Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Distributed detection and fusion in a large wireless sensor network of random size
EURASIP Journal on Wireless Communications and Networking
Information bounds and quickest change detection in decentralized decision systems
IEEE Transactions on Information Theory
A generalized change detection problem
IEEE Transactions on Information Theory
Delay optimal event detection on ad hoc wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
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We consider the problem of event detection in wireless sensor networks (WSNs) that are large in the sense that an event affects the statistics of the observations of a small number of sensors in the vicinity of where it occurs. An event occurs at a random time at a random location in the region (called the region of interest, ROI) covered by the WSN. We consider a distance based sensing model in which the physical signal sensed by a sensor at a distance d from the event is attenuated by a factor ρ(d). We formulate the problem of detecting an event as early as possible and locating it to a subregion of the ROI under the constraints that the average time to false alarm (TFA) and the average time to false isolation (TFI) are bounded by γ. This formulation is motivated by the change detection/isolation framework introduced by Nikiforov [6]. We extend the decentralized detection procedures MAX [11] and ALL [9], [5], which are designed for colocated networks, to the case of a large WSN where the event localization is also a critical issue. The extended MAX and ALL detect the change and identify a subregion where the event is located. Sensor noise can make the local decisions of ALL toggle rapidly. Motivated by this fact, we propose a distributed change detection/isolation procedure, HALL (Hysteresis modified ALL). We study the supremum average detection delay (SADD) performance of the change detection/isolation procedures MAX, ALL and HALL for a required min{TFA, TFI} ≥ γ. We show that as γ → ∞, the (asymptotic) SADD(ALL) ≤ ln γ/ω0 MI, SADD (HALL) ≤ ln (γ+1)/ω0 (1-1/β) MI + C, and SADD (MAX) ≤ ln γ/ω0 I, where ω0, C, β and M are constants that depend on the sensor deployment, the postchange and the prechange distributions of sensor measurements, and I is the Kullback-Leibler divergence between a worst-case postchange distribution and the prechange distribution of sensor measurements. We also compare the SADD of the distributed procedures with that of the asymptotically optimal centralized procedure given by Nikiforov [6] for a Boolean sensing model. We show that the SADD performance of ALL and HALL is of the same order as that of Nikiforov's. We also provide numerical comparison of SADD and TFA for the centralized asymptotically optimal scheme [6], and the distributed schemes MAX, ALL and HALL.