DynamicSD: Discover Dynamic and Uncertain Services in Pervasive Computing Environments

Abstract

Pervasive computing environments consist of thousands of heterogeneous devices and network services. Service discovery protocols provide essential functionalities for users and client devices to discover and access services. Most existing protocols, however, only support discovery via static service attributes. Dynamic information such as service conditions, quality, and reliability is not available to users and clients. To disseminate dynamic service information, we need to properly control the communication and computational overhead. Thus, the solution will be viable for resource-constrained devices. We propose a novel service discovery approach, called DynamicSD. We use mathematical models-Markov Chains-to represent dynamic and uncertain service states. The Markov Chains are disseminated among clients, services, and directories. By deriving the properties of the Markov Chains, we attain dynamic service information. To the best of our knowledge, this is the first formal model to represent dynamic and uncertain service conditions in service discovery protocols. We implement a prototype protocol on wireless sensors. The performance measurements show that the communication and computational overhead that we introduce is low.