|Title:||Self-Healing Wireless Sensor Networks|
|Affiliation:||Imperial College London|
PhD project description
Pervasive computing and its applications are increasingly attracting attention from the academic community. However, for pervasive systems to become widely adopted by users, they need to support automatic configuration and management. These requirements become more apparent as the number of nodes employed by those systems rises. Management of the system's components should happen transparently from the users, who may not be adequately technically trained to cope with details of operation. Consequently, adaptation of Autonomic Computing techniques to the area of Pervasive Computing is implied.
Our goal is to construct self-managed Body Sensor Networks (BSN), which are practical to use without demanding high technical expertise from their users. In that direction the basic architecture of self-managed system is presented in and self-configuration and adaptation of components has been investigated in. The objective of my thesis is to study the self-healing property in such systems. The Self-healing should be achieved in two levels; the sensor level and node/network level. In the former, faults on the sensing devices are handled that may report irregularly erroneous values or they incrementally drift. Such behaviors are typically caused by external damaging factor or inherent physical issues. In the node scope issues that rise typically involve battery life of nodes, which may be depleted, or malfunctioning of the communication mechanism.
A framework for self-healing on BSNs should also be aware of the limitations of the platform. We should be able to provide autonomic services with the minimum communication overhead so that we may not have a negative impact in the life-time of sensors in the network.