Wireless Sensor Networks Application Centric Design Part 11 doc

MAC & Mobility In Wireless Sensor Networks 289 0 0.5 1 1.5 2 2.5 3 3.5 4 0 100 200 300 400 500 600 700 Time(s) Avg. Message Delay S-MAC-PP SEA-MAC-PP Fig. 9-C: PP+S-MAC vs. PP+SEA-MAC Delay effeciny at 5% Duty-Cycle. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 100 200 300 400 500 600 700 Time(s) Avg. Message Delay S-MAC-PP SEA-MAC-PP Fig. 9-D: PP+S-MAC vs. PP+SEA-MAC Delay comparison at 25% Duty-Cycle. To summerize the result show above, The proposed scheme gave the effect on S-MAC and made the consumption in terms of energy at low Duty-Cycle operation better than the original scheme of S-MAC. The proposed approach provided better operation in terms of energy consumption at high Duty-Cycle operation than the original SEA-MAC scheme. Both protocols provided better throughput for most of the scenarios after adding the proposed scheme to the original scheme of the protocols. 4.2 The proposed Scheme effect for the second scenario The second scenario has a new factor that gave an effect on the operation of both protocols S-MAC and SEA-MAC (with or without the implementation of the proposed theory). This is represented by the number of the deployed nodes. Increasing the number of the nodes can give a positive effect on the network operation as it will help to conduct the inquiry collection of the phenomena in a more fast paced operation. Figure 10-A,B &C shows the energy consumption, Delay and collisions occurrences. This effect is observed in Figure 10- A, where we can see the gap of consumption between SEA-MAC and S-MAC. 93000 94000 95000 96000 97000 98000 99000 100000 101000 0 1000 2000 3000 4000 5000 6000 7000 Time(s) E nerg y(m J) S-MAC SEA-MAC Fig. 10-A: S-MAC vs. SEA-MAC energy consumption at 5% Duty-Cycle. 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0 1000 2000 3000 4000 5000 6000 7000 Time(s) Avg. Message Delay(s) S-MAC SEA-MAC Fig. 10-B: S-MAC vs. SEA-MAC Delay average at 5% Duty-Cycle. Wireless Sensor Networks: Application-Centric Design290 0 20 40 60 80 100 120 0 1000 2000 3000 4000 5000 6000 7000 Time(s) No. of collision s S-MAC SEA-MAC Fig. 10-C: S-MAC vs. SEA-MAC collisions ocurrances at 5% Duty-Cycle. Adding the proposed approach to both protocols resulted in a different operation than the original ones. Figure 11-A,B&C shows that, it is observed that S-MAC was improved over SEA-MAC operation at low Duty-Cycle. This is due to the fact that S-MAC goes through four stages of operation (SYNC+RTS+CST+ACK) while SEA-MAC has (TONE+SYNC+RTS+CTS+ACK) which leads to a longer operation even with the compression of two control packets (SYNC&RTS), SEA-MAC has longer operation time than S-MAC at shorter duty-cycle. 98800 99000 99200 99400 99600 99800 100000 100200 0 1000 2000 3000 4000 5000 6000 7000 Time(s) E n ergy(m J) S-MAC-PP SEA-MAC-PP Fig. 11-A: PP+S-MAC vs. PP+SEA-MAC energy consumption at 5% Duty-Cycle. 0 0.5 1 1.5 2 2.5 3 3.5 0 1000 2000 3000 4000 5000 6000 7000 Time(s) Avg. Message Delay(s) S-MAC-PP SEA-MAC-PP Fig. 11-B: S-MAC-PP vs. SEA-MAC-PP average Delay at 5% Duty-Cycle. 0 10 20 30 40 50 60 0 1000 2000 3000 4000 5000 6000 7000 Time(s) No. of collisions S-MAC-PP SEA-MAC-PP Fig. 11-C: S-MAC-PP vs. SEA-MAC-PP collisions at 5% Duty-Cycles. 4.3 Pros and Cons of the proposed theory Overall, the proposed approached satisfied the quest as it does improve the operation of both protocols at different ranges of duty-cycle (we must note SEA-MAC with the proposed approach offered better energy consumption and delay operation at higher duty-cycles than S-MAC also implemented with the approach). Increasing the number of nodes result in collision occurance rather than the situation with the straight line deployment. Overall message delay is in favor of S-MAC at shorter duty-cycles and the advantage is to SEA- MAC at longer duty-cycle. In the next section we will discuss breifly the mobility issues in WSN as it is considered an important part of this research area. 5. Mobility in WSN Wireless sensor networks (WSN) offers a wide range of applications and it is also an intense area of research. However, current research in wireless sensor networks focuses on MAC & Mobility In Wireless Sensor Networks 291 0 20 40 60 80 100 120 0 1000 2000 3000 4000 5000 6000 7000 Time(s) No. of collision s S-MAC SEA-MAC Fig. 10-C: S-MAC vs. SEA-MAC collisions ocurrances at 5% Duty-Cycle. Adding the proposed approach to both protocols resulted in a different operation than the original ones. Figure 11-A,B&C shows that, it is observed that S-MAC was improved over SEA-MAC operation at low Duty-Cycle. This is due to the fact that S-MAC goes through four stages of operation (SYNC+RTS+CST+ACK) while SEA-MAC has (TONE+SYNC+RTS+CTS+ACK) which leads to a longer operation even with the compression of two control packets (SYNC&RTS), SEA-MAC has longer operation time than S-MAC at shorter duty-cycle. 98800 99000 99200 99400 99600 99800 100000 100200 0 1000 2000 3000 4000 5000 6000 7000 Time(s) E n ergy(m J) S-MAC-PP SEA-MAC-PP Fig. 11-A: PP+S-MAC vs. PP+SEA-MAC energy consumption at 5% Duty-Cycle. 0 0.5 1 1.5 2 2.5 3 3.5 0 1000 2000 3000 4000 5000 6000 7000 Time(s) Avg. Message Delay(s) S-MAC-PP SEA-MAC-PP Fig. 11-B: S-MAC-PP vs. SEA-MAC-PP average Delay at 5% Duty-Cycle. 0 10 20 30 40 50 60 0 1000 2000 3000 4000 5000 6000 7000 Time(s) No. of collisions S-MAC-PP SEA-MAC-PP Fig. 11-C: S-MAC-PP vs. SEA-MAC-PP collisions at 5% Duty-Cycles. 4.3 Pros and Cons of the proposed theory Overall, the proposed approached satisfied the quest as it does improve the operation of both protocols at different ranges of duty-cycle (we must note SEA-MAC with the proposed approach offered better energy consumption and delay operation at higher duty-cycles than S-MAC also implemented with the approach). Increasing the number of nodes result in collision occurance rather than the situation with the straight line deployment. Overall message delay is in favor of S-MAC at shorter duty-cycles and the advantage is to SEA- MAC at longer duty-cycle. In the next section we will discuss breifly the mobility issues in WSN as it is considered an important part of this research area. 5. Mobility in WSN Wireless sensor networks (WSN) offers a wide range of applications and it is also an intense area of research. However, current research in wireless sensor networks focuses on Wireless Sensor Networks: Application-Centric Design292 stationary WSN where they are deployed in a stationary position providing the base station with information about the subject under observation. However, a mobile sensor network is a collection of WSN nodes. Each of these nodes is capable of sensing, communication and moving around. It is the mobility capabilities that distinguish a mobile sensor network from the conventional ‘fixed’ WSN (Motari'c et al, 2002). Mobile sensor networks offer many opportunities for research as these sensors involves: the estimate location of the node in a movement scenario, an efficient DATA and information processing schemes that can cope with the mobility measurements and requirements (this includes the routing theory and the potential MAC Protocol Used). Most of the discussed approaches interms of routing theory, MAC and also allocation the location of the sensors are ment for stationary sensor nodes. Mobile sensor networks requiers extra care when it comes to design and implementing a network related protocols the conserns includes ad not exclusive to: energy consumption, message delay, location estimation accuracy and scurity of information traveled between the nodes to the base station. To list some of the aspects that effects on designing an operapable Mobile sensor networks, the next sections will give a brief explination about routing theory, MAC approaches and Localaization scheme aimed for mobility applications. 5.1 Routing theory Routing protocols are protocols aimed to offer transmitting the DATA through the network by utilizing the best available routes (not always the shortest ones) to the destination. When it comes to design routing protocols for mobile Sensor nodes, extra care should be taken in terms of timing the transportation between the nodes. Most of the routing protocol that are used and implemented for Wireless sensor networks (e.g. Ad hoc on demand Distance Vector (AODV) and Dynamic Source Routing (DSR)) are originally designed and optimized for ad hoc networks which utilizes devices like (Laptop computers and mobile phones) which has much powerful energy sources than the ones available in sensor nodes. And to the power issue mobility make the task even tougher. 5.2 MAC approaches Even the approach discussed in this chapter does not satisfy the mobility issues in MAC protocols aimed for mobile sensor networks. The results from the current work suggest that the CSMA based MAC protocols has a better chance in overcoming this issue than TDMA based MAC protocols because of the time slotting issues that comes along with TDMA based systems. IEEE802.15.4 or best known as (Zigbee) is a MAC layer standard provided by IEEE organization aimed for low power miniatures. Still, it cannot be considered yet as a standard MAC protocols for mobile sensor networks as it is still in the development stages for such applications. 5.3 Localization Issues Locating the sensor is an important task in WSN as it provides information about the phenomena monitored and what action should be taken at the occurrence of an action. Proposed localization schemes are aimed manly for stationary networks and partially for mobile networks. Some of the examples of localization techniques are (Boukerche et al, 2007): RSSI: Received Signal Strength Indicator, which is the cheapest technique to establish a node location as the medium used is wireless medium and most of the wireless adapter are capable of capturing such information. The disadvantage of such approach is the accuracy of the information calculated by such approach. GPS: Geo- Positioning System, the most used approach mobile nodes application and in some cases considered the easiest. The disadvantage of GPS systems is that it adds extra cost to systems in terms of financial cost and energy consumption costs and also accuracy issues. TOA: Time On Arrival systems, the most accurate approach to achieve the location of the nodes. However there are some cons for this technique: first of all the cost is higher than GPS systems. Second the accuracy issue is dependent on how violent the environment being applied on as it requires a line-of-sight connection to capture the required information. And the last issue, because it is a mounted platform so it will consume energy like the issue with the GPS systems. 6. Future Research goals The future research goal is to devise a template Network Model aimed for Mobile Wireless Sensor Networks. The template will take in consideration the concerns discussed in section five of this chapter. It is envisage that the proposed approach provided in this chapter can assist to devise a MAC approach that can be applied for various applications in WSN. The proposed template is designed for Habitat monitoring applications as they share some similarities in terms of the configurations and crucial guarantees. Future work would to utilize a Signal – to – noise Ratio estimator (Kamel, Jeoti, 2007) as a metric to define which route is the best to chose and on which nodes signal can estimate the location of the node. Cross-layer approach a definite approach and consideration that we aim utilize in our template. 7. Acknowledgments We are greateful to both Dr. Brahim Belhaouari Samir, department of Electrical and Electronic Engineering in Universiti Teknologi PETRONAS, 31750 Tronoh, Perak for helping us with proposed scheme mathmatical analysis. and Mr. Megual A. Erazo, Computer science department in Florida international University for helping us to develop SEA-MAC protocol. Our thanks goes also to Universiti Teknologi PETRONAS for funding this research and achieve the aimed results. MAC & Mobility In Wireless Sensor Networks 293 stationary WSN where they are deployed in a stationary position providing the base station with information about the subject under observation. However, a mobile sensor network is a collection of WSN nodes. Each of these nodes is capable of sensing, communication and moving around. It is the mobility capabilities that distinguish a mobile sensor network from the conventional ‘fixed’ WSN (Motari'c et al, 2002). Mobile sensor networks offer many opportunities for research as these sensors involves: the estimate location of the node in a movement scenario, an efficient DATA and information processing schemes that can cope with the mobility measurements and requirements (this includes the routing theory and the potential MAC Protocol Used). Most of the discussed approaches interms of routing theory, MAC and also allocation the location of the sensors are ment for stationary sensor nodes. Mobile sensor networks requiers extra care when it comes to design and implementing a network related protocols the conserns includes ad not exclusive to: energy consumption, message delay, location estimation accuracy and scurity of information traveled between the nodes to the base station. To list some of the aspects that effects on designing an operapable Mobile sensor networks, the next sections will give a brief explination about routing theory, MAC approaches and Localaization scheme aimed for mobility applications. 5.1 Routing theory Routing protocols are protocols aimed to offer transmitting the DATA through the network by utilizing the best available routes (not always the shortest ones) to the destination. When it comes to design routing protocols for mobile Sensor nodes, extra care should be taken in terms of timing the transportation between the nodes. Most of the routing protocol that are used and implemented for Wireless sensor networks (e.g. Ad hoc on demand Distance Vector (AODV) and Dynamic Source Routing (DSR)) are originally designed and optimized for ad hoc networks which utilizes devices like (Laptop computers and mobile phones) which has much powerful energy sources than the ones available in sensor nodes. And to the power issue mobility make the task even tougher. 5.2 MAC approaches Even the approach discussed in this chapter does not satisfy the mobility issues in MAC protocols aimed for mobile sensor networks. The results from the current work suggest that the CSMA based MAC protocols has a better chance in overcoming this issue than TDMA based MAC protocols because of the time slotting issues that comes along with TDMA based systems. IEEE802.15.4 or best known as (Zigbee) is a MAC layer standard provided by IEEE organization aimed for low power miniatures. Still, it cannot be considered yet as a standard MAC protocols for mobile sensor networks as it is still in the development stages for such applications. 5.3 Localization Issues Locating the sensor is an important task in WSN as it provides information about the phenomena monitored and what action should be taken at the occurrence of an action. Proposed localization schemes are aimed manly for stationary networks and partially for mobile networks. Some of the examples of localization techniques are (Boukerche et al, 2007): RSSI: Received Signal Strength Indicator, which is the cheapest technique to establish a node location as the medium used is wireless medium and most of the wireless adapter are capable of capturing such information. The disadvantage of such approach is the accuracy of the information calculated by such approach. GPS: Geo- Positioning System, the most used approach mobile nodes application and in some cases considered the easiest. The disadvantage of GPS systems is that it adds extra cost to systems in terms of financial cost and energy consumption costs and also accuracy issues. TOA: Time On Arrival systems, the most accurate approach to achieve the location of the nodes. However there are some cons for this technique: first of all the cost is higher than GPS systems. Second the accuracy issue is dependent on how violent the environment being applied on as it requires a line-of-sight connection to capture the required information. And the last issue, because it is a mounted platform so it will consume energy like the issue with the GPS systems. 6. Future Research goals The future research goal is to devise a template Network Model aimed for Mobile Wireless Sensor Networks. The template will take in consideration the concerns discussed in section five of this chapter. It is envisage that the proposed approach provided in this chapter can assist to devise a MAC approach that can be applied for various applications in WSN. The proposed template is designed for Habitat monitoring applications as they share some similarities in terms of the configurations and crucial guarantees. Future work would to utilize a Signal – to – noise Ratio estimator (Kamel, Jeoti, 2007) as a metric to define which route is the best to chose and on which nodes signal can estimate the location of the node. Cross-layer approach a definite approach and consideration that we aim utilize in our template. 7. Acknowledgments We are greateful to both Dr. Brahim Belhaouari Samir, department of Electrical and Electronic Engineering in Universiti Teknologi PETRONAS, 31750 Tronoh, Perak for helping us with proposed scheme mathmatical analysis. and Mr. Megual A. Erazo, Computer science department in Florida international University for helping us to develop SEA-MAC protocol. Our thanks goes also to Universiti Teknologi PETRONAS for funding this research and achieve the aimed results. Wireless Sensor Networks: Application-Centric Design294 8. References Kazem Sohraby, Daniel Minoli and Taieb Znati “WIRELESS SENSOR NETWORKS Technology, Protocols, and Applications”, 2007 by John Wiley & Sons, Inc. Yang Yu, Viktor K Prasanna and Bhaskar Krishnamachari “Information processing and routing in wireless sensor networks”, 2006 by World Scientific Publishing Co. Pte. Ltd. Bhaskar Krishnamachari “Networking Wireless Sensors”, Cambridge University Press 2005. Alan Mainwaring, Joseph Polastre, Robert Szewczyk, David Culler and John Anderson “Wireless Sensor Networks for Habitat Monitoring”, WSNA’02, September 28, 2002, Atlanta, Georgia, USA, ACM. Vijay Raghunathan, Curt Schurgers, Sung Park, and Mani B. Srivastava “Energy Aware Wireless Sensor Networks”, IEEE Signal Processing Magazine, 2002. Azzedine Boukerche, Fernando H. S. Silva, Regina B. Araujo and Richard W. N. Pazzi “A Low Latency and Energy Aware Event Ordering Algorithm for Wireless Actor and Sensor Networks”, MSWiM’05, October 10–13, 2005, Montreal, Quebec, Canada, ACM. Rebecca Braynard, Adam Silberstein and Carla Ellis “Extending Network Lifetime Using an Automatically Tuned Energy-Aware MAC Protocol”, Proceedings of the 2006 European Workshop on Wireless Sensor Networks, Zurich, Switzerland (2006). Lodewijk van Hoesel and Paul J.M. Havinga “MAC Protocol for WSNs”, SenSys'04, November 3-5, 2004, Baltimore, Maryland, USA, ACM. Yee Wei Law, Lodewijk van Hoesel, Jeroen Doumen, Pieter Hartel and Paul Havinga “Energy Efficient Link Layer Jamming Attacks against Wireless Sensor Network MAC Protocols”, SASN’05 , November 7, 2005, Alexandria, Virginia, USA, ACM. Ioannis Mathioudakis, Neil M.White, Nick R. Harris, Geoff V. Merrett, “Wireless Sensor Networks: A Case Study for Energy Efficient Environmental Monitoring”, Eurosensors Conference 2008, 7-11 September 2008, Dresden, Germany. Marwan Ihsan Shukur, Lee Sheng Chyan and Vooi Voon Yap “Wireless Sensor Networks: Delay Guarentee and Energy Efficient MAC Protocols”, Proceedings of World Academy of Science, Engineering and Technology, WCSET 2009, 25-27 Feb. 2009, Penang, Malaysia. Wei Ye, John Heidemann and Deborah Estrin “An Energy-Efficient MAC protocol for Wireless Sensor Networks”, USC/ISI Technical Report ISI-TR-543, September 2001. Tijs Van Dam and Keon Langendoen “An Adaptive Energy-Efficeint MAC Protocol for Wireless Sensor Networks”, SenSys’03, November 5-7, 2003, ACM. Shu Du, Amit Kumar Saha and David B. Johnson, “RMAC: A Routing-Enhanced Duty- Cycle MAC Protocol for Wireless Sensor Networks”, INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE. Jin Kyung PARK, Woo Cheol Shin and Jun HA “Energy-Aware Pure ALOHA for Wireless Sensor Networks”, IEIC Trans. Fundamentals, VOL.E89-A, No.6 June 2006. Changsu Suh and Young-Bae Ko, “A Traffic Aware, Energy Efficient MAC protocol for Wireless Sensor Networks”, Proceeding of the IEEE international symposium on circuits and systems (IS CAS’05), May. 2005. Sangheon Pack, Jaeyoung Choi, Taekyoung Kwon and Yanghee Choi, “TA-MAC: Task Aware MAC Protocol for Wireless Sensor Networks”, Vehicular Technology Conference, 2006. VTC 2006-Spring. IEEE 63rd. Miguel A. Erazo, Yi Qian, “SEA-MAC: A Simple Energy Aware MAC Protocol for Wireless Sensor Networks for Environmental Monitoring Applications”, Wireless Pervasive Computing, 2007. ISWPC '07. IEEE 2 nd international symposium 2007. Rajgopal Kannan, Ram Kalidini and S. S. Iyengar “Energy and rate based MAC protocol for Wireless Sensor Networks” SIGMOD Record, Vol.32, No.4, December 2003. Anirudha Sahoo and Prashant Baronia “An Energy Efficient MAC in Wireless Sensor Networks to Provide Delay Guarantee”, Local & Metropolitan Area Networks, 2007. LANMAN 2007. 15th IEEE Workshop on. Saurabh Ganeriwal, Ram Kumar and Mani B. Srivastava “Timing-sync Protocol for Sensor Networks”, SenSys ’03, November 5-7, 2003, Los Angeles, California, USA, ACM. Esteban Egea-L´opez, Javier Vales-Alonso, Alejandro S. Mart´nez-Sala, Joan Garc´a-Haro, Pablo Pav´on-Mari˜no, and M. Victoria Bueno-Delgado “A Real-Time MAC Protocol for Wireless Sensor Networks: Virtual TDMA for Sensors (VTS)”, ARCS 2006, LNCS 3894, pp. 382–396, 2006, Springer-Verlag Berlin Heidelberg 2006. Teerawat Issariyakul and Ekram Hossain “Introduction to Network Simulator NS2”, SpringerLink publications-Springer US 2008. Marwan Ihsan Shukur and Vooi Voon Yap “An Approach for efficient energy consumption and delay guarantee MAC Protocol for Wireless Sensor Networks”, Proceedings of International Conference on Computing and Informatics, ICOCI 2009, 24-25 June 2009, Kuala Lumpur, Malaysia, a. Marwan Ihsan Shukur and Vooi Voon Yap “Enhanced SEA-MAC: An Efficient MAC Protocol for Wireless Sensor Networks for Environmental Monitoring Applications”, Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, IEEE CITISIA 2009, 25 July 2009, MONASH University Sunway Campus, Malaysia, b. Howard, A, Matari´c, M.J., and Sukhatme, G.S., “Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem”, Proceedings of the 6th International Symposium on Distributed Autonomous Robotics Systems (DARS02) Fukuoka, Japan, June 25-27, 2002. Azzedine Boukerche, Horacio A. B. F. Oliveira and Eduardo F. Nakamura “Localization Systems For Wireless Sensor Networks”, IEEE Wireless Communications Mag. Dec. 2007. Nidal S. Kamel and Varun Jeoti “A Linear Prediction Based Estimation of Signal-to-Noise Ratio in AWGN Channel”, ETRI journal, Volume 29, Number 5, October 2007. MAC & Mobility In Wireless Sensor Networks 295 8. References Kazem Sohraby, Daniel Minoli and Taieb Znati “WIRELESS SENSOR NETWORKS Technology, Protocols, and Applications”, 2007 by John Wiley & Sons, Inc. Yang Yu, Viktor K Prasanna and Bhaskar Krishnamachari “Information processing and routing in wireless sensor networks”, 2006 by World Scientific Publishing Co. Pte. Ltd. Bhaskar Krishnamachari “Networking Wireless Sensors”, Cambridge University Press 2005. Alan Mainwaring, Joseph Polastre, Robert Szewczyk, David Culler and John Anderson “Wireless Sensor Networks for Habitat Monitoring”, WSNA’02, September 28, 2002, Atlanta, Georgia, USA, ACM. Vijay Raghunathan, Curt Schurgers, Sung Park, and Mani B. Srivastava “Energy Aware Wireless Sensor Networks”, IEEE Signal Processing Magazine, 2002. Azzedine Boukerche, Fernando H. S. Silva, Regina B. Araujo and Richard W. N. Pazzi “A Low Latency and Energy Aware Event Ordering Algorithm for Wireless Actor and Sensor Networks”, MSWiM’05, October 10–13, 2005, Montreal, Quebec, Canada, ACM. Rebecca Braynard, Adam Silberstein and Carla Ellis “Extending Network Lifetime Using an Automatically Tuned Energy-Aware MAC Protocol”, Proceedings of the 2006 European Workshop on Wireless Sensor Networks, Zurich, Switzerland (2006). Lodewijk van Hoesel and Paul J.M. Havinga “MAC Protocol for WSNs”, SenSys'04, November 3-5, 2004, Baltimore, Maryland, USA, ACM. Yee Wei Law, Lodewijk van Hoesel, Jeroen Doumen, Pieter Hartel and Paul Havinga “Energy Efficient Link Layer Jamming Attacks against Wireless Sensor Network MAC Protocols”, SASN’05 , November 7, 2005, Alexandria, Virginia, USA, ACM. Ioannis Mathioudakis, Neil M.White, Nick R. Harris, Geoff V. Merrett, “Wireless Sensor Networks: A Case Study for Energy Efficient Environmental Monitoring”, Eurosensors Conference 2008, 7-11 September 2008, Dresden, Germany. Marwan Ihsan Shukur, Lee Sheng Chyan and Vooi Voon Yap “Wireless Sensor Networks: Delay Guarentee and Energy Efficient MAC Protocols”, Proceedings of World Academy of Science, Engineering and Technology, WCSET 2009, 25-27 Feb. 2009, Penang, Malaysia. Wei Ye, John Heidemann and Deborah Estrin “An Energy-Efficient MAC protocol for Wireless Sensor Networks”, USC/ISI Technical Report ISI-TR-543, September 2001. Tijs Van Dam and Keon Langendoen “An Adaptive Energy-Efficeint MAC Protocol for Wireless Sensor Networks”, SenSys’03, November 5-7, 2003, ACM. Shu Du, Amit Kumar Saha and David B. Johnson, “RMAC: A Routing-Enhanced Duty- Cycle MAC Protocol for Wireless Sensor Networks”, INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE. Jin Kyung PARK, Woo Cheol Shin and Jun HA “Energy-Aware Pure ALOHA for Wireless Sensor Networks”, IEIC Trans. Fundamentals, VOL.E89-A, No.6 June 2006. Changsu Suh and Young-Bae Ko, “A Traffic Aware, Energy Efficient MAC protocol for Wireless Sensor Networks”, Proceeding of the IEEE international symposium on circuits and systems (IS CAS’05), May. 2005. Sangheon Pack, Jaeyoung Choi, Taekyoung Kwon and Yanghee Choi, “TA-MAC: Task Aware MAC Protocol for Wireless Sensor Networks”, Vehicular Technology Conference, 2006. VTC 2006-Spring. IEEE 63rd. Miguel A. Erazo, Yi Qian, “SEA-MAC: A Simple Energy Aware MAC Protocol for Wireless Sensor Networks for Environmental Monitoring Applications”, Wireless Pervasive Computing, 2007. ISWPC '07. IEEE 2 nd international symposium 2007. Rajgopal Kannan, Ram Kalidini and S. S. Iyengar “Energy and rate based MAC protocol for Wireless Sensor Networks” SIGMOD Record, Vol.32, No.4, December 2003. Anirudha Sahoo and Prashant Baronia “An Energy Efficient MAC in Wireless Sensor Networks to Provide Delay Guarantee”, Local & Metropolitan Area Networks, 2007. LANMAN 2007. 15th IEEE Workshop on. Saurabh Ganeriwal, Ram Kumar and Mani B. Srivastava “Timing-sync Protocol for Sensor Networks”, SenSys ’03, November 5-7, 2003, Los Angeles, California, USA, ACM. Esteban Egea-L´opez, Javier Vales-Alonso, Alejandro S. Mart´nez-Sala, Joan Garc´a-Haro, Pablo Pav´on-Mari˜no, and M. Victoria Bueno-Delgado “A Real-Time MAC Protocol for Wireless Sensor Networks: Virtual TDMA for Sensors (VTS)”, ARCS 2006, LNCS 3894, pp. 382–396, 2006, Springer-Verlag Berlin Heidelberg 2006. Teerawat Issariyakul and Ekram Hossain “Introduction to Network Simulator NS2”, SpringerLink publications-Springer US 2008. Marwan Ihsan Shukur and Vooi Voon Yap “An Approach for efficient energy consumption and delay guarantee MAC Protocol for Wireless Sensor Networks”, Proceedings of International Conference on Computing and Informatics, ICOCI 2009, 24-25 June 2009, Kuala Lumpur, Malaysia, a. Marwan Ihsan Shukur and Vooi Voon Yap “Enhanced SEA-MAC: An Efficient MAC Protocol for Wireless Sensor Networks for Environmental Monitoring Applications”, Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, IEEE CITISIA 2009, 25 July 2009, MONASH University Sunway Campus, Malaysia, b. Howard, A, Matari´c, M.J., and Sukhatme, G.S., “Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem”, Proceedings of the 6th International Symposium on Distributed Autonomous Robotics Systems (DARS02) Fukuoka, Japan, June 25-27, 2002. Azzedine Boukerche, Horacio A. B. F. Oliveira and Eduardo F. Nakamura “Localization Systems For Wireless Sensor Networks”, IEEE Wireless Communications Mag. Dec. 2007. Nidal S. Kamel and Varun Jeoti “A Linear Prediction Based Estimation of Signal-to-Noise Ratio in AWGN Channel”, ETRI journal, Volume 29, Number 5, October 2007. X Hybrid Optical and Wireless Sensor Networks Lianshan Yan, Xiaoyin Li, Zhen Zhang, Jiangtao Liu and Wei Pan Southwest Jiaotong University Chengdu, Sichuan, China 1. Introduction Wireless sensor network (WSN) has attracted considerable attentions during the last few years due to characteristics such as feasibility of rapid deployment, self-organization (different from ad hoc networks though) and fault tolerance, as well as rapid development of wireless communications and integrated electronics [1]. Such networks are constructed by randomly but densely scattered tiny sensor nodes (Fig. 1). As sensor nodes are prone to failures and the network topology changes very frequently, different protocols have been proposed to save the overall energy dissipation in WSNs [2-5]. Among them, Low-Energy- Adaptive-Clustering-Hierarchy (LEACH), first proposed by researchers from Massachusetts Institute of Technology [5], is considered to be one of the most effective protocols in terms of energy efficiency [6-7]. Another protocol, called Power-Efficient Gathering in Sensor Information Systems (PEGASIS), is a near optimal chain-based protocol [8]. WSN WSN Nodes SINK WSN WSN Nodes SINK Fig. 1. Illustration of a wireless sensor network (WSN) with randomly scattered nodes (sink node: no energy restriction; WSN nodes: with energy restriction); On the other hand, distributed fiber sensors (DFS) have been intensively studied or even deployed for analyzing loss, external pressure and temperature or birefringence distribution along the fiber link, ranging from hundreds of meters to tens of kilometers [9-13]. Mechanisms include Rayleigh, Brillouin or Raman scattering or polarization effects, through either time or frequency-domain analysis. Compared with conventional sensors including Hybrid Optical and Wireless Sensor Networks 297 Hybrid Optical and Wireless Sensor Networks Lianshan Yan, Xiaoyin Li, Zhen Zhang, Jiangtao Liu and Wei Pan 16 wireless ones, optical fiber sensors have intrinsic advantages such as high sensitivity, the immunity to electromagnetic interference (EMI), superior endurance in harsh environments and much longer lifetime. Apparently it would be highly desirable to have integrated sensor networks that can take advantages of both WSNs and fiber sensor networks (FSNs). Such hybrid sensor networks can find major applications including monitoring inaccessible terrains (military, high- voltage electricity facilities, etc.), long-term observation of earthquake activity and large area environmental control with tunnels, and so on. So far hybrid sensor networks have been studied as well [14-16], while optical sensors in these networks are generally point-like (e.g. fiber-Bragg-grating based), and such nodes can be regarded as normal WSN nodes after optical to wireless signal conversion. In this chapter, we first review typical WSN protocols, mainly about LEACH and PEGASIS, then evaluate the performance of LEACH protocols for different topologies, especially the rectangle one. We propose an improved algorithm based on LEACH and PEGASIS for the WSN, finally and most importantly, we propose an O-LEACH protocol for the hybrid sensor network that is composed of a DFS link and two separated WSNs. Most analyses about performance are done in terms of lifetime of the sensor networks. 2. Overview of WSN protocols Wireless sensor networks (WSNs) generally are composed of small or tiny nodes with sensing, computation, and communication capabilities. Various routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy awareness is an essential design issue. Among them, routing protocols might differ depending on the applications and network architectures. In general, routing protocols for the wireless sensor networks can be divided into flat-based, hierarchical-based, and location-based in terms of the underlying network structures [17-19]. As some protocols may be discussed intensively in other chapters of this book, here we give a brief review about major protocols. (1) Flat-based routing protocol Sensor nodes in flat-based routing protocols have the same role and collaborate together to perform the sensing task and multi-hop communication. Since the flat routing is based on flooding, it has several demerits, such as large routing overhead and high energy dissipation. Flat-based routing protocol is used in the early stage of WSNs, such as Flooding, Gossiping, SPIN, and Rumor. (2) Hierarchical-based routing protocol Hierarchical-based routing protocol is the main trend for WSN’s routing protocols. In hierarchical-based routing protocols, the network is divided into several logical groups within a fixed area. The logical groups are called clusters. Sensor nodes collect the information in a cluster and a head node aggregates the information. Each sensor node delivers the sensing data to the head node in the cluster and the head node delivers the aggregated data to the base station which is located outside of the sensor network. Contrary to flat routing protocols, only a head node aggregates the collected information and sends it to the base station. Due to these advantages, sensor nodes can remarkably save their own s energy. In general, a hierarchical routing technique is regarded as superior to flat routing approaches. The classical Hierarchical-based routing protocols are LEACH, PEGASIS, H- PEGASIS, TEEN, and APTEEN. We will discuss the LEACH and PEGASIS protocols in more details later. (3) Location-based routing protocol Such protocol is based on the location information of senor nodes in WSNs. It assumes that each node would know its own location and its neighbor sensor nodes’ location before sensor nodes sensing and collecting the peripheral information. The distance between neighbouring sensor nodes can be computed based on the incoming signal strength [17-18]. 2.1 LEACH Low Energy Adaptive Clustering Hierarchy (LEACH) was first introduced by Heinzelman, et al. in [5, 20] with advantages such as energy efficiency, simplicity and load balancing ability. LEACH is a cluster-based protocol, therefore the numbers of cluster heads and cluster members generated by LEACH are important parameters for achieving better performance. In LEACH protocol, the sensor nodes in the network are divided into a number of clusters, the nodes organize themselves into preferred local clusters, a sensor node is selected randomly as the cluster head (CH) in each cluster and this role is rotated to evenly distribute the energy load among nodes of the network. The CH nodes compress data arriving from nodes that belong to the respective cluster, and send an aggregated packet to the BS in order to further reduce the amount of information that must be transmitted to the BS, thus reducing energy dissipation and enhancing system lifetime. After a given interval of time, randomized rotation of the role of CH is conducted to maximize the uniformity of energy dissipation of the network. Sensors elect themselves to be local cluster heads at any time with a certain probability. Generally only ~ 5% of nodes need to act as CHs based on simulation results. LEACH uses a TDMA/CDMA MAC to reduce intercluster and intracluster collisions. As data collection is centralized and performed periodically, this protocol is most appropriate when there is a need for constant monitoring by the sensor network. The operation of LEACH is broken up into rounds, where each round begins with a set-up phase followed by a steady-state phase. In order to minimize overhead, the steady-state phase takes longer time compared to the set-up phase. In the setup phase, the clusters are organized and CHs are selected. In the steady state phase, the actual data transfer to the BS takes place. During the setup phase, each node decides whether or not to become a cluster head for the current round. A predetermined fraction of nodes, p, elect themselves as CHs. A sensor node chooses a random number between 0 and 1. If this random number is less than a threshold value T(n), , the node becomes a cluster head for the current round. The threshold value is calculated based on Eq. (2-1): 1 1 *( mod ) ( ) 0 p if n G p r T n p otherwise           (2-1) Wireless Sensor Networks: Application-Centric Design298 [...]... 5 0 0 200 400 Fig 4.3 Relationship between rounds and number of survival nodes 310 Wireless Sensor Networks: Application- Centric Design WSNs are generally deployed in harsh environments, where the base station is far away from the sensor field The location of the base station has a great effect on the lifetime of the sensor network When the distance between the base station and the monitored region... iterations to get the statistical average value The performance (network delay) is evaluated in terms of the average distance before the first dead node appearing in wireless sensor network 312 Wireless Sensor Networks: Application- Centric Design Average distance/Round 1200 1000 800 LEACH LEACH-P PEGASIS 600 400 200 0 20 40 60 80 100 Number of Rounds Fig 4.6 Comparison of transmission delay in terms... required for such hybrid sensor networks to employ OLEACH with two isolated WSNs Furthermore, typical lifetime evolutions are compared as well in Fig 5.4 (c) where D equals 20 These curves are specially chosen from thousands of simulated iterations with performance close to average ones Results of LEACH protocol are also included 316 Wireless Sensor Networks: Application- Centric Design 4000 4000 Round... parameter More wireless sinks are required for longer DFS links, and the performance evaluation of various optical wireless sensor network topologies are of great interests for further investigation On the other hand, our protocol and the simulation model can be adapted into networks with different parameters so that we can find the optimized network design s Hybrid Optical and Wireless Sensor Networks. .. introducing the DFS into the rectangular sensor area We can see from Fig 5.6 that the numbers of the first dead node in the four regions are 663, 805, 779 and 698, respectively The first 20% nodes die slowly, but the remaining ones die 318 Wireless Sensor Networks: Application- Centric Design rapidly in the total region The results further demonstrate that the hybrid sensor network incorporating DFS with... WSNs or hybrid sensor networks with distributed fiber sensors involved As sensor networks are much more complicated in real applications, more thorough and careful optimization of routing algorithms are required to meet specific requirements, such as real-time, long lifetime, security, and so on 7 References [1] I F Akyildiz, W Su, Y Sankarasubramaniam, E Cayirci (2002) A survey on sensor networks, IEEE... Balakrishnan (2000) Energy-efficient communication protocol for wireless microsensor networks, IEEE Proc Hawaii Int’l Conf Sys Sci., pp 1–10 [6] X Fan, Y Song (2007) Improvement on LEACH protocol of wireless sensor network, IEEE SENSORCOMM, pp.260-264 [7] H Jeong, C.-S Nam, Y.-S Jeong, D.-R Shin (2008) A mobile agent based LEACH in wireless sensor network, Conf on Advanced Comm Technol (ICACT), pp 75-78... changes Simulation results indicate that the lifetime for the BS in (0, 50) is longer that in (50,175) As the BS in (0, 50) is nearer to the sensor area, the energy for transmitting data to the BS is reduced 304 Wireless Sensor Networks: Application- Centric Design Topology Circle Square Rectangle Sink (0, 0) (0,50) (50, 175) (0, 25) (100,150) 1% 757 740 639 470 555 20% 854 846 715 644 652 50% 923... nodes, p, elect themselves as CHs A sensor node chooses a random number between 0 and 1 If this random number is less than a threshold value T(n), , the node becomes a cluster head for the current round The threshold value is calculated based on Eq (2-1): p   1  T (n)  1  p *(r mod ) p  0  if n  G otherwise (2-1) 300 Wireless Sensor Networks: Application- Centric Design Where p is the desired percentage... nodes The flowchart of the O-LEACH protocol is shown in Fig 5.3 As the operation of the standard LEACH protocol is separated into the setup phase and the steady phase, we also 314 Wireless Sensor Networks: Application- Centric Design separate the O-LEACH operation into two phases The steady phase is as same as the LEACH one During the setup phase, there are two major differences between O-LEACH and LEACH: . results. Wireless Sensor Networks: Application- Centric Design2 94 8. References Kazem Sohraby, Daniel Minoli and Taieb Znati WIRELESS SENSOR NETWORKS Technology, Protocols, and Applications”,. intense area of research. However, current research in wireless sensor networks focuses on Wireless Sensor Networks: Application- Centric Design2 92 stationary WSN where they are deployed in a. conventional sensors including Hybrid Optical and Wireless Sensor Networks 297 Hybrid Optical and Wireless Sensor Networks Lianshan Yan, Xiaoyin Li, Zhen Zhang, Jiangtao Liu and Wei Pan 16 wireless