Autonomic Wireless Sensor Networks: A Systematic Literature Review

Autonomic computing (AC) is a promising approach to meet basic requirements in the design of wireless sensor networks (WSNs), and its principles can be applied to efficiently manage nodes operation and optimize network resources. Middleware for WSNs supports the implementation and basic operation of such networks. In this systematic literature review (SLR) we aim to provide an overview of existing WSN middleware systems that address autonomic properties. The main goal is to identify which development approaches of AC are used for designing WSN middleware system, which allow the self-management of WSN. Another goal is finding out which interactions and behavior can be automated in WSN components. We drew the following main conclusions from the SLR results: (i) the selected studies address WSN concerns according to the self-* properties of AC, namely, self-configuration, self-healing, self-optimization, and self-protection; (ii) the selected studies use different approaches for managing the dynamic behavior of middleware systems for WSN, such as policy-based reasoning, context-based reasoning, feedback control loops, mobile agents, model transformations, and code generation. Finally, we identified a lack of comprehensive system architecture designs that support the autonomy of sensor networking

Runtime variability for dynamic reconfiguration in wireless sensor network product lines

Runtime variability is a key technique for the success of Dynamic Software Product Lines (DSPLs), as certain application demand reconfiguration of system features and execution plans at runtime. In this emerging research work we address the problem of dynamic changes in feature models in sensor networks product families, where nodes of the network demand dynamic reconfiguration at post-deployment time

An autonomic plane for wireless body sensor networks

Wireless Body Sensor Networks (WBSN) have proved to be a potential technology for developing applications that can monitor and control physical and biochemical parameters on the human body. Developing such applications is rather cumbersome, since these have to meet a combination of conflicting requirements. Achieving accuracy, efficiency, correctness, fault-tolerance, adaptability and reliability on WBSN is tricky because these features have to be provided beyond the design/implementation phase, notably at execution time. In this paper we explore the viability and convenience of autonomic computing in the context of WBSNs. In particular, we propose to extend a conventional WBSN framework with an autonomic plane, a way for separating out the provision of self-* properties from the WBSN application logic. This separation of concerns leads to an ease of deployment and run-time management of new applications. We study this approach in the context of SPINE2 framework, showing how this can be readily enhanced with an autonomic layer. We find that this enhancement brings not only considerable functional improvements but also measurable performance benefits

An autonomic plane for wireless body sensor networks

Wireless Body Sensor Networks (WBSN) have proved to be a potential technology for developing applications that can monitor and control physical and biochemical parameters on the human body. Developing such applications is rather cumbersome, since these have to meet a combination of conflicting requirements. Achieving accuracy, efficiency, correctness, fault-tolerance, adaptability and reliability on WBSN is tricky because these features have to be provided beyond the design/implementation phase, notably at execution time. In this paper we explore the viability and convenience of autonomic computing in the context of WBSNs. In particular, we propose to extend a conventional WBSN framework with an autonomic plane, a way for separating out the provision of self-* properties from the WBSN application logic. This separation of concerns leads to an ease of deployment and run-time management of new applications. We study this approach in the context of SPINE2 framework, showing how this can be readily enhanced with an autonomic layer. We find that this enhancement brings not only considerable functional improvements but also measurable performance benefits