The tiny and low-cost sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive sensor netw More
The tiny and low-cost sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive sensor networks (CSNs). One solution for this problem is that the sensors sense various channels at different sensing periods. Due to the energy limitation in these scenarios, the lifetime maximization will become an important issue. In this paper, maximizing the lifetime of a CSN is investigated by selecting both the cooperative sensors and their detector threshold, such that the desired detection performance constraints are satisfied. This is a NP-complete problem, and obtaining the optimum solution needs exhaustive search with exponential complexity order. Here we have proposed two convex-based optimization algorithms with low order of complexity. First algorithm applies the known instantaneous Signal-to-Noise-Ratio (SNR) and obtains the proper detector thresholds by solving an equation for every channel. Investigation the effect of detector thresholds on the energy consumption, the false alarm probability and the detection probability shows that we can minimize the detector thresholds such that the detection constraints are met. In the second algorithm in order to reduce the complexity of the problem it is proposed the Bisection method for determining detector thresholds. Because knowing the instantaneous SNR is difficult, we have investigated the performance of the second algorithm by average value of SNR. Simulation results show that the proposed algorithms improve the performance of the network in case of lifetime and energy consumption.
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The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, a sensor network of nodes with wireless transceiver capabilities and limited energy is considered. Clustering is one of the most eff More
The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, a sensor network of nodes with wireless transceiver capabilities and limited energy is considered. Clustering is one of the most efficient techniques to save more energy in these networks. Therefore, the proper selection of the cluster heads plays important role to save the energy of sensor nodes for data transmission in the network. In this paper, we propose an energy efficient data transmission by determining the proper cluster heads in wireless sensor networks. We also obtain the optimal location of the base station according to the cluster heads to prolong the network lifetime. An efficient method is considered based on particle swarm algorithm (PSO) which is a nature inspired swarm intelligence based algorithm, modelled after observing the choreography of a flock of birds, to solve a sensor network optimization problem. In the proposed energy- efficient algorithm, cluster heads distance from the base station and their residual energy of the sensors nodes are important parameters for cluster head selection and base station localization. The simulation results show that our proposed algorithm improves the network lifetime and also more alive sensors are remained in the wireless network compared to the baseline algorithms in different situations.
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In this paper, the problem of joint energy efficient spectrum sensing and determining the mobile primary user location is proposed based on compressive sensing in cognitive sensor networks. By utilizing compressive sensing, the ratio of measurements for the sensing node More
In this paper, the problem of joint energy efficient spectrum sensing and determining the mobile primary user location is proposed based on compressive sensing in cognitive sensor networks. By utilizing compressive sensing, the ratio of measurements for the sensing nodes are considerably reduced. Therefore, energy consumption is improved significantly in spectrum sensing. The multi-antenna sensors is also considered to save more energy. On the other hand, multi-antenna sensor utilization is a proper solution instead of applying more sensors. The problem is formulated to maximize the network lifetime and find the mobile primary user position by sensors selection under the detection performance and accuracy of localization constraints. For this purpose, a cooperative game is proposed to study this problem. It is shown that with the proposed game, the network lifetime is maximized while the proper sensors which participate in spectrum sensing and primary user localization are determined. Simulation results show that the network lifetime is improved while the detection performance constraint is satisfied and the location of the primary user is determined with high accuracy.
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