An Analytical Technique to Determine the Decision Thresholds of Multi-bit Distributed Detection in Sensor Networks

Distributed Sequential Hypothesis Testing With Byzantine Sensors

“…Distributed inference with sensor networks has drawn substantial research attention due to its wide application in wireless sensor networks [1], power grids, cognitive radio [2], smart building, Internet of Things, etc. As sensors in the network are usually spatially separated and communicate by wired/wireless channels, they are exposed to various interference and attacks.…”

“…Our previous result has been published in [22]. The main contributions of this paper are summarized as follows: (1) We propose a voting detection scheme named VSPRT (voting SPRT, Section III, IV) in fusion center scenario and a generalized scheme named DVSPRT (distributed voting SPRT, Section V) in fully-distributed scenario. (2) We quantify the limit distribution of the sample number of VSPRT and DVSPRT while most existing works consider the expectation of sample number.…”

Distributed Decision Fusion with M-ary Signaling and k-bit Transmission on Sensor Observation in Wireless Sensor Networks

“…When the decision made by each local sensor is binary, the sensor observation in the multiple-sensor system can be considered being recorded by each sensor via one single bit only, which is usually referred to as hard-decision fusion [10]. In contrast to the binary local decision case, many works have also studied the case of soft-decision fusion, in which the sensor observation is divided into M regions, where M is an arbitrary integer with M > 2 [1,2,[11][12][13][14][15][16][17]. The work in previous study [11] considers the system in which the local sensor sends an additional quality information bit along with the binary decision bit to the FC.…”

“…The work in previous study [11] considers the system in which the local sensor sends an additional quality information bit along with the binary decision bit to the FC. In the works [1,2,12,13,14,18,19], the local soft-decision is made using log-likelihood ratio (LLR) quantizer. In the study [15], the multiple-bit local decision is made in accordance with the quantization level of received signal energy.…”

“…However, the works mentioned above all assume that the information of soft local decision is digitally transmitted to the FC, in which the information of each quantization level is mapped to several digital bits and the local softdecision (quantizer output) or the local decision performance corresponding to the received quantization level is then utilized by the FC to make its final decision, and usually Gray coding is employed in the mapping of the multiple bits. Besides, the demanding channel bandwidth in the literature [1,2,[11][12][13][14][15][16][17] is scaled with the number of quantization levels. As opposed to the existing works mentioned above, this study considers both uncoded (analog) and digital transmission schemes between the local sensors and the FC.…”