Technical Report Writing Final Report

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December 9th, 2011

To:

Mr Abdul Waheed Haris

Lecturer Technical Report Writing

FAST-NUCES (Main Campus)

Karachi

Dear Sir,

I have enclosed the Final Year Project Report of IEEE 802.15.4/ZigBee Network Lifetime

Optimization.

As I had predicted, the introduction of Cluster Heads or ZigBee Coordinators to the

simulated network on OMNeT++ and adding a little more intelligence to them increased the

network uptime by at least 10%. The details are discussed in the enclosed report. If the

methodologies that I have discussed are implemented, it would be a great achievement for

the WSN Committee to have an even better and efficient network at their disposal.

It was a great honour working with you on this project.

I would be available for contact on my email address if there are any queries regarding this

project and/or project report.

Thank You.

Yours Sincerely,

Usman Muhammad Nooruddin

Roll Number: 07-0039

Section A, BS (TE)

Email Address: [email protected]


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NETWORK LIFETIME OPTIMIZATION OF

IEEE 802.15.4/ZIGBEE

National University of Computer and Emerging Sciences FAST

Dated: December 9th

, 2011

Prepared By:

Usman Muhammad NooruddinRoll Number: 07-0039

Prepared For:

Abdul Waheed HarisLecturer Technical Report Writing


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[This page is intentionally left blank]


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Table of Contents

01. Abstract 4

02. Summary 5

03. Introduction 5

04. Background 5

05. Scope 6

06. Applicable Documents 6

07. Nomenclature / Definitions 7

08. References 7

09. Methodology 7

10. Theory

10.1 Evaluation of Low Rate Wireless Personal Area Network (LR-WPAN) Standardization

10.2 Why is it called ZigBee?

10.3 Device Types10.4 Network Topologies

10.5 Architecture

10.6 Network and Application Support Layer

10.7 Physical (PHY) Layer

10.8 Media Access Control (MAC) Layer

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11. Design Parameters 11

12. Technical Requirements 12

13. Assumptions 12

14. Technical Description 13

15. Instructions & Procedures 13

16. Evaluations / Analysis 14

17. Acceptance Criteria 14

18. Results & Conclusion 15

19. Discussion of Results 15

20. Recommendations & Alternatives 15

21. Bibliography 16

22. Appendix

Annealing Sensor Networks, Andrew Jennings and Daud A. Channa

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List of Figures:

Figure 1: Star Topology 9

Figure 2: Peer to Peer Topology 9

Figure 3: Mesh Topology 10

Figure 4: ZigBee Stack 10

Figure 5: Proposed Scenario 12

List of Graphs:

Graph 1: Results 15


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01. ABSTRACT

IEEE 802.15.4 or ZigBee is a standards-based technology for remote monitoring, control and sensor

network applications. The ZigBee standard was created to address the need for a cost-effective, standards-

based wireless networking solution that supports low data-rates, low-power consumption, security, and

reliability.

Although the ZigBee Alliance employs enough intelligence for a network to grant ZigBee devices a long

uptime, this report studies another method that can well increase the Network Lifetime up to 10% - 15%

more than the conventional methods that are proposed by default. This is done by intelligently routing the

packets from source to sink by looking at the LQI (Link Quality Indicator) of the link currently under

consideration.


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02. SUMMARY:

This report focuses on the possibility of an introduction of a new protocol to route the packets from the

ZigBee end-devices to the Monitoring station at the other end. A packet may have to pass many devices in

between to reach the predefined destination. But, it all comes down to the cumulative intelligence of the

network to route that packet efficiently from the source to the destination to save energy in this process.

03. INTRODUCTION:

The technology defined by the ZigBee specification is intended to be simpler and less expensive than other

WPANs, such as Bluetooth. ZigBee is targeted at radio-frequency (RF) applications that require a low data

rate, long battery life, and secure networking.

This research report studies the implementation of a new Networking protocol that provides further

intelligence to the ZigBee Coordinators so that they can well manage the routing of packets from source to

sink.

This report complies with the standards set by the IEEE 802.15.4.

04. BACKGROUND:

The IEEE 802.15.4 standard is a simple packet data protocol for lightweight wireless networks and specifies

the Physical (PHY) and Medium Access Control (MAC) layers for Multiple Radio Frequency (RF) bands,

including 868 MHz, 915 MHz, and 2.4 GHz. The IEEE 802.15.4 standard is designed to provide reliable data

transmission of modest amounts of data up to 100 meters or more while consuming very little power.

ZigBee technology takes full advantage of the IEEE 802.15.4 standard and extends the capabilities of this

new radio standard by defining a flexible and secure network layer that supports a variety of architectures

to provide highly reliable wireless communication. ZigBee technology also offers simplicity and a cost-

effective approach to building, construction and remodelling with wireless technology. ZigBee is all set to

provide the consumers with ultimate flexibility, mobility, and ease of use by building wireless intelligence

and capabilities into every day devices.

Thus, ZigBee technology is a low data rate, low power consumption, low cost, wireless networking

protocol targeted towards automation and remote control applications.

The focus of network applications under the IEEE 802.15.4 / ZigBee standard include the features of low

power consumption, needed for only two major modes (Tx/Rx or Sleep), high density of nodes per

network, low costs and simple implementation. These features are enabled by the following characteristics

2.4GHz and 868/915 MHz dual PHY modes.

This represents three license-free bands: 2.4-2.4835 GHz, 868-870 MHz and 902-928 MHz. The

number of channels allotted to each frequency band is fixed at 16 channels in the 2.45 GHz band,

10 channels in the 915 MHz band, and 1 channel in the 868 MHz band

Maximum data rates allowed for each of these frequency bands are fixed as 250kbps @2.4 GHz, 40

kbps @ 915 MHz, and 20 kbps @868 MHz.

Allocated 16 bit short or 64 bit extended addresses.

Allocation of guaranteed time slots (GTSs)

Carrier sense multiple access with collision avoidance (CSMA-CA) channel access Yields highthroughput and low latency for low duty cycle devices like sensors and controls.


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Fully hand-shake acknowledged protocol for transfer reliability.

Low power consumption with battery life ranging from months to years.

Energy detection (ED).

Link quality indication (LQI).

Multiple topologies : star, peer-to-peer, mesh topologies

05. SCOPE:

Increasing network lifetime is a great advantage to keeping the network up even under desolate

conditions. We want the network to be robust and resistant to failure.

Since the core markets include consumer electronics, energy management and efficiency, health care,

home automation, telecommunication services, building automation and industrial automation, an

increase in network lifetime would mean increase in efficiency of all the above mentioned and not leaving

out the increase in revenue of the company due to a resistant to failure network.

06. APPLICABLE DOCUMENTS:

[1] LAN/MAN Standards Committee of the IEEE Computer Society, IEEE Standard for Information

technology Telecommunications and information exchange between systems Local and

metropolitan area networks Specific requirements Part 15.4: Wireless Medium Access Control

(MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks

(WPANs), 2006.

[2] Holger Karl and Andreas Willig, Protocols and Architectures for Wireless Sensor Networks, 2005

John Wiley & Sons, Ltd. ISBN: 0-470-09510-5.[3] Shahin Farahini, ZigBee Wireless Networks and Transceivers, 30 September, 2008, NewNes

Publishers, ISBN: 0-750-68393-7.[4] Andrs Varga, OMNeT++ User Guide for v4.2b2, May 5th, 2011.[5] INET Framework for OMNeT++ Manual, 2011.[6] The ZigBee Alliance, .[7] OMNeT++ in a Nutshell,

[8] MiXiM,


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07. NOMENCLATURE / DEFINITIONS:

APL Application Layer

FFD Forward Function Device

LQI Link Quality Indicator

LR-WPAN Low Rate Wireless Personal Area NetworkMAC Medium Access Layer

Mote A ZigBee Coordinator

Node A ZigBee End Device

NWK Network Layer

PAN Personal Area Network

PHY Physical Layer

QOS Quality of Service

RFD Reduced Function Device

WLAN Wireless Local Area Network

WPAN Wireless Personal Area Network

08. REFERENCES:

[1] Andrew Jennings and Daud A. Channa, Annealing Sensor Networks, Lecture Notes in Computer

Science, Volume 3684, Aug 2005, Pages 581 - 586.

[2] Arun S., Seminar Report on ZigBee, September 2008.

[3] Feng Chen and Falko Dressler, A Simulation Model of IEEE 802.15.4 in OMNeT++, 2006.

[4] Feng Chen, Isabel Dietrich, Reinhard German and Falko Dressler, An Energy Model for SimulationStudies of Wireless Sensor Networks using OMNeT++, 2007.

[5] Feng Chen, Nan Wang, Reinhard German and Falko Dressler, Performance Evaluation of IEEE

802.15.4 LR-WPAN for Industrial Applications, 2008.

[6] Sajjad Hussain Shah, Kashif Naseer, Wajid Ali, Sohail Jabbar, Abid Ali Minhas, Prolonging the

Network Lifetime in WSN through Computational Intelligence, Proceedings of the World Congress

on Engineering and Computer Science 2011 Vol I, WCECS 2011, October 19-21, 2011, San

Francisco, USA.

09. METHODOLOGY:

The network lifetime of the ZigBee transceiver is obtained by simulating a ZigBee network on a network

simulator. In this project we have opted for OMNeT++.

The simulation is first performed first by turning off the sleep cycles and testing the transmissions and the

energy consumed. Then the sleep cycles would be introduced and the energy consumed would be

recorded. Lastly, the protocol that would be discussed in the theory would be introduced and the energy

consumption would be recorded.

A graphing utility would be used to plot the results of the energy consumed for every iteration of the test.


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10. THEORY:

The cellular network was a natural extension of the wired telephony network that became persistent

during the mid-20th century. As the need for mobility and the cost of laying new wires increased, the

motivation for a personal connection independent of location to that network also increased. Coverage of

large area is provided through (1-2km) cells that co-operate with their neighbours to create a seamlessnetwork. Cellular standards basically aimed at facilitating voice communications throughout a metropolitan

area. During themid-1980s, it turned out that an even smaller coverage area is needed for higher user

densities and the emergent data traffic.

10.1 Evolution of Low-Rate Wireless Personal Area Network (LR-WPAN) Standardization

The IEEE 802.11 working group for Wireless Local Area Network (WLAN) was formed, to create a wireless

local area network standard. Whereas IEEE 802.11 was concerned with features such as Ethernet matching

speed, long range(100m), complexity to handle seamless roaming, message forwarding, and data

throughput of 2-11 Mbps.

Wireless personal area networks (WPANs) are used to convey information over relatively short distances.WPANs are focused on a space around a person or object that typically extends up to 10m in all directions.

The focus of WPANs is low-cost, low power, short-range and very small size.

The high data rate WPAN (IEEE 802.15.3) is suitable for multi-media applications that require very

high quality of services.

Medium rate WPANs (IEEE 802.15.1/Bluetooth) will handle a variety of tasks ranging from cell

phones to PDA communications and have QoS suitable for voice communications.

The low rate WPANs (IEEE 802.15.4/LR-WPAN) is intended to serve a set of industrial, residential

and medical applications with very low power consumption, with relaxed needs for data rate and

QoS. The low data rate enables the LR-WPAN to consume very little power. This feature allows

small, power-efficient, inexpensive solutions to be implemented for a wide range of devices.

10.2 Why is it called ZigBee?

It has been suggested that the name evokes the haphazard paths that bees follow as they harvest pollen,

similar to the way packets would move through a mesh network. Using communication system, whereby

the bee dances in a zigzag pattern, worker bee is able to share information such as the location, distance,

And direction of a newly discovered food source to her fellow colony members. Instinctively implementing

the ZigBee Principle, bees around the world actively sustain productive itchiness and promote future

generations of Colony members.

10.3 Device Types

ZigBee devices are required to conform to the IEEE 802.15.4-2006 Low-Rate Wireless Personal AreaNetwork (WPAN) standard. ZigBee wireless devices are expected to transmit 10-75 meters, depending on

the RF environment and the power output consumption required for a given application, and will operate

in the unlicensed Worldwide (2.4GHz global, 915MHz Americas or 868 MHz Europe). The data rate

is250kbps at 2.4GHz, 40kbps at 915MHz and 20kbps at 868MHz.

There are three different ZigBee device types that operate on these layers in any self-organizing application

network. These devices have 64-bit IEEE addresses, with option to enable shorter addresses to reduce

packet size, and work in either of two addressing modes star and peer-to-peer.

The ZigBee (PAN) coordinator node: The most capable device, the coordinator forms the root of

the network tree and might bridge to other networks. It is able to store information about the


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network. There is one, and only one, ZigBee coordinator in each network to act as the router to

other network. It also acts as the repository for security keys.

The Full Function Device (FFD): The FFD is an intermediary router transmitting data from other

devices. It needs lesser memory than the ZigBee coordinator node, and entails lesser

manufacturing costs. It can operate in all topologies and can act as coordinator.

The Reduced Function Device (RFD): This device is just capable of talking in the network; it cannotrelay data from other devices. Requiring even less memory, (no flash, very little ROM and RAM), an

RFD will thus be cheaper than an FFD. This device talks only to a network coordinator and can be

implemented very simply in star topology.

An FFD can talk to RFDs or other FFDs, while an RFD can talk only to an FFD. An RFD is intended for

applications that are extremely simple, such as a light switch or a passive infrared sensor; they do not have

the need to send large amounts of data and may only associate with a single FFD at a time. Consequently,

the RFD can be implemented using minimal resources and memory capacity.

10.4 Network Topologies

There are 3 basic topologies that exist in ZigBee networks. These can be mingled together to create moretopologies.

Star Topology

Peer-to-Peer Topology

Mesh Topology

Figure 1: Star Topology

Figure 2: Peer - to - Peer Topology


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Figure 3: Mesh Topology

10.5 Architecture

The LR-WPAN architecture is defined in terms of a number of blocks in order to simplify the standard.

These blocks are called layers. Each layer is responsible for one part of the standard and offers services to

the higher layers. The layout of the blocks is based on the open systems interconnection (OSI) seven-layer

model. The interfaces between the layers serve to define the logical links between layers. The LR-WPAN

architecture can be implemented either as embedded devices or as devices requiring the support of an

external device such as a PC.

An LR-WPAN device comprises a PHY, which contains the radio frequency (RF) transceiver along with its

low-level control mechanism, and a MAC sub layer that provides access to the physical channel for all types

of transfer.

Figure 4: ZigBee Stack


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10.6 Network and Application Support layer:

The network layer permits growth of network without high power transmitters. This layer can handle huge

numbers of nodes.

This level in the ZigBee architecture includes

The ZigBee Device Object (ZDO)

User-Defined Application Profile(s)

The Application Support (APS) Sub-layer.

The APS sub-layer's responsibilities include maintenance of tables that enable matching between two

devices and communication among them, and also discovery, the aspect that identifies other devices that

operate in the operating space of any device.

The responsibility of determining the nature of the device (Coordinator / FFD or RFD) in the network,

commencing and replying to binding requests and ensuring a secure relationship between devices rests

with the ZDO (ZigBee Define Object). The user defined application refers to the end device that conformsto the ZigBee Standard.

10.7 Physical (PHY) layer:

The PHY service enables the transmission and reception of PHY protocol data units (PPDU) across the

physical radio channel.

The features of the IEEE 802.15.4 PHY physical layer are Activation and deactivation of the radio

transceiver, energy detection (ED), Link quality indication (LQI), channel selection, clear channel

assessment (CCA) and transmitting as well as receiving packets across the physical medium.

10.8 Media access control (MAC) layer:The MAC service enables the transmission and reception of MAC protocol data units (MPDU) across the

PHY data service. The features of MAC sub layer are beacon management, channel access, GTS

management, frame validation, acknowledged frame delivery, association and disassociation.

11. DESIGN PARAMETERS:

1. The simulation should be a large scale implementation, that is, not less than 200 nodes.

2. At least 3 ZigBee Coordinator cluster heads (CH) should be involved.

3. Attest 30 ZigBee Routers should be involved.

4. At least 60 ZigBee End-devices should be involved.

5. The protocol to be tested would have the following specifications: If the CH is connected to two routers, and one of them is sleeping, the CH would route the

frame through the other Router.

If this has been repeated a certain number of times, the CH would wake the other router and

let the previous one sleep and save its battery.

If both of them are asleep, the CH would look at the time they have been sleeping and the

battery life they have remaining with them and wake the one who has been asleep the

longest with more battery life.

If both of them are awake, the CH would determine who has the longest uptime and the

least battery remaining of the two, and would put that node to sleep.


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14. TECHNICAL DESCRIPTION:

The current applications of the ZigBee transceiver are numerous, which also leads to much possible

advancements and enhancements. Most of the applications in various different criterions require only

major enhancement: Network Lifetime Prolongation.

The longer we can maintain the nodes alive, i.e., prolonging the battery life in the network, the better the

application and hence broaden the scope of the ZigBee Transceiver.

We would be using two tools to accomplish this:

1. OMNeT++. This is an open source Network Simulator

OMNeT++ is a discrete event simulation environment. Its primary application area is the simulation of

communication networks, but because of its generic and flexible architecture, is successfully used in other

areas like the simulation of complex IT systems, queuing networks or hardware architectures as well.

OMNeT++ provides a component architecture for models. Components (modules) are programmed in C++,

then assembled into larger components and models using a high-level language (NED). Reusability of

models comes for free. OMNeT++ has extensive GUI support, and due to its modular architecture, the

simulation kernel (and models) can be embedded easily into your applications.

2. MiXiM Framework

MiXiM is an OMNeT++ modelling framework created for mobile and fixed wireless networks (wireless

sensor networks, body area networks, ad-hoc networks, vehicular networks, etc.). It offers detailed models

of radio wave propagation, interference estimation, radio transceiver power consumption and wireless

MAC protocols (e.g. ZigBee).

15. INSTRUCTIONS AND PROCEDURES:

1. Install OMNeT++

a. Download OMNeT++ from

b. Extract the latest version of OMNeT++ on the disk.

c. Run the bash terminal.

WARNING: Do not install OMNeT++ on any directory which has a space in its name,

otherwise the bash terminal would register an error.

d. Type ./configure and press Enter.e. After the process is over, type make and press Enter. This is a time consuming process.

f. After the process is over, type omnetpp to run OMNeT++.

g. If the need to log out arises, type exit and press Enter.

2. Install MiXiM framework over OMNeT++

a. Download the latest version of MiXiM .

b. Open OMNeT++.

c. Go to File-> Import.

d. Select the .tar.gz file and press import.

e. After the import process completes, press CTRL+B to build all configurations and to

append MiXiM to OMNeT++. This is a time consuming process.

f. Now, all the modules of MiXiM have been integrated on to OMNeT++ for use.


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3. Start a new project file of MiXiM by using the Wizard.

4. Make the topology shown in Figure xx .

5. Build the project.

NOTE: Add the record-eventlog = true to the omnetpp.ini file.

6. Run the project and simulate up to 100,000 events.

7. Save the simulation and exit.8. Open the results sub-folder in OMNeT++ file browser and plot the scalar values.

9. Repeat the process from 5 to 8 for:

a. No Sleep cycles introduced into the topology.

b. Sleep Cycles introduced into the topology.

c. The protocols that is proposed in this project, specific to the cluster heads or ZigBee

Coordinators.

16. EVALUATIONS AND ANALYSIS:

When the plots start appearing, we can see how the results vary from one another. It may be quite vague

at first but, after filtering is applied to the results, the plots would become clear to reveal the results asshown in Graph 1.

The parameter record-eventlog = true enables to capture every moment and every magnitude

that happens during a simulation. It is also clear to note that unless until it is required, like here, this

parameter puts a lot of load on the CPU hence should be used carefully, since it is capturing the most

minute of details happening inside the simulated environment.

After filtering, the scalar graphs would show how the network life of the whole ZigBee network decreases

until one of the node fails. The simulation would end where one node fails and gives an interrupt flag to

stop the simulation. Here the total events would be displayed in the Tkenv status bar.

17. ACCEPTANCE CRITERIA:

1. The whole simulation should show that the proposed method increases the network lifetime.

2. The Network Lifetime increased should be of a measurable factor.

3. The model drawn out of this proposal should be implementable and should follow the standards

specified by IEEE 802.15.4.

4. Since the battery lifespan specified by ZigBee Alliance is minimum 2 years, no node should die

before 2 years simulated time.


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18. RESULTS AND CONCLUSION:

The following are the results from the simulations successfully completed in OMNeT++:

Graph 1: Results

1. With no sleep cycles in the ZigBee Network, the network dies very soon.

2. As soon as the sleep cycles are introduced, the Network lives up to the ZigBee standard.

3. When the Network is tested on the protocols that are mentioned in this report, the Network life is

increased by approximately 15%, which is a measurable and a considerable amount.

19. DISCUSSION OF RESULTS:

As we can clearly see how the results vary from when no sleep cycles are introduced to when the protocol

involving the proposed scenario is introduced. This concludes that our method of approach was correct

and properly defined.

20. RECOMMENDATIONS AND ALTERNATIVES:

Recommendations:

1. The scenario should be followed strictly. Any changes would lead to a different scenario and would

need to be evaluated.

2. Since this applies to a ZigBee configuration employing ZigBee end-devices, the same scenario canbe evaluated with more ZigBee Routers. It might lead to a better Network Lifetime since we are

introducing more intelligent devices into the network premises.

Technology alternatives can be used which apply the IEEE 802.15.4 stack in a different way as opposed to

ZigBee:

1. CEBus

2. LonWorks

3. Insteon

4. Z-Wave

One should note that an alternative would lead to the same amount of paper work as done here.


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21. BIBLIOGRAPHY:

[1] Fabrizio Granelli, Dzmitry Kliazovich and Nelsom L. S. Da Fonseca, Performance Limitations of IEEE

802.15.4 Networks and Potential Enhancements, 2007.

[2] Nicky van Frost, Simulating Queuing Networks with OMNeT++, January 23rd

, 2004.

[3] C. Mallanda, A. Suri, V. Kunchakarra, S. S. Iyengar, R. Kannan and A. Durresi, Simulating Wireless

Sensor Networks with OMNeT++, 2005.

[4] E. Egea-Lpez, J. Vales-Alonso, A. S. Martnez-Sala, P. Pavn-Mario and J. Garca-Haro, Simulation

Tools for Wireless Sensor Networks, Summer Simulation Multiconference SPECTS 2005.

[5] Tao Shu and Marwan Krunz, Coverage-Time Optimization for Clustered Wireless Sensor Networks:

A Power-Balancing Approach, IEEE/ACM Transactions on Networking, Vol. 18, No. 1, February

2010.

[6] Deborah Estrin, Ramesh Govindan, John Heidemann and Satish Kumar, Next Century Challenges:

Scalable Coordination in Sensor Networks, 1999.

[7] Pablo Suarez, Carl-Gustav Renmarker, Adam Dunkels and Thiemo Voigt, Increasing ZigBee

Network Lifetime with X-MAC, 2008.

[8] Myung-Gon Park, Kang-Wong Kim and Chan-Gun Lee, A Holistic Approach for Optimizing Lifetime

of IEEE 802.15.4/ZigBee Networks with Deterministic Guarantee of Real-Time Flows, 2009.


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22. APPENDIX


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Annealing Sensor Networks

Andrew Jennings & Daud Channa

Electrical & Computer Engineering School,

RMIT University, Melbourne, Australia

{[email protected], [email protected]}

Abstract. With a continuing improvement in the capabilities of intelligence perunit of energy, we should reconsider the organisation of sensor networks. We

contend that solutions should be model-free, locally based and need to behighly dynamic in nature. Here we propose an approach inspired by simulatedannealing. In the context of several application scenarios we explore the poten-

tial for adding intelligence to sensor networks.

1. Introduction

Sensor networks [1] are envisioned to become an integral part of our lives. Thesenetworks are being applied to provide various tasks such as surveillance and monitor-ing systems for commercial and military applications. Applications are being devel-

oped to gather process and utilize the information from the surrounding environmentas required. These requirements have kept challenging researchers in the design ofbetter architectures and protocols for sensor networks. We now have some early de-ployment of sensor networks, showing that we have successfully established the basicprotocols. These follow two main directions: clustering of nodes [2], and synchronisedsleep cycle networks with a flatter structure [3]. Now the main challenge is to estab-lish a wide range of application systems. To deploy applications we need methods ofcoordination that are efficient both in delivering services and conserving energy.

The growth and advancements in technologies and the constant reduction in thesize and cost of Micro Electro Mechanical Systems (MEMS) have given rise to awhole new dimension of networking which involves sensors and actuators that arequickly deployable and self organizing. They have resulted in a new dimension in

network computing, namely pervasive sensing and control. The ongoing rapid ad-vancements, developments, and research in the sensor and actuator networks onlyleaves one to foresee that they will soon intervene and associate into all living habitatsof humans and their surrounding environment.

In most scenarios the network must be functional over long periods of time, it iscrucial for the operation, management and continued lifespan of the network to con-trol the behaviour/reaction of the sensors and actuators to the different occurrences ofevents in the network. The sensors in these networks are limited in energy, memory

To appear in KES Invited Session on Evolutionary and Self-Organizing Sensors, Actua-

tors and Processing Hardware. Jennings A. and Channa D., Melbourne, 2005


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and computational facilities, while generally the actuators have an ample supply ofresources as their mobility can enable them to recharge thus utilizing their resources tothe fullest without energy constraint.

The deployment and maintenance of the nodes must be cost effective, because itwill be unfeasible to configure these large networks of small devices. The sensornodes along with the actuators must be self organizing and provide a means of pro-gramming and managing the network as a whole, rather than administering individualnodes and actuators.

2 Status & scenarios

Although it is not yet clear which applications are viable for sensor networks, wehave selected three scenarios to motivate our work. They serve to highlight the issuesthat we now face. We aim to create solutions for these situations.

2.1 Pedestrian Crossing Guard

Fig. 1. A proposal for an instrumented pedestrian crossing

We aim to improve the safety of pedestrian crossings using sensor networks. Howmight we prevent the running down of pedestrians?

One possible solution is to use an array of pressure sensors on the roadway surfaceto detect pedestrians walking. This would be in addition to proximity sensors and vis-ual surveillance [5], as illustrated in Figure 1. Through combination of sensor read-ings, we can improve the accuracy of recognition. If we want to make use of the sen-sor readings then we need confidence that there is a low probability of false positives -otherwise car drivers will not accept the system.

With a reliable method of pedestrian detection, we can perhaps move to the nextlevel with these systems. If we can reliably detect a car traveling at a speed likely to


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result in a collision, then the crossing system can intervene and communicate with thecar - potentially it could also override car braking systems. This would bring the car toa halt. We have the possibility of eliminating the possibility of cars contacting pedes-trians at crossings. There may also be a role for coordinating with robot teams to en-able more active monitoring of pedestrians here we need development of team be-haviour [6]

2.2 Animal Counting

Environmental monitoring was one of the earliest motivations for exploring sensornetworks. A typical task is the estimation of animal populations. We would like toknow how many of a particular type of animal are within a geographic area. In con-

trast to urban applications, this setting is very demanding in terms of energy manage-ment. Note that the estimation of populations is more difficult than simply trackinganimals - we need some confidence of the identity of animals. Is the set of readings fora single animal, or two that are within the same area?

2.3 Perimeter Surveillance

This is a classical application of sensory technology. We have a perimeter that wewish to patrol, with video and movement surveillance. To augment this, we would liketo deploy proximity sensors to improve accuracy. These scenarios can give us aframework to consider sensor network application approaches. They provide chal-lenges and a range of difficulties. All are real applications that may have some pros-

pect of widespread deployment. At this stage of development of the field, it is impor-tant to focus on feasible applications to focus the research.

3 Energy and Intelligence

One of the central tenets of sensor networks has been the need to keep nodes simpleand careful in their use of energy. We could not, for example, implement the fullTCP/IP stack on sensor nodes. This would be a waste of energy, since the nature ofthe communication is quite different.

Progress in battery technology is painful and very slow. But when we consider in-telligence per unit of energy, then progress is quite dramatic. So we should be moreopen to incorporating intelligence in the sensor node, as long as that results in signifi-cant energy conservation.


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3.1 Local Resolution

Fig. 2. Directed diffusion (network wide) versus local resolution

One of the original proposals for sensor network protocols was "directed diffusion"(DD) [4]. It is a robust protocol that can work in very tough environments. Even withextensive network breakages, it will continue to operate. As Figure 2 illustrates, "in-terests" are propagated to areas of the network, and "gradients" are used to reinforcethe successful delivery of packets across the network.

Given the constraints placed on directed diffusion, it is an appealing solution. Buthow might it change if we allowed local processing, rather than propagating resultsacross the broader sensor network? DD assumes that we have to send this outside thesensor network, but with local processing we can avoid the energy consumption ofnetwork wide reporting.

This creates both a need for local algorithms that can actually resolve sensor data,and also a means of coordination. There are clear energy advantages in local resolu-tion.

Similar difficulties arise in the location of mobile sensor nodes. The author in [7]has presented the use of simulated annealing for this problem. Here we are concernedwith organisation and messaging for fixed nodes.

3.2 Model Based, or Model Free?

If we want to improve monitoring, then perhaps a more accurate model of the con-text will help? In the case of the pedestrian crossing, we could develop a trackingmodel. For example, Kalman filtering could aid following an individual through thenetwork. But if we incorporate this model, what will happen in non-modelled cases?How will our model-based approach react when a person falls over, and lies stationary


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on the crossing? In the worst case, we might decide that the crossing is clear, and letthe cars proceed.

Similarly, in surveillance of a perimeter, we might improve accuracy by statisticaltraining to detect people walking across the field. But will we detect somebody crawl-ing across the space?

Once we fix a model for the sensing environment, defining the range of possibletargets, then the task of constructing the sensor network is reduced to optimisation.There is no need for further intelligence. So the real challenge for sensor networks ishow to deal with unusual data. Consider surveillance when a bunch of leaves falls tothe ground. Do we raise an alarm or simply log the event for further processing? If welog it, what priority do we give to the event?

This is a familiar problem in AI, bringing us to the very familiar challenges of se-mantics. How do we deal with images that do not have familiar content? How can wego beyond simple statistical pattern matching? These are very difficult, but also veryimportant problems.

3.3 Dynamics

Consider the problem of tracking (and identifying) an animal that gives us unusualreadings. Perhaps it is of a size that we have not encountered, or it genuinely is a newentry to the region. Clearly this is important, and we would like to track its trajectory.But in order to do this, we need to estimate velocities and alert the relevant part of the

sensor network. Once we have lost contact, it will be difficult to sustain the identity.Remember, we are interested in counting animals, so identity is important. Clearly weneed application protocols that can deal effectively with highly dynamic situations.

4 Annealing Sensor Networks

The simulated annealing algorithm is a successful method of searching for optimalsolutions in complex spaces. Most importantly, it is model free : any problem can beformulated as an annealing process. In analogy with the process of annealing crystals,it has an associated temperature. At high temperature, large parameter changes arepossible, but as the process cools only smaller changes are possible.

Fig. 3. Activation cycles (sleep cycles) for a single node


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We propose an approach to organising sensor networks that is inspired by simu-lated annealing. Regions have a temperature, which indicates the intensity of sensing.Figure 3 illustrates the sleep cycle of a single node. At a low temperature, the nodescycle only at A, but as the temperature increases we also cycle at B,C,D progressively.Since these cycles are divisions of the fundamental cycle (the A cycle), these sched-ules do not conflict.

Fig. 4. Network temperature heating process

In the event that a node encounters an unexpected stimulus, it can cause a local risein temperature. If several nodes in a region send this message, then a rapid rise in lo-cal temperature can take place. We can allow this temperature to spread rapidly inspace if we desire, or rapidly decay. In accordance with the physical analogy, localheating cannot spread vast distances without decay. Figure 4 illustrates the process.

To effectively make use of annealing sensor networks, we need local resolution ofsensor information. For example, in the case of animal tracking, a local decision isneeded on the temperature response. Note that an identification is not needed, but onlylocal decision making. We are investigating how to provide this on the typical proces-sors used for sensor nodes. It certainly seems possible to accomplish this computationon the nodes. Of course over time, we can expect the intelligence/energy quotient tocontinue to grow.

Annealing sensor networks (ASN) are a development of directed diffusion net-works. There are some important differences. The adaptive sleep cycle provides forrapid response. Local resolution of control is an important difference. Where directeddiffusion incorporates routing, ASN's only advise routing.


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5 Discussion

We have proposed an approach to model-free local behaviour for sensor networks.Given that we have no local model of expected behaviour, how can we achieve localresolution? Each node can keep a statistical database of patterns it has encountered.When patterns within a statistical tolerance appear, this can trigger the appropriatebehaviour.

Fully distributed control of sensor networks in this manner raises some importantnew issues. How do we maintain the currency of statistical data? How can we makechanges to behaviour whilst ensuring network stability?

It is interesting that classical problems of semantics come to the forefront when wewant to further explore sensor networks. Here the resources we have to bring to the

problem are limited. We have an unlimited source of data, through lifelong observa-tion of the world through the network sensors. Potentially we can bring vast computa-tion to the task, through recording and processing offline. But we are limited in humanintervention. This leads us to explore computationally intensive approaches. Perhapswe should consider the task as data mining for sensor organisation methods.

References

1. Deborah Estrin, Ramesh Govindan, John Heidemann and Satish Kumar Next CenturyChallenges: Scalable Coordination in Sensor NetworksIn Proceedings of the Fifth An-

nual International Conference on Mobile Computing and Networks (MobiCOM '99),August 1999, Seattle, Washington.

2. Wendi Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan Energy-EfficientCommunication Protocols for Wireless Microsensor Networks, Proc. Hawaiian Int'lConf. on Systems Science, January 2000.

3. Wei Ye and John Heidemann and Deborah Estrin An Energy-Efficient MAC Protocolfor Wireless Sensor Networks Proceedings 21st International Annual Joint Conference

of the IEEE Computer and Communications Societies, 2002

4. Chalermek Intanagonwiwat, Ramesh Govindan and Deborah Estrin Directed Diffusion:A Scalable and Robust Communication Paradigm for Sensor Networks In Proceedingsof the Sixth Annual International Conference on Mobile Computing and Networks

(MobiCOM 2000), August 2000, Boston, Massachusetts.5. Christian Wohler Autonomous in situ training of classification modules in real-time vi-

sion systems and its application to pedestrian recognition Pattern Recognition Letters

Volume 23 , Issue 11 Pages: 1263 - 1270 (September 2002)6. H. Van Dyke Parunak, Sven. A. Bruekner & James Odell Swarming pattern detection

in Sensor and Robot Networks Altarium Institute, Working Document

7. Martinson E. and Payton D. Lattice Formation in Mobile Autonomous Sensor Arrays,HRL Laboratories, LLC. 2003-2004