Heuristic Based Coverage Aware Load Balanced Clustering in WSNs and Enablement of IoT

Wireless Sensor Networks (WSNs) have become a cornerstone of modern technology, especially in applications like the Internet of Things (IoT), where they provide scalable and effective solutions for communication and monitoring applications. However, the finite energy of sensor nodes introduces a significant challenge, as it directly influences the network's lifespan and reliability. Routing protocols and clustering solutions have emerged as ideal ways to address these energy concerns. By forming sensors into clusters and enhancing data transmission routes, these protocols reduce energy consumption and improve network efficiency. Modern approaches incorporate techniques such as metaheuristics, fuzzy logic, and machine learning to address issues like load balancing, node mobility, and network topology changes. Despite considerable progress, gaps remain in scalability, Quality of Service (QoS) integration, and the adaptability of clustering protocols for dynamic environments. This research reviews the state of the art in optimizing energy efficiency in Wireless Sensor Networks. It addresses routing approaches and clustering solutions and highlights performance and quality key metrics and future trends.

The area of wireless sensor networks (WSNs) has gained significant attention from researchers due to its expansive range of applications, such as industrial regulation, human detection, and medical diagnosis. WSN is a vast number of geographically scattered devices that use sensor nodes to communicate and gather information from the target region. In clustered WSNs, the cluster head (CH) is deliberated as the relay node at a higher energy level than the non-CH nodes. The relay nodes perform more tasks related to the non-CHs, and these relay nodes are restricted by energy and communication capacity. Therefore, balancing the load of the relay node is a significant concern for improving the performance of the WSNs. Clustering is a well-known technique enforced to balance the load of the relay nodes. Often, densely loaded relay nodes dissolve their energy in less time and may cause changes in the topology of the network. In this study, we propose a Quasi-oppositional based Jaya load balancing strategy (QOJ-LBS) with a novel fitness function to address the issue of load balancing. The novel fitness function derived from the convex combination of the least lifespan of the relay node across the network and the entropy value of the lifespan of all relay nodes. The proposed QOJ-LBS justifies the network performance under two different WSN conditions called scenario-1 and scenario-2 with single-hop and multi-hop routing. The experimental analysis shows that improvement in network lifespan, total energy utilization, and the number of active sensor nodes of WSN is statistically significant in proposed QOJ-LBS compared to other state-of-the-art algorithms.

Recent trend to steer Wireless Sensor Networks towards a broad outdoor environment allows to monitor harsh areas and prevent a disaster situation. To guarantee an extensive work of the system and smooth cooperation among involved devices a new clustering approach needs to be found. In this paper, we present a new algorithm for an Event-oriented Focal Weight-based clustering for Wireless Sensor Network which will be able to monitor various parameters (physical, biological, and chemical) of the outdoor environment and signal the current situation further to the network. Shown results demonstrate the applicability of the algorithm for the large amount of nodes with various power levels. The use of a dual-band transceiver and advanced processing elements allow to accommodate new features in the nodes and guarantee a high performance with the low power consumption which is crucial for the outdoor system with no human attendance.

The minimisation of energy consumption has become an emerging topic in wireless sensor networks (WSNs) as these networks enable a wealth of new applications. The internet of things (IoT) application is one of them and the current hype around the IoT is huge. Therefore, the development of efficient communication protocols for WSNs is a major concern. In this context, various research communities have triggered several optimisation techniques to provide energy‐efficient solutions to WSNs. This study aims to apply the genetic algorithm (GA) in WSNs clustering and to evaluate its performance over another optimisation technique. The proposed protocol is analytically analysed and compared with a fuzzy logic (FL)‐based routing protocol and traditional routing protocol like LEACH and K‐means using a Java‐based custom simulator. Simulation results show that there is a trade‐off between GA‐clustering and FL‐clustering, but the overall performance of GA‐clustering is very promising for obtaining optimal energy consumption.

The technology of Heterogeneous Wireless Sensor Networks (HWSNs) is critical to the efficient operation and deployment of a variety of real-time Internet of Things (IoT) and Mobile Ad hoc Networks (MANETs). It is crucial for reducing overall energy dissipation and ensuring consistent energy distribution throughout the network. Bio-inspired hybrid optimization algorithms are emerging as a possible option for overcoming basic difficulties in Wireless Sensor Networks (WSNs), with a focus on sensor lifespan restrictions. A significant topic that must be considered prior to network configuration is attaining energy efficiency and optimal communication. Several papers have been published on the use of bio-inspired algorithms in WSNs. Few articles, however, addressed the hybrid strategy for routing and clustering in WSNs with communication. This research focuses on hybrid bio-inspired optimization algorithms and elaborates on their taxonomy and problem domains in WSNs. Furthermore, we explored and investigated the hybridization of the Whale Optimization Algorithm (WOA) with other meta-heuristic algorithms such as the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and others. This review can assist researchers in exploring the uses of such algorithms within and outside of this study area.

Energy efficiency is critical in Wireless Sensor Networks (WSNs), pivotal for Internet of Things (IoT) applications like environmental monitoring and smart cities. Clustering protocols like Low-Energy Adaptive Clustering Hierarchy (LEACH) extend network lifetime through Cluster Head (CH) rotation, but random CH selection often leads to suboptimal energy use. This study evaluates eight hybrid Particle Swarm Optimization-based LEACH (PSO-LEACH) variants to optimize CH selection and clustering: FA-PSO-LEACH (Firefly Algorithm), CSA-PSO-LEACH (Crow Search Algorithm), PSO-LEACH with CBTEERA, Adaptive PSO-LEACH, ABC-PSO-LEACH (Artificial Bee Colony), ACO-PSO-LEACH (Ant Colony Optimization), WOA-PSO-LEACH (Whale Optimization Algorithm), and Hybrid GA-PSO-LEACH (Genetic Algorithm). The primary research objective is to compare these variants in terms of energy efficiency, network lifetime, and clustering quality to identify the most effective strategies for prolonging WSN operation in IoT environments. Specifically, this paper addresses the research question: Which hybrid PSO-LEACH variant provides the optimal balance between early-stage stability and long-term energy conservation in a simulated WSN with 50 nodes? Each variant uses a unique metaheuristic for CH selection based on residual energy and distance, with PSO refining clustering. A Python simulation modelled a 100m × 100m WSN with 50 nodes, assessing network lifetime (First Node Died, FND; Half Nodes Died, HND; Last Node Died, LND), energy consumption, and clustering quality. Results show ABC-PSO-LEACH leading with FND at 57 rounds and LND at 462 rounds, followed by WOA-PSO-LEACH (HND 148, LND 406), surpassing others (e.g., PSO-LEACH with CBTEERA: FND 10, LND 280). WOA-PSO-LEACH’s balanced energy distribution excels in mid-to-late stages, despite computational overhead. These bio-inspired hybrids outperform traditional LEACH, with ABC-PSO-LEACH and WOA-PSO-LEACH ideal for prolonged WSN operation. Future work aims to enhance early-stage performance and explore dynamic topologies.

This paper considers wireless sensor (hyper) networks by single-valued neutrosophic (hyper)graphs. It tries to extend the notion of single-valued neutrosophic graphs to single-valued neutrosophic hypergraphs and it is derived single-valued neutrosophic graphs from single-valued neutrosophic hypergraphs via positive equivalence relation. We use single-valued neutrosophic hypergraphs and positive equivalence relation to create the sensor clusters and access to cluster heads. Finally, the concept of (extended) derivable single-valued neutrosophic graph is considered as the energy clustering of wireless sensor networks and is applied this concept as a tool in wireless sensor (hyper) networks.

Clustering has become a crucial operation in Wireless Sensor Networks (WSNs). Affinity Propagation (AP) is a relatively new clustering technique that has been shown to possess several advantages over long-standing algorithms such as K-means, particularly in terms of quality of clustering and multi-criteria support. However, the original AP algorithm is computationally intensive making it unsuitable for clustering in WSNs. A hierarchical decentralized variation of AP (Hi-WAP) has been recently proposed to reduce the processing cost of AP while minimizing the potentially negative effect of distribution (due to the lack of a global view) on clustering quality. In this paper, we explore the suitability of Hi-WAP for clustering in WSNs. We employ the level of distortion and the processing time as evaluation metrics. We propose an extension to Hi-WAP, termed LAP; Location-aware Affinity Propagation, where clustering is performed while being cognizant of nodes’ locations. Simulation results reveal that LAP, in general, outperforms Hi-WAP. We further study the optimization of LAP parameter values with the objective of minimizing processing time while maintaining a desirable low level of distortion.

The Internet of things (IoT) and wireless sensor networks (WSNs) have been rapidly and tremendously developing recently as computing technologies have brought about a significant revolution. Their applications and implementations can be found all around us, either individually or collaboratively. WSN plays a leading role in developing the general flexibility of industrial resources in terms of increasing productivity in the IoT. The critical principle of the IoT is to make existing businesses sufficiently intelligent to recognize the need for significant fault mitigation and short-cycle adaptation to improve effectiveness and financial profits. This article presents efficiently applied security protocols at the data link layer for WSN and IoT-based frameworks. It outlines the importance of WSN–IoT applications as well as the architecture of WSN in the IoT. Our primary aim is to highlight the research issues and limitations of WSNs related to the IoT. The fundamental goal of this work is to emphasize a suggested architecture linked to WSN–IoT to enhance energy and power consumption, mobility, information transmission, QoS, and security, as well as to present practical solutions to data link layer difficulties for the future using machine learning. Moreover, we present data link layer protocol issues, attacks, limitations, and research gaps for WSN frameworks based on the recent work conducted on the data link layer concerning WSN applications. Current significant issues and challenges pertain to flow control, quality of service (QoS), security, and performance. In the context of the literature, less work has been undertaken concerning the data link layer in WSN and its relation to improved network performance.

Researchers and industry professionals are highly interested in Wireless Sensor Networks (WSNs) due to their importance in utilizing low-cost, low-power microelements, such as radios, computers, and sensors, which are often integrated onto a single chip. Recently, the integration of the Internet of Things (IoT) with WSNs has been extensively explored. Effective routing techniques are crucial for optimizing power usage, ensuring Quality of Service (QoS), and maintaining network reliability in IoT-enabled WSNs. This paper presents an enhanced energy-aware navigation system that employs the Quantum Firefly Optimization (QFO) method and Neuro-Fuzzy Clustering for IoT-enabled WSNs. The Neuro-Fuzzy Clustering method extends the system's lifetime by automatically grouping sensor nodes into energy-efficient clusters. The QFO method is used to determine the optimal routing paths by considering factors such as energy consumption, QoS, and trust metrics. By incorporating these advanced methodologies, the proposed solution outperforms existing approaches in terms of energy efficiency, routing accuracy, and overall network stability. Simulation results demonstrate that this novel approach has the potential to significantly improve current routing protocols and expand the capabilities of IoT-enabled WSNs. Additionally, to enhance efficiency in mobile computing environments, the security of the intrusion detection system was strengthened through the use of deep learning techniques.

Clustering in wireless sensor networks has been one of the most effective mechanisms for addressing the issues of network lifetime, scalability and bandwidth reusability. In this paper, application of a relatively new meta-heuristic called as quantum inspired genetic algorithm has been proposed for dealing with the problem of multi-hop energy balanced unequal clustering and prolonging the network lifetime. Quantum inspired genetic algorithm conflates the concepts of evolutionary computation with the principles of quantum computing. The quantum computing principles impart huge parallel processing capabilities to the genetic algorithm due to which these algorithms provide better quality solutions in lesser amount of time. Quantum inspired algorithms are capable of providing competitive solutions even with a very small population size which results in reduced computational cost as population size is a factor in complexity calculation for evolutionary and swarm algorithms. The effectiveness of proposed approach has been evaluated by comparing it with traditional genetic algorithm based clustering for wireless sensor networks. Simulation results clearly indicate that quantum inspired genetic algorithm is capable of performing clustering in more efficient way and increasing the network lifetime as compared to traditional genetic algorithm.

Wireless Sensor Networks (WSN) has been attracting growing interests for developing a new generation of large-scale embedded computing systems. However, the communication paradigms in wireless sensor networks differ from the ones associated to traditional wireless networks, triggering the need for new communication protocols and energy-consuming model. The NDSA (A Novel Data-sending Algorithm) Based on Cluster for wireless sensor networks aims at the design of a scalable data-sending model for supporting large-scale embedded computing applications with critical requirements. In this paper, we assume intra-cluster data taking on Gaussian distribution. According to the desired accuracy given by system, NDSA can automatically adjust the number of data-sending nodes. Experimental results show that NDSA can lengthen the life of network significantly better than other similar algorithms.

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

Industrial monitoring environments have evolved from single monolithic systems to widely distributed heterogeneous systems. These include the Internet of Things (IoT), Industrial IoT, Cyber-Physical Systems, and Enterprise Application. One of the key challenges is the integration of heterogeneous systems and data exchange interoperability. In the industrial smart gas project, in which this work takes place, current standard-based or middleware solutions are not sufficient to handle these issues and often require specific ad hoc developments. This paper proposes a generic, modular, and extensible interoperability architecture based on modeling principles. We provide a free software implementation and illustrate the approach on industrial use cases. Some criteria are then proposed for a first qualitative evaluation.