Sensor Selection for Cooperative Spectrum Sensing in Multiantenna Sensor Networks Based on Convex Optimization and Genetic Algorithm

Hojjati, Seyed Hani; Ebrahimzadeh, Ataollah; Najimi, Maryam; Reihanian, Ali

doi:10.1109/jsen.2016.2535862

Cited by 32 publications

(9 citation statements)

References 2 publications

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“…Moreover, Peng et al [21] and Hojjati et al [22] proposed a strategy to reduce the energy consumption in the WSN by jointly selecting sensor node with proper modulation constellation sized through cooperative spectrum sensing. Also, Peng et al [23] considered the optimization techniques to minimize the energy consumption and prolong the lifetime in multi-hop clustered WSNs.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Cluster-Based Wireless Sensor Networks Using Adaptive Modulation: Performance Analysis

Abu-Baker¹,

Alshamali

Shawaheen

2021

IEEE Access

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Wireless Sensor Networks (WSNs) play a vital role in modern technology since they have recently emerged into enormous essential applications of the Internet of Things (IoT). However, WSNs encounter a shortage in the lifetime due to limitations in the power supply. Accordingly, many solutions are reported in literature to deal with energy saving problem in the WSNs. In this paper, a novel method is presented to minimize energy consumption using adaptive modulation that is jointly integrated with clustering technique. This method is considered as a promising solution for dense and sparse cluster-based WSNs to improve energy efficiency. In the suggested solution, the adaptive modulation is implemented in the communication link between cluster members (CMs) and cluster head (CH). Besides, distancebased adaptive modulation step function is proposed in which the optimum modulation order is selected to achieve the minimum energy consumption between CMs and CH. The proposed method is evaluated extensively in order to investigate the impact of adaptive modulation in cluster-based WSNs using M-ary Quadrature Amplitude Modulation (MQAM) system. The performance evaluation addresses both energy consumption and throughput by using two metrics: cluster density, and cluster size. Regarding simulation results, by varying cluster density and cluster size, the adaptive modulation shows significant saving in energy consumption where it constitutes a lower bound for energy consumption. Also, it shows a great impact on throughput where it constitutes an upper bound for the throughput. Moreover, the adaptive modulation shows a considerable leverage on energy saving for small number of clusters, conversely, the energy saving decreases as the number of clusters increase. Finally, it is concluded that these findings can provide a remarkable guidance for designing an energy efficient WSNs.

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Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Cluster-Based Wireless Sensor Networks Using Adaptive Modulation: Performance Analysis

Abu-Baker¹,

Alshamali

Shawaheen

2021

IEEE Access

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“…Function approximation algorithm includes Newton-Raphson method, Levenberg-Marquardt algorithm (L-M), Gauss-Newton method and so on [16], [17], [18]. Among them, L-M algorithm is the most widely used algorithm due to its characteristics of low local extremum probability, strong stability and fast convergence; Meta-heuristic algorithm includes simulated annealing, genetic algorithm, particle swarm optimization (PSO) and so on, and comparing with other algorithms, PSO has the advantages of easy implementation, high precision and fast convergence [19], [20], [21]. In this paper, we present two calibration algorithm based on L-M and PSO, and the final calibration algorithm is determined by comparing the advantages and disadvantages of the two methods.…”

Section: Calibration Algorithmmentioning

confidence: 99%

Development and Verification of a Novel Measurement and Position System for Confined Cabin

et al. 2021

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The high-accuracy key points measurement is necessary for shipbuilding, and numerous measuring systems have been invented. However, there are still limitations and challenges in measurement frequency, calibration method and environmental disturbances. This paper presents a novel and automatic measurement and position system based on laser scan technology. The system components and mathematical model are introduced. Subsequently, a fast calibration method is established, also different calibration schemes and calibration algorithms are discussed. To avoid the environmental disturbances to this system, a robust positioning algorithm based on maximum likelihood estimation is presented. A series of simulation and verification experiments in normal and interferential environment are designed to evaluate the precision of the proposed system. The results shows that the system's position accuracy can reach 1.00mm with 50Hz output whether there are disturbances or not, which can meet the need of position in the confined cabin.

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“…The design of decoupled subsystems allows the implementation on a multi-core computer or separate processors. Based on that, subsystems (28), (30), and (31) are discretized with zero-order hold as follows:…”

Section: Adaptive Mpc Using Esomentioning

confidence: 99%

“…Since the cost

{\overset{J}{true}}_{i} false(k false)

is complex and non‐differentiable, using the traditional search techniques is difficult to solve the optimization problem. In the works of Lee et al 29 and Hojjati et al, 30 the population‐based optimization approaches provide good solutions to similar complicated problems. Inspired by the facts, the DE algorithm is utilized to solve the local optimization, then the control law u i can be worked out directly.…”

Section: Adaptive Mpc Strategymentioning

confidence: 99%

“…The design of decoupled subsystems allows the implementation on a multi‐core computer or separate processors. Based on that, subsystems (28), (30), and (31) are discretized with zero‐order hold as follows:

\{\begin{matrix} left \\ {bold-italicx}_{italicxi} false(k + 1 false) = {bold-italicA}_{italicfxc} {bold-italicx}_{italicxi} false(k false) + {bold-italicβ}_{italicfxc} a_{italicxi} false(k false) + {bold-italicD}_{italicfxc} {bold-italicx}_{italicfxi} false(k + 1 false) \\ y_{italicxi} false(k false) = {bold-italicC}_{italicfxc} {bold-italicx}_{italicxi} false(k false) \end{matrix}

\{\begin{matrix} left \\ {bold-italicx}_{italicyi} false(k + 1 false) = {bold-italicA}_{italicfyc} {bold-italicx}_{italicyi} false(k false) + {bold-italicβ}_{italicfyc} a_{italicyi} false(k false) + {bold-italicD}_{italicfyc} {bold-italicx}_{italicfyi} false(k + 1 false) \\ y_{italicyi} false(k false) = {bold-italicC}_{italicfyc} {bold-italicx}_{italicyi} false(k false) \end{matrix}

\{\begin{matrix} left \\ {bold-italicx}_{italiczi} false(k + 1 false) = {bold-italicA}_{italicfzc} {bold-italicx}_{italicxi} false(k false) + {bold-italicβ}_{italicfzc} a_{italicxi} false(k false) + {bold-italicD}_{italicfzc} {bold-italicx}_{italicfzi} false(k + 1 false) \\ y_{italiczi} false(k false) = {bold-italicC}_{italicfzc} {bold-italicx}_{italiczi} false(k false) \end{matrix}

…”

Section: Adaptive Mpc Strategymentioning

confidence: 99%

See 1 more Smart Citation

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

Zhang

Sun

Deng

2020

Adaptive Control & Signal

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This article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme.

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Sensor Selection for Cooperative Spectrum Sensing in Multiantenna Sensor Networks Based on Convex Optimization and Genetic Algorithm

Cited by 32 publications

References 2 publications

Energy-Efficient Cluster-Based Wireless Sensor Networks Using Adaptive Modulation: Performance Analysis

Energy-Efficient Cluster-Based Wireless Sensor Networks Using Adaptive Modulation: Performance Analysis

Development and Verification of a Novel Measurement and Position System for Confined Cabin

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

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