Autonomic computing-based wireless sensor networks

Abstract

Wireless Sensor Networks (WSNs) have grown in popularity in the last years by proving to be a bene cial technology for a wide range of application do- mains, including but not limited to health-care, environment and infrastruc- ture monitoring, smart home automation, industrial control, intelligent agri- culture, and emergency management. However, developing applications on such systems requires many e orts due to the lack of proper software abstractions and the di culties in man- aging resource-constrained embedded environments. Moreover, these appli- cations have to meet a combination of con icting requirements. Achieving accuracy, e ciency, correctness, fault-tolerance, adaptability and reliability on WSN is a major issue because these features have to be provided beyond the design/implementation phase, notably at execution time. This thesis explores the viability and convenience of Autonomic Comput- ing in the context of WSNs by providing a novel paradigm to support the development of autonomic WSN applications as well as speci c self-adaptive protocols at networking levels. In particular, this thesis provides three main contributions. The rst is the design and realization of a novel framework for the development of e cient distributed signal processing applications on heterogeneous WSNs, called SPINE2. It provides a programming abstraction based on the task-oriented paradigm for abstracting away low-level details and has a platform-independent architecture enabling code reusability and portability, application interoperability and platform heterogeneity. The sec- ond contribution is the development of SPINE-* which is an enhancement of SPINE2 by means of an autonomic plane, a way for separating out the provision of self-* techniques from the WSN application logic. Such a separa- tion of concerns leads to an ease of deployment and run-time management of new applications. We nd that this enhancement brings not only considerable functional improvements but also measurable performance bene ts. Third, since we advocate that the agent-oriented paradigm is a well-suited approach in the context of autonomic computing, we propose MAPS, an agent-based programming framework for WSNs. Speci cally designed for supporting Java- iii based sensor platforms, MAPS allows the development of general-purpose mobile multi-agent applications by adopting a multi-plane state machine for- malism for de ning agents' behavior. Finally, the fourth contribution regards the design, analysis, and simulations of a self-adaptive AODV routing protocol enhancement, CG-AODV, and a novel contention-based MAC protocol, QL- MAC. CG-AODV adopts a \node concentration-driven gossiping" approach for limiting the ooding of control packets, whereas QL-MAC, based on a Q-learning approach, aims to nd an e cient radio wake-up/sleep scheduling strategy to reduce energy consumption on the basis of the actual network load of the neighborhood. Simulation results show that CG-AODV outper- forms AODV, whereas QL-MAC provides better performance over standard MAC protocols.