**7. Conclusion**

This chapter describes several aspects which should be considered during developing a network protocol for wireless sensor network (WSN). WSN has been used in both surveillance and civil applications. It is considered application specific as each application has its own set of requirements. Two main categories are proposed including event-based and periodic-based application. Throughput is the key requirement in the event-based whilst lifetime is the key in the periodic-based. Moreover, one of major drawbacks of WSN

Chintalapudi, K.; Fu, T.; Paek, J.; Kothari, N.; Rangwala, S.; Caffrey, J.; Govindan, R.;

Juang, P.; Oki, H.; Wang, Y.; Martonosi, M.; Peh, L.S.; & Rubenstein, D. (2002). Energy-

Szewczyk, R.; Mainwaring, A.; Polasrte, J.; Anderson, J. & Culler, D. (2004). An Analysis of a Large Scale Habitat Monitoring Application, *SenSys'04*, Baltimore, Maryland, USA

Martinez, K.; Padhy, P.; Riddoch, A.; Ong, H.L.R. & Hart, J.K. (2005). Glacial Environment Monitoring Using Sensor Networks, *REALWSN'05*, Stockholm, Sweden Kottapalli, V.A.; Kiremidjian, A.S.; Lynch, J.P.; Carryer, E.; Kenny, T.W.; Law, K.H. & Lei, Y.

Paek, J.; Chintalapudi, K.; Caffrey, J.; Govindan, R. & Masri, S. (2005). A Wireless Sensor

Schmid, T.; Dubois-Ferrière, H. & Vetterli, M. (2005). SensorScope: Experiences with a Wireless Building Monitoring Sensor Network, *REALWSN'05*, Stockholm, Sweden Dreicer, J.S.; Jorgensen, A.M. & Dors, E.E. (2002). Distributed Sensor Network with

Simon, G.; Balogh, G.; Pap, G.; Maróti, M.; Kusy, B.; Sallai, J. ; Lédeczi, Á.; Nádas, A. &

Coleri, S.; Cheung, S.Y. & Varaiya, P. (2004). Sensor Networks for Monitoring Traffic,

Brignone, C.; Conners, T.; Lyon, G. & Pradhan, S. (2005). SmartLOCUS: An Autonomous,

Sankarasubramaniam, Y.; Akan, O.B. & Akyildiz, I.F. (2003). ESRT: Event-to-Sink Reliable

Ee, C.T. & Bajcsy, R. (2004). Congestion Control and Fairness for May-to-One Routing in

Hull, B.; Jamieson, K. & Balakrishnan, H. (2004). Mitigating Congestion in Wireless Sensor

Lu, C.; Blum, B.M.; Abdelzaher, T.F.; Stankovic, J.A. & He, T. (2002). RAP: A Real-Time

Wan, Chieh-Yih; Eisenman, S.B. & Campbell, A.T. (2003). CODA: Congestion Detection and

Avoidance in Sensor Networks, *ACM SenSys'03*, Los Angeles, USA

Communication Architecture for Large-Scale Wireless Sensor Networks, *RTAS*,

*University of California Berkeley Technical Report*, August 2004

*Hewlett-Packard Development Company Technical Report*, June 2005

Sensor Networks, *ACM SenSys'04*, Bultimore, Maryland, USA

Networks, *ACM SenSys'04*, Bultimore, Maryland, USA

Transport in Wireless Networks, *ACM MobiHoc'03*, Maryland, USA

Vol.46, Issue 5, pp.605-634

*Materials*, San Diego, USA

Sydney, Australia

Vol.632, pp.235-243

Maryland, USA

September 2002

Network, *IEEE Internet Computing*

Experiences with ZebraNet, *ASPLOS'02*, ACM

Sensor Network for Target Detection, Classification, and Tracking. *Computer Networks: The International Journal of Computer and Telecommunications Networking*,

Johnson, E. & Masri, S. (2006). Monitoring Civil Structures with a Wireless Sensor

Efficient Computing for Wild-Life Tracking: Design Tradeoffs and Early

(2003). Two-Tiered Wireless Sensor Network Architecture for Structural Health Monitoring, *Proceedings of SPIE's 10th Annual Symposium on Smart Structures and* 

Network for Structural Health Monitoring: Performance and Experience, *Proceedings of the 2nd IEEE Workshop on Embedded Networked Sensors (EmNetS-II)*,

Collective Computation for Situational Awareness, *AIP Conference Proceedings*,

Frampton, K. (2004). Sensor Network-Based Countersniper System, *SenSys'04*,

Self-Assembly Sensor Network for Indoor Asset and Systems Management,

is resource constraint. The power for all operations comes from tiny batteries. Under some circumstances, it is uneconomical or impractical to change or recharge the batteries. In WSN, the data is delivered via wireless link which is susceptible to the surrounding environments. The radio unit is responsible for data delivery has a limited buffering capacity. Control information should be minimised to be included in a packet.

The Power & Reliability Aware Protocol (PoRAP) is developed and its main objective is to provide an efficient data communication by means of energy conservation whilst reliability is maintained. Its three key elements include direct communication, adaptive transmission power and intelligent scheduling. With adaptive transmission power and intelligent scheduling, the power consumption is minimised as a result of a lower transmitting power, collision avoidance and minimised idle listening without unnecessary data losses. The key capabilities of PoRAP make it suitable for use in the periodic-based WSN applications with regular reporting patterns where maximising bandwidth is not the prime concern. PoRAP thus applies to some of the WSN applications such as environmental and habitat monitoring where the sources often remain at their positions throughout the operation. Slots are allocated to the sources for data transmissions. In PoRAP, it is assumed that the number of allocated slots is equal to that of sources. A low duty cycle application is more efficient using PoRAP when the percentage of slot usage is high. The evaluation results indicate up to 50% of power can be yielded whilst the reliability is within the desired range. However, PoRAP is not applicable if a source has to wait longer until the next cycle is started. Therefore, a limitation of PoRAP arises when there is a high slot overhead because there are many sources in the network.

#### **8. References**


is resource constraint. The power for all operations comes from tiny batteries. Under some circumstances, it is uneconomical or impractical to change or recharge the batteries. In WSN, the data is delivered via wireless link which is susceptible to the surrounding environments. The radio unit is responsible for data delivery has a limited buffering capacity. Control

The Power & Reliability Aware Protocol (PoRAP) is developed and its main objective is to provide an efficient data communication by means of energy conservation whilst reliability is maintained. Its three key elements include direct communication, adaptive transmission power and intelligent scheduling. With adaptive transmission power and intelligent scheduling, the power consumption is minimised as a result of a lower transmitting power, collision avoidance and minimised idle listening without unnecessary data losses. The key capabilities of PoRAP make it suitable for use in the periodic-based WSN applications with regular reporting patterns where maximising bandwidth is not the prime concern. PoRAP thus applies to some of the WSN applications such as environmental and habitat monitoring where the sources often remain at their positions throughout the operation. Slots are allocated to the sources for data transmissions. In PoRAP, it is assumed that the number of allocated slots is equal to that of sources. A low duty cycle application is more efficient using PoRAP when the percentage of slot usage is high. The evaluation results indicate up to 50% of power can be yielded whilst the reliability is within the desired range. However, PoRAP is not applicable if a source has to wait longer until the next cycle is started. Therefore, a limitation of PoRAP arises when there is a high slot overhead because there are

Warneke, B. & Pister, K.S.J. (2002). MEMS for Distributed Wireless Sensor Networks,

Mainwaring, A.; Polasrte, J. ; Szewczyk, R.; Culler, D. & Anderson, J. (2002). Wireless Sensor Networks for Habitat Monitoring, *WSNA'02*, Atlanta, Georgia, USA. Allen, G.W.; Lorincz, K.; Ruiz, A.; Marcillo, O.; Johnson, J.; Lees, J. & Welsh, M. (2006).

Essa, I.A. (2000). Ubiquitous Sensing for Smart and Aware Environments. *IEEE Personal* 

Srivastava, M.; Muntz, R. & Potkonjak, M. (2001). Smart Kindergarten: Sensor-Based

Jovanov, E.; O'Donnell Lords, A.; Raskovic, D.; Cox, P.G.; Adhami, R. & Andrasik, F. (2003).

Otto, C.; Milenković, A.; Sanders, C. & Jovanov, E. (2006). System Architecture of a Wireless

Arora, A.; Dutta, P.; Bupat, S.; Kulathumani, V.; Zhang, H.; Naik, V.; Mittal, V.; Cao, H.;

*Proceeding of the 9th International Conference on Electronics, Circuits and Systems*.

Deploying a Wireless Sensor Network on an Active Volcano. *IEEE Internet* 

Wireless Networks for Smart Developmental Problem-Solving Environments. *ACM* 

Stress Monitoring Using a Distributed Wireless Intelligent Sensor System. *IEEE* 

Body Area Sensor Network for Ubiquitous Health Monitoring, *Journal of Mobile* 

Demirbas, M.; Gouda, M.; Choi, Y.; Herman, T.; Kulkarni, S.; Arumugam, U.; Nesterenko, M.; Vora, A. & Miyashita, M. (2004). A Line in the Sand: A Wireless

information should be minimised to be included in a packet.

many sources in the network.

Dubrovnik, Croatia

*Communications*

*SIGMOBILE*, Rome, Italy

*Computing*, Vol.10, No.2, pp.18-25

*Engineering in Medicine and Biology Medicine*

*Multimedia*, Vol.1, No.4, pp.307-326

**8. References** 

Sensor Network for Target Detection, Classification, and Tracking. *Computer Networks: The International Journal of Computer and Telecommunications Networking*, Vol.46, Issue 5, pp.605-634


**28** 

*USA* 

**Standardised Geo-Sensor Webs for** 

*Massachusetts Institute of Technology,* 

**Integrated Urban Air Quality Monitoring** 

Bernd Resch, Rex Britter, Christine Outram, Xiaoji Chen and Carlo Ratti

*'In the next century, planet earth will don an electronic skin. It will use the Internet as a scaffold to support and transmit its sensations. This skin is already being stitched together. It consists of millions of embedded electronic measuring devices: thermostats, pressure gauges, pollution detectors, cameras, microphones, glucose sensors, EKGs, electroencephalographs. These will probe and monitor cities and endangered species, the atmosphere, our ships, highways and fleets of trucks, our* 

Following this comprehensive vision by Neil Gross (1999), it can be assumed that sensor network deployments will increase dramatically within the coming years, as pervasive sensing has recently become feasible and affordable. This enriches knowledge about our

However, leveraging sensor data in an ad-hoc fashion is not trivial as ubiquitous geo-sensor web applications comprise numerous technologies, such as sensors, communications, massive data manipulation and analysis, data fusion with mathematical modelling, the production of outputs on a variety of scales, the provision of information as both hard data and user-sensitive visualisation, together with appropriate delivery structures. Apart from this, requirements for geo-sensor webs are highly heterogeneous depending on the

This chapter addresses the nature of this supply chain; one overarching aspect is that all elements are currently undergoing both great performance enhancement combined with drastic price reduction (Paulsen & Riegger, 2006). This has led to the deployment of a number of geo-sensor networks. On the positive side the growing establishment of such networks will further decrease prices and improve component performance. This will particularly be so if the environmental regulatory structure moves from a mathematical

Of specific interest in this chapter is our concern that most sensor networks are being built up in monolithic and specific application-centred measurement systems. In consequence, there is a clear gap between sensor network research and mostly very heterogeneous end user requirements. Sensor network research is often dedicated to a long-term vision, which tells a compelling story about potential applications. On the contrary, the actual implementation is mostly not more than a very limited demonstration without taking into account well-known issues such as interoperability, sustainable development, portability or

*conversations, our bodies – even our dreams.' (Gross, 1999)* 

modelling base to a more pervasive monitoring structure.

the coupling with established data analysis systems.

environment with previously uncharted real-time information layers.

**1. Introduction** 

functional context.

