Articles written in Sadhana

    • Implementation of the vehicle recognition systems using wireless magnetic sensors


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      Wireless network sensors and their use in traffic monitoring, traffic density determination or vehicle speed detection and classification have recently been the focus of interest for researchers. This article describes how a new sensor circuit was designed to deliver instantaneous, real-time and novel solutions as a vehicle detection system, which is more powerful than the nodes used in other studies, and gives results with smaller error margins due to its serial communication qualification. With the proposed logic algorithm, it was possible tocategorise the instantaneous traffic status of a road in four levels: no traffic, mild traffic, heavy traffic and very heavy traffic. Additionally, with the nodes placed at the beginning and the end of the road, the number of vehicles per hour for a day was determined and traffic was analysed. Then, vehicles passing by were classified with a proposed classification algorithm and magnetic signature length (MSL) parameter as cars, minibuses, buses and trucks, and an accuracy rate of 95% was obtained. As the last application, the direction of motion ofthe vehicle on the x-axis as well as left-to-right or right-to-left directions was determined, and the result was 94% accurate. The simplicity of the proposed algorithms, the absence of any complex mathematical calculations, thelow cost of the sensor node and circuit and the low power consumption of the communication system demonstrate the superiority of this system in comparison with other studies.

    • A mobile fault detection algorithm in heterogeneous wireless sensor networks: a bio-inspired approach


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      This paper puts forth a novel mobile fault detection algorithm for wireless sensor networks (WSNs) based on bacterial-inspired optimization. We introduce a bio-swarm intelligence approach to mobile fault detection in WSNs by using voltage values. At certain times, the sensor nodes in the clustered network send datapackets containing health-fitness information to cluster heads (CHs) selected by the proposed CH selection algorithm. A mobile sink (MS) collects the health status via data from all the nodes as they reach the intersection point of the CHs. After this stage, the data packets are analyzed by the MS, and hardware or software faults are detected by assessing the fitness values of the nodes. The faulty nodes are eventually discarded from the network, and recovery of the rest of the nodes in the network is satisfied. Inspired by the interaction of bacteria for feed collection, their response to chemicals, and their interaction and communication with one another, we bring an innovative approach to finding node failures or software faults in WSNs, and these failures are removed from the network to help its operation and to take measures to maintain the electrical structures. In fact, we adaptour algorithm to low energy harvesting electrical components as an example. We compare our novel algorithm with existing studies through extensive simulations in NS 2 environment based on fault detection accuracy, false alarm rate, and false positive rate criteria versus fault probability, number of nodes, and sink speed. Considering detection accuracy, the simulation results validate that our algorithm shows better performance as compared with others.

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