Abstract. Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called "A Line in the Sand" and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensors nodes at MacDill Air Force Base in Tampa, Florida, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes.

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Abstract-We present a fast, local clustering service, FLOC, that partitions a multihop wireless network into nonoverlapping and approximately equal-sized clusters. Each cluster has a clusterhead such that all nodes within unit distance and some nodes within distance m of the clusterhead belong to the cluster. We show that, by asserting a stretch factor m ! 2, FLOC achieves locality of clustering and fault-local self-stabilization: The effects of cluster formation and faults/changes at any part of the network are contained within at most m þ 1 units. Through simulations and experiments with actual deployments, we analyze the trade-offs between clustering time and the quality of clustering and suggest suitable parameters for FLOC to achieve a fast completion time without compromising the quality of the resulting clustering.