A decentralized version of particle swarm optimization called the distributed particle swarm optimization (DPSO) approach is formulated and applied to the generation of sensor network configurations or topologies so that the deleterious effects of hidden nodes and asymmetric links on the performance of wireless sensor networks are minimized. Three different topology generation schemes, COMPOW, Cone-Based and the DPSO--based schemes are examined using ns-2. Simulations are executed by varying the node density and traffic rates. Results contrasting heterogeneous vs. homogeneous power reveal that an important metric for a sensor network topology may involve consideration of hidden nodes and asymmetric links, and demonstrate the effect of spatial reuse on the potency of topology generators.
Since untethered sensor nodes operate on battery, and because they must communicate through a multi-hop network, it is vital to optimally configure the transmit power of the nodes both to conserve power and optimize spatial reuse of a shared channel. Current topology control algorithms try to minimize radio power while ensuring connectivity of the network. We propose that another important metric for a sensor network topology will involve consideration of hidden nodes and asymmetric links. Minimizing the number of hidden nodes and asymmetric links at the expense of increasing the transmit power of a subset of the nodes may in fact increase the longevity of the sensor network. In this paper we explore a distributed evolutionary approach to optimizing this new metric. Inspiration from the Particle Swarm Optimization technique motivates a distributed version of the algorithm. We generate topologies with fewer hidden nodes and asymmetric links than a comparable algorithm and present some results that indicate that our topologies deliver more data and last longer.
Untethered, underwater sensors, deployed for event detection and tracking and operating in an autonomous mode will be required to self-assemble into a configuration, which optimizes their coverage, effectively minimizing the probability that an event in the target area goes undetected. This organized, cooperative, and autonomous, spreading-out of the sensors is complicated due to sensors localized communication. A given sensor will not in general have position and velocity information for all sensors, but only for those in its communication area. A possible approach to this problem, motivated by an evolutionary optimization technique, Particle Swarm Optimization (PSO) is proposed and extended in a novel way. A distributed version of PSO is developed. A distributed version of PSO is explored using experimental fitness to address the coverage problem in a two dimensional area.
When wireless sensors are capable of variable transmit power and are battery powered, it is important to select the appropriate transmit power level for the node. Lowering the transmit power of the sensor nodes imposes a natural clustering on the network and has been shown to improve throughput of the network. However, a common transmit power level is not appropriate for inhomogeneous networks. A possible fitness-based approach, motivated by an evolutionary optimization technique, Particle Swarm Optimization (PSO) is proposed and extended in a novel way to determine the appropriate transmit power of each sensor node. A distributed version of PSO is developed and explored using experimental fitness to achieve an approximation of least-cost connectivity.
Clustering is necessary for data aggregation, hierarchical routing, optimizing sleep patterns, election of extremal sensors, optimizing coverage and resource allocation, reuse of frequency bands and codes, and conserving energy. Optimal clustering is typically an NP-hard problem. Solutions to NP-hard problems involve searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of NP-hard problems. We explore one such approach, Particle Swarm Optimization (PSO), an evolutionary programming technique where a 'swarm' of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. We use the PSO approach to cluster sensors in a sensor network. The energy efficiency of our clustering in a data-aggregation type sensor network deployment is tested using a modified LEACH-C code. The PSO technique with a recursive bisection algorithm is tested against random search and simulated annealing; the PSO technique is shown to be robust. We further investigate developing a distributed version of the PSO algorithm for clustering optimally a wireless sensor network.