The Subodh Journal of Recent Trends in Information ... · A Survey of Load Balancing Algorithms in Cloud Computing Manisha Agarwal, Manisha Jailia, Neha Agarwal ... Artificial Bee

The Subodh Journal of Recent Trends in Information Technology • [ISSN No. 0975 – 9875] • May-2013

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The Subodh Journal of Recent Trends in Information Technology SJRTIT

Chief Patron: Sh. S. S. Bothra, Hony. Secretary, S. S. Jain Subodh Shiksha Samiti, Jaipur

Patron: Sh. Vinod Lodha, Convener S. S. Jain Subodh PG College

Sh. Rajendra Kumar Jain (Raja), Convener MCA Institute, S. S. Jain Subodh PG College

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Editor-in-Chief: Ashish Chandra Swami Associate Professor, S. S. Jain Subodh PG College

Executive Editor: Dr. Madhu Sharma Associate Professor, S. S. Jain Subodh PG College

Publisher: S.S. Jain Subodh P.G. College, MCA Institute Rambagh Circle, Jaipur-302004

Editorial Advisory Board: Prof. S. Sancheti Prof. R. P. Yadav Prof. M.C. Govil Prof. M.S. Gaur

The Subodh Journal of Recent Trends in Information Technology is an academically peer reviewed journal. All submissions are referred by three or more peers.

ISSN No. 0975 – 9875 Copyright 2010 by S.S. Jain Subodh P.G. College. All rights are reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher. Note : Authors will be solely responsible for the genuinity and originality of the contents of their papers.

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The Subodh Journal of Recent Trends in Information Technology

(SJRTIT)

The Mission of the Journal is:

To provide an authoritative journal in IT enhancements and explorations.

To advance IT edification around the globe by publishing high quality articles on best practices with their utilization.

To expose the readers to a variety of epistemologies related to the ongoing research activities.

To recognize and clinch the range of teaching and knowledge models in use around the globe.


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Table of Contents

1. A Survey of Load Balancing Algorithms in Cloud Computing

Manisha Agarwal, Manisha Jailia, Neha Agarwal

2. E-Agro Market: An Inventive Scaffold for Agro-Based Industries Dr. Madhu Sharma

3. Wireless Sensor Networks and its Future Trends

Vandana Vijay

4. Analysis of Healthcare by using Digital Image Processing Based on Medical Palmistry

Dr. Vipin Kumar Jain

5. Utilization of Cloud Computing for providing Information Technology based Services

Ashish Chandra Swami

6. Impact and Implementation of Green IT practices: Need of current era Sangeeta Vaibhav Meena

7. Artificial Bee Colony Algorithm- A Dynamic Approach for Query Optimization in DDBMS

Anjali Soni


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A Survey of Load Balancing Algorithms in Cloud Computing Manisha Agarwal *, Manisha Jailia* , Neha Agarwal**

*Senior Assistant Professor, Banasthali University, Banasthali, **Assistant Professor, S.S. Jain Subodh P.G. College, Jaipur

Abstract:

Cloud computing offers pool of resources which tremendously gives lot of changes in distributed system environment. Cloud provides user friendly environment through user can access hardware, software over the internet. It provides virtual computing environment and deliver services in less costly manner. Cloud provides efficient resource utilization and gives maximum throughput. But there are various issues like virtualization, dynamic resource allocation, security, load balancing & fault tolerance etc. Load Balancing means the proper utilization of resources by maintaining balance load on participating nodes. In this paper, we are going to discuss brief summary of load balancing algorithm in cloud computing.

Keywords:

Cloud computing, Load Balancing, Load Balancing in Cloud Computing

1. INTRODUCTION

Using distributed technologies, Cloud computing fullfill various user needs and applications. Share software, information and resources, through internet are the major functions of cloud computing to satisfy needs, performance improvement and cost reduction. Many commodity nodes are used by cloud computing that can cooperate to perform a specific service together [11]. In addition to that, the vigorous development of the device and internet applications continuously enhanced with multimedia occurs quickly in the network system. Thus, the considerable attention have gradually attracted by applications associated with network integration. To provide high

performance computing and integrate environment for users, cloud computing provides a scalable and flexible approach of managing thousands of distributed heterogeneous nodes by the development of distributed systems [12]. It has been successfully applied in applications such as business, scientific research and industries. In cloud computing one of the central issues is Load balancing. In Load balancing, the distribution of jobs throughout a networked computer system is involved, thus throughput is increased without obtaining faster or additional computer hardware. At any point of time, the work done by every processor in the system should approximately the same is ensured by Load


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balancing. It helps to achieve a high resource utilization ratio and user satisfaction, hence improving the overall performance and resource utility of the system [1]. It also ensures that every computing resource is distributed efficiently and fairly. Load balancing also enables scalability which one of the very important features of cloud is computing. Load balancing algorithms dose not taken the previous state or behavior of the system, it depends upon the present behavior of the system because it is dynamic in nature[11].

2. LOAD BALANCING IN CLOUD COMPUTING

Load balancing is the method to perk up both the job response time and improve resource consumption by reallocating the work load among nodes of the cloud computing system.

Figure 1: Classification of Load Balancing Algorithms

A dynamic load balancing algorithm, does not consider previous behavior or state of the system, but, it depends on the present behavior of the system. Depending on

process initialization, load balancing algorithms can be divided into three types as Receiver Initiated, Sender Initiated and Symmetric [3].

The load estimation, load comparison, stability of different system, system performance, nodes interaction, nature of work to be transferred and node selection, are the important things to consider while developing such algorithm. The load can be considered in terms of amount of memory used, CPU load, Network load or delay [7].

3. EFFICIENT LOAD BALANCING ALGORITHMS

The brief review of few existing load balancing algorithms is presented here as:

1) Token Routing: In this algorithm the system cost is optimized by moving the tokens around the system. But due to communication bottleneck, agents can not have the enough information of distributing the work load in a scalable cloud system. So, the workload distribution is not fixed among the agents. The “heuristic approach of token based load balancing” can be used to remove drawback of the token routing algorithm. The efficient and fast routing decision is provides by this algorithm. In this algorithm, it’s not necessary for the agent to have an idea of neighbor’s working load and the complete knowledge of their global state. Their own knowledge base is build to make decision for the passing of the token. And the previously received tokens are actually derived to form this knowledge base. So, no


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communication overhead is generated in this approach [2].

2) Randomized: This is a static algorithm. In Randomized algorithm a particular node n with a probability p can handle a process. Independent of allocation from remote processor, for each processor the process allocation order is maintained. For equally loaded processes, this algorithm works well. In case of different computational complexities load, problem may arise. Deterministic approach is not maintained in this algorithm. When Round Robin algorithm generates overhead for process queue then Randomized algorithm works well [2].

3) Central Queuing: Dynamic distribution principal is used in central queuing algorithm. In this algorithm, queue manager insert each new activity into the queue. When queue manager received a request for an activity it fetches the first activity from the queue and sends it to the requester. If in the queue no ready activity is present then the request is buffered, until the availability of new activity. But in case there are unanswered requests in the queue and new activity comes, the first such request is removed from the queue and new activity is assigned to it. The local load manager sends a request for the new activity to the central load manager when a processor load falls under the threshold. If ready activity is found the request is answered by central manager, otherwise until new activity

arrives the request is queued [2].

4) Round Robin: This algorithm divides the processes between all processors. Each process is assigned in a round robin order to the processor. Independent of the allocations from remote processors, the process allocation order is maintained locally. For different processes the job processing time are not same, though the work load distributions between processors are equal. So, it is possible that some nodes remain idle and others may be heavily loaded at any particular time. Web servers mostly use this algorithm where requests are of distributed nature and similar equally [2].

5) Least Connection Mechanism: In Least Connection Mechanism, to estimate the load the number of connections for each server is counted dynamically. Corresponding to each server number of connection is recorded by the load balancer. Whenever a timeout happens or when connection finishes the number of connection decreases and when a new connection is dispatched to load balancer, connection number increases [2].

6) Biased Random Sampling: In this algorithm the load is balanced through investigation of scalable and distributed load balancing approach, that uses random sampling of the system domain to achieve self-organization. Here a server is treated as a node and with the connectivity of each node, a virtual graph is constructed, that graph represents the server load. In the graph, free server resources are directed at


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each node as indegree. This is a fully decentralized load balancing scheme, thus making it suitable for large network systems like that in a cloud. With an increase in population diversity the performance is degraded [6].

7) Join-Idle-Queue – This algorithm is for dynamically scalable web services. By balancing the load at each dispatcher by finding idle processors and then, reduce average queue length at each processor by job assignment to processors, this algorithm provides large scale load balancing with distributed dispatchers. It effectively reduces the system load by removing the work from the critical path of request processing. It does not Increase actual response time and there is no communication overhead at job arrivals [14][15].

8) Biased Random Sampling – [13][15] This algorithm increased the throughput by effectively utilizing the increased system resources for which the performance of the system is improved with similar and high population of resources. This is a scalable and distributed load balancing approach. Load balancing is achieved by random sampling across all nodes of the system domain to achieve self-organization. With an increase in population diversity the system performance is degraded.

4. CONCLUSION

Load Balancing is the most important aspect of cloud computing. The dynamic workload

is reduced by load balancing across the nodes and hence provides higher user and resource satisfaction. It helps in reducing the overhead, response time and increasing scalability. This paper depicts the various load balancing techniques in cloud computing in detail. This paper elaborates the fundamental concept of load balancing defining its goals and challenges; it presents the review of the some of the most prominent load balancing algorithms in the system.

References: [1] Abhijit A. Rajguru, S.S. Apte, “A

Comparative Performance Analysis of Load Balancing Algorithms in Distributed System using Qualitative Parameters”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-1, Issue-3, August 2012.

[2] Ajanta De Sarkar and Soumya Ray, “ Execution Analysis of Load Balancing Algorithms in Cloud Computing Environment”, International Journal on Cloud Computing: Services and Architecture (IJCCSA),Vol.2, No.5, October 2012.

[3] Dr. Hemant S. Mahalle, Prof. Parag R. Kaveri and Dr.Vinay Chavan, “ Load Balancing On Cloud Data Centres”, International Journal of Advanced Research in Computer Science and Software Engineering ,Volume 3, Issue 1, January 2013 ISSN: 2277 128X.


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[4] Jaspreet kaur, “Comparison of load balancing algorithms in a Cloud”, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 ,Vol. 2, Issue 3, May-Jun 2012, pp.1169-1173 1669 .

[5] Yatendra Sahu and R.K Pateriya,Cloud Computing Overview with Load Balancing Techniques.

[6] Nayandeep Sran and Navdeep Kaur, “Comparative Analysis of Existing Load Balancing Techniques in Cloud Computing”, International Journal of Engineering Science Invention ISSN (Online): 2319 – 6734, Volume 2 Issue 1 , January. 2013, PP.60-63.

[7] Ram Prasad Padhy and P Goutam Prasad Rao, Load Balancing in Cloud Computing Systems.

[8] Sandeep Sharma, Sarabjit Singh, and Meenakshi Sharma, Performance Analysis of Load Balancing Algorithms.

[9] Ajay A. Jaiswal S. K. Shriwastava, An approach towards Load Balancing and Cloud Security.

[10] Ram Prasad Padhy ,P Goutam Prasad Rao, Load balancing in Cloud Computing Systems.

[11] Ms. Nitika, Comparative Analysis of Load Balancing Algorithms in Cloud Computing.

[12] Shu-Ching Wang, Kuo-Qin Yan ,Wen-Pin Liao and Shun-Sheng Wang,

Towards a Load Balancing in a Three-level Cloud Computing Network.

[13] Martin Randles, David Lamb, A. Taleb-Bendiab,” A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing”, WAINA '10 Proceedings of the 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops pp551-556.

[14] Nidhi Jain Kansal, Inderveer Chana,” Cloud Load Balancing Techniques: A Step towards Green Computing”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 1, January 2012 ISSN (Online): 1694-0814,pp 238-246.

[15] Jaydeep R Viradiya , Milankumar Sanandia, “Weighted Diffusive Load Balancing Algorithm In Cloud Environment”, International Journal of Engineering Research & Technology, Vol.2 - Issue 5 (May - 2013), e-ISSN: 2278-0181.


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E-Agro Market: An Inventive Scaffold for Agro-Based Industries Dr. Madhu Sharma

Associate Professor, Department of Computer Science S. S. Jain Subodh PG College, Jaipur, Rajasthan

Abstract:

Marketing is the key requirement for trading and convincing other persons for trading of any product and so on market to provide platform for trading activities. With the continuous changes in the current world, the structure of market is also changing and improving, to match with the pace of technological advancements and to provide more better and efficient output. The concept of e-market and e-marketing has been established to utilize the power of Information and communication technologies in the commercial field. In this paper, the structure of e-market has been explored with their area of application and the advantages more specifically in agro based industries. The proposed e-agro market system is expected to be an inventive, effective and beneficial trading approach for the country’s farmers.

Keywords:

Marketing, e-market, e-marketing, e-agro market, business-to-business

1. INTRODUCTION

E-market is a platform to provide services to customers or retail dealers directly using internet services, rather than physical area occupancy for trading. E-market services are open to several buyers and several sellers, but it itself does not sell nor buy any product or services. E-Market supports in establishing new business relationships with supplier services, without any actual trading at these platforms [1][2][3]. E-markets and supplier service provision is also called Business-to-Business Internet platform. This platform provides all internetwork supported technical solutions for founding of new trading affairs between companies. E-market

could also be stated as a website of a company or organization meant to sell their products as an individual or retailer, to sell other’s products, trading between customers or facilitating non trading activities collaborative design or people collaboration [3].

E-marketing originated from e-market, also known as Internet marketing or online marketing, includes the processes or activities done through e-market. It includes the use of a website in combination with online promotional methods like: Social medial marketing, Interactive online ads, E-mail marketing, affiliate marketing, display advertising etc. It’s very important to know


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the skills of e-marketing strategies in advance before implementing it to avoid the valuable resources especially in e-markets.

2. CONVENTIONAL MARKET STRUCTURE FOR AGRO BASED INDUSTRIES

In general, agro based industries are those industries which depend on agricultural yields as raw resources, For example a cotton textile industries is an agro based industry, as it uses cotton as the raw resource for its final product making to make clothes and sheets [4][5]. To facilitate the availability of raw materials to these industries, markets are established. To match with the pace of demand from industrialists and supply from raw material producers or farmers proper markets are required. The retail traders or any company are not allowed to buy these raw products directly from farmer and farmers too not allowed to sell their material directly, instead the buying and selling activities are done through market or mandis under government acts and its mechanism [4][5]. In India, usually every state or district has their own market or mandis for buying and selling of the raw material. Farmers sell their material through the auction system or through agents or traders in mandi after getting license from government. But in this trading system, usually farmers face loss and problems due to some tricks like price manipulations, delayed payments, excuses for full payments, non provision of sale slips etc. from

mediators or retails traders. As a result, the farmers do not get higher returns on their material and their condition remains almost same even after good production [6].

3. E-AGRO MARKET OBJECTIVES AND STRUCTURE

Making the right strategic and commercial schemes for a smooth business is a vital need for success of any business activity. E-market and e-marketing both helps in finding right choices for business partners for the growth of their business. E-market provides an inducement to focus on significant areas and devise strategies to assist and attain intended objectives as per the current requirements. The e-market framework proposed for agro based industries is expected to solve the problems of farmers by providing a smooth and transparent mechanism for both seller as well as buyer. The five S’s of the e-agro market objectives are as depicted in figure 1.

Figure 1: E- Agro Market Objectives


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These five S’s of the e-agro market objective provides a useful scaffold for developing an effectual system and can be explained as:

Sell – to sell products or services using internet services.

Serve – to serve customers using internet services.

Speak – to speak or communicate with clientele using internet services.

Save – to save cost using internet services.

Sizzle – sizzling or brand identity in market using internet services.

While setting the e-agro market objectives, it is to be assured that they are specific, measureable, action-oriented, realistic and time specific. It is claimed that the idea of e- agro market will convey additional precise and fair competition between diverse sellers and buyer in a positive direction in this context.

Fig.ure 2: E- Agro Market Framework

The e-agro Market framework includes the terminals for seller or farmers and buyers or

retail traders to access the system. The farmers are expected to feed the value or quantity of the material for sale along with the price quotes. The price quotes are governed by the policies and rules defined by the government through the controlling system, similarly buyers could view the available quantity with price tags and quote their demand in the system. If the demand matches with the stock, the deal is fixed electronically and the material is shipped for the buyer after its payment confirmation. Here, farmers are expected to deliver their material physically at the warehouse designated by private sub e market for their quality testing and certification for the online trading.

4. CONCLUSION

It could be concluded that the farmers and the agro based industries are here to stay, grow and to continue for the years to come at the global level with a great support structure of e-agro market. With the appropriate combination of the e-market, its objectives and strategies a perfect e-business model in the country’s’ agro industries could be established with the higher growth and returns. The important factors relevant to e-market desires are to be carefully visualized and implemented for a healthier and trustworthy agro business network establishment. The e-agro market is expected to ensure a transparent and reliable system favorable for our country’s farmers.


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References: [1] E-market architecture for supporting

multiple roles and reconfigurable business porcesses, US20020032590 A1, Mar 14, 2002.

[2] Pu Huang,Alan Scheller–Wolf2 andKatia SycaraDesign of a Multi–Unit Double Auction E–Market, Computational Intelligence Volume 18, Issue 4, November 2002, pages 596–617.

[3] Jing Zhao, Shan Wang, Wilfred V. Huang, A study of B2B e-market in China: E-commerce process perspective,O. Research Article, Information & Management, Volume 45, Issue 4, June 2008, Pages 242-248.

[4] Jean-Emile Denis and Daniel Depelteau, Market Knowledge, Diversification and Export Expansion, Journal of International Business Studies, Vol. 16, No. 3 (Autumn, 1985), pp. 77-89

[5] IRoderick J. Brodie, Heidi Winklhofer, Nicole E. Coviello1 and Wesley J. Johnston, s e-marketing coming of age? An examination of the penetration of e-marketing and firm performance,Volume 21, Issue 1, Winter 2007, pages 2–21.

[6] K. Kalyanam;Shelby Mclntyre, The E-Marketing Mix: A contribution of E-Tailing Wars.

[7] Venkaiah, V, Impact of agro-based industries on rural economy, cabdirect, 1987 pp. 366 pp.

[8] Mohamad Husin, Zin Zawawi Zakaria, Abdul Halim Hassan, P Proceedings of the National Symposium on Oil Palm By-products for Agro-based Industries, Institut Penyelidikan Minyak Kelapa Sawit Malaysia, Bangi, Selangor (Malaysia): Kuala Lumpur, 5-6 Nov 1985, p. 7-15.


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Wireless Sensor Networks and Its Future Trends Vandana Vijay

Lecturer, Department of Computer Science S. S. Jain Subodh PG College, Jaipur, Rajasthan

Abstract:

One of the emerging technologies that have had big impact on the field of research is Wireless sensor Networks. It offers a new paradigm regards to the way that the traditional monitoring systems have been designed. It is basically composed of a number of wireless sensor nodes which form a sensor field and a sink. These large numbers of nodes, having the abilities to sense their surroundings, perform limited computation and communicate wirelessly, form the WSNs. Sensor networks are used in many applications. In military applications they are used for surveillance and target tracking. In industrial applications, sensor networks are used in monitoring hazardous chemicals. They are also used in monitoring the environment and in early fire warning in forests as well as seismic data collections. There are many new challenges that have surfaced for the designers of WSNs, in order to meet the requirements of various applications like sensed quantities, size of nodes and nodes’ autonomy. Therefore, improvements in the current technologies and better solutions to these challenges are required.

Keywords:

Future trends, recent advances, research challenges, wireless sensor networks

1. INTRODUCTION

Various advancements in VLSI (Very Large Scale Integrated Circuits)technology, and MEMS(Micro-Electro-Mechanical Systems), as well as in wireless communication technology made it possible to manufacture sensor networks where very large numbers of very small nodes are scattered across some environment in order to sense and report to a central node (user).

A WSN, generally consists of a base station (also called as gateway sometimes) that can communicate with a number of wireless

sensors via a radio link. Wireless sensor nodes collect the data, compress it, and transmit it to the gateway directly or indirectly with the help of other nodes. The transmitted data is then presented to the system by the gateway connection.

2. ARCHITECTURE OF SENSOR NODES

Sensor networks consist of very small nodes (sensors) that are deployed in some geographical area. Sensor networks are used to measure temperature or pressure, or it


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could be used for target tracking or border surveillance. It could be also deployed in factories in order to monitor toxic or hazardous materials. It is also used to measure the weakness in building structures, or in vehicles and airplanes. The architecture of sensor nodes is as depicted in figure 1.

Figure 1: The Architecture of a Sensor Node

A typical sensor node consists of 4 main parts–Power supply, sensor and analog to digital converter (ADC), processor and storage memory and finally, transceiver to send and receive data as shown in Figure 1. The power supply is to power the node. The sensor circuitry can transform physical quantities into an electric signal. The ADC changes the analog signal generated by the sensor into a digital signal and sends it to the processor. The processor can perform simple operations on the received digital signal, and can store it into memory. Finally, the transceiver sends and receives data.

The nodes are scattered in a geographical area and the area is divided into clusters with a gateway in each cluster. Nodes in each cluster communicate with the gateway. The gateway collects the data and forwards it to the user. Nodes can directly communicate with the gateway in its cluster, or nodes can

use chaining in order to communicate with the gateway. Using chaining reduces the energy used in transmission, but increases the energy used in processing since each node should receive and forward the message to and from other nodes. Some sensor networks may have more than one level of aggregation.

Typically, sensor networks works in one of two modes–Continuous operation or Query mode. In continuous operation mode, the node is continuously sensing the environment and sending the data (or the processed data) to neighboring or a central node. In query mode, the node is usually powered down waiting for a command from a central node, or neighboring node. When the node receives the commands (usually in the form of report), it collects data from the sensor, processes it and sends it to the requesting node. The design of sensor networks must concentrate on the challenges that are inherent to sensor networks. The most important design factors are fault tolerance, scalability, cost, and power consumption.

3. APPLICATIONS AREAS

As with most things in life there are positive and negative sides to social networking too. Few of application areas of WSM are as follows :

3.1 Military

New and emerging technologies, such as networks, support military operations by delivering critical information rapidly and


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dependably to the right individual or organization at the right time. This improves the efficiency of combat operations. The new technologies must be integrated quickly into a comprehensive architecture to meet the requirements of present time. Some other important application is detection of enemy units’ movements on land/sea, sensing intruders on bases, chemical/biological threats and offering logistics in urban warfare.

3.2 Industrial and Commercial

Successful use of wireless sensors in systems such as supervisory control and data acquisition has proved that these devices could effectively address the needs of industrial applications. The critical process applications of WSNs in industry are monitoring temperature, flow-level, and pressure parameters. With rapidly increasing technological advances in wireless technology and its subsequently decreasing prices, numerous wireless applications are being developed in industry. WSN in manufacturing industries can monitor and optimize quality control.

3.3 Structural Healthcare

Structures are inspected at regular time intervals, and repairing or replacing based on the time of use, rather than on their working conditions. Wireless sensing will allow assets to be inspected when the sensors indicate that there may be a problem. This will reduce the cost of maintenance and preventing harmful failure. These

applications include sensors mounted on heavy duty bridges, within concrete and composite materials and big buildings.

3.4 Agriculture

Agriculture can also be benefited by the deployment of WSN to get the information regarding soil degradation and water scarcity. With help of WSNs, we can check the clean water consumed in irrigation and manage it.

3.5 Traffic Management and Monitoring

Every big city is suffering from traffic congestion around the world. A sincere effort is being made to solve the traffic congestion. Congestion can be alleviated by planning managing traffic. A real-time automatic traffic data collection must be employed for efficient management of rush-hour traffic. The vehicle tracking application is to locate a specific vehicle or moving object and monitor its movement. This work also describes design of WSN for vehicular monitoring. As the power source (battery) is limited, it is important that a design of sensor node is power efficient.

3.6 Biomedical/Medical

The uses of WSNs in biomedical and medical are in growing phase. Biomedical wireless sensor networks (BWSNs) show the future opportunities for supporting mobility while monitoring vital body functions in hospital and home care. There is a requirement for BWSN to develop in order to cover security handling, improved signal


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integration and visualization. They can also be used to achieve extended mobility outside the surgery room, monitoring of several patients/persons at the same time, and further adaptations to medical experts needs for information. As the Internet usage has become popular among people, e-services for the healthcare which is commonly known as e-Health, have recently attracted significant attention within both the research society and industry.

Followings are several ongoing projects for healthcare using WSN:

1. GlucoWatch G2 (Dario et al., 2004) – use WSN to research wearable personal health system that will monitor and evaluate human vital signs.

2. ALARM-NET (Wood et al., 2006) – a WSN built for assisted-living and residential monitoring.

3. AMON (Anliker et al., 2004) – a Wireless Body Area Sensor Network System

4. CodeBlue (Lorincz et al., 2004) – an architecture proposed for tracking and monitoring of patients.

4. FUTURE TRENDS The future development in sensor nodes must produce very powerful and cost effective devices, so that they may be used in applications like underwater acoustic sensor system, sensing based cyber physical systems, time critical applications, cognitive sensing, spectrum management and security and privacy management.

4.1 Cognitive Sensing

Cognitive sensor networks are used for acquiring localized and situated information of the sensing environment by the deploying a large number of sensors intelligently and autonomically.

Two well known examples of cognitive sensing are swarm intelligence and quorum sensing:

1. Swarm intelligence is developed in artificial intelligence for studying the collective behavior of decentralized, self organized systems.

2. Quorum sensing is an example of bio inspired sensing and networking. Quorum sensing is the ability of bacteria to communicate and coordinate behavior via signaling molecules.

4.2 Underwater Sensor Systems

Underwater sensor networks are designed to enable applications for oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. Underwater sensors are also being in use for exploration of natural undersea resources and gathering of scientific data. So a need of underwater communications among underwater devices arises. Underwater sensor nodes and vehicles should be capable of coordinate their operation, exchanging their location and movement information and hence relay monitored data to an onshore basestation.


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A protocol named DUCS (Distributed Underwater Clustering Scheme) described by Domingo and Prior (2008), is a GPS free routing protocol. It minimizes the proactive routing message exchange and does not use flooding techniques. It also uses data aggregation to eliminate redundant information.

4.3 Coordination in Heterogeneous Networks

Since the sensor nodes have energy constraints, so the main obstacle in the coordination with other networks is limited energy of sensor nodes.

The data produced by sensor nodes should be accessible. This can be done by connecting the WSN with existing network infrastructure such as global Internet, a local area network or private internet.

5. CONCLUSION

The paper concludes the future developments in sensor nodes and indicates that the sensor nodes are capable of producing powerful and cost effective devices, which are helpful in developing innovative applications. Thus, the sensor node technology is required to be explored in current era for better solutions and meet upcoming challenges.

References:

[1] A. Tiwari, A. Lewis, and S¬G. Shuzhi, “Design & Implementation of Wireless Sensor Network for Machine Condition Based Maintenance” Int’l Conf. Control, Automation, Robotics, & Vision (ICARV), Kunming, China, 6–9 Dec. 2004.

[2] R. Kay and F. Mattern, "The Design Space of Wireless Sensor Networks" IEEE Wireless Communications Vol.11.No.6 (2004) pp 54–61.

[3] A. Tiwari, P. Ballal and F.L. Lewis, “Energy-efficient wireless sensor network design and implementation for condition-based maintenance” ACM Transactions on Sensor Networks (TOSN) Vol.3.No.1 (2007) pp 1-7.

[4] S. Hadim, N. Mohamed, "Middleware: middleware challenges and approaches for wireless sensor networks" Distributed Systems Online, IEEE Vol.7.No.3 (2006) pp 1-1.

[5] S. Hussain, S. Schaffner and D. Moseychuck, "Applications of Wireless Sensor Networks and RFID in a Smart Home Environment" Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference (2009) pp 153-157.


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[6] Chien-Chung Shen, C. Srisathapornphat and C. Jaikaeo, “Sensor Information Networking Architecture and Applications” IEEE Personal Communications (2001) pp 52-59.

[7] S. S. Doumit and D. P. Agrawal, “Self-Organizing and Energy-Efficient Network of Sensors” IEEE (2002) pp 1-6.

[8] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “A Survey on Sensor Networks” IEEE Communications Magazine (2002) pp 102¬114.

[9] Chinrungrueng, U. Sununtachaikul and S. Triamlumlerd, "A Vehicular Monitoring System with Power-Efficient Wireless Sensor Networks" ITS Telecommunications Proceedings, 6th International Conference on (2006) pp 951-954.


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Analysis of Healthcare by using Digital Image Processing Based on Medical Palmistry

Dr. Vipin Kumar Jain Lecturer

S. S. Jain Subodh PG College, Jaipur

Abstract:

Palmprint are believed to have the critical properties of universality, stability and uniqueness, permanence and collect ability for personal authentication. Palm is large in size and contains abundant feature of different levels, such as creases, palm lines, texture, ridges, delta points and minutiae. Human being was interested in the palm lines for fortune telling long time ago. In this century scientists discovered that palm line were associated with some genetic diseases including Down syndrome, Aarskog Syndrome, Cohen Syndrome and fetal alcohol Syndrome. The medical science detected some diseases like Anemia, Calcium & Iron deficiencies, Jaundice etc. based on symptoms of palm features. The palmistry also defines some features like shape and size of palm, fingers, nails, palm lines, palm temperature, thump size and strength. In this paper we have combined both symptoms from medical science and signs on palm according to palmistry. It is tried to analyze the features of palm by digital image processing technique to detect symptomatic diseases. The images of human palm form input to the system. Then, system applies digital image processing and analysis techniques on input images to identify certain features in the image. The valuable signs and color of palm as well its surroundings are extracted. By using knowledge base of medical palmistry it analyzes certain features in image and predicts probable disease.

Keywords:

Digital Image Processing, Digital Image, Medical Palmistry, Knowledge Base

1. INTRODUCTION

The palm print is defined as the inner surface of our hand from the wrist to the root of fingers. A print is the impression made in or on surface by pressure. A Palmprint is defined as the skin pattern of a palm composed of the physical characteristics of

the skin patterns such as lines, points and texture. Palmprint may be found on the surface of an object mainly due to perspiration. Palmprint feature, which may or may not be observable to the necked eye. It can be achieved by designing an appropriate algorithm capable of separating two people by their palm print features.


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Human being was interested in the palm lines for fortune telling long time ago. In this century scientists discovered that palm line were associated with some genetic diseases including Down syndrome, Aarskog Syndrome, Cohen Syndrome and fetal alcohol Syndrome. Scientist and fortune tellers named the lines and the region differently, fortune tellers named heart line, head line, Life line as shown in Fig. 1.

Figure 1: Definitions of Palm Lines and Regions

(a) From Scientists (b) From Fortune-tellers

Features of Palmprint Features play very important role in palm print authentication. The features include principal lines, wrinkles, ridges etc. as shown in figure 2.

Figure 2: Palmprint shows Wrinkles, Ridges

and Principal Lines.

Type of Features of Palmprint The six major type of features can be observed on a palm are:-

1. Geometry Feature 2. Principal Line Feature

3. Wrinkles Feature 4. Datum Point

5. Delta Point Feature 6. Minutiae Feature

The techniques used to provide perception to digital computer are called image processing and analysis techniques. An image may be defined as a two-dimensional function, f(x,y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x,y) is called the intensity or grey level of the image at that point [1]. When x, y and the amplitude


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values are infinite, we call the image an analog image. When these values are finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer [1]. Once computer has visual information in appropriate format, computer can analyze it, which is called image analysis. Image understanding and analysis is difficult task. In healthcare industry, there are so many applications of digital image processing, Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT) scan are most popular among them [6]. Apart from healthcare, Digital Image Processing and analysis is also applied in different areas like office and industrial automation, Remote sensing, natural resources survey and management, Criminology, Astronomy, Meteorology, and artillery applications.

1.2 Medical Palmistry Palmistry is a science which observes human palm by different aspects and derives conclusions about nature of the person. Medical palmistry is one branch of palmistry, which works on identification of probable diseases by observing some symbols in human palms. According to principles of medical palmistry, there are some symbols like Iceland, cross, star, square, grill, spot and circle. If one or more of them is/are found on specific region of palm, or on specific line of palm, it indicates probability of disease of respective organ of body. Apart of symbols, color and surface of palm and nails, shape of palm and fingers also plays important role in decision making.

2. SYMBOLS /SYMPTOMS IN HUMAN PALM THAT INDICATE CERTAIN

DESEASES ACCORDING TO PALMISTRY - Symbols shown in figure 1 indicate specific diseases, based on their position on lines, mounts and fingers [6].

Fig. 3. : Different signs & symbols

Island: Island on the line of heart denotes heart disease inherited [3]. Spot: Bright-red spot on the line of head indicates a shock or injury from some blow or fall [3].

Square: Square is called a mark of preservation, because it shows that the


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subject is protected at that particular point from whatever danger menaced [3].

Star: Star on the mount of moon indicates probability of ascites, or urinal diseases [4].

Grille: Grille on the mount of Venus indicates probability of problems with reproductive system [4].

In additions to these marks there are some other patterns like triangle, cross, and circle. They are more related to nature and psychology of a person rather than physical characteristics.

Figure 4: Different signs &

symbols on palm

3. SYMBOLS /SYMPTOMS IN HUMAN PALM THAT INDICATE CERTAIN DESEASES ACCORDING TO MEDICAL SCIENCE – Medical researchers studying skin patterns (dermatoglyphics) have discovered a correspondence between genetic abnormalities and unusual markings in the hand. Research has confirmed a link between specific fingerprint patterns and heart

disease. These days palmistry is well accepted throughout the world. Professional palmists can be found reading palms in every country in the world.

Color of the Skin in Palmistry: The health of a person is often indicated by the color of their hands. Dead white hands often indicate a lack of circulation. Pink hands are usually a sign of good health in palmistry. Red hands may indicate high blood pressure. Blue-ish hands indicate a sluggish condition of the circulation of the blood supply. The normal color of the palm should be rosy and pinkish.

Figure 5: Different signs & shapes of nails

Discoloured Nails:

? Your nails should be a healthy, pinkish colour.

? If we have a greenish-yellow tinge then you probably have a fungal infection.

? Besides, yellow nails can be a sign of a lung disease.

? Brown marks below the nail line can point to a suspicious mole underneath and pitted-looking nails, where the normally smooth surface has several


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small dents on it, are normally associated with psoriasis.

Spoon-shaped nails

? A healthy nail should be slightly raised in the middle and slightly curved down at the tip. If you nail looks the opposite of this, like a spoon, it's a symptom of iron deficiency and anemia.

? Nail clubbing, where the nail takes on a ball-like shape, is even more worrisome as it can mean an internal disease such as lung cancer or heart problems.

Finger lumps

? Small lumps close to the nail beds are the first signs of osteoarthritis. These lumps come up as the disease causes a loss of joint space, leading to broadening of joints while new bones begin to form. Osteoarthritis, which is general wear and tear of the joints.

Trembling hands

? Everyone's hands shake to some degree but if yours tremble noticeably, it may be an early warning sign that you are developing Parkinson's disease, which affects the nervous system. Shaky hands could also be blamed on stress, anxiety or drinking too much alcohol or coffee.

Dry skin

? Very dry skin can be a sign of an under-active thyroid as it causes the skin to lose moisture. It can also mean that one may be sensitive to soaps and other cosmetics. Hands also tend to lose moisture after menopause because the skin dries out when a women's oestrogen level drops.

Red palms

? For centuries doctors have associated a reddening of the palm with liver disorders, in particular cirrhosis. Liver palm, or palmar erythema, is thought to be caused by blood vessels which are dilating in response to the hormone imbalance caused by the damage to the liver. The reddening occurs on the outer edge of the wrist, from the base of the thumb, along the wrist to the little finger.

Other signs are a whitening of the nails, caused by the protein deficiency typical in liver disease, and jaundiced skin. Red palms can also be a symptom of rheumatoid arthritis, thyroid disorders and, sometimes, pregnancy.

4. THE PROPOSED SYSTEM The main function of the proposed model is to take as an input, the image of human palm, process it and as an output, predict diseases, using knowledge of medical palmistry. The architecture of the system is shown in figure 2 [5].

Figure 2: The Architecture of the System

Medical + Palmistry

Symptoms


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As shown in figure 2, the system takes as an input the image of palm, using IPAA techniques, and using knowledgebase of medical palmistry, it identifies the certain symbols in specific part of palm.

5. MODULES OF THE SYSTEM The system includes Image processing module and knowledge management & prediction module. Image processing module deals with operations on image up to pattern recognition[6]. The knowledge about medical palmistry is fed into the system in second module. In this module, sample patterns are fed into knowledge base. At last these two modules combine their work products, and system generates final output, that is prediction. Steps specified in each module are as follows.

(A) Image Processing and Analysis Module

(i) Image Formation Image formation means conversion of radiant energy emitted from source into 2D image. In this system, digital camera is used for good quality of image. This module forms input to the system.

(ii) Digitization

Since we are using digital camera, we don‟t have to perform digitization techniques like sampling and quantization. The input image itself is in digital form.

(iii) Image enhancement This step will improve the quality of image by applying enhancement techniques like contrast intensification, noise cleaning and edge sharpening. To determine certain patterns as shown in fig 1, we are performing this step as primary requirement.

(iv) Segmentation In this step system will divide the spatial domain into „meaningful‟ parts or regions, which are of our interest. Specific patterns will be searched according to these segments. Image can be divided into four quadrants, because all mounts in the palm image can be easily identified [6].

(v) Edge, line detection and color processing In this step, palm image is processed for identification of edges and lines, which are usually found in human palm. For example, the heart line, the life line and so on. Color of palm is also identified in this step.

Medical + Palmistry Symptoms


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Moreover, shape of palm is also decided in this step [6].

(vi) Feature Extraction This step will extract specific features like patterns of star, grille, Iceland, square, and spots from the image [6].

(vii) Image Description This step will identify the pattern and its exact location in the image. The partial algorithm is shown in following section.

(B) Knowledge Management & Prediction module The Knowledge Base system is prepared by using knowledge of medical symptoms and palmistry. Knowledge base is the backbone of the system. This module and knowledge base system predicts the probable diseases of a palm holder [5].

References: [1] R. C. Gonzalez and R. E. Woods

“Digital Image Processing”, 2nd edition, Pearson Education, 2004

[2] D. M. Shah “Decision Support system for Image Analysis” in journal of Advanced Research in Computer Engineering, 1(1-2) January-December 2007, pp 51-56.

[3] Cheiro, “Language of The Hand”, Manoj Paperbacks, Delhi.

[4] Bhupendra Dholakiya, “Sampurna Hastarekha Shastra”, Uzma publication, Ahmedabad.

[5] Hardik Pandit and Dipti Shah, “Decision Support System for Medical Palmistry” - in “Advances in Applied Research”, vol.2, July-December 2010, pp 173-178.

[6] Hardik Pandit and Dipti Shah, “Decision Support System for Healthcare Based on Medical Palmistry”, presented in ICISD – 2011, GCET Engineering College, Vallabh Vidyanagar


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Utilization of Cloud Computing for providing Information Technology based Services

Ashish Chandra Swami Associate Professor

S. S. Jain Subodh PG College, Jaipur

Abstract :

The term cloud means collection of various objects intermixing and clear vision about individual things is not seen properly as seen from far distance. Keeping this concept in mind various Information Technology things vide hardware and software are clubbed together to form a single unit as seen from far distance to from CLOUD for Information Technology. This cloud further is categorized under many branches based on various combinations of hardware and software. Using these clouds as resource various Information Technology based services are provided to users at economical price. These services includes all types of services which are related to Information Technology like Health sector, Food Sector, Security sector, Social sector, Education sector, Scientific sector, Research innovation sector are few services to be named. Thus by using cloud based Information Technology services wide range of services can be made available to users benefiting society as whole. Thus by implementing these services through cloud based information technology system will benefit the society and overall system in many ways.

Keywords :

Cloud Computing, Software as a Service, Platform as a Service, Infrastructure as a Service

1. INTRODUCTION

Today Cloud computing is one of the hottest topic in the field of Information technology. Basic meaning of ‘Cloud’ refers to the collection of services provide through widespread internet that means Cloud Computing is an internet based computing that are provided to users through high speed internet. References to cloud computing in its modern sense appeared early as 1996,

with the earliest known mention in a Compaq internal document. The popularization of the term can be traced to 2006 when Amazon.com introduced the Elastic Compute Cloud. Cloud computing is becoming need of hour and back bone of Information Technology Industry. It eases the user’s accessibility to Information Technology based services and provides a virtual storage space to the user which could


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be used without concerning about the details of the hardware or software. Every industry weather it may be Government Enterprise, Business organization, Public sector enterprise or any other industry they are becoming focused towards cloud computing because it has many advantages like Cost savings, Focusing on the business, Performance, Security, Flexibility etc.

As cloud computing has advantages these clouds are becoming very popular in providing various services based on the use of Information Technology. Major services that could be provided using cloud based techniques are implementation of Government projects (E-Governance), E-Commerce, M-Commerce, Mobile Applications, Public dealing systems, Social Media applications, Online Gaming, E- Medical consultations and services, Web based services etc.

2. ARCHITECTURE FOR CLOUD BASE SERVICES

Cloud computing services follow the three layer architecture that supports in providing various services. These three layers include:

a) Software as a Service (SaaS)

b) Platform as a Service (PaaS)

c) Infrastructure as a Service (IaaS)

Figure 1.1

Software as a service includes various software based services developed by the service providers for the user which are accesable through internet. Major benefit of providing these kind of services to user is that they are free from maintaining and updating the software services instead they just need to use internet based software’s to use these services. Softwares services like Microsoft Office 365, Google Apps, Salesforce, Workday, Concur, Citrix GoToMeeting, Cisco WebEx are examples of SaaS.

Platform as a service model of cloud provider delivers hardware and software tools usually those needed for application development to its users as a service. A PaaS provider hosts the hardware and software on its own infrastructure. As a result, PaaS gives user freedom to install in-house hardware and software to develop or run a new application. This model does not typically replace a business' entire infrastructure but a business relies on PaaS providers for key


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services, such as Java development or application hosting.

Infrastructure as a Service is another fundamental model of cloud based computing. It provides user big Servers, Data Storage, Networking, Virtualization, Hard disk management, Storage Management, backup like infrastructure facilities. Amazon Web Services (AWS), Microsoft Azure, Google Compute Engine (GCE), Joyent are few examples of IaaS

Figure 1.2: Top companies providing services in different models of cloud

3. VARIOUS SERVICES AREAS BASED ON CLOUD

Using cloud architecture various Information Technology based services are provided by different agencies including Government agencies as well as Private agencies benefiting the society in various fields. Major areas where these Information Technology based services are implemented are in Medical Field, Insurance Field, Online Gaming, Entertainment field, Education

Field, Business Modeling, ERP solutions, etc are the focus areas of private agencies. Similarly government agencies are also using various Information Technology based services in providing social upliftment of the society. All these agencies providing cloud based services are able to provide these services at very economical price range to the users as expensive resources are shared among users, service providers and infrastructure providers. Major benefits of using Cloud computing for providing Information Technology based services are Cost savings that is lower costs in every respect whether it may be Software or Hardware, elasticity, scalability, storage on demand, Big Data, Better upgradeability, Easier management, Reduce spending on technology infrastructure, Globalize your workforce on the cheap, Better use of resources , Easy to get the latest and greatest updates, Improved information security etc.

4. CONCLUSION

Using cloud computing for Information Technology based services is a becoming very useful as user are being benefited in many ways. Key benefit of using these services is cost effectiveness and security of data. They are getting access to these services by breaking the barriers of geographical boundaries. These services are becoming trend setter in their respective fields by providing those to users at economical price. Modern users will going to use these services more and more as all the major area if Information technology is using


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these cloud computing based services. Thus next decade will be of Cloud Computing for providing Information Technology based services.

References: [1] http://www.qsiclouds.com/vcloud/

[2] http://www.slideshare.net/ milindkoyande/introduction-to-cloud-computing-13011219

[3] http://en.wikipedia.org/wiki/ Cloud_computing

[4] White paper Introduction to Cloud Computing: The Future of Service Provider Networks

[5] Cover Story: Platforms for Building and Deploying Applications for Cloud Computing by Rajkumar Buyya and Karthik Sukumar.


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Impact and Implementation of Green IT practices: Need of current era Sangeeta Vaibhav Meena

Assistant Professor, S.S. Jain Subodh P.G. College, Jaipur

Abstract :

In yesteryears a great revolution in Information technology has been seen in our life and our economic as well as social prosperity has not been untouched from it. The use of IT related devices have been increased rapidly and we know that they have a very short life span. This created very serious problems including lots of energy consumption, health hazardous e-waste. These effects which are being introduced by the technology are needed to be talked about. Governments and IT organizations have taken a lot of initiative to encourage the use of Green-IT. In the last few years, the IT organizations have realized that some initiative has to be taken towards green computing. Going green is not only in their public interest but it also reduces cost of overall expenditures. Now IT industries are trying to adopt those strategies which not only resolve the issue of rising cost but also can reduce the bad impacts on our environment. Green IT is an emerging application and essential component that should be seriously considered by the current and coming generation technology designers. There are four aspects to be “Green”, which are Green design, Green manufacturing, Green use, and Green disposal.

The goals of green computing are not only to reduce the increasing use of unsafe high risk materials and to reduce the energy consumption but also encourage the efficient designing and recycling of the produces used in IT. The idea is to make the whole process in such a way which gives independence of using computers with no harm on environment, economy, and society.

The aim of this paper is to offer an understanding of needs and benefits of Green-Computing and also shedding light on the impact and implementation of Green IT.

Keywords:

Green-IT, Green Computing, EPEAT, RoHS, MAIT, Energy-efficient, Environment.


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1. INTRODUCTION

Today Information technology can be seen as one of the pillars of our society. It plays a major role in our professional as well as private life. It is also become one of the most important drivers of economic growth. Even though this provides numerous good impacts, it is also playing a vital role in damaging our environment. From last few years the environmental effects are coming into the notice which is a very dangerous gift of information technology to our environment. Global warming is one of the most heard terms, which must be handled. IT is providing us a faster but unsafe life due to regular use on large scale. Green computing is a step toward decreasing harmful impacts of information technology.

1.1 What is Green IT?

It is an Eco-friendly mechanism, which can be used in all the steps from designing to utilizing and disposing the IT infrastructure in a very efficient and effective manner with minimum or no impact on the environment. The goal of green IT is to reduce the use of hazardous material and save our environment from its harmful impacts. More directly, we can say that using computers or devices in ways that save the environment save energy and overall save money. Green IT practices is a combination of energy efficiency practices and renewable energy practices. It is a tool by which global warming can be controlled and reduced.

1.2 Why we need Green IT?

We need IT to be green because of the following reasons:

● IT is the fastest growing sector of energy consumption. People are unaware of the consequences of mishandling. It is observed that most of the computer energy is often wasteful, because we leave the computer ON even when it is not in use. Energy is used to run the CPU, motherboard, monitor, printers, running the fan and spinning the disks. An average desktop computer has a power usage of between 65 to 250 watts and an average laptop consumes 15-45 watts of electricity.

● A very big problem called “e-waste” has been introduced because of the rapid technological advancement. This advancement obsolete the products within a very lesser time period, which creates a large stock of unwanted electronic products named as “e-waste”.

● Approximately 2% of total carbon footprint emission is contributed by IT sector. Carbon footprint is the amount of greenhouse gases produced. An increase in the concentration of this is responsible for Earth's increasing temperature, which could lead to severe floods and droughts, rising sea levels, and other environmental effects, affecting both life and the world's economy.

● Toxic chemicals like lead, mercury used in the manufacturing of computers and components which can enter in the food


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chain and water. A computer is made of approximately 17% lead, copper, zinc, mercury, and cadmium; 23% is plastic, 14% is aluminum, and 20% is iron [11]. Some of the toxic chemicals found in our average computer are:

Brominated flame retardants (BFRs) in plastic casings

Cadmium and selenium in circuit boards

Cathode ray tubes (CRT) in old monitors

Lead in solder

Mercury in LCD screens backlights.

● The public is now aware about the increasing risk of the sustainability of human being due to the fast depletion of the resources given by our nature. Now it has been mandatory for the engineers to think upon it and provide us a clean green technology.

1.3 How to implement?

There are plenty of ways to be green and also in the IT world, the solution falls into four general categories:

Green design: Designing the computers, application servers, data centers and all other IT related components in an environment friendly and energy efficient manner.

Green manufacturing: Manufacturing the electronic devices and other associated subsystems by considering the impact on the environment

Green use: The use of all IT devices is to be monitored and managed so that they consume lesser energy and help the environment.

Green disposal: Proper disposal and recycling of old and unwanted computers. Reuse by updating older electronic devices.

2. GREEN DESIGN

While designing a new system, Green design covers the whole life cycle of IT product not just thinking about only its performance. Green designers look at the entire chain starting from designing, to manufacturing, to use, and at last disposal. They focused on eliminating or minimizing the negative impacts. There is a method called EPEAT for consumers to evaluate the effect of a product on the environment. It provides a vibrant and reliable set of performance criteria for the design of products.

The Electronic Product Environmental Assessment Tool (EPEAT) is a tool which gives some clearly defined set of performance criteria for the designing of products. EPEAT’s environmental criteria cover the complete product life cycle from design to recycling. The Council evaluates on 51 criteria - 23 required and 28 optional. Products must meet all 23 required criteria to qualify for EPEAT. Products are rated Bronze, Silver or Gold based on how many of 28 optional criteria they meet (below 50%, 50%, and 75%). It is mandatory for Bronze-rated products to meet all required


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criteria, Silver-rated products must meet all required criteria plus at least 50% of the optional criteria and Gold-rated products should meet all required criteria and at least 75% of the optional criteria [3]. For last few years, EPEAT ratings have helped governments, companies, and consumers residing in various parts of the world. Now they are comparing and purchasing greener devices, resulting in major environmental benefits. Now, EPEAT registration and verification services are also available for India. India becomes the 43rd registered country of EPEAT which helps purchasers to select environment friendly products.

3. GREEN MANUFACTURING

Growing public awareness about the effects of the hazardous materials, research work is started by lots of companies to find more effective ways to build greener IT devices that do not make use of the dangerous chemicals.

Restriction of Hazardous Substances Directive or RoHS is the directive on the restriction of the use of certain hazardous substances in electrical and electronic equipment. This directive restricts the use of six hazardous materials in the manufacture. WEEE – Waste Electrical and Electronic Equipment Directive is closely linked to this directive.

Not only lead is restricted by RoHS, but there are six substances which use is restricted by RoHS[5]:

Mercury (Hg)

Cadmium (Cd)

Polybrominated biphenyls (PBB)

Polybrominated diphenyl ether (PBDE)

Hexavalent chromium (Cr6+)

Lead (Pb)

Rules 2011 is passed by the Government of India, which regulate the restriction of dangerous materials used in electronic and electrical equipment and components.

The producers and distributors, which involvement is in manufacturing, sales and processing of electronic and electrical components, all are covered in this Rules 2011. Its area is not limited to this only but it also restricts the waste collection centers, recyclers and product dismantlers.

Regulations in India and EU regulations have noticeable similarity. Rules 2011 does not deal with radioactive waste and Batteries because existing Indian laws are already regulating both of these. There are 39 substances which are exempted in EU RoHS directive. Slight variations can be seen in between India RoHS and EU RoHS, but these are negligible.

4. GREEN USE

Green use means reducing the energy consumption of IT devices as well as using them in an environmentally sound manner. The first major landmark in the history of green computing was the U.S. Environmental Protection Agency (EPA)’s Energy Star program, launched in 1992. This “Energy Star” is a labeling program that split


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up the computers, monitors and other devices according to the energy efficiency and it runs voluntarily [2]. Another way of green use of computer is to set default printer setting to double-sided, also preferred gray scale copies and before printing use print preview option. Don’t print unless necessary. Refill old cartridges instead of buying new. Turn on Power management features like sleep mode and also avoid the use of screen savers. It’s better to do computer-related tasks during contiguous blocks of time, leaving hardware off at other times. Power management in computer systems is considered due to many reasons, some of these are:

Overall energy consumption can be reduced

Battery life can be extended for portable and embedded systems

Cooling requirements can be reduced

Noise reduction

Today Virtualization is being seen as a most efficient and effective tool to reduce the energy consumption and gives more cost effective solution. Using virtualization a single server can be divided into multiple virtual machines, where multiple applications can be run. In this way server utilization rates can also be increased.

The efficiently written algorithm put its impact on the computer resources requirement for any given computing method. As much the efficient algorithm is proportional to the lower cost and energy

consumption. A slower search algorithm (e.g. linear) takes more energy than a faster search algorithm (e.g. hashed or indexed).

5. GREEN DISPOSAL

Green disposal can be achieved by refurbishing and reusing old computers and properly recycling unwanted computers. Less than 5% of India's total electronic waste gets recycled. 8,00,000 tons of E-waste is generated annually in India and the trend is rising. India’s Ministry of Environment and Forests (MoEF) introduced the “e-waste (Management and Handling) Rules 2011”, which came into effect on May 1, 2012 [10]. Hewlett Packard, Dell and several other companies have adopted a recycling methodology named as “Take Back”, but right now it is recycling approximately 2% only of their current sales.

Manufacturers' Association for Information Technology (MAIT) is the apex body representing the interests of the IT Hardware Industry in India, MAIT under the guidance of the Ministry of Environment and Forests and the CPCB/SPCBs has played a crucial role in formulating the e-Waste Rules 2011 as well the E-Waste Implementation Guidelines [9].

There are various organizations which support recycling of computer products and always try to accept out of order or unused computer devices and remake them. So, green disposal can be archived by donating still-working computers to a non-profit agency or upgrade them rather than replaced.


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6. CONCLUSION

Today’s concerns over climate change and the environment has created a unique opportunity for businesses to be responsible corporate and creates smart technology solutions along with cost and energy efficiencies at the same time. Companies globally are acting to become more environment friendly in their IT operations now they are discussing green IT strategy. Green computing represents a responsible way to address the issue of global warming. It is not only the responsibility of governments and organizations to protect our natural environment for the benefit of both the environment and humans but also it’s a moral duty of individuals to protect it. IT users should be aware about the harmful impacts of the use of computing devices over environment and takes some steps at his\her own level to reduce electricity consumption, e-waste etc. so that the concept of green computing can be implemented.

References: [1] http://en.wikipedia.org/wiki/

Green_computing

[2] http://www.energystar.gov

[3] http://www.epeat.net/resources/criteria

[4] http://www.greenit.net/

[5] http://en.wikipedia.org/wiki/ Restriction_of_Hazardous_Substances_Directive

[6] http://www.infosys.com/infosys-labs/publications/Documents/green-IT.pdf

[7] http://www.carnegiecyber academy.com/facultyPages/environment/issues.html

[8] http://cmet.gov.in/rohs.html

[9] http://mait.com/ewaste/

[10] http://www.moef.nic.in/downloads/ rules-and-regulations/1035e_eng.pdf

[11] http://en.wikipedia.org/wiki/ Personal_computer


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Artificial Bee Colony Algorithm- A Dynamic Approach for Query Optimization in DDBMS

Anjali Soni Research Scholar, RTU, Kota

Assistant Professor, S.S. Jain Subodh, P.G. College, Jaipur

Abstract :

From past many years, meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. The primary reason for adopting these algorithms are their broad applicability and ease of use. The Artificial Bee Colony (ABC) Algorithm is considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of bee colony behavior. In this paper, the Artificial Bee Colony Algorithm (ABC Algorithm) is reviewed that simulates foraging behavior of honey bee swarm. The performance of the algorithm is quite comparable with the results of the well-developed genetic algorithm. Dynamic Query Optimization is an important technique for designing and implementing database management system. It is crucial factor that affects the capability of database. The concept of distributed database systems has emerged as a natural solution to the information processing problems of geographically dispersed organizations. In this paper, we are concerned with processing a query in a distributed relational database system implemented with ABC algorithm.

Keywords:

Query optimization, Artificial Bee Colony Algorithm

1. INTRODUCTION

A distributed relational database is basically a distributed database which consist of multiple physical locations or sites and a number of relations distributed on multiple sites. These relations may be replicated and/or fragmented at different sites in the system. Replication of data leads to greater availability whereas fragmentation helps in reducing space for storage of relations. In

present scenario, having data distributed across multiple sites is particularly advantageous for large organizations who have offices and their work force distributed across the world. distributed data can then be access from multiple locations and offer other benefits including improving the availability of data. By distributing data we have the following advantages:


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? Single site failure- As the data is distributed over multiple sites , if one site fails data can further be recover from another one

? Replicated/ Fragmented Data - Data can be recovered by having an alternate copy. Fragmentation reduce the load on owning sites

? No interference- Every site can work independently as well as with coordination, depending on the need of query

? It helps in reducing the number of site failures, network accesses

? User need not bother about the storage and accessing methods thus improves the performance of users

In a distributed environment the database should always hold the ACID properties that is database should be consistent so the two desirable properties that a distributed database management system should possess are Distributed Data Independence and Distributed Transaction Atomicity.

Data Independence refers to the fact that a user is not having knowledge about the location of data, he is querying for. Query optimizer should use a cost model to give the output in optimal time i.e. it is the role of optimizer to select the best site to fetch the data from, to execute the query, so as to get the desired solution in minimum cost. In case

if SPJ (Select-Project-Join) Query, the order of relation to execute in join query should also determined. Distributed transaction atomicity refers to the fact that after executing the transactions in a distributed environment, these transactions should be atomic. If a user completes a transaction, the effect of that transaction should be visible to all the sites, be it updation or deletion [1].

2. DISTRIBUTED QUERY PROCESSING

Query processing is the process of translating a high level query (relational calculus) in a sequence of database operators (relational algebra +communication operators). In a distributed environment, location and data transparency is there, i.e. user is not required to know about the location of data. Here, Query optimization cost is the addition CPU cost, I/O cost and communication costs. Data is localized on different sites in a distributed environment. By following the specified procedure one can access the data in distributed environment but there may be some other problems while data access. There are:

? One high level query can have many equivalent transformations, the main difficulty is to select the most efficient one

? An efficient exchange strategies for distributed data must be found [2].


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Figure 1.1

3. ARTIFICIAL BEE ALGORITHM

Swarm Intelligence has been evolving as an active area of optimization over the past years. The major emphasis is to design adaptive, decentralized, flexible and robust artificial systems, capable of solving problems through solutions inspired by the behavior of social insects [3].

Artificial Bee Algorithm is one of the technique of finding the optimal solution in query optimization. In this algorithm, after returning from a foraging trip, bees communicate the distance, direction, and quality of a flower site to their fellow foragers by making waggle dances on a dance floor inside the hive [4]. By dancing zealously for a good foraging site they recruit foragers for the site. In this way a good flower site is exploited, and the number of foragers at this site is reinforced.

Honey bees evaluate the quality of each discovered food site and only perform waggle dance for it on the dance floor if the

quality is above a certain threshold. So each discovered site does not receives reinforcement. As a result, quality flower sites are exploited quite extensively. The foraging bees are classified into three categories; employed bees, onlookers and scout bees [5]. All bees that are currently exploiting a food source are known as employed. The employed bees exploit the food source and they carry the information about food source back to the hive and share this information with on looker bees. Onlookers bees are waiting in the hive for the information to be shared by the employed bees about their discovered food sources and scouts bees will always be searching for new food sources near the hive. Employed bees share information about food sources by dancing in the designated dance area inside the hive. The nature of dance is proportional to the nectar content of food source just exploited by the dancing bee. Onlooker bees watch the dance and choose a food source according to the probability proportional to the quality of that food source. Therefore, good food sources attract more onlooker bees compared to bad ones. Whenever a food source is exploited fully, all the employed bees associated with it abandon the food source, and become scout. Scout bees can be visualized as performing the job of exploration, whereas employed and onlooker bees can be visualized as performing the job of exploitation.


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Figure: 1.2

In the Bees algorithm [6], each food source represents a possible solution for the problem and the nectar amount of a food source represents the quality of the solution represented by the fitness value For every food source, only one bee can be employed and vice versa. It implies that the number of food sources is same as the number of employed bees and there is exactly one employed bee for every food source. This algorithm starts by associating all employed bees with randomly generated food sources (solution). In each iteration, every employed bee determines a food source in the neighborhood of its current food source and evaluates its nectar amount (fitness).

Pseudo code for Bees algorithm 1: Initialize 2: REPEAT. 3: Move the employed bees onto their

food source and evaluate the fitness

4: Move the onlookers onto the food source and evaluate their fitness

5: Move the scouts for searching new food source

6: Memorize the best food source found so far

7: UNTIL (termination criteria satisfied) In our dynamic approach of query optimization we consider each preferred path which a bee takes is a complete QEP which passes contains all relations. So each relation is connected to other relations who are called nearby neighborhoods of this relation. The decision of each bee for changing these nearby neighborhoods results in the invention of new QEP which are considered as neighborhood QEP of the preferred path [7].

The ABC algorithm basically has a search strategy, which is considerably similar to the standard DE algorithm (i.e., DE/rand/1). However, the ABC algorithm has a very successful decision mechanism that decides which areas within the search space require to be surveyed in more detail. The strategy of the ABC algorithm used to discover new nectar sources within the ABC algorithm and manage the capacity of the nectar sources discovered is also substantially powerful. Jeff Sidell has drawn a good framework to work on dynamic optimizers in paper ”Performance of Adaptive Query Processing in the Mariposa Distributed Database Management System”. Optimizers that work according to the workload of sites are dynamic optimizers. If a site is busy then these optimizers can utilize other site resources such as CPU time for optimizing the code [3] . Chung and Irani , suggested an approach


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which minimizes the intersite data traffic incurred by a distributed query by using a sequence of semi joins. A heuristic algorithm is developed to determine a low cost sequence of semi joins [5]. Later in March ,2013, Sunita M. Mahajan, and Vaishali P. Jadhav devised algorithms for semi join queries which works on one or more than one joining attribute. [6]. Many researchers have been worked on these swarm optimization algorithms. Bees algorithm is being used on Multi Join Query optimization by various authors and prove to be an efficient algorithm [8][11][12]. Authors have describe the whole process of food search as well as about their waggle dance connected with their objective in networking. Upon the route they choose the routing tables gets updated [9]. The problem of optimization is connected to PSO algorithm , Genetic Algorithm, Bees Algorithm [10]. Problem of protein structure prediction is also solved using this bees algorithm [13].

4. CONCLUSION

Swarm Optimization is gaining attention of researchers from the last decade. Many applications have been optimized using these intelligence up till now. Researchers are also focusing on these algorithm as these algorithm are easy to employ on any problem. The well-known artificial bee colony (ABC) algorithm is one of the most effective swarm-based algorithms. The ABC system combines both local and global search methods in an attempt to balance exploration and exploitation processes, and hopefully, it can be successfully applied to solve real-world problems. In this paper, these algorithms are reviewed for optimization problems to check the effectiveness of this algorithm on query

optimization process. Several papers are reviewed to test the capability of solving problems through this algorithm. This algorithm is gaining attention from researchers of different fields. In query optimization, choosing an effective query execution plan is the job of DBMS. Several execution plans can be implemented with the help of these algorithm. With several options available, query optimizer can choose the best query execution plan, thus giving the better result with the help of these algorithms, this process may yield some better results in future.

References: [1] http://www.cs.ox.ac.uk/people/

dan.olteanu/theses/Robe rt.Taylor.pdf

[2] Query processing in a distributed DBMS by Moritz Wilfer

[3] Bijaya Ketan Panigrahi, Lim Leng Hiot, Yuhui shi, “Handbook of swarm intelligence: concepts, principles and applications”

[4] R. Sagayam1, Mrs. K. Akilandeswari “Comparison of ant colony and bee colony optimization for spam host detection”, International Journal of Engineering Research and Development, Volume 4, PP. 26-32, November 2012

[5] Yucheng Kao, Ming-Hsien Chen, and Yi-Ting Huang, “A hybrid algorithm based on ACO and PSO forcapacitated vehicle routing problems”, Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2012, May 2012


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[6] V.Selvi, Dr.R.Umarani, “Comparative analysis of ant colony and particle swarm optimization techniques”, International Journal of Computer Applications ,Volume 5– No.4, August 2010

[7] K. von Frisch. The dance language and orientation of Bees. Harvard University Press, Cambridge, 1967.

[8] Karaboga, D., Basturk, B., “On the performance of artificial bee colony (ABC) algorithm.”, Appl. Soft Comput, 2008

[9] Cui Qiao1 &Hengshan Wang, “The further research on the application of ABC to the optimization and control of project”, Engineering Management Research, Vol. 1, No. 2, 2012

[10] Mohammad Alamery, Ahmad Faraahi, Hossein Erfani ” Multi-join query optimization using the bees algorithm”, Advances in Intelligent and Soft Computing Volume, 2010

[11] Nana Li, Yujuan Liu, Yongfeng Dong, Junhua Gu “Application of ant colony optimization algorithm to multi join query optimization”, Advances in Computation and Intelligence”, 2008

[12] Dokeroglu T., “Particle Swarm Intelligence as a new heuristic for the optimization of distributed database queries”, Application of Information and Communication Technologies (AICT), 6th International Conference, 2012

[13] Deepak Sukheja, Umesh Kumar Singh,” A novel approach of query optimization for distributed database systems” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 4, No 1, July 2011.


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