Improve the Performance of Adaptive Sleep Scheduled Wireless Sensor Network

The conventional methods of improving the performance of wireless sensor network focus on proposed algorithm mechanism to increase the packet delivery ratio, reduce the delay and so on. In this paper, firstly we propose an adaptive sleep scheduled scheme to reduce the energy consumption of the whole network. Based on this, we introduce weight w to measure the importance of data. We assume that the faster important the data is transmitted to sink node, the better the performance of whole network is. Applied the scheduling policy, system sleep optimum is impossible. The simulation shows that the performance has increased compared with full connect network.

[1]  Maciej J. Zawodniok,et al.  A dynamic programming approach: Improving the performance of wireless networks , 2011, J. Parallel Distributed Comput..

[2]  Deborah Estrin,et al.  An energy-efficient MAC protocol for wireless sensor networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[3]  Brahim Bensaou,et al.  Tradeoff Between Lifetime and Rate Allocation in Wireless Sensor Networks: A Cross Layer Approach , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[4]  Carlos E. Palau,et al.  Multimode WSN: Improving Robustness, Fault Tolerance and Performance of Randomly Deployed Wireless Sensor Network , 2010, 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks.

[5]  Wang Jian-xin Survey of Sleep Scheduling in Wireless Sensor Network , 2008 .

[6]  Rong Zheng,et al.  Optimal Block Design for Asynchronous Wake-Up Schedules and Its Applications in Multihop Wireless Networks , 2006, IEEE Transactions on Mobile Computing.

[7]  Calvin C. Newport Improving Wireless Network Performance Using Sensor Hints , 2011, NSDI.

[8]  Yan Gao,et al.  SOFA: A Sleep-Optimal Fair-Attention Scheduler for the Power-Saving Mode of WLANs , 2011, 2011 31st International Conference on Distributed Computing Systems.

[9]  Tian He,et al.  Data forwarding in extremely low duty-cycle sensor networks with unreliable communication links , 2007, SenSys '07.

[10]  Özgür Erçetin,et al.  Energy efficient random sleep-awake schedule design , 2006, IEEE Communications Letters.

[11]  Nurhayati,et al.  A Cluster Based Energy Efficient Location R outing Protocol in Wireless Sensor Networks , 2011 .

[12]  R. B. Patel,et al.  Multi-Hop Data Communication Algorithm for Clustered Wireless Sensor Networks , 2011, Int. J. Distributed Sens. Networks.

[13]  Subhasis Dash,et al.  Congestion-less energy aware token based MAC protocol integrated with sleep scheduling for Wireless Sensor Networks , 2011, WCE 2011.

[14]  Jie Wu,et al.  An unequal cluster-based routing protocol in wireless sensor networks , 2009, Wirel. Networks.

[15]  Yu-Chee Tseng,et al.  Power-saving protocols for IEEE 802.11-based multi-hop ad hoc networks , 2003, Comput. Networks.

[16]  Thomas Noël,et al.  Auto-adaptive MAC for energy-efficient burst transmissions in wireless sensor networks , 2011, 2011 IEEE Wireless Communications and Networking Conference.

[17]  Wendi Heinzelman,et al.  Proceedings of the 33rd Hawaii International Conference on System Sciences- 2000 Energy-Efficient Communication Protocol for Wireless Microsensor Networks , 2022 .

[18]  Nitin H. Vaidya,et al.  A MAC protocol to reduce sensor network energy consumption using a wakeup radio , 2005, IEEE Transactions on Mobile Computing.

[19]  Mihaela Cardei,et al.  Improving network lifetime with mobile wireless sensor networks , 2010, Comput. Commun..

[20]  Deborah Estrin,et al.  Geography-informed energy conservation for Ad Hoc routing , 2001, MobiCom '01.

[21]  N. A. Vasanthi,et al.  AWS: asynchronous wakeup schedule to minimize latency in wireless sensor networks , 2006, IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC'06).

[22]  Georgios I. Papadimitriou,et al.  A novel HMM-based learning framework for improving dynamic wireless push system performance , 2011, Computers and Mathematics with Applications.

[23]  Zongwei Luo,et al.  A Model for Evaluating Connectivity Availability in Random Sleep Scheduled Delay-Tolerant Wireless Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.