Autonomic computing system for self-management of machine-to-machine networks

Abstract

The internet of things consists of a high amount of heterogeneous objects that are widely distributed and evolve frequently according to their context changes. Management of such a complex environment is costly in terms of time and money. Machine to Machine (M2M) networks will soon become virtually impossible to administer. Designing context aware autonomic system for M2M networks with capability of self-management is a challenge. This paper proposes an autonomic computing system based on standardized M2M architecture and composed of generic and extensible autonomic managers. Autonomic managers communicate with each other to enable self-management of application, service and communication layers based on knowledge models and reasoning rules. A smart metering use case is experimented to illustrate the proposed solution.