A wide variety of sensors have been incorporated into a spectrum of wireless sensor network (WSN) platforms, providing flexible sensing capability over a large number of low-power and inexpensive nodes. Traditional signal processing algorithms, however, often prove too complex for energy-and-cost-effective WSN nodes. This study explores how to design efficient sensing and classification algorithms that achieve reliable sensing performance on energy-andcost-effective hardware without special powerful nodes in a continuously changing physical environment. We present the detection and classification system in a cutting-edge surveillance sensor network, which classifies vehicles, persons, and persons carrying ferrous objects, and tracks these targets with a maximum error in velocity of 15%. Considering the demanding requirements and strict resource constraints, we design a hierarchical classification architecture that naturally distributes sensing and computation tasks at different levels of the system. Such a distribution allows multiple sensors to collaborate on a sensor node, and the detection and classification results to be continuously refined at different levels of the WSN. This design enables reliable detection and classification without involving high-complexity computation, reduces network traffic, and emphasizes resilience and adaptation to the realistic environment. We evaluate the system with performance data collected from outdoor experiments and field assessments. Based on the experience acquired and lessons learned when developing this system, we abstract common issues and introduce several guidelines which can direct future development of detection and classification solutions based on WSNs.
Evolutionary computation (EC) techniques have been successfully applied to compute near-optimal paths for unmanned aerial vehicles (UAVs). Premature convergence prevents evolutionary-based algorithms from reaching global optimal solutions. This often leads to unsatisfactory routes that are suboptimal to optimal path planning problems. To overcome this problem, this paper presents a framework of parallel evolutionary algorithms for UAV path planning, in which several populations evolve simultaneously and compete with each other. The parallel evolution technique provides more exploration capability to planners and significantly reduces the probability that planners are trapped in local optimal solutions.
Most large blackouts are caused by cascading failures-sequences of equipment outages, one set of outages precipitating another. We study the application of distributed, autonomous agents for shortening such sequences. Each agent controls a single variable-the consumption of a load or the output of a generator. Each agent uses model predictive control and cooperates with its neighbors in making its decisions. Experiments using the IEEE 118 bus test case illustrate the effectiveness of this method.
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