Abstract-Faults in WSN are very common and appear in different levels of the system. For pervasive applications to be adopted by end-users there is a need for autonomic selfhealing. This paper discusses our initial approach to selfhealing in WSN and describes experiments with two case studies of body sensor deployment. We evaluate the impact of sensor faults on activity and gesture classification accuracy respectively and develop mechanisms that will allow detection of those faults during system's operation.

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Wireless sensor networks are a key aspect of many pervasive systems designed to aid people in their normal activities and adapt to their current context. However, these systems also need to be self-managing in discovering and configuring devices for services, detecting and responding to attacks, determining errors and faults and reconfiguring the system to mitigate these. In this paper we describe the Starfish framework for specifying and dynamically managing policies in sensor nodes. We discuss the components in the framework which include the Finger2 policy system for specifying dynamic adaptivity, a module library to simplify the programming the basic funtionality of nodes and a client side editor for managing policies. We describe policies for an adaptive healthcare body network then focus on policies for self-healing aspects of sensor networks and give examples of policy-based reconfigurations to deal with faults.

Abstract-Pervasive applications incorporate physical components that are exposed to everyday use and a large number of conditions and external factors that can lead to faults and failures. It is also possible that application requirements change during deployment and the network needs to adapt to a new context. Consequently, pervasive systems must be capable to autonomically adapt to changing conditions without involving users becoming a transparent asset in the environment. In this paper, we present an autonomic mechanism for initial task assignment in sensor networks, an NP-hard problem. We also study on-line adaptation of the original deployment which considers real-time metrics for maximising utility and lifetime of applications and smooth service degradation in the face of component failures.