The problem of scalar parameter estimation in a distributed wireless sensor network (WSN), in the presence of communication failures, is considered in this work. When sensors obtain measurements and attempt to transmit their measurements to the fusion center (FC) for parameter estimation, the transmissions to the FC may be unsuccessful due to various reasons such as poor communication channels, large distance between the sensors and FC or insufficient transmit power. To overcome the degradation of estimation performance due to missing data, we consider linear inter-sensor collaboration, where sensors exchange measurements with neighboring sensors, before transmitting to the FC. We consider two objectives: 1) maximize the estimation accuracy subject to collaboration power constraints and 2) minimize the collaboration power subject to the required estimation accuracy. We consider linear estimators for the parameter inference task, and propose methods for designing the collaboration scheme (collaboration weights). The performances of the estimators and collaboration design are compared using numerical results and simulations.

In this work, we consider a binary hypothesis testing problem involving human decision-makers. Due to the nature of human behavior, human decision-makers observe the phenomenon of interest sequentially up to a random length of time. The humans use a belief model to accumulate the log-likelihood ratios until they cease observing the phenomenon. The belief model is used to characterize the perception of the human decision-maker towards observations at different instants of time, i.e., some decision-makers may assign greater importance to observations that were observed earlier, rather than later and vice-versa. We further consider the performance of a group of humans using a global decision-maker that fuses human decisions using the Chair-Varshney rule. When the number of observations that were used by the humans to arrive at their respective decisions are available to the fusion center (FC), the weights in the Chair-Varshney rule are modified to include this information in the decision fusion rule. Numerical and simulation results are presented to corroborate and validate theoretical results.

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).