Improved Whale Optimization Algorithm for Optimal Network Coverage in Industrial Wireless Sensor Networks

Devan, P. Arun Mozhi; Ibrahim, Rosdiazli; Omar, Madiah Binti; Bingi, Kishore; Abdulrab, Hakim; Hussin, Fawnizu Azmadi

doi:10.1109/icftsc57269.2022.10040067

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…This mechanism is also observed in other competitive situations in nature. The movement of the hawks in this phase is obtained using the following position update rule given in Equation (7).…”

Section: Soft Besiege With Progressive Rapid Divesmentioning

confidence: 99%

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A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks

Devan

Ibrahim

Omar

et al. 2023

Sensors

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A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications.

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Section: Soft Besiege With Progressive Rapid Divesmentioning

confidence: 99%

“…It is crucial to consider the technical constraints of the deployment location and the basic topology before deploying wireless mesh routers. These constraints include factors such as radio signal strength, frequency range, and interference from other wireless networks [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks

Devan

Ibrahim

Omar

et al. 2023

Sensors

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“…3,6 The advantages provided by IWSNs have convinced industrial plants of their potential benefits. 7,8 Composed of multiple sensor nodes deployed within industrial facilities, IWSNs facilitate the collection of diverse measurements, including regulation, temperature, vibration, and pressure, allowing for comprehensive device monitoring. The primary function of IWSNs is to transmit data to a central base station (BS) through self-organization.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, IWSNs have emerged as a low‐cost and efficient solution for industrial automation and process control 3,6 . The advantages provided by IWSNs have convinced industrial plants of their potential benefits 7,8 …”

Section: Introductionmentioning

confidence: 99%

JOCP: A jointly optimized clustering protocol for industrial wireless sensor networks using double‐layer selection evolutionary algorithm

Wu,

Yang,

et al. 2023

Concurrency and Computation

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SummaryIndustrial Wireless Sensor Networks (IWSNs) have gained significant popularity for their ability to improve plant productivity and production efficiency through self‐organization and rapid deployment. However, the challenge of achieving reliable and sustainable data transmission remains due to the large amount of heterogeneous data generated by large‐scale IWSNs. In this paper, we present a systematic approach that addresses this challenge by focusing on data transmission clustering strategies, optimal cluster head selection, and routing design. We propose a novel Jointly Optimized Clustering Protocol (JOCP), which enhances cluster head selection by considering multiple critical factors that impact the IWSN life cycle. JOCP incorporates two key modules: the many‐objective clustering model and the double‐layer selection evolutionary algorithm. Specifically, the many‐objective clustering model considers cluster head selection from different perspectives, including maximum node survival cycle, minimum node distance, minimum network overall energy consumption, and balanced cluster energy consumption, with the aim of extending the network life cycle. Additionally, the double‐layer selection evolutionary algorithm optimizes the many‐objective clustering model to select appropriate cluster heads. Through performance verification, we demonstrate that the JOCP protocol effectively enhances the network life cycle and increases the number of surviving nodes compared to baseline clustering algorithms. Our research provides a comprehensive solution to the challenges associated with reliable and sustainable data transmission in large‐scale IWSNs, highlighting the potential for improved performance in industrial applications.

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“…PID controllers have gained popularity in automatic control systems due to their simplicity, good performance, and costeffectiveness [1][2][3][4]. However, their effectiveness must be improved to cater to more complex and higher-order systems [5]. Recent research suggests the integration of more complex PID controller configurations, such as fractional order calculus and higher-order derivative terms, such as PIDD 2 and proportional integral derivative acceleration controllers, to regulate more complex systems [1,3,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Design of PIDDα Controller for Robust Performance of Process Plants

Fawwaz,

Bingi,

Ibrahim

et al. 2023

Algorithms

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Managing industrial processes in real-time is challenging due to the nonlinearity and sensitivity of these processes. This unpredictability can cause delays in the regulation of these processes. The PID controller family is commonly used in these situations, but their performance is inadequate in systems and surroundings with varying set-points, longer dead times, external noises, and disturbances. Therefore, this research has developed a novel controller structure for PIDDα that incorporates the second derivative term from PIDD2 while exclusively using fractional order parameters for the second derivative term. The controllers’ robust performance has been evaluated on four simulation plants: first order, second order with time delay, third-order magnetic levitation systems, and fourth-order automatic voltage regulation systems. The controllers’ performance has also been evaluated on experimental models of pressure and flow processes. The proposed controller exhibits the least overshoot among all the systems tested. The overshoot for the first-order systems is 9.63%, for the third-order magnetic levitation system, it is 12.82%, and for the fourth-order automatic voltage regulation system, it is only 0.19%. In the pressure process plant, the overshoot is only 4.83%. All controllers for the second-order systems have a time delay, while the flow process plant has no overshoot. The proposed controller demonstrates superior settling times in various systems. For first-order systems, the settling time is 14.26 s, while in the pressure process plant, the settling time is 8.9543 s. Similarly, the proposed controllers for the second-order system with a time delay and the flow process plant have the same settling time of 46.0495 s. In addition, the proposed controller results in the lowest rise time for three different systems. The rise time is only 0.0075 s for the third-order magnetic levitation system, while the fourth-order automatic voltage regulation system has a rise time of 0.0232 s. Finally, for the flow process plant, the proposed controller has the least rise time of 25.7819 s. Thus, in all the cases, the proposed controller results in a more robust controller structure that provides the desired performance of a regular PIDD2 controller, offering better dynamic responses, shorter settling times, faster rise times, and reduced overshoot. Based on the analysis, it is evident that PIDDα outperforms both PID and FOPID control techniques due to its ability to produce a more robust control signal.

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Improved Whale Optimization Algorithm for Optimal Network Coverage in Industrial Wireless Sensor Networks

Cited by 4 publications

References 17 publications

A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks

A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks

JOCP: A jointly optimized clustering protocol for industrial wireless sensor networks using double‐layer selection evolutionary algorithm

Design of PIDDα Controller for Robust Performance of Process Plants

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