Issues and Challenges in Next Generation Networks[Context: IoT, Big Data and Cloud]

APHRDI-Three Day Residential Training Programme on “Internet of Things” , 19 -21 December, 2016

P. Venkata Krishna

Dr. P. Venkata KrishnaSenior Member of IEEE & ACM

Professor & Head, Department of Computer ScienceDirector, UCC

Sri Padmavati Mahila UniversityTirupati, India

dr.krishna@ieee.org

Outline

▪ Introduction

▪Objectives

▪Addressing Issues

▪ Case Analysis

▪ Conclusion

▪ References

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INTRODUCTION

▪ The exponential growth in mobile computing technology has resulted in the form of newer mobile devices with variable features and capabilities.

▪ To maximise the utilisation of such devices, the mobile applications also need to be designed with capabilities to address the real-time as well as non-real-time services requirements using virtualization and distributed computing techniques.

▪ The prime disadvantage of conventional networks is fixed and inflexible in their operation.

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Introduction

▪Due to their hierarchical architecture, traditional networks are unable to meet the future network requirements such as cloud services and big data analysis that requires classified network traffics based on user demands.

▪Hence, the future network infrastructure has to fulfil few critical criteria such as dynamicity, scalability, adaptability, better quality of service in terms of improved bandwidth and reduced latency.

▪ The major factor such as node addressing at lower layers could be a critical issue which affects the above mentioned criteria equally.

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Introduction

▪ The networks have also gone through few procedural changes such as OSI layered Protocols, TCP/IP Protocols [8-10] and Cross-Layered Protocols.

▪ However, due to the ever increasing demand for pervasive computing services, computer networks are again in the process of an evolution to fulfil the pervasive requirements by interfacing cyber world with any physical systems for reactive environments or by making Internet of Things for less reactive environments.

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Cloud Computing

▪ The NIST cloud computing definition [1] is widely accepted as a valuable contribution toward providing a clear understanding of cloud computing technologies and cloud services.

▪ It provides a simple and unambiguous taxonomy of three service models available to cloud consumers: cloud software as a service (SaaS), cloud platform as a service (PaaS), and cloud infrastructure as a service (IaaS).

▪ It also summarizes four deployment models describing how the computing infrastructure that delivers these services can be shared: private cloud, community cloud, public cloud, and hybrid cloud.

▪ Finally, the NIST definition also provides a unifying view of five essential characteristics that all cloud services exhibit: on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service.

Cloud Conceptual Reference Architecture

Actors in Cloud

Cloud Support

▪ Virtualization

▪ On Demand Business

▪ SOA

▪ Autonomic Computing

▪ Semantic web

▪ After careful evaluation, this paper has found that longer MAC addressing scheme has affected networks such as Ethernet LAN and WLAN by making Ethernet frame stay longer due to extra bits.

▪ Because of this, there is a significant loss of bandwidth, longer latency and less optimal collision detection. In networks with light weight protocol such as ZigBee, Bluetooth and Wireless Sensor Networks which are predominantly required to deal with smaller packets, waste of bandwidth and energy would be very high.

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Internet of Things (IoT)

▪ The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

▪ IoT facilitates machine to machine communication that evolved from the convergence of wireless technologies, micro-electromechanical systems (MEMS), and the internet.

HOW

▪ Sensors

▪ Actuators

▪ Networks

▪ Protocols

How cont..

How cont..

Internet of Things (IoT)

▪ Three Things which power IoT :

❖ Sensing

❖ Computing

❖ Communication

IΘTᴧCΞLL

▪ Posting a photo on Facebook

▪ Following someone on Twitter

▪ Sending/Receiving an e-mail

▪ Catching up on NEWS

▪ Reading/Publishing a blog

▪ Learning about IoT

Things People do on INTERNET

People share information with other People

What if

THINGS could share information with

other THINGS(or people)?

▪ Information what they see hear feel and do!

▪ For example▪ Soil-tells the moisture content ▪ Chair-who is sitting on it and what is his weight▪ Room-what is the temperature and presence▪ Car-at what speed it is going and where▪ Bridge-how many vehicles are on it & how old it is?

▪ This information could be used by the other things/people

What information can things share?

▪ It helps “Things”(homes, cities, cars, roads etc) to communicate what they know, feel and do with other things (or people)

▪ These communications can be gathered and analyzed by other things or people (Home control system, traffic control system, etc)

▪ And be able to give instructions to other “things” to do stuff

▪ How do THINGS Communicate?

Idea of Internet of Things

How people communicate?

SENSE-ANALYZE-RESPOND

SENSORS

Temp Sensing and Sending it to Mobile App

Control Bulb through Mobile

App

Build Occupancy Detector from

the first Principle

Home Automation System

Cyber Physical System as Internet of Things in Critical Applications

▪ Internet of Things (IoT) (Coetzeeand Eksteen, 2011) is a wireless capability which shows that how machine can interact with other device to perform certain task. This device to device (Wan et al., 2013) communication is possible only through Internet.

▪ Cyber’ is an integration of computation, communication and control systems. ‘Physical’ means natural and human-made systems governed by the laws of physics and functioning in continuous time.

▪ Cyber Physical Systems in which the cyber and physical systems are those firmly incorporated at all scales and levels.

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▪ Starting in late 2006, the US National Science Foundation (NSF) and other United State federal agencies sponsored several workshops on CPSs.

▪ In 2007, the NSF has identified CPSs as a key area of research. CPS uses embedded computers and networks to compute, communicate and control the physical processes.

▪ Simultaneously a CPS receives feedbacks on how physical processes affect computations and vice versa, which is shown in Fig.1. Just as the internet transforms how humans interact with one another. CPSs will transform how we interact with the physical world around us.

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CPS KEY OBJECTIVES

▪ The objective of the research program of the CPSs is to incorporate physical and cyber design. CPSs are different from desktop computing, traditional embedded real-time systems and Wireless Sensor Network (WSN). The following are some of the characteristics of CPSs [12].

▪ Closely integrated: CPSs are the integrations of computation and physical processes.

▪ Cyber capability in every physical component and resource-constrained: The software is embedded in every embedded system or physical component and the system resources such as computing, network bandwidth, etc. are usually limited.

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▪ Networked at multiple and extreme scales: The networks of the CPSs include wired or wireless network, WLAN, Bluetooth, GSM, etc which are distributed systems. Moreover, the system scales and device categories appear to be highly varied.

▪ Complex at multiple temporal and spatial scales: The different components of CPS have probably in equably constrained by spatiality and real time.

▪ Dynamically reorganizing/reconfiguring: CPSs as very complicated systems must have adaptive capabilities.

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▪ High degrees of automation control loops must close: CPSs are in favour of convenient human-machine interaction and the advanced feedback control technologies are widely applied to these systems.

▪ Operation must be dependable, certified in some cases: As a large scale or complicated system, the reliability and security are necessary for CPSs.

▪ Cyber capability in every physical component: CPSs have sensing technology and predictable behaviour, high confidence software and systems.

▪ Cyber and physical components are integrated: CPSs are integrated for learning and adaption and are high performance, self-organization and self-assembly.

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Applications

▪ A Distributed Robot Garden at MIT, Cambridge, in which a team of robots tend a garden of tomato plants. This system combines distributed sensing. Each plant is equipped with a sensor node monitoring its status, navigation, manipulation (objects to pick up, modify, destroy) and wireless networking.

▪ A focus on the control system aspects of CPSs that pass through critical infrastructure (electricity generation, water supply and telecommunication) can be found in the efforts of the Idaho National Laboratory at Eastern Idaho and collaborators researching resilient control systems (tolerate fluctuations via their structure and control parameters).

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Applications

▪ MIT’s CarTel project in which a fleet of taxis collect real-time traffic information in the Boston area. Together with historical data, this information is then used for calculating fastest routes for a given time of the day.

▪ Health care and medicine domain includes National health information network, Electronic patient record initiative, Home care, Operating room, etc. Some of these are controlled by computer systems with hardware and software components and real-time systems with safety and timing requirements.

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▪ Many sensor nodes construct wireless networks with features of dynamic reorganizing and reconfiguring. Integrate intelligent road with unmanned vehicle with sensor nodes get data from wireless sensor networks and process information to determine the behaviour of vehicles. This comprises vision system, Global Positioning System (GPS), main board, etc. The GPS and vision system serve as an auxiliary location, while the unmanned vehicles primarily realize navigation depending on WSNs.

▪ The distributed micro power generation is coupled with the power grid, where timing precision and security issues appear large. The power electronics, power grid and embedded control software form a CPS (Electric Power Grid), whose design is influenced by fault tolerance, security and decentralized control.

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▪ Transportation systems could be benefit considerably from better embedded intelligence in automobiles, which could improve safety and efficiency.

▪ Networked autonomous vehicles could dramatically improve the efficiency of military and could offer significantly more effective disaster recovery techniques.

▪ Networked building control systems such as Heating, Ventilation, and Air-Conditioning (HVAC) system and lighting could significantly improve energy efficiency and demand variability, reducing reliance on hydrocarbon fuels.

▪ In communications, cognitive radio could be benefit a lot from distributed consensus about available bandwidth and from distributed control technologies.

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What is Technology convergence?

▪ In simple terms technology convergence is an amalgamation of three domains Sensing, Computing and Communication.

▪ Sensing helps us to monitor the environment and measure all the critical parameters such as temperature, moisture, humidity, wind speed, wind direction, chemical radiation, and many more physical phenomenon.

▪ Computing helps us to interpret and build the right context by eliminating the redundant data. Computing also provide better facility to take an appropriate decision based on derived context.

▪ Finally, communicating means passing the context and conveying arrived decisions to the relevant end users. Communication also helps us to reach the end users dynamically as well as in real-time.

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Why technology convergence is required?

▪ The simple answer is to make a better living space.

▪ Better living space means with less or no pollution, better water conservation and harvesting facilities, more natural energy production facilities, green technology based utility services like healthcare, transportation, power supply, water supply, telecommunication, environment-friendly precision farming.

▪ The evolution of technology has scaled up the sophistication with respect to the devices and interfaces across sensing, computing and communication domains. But to solve issues that threaten the better living space, we need a greater paradigm shift in our approach.

▪ The conceptualization of technological solution is already a major issue and will also stay as an issue in near future.

▪

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▪ With the help of remote sensing and localized sensing via distributed wireless sensor networks coupled with technologies such as pervasive computing, data analytics, cloud computing and web services, a true and proactive living space could be established.

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OBJECTIVES

▪The Next-Generation Networks such as Internet of Things or Cyber Physical Systems need to accomplish the following objectives as given below:

▪ Effective Utilization of Bandwidth▪ Possible reduction of latency▪ Improved data flow per packet▪ Better network management

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▪ The stated objectives are directly related to the size of a network.

▪ Since the size of Next-generation network is highly unpredictable as well as tends to change persistently due to different application requirements, achievement of these objectives would be a challenging as well as a complex task.

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Sample Scenario

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▪ Four familiar networking standards have been partially combined together to make a larger network.

▪ A conventional Ethernet LAN is shown with one server and four client nodes.

▪ A wireless LAN consists of four nodes which are linked with Ethernet LAN via access point (AP).

▪ A Bluetooth network consists of three nodes and Zigbee network that is composed of four sensor nodes.

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▪ The intention of each of these networks is to share information with select group of nodes in a meaningful manner.

▪ In simple term the meaningful manner means, the intended information from the sender needs to be transferred to the relevant receiver with accepted level of bandwidth, latency, timeliness, functional correctness and speed.

▪ The identification of relevant sender and receiver pair in case of unicast communication as well as appropriate sender and multiple receivers in case of multicast communication will be a key challenge.

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▪ The local area network comprises of simple network devices such as hubs, switches and NICs whereas a wide area network consists of hubs, switches routers and NICs.

▪ For every data network, establishing connectivity among communicating devices is the fundamental requirement.

▪ Nevertheless, in order to set up a purposeful connection between any two systems, identification of individual systems is an essential prerequisite. Hence all systems in a network need to be categorized using unique addressing procedures.

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▪ The OSI layer model has used different addressing schemes at various levels such as MAC addressing at data-link layer, IP addressing at network layer and PORT addressing at transport layer [8, 9, 10].

▪ In the past, different MAC schemes have been benchmarked for both wired and wireless networks.

▪ The MAC method that uses Carrier Sense Multiple Access (CSMA) technique is widely used across different networking standards [8, 9].

▪ In [3], the impact of large signal propagation delay on various MAC schemes is evaluated.

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▪ In CSMA based networks, nodes are expected to sense the medium before they try to access it. CSMA requires a node to sense medium for the potential ongoing communication before it attempts to access this medium.

▪ However, due to larger signal propagation delay, nodes might face problem in detecting any ongoing communication [3, 5, 7].

▪ Moreover, the variable packet size also affects the signal propagation time which further degrades overall performance [1, 2, 3, 4, 7].

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▪ The 48-bit MAC may bring few issues such possible duplicates [6] and security violation.

▪ Many contributions have been made regarding address allocation strategies especially in MANETS [4, 5, 7].

▪ However, enough work has not been carried out principally as related to the MAC overhead in any heterogeneous networks.

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▪ The stated objectives such as bandwidth, latency, network management and data flow are heavily dependent on two common challenges such as implementation of unique addressing method and minimization of addressing overhead.

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MAC Frame Format and IPv4 Header Format

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MAC Frame Format and IPv4 Header Format

▪ The source and destination addresses in both formats are used to identify the relevant communication entities or sender and receiver.

▪ The MAC address is made up of 6-octets or 48-bits and the same is burned onto the hardware which is hard-coded and permanent whereas the IP address contains 4 octets or 32 bits which is logical and reconfigurable.

▪ The MAC address is unique and the same is assigned by hardware manufacturer in association with Internet Assigned Numbers Authority (IANA).

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▪ Both IP as well as MAC addressing schemes have been found and used in computer networks from the beginning.

▪ At present, networks have become more dynamic in size, ad hoc in nature, hungry for bandwidth, diverse in connectivity, unique in service provisioning such as QOS and also highly based on application requirements.

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MAC Frame Formats

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IEEE 802.3 Ethernet Frame Format

IEEE 802.11 WLAN Frame Format

IEEE 802.5 Token ring Frame Format

IEEE 802.15.4 ZigBee Frame Format

Consolidated Address Information

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▪ Various MAC protocols reveal the frame format for different networking standards such as IEEE 802.3, IEEE 802.5, IEEE 802.11 and IEEE 802.15.4.

▪ A simple investigation confirms that all these networking standards use any one among the IEEE addressing schemes such as MAC48, Extended Unique Identifier (EUI48) and EUI64.

▪ Here, IEEE 802.3, IEEE 802.5 and IEEE 802.11 uses 48 bit MAC addresses and IEEE 802.15.4 uses extended 64 bit MAC addresses.

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Locality of reference

▪ In accordance with the locality of reference theory, there are two types of locality of reference such as physical locality of reference and temporal locality of reference.

▪ Based on physical locality of reference, nodes that reside in close proximity are likely to communicate among themselves.

▪ According to the temporal locality of reference, nodes are expected to communicate with the same set of nodes repetitively.

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▪ A smaller LAN or a campus LAN would have more communication activities than LAN to WAN.

▪ Since networking standards such as IEEE 802.3, IEEE 802.11 and IEEE 802.15.4 are used often, this paper has done a restricted investigation regarding the impact of addressing in the above mentioned standards.

▪ Here, two simple scenarios are used to evaluate the issue of MAC addressing overhead.

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Scenario 1: MAC under IEEE 802.3

▪ Suppose node n1 would like to send 1,500KBs of information to node n2 using Ethernet LAN.

▪ Since both nodes n1 and n2 belong to the same subnet, they do not require any router to facilitate their data communication.

▪ The maximum transfer unit for MAC frame is 1500 bytes. Hence n1 needs to use 1000 frames to transmit 1500KBs of information to n2. The MAC frame overhead with respect to address fields is 12 bytes per frame.

▪ Consider a data pay load of 50 bytes per second. Now the MAC overhead per minute will be 720 bytes (12*60). Hence, smaller packets induce more MAC overhead.

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Scenario 2: MAC under IEEE 802.15.4

▪ Smart sensor nodes such as temperature sensor, pressure sensor, moisture sensor, humidity sensor, light sensor, smoke sensor, gas leak sensor, acoustic sensor, vibration sensor, image sensor could monitor relevant physical phenomena and would produce tiny data units of nearly constant size.

▪ These data units are then transferred to the base station in a smart sensor network. However, the data transfer rate depends upon the application requirements and based on type of query scheme.

▪ Consider a wireless sensor network deployed using IEEE 802.15.4 standard. Assume that a sensor node sensing an event and constantly generating 3 bytes of information.

▪ Since IEEE 802.15.4 using 16 bytes for extended addressing [11], per frame overhead is 16 bytes. Hence, the MAC overhead becomes more than 5 times the actual information.

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▪ From these two scenarios, it is clearly understood that MAC overhead will be a major constraint, especially in Internet of Things (IoT) systems where more physical systems will be connected using MAC-based networks.

▪ Hence, a different approach is required to address the above mentioned issues to make Next- generation networks more optimal.

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Adaptive Node Configuration Protocol (ANCP)

▪ Krishna et al. (2015) designed solution using ANCP

▪ To overcome the MAC overhead, particularly in Ethernet LAN and resource constrained networks such as ZigBee and wireless sensor networks, this paper has proposed an Adaptive Node Configuration Protocol (ANCP).

▪ It is a simple overlay protocol just sandwiched between network layer and data-link layer.

▪ It reads the IP packet from the higher layer and determines whether both destination address and source address are having same network id.

▪ If both are having same net-id, then it by pass the conventional MAC addressing scheme. It uses 12-bit unique two tier node addressing scheme.

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▪ In case that both are having different net-id, then control will be handed back to the conventional MAC addressing scheme.

▪ Based on the locality of reference, ANCP could solve major MAC overhead in conventional LANs as well as in resource limited networks.

▪ Since ANCP uses only 24 bits, it could almost save 72 bits per packet over conventional MAC addressing scheme.

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Packet Transmission time versus Packet size

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▪ The Next-Generation Networks (NGN) would be based on hybrid standards of networking technologies such as IEEE 802.3 (Ethernet), IEEE 802.11 (Wi-Fi), IEEE 802.15.1 (Bluetooth) and IEEE 802.15.4 (ZigBee).

▪ In the future, multiple of these networking technologies would be integrated together to serve applications customized for end users.

▪ Hence, network boundary, network access, network protocols, network topology and network bandwidth would be decided as well as designed by the user applications

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▪ Large scale services systems leveraging RFID and other technologies for tracking of goods and services could acquire the nature of distributed real-time control systems.

▪ Distributed real-time games that integrate sensors and actuators could change the nature of online social interactions.

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Basic Building Blocks of U-CAMPa

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Sample workflow based on U-CAMPa

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Integration of Sensors with Cloud Services

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Conclusion

▪ NGN challenges and Future aspects

▪ How to use and deploy cloud models

▪ IoT/CPS- Future of NGN

▪ Data Analytics

▪ Finally, for better living space…

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References

▪ Emerging Technologies and Applications for Cloud Based Gaming, IGI-Global Publishers 2016, ISBN13: 9781522505464|ISBN10: 1522505466|EISBN13: 9781522505471|DOI: 10.4018/978-1-5225-0546-4

▪ Challenges, Opportunities, and Dimensions of Cyber-Physical Systems, IGI-Global Publishers, 2015 ISBN13: 9781466673120|ISBN10: 1466673125|EISBN13: 9781466673137|DOI: 10.4018/978-1-4666-7312-0

▪ P. Venkata Krishna, Sumanth Yenduri, and Eunmi Choi, “Intelligent Systems Architectures for Big Data”, International Journal of Communication Networks and Distributed Systems, Vol. 15 No. 2/3, 2015. (ISSN: 1754-3916)

▪ S. Misra, P. V. Krishna, K. Kalaiselvan, V. Saritha and M. S. Obaidat, “Learning Automata-Based QoS Framework for Cloud IaaS”, IEEE Transactions on Network and Service Management, Vol. 11, Issue. 1, pp. 15-24, 2014 (ISSN: 1932-4537 )

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THANK YOU

ANY QUESTIONS??