Distributed M-ary hypothesis testing for decision fusion in multiple-input multiple output wireless sensor networks

In this study, the authors study binary decision fusion over a shared Rayleigh fading channel with multiple antennas at the decision fusion centre (DFC) in wireless sensor networks. Three fusion rules are derived for the DFC in the case of distributed M-ary hypothesis testing, where M is the number of hypothesis to be classified. Namely, the optimum maximum a posteriori (MAP) rule, the augmented quadratic discriminant analysis (A-QDA) rule and MAP observation bound. A comparative simulation study is carried out between the proposed fusion rules in-terms of detection performance and receiver operating characteristic (ROC) curves, where several parameters are taken into account such as the number of antennas, number of local detectors, number of hypothesis and signal-to-noise ratio. Simulation results show that the optimum (MAP) rule has better detection performance than A-QDA rule. In addition, increasing the number of antennas will improve the detection performance up to a saturation level, while increasing the number of the hypothesis will deteriorate the detection performance

Distributed M-ary hypothesis testing for decision fusion in multiple-input multipleoutput wireless sensor networks

In this study, the authors study binary decision fusion over a shared Rayleigh fading channel with multiple antennas at the decision fusion centre (DFC) in wireless sensor networks. Three fusion rules are derived for the DFC in the case of distributed M-ary hypothesis testing, where M is the number of hypothesis to be classified. Namely, the optimum maximum a posteriori (MAP) rule, the augmented quadratic discriminant analysis (A-QDA) rule and MAP observation bound. A comparative simulation study is carried out between the proposed fusion rules in-terms of detection performance and receiver operating characteristic (ROC) curves, where several parameters are taken into account such as the number of antennas, number of local detectors, number of hypothesis and signal-to-noise ratio. Simulation results show that the optimum (MAP) rule has better detection performance than A-QDA rule. In addition, increasing the number of antennas will improve the detection performance up to a saturation level, while increasing the number of the hypothesis will deteriorate the detection performance

Evidential reasoning-based airline network design for long-haul transportation in express delivery

Kod hitne isporuke, za ekspresni prijevoz robe uvelike se koristi avionska dostava zbog visoke učinkovitosti i sigurnosti, najvažnijih za kupce. Velika količina tereta prisiljava ekspresne kompanije na uspostavu vlastitih avionskih mreža u svrhu poboljšanja efikasnosti isporuke i snižavanja troškova prijevoza. Uobičajena optimalna rješenja imaju vrlo složeno dizajnirane mreže s čvorištima za prijelaz; štoviše, uvjeti, uključujući količinu oborina, volumen željezničkog prijevoza, prosječnu propusnost i infrastrukturu hardwera, ne mogu se u potpunosti uzeti u obzir. U ovom radu formuliramo problem dizajna ekspresne avionske mreže kao proces vrednovanja zasnovan na evidentnom zaključivanju s multidimenzijskim podacima. U svrhu osiguranja fleksibilne scheme za rješavanje zadatka procjenjivanja primijenjena je Dempster-Shafer teorija dokaza, a postupak evidentnog zaključivanja specifičan za dizajniranje ekspresnih mreža predlaže se kako bi se osigurao najbolji izbor čvorišta za prijelaz. Predloženi pristup je primijenjen kod projektiranja avionske mreže za ShunFeng ekspres kompaniju, a analiza tog slučaja pokazuje da se predloženom schemom može osigurati razumna transportna mreža uz više učinkovitosti i niže cijene.In express delivery, air couriers have been used extensively for transporting express freight due to its high efficiency and security, which are the most important considerations of the customers. The large volume of cargo forces the express company to build its own airline networks, aiming at improving the efficiency of delivery and reducing the transport cost. The conventional optimal solutions are very complex for use in designing networks with transferring hubs, and, in addition, the port conditions, including the volume of rainfall, the volume of railway transportation, average throughput, and hardware infrastructure, cannot be considered fully. In this paper, we formulate the express airline network design problem as an evidential reasoning-based evaluation process with multi-dimensional data. We used the Dempster-Shafer evidence theory to provide a flexible scheme to solve the evaluation task, and we proposed an evidential reasoning process specific for designing an express network to determine the best choice of the transferring hub. The proposed approach was applied in the design of the airline network for the ShunFeng express company, and the case study demonstrated that the proposed scheme can obtain a reasonable transport network with higher efficiency and lower cost

Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks

Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier’s training output confusion matrix and real-time observations. We also derive explicit global BBA in the fusion center under Dempster’s combinational rule, making the decision making operation in the fusion center greatly simplified. Also, sending the whole BBA structure to the fusion center is avoided. Experimental results demonstrate that the proposed fusion rule has better performance in fusion accuracy compared with the naïve Bayes rule and weighted majority voting rule