9 research outputs found
Energy-efficient Decision Fusion for Distributed Detection in Wireless Sensor Networks
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 sensor network, the local sensors
transmit binary decisions to the fusion center, where the number of all
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
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 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 sensor transmissions; in
some cases, the savings in transmission approaches .Comment: 7 pages, 3 figures. Proceedings of FUSION 2018, Cambridge, U
The probability of large deviations for the sum functions of spacings
Let 0=U0,n≤U1,n≤⋯≤Un−1,n≤Un,n=1 be an ordered sample from uniform [0,1] distribution, and Din=Ui,n−Ui−1,n, i=1,2,…,n; n=1,2,…, be their spacings, and let f1n,…,fnn be a set of measurable functions. In this paper, the probabilities of the
moderate and Cramer-type large deviation theorems for statistics
Rn(D)=f1n(nD1n)+⋯+fnn(nDnn) are proved. Application of these theorems for determination of the
intermediate efficiencies of the tests based on Rn(D)-type statistic is presented here too
Resource management in sensing services with audio applications
Middleware abstractions, or services, that can bridge the gap between the increasingly pervasive sensors and the sophisticated inference applications exist, but they lack the necessary resource-awareness to support high data-rate sensing modalities such as audio/video. This work therefore investigates the resource management problem in sensing services, with application in audio sensing. First, a modular, data-centric architecture is proposed as the framework within which optimal resource management is studied. Next, the guided-processing principle is proposed to achieve optimized trade-off between resource (energy) and (inference) performance.
On cascade-based systems, empirical results show that the proposed approach significantly improves the detection performance (up to 1.7x and 4x reduction in false-alarm and miss rate, respectively) for the same energy consumption, when compared to the duty-cycling approach. Furthermore, the guided-processing approach is also generalizable to graph-based systems. Resource-efficiency in the multiple-application setting is achieved through the feature-sharing principle. Once applied, the method results in a system that can achieve 9x resource saving and 1.43x improvement in detection performance in an example application.
Based on the encouraging results above, a prototype audio sensing service is built for demonstration. An interference-robust audio classification technique with limited training data would prove valuable within the service, so a novel algorithm with the desired properties is proposed. The technique combines AI-gram time-frequency representation and multidimensional dynamic time warping, and it outperforms the state-of-the-art using the prominent-region-based approach across a wide range of (synthetic, both stationary and transient) interference types and signal-to-interference ratios, and also on field recordings (with areas under the receiver operating characteristic and precision-recall curves being 91% and 87%, respectively)