Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

Abstract

The optimal performance of a wireless mesh network (WMN) can be greatly improved by strategically placing wireless mesh routers. As a result, it is crucial to optimally locate the WMN routers for better coverage and connectivity. Besides the optimal placement, the network congestion due to overlaying routers has to be taken into consideration. These issues have become a motivation for researchers to identify a variety of approaches to optimize WMN performance. Multiple metaheuristic algorithms have been employed for identifying the trade-offs between coverage and connectivity in WMN. Consequently, a novel hybrid Harris Hawks optimization with the sine cosine algorithm (HHOSCA) is presented in this work to tackle the aforementioned WMN optimization problems. The proposed HHOSCA seeks optimal router placement that leads to significantly increased network coverage and achieves full connectivity between the mesh routers. In addition, the proposed HHOSCA produces a cost-effective WMN by reducing the congestion in the network to the minimum number of routers whilst ensuring maximum coverage and connectivity. The superiority of the proposed HHOSCA in comparison to the other algorithm was validated by using 33 benchmark functions. It was compared against four well-known algorithms including Sine Cosine Algorithm (SCA), Harris Hawks optimization (HHO), Gray Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). These algorithms are statistically analyzed and compared to the simulated results of the proposed method. In addition, the performance of HHOSCA is compared to the state-of-the-art to highlight the efficacy of the proposed algorithm. The statistical analyses and simulation findings confirm that the HHOSCA outperforms the other algorithms in terms of network connectivity, coverage, network reduction, and convergence. The experimental results reveal that the proposed HHOSCA method achieves favourable optimization results compared with other relevant methods.INDEX TERMS Optimal node placement, reliable wireless networks, network deployment optimization, particle swarm optimization, gray wolf optimization, Harris Hawks optimization, sine cosine optimization, industrial wireless mesh networks.The associate editor coordinating the review of this manuscript and approving it for publication was Xiaolong Li .

Energy efficient optimal deployment of industrial wireless mesh networks using transient trigonometric Harris Hawks optimizer

“…While the previously developed HHOSCA [48] demonstrates commendable performance, it still exhibits certain limitations, including premature convergence and the struggle to reach global optima. To overcome these shortcomings, we propose a novel approach known as Transient Trigonometric Harris Hawks optimizer (TTHHO), which integrates the strengths of HHO, SCA, and TSO.…”

“…Building upon these advancements, the authors of [48] developed a hybrid optimization algorithm called Hybrid Harris Hawk Optimization and Sine Cosine Algorithm (HHOSCA). By combining the strengths of both algorithms, HHOSCA aims to further improve optimization performance and address complex problems effectively.…”

“…By combining the strengths of both algorithms, HHOSCA aims to further improve optimization performance and address complex problems effectively. However, the lack of full coverage in [48] may result in areas with limited or no access, compromising the overall effectiveness of the network. Moreover, energy efficiency is a critical factor in WMN design, as the limited energy resources of individual routers directly impact the network's longevity and sustainability.…”

A novel fractional-order dead-time compensating controller for the wireless networks

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Optimizing Lifetime of Internet-of-Things Networks with Dynamic Scanning

“…Research on connection-based wireless mesh networks (WMNs) focuses on topics related to efficient communication of relay nodes, as the goal is to improve the lifetime of the network through efficient routing. In [5], the study shows the efficient delivery of data by enhancing backbone networking using deep learning and taking quality of service (QoS) into account; in [6], the study applied machine learning to controlling large WMNs to increase network reliability; and in [7], the study optimized router placement to reduce congestion. A routing method that considers delay and interference to find optimal routes in wireless mesh networks was proposed in [8], and a hybrid routing strategy with multiple topologies and a multiple mesh-ring (MMR) configuration for load balancing in Bluetooth Low Energy (BLE) networks was studied in [9].…”