Energy-Efficient Decision Fusion for Distributed Detection in Wireless Sensor Networks

Abstract

This paper proposes an energy-efficient counting rule for distributed detection by ordering sensor transmissions in wireless sensor networks. In the counting rule-based detection in an N −sensor network, the local sensors transmit binary decisions to the fusion center, where the number of all N local-sensor detections are counted and compared to a threshold. In the ordering scheme, sensors transmit their unquantized statistics to the fusion center in a sequential manner; highly informative sensors enjoy higher priority for transmission. When sufficient evidence is collected at the fusion center for decision making, the transmissions from the sensors are stopped. The ordering scheme achieves the same error probability as the optimum unconstrained energy approach (which requires observations from all the N sensors) with far fewer sensor transmissions. The scheme proposed in this paper improves the energy efficiency of the counting rule detector by ordering the sensor transmissions: each sensor transmits at a time inversely proportional to a function of its observation. The resulting scheme combines the advantages offered by the counting rule (efficient utilization of the network's communication bandwidth, since the local decisions are transmitted in binary form to the fusion center) and ordering sensor transmissions (bandwidth efficiency, since the fusion center need not wait for all the N sensors to transmit their local decisions), thereby leading to significant energy savings. As a concrete example, the problem of target detection in large-scale wireless sensor networks is considered. Under certain conditions the ordering-based counting rule scheme achieves the same detection performance as that of the original counting rule detector with fewer than N/2 sensor transmissions; in some cases, the savings in transmission approaches (N − 1).

Reduced-rank Analysis of the Total Least Squares

“…The notion of ordering is similar to the one introduced in Section II-B; however, it will soon become evident that such a low rank estimator does not exist (except under certain conditions) and that the ordering mechanism similar to the one shown in (21) cannot be realized for the TLS problem. Theorem 2: Consider the TLS formulation given by (22), and hypothesize that there exists a low rank estimatorx q,TLS given by (24). There exists no q < p (except in some special cases which will be explained in Section IV) independent of the unknown parameter θ that minimizes the mean squared error between x andx q,TLS .…”

Tight Bounds on the Weighted Sum of MMSEs with Applications in Distributed Estimation

“…By "target" we are referring to some activity; for example, fire in the ROI. The target's signal power is assumed to follow the isotropic power attenuation model [22]. The signal power at sensor j is given by ρ 2 j = ρ 2 0 /(1 + γd m j ), j = 1, .…”

“…For the purpose of this paper, let us consider the simple goal of estimating the distances d j based on the noisy observations made by the sensors. The knowledge of d j is typically used to infer the presence/absence of a target in the ROI (see [22]). In conventional distributed estimation, the estimates of d j computed by the j th local sensor is transmitted to a central processing unit, which aggregates d j , j = 1, .…”

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

“…These analyses can be categorized into two main classes such as partial data forwarding and no data forwarding. Since energy efficiency is an essential factor compared with other systems for WSNs . For the functionality of the nodes, the energy source is the major severe drawback in WSN.…”

“…Since energy efficiency is an essential factor compared with other systems for WSNs. 3 For the functionality of the nodes, the energy source is the major severe drawback in WSN. Since charging or replacement of energy sources is practically impossible.…”