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Decentralized detection is one of the key tasks that a wireless sensor network (WSN) is faced to accomplish. Among several decision criteria, the Rao test is able to cope with an unknown (but parametrically-specified) sensing model, while keeping computational simplicity. To this end, the Rao test is employed in this paper to fuse multivariate data measured by a set of sensor nodes, each observing the target (or the desired) event via a nonlinear mapping function. In order to meet stringent energy/bandwidth requirements, sensors quantize their vector-valued observations into one or few bits and send them over error-prone (to model low-power communications) reporting channels to a fusion center (FC). Therein, a global (better) decision is taken via the proposed test. Its closed form and asymptotic (large-size WSN) performance are obtained, and the latter leveraged to optimize quantizers. The appeal of the proposed approach is confirmed via simulations. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Detection is a basic task of a wireless sensor network (WSN). To meet severe bandwidth and energy limitations of WSNs, network nodes are usually programmed to decide locally about a desired event occurrence and send just one bit to a fusion center (FC) wherein an optimum decision must be taken. In this paper, we employ the Rao test for fusing local decisions of nodes. The Rao test is well-known for its much lower computational complexity than the more common (but computationally heavier) generalized likelihood ratio test (GLRT). We obtain the closed forms of the Rao-test-based fusion rules in both homogeneous and heterogeneous WSNs in the presence of imperfect communication channels. The importance of the presented formulations lies in their simplicity while taking practical issues into account. We show that the Rao-test-based decision fusion in homogeneous WSNs coincides with the counting rule (CR). Moreover, simple methods of adjusting local detection thresholds are proposed. The effectiveness of RAPID in the improvement of the overall network performance is shown through simulations in different scenarios.

Distributed detection is an essential task of wireless sensor networks (WSNs) with stringent limitations of energy and processing resources. In most of detection scenarios, the optimal threshold depends on the noise power which itself consists of some uncertainty in practice. In this paper, we use fuzzy hypothesis test (FHT) for modeling the noise power uncertainty. Moreover, we propose an optimal censoring scheme by applying the Neyman–Pearson lemma on the FHT. It is proved that the optimal censoring method is obtained by simply comparing the energy of the observed data with a threshold. Interestingly, we show that the threshold would depend on the local communication constraint as well as the noise uncertainty bound. We evaluate the performance of the proposed censoring algorithm by resorting to the equal gain combining (EGC) as the relevant fusion rule, using common receiver operating curves. Simulation results show the effectiveness of the proposed method in counteracting the uncertainty.