Figure - available from: Journal of Sensors
This content is subject to copyright. Terms and conditions apply.
Source publication
Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self-organizing capabilities of each n...
Citations
... The Assignment problem of IUWSNs is solved by expanding the solution space and improving the global search ability of the algorithm. This paper introduces a task allocation model [27,28] aimed at maximizing network revenue within Intelligent Unmanned Wireless Sensor Networks (IUWSNs). This model factors in the impact of IUWSNs on routing energy consumption. ...
In recent times, the progress of Intelligent Unmanned Wireless Sensor Networks (IUWSNs) has inspired scientists to develop inventive task allocation algorithms. These efficient techniques serve as robust stochastic optimization methods, aimed at maximizing revenue for the network’s objectives. However, with the increase in sensor numbers, the computation time for addressing the challenge grows exponentially. To tackle the task allocation issue in IUWSNs, this paper introduces a novel approach: the Chaotic Elite Adaptive Genetic Algorithm (CEAGA). The optimization problem is formulated as an NP-complete integer programming challenge. Innovative elite and chaotic operators have been devised to expedite convergence and unveil the overall optimal solution. By merging the strengths of genetic algorithms with these new elite and chaotic operators, the CEAGA optimizes task allocation in IUWSNs. Through simulation experiments, we compare the CEAGA with other methods—Hybrid Genetic Algorithm (HGA), Multi-objective Binary Particle Swarm Optimization (MBPSO), and Improved Simulated Annealing (ISA)—in terms of task allocation performance. The results compellingly demonstrate that the CEAGA outperforms the other approaches in network revenue terms.
... In-depth research based on these traditional heuristics led to the proposal of many new heuristics to solve task allocation problems. Li et al. [36] proposed an improved adaptive cloning genetic algorithm (IACGA) and achieved good results, though it was prone to evolutionary stagnation. Baniabdelghany et al. [35] proposed a discrete particle swarm optimization (DPSO) for distributing tasks between sensor nodes. ...
Wireless sensor technology advancements have made soil moisture wireless sensor networks (SMWSNs) a vital component of precision agriculture. However, the humidity nodes in SMWSNs have a weak ability in information collection, storage, calculation, etc. Hence, it is essential to reasonably pursue task allocation for SMWSNs to improve the network benefits of SMWSNs. However, the task allocation of SMWSNs is an NP (Non-deterministic Polynomial)-hard issue, and its complexity becomes even higher when constraints such as limited computing capabilities and power are taken into consideration. In this paper, a novel differential evolution adaptive elite butterfly optimization algorithm (DEAEBOA) is proposed. DEAEBOA has significantly improved the task allocation efficiency of SMWSNs, effectively avoided plan stagnation, and greatly accelerated the convergence speed. In the meantime, a new adaptive operator was designed, which signally ameliorates the accuracy and performance of the algorithm. In addition, a new elite operator and differential evolution strategy are put forward to markedly enhance the global search ability, which can availably avoid local optimization. Simulation experiments were carried out by comparing DEAEBOA with the butterfly optimization algorithm (BOA), particle swarm optimization (PSO), genetic algorithm (GA), and beluga whale optimization (BWO). The simulation results show that DEAEBOA significantly improved the task allocation efficiency, and compared with BOA, PSO, GA, and BWO the network benefit rate increased by 11.86%, 5.46%, 8.98%, and 12.18% respectively.
... This method deploys remote sensing sensor nodes within high yield and low energy consumption for complex traffic parameter coordination. Z. Zha et al. [4] have proposed a modified clone genetic algorithm with adaptive ability to solve task allocation in ITWSNs. It employs operator of clonal expansion to speed up the convergence rat, while adaptive operator is updated to improve the global search capability. ...
... At the same time, CSA can be used to solve multiobjective problems. Comparing CSA with the GA [19][20][21], the main difference is the way the population evolves. In the GA, the population evolves through crossover and mutation, and in the CSA, cell reproduction is asexual, with each offspring produced by one cell being an exact copy of its parents, and mutation and selection are made through these offspring. ...
With the continuous development of computer and network technology, the large-scale and clustered operations of drones have gradually become a reality. How to realize the reasonable allocation of UAV cluster combat tasks and realize the intelligent optimization control of UAV cluster is one of the most challenging difficulties in UAV cluster combat. Solving the task allocation problem and finding the optimal solution have been proven to be an NP-hard problem. This paper proposes a CSA-based approach to simultaneously optimize four objectives in multi-UAV task allocation, i.e., maximizing the number of successfully allocated tasks, maximizing the benefits of executing tasks, minimizing resource costs, and minimizing time costs. Experimental results show that, compared with the genetic algorithm, the proposed method has better performance on solving the UAV task allocation problem with multiple objectives.
Traffic signal control is significant to solve the real world issues such as the fuel wastage, time wastage, environmental pollution, accidents due to traffic congestion and several other factors. Hence, this research introduces a novel traffic management system based on the multi-objective function. Initially, the smart city map is taken from the real satellite image and is then segmented to gather more detailed information. Then, using the network simulation through MATLAB, the information regarding the traffic is gathered and then the paths are identified for efficient routing to avoid the traffic congestion. From, the identified path, the traffic signal control is employed optimally based on the solution encoding, multi-objective function and the seagull optimization algorithm (SOA). Finally, the performance of the proposed method is evaluated based on the performance metrics, like travel time, distance and average traffic density.KeywordsTraffic signal controlOptimizationRoutingVehiclePath
In the process of power grid monitoring, image data are mostly transmitted in segments by wireless image sensor networks (WISNs), and the external attacks or interference on a communication link easily causes the data to be retransmitted. Therefore, high quality of service (QoS) routing is essential to the secure and robust operation of the WISNs. Specifically, the QoS routing optimization in WISN is a nondeterministic polynomial hard problem, which usually cannot be solved by ordinary algorithms. Based on this motivation, a novel heuristic trust scheme (i.e., multi-objective trust routing scheme based on cloned elite slime mould algorithm (CESMA-MTRS)) is proposed to greatly improve the security and QoS of WISN. The scheme designs a novel trust difference test mechanism to enhance the identification of malicious nodes. Furthermore, a new elite archiving strategy and clone optimization strategy are designed to improve the convergence speed of grid monitoring data transmission routing search. The simulation results show that when the network scale is 100 and the attacked node scale is 30%, CESMA-MTRS reduces the delay by 8% and 15% and the packet loss rate by 24% and 27%, respectively, compared with trust-based routing protocol called the On-demand trust-based multicast routing protocol (ODTMRP) and evolutionary selfcooperative trust approach.
