Service-Oriented Sensor-Actuator Networks
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In this dissertation, we propose service-oriented sensor-actuator networks (SOSANETs) as a new paradigm for building the next generation of customizable, open, interoperable sensor-actuator networks. In SOSANETs, nodes expose their capabilities to applications in the form of service profiles. A node's service profile consists of a set of services (i.e., sensing and actuation capabilities) that it provides and the quality of service (QoS) parameters associated with those services (delay, accuracy, freshness, etc.). SOSANETs provide the benefits of both application-specific SANETs and generic SANETs. We first define a query model and an architecture for SOSANETs. The proposed query model offers a simple, uniform query interface whereby applications specify sensing and actuation queries independently from any specific deployment of the underlying SOSANET. We then present uRACER (Reliable Adaptive serviCe-driven Efficient Routing), a routing protocol suite for SOSANETs. uRACER consists of three routing protocols, namely, SARP (Service-Aware Routing Protocol), CARP (Context-Aware Routing Protocol), and TARP (Trust-Aware Routing Protocol). SARP uses an efficient service-aware routing approach that aggressively reduces downstream traffic by translating service profiles into efficient paths. CARP supports QoS by dynamically adapting each node's routing behavior and service profile according to the current context of that node, i.e. number of pending queries and number and type of messages to be routed. Finally, TARP achieves high end-to-end reliability through a scalable reputation-based approach in which each node is able to locally estimate the next hop of the most reliable path to the sink. We also propose query optimization techniques that contribute to the efficient execution of queries in SOSANETs. To evaluate the proposed service-oriented architecture, we implemented TinySOA, a prototype SOSANET built on top of TinyOS with uRACER as its routing mechansim. TinySOA is designed as a set of layers with a loose interaction model that enables several cross-layer optimization options. We conducted an evaluation of TinySOA that included a comparison with TinyDB. The obtained empirical results show that TinySOA achieves significant improvements on many aspects including energy consumption, scalability, reliability and response time.
- Doctoral Dissertations