Abstract-In this paper, a new type of collaboration in wireless sensor networks (WSN) is suggested that exploits array processing algorithms for better reception of a signal. For receive collaboration, the transmission power during intra-cluster transmissions decreases at the expense of increasing the intercluster communications. It is shown that, as a result of using receive collaboration, the destination node's power consumption and the network interference level decrease which considerably improve the data transmission performance and network life time. This method is applicable both for cluster based and noncluster based WSNs. In order to show the feasibility of receive collaboration and also to evaluate its performance, an LS-CMA based channel equalization scheme is also simulated which is performed during cooperation between cluster nodes. The comparison of the output BER between random distributed and uniform linear distributed cases shows a good performance of receive collaboration.
Abstract-In this paper, a new traffic monitoring technique is introduced which works based on the emitted RF noise from the vehicles. In comparison with the current traffic sensing systems, our light-weight technique has simpler structure in both terms of hardware and software. An antenna installed to the roadside receives the signal generated during electrical activity of the vehicles' sub-systems. This signal feeds the feature extraction and classification blocks which recognize different classes of traffic situation in terms of density and flow. Different classifiers like Naive Bayes, Decision Tree and k-Nearest Neighbor are applied in real-world scenarios and performances higher than 95% are reported. Although the electrical noises of the various vehicles do not have the same statistical characteristics, experimental analysis shows that they are applicable for traffic monitoring goals. Due to the acceptable classification results and the differences between the proposed and current traffic monitoring techniques in terms of interfering factors, advantages and disadvantages, we propose it to work in parallel with the current systems to improve the coverage and efficiency of the traffic control network.
Collaborative data communication is one of the efficient approaches in wireless sensor networks (WSN) in terms of lifetime, reliability and quality of service (QoS) enhancement. In this paper, we propose a new self-optimized collaborative algorithm which minimizes the energy consumption by decreasing the number of collaborative nodes and at the same time guarantees the demanded quality. To do this, we focus on the fact that during the collaboration, a receiver node aggregates the signals of the collaborative nodes separately. The major task of this node is the time adjustment of the collaborative nodes to receive their signals synchronously.The proposed algorithm performs an extra process to sort the aggregated signals based on their bit error rate (BER) as the quality and select the minimum number of the nodes with higher rank for collaboration. It is because the low quality signals have negative effect on the collaboration performance, as confirmed experimentally. The new algorithm gains higher level of energy storage balance without increasing of the inter-node communications or computational load by modification of the node selection metric. It also guarantees the demanded QoS through modification of the collaboration based on the signal quality at the destination which results in higher reliability. Based on the proposed algorithm, sensor nodes can gain the optimum efficiency during collaborative data communication without external management resources. The algorithm is applicable in various scenarios and network structures.
In this article, a new traffic sensing and monitoring technique is introduced which works based on the emitted RF noise from the vehicles. In comparison with the current traffic sensing systems, our light-weight technique has simpler structure in both terms of hardware and software. An antenna installed to the roadside or the inside of a car receives the signal generated during electrical activity of the vehicles' sub-systems. This signal feeds the feature extraction and classification blocks which recognize different classes of traffic situation in terms of density, flow and location. Different classifiers like naive Bayes, Decision Tree and k-Nearest Neighbor are applied in real-world scenarios and performances for instance of traffic situation detection are reported with higher than 95%. Although the electrical noises of the various vehicles do not have the same statistical characteristics, results from two experiments with an implementation on RF receiver illustrate that our approach is practically feasible for traffic monitoring goals. Due to the acceptable classification results and the differences between the proposed and current traffic monitoring techniques in terms of interfering factors, advantages and disadvantages, we propose it to work in parallel with the current systems to improve the coverage and efficiency of the traffic control network.
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