With the wide-adoption of the Internet of Things, heterogeneous smart things, serving as sensor nodes, require to work in a collective fashion for achieving complex applications. To address this challenge, this article proposes a service-oriented wireless sensor networks (WSNs) framework, where sensor nodes are encapsulated and represented as WSN services, which are energy-aware, and typically have constraints on their spatial and temporal aspects. Generally, WSN services are categorized into service classes according to the their functionalities. Consequently, service classes chains are generated with respect to the requirement of domain applications, and the composition of WSN services is constructed through discovering and selecting appropriate WSN services as the instantiation of service classes contained in chains. This WSN services composition is reduced to a multi-objective and multi-constrained optimization problem, which can be solved through adopting particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Experimental evaluation shows that PSO outperforms GA in finding approximately optimal WSN services compositions