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Wang Z, Duan J, Xu H, Song X, Yang Y. Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks. Sensors (Basel) 2023; 23:7711. [PMID: 37765767 PMCID: PMC10536519 DOI: 10.3390/s23187711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 09/29/2023]
Abstract
In the research of heterogeneous wireless sensor networks, clustering is one of the most commonly used energy-saving methods. However, existing clustering methods face challenges when applied to heterogeneous wireless sensor networks, such as energy balance, node heterogeneity, algorithm efficiency, and more. Among these challenges, a well-designed clustering approach can lead to extended node lifetimes. Efficient selection of cluster heads is crucial for achieving optimal clustering. In this paper, we propose an Enhanced Pelican Optimization Algorithm for Cluster Head Selection (EPOA-CHS) to address these issues and enhance cluster head selection for optimal clustering. This method combines the Levy flight process with the traditional POA algorithm, which not only improves the optimization level of the algorithm, but also ensures the selection of the optimal cluster head. The logistic-sine chaotic mapping method is used in the population initialization, and the appropriate cluster head is selected through the new fitness function. Finally, we utilized MATLAB to simulate 100 sensor nodes within a configured area of 100 × 100 m2. These nodes were categorized into four heterogeneous scenarios: m=0,α=0, m=0.1,α=2, m=0.2,α=3, and m=0.3,α=1.5. We conducted verification for four aspects: total residual energy, network survival time, number of surviving nodes, and network throughput, across all protocols. Extensive experimental research ultimately indicates that the EPOA-CHS method outperforms the SEP, DEEC, Z-SEP, and PSO-ECSM protocols in these aspects.
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Affiliation(s)
- Zhen Wang
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Jin Duan
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Haobo Xu
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Xue Song
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Yang Yang
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
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Qiu S, Zhao J, Zhang X, Li A, Wang Y, Chen F. Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm. Sensors (Basel) 2023; 23:7572. [PMID: 37688024 PMCID: PMC10490593 DOI: 10.3390/s23177572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/16/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
Sensor nodes are widely distributed in the Internet of Things and communicate with each other to form a wireless sensor network (WSN), which plays a vital role in people's productivity and life. However, the energy of WSN nodes is limited, so this paper proposes a two-layer WSN system based on edge computing to solve the problems of high energy consumption and short life cycle of WSN data transmission and establishes wireless energy consumption and distance optimization models for sensor networks. Specifically, we propose the optimization objective of balancing load and distance factors. We adopt an improved sparrow search algorithm to evenly distribute sensor nodes in the system to reduce resource consumption, consumption, and network life. Through the simulation experiment, our method is illustrated, effectively reducing the network's energy consumption by 26.8% and prolonging the network's life cycle.
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Affiliation(s)
- Shaoming Qiu
- Communication and Network Laboratory, Dalian University, Dalian 116622, China (F.C.)
| | - Jiancheng Zhao
- Communication and Network Laboratory, Dalian University, Dalian 116622, China (F.C.)
| | - Xuecui Zhang
- North Automatic Control Technology Institute, Taiyuan 030006, China
| | - Ao Li
- Communication and Network Laboratory, Dalian University, Dalian 116622, China (F.C.)
| | - Yahui Wang
- Communication and Network Laboratory, Dalian University, Dalian 116622, China (F.C.)
| | - Fen Chen
- Communication and Network Laboratory, Dalian University, Dalian 116622, China (F.C.)
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Repuri RK, Darsy JP. Energy-Efficient LoRa Routing for Smart Grids. Sensors (Basel) 2023; 23:s23063072. [PMID: 36991783 PMCID: PMC10059010 DOI: 10.3390/s23063072] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 02/25/2023] [Accepted: 03/07/2023] [Indexed: 05/27/2023]
Abstract
Energy-efficient routing protocols in Internet of Things (IoT) applications are always of colossal importance as they improve the network's longevity. The smart grid (SG) application of the IoT uses advanced metring infrastructure (AMI) to read and record power consumption periodically or on demand. The AMI sensor nodes in a smart grid network sense, process, and transmit information, which require energy, which is a limited resource and is an important parameter required to maintain the network for a longer duration. The present work discusses a novel energy-efficient routing criterion in an SG environment realised using LoRa nodes. Firstly, a modified LEACH protocol-cumulative low-energy adaptive clustering hierarchy (Cum_LEACH) is proposed for cluster head selection among the nodes. It uses the cumulative energy distribution of the nodes to select the cluster head. Furthermore, for test packet transmission, multiple optimal paths are created using the quadratic kernelised African-buffalo-optimisation-based LOADng (qAB_LOADng) algorithm. The best optimal path is selected from these multiple paths using a modified version of the MAX algorithm called the SMAx algorithm. This routing criterion showed an improved energy consumption profile of the nodes and the number of active nodes after running for 5000 iterations compared to standard routing protocols such as LEACH, SEP, and DEEC.
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Asaithambi S, Ravi L, Kotb H, Milyani AH, Azhari AA, Nallusamy S, Varadarajan V, Vairavasundaram S. An Energy-Efficient and Blockchain-Integrated Software Defined Network for the Industrial Internet of Things. Sensors (Basel) 2022; 22:7917. [PMID: 36298266 PMCID: PMC9607010 DOI: 10.3390/s22207917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/11/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
The number of unsecured and portable Internet of Things (IoT) devices in the smart industry is growing exponentially. A diversity of centralized and distributed platforms have been implemented to defend against security attacks; however, these platforms are insecure because of their low storage capacities, high power utilization, single node failure, underutilized resources, and high end-to-end delay. Blockchain and Software-Defined Networking (SDN) are growing technologies to create a secure system and to ensure safe network connectivity. Blockchain technology offers a strong and trustworthy foundation to deal with threats and problems, including safety, privacy, adaptability, scalability, and security. However, the integration of blockchain with SDN is still in the implementation phase, which provides an efficient resource allocation and reduced latency that can overcome the issues of industrial IoT networks. We propose an energy-efficient blockchain-integrated software-defined networking architecture for Industrial IoT (IIoT) to overcome these challenges. We present a framework for implementing decentralized blockchain integrated with SDN for IIoT applications to achieve efficient energy utilization and cluster-head selection. Additionally, the blockchain-enabled distributed ledger ensures data consistency throughout the SDN controller network and keeps a record of the nodes enforced in the controller. The simulation result shows that the proposed model provides the best energy consumption, end-to-end latency, and overall throughput compared to the existing works.
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Affiliation(s)
- Sasikumar Asaithambi
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 600062, Tamil Nadu, India
| | - Logesh Ravi
- SENSE, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
- Data Engineering Research Group (DERG–SENSE), Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
| | - Hossam Kotb
- Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
| | - Ahmad H. Milyani
- Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | | | - Senthilkumar Nallusamy
- Department of Electronics and Communication Engineering, M.Kumarasamy College of Engineering, Karur 639113, Tamil Nadu, India
| | - Vijayakumar Varadarajan
- School of Computer Science and Engineering, University of New South Wales, Sydney 2052, Australia
- Ajeenkya DY Patil University, Pune 412105, Maharashtra, India
- Swiss School of Business Management, SSBM, 1213 Geneva, Switzerland
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Yang X, Yu T, Chen Z, Yang J, Hu J, Wu Y. An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks. Sensors (Basel) 2022; 22:3236. [PMID: 35590924 PMCID: PMC9105341 DOI: 10.3390/s22093236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 04/13/2022] [Accepted: 04/21/2022] [Indexed: 06/15/2023]
Abstract
Flying ad hoc networks (FANETs) have been gradually deployed in diverse application scenarios, ranging from civilian to military. However, the high-speed mobility of unmanned aerial vehicles (UAVs) and dynamically changing topology has led to critical challenges for the stability of communications in FANETs. To overcome the technical challenges, an Improved Weighted and Location-based Clustering (IWLC) scheme is proposed for FANET performance enhancement, under the constraints of network resources. Specifically, a location-based K-means++ clustering algorithm is first developed to set up the initial UAV clusters. Subsequently, a weighted summation-based cluster head selection algorithm is proposed. In the algorithm, the remaining energy ratio, adaptive node degree, relative mobility, and average distance are adopted as the selection criteria, considering the influence of different physical factors. Moreover, an efficient cluster maintenance algorithm is proposed to keep updating the UAV clusters. The simulation results indicate that the proposed IWLC scheme significantly enhances the performance of the packet delivery ratio, network lifetime, cluster head changing ratio, and energy consumption, compared to the benchmark clustering methods in the literature.
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Affiliation(s)
- Xinwei Yang
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China; (X.Y.); (T.Y.); (Z.C.); (J.H.); (Y.W.)
| | - Tianqi Yu
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China; (X.Y.); (T.Y.); (Z.C.); (J.H.); (Y.W.)
| | - Zhongyue Chen
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China; (X.Y.); (T.Y.); (Z.C.); (J.H.); (Y.W.)
| | - Jianfeng Yang
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China; (X.Y.); (T.Y.); (Z.C.); (J.H.); (Y.W.)
| | - Jianling Hu
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China; (X.Y.); (T.Y.); (Z.C.); (J.H.); (Y.W.)
- School of Electronic and Information Engineering, Wuxi University, Wuxi 214105, China
| | - Yingrui Wu
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China; (X.Y.); (T.Y.); (Z.C.); (J.H.); (Y.W.)
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Wei Q, Bai K, Zhou L, Hu Z, Jin Y, Li J. A Cluster-Based Energy Optimization Algorithm in Wireless Sensor Networks with Mobile Sink. Sensors (Basel) 2021; 21:s21072523. [PMID: 33916559 PMCID: PMC8038497 DOI: 10.3390/s21072523] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/01/2021] [Accepted: 04/01/2021] [Indexed: 11/24/2022]
Abstract
Aiming at high network energy consumption and data delay induced by mobile sink in wireless sensor networks (WSNs), this paper proposes a cluster-based energy optimization algorithm called Cluster-Based Energy Optimization with Mobile Sink (CEOMS). CEOMS algorithm constructs the energy density function of network nodes firstly and then assigns sensor nodes with higher remaining energy as cluster heads according to energy density function. Meanwhile, the directivity motion performance function of mobile sink is constructed to enhance the probability of remote sensor nodes being assigned as cluster heads. Secondly, based on Low Energy Adaptive Clustering Hierarchy Protocol (LEACH) architecture, the energy density function and the motion performance function are introduced into the cluster head selection process to avoid random assignment of cluster head. Finally, an adaptive adjustment function is designed to improve the adaptability of cluster head selection by percentage of network nodes death and the density of all surviving nodes of the entire network. The simulation results show that the proposed CEOMS algorithm improves the cluster head selection self-adaptability, extends the network life, reduces the data delay, and balances the network load.
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Affiliation(s)
- Qian Wei
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China; (Q.W.); (L.Z.); (Z.H.); (Y.J.); (J.L.)
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
| | - Ke Bai
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China; (Q.W.); (L.Z.); (Z.H.); (Y.J.); (J.L.)
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
- Correspondence:
| | - Lin Zhou
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China; (Q.W.); (L.Z.); (Z.H.); (Y.J.); (J.L.)
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
| | - Zhentao Hu
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China; (Q.W.); (L.Z.); (Z.H.); (Y.J.); (J.L.)
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
| | - Yong Jin
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China; (Q.W.); (L.Z.); (Z.H.); (Y.J.); (J.L.)
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
| | - Junwei Li
- School of Artificial Intelligence, Henan University, Kaifeng 475004, China; (Q.W.); (L.Z.); (Z.H.); (Y.J.); (J.L.)
- School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
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Ren Q, Yao G. An Energy-Efficient Cluster Head Selection Scheme for Energy-Harvesting Wireless Sensor Networks. Sensors (Basel) 2019; 20:E187. [PMID: 31905712 DOI: 10.3390/s20010187] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 12/21/2019] [Accepted: 12/27/2019] [Indexed: 11/16/2022]
Abstract
Concerning the large amount of energy consumption during the cluster head selection stage and the unequal harvested energy among nodes in energy-harvesting wireless sensor networks (EH-WSNs), an energy- efficient cluster head selection scheme called EECHS is proposed in this paper. The scheme divides all nodes from one cluster into three types: cluster head (CH), cluster member (CM), and scheduling node (SN). The SN is designed to monitor and store real-time information about the residual energy of all nodes, including CMs and the CH, in the same cluster. In the CH selection stage, the SN specifies a corresponding CM as the new CH according to the monitored results, thereby reducing the energy consumption caused by CH selection. In this way, the task of CH selection is migrated from CHs to SNs and, thus, the CHs can preserve more energy for data forwarding. Moreover, the EECHS adjusts the transmission radius of some nodes dynamically to prevent these nodes from discarding the harvested energy if their batteries are fully charged. A series of experiments were conducted to verify the effectiveness of the proposed EECHS, and the results demonstrate that EECHS can provide an efficient CH selection scheme for EH-WSNs and is able to use the harvested energy more efficiently than corresponding competitors.
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Yalçın S, Erdem E. Bacteria Interactive Cost and Balanced-Compromised Approach to Clustering and Transmission Boundary-Range Cognitive Routing In Mobile Heterogeneous Wireless Sensor Networks. Sensors (Basel) 2019; 19:E867. [PMID: 30791482 DOI: 10.3390/s19040867] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 02/15/2019] [Accepted: 02/16/2019] [Indexed: 11/20/2022]
Abstract
The improvement of stable, energy-efficient mobile-based clustering and routing protocols in wireless sensor networks (WSNs) has become indispensable so as to develop large-scale, versitale, and adaptive applications. Data is gathered more efficiently and the total path length is shortened optimally by means of mobile sink (MS). Two algorithms as bacterial interaction based cluster head (CH) selection and energy and transmission boundary range cognitive routing algorithm with novel approach for heterogeneous mobile networks are proposed in this study. The more reliable and powerful CH selection is made with the greedy approach that is based on the interaction fitness value, energy node degree, and distance to adjacent nodes in a compromised manner. The best trajectories, thanks to intersection edge points of the visited CHs, are obtained in the proposed routing algorithm. In this way, the MS entry to transmission range boundaries of the CH has been a sufficient strategy to collect information. As in energy model, we adopt energy consumption costs of listening and sensing channel as well as transmit and receive costs. Comprehensive performance analyzes have been seriously carried out via the Matlab 2016a environment. We validate that the proposed scheme outperforms existing studies in terms of several performance metrics as simulations.
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Uddin MA, Mansour A, Jeune DL, Ayaz M, Aggoune EHM. UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring. Sensors (Basel) 2018; 18:s18020555. [PMID: 29439496 PMCID: PMC5855258 DOI: 10.3390/s18020555] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 02/02/2018] [Accepted: 02/05/2018] [Indexed: 11/18/2022]
Abstract
In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use.
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Affiliation(s)
- Mohammad Ammad Uddin
- Lab STICC, ENSTA Bretagne, Brest 29200, France.
- Sensor Networks and Cellular Systems Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia.
| | - Ali Mansour
- Lab STICC, ENSTA Bretagne, Brest 29200, France.
| | | | - Mohammad Ayaz
- Sensor Networks and Cellular Systems Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia.
| | - El-Hadi M Aggoune
- Sensor Networks and Cellular Systems Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia.
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