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Topology control is an effective method to improve the energy efficiency of wireless sensor networks (WSNs). Traditional approaches are based on the assumption that a pair of nodes is either "connected" or "disconnected." These approaches are called connectivity-based topology control. In real environments, however, there are many intermittently connected wireless links called lossy links. Taking a succeeded lossy link as an advantage, we are able to construct more energy-efficient topologies. Toward this end, we propose a novel opportunity-based topology control. We show that opportunity-based topology control is a problem of NP-hard. To address this problem in a practical way, we design a fully distributed algorithm called CONREAP based on reliability theory. We prove that CONREAP has a guaranteed performance. The worst running time is O(∣E∣), where E is the link set of the original topology, and the space requirement for individual nodes is O(d), where d is the node degree. To evaluate the performance of CONREAP, we design and implement a prototype system consisting of 50 Berkeley Mica2 motes. We also conducted comprehensive simulations. Experimental results show that compared with the connectivity-based topology control algorithms, CONREAP can improve the energy efficiency of a network up to six times. [ABSTRACT FROM AUTHOR]

The Peer-to-Peer (P2P) model, being widely adopted in today's Internet computing, suffers from the problem of topology mismatch between the overlay networks and the underlying physical network. Traditional topology optimization techniques identify physically closer nodes to connect as overlay neighbors, but could significantly shrink the search scope. Recent efforts have been made to address the mismatch problem without sacrificing the search scope, but they either need time synchronization among peers or have a low convergent speed. In this paper, we propose a scalable bipartite overlay (SBO) scheme to optimize the overlay topology by identifying and replacing the mismatched connections. In SBO, we employ an efficient strategy for distributing optimization tasks in peers with different colors. We conducted comprehensive simulations to evaluate this design. The results show that SBO achieves approximately 85 percent of reduction on traffic cost and about 60 percent of reduction on query response time. Our comparisons with previous approaches to address the topology mismatch problem have shown that SBO can achieve a fast convergent speed, without the need of time synchronization among peers. [ABSTRACT FROM AUTHOR]