Autonomic approaches for enhancing communication QoS in dense Wireless Sensor Networks with real time requirements

Abstract

Wireless Sensor Networks (WSN) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) in each node can not be easily replaced. One solution is to deploy a large number of sensor nodes, since the lifetime and dependability of the network can be increased through cooperation among nodes. In addition to energy consumption, applications for WSN may also have other concerns, such as, meeting deadlines and maximizing the quality of information. In this paper, two autonomic approaches for dense WSN are presented. The first approach is a Genetic Machine Learning algorithm aimed at applications that make use of trade-offs between different metrics. Simulations were performed on random topologies assuming different levels of faults. GMLA showed a significant improvement when compared with the use of IEEE 802.15.4 protocol. Moreover, an approach that autonomically provides QoS for dense WSN called VOA ( Variable Offset Algorithm) is presented. Experimental results had showed that VOA can significantly improve communication efficiency in dense WSN.