Discharge Curve Backoff Sleep Protocol for Energy Efficient Coverage in Wireless Sensor Networks

AbstractIn energy constrained wireless sensor networks, maximizing network coverage lifetime while ensuring optimized coverage is important. The challenge is to determine an appropriate duty cycle for the nodes while maintaining sufficient count of active nodes for optimal network coverage. Most of the existing work, for coverage optimization based on duty cycle, does not consider the residual energy of the active nodes. This can result in suboptimal wake-up of sleeping nodes. RBSP considers the residual energy but ignores the active nodes’ battery discharge rate. In this paper, we propose DCBSP (Discharge Curve Backoff Sleep Protocol), which considers the battery discharge curve of the active nodes to determine the duty cycle of the inactive nodes. Thus in DCBSP, inactive nodes wake-up close to death of the active nodes which leads to lesser energy consumption and increased network lifetime. NS-2 simulations show the energy consumption of DCBSP is lesser than that of PEAS by 39% and lesser by 25% and 15% as compared to RBSP and PECAS respectively. Further, the coverage ratio of DCBSP is higher than PEAS by 32% and higher by 17% and 6% as compared to RBSP, PECAS respectively. Hence, DCBSP is effective in ensuring higher coverage while extending the network lifetime

Power saving and energy optimization techniques for Wireless Sensor Networks

Wireless sensor networks have become increasingly popular due to their wide range of applications. Energy consumption is one of the biggest constraints of the wireless sensor node and this limitation combined with a typical deployment of large number of nodes have added many challenges to the design and management of wireless sensor networks. They are typically used for remote environment monitoring in areas where providing electrical power is difficult. Therefore, the devices need to be powered by batteries and alternative energy sources. Because battery energy is limited, the use of different techniques for energy saving is one of the hottest topics in WSNs. In this work, we present a survey of power saving and energy optimization techniques for wireless sensor networks, which enhances the ones in existence and introduces the reader to the most well known available methods that can be used to save energy. They are analyzed from several points of view: Device hardware, transmission, MAC and routing protocols.Sendra Compte, S.; Lloret, J.; García Pineda, M.; Toledo Alarcón, JF. (2011). Power saving and energy optimization techniques for Wireless Sensor Networks. Journal of Communications. 6(6):439-459. doi:10.4304/jcm.6.6.439-459S4394596

Saving Energy and Improving Communications using Cooperative Group-based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) can be used in many real applications (environmental monitoring, habitat monitoring, health, etc.). The energy consumption of each sensor should be as lower as possible, and methods for grouping nodes can improve the network performance. In this work, we show how organizing sensors in cooperative groups can reduce the global energy consumption of the WSN. We will also show that a cooperative group-based network reduces the number of the messages transmitted inside the WSNs, which implieasa reduction of energy consumed by the whole network, and, consequently, an increase of the network lifetime. The simulations will show how the number of groups improves the network performance. © 2011 Springer Science+Business Media, LLC.García Pineda, M.; Sendra Compte, S.; Lloret, J.; Canovas Solbes, A. (2013). Saving Energy and Improving Communications using Cooperative Group-based Wireless Sensor Networks. Telecommunication Systems. 52(4):2489-2502. doi:10.1007/s11235-011-9568-3S24892502524Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Journal of Computer Networks, 38(4), 393–422.Garcia, M., Bri, D., Sendra, S., & Lloret, J. (2010). Practical deployments of wireless sensor networks: a survey. Journal on Advances in Networks and Services, 3(1&2), 1–16.Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A wireless sensor network deployment for rural and forest fire detection and verification. Sensors, 9(11), 8722–8747.Mainwaring, A., Polastre, J., Szewczyk, R., & Culler, D. (2002). Wireless sensor networks for habitat monitoring. In ACM workshop on sensor networks and applications (WSNA’02), Atlanta, GA, USA, September.Garcia, M., Sendra, S., Lloret, G., & Lloret, J. (2010, in press). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, pp. 1–9. doi: 10.1049/iet-com.2010.0654 .Sinha, A., & Chandrakasan, A. (2001). Dynamic power management in wireless sensor networks. IEEE Design & Test of Computers, 18(2), 62–74.Garcia, M., Coll, H., Bri, D., & Lloret, J. (2008). Using MANET protocols in wireless sensor and actor networks. In The second international conference on sensor technologies and applications (SENSORCOMM 2008), Cap Esterel, Costa Azul, France, 25–31 August.Lloret, J., García, M., Boronat, F., & Tomás, J. (2008). MANET protocols performance in group-based networks. In Wireless and mobile networking: Vol. 284 (Chap. 13, pp. 161–172). Berlin, Heidelberg, Boston: Springer.Lloret, J., García, M., & Tomás, J. (2008). Improving mobile and ad-hoc networks performance using group-based topologies. In Wireless sensor and actor networks 2008 (WSAN 2008), Ottawa, Canada, 14–15 July. Berlin, Heidelberg, New York: Springer.Lloret, J., Palau, C., Boronat, F., & Tomas, J. (2008). Improving networks using group-based topologies. Journal of Computer Communications, 31(14), 3438–3450.Lloret, J., Garcia, M., Tomás, J., & Boronat, F. (2008). GBP-WAHSN: a group-based protocol for large wireless ad hoc and sensor networks. Journal of Computer Science and Technology, 23(3), 461–480.Lloret, J., García, M., Boronat, F., & Tomás, J. (2008). MANET protocols performance in group-based networks. In 10th IFIP international conference on mobile and wireless communications networks (MWCN 2008), Toulouse, France, 30 September–2 October.Garcia, M., Sendra, S., Lloret, J., & Lacuesta, R. (2010). Saving energy with cooperative group-based wireless sensor networks. In LNCS: Vol. 6240. Cooperative design, visualization, and engineering: CDVE 2010 (pp. 231–238), September. Berlin: Springer.Lloret, J., Sendra, S., Coll, H., & García, M. (2010). Saving energy in wireless local area sensor networks. Computer Journal, 53(10), 1658–1673.Meiyappan, S. S., Frederiks, G., & Hahn, S. (2006). Dynamic power save techniques for next generation WLAN systems. In Proceedings of the 38th southeastern symposium on system theory (SSST), Cookeville, Tennessee, USA, 5–7 March.Raghunathan, V., Schurgers, C., Park, S., & Srivastava, M. (2002). Energy aware wireless microsensor networks. IEEE Signal Processing Magazine, 19(2), 40–50.Min, R., Bhardwaj, M., Cho, S.-H., Shih, E., Sinha, A., Wang, A., & Chandrakasan, A. (2001). Low power wireless sensor networks. In Proceedings of international conference on VLSI design, India, Bangalore, 3–7 January.Salhieh, A., Weinmann, J., Kochha, M., & Schwiebert, L. (2001). Power efficient topologies for wireless sensor networks. In Proceedings of the IEEE international conference on parallel processing (pp. 156–163), 3–7 September.Jayashree, S., Manoj, B. S., & Murthy, C. S. R. (2004). A battery aware medium access control (BAMAC) protocol for Ad-hoc wireless network. In Proceedings of the 15th IEEE international symposium on personal, indoor and mobile radio communications (PIMRC 2004), Barcelona, Spain, 5–8 September (Vol. 2, pp. 995–999).Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient MAC protocol for wireless sensor networks. In Proceedings IEEE INFOCOM 2002, the 21st annual joint conference of the IEEE computer and communications societies, New York, USA, 23–27 June.Ching, C., & Schindelhauer, C. (2010). Utilizing detours for energy conservation in mobile wireless networks. Journal of Telecommunication Systems. doi: 10.1007/s11235-009-9188-3 .Gao, Q., Blow, K., Holding, D., Marshall, I., & Peng, X. (2004). Radio range adjustment for energy efficient wireless sensor networks. Journal of Ad Hoc Networks, 4(1), 75–82.Li, D., Jia, X., & Liu, H. (2004). Energy efficient broadcast routing in static ad hoc wireless networks. IEEE Transactions on Mobile Computing, 3(1), 1–8.Camilo, T., Carreto, C., Silva, J., & Boavida, F. (2006). An energy-efficient ant-based routing algorithm for wireless sensor networks. In Lecture notes in computer science: Vol. 4150. Ant colony optimization and swarm intelligence (pp. 49–59). Berlin: Springer.Younis, M., Youssef, M., & Arisha, K. (2002). Energy-aware routing in cluster-based sensor networks. In Proceedings of the 10th IEEE international symposium on modeling, analysis, and simulation of computer and telecommunications systems (MASCOTS ’02) (pp. 129–136). Washington: IEEE Computer Society.Cheng, Z., Perillo, M., & Heinzelman, W. B. (2008). General network lifetime and cost models for evaluating sensor network deployment strategies. IEEE Transactions on Mobile Computing, 7(4), 484–497.Heo, N., & Varshney, P. K. (2005). Energy-efficient deployment of intelligent mobile sensor networks. IEEE Transactions on Systems, Man and Cybernetics Part A Systems and Humans, 35(1), 78–92.Vlajic, N., & Xia, D. (2006). Wireless sensor networks: to cluster or not to cluster? 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Upper bounds on the lifetime of sensor networks. In: International conference on communications (ICC’01). June 2001 (pp. 785–790).Gibbons, A. (1985). Algorithmic graph theory. Cambridge: Cambridge University Press.Fraigniaud, P., Pelc, A., Peleg, D., & Perennes, S. (2000). Assigning labels in unknown anonymous networks. In Proceedings of the 19th annual ACM SIGACT-SIGOPS symposium on principles of distributed computing, Portland, OR, USA (Vol. 1, pp. 101–111).OPNET Modeler® Wireless Suite network simulator (2011). Available at http://www.opnet.com/solutions/network_rd/modeler_wireless.html

Use of wireless sensors to improve robot lifetime for multi-threat containment

Autonomous robots can be used in a decentralized environment to contain threats. While working in this capacity, these motor propelled robots are constantly moving, therefore drawing a large amount of current from the battery. If these algorithms are to be implemented in hardware, it is important to ensure that the robots move only when necessary in an effort to optimize battery life. This work introduces static wireless sensors to assist robots in detecting threats. By having a sufficient number of wireless sensors available to detect threats, it is hypothesized that a similar containment performance can be achieved with less robot movements. When not actively containing threats, the robots may enter a sleep mode thus optimizing energy conservation. The notion of multimode operations has been utilized in other wireless sensor network applications. In the field of cooperative robotics, however, little has been investigated for system performance when both mobile robots and static sensors coexist. This work leverages previously developed multi-threat containment algorithms and the notion of multi-mode operations from wireless sensor network research community and examines the scenarios where wireless sensors can benefit the overall system performance. Battery models and additional sensor and obstacle objects are introduced to a previously developed simulator, MAHESHDAS. Various battery models and parameters are considered to mimic a realistic environment. The sensor nodes occupy a small amount of physical space and therefore assist the robots while also limiting their movements. Robots are assumed to be ground vehicles, and will need to avoid collisions of each other as well as sensor nodes and other obstacles. Repulsion forces are used to model the collision avoidance between the various objects. The percentage of threats contained, the time to contain threats, and the average robot lifetime are compared in different operational scenarios. The simulation results demonstrate that the introduction of wireless sensors improve the average robot lifetime when the threats do not occur too often and when the sensor repulsion force is relatively small. Uniform sensor placement is also shown to perform better than random deployment

Technologies to improve the performance of wireless sensor networks in high-traffic applications

The expansion of wireless sensor networks to advanced areas, including structure health monitoring, multimedia surveillance, and health care monitoring applications, has resulted in new and complex problems. Traditional sensor systems are designed and optimised for extremely low traffic loads. However, it has been witnessed that network performance drops rapidly with the higher traffic loads common in advanced applications. In this thesis, we examine the system characteristics and new system requirements of these advanced sensor network applications. Based on this analysis, we propose an improved architecture for wireless sensor systems to increase the network performance while maintaining compatibility with the essential WSN requirements: low power, low cost, and distributed scalability. We propose a modified architecture deriving from the IEEE 802.15.4 standard, which is shown to significantly increase the network performance in applications generating increased data loads. This is achieved by introducing the possibility of independently allocating the sub-carriers in a distributed manner. As a result, the overall efficiency of the channel contention mechanism will be increased to deliver higher throughput with lower energy consumption. Additionally, we develop the concept of increasing the data transmission efficiency by adapting the spreading code length to the wireless environment. Such a modification will not only be able to deliver higher throughput but also maintain a reliable wireless link in the harsh RF environment. Finally, we propose the use of the battery recovery effect to increase the power efficiency of the system under heavy traffic load conditions. These three innovations minimise the contention window period while maximising the capacity of the available channel, which is shown to increase network performance in terms of energy efficiency, throughput and latency. The proposed system is shown to be backwards compatible and able to satisfy both traditional and advanced applications and is particularly suitable for deployment in harsh RF environments. Experiments and analytic techniques have been described and developed to produce performance metrics for all the proposed techniques

Improving the Performance of Wireless LANs

This book quantifies the key factors of WLAN performance and describes methods for improvement. It provides theoretical background and empirical results for the optimum planning and deployment of indoor WLAN systems, explaining the fundamentals while supplying guidelines for design, modeling, and performance evaluation. It discusses environmental effects on WLAN systems, protocol redesign for routing and MAC, and traffic distribution; examines emerging and future network technologies; and includes radio propagation and site measurements, simulations for various network design scenarios, numerous illustrations, practical examples, and learning aids