Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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On the Handling of Node Failures: Energy-Efficient Job Allocation Algorithm for Real-time Sensor Networks

  • Karimi, Hamid (School of Electrical and Computer Engineering, College of Engineering, University of Tehran) ;
  • Kargahi, Mehdi (School of Electrical and Computer Engineering, College of Engineering, University of Tehran) ;
  • Yazdani, Nasser (School of Electrical and Computer Engineering, College of Engineering, University of Tehran)
  • Received : 2010.06.16
  • Accepted : 2010.08.23
  • Published : 2010.09.30
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Abstract

Wireless sensor networks are usually characterized by dense deployment of energy constrained nodes. Due to the usage of a large number of sensor nodes in uncontrolled hostile or harsh environments, node failure is a common event in these systems. Another common reason for node failure is the exhaustion of their energy resources and node inactivation. Such failures can have adverse effects on the quality of the real-time services in Wireless Sensor Networks (WSNs). To avoid such degradations, it is necessary that the failures be recovered in a proper manner to sustain network operation. In this paper we present a dynamic Energy efficient Real-Time Job Allocation (ERTJA) algorithm for handling node failures in a cluster of sensor nodes with the consideration of communication energy and time overheads besides the nodes' characteristics. ERTJA relies on the computation power of cluster members for handling a node failure. It also tries to minimize the energy consumption of the cluster by minimum activation of the sleeping nodes. The resulting system can then guarantee the Quality of Service (QoS) of the cluster application. Further, when the number of sleeping nodes is limited, the proposed algorithm uses the idle times of the active nodes to engage a graceful QoS degradation in the cluster. Simulation results show significant performance improvements of ERTJA in terms of the energy conservation and the probability of meeting deadlines compared with the other studied algorithms.

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References

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