On prolonging network lifetime by adjusting sleep/awake cycles in wireless sensor networks

To save their energy, sensors spent most of their life in sleeping mode waking up for short intervals to achieve any required tasks. While performing their sensing and communicational obligations to the network and due to the random nature of sleep and awake cycles, sensors are usually assigned different workloads which in turn consume their energy unevenly. Eventually, the energy of those heavily loaded sensors will be totally depleted. This results in reducing network density around those sensors and creates energy holes that isolate the network into disconnected islands. These problems have their negative impacts on network durability and reliability. To avoid these problems, we propose and evaluate a distributed protocol that adjusts sensor sleep and wake up times based on the relative differences between sensor energy and the energy of surrounding sensors. Using simulation, we first show that during network lifetime, variations in sensors energy can be very large compared to what can be achieved using our proposed protocol. In addition to this, we show the impact of these variations on the network lifetime measured in terms of the number of tasks the network can perform till the network density goes below certain threshold.

[1]  Wolfgang Effelsberg,et al.  Scriptable Sensor Network Based Home-Automation , 2007, EUC Workshops.

[2]  Xu Du,et al.  An energy-efficient real-time routing protocol for sensor networks , 2007, Comput. Commun..

[3]  Faramarz Fekri,et al.  Sleep scheduling and lifetime maximization in sensor networks: fundamental limits and optimal solutions , 2006, IPSN.

[4]  Tarek F. Abdelzaher,et al.  Towards optimal sleep scheduling in sensor networks for rare-event detection , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[5]  Stephan Olariu,et al.  Energy-based task load balancing in wireless sensor networks , 2008, 2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems.

[6]  Wendi Heinzelman,et al.  Balanced-energy sleep scheduling scheme for high density cluster-based sensor networks , 2004 .

[7]  Katia Obraczka,et al.  Energy-efficient collision-free medium access control for wireless sensor networks , 2003, SenSys '03.

[8]  Edmund M. Yeh,et al.  Distributed energy management algorithm for large-scale wireless sensor networks , 2007, MobiHoc '07.

[9]  Nuria Oliver,et al.  HealthGear: a real-time wearable system for monitoring and analyzing physiological signals , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[10]  Sajal K. Das,et al.  On the Energy Hole Problem of Nonuniform Node Distribution in Wireless Sensor Networks , 2006, 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Systems.

[11]  Matt Welsh,et al.  Sensor networks for medical care , 2005, SenSys '05.

[12]  Artin Der Minassians,et al.  Wireless Sensor Networks for Home Health Care , 2007, 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07).

[13]  Andrew S. Tanenbaum,et al.  Taking Sensor Networks from the Lab to the Jungle , 2006, Computer.

[14]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[15]  M. Lakshmanan,et al.  AN ADAPTIVE ENERGY EFFICIENT MAC PROTOCOL FOR WIRELESS SENSOR NETWORKS , 2009 .

[16]  Jindong Tan,et al.  Heartbeat-driven medium-access control for body sensor networks , 2010, IEEE Trans. Inf. Technol. Biomed..

[17]  Bhaskar Krishnamachari,et al.  Delay efficient sleep scheduling in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[18]  Tu Shiliang,et al.  A realtime dynamic traffic control system based on wireless sensor network , 2005, 2005 International Conference on Parallel Processing Workshops (ICPPW'05).

[19]  Luís Bernardo,et al.  A Fire Monitoring Application for Scattered Wireless Sensor Networks - A Peer-to-Peer Cross-layering Approach , 2007, WINSYS.

[20]  Sanjay Jha,et al.  Wireless Sensor Networks for Battlefield Surveillance , 2006 .