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Efficient energy conservation to maximize the network lifetime is always the most critical challenge in wireless sensor networks due to their energy-constrained nodes and harsh working environments. Clustering routing is considered the most popular method to lessen energy utilization at present. In this paper, a fuzzy logic and particle swarm optimization-based clustering routing protocol called PFCRE is proposed to improve energy efficiency, mitigate the energy holes and enhance the network lifetime as well. The proposed PFCRE considers energy minimum and balance to form clusters by using an improved particle swarm optimization algorithm. Moreover, a fuzzy inference system is employed to find the optimal route for each CH, which considers effective parameters including residual energy, distance to base station and number of being selected as relay so as to not only minimize energy consumption but also balance traffic load. Especially, the rules of the fuzzy inference system are tuned by another particle swarm optimization algorithm. Furthermore, an adaptive maintenance mechanism is used to organize the clusters instead of periodic clustering to further diminish computation and message overheads. The performance of PFCRE is evaluated by extensive experiments, and the results indicate that it outperforms IBRE-LEACH, EAUCA, DAPFL, IPSOGWO along with FMSFLA in terms of network lifetime, throughput, energy consumption, and standard deviation of CH's traffic load. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) play a crucial role in healthcare and various other domains by enabling continuous monitoring of vital signs and activities. However, challenges such as low-powered sensors, unstable links, and routing issues hinder their efficiency and reliability. In this paper, we propose the Thermal Aware High Throughput (TAHT) routing protocol to address these challenges. TAHT utilizes a multi-hop topology and prioritizes stable links for efficient routing, considering parameters like energy, link quality, and temperature. Through experimental evaluation, TAHT demonstrates a 10% to 20% improvement in throughput, a 15% reduction in temperature overheads, and enhanced overall performance compared to benchmark protocols. By effectively managing energy consumption, minimizing duplicate data transmissions, and optimizing routing paths, TAHT offers a novel solution to improve the reliability and sustainability of WBANs, thus advancing the field beyond previous efforts in the literature. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) represent the collection of restricted energy sensor nodes that are deployed in an area of target for gathering potential environment data for decision‐making with respect to their objective of application. However, the implementation of energy‐effective data gathering strategies in large‐scale WSNs is the most challenging due to the limited energy resources. Clustering‐based data gathering strategies are identified to be quite effective for energy saving that directly attributes to extended network lifetime. Moreover, optimal path amid the cluster head (CH) and sink node needs to be selected for sustaining energy efficiency and improving network lifespan. In this article, Hybrid Salp Swarm and Improved Whale Optimization Algorithm (HSSIWOA)‐based clustering scheme is proposed for improving the network lifetime and routing optimization with maximized energy efficacy. It integrated the exploration capability of Salp Swarm Optimization Algorithm (SSOA) with exploitation benefits of Improved Whale Optimization Algorithm (IWOA) for balancing the trade‐off between the rate of exploration and exploitation during CH selection process. It utilized the parameters of residual energy, load balance, intra‐cluster distance, inter‐cluster distance, and node centrality into account during the process of fitness evaluation. It performed well by constructing an optimized number of clusters, such that energy stability and network lifetime are maintained in the network. The experimental results of the proposed HSSIWOA scheme confirmed extended network lifetime of 21.64%, minimized energy utilization of 23.42%, and maximized throughput of 18.56%, better than the baseline approaches. [ABSTRACT FROM AUTHOR]

Recently, wireless body area network (WBAN) becomes a hot research topic in the advanced healthcare system. The WBAN plays a vital role in monitoring the physiological parameters of the human body with sensors. The sensors are small in size, and it has a small-sized battery with limited life. Hence, the energy is limited in the multi-hop routing process. The patient data is collected by the sensor, and the data are transmitted with high energy consumption. It causes failure in the data transmission path. To avoid this, the data transmission process should be optimized. This paper presents an advanced authentication and energy-efficient routing protocol (AAERP) for optimal routing paths in WBAN. Patients' data are aggregated from the WBAN through the IoMT devices in the initial stage. To secure the patient's private data, a hybrid mechanism of the elliptic curve cryptosystem (ECC) and Paillier cryptosystem is proposed for the data encryption process. Data security is improved by authenticating the data before transmission using an encryption algorithm. Before the routing process, the data encryption approach converts the original plain text data into ciphertext data. This encryption approach assists in avoiding intrusions in the network system. The encrypted data are optimally routed with the help of the teamwork optimization algorithm (TOA) approach. The optimal path selection using this optimization technique improves the effectiveness and robustness of the system. The experimental setup is performed by using Python software. The efficacy of the proposed model is evaluated by solving parameters like network lifetime, network throughput, residual energy, success rate, number of packets received, number of packets sent, and number of packets dropped. The performance of the proposed model is measured by comparing the obtained results with several existing models. [ABSTRACT FROM AUTHOR]

Summary: In wireless sensor networks (WSNs), routing based on cluster construction is highly preferred as it greatly supports reliable data communication, load balancing, and fault tolerance with extended network lifetime. In specific, metaheuristic approach‐based dynamic cluster heads (CHs) selection has the possibility of enhancing the lifespan of network and at the same time is capable in reducing the energy consumption. In this paper, hybrid grasshopper and Harris hawk optimization algorithm‐based energy efficient routing protocol (HGHHOA) is propounded for optimal CH selection. This proposed HGHHOA approach adopted a fitness function that incorporated the factors of residual energy, distance between CH and cluster members, distance between selected CHs and the sink, node centrality, and node degree into account. The fitness function values of optimality facilitate a potential CH selection with significant cost‐effective routing. It is proposed with primary objective of improving the network lifespan through optimized selection of CHs that balances the available energy in a predominant way. It is proposed with significance of handling premature convergence with minimized energy consumption and network lifetime through the possibility of establishing an ideal balance between number of alive and dead nodes. The results of this proposed HGHHOA approach with varying rounds of implementation exhibited better results in throughput and residual energy which is 23.98% and 29.21%, better than the bassline CH selection mechanisms. [ABSTRACT FROM AUTHOR]

A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR. [ABSTRACT FROM AUTHOR]

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has developed into a new area of study that promises to elevate human culture to a higher level of sophistication. The network is essential in IoT since it is responsible for relaying information from sensors to the sink. In the IoT, where many devices share finite resources, extending the lifespan of the network is a difficult challenge. The lifespan of a network can be prolonged by the use of clustering. However, initial network nodes' energy might be quickly depleted by incorrectly selecting cluster heads (CHs). This research aims to provide a solution by suggesting a fuzzy-based optimized nature-inspired clustering technique (FONIC) to choose the best CH to sustain the network over time. When dealing with unreliable network conditions, the precise solution provided by fuzzy logic (FL) is invaluable. Therefore, in order to calculate a fitness value, FL is used on network metrics such as energy, distance, degree, and centrality. In the end, the right CH is chosen with the help of the Penguin Search Optimization Algorithm (PeSOA). Python is utilized to do extensive simulations that confirm the effectiveness of the suggested FONIC protocol. Other protocols, including FIGWO, HMGWO, LEACH-PRO, FGWSTERP, and SSMOECHS, are contrasted with the proposed FONIC protocol. Compared to other top-tier protocols, the suggested FONIC protocol was shown to perform better than any of them, improving the ratio of packet transmission by 10% and network lifespans by 10–15%. [ABSTRACT FROM AUTHOR]

MANET is widely utilized for interactions among devices without relying on a central governing body. The frequent changes in network topologies expose it to various security vulnerabilities. Enhancing security within MANET is of paramount concern and demands resolution. Addressing these security-related issues stands as the sole approach to ensuring proper data sharing in MANET. Numerous security-oriented techniques have been introduced, but they have encountered several challenges, leading to increased costs and time complexity. These difficulties have necessitated additional resources to be allocated towards mitigating issues and grappling with the growing complexity, thereby introducing a layer of complexity that can impede seamless implementation and efficacy. In this context, a Dynamic Algorithm Switching (DAS) algorithm is proposed to strengthen security in MANET. The process begins with Node clustering, followed by cluster formation through the implementation of the Modified Density-Based Spatial Clustering of Applications (DBSCAN). The proposed method incorporates Cluster Head selection (CHS), which in turn enhances energy efficiency within MANET. The effectiveness of the DAS method is evaluated through experimentation, measuring key aspects such as Packet Delivery Ratio (PDR), end-to-end delay, throughput97.9, network lifetime, and energy consumption with the attained values 98.7%, 15.81mas, 97.9% 952 s as well as 11 J/s respectively. However the existing approaches such as ANFIS-EESC, QoSDSBS, EBR and IBADConBE achieved rate of PDR as 98.3%, 97.6%, 97.1% and 96.9% that diminished the performance. Comparative analysis demonstrates that the proposed method outperforms other techniques across all metrics, establishing its superior efficiency. [ABSTRACT FROM AUTHOR]

Owing to the inexpensive, adaptable, and scalable features of WSNs (wireless sensor networks), the network is viewed as a vital technology to support distinct smart applications. The primary issue is to strengthen the lifespan of the network, as the node devices have bounded lifecycles owing to notable power constraints. Thus, to sustain the lifespan of WSNs, it is crucial to reinforce the energy administration at the node devices, as they are routinely deployed in secluded areas. Several energy management strategies have recently been used in this context. Among these alternatives, cluster-chain hybrid networks have demonstrated the ability to significantly reduce the node energy usage. However, the chain formation techniques often result in increased latency in large-scale scenarios. Similarly, it has been proven that coding techniques preserves the network reliability and energy efficiency. While utilising the benefits of these coding schemes it is necessary to carefully secure the topology, link quality, and coding vectors. In this paper, an improved energy-saving distributed cluster–chain K-communication mechanism for smart sensor networks is proposed. In the proposed solution named IESDCC-KM, a dual K-means technique is adapted to form unequal clusters. IESDCC-KM implements a competing and ideal weight function to select the cluster heads and establishes perpendicular chain trees among heads based on their distances and a threshold value. It establishes gradient-based dis-joint multiple routes from the source to the destination and implements discrete wavelet transform to compress the accumulated inter-cluster data. At the intermediate nodes on the path along the source and destination, the packets from the different link nodes are encoded utilizing linear network coding. MATLAB 2018b experimental analysis demonstrates the proposed IESDCC-KM improves the reception ratio by 0.20 to 0.03 at 0.99 to 0.96 precision rate. Furthermore, it showed a 28.57% throughput increase with a 1% reduction in delay and a 2.86% boost in energy-saving for 4000 rounds. [ABSTRACT FROM AUTHOR]

Summary The sensor networks are the primary and essential components on which the world of Internet of Things (IoT) is built. IoT empowers smart communication, computation, and sensing capabilities. In sensor networks, the data are collected by the sensor nodes and sent to the sink along a communication path. These communication paths are collaboratively established by the nodes and the sink. By incorporating energy‐efficient data gathering techniques, the lifetime of these networks is improved. The major contribution of the study in this work is to provide a survey of various techniques for data aggregation (DA) and the employed algorithmic strategies that facilitate and influence network lifetime (NL) in these environments. DA in wireless sensor networks (WSN), IoTs, and cloud computing extend the lifetime of these networks since it enables efficient merging of traffic flows, thus reducing transmissions and energy consumption of devices. In sensor networks, data aggregation tree (DAT)‐based routing facilitates energy‐efficient routing that extends NL. NL maximization using DATs constructs DATs with optimal NL and is a known NP‐complete problem. Subsequently, the study in this work surveys the various approaches employed by researchers to construct DATs and discusses techniques for DAT scheduling. This work further explores various sensor deployment techniques and discusses real world scenario in which NL is influenced by uncertainty in communication links. Finally, the study in this survey highlights the achievements in realizing NL improvement using DAT and identifies the limitations and research challenges. [ABSTRACT FROM AUTHOR]

This paper proposes an improved dual-phased framework for energy-efficient, cluster-based routing in Wireless Sensor Networks (WSNs). It addresses the critical challenge of balancing energy consumption with reliable network performance. Cluster-based routing is a crucial parameter for WSN operational efficiency, especially in applications demanding minimal energy use and dependable data transmission. The proposed framework integrates two advanced meta-heuristic algorithms: Sailfish Optimization (SFO) and Spotted Hyena Optimization (SHO). This combined approach leverages SFO's rapid exploration for efficient clustering and optimal Cluster Head (CH) selection. Additionally, SHO's refined exploitation capabilities optimize efficient routing paths. This innovative methodology significantly improves network performance metrics like energy efficiency, network lifetime, and Packet Delivery Ratio (PDR). The originality of this work lies in the dual-phased optimization strategy. It distinctively outperforms traditional single-algorithm based approaches by employing a unique hybrid optimization approach, offering greater originality and value. Experimental simulations demonstrate that the proposed framework outperforms several popular algorithms in terms of key performance metrics. This makes it a valuable contribution to the field and an efficient solution for diverse applications. [ABSTRACT FROM AUTHOR]

Relay node placement with a hop count bound is a crucial problem in enhancing connectivity, lifetime, and reliability in multi-hop wireless sensor networks. However, existing approaches focus solely on minimizing the number of used relay nodes without considering the energy consumption among nodes. This work investigates a relay node placement problem in multi-hop wireless sensor networks with two objectives: minimize the number of used relay nodes, and minimize the maximum node energy consumption to prolong the network's lifetime while still ensuring the network's connectivity. In particular, we consider a hop count bound as a delay constraint to elevate the network's reliability. We propose a multi-objective evolutionary algorithm called GPrim to solve our problem. The algorithm is a combination of edge-set encoding and NSGA-II framework. Leveraging problem-specific properties, we introduce objective-oriented heuristics incorporated into initialization, crossover, and mutation operators to improve the algorithm's convergence. Simulation results on 3D datasets show that the proposed algorithm performs significantly better than existing algorithms on all measured metrics. [ABSTRACT FROM AUTHOR]

One of the key goals in the design of the networks is to increase the lifespan of wireless sensor networks (WSNs). Using different models of intelligent energy management could help designers achiseve this objective. By reducing the number of sensors required to collect data on the environment, these models can achieve higher levels of energy efficiency without sacrificing the quality of the readings. When battery power is an issue, wireless sensor networks (WSNs) are often employed for applications such as monitoring or tracking. Several routing protocols have been developed in the last several years as possible answers to this problem. Despite this, the issue of extending the lifetime of the network while considering the capacities of the sensors remain open. As a result of applying neural networks, Low-Energy Adaptive Clustering Hierarchy (LEACH) and Energy-Efficient Sensor Routing (EESR) can be improved in terms of their overall efficiency as well as their level of dependability, as is shown in this research EESR. Energy-Efficient Sensor Routing (ESR) and Low-Energy Adaptive Clustering Hierarchy (LEACH) are the names of the two protocols that are being utilized here EESR. The system incorporates a refined version of the Levenberg–Marquardt Neural Network (LMNN), which serves to enhance the efficiency with which it uses energy. The ability of an Intrusion Detection Systems (IDS) based on an artificial neural system to detect anomalies has also been proven. Anomalies can be identified using this system's optimum feature selection. Simulations showed that the proposed ANN-ILMNN model worked better, as shown by these results. [ABSTRACT FROM AUTHOR]

The study develops an oxidative kinetic model of acrylic‐urethane network during photo‐aging. The kinetic model can track chemical reaction dynamics well and also enables to estimate the network lifetime by counting scission/crosslinking events. Estimated network lifetime is semiquantitively coincided with the one derived from different statistical approach. [ABSTRACT FROM AUTHOR]

Evolution of Wireless sensor networks (WSN) has found its identity in Wireless Body Area Networks (WBAN) to monitor the health status of adults undergone a surgery, etc. In addition, the mobility of the sensor nodes deployed on/in the human body plays a vital role in maintaining the accuracy of the physiological signal. In this paper, a Distributed Posture-based Traffic-Medium Access Control (DPT-MAC) protocol is proposed where the sensor nodes follow the Disc Communication model for the detection of neighbor nodes based on the proximity of the sensing range. Further, the modified IEEE 802.15.4 superframe structure determines the potential sleep posture of a subject to ensure a collision-free data transmission and prioritize the data traffic based on the criticality of the data like critical, periodic and nonregular data. The critical data detection occupies the channel on high priority and will have the probability to access the other channel space during the contention period of the noncritical data. The regular guaranteed time slot (GTS) is reduced from the tradition number of slots to five, which allows the nodes to handle the throughput. This uninterrupted data transmission minimizes the delay rate and collision ratio with any number of sensor nodes as the channel synchronization is strictly followed using the Carrier Sense Multiple Access/Collision Avoidance mechanism. Here, the optimization of the network quality parameters like energy consumption, throughput, delay and collision ratio assures a guaranteed increase in the Network Lifetime. The results show that the distributed topology outperforms the conventional star topology in sustaining the Network Lifetime. [ABSTRACT FROM AUTHOR]

Energy limitation is one of the essential parameters in the design of a Wireless body area networks (WBANs) as it is important to improve the lifetime of the network. WBAN routing is an effective approach for establishing energy efficiency sets and assign time slots for the network. Many algorithms that deal with interference model treats the whole WBAN as a minimum interference unit and increase their lifetime cycle. In this research, we report an effective low-energy adaptive clustering hierarchy (LEACH) routing protocol using MATLAB simulation and related C++ simulation codes to enhance the overall performance of the network by improving the energy efficiency and network lifetime cycles. Furthermore, the study sheds light up on the comparison of the protocol and proposes a modified protocol for WBAN. Based on the results obtained from conducting different configurations in the proposed design, the base station should be situated near the network to insure high network performance. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) play a crucial role in healthcare and various other domains by enabling continuous monitoring of vital signs and activities. However, challenges such as low-powered sensors, unstable links, and routing issues hinder their efficiency and reliability. In this paper, we propose the Thermal Aware High Throughput (TAHT) routing protocol to address these challenges. TAHT utilizes a multi-hop topology and prioritizes stable links for efficient routing, considering parameters like energy, link quality, and temperature. Through experimental evaluation, TAHT demonstrates a 10% to 20% improvement in throughput, a 15% reduction in temperature overheads, and enhanced overall performance compared to benchmark protocols. By effectively managing energy consumption, minimizing duplicate data transmissions, and optimizing routing paths, TAHT offers a novel solution to improve the reliability and sustainability of WBANs, thus advancing the field beyond previous efforts in the literature.

Abstract A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR.

This paper proposes an improved dual-phased framework for energy-efficient, cluster-based routing in Wireless Sensor Networks (WSNs). It addresses the critical challenge of balancing energy consumption with reliable network performance. Cluster-based routing is a crucial parameter for WSN operational efficiency, especially in applications demanding minimal energy use and dependable data transmission. The proposed framework integrates two advanced meta-heuristic algorithms: Sailfish Optimization (SFO) and Spotted Hyena Optimization (SHO). This combined approach leverages SFO's rapid exploration for efficient clustering and optimal Cluster Head (CH) selection. Additionally, SHO's refined exploitation capabilities optimize efficient routing paths. This innovative methodology significantly improves network performance metrics like energy efficiency, network lifetime, and Packet Delivery Ratio (PDR). The originality of this work lies in the dual-phased optimization strategy. It distinctively outperforms traditional single-algorithm based approaches by employing a unique hybrid optimization approach, offering greater originality and value. Experimental simulations demonstrate that the proposed framework outperforms several popular algorithms in terms of key performance metrics. This makes it a valuable contribution to the field and an efficient solution for diverse applications.

The development of computer technology and wireless communication has introduced the wireless body area network (WBAN) to the world. In WBAN, the patient's vital signs are monitored by small sensors embedded in the body. Sensor nodes work with a limited energy source, so energy consumption is a major issue in these networks. The increase in temperature caused by data transmissions can cause serious damage to body tissue. This paper proposes an Energy-Temperature Aware Routing (ETAR) protocol to solve this problem. In ETAR, routing is done directly and multi-hop using relay nodes. Multi-hop data forwarding plays a significant role in reducing energy consumption. In the proposed method, relay nodes are selected using a fuzzy inference system. Energy, temperature, and distance parameters are defined as the inputs of the fuzzy system. Therefore, in each round, a node with more remaining energy, lower temperature, and less distance from its neighbors is selected as the relay node. The proposed protocol reduces the adverse effects of temperature on the body by setting temperature limits for sensors. The performance of ETAR was evaluated for homogeneous and heterogeneous networks. In homogenous network, this protocol improves energy consumption by 44% and 55% compared to THE and EEMR. Network lifetime is enhanced by 46% and 55% compared to THE and EEMR. The throughput is improved by 40% compared to THE and 34% compared to EEMR, respectively. In a heterogeneous network, this protocol improves energy consumption by 47% and 52% compared to THE and EEMR. Network lifetime is enhanced by 62% and 65% compared to THE and EEMR, respectively. The throughput is improved by 100% compared to THE and 97% compared to EEMR. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) consist of sensor nodes that detect, process, and transmit various types of information to a base station unit. The development of energy-efficient routing protocols is a crucial challenge in WSNs. This study proposes a novel algorithm called RMOWOA, i.e., Revamped Multi-Objective Whale Optimization Algorithm, which utilizes concentric circles with different radii to partition the network. The circles are divided into eight equal sectors, and sections are formed at the intersections of sectors and layers. Each section contains a small number of nodes, and an agent is selected based on specific criteria. The nodes within each section transmit their detected information to the corresponding agent or cluster head. This process is repeated until the base station receives the data. The selection of agents is based on a WOA-based approach, known for enhancing the network's lifetime. The selected agent aggregates the data, performs redundant residue number-based error detection and rectification, and forwards the information to the lower segment's agent within that sector. The proposed RMOWOA algorithm is evaluated through simulation analysis and compared with established benchmark cluster head selection schemes such as SFA- Cluster Head Selection, FCGWO-Cluster Head Selection, and ABC-Cluster Head Selection. The experimental results of the RMOWOA algorithm demonstrate reduced energy consumption and extended network lifespan by effectively balancing the ratio of alive and dead nodes in WSNs. [ABSTRACT FROM AUTHOR]

Summary: In recent years, the use of wireless sensor devices in several applications, for example, monitoring in dangerous geographical spaces and the Internet of Things, has dramatically increased. Though sensor nodes (SNs) have limited power, battery replacement is not feasible in most cases. Therefore, energy saving in wireless sensor networks (WSN) is the major concern in the design of effective transmission protocol. Clustering might lower energy usage and increase network lifetime. Routing protocol for WSN represents an engineering area that has gained considerable interest among researchers due to its rapid evolution and development. Among them, the clustering routing protocol corresponds to the most effective technique to manage the energy consumption of each SN. In this manuscript, we focus on the design of a new metaheuristic optimization‐based energy‐aware clustering with routing protocol for lifetime maximization (MOEACR‐LM) method in WSN. The purpose of the MOEACR‐LM method is to improve network efficiency via proper selection of cluster heads (CHs) and effective data transmission. Initially, a hunter–prey optimization (HPO) method‐based clustering technique is used for cluster construction and the CH selection process. Next, the clouded leopard optimization (CLO) model is used for the route selection process in WSN. The HPO and CLO models derive a fitness function involving multiple parameters for clustering and routing processes. A comprehensive experimental analysis is carried out to demonstrate the enhanced performance of the MOEACR‐LM technique. The overall comparison study pointed out the improved energy efficiency results of the MOEACR‐LM technique over other existing approaches. [ABSTRACT FROM AUTHOR]

The paper presents a new routing scheme using the information on the locations of nodes to create a routing region that controls the region of packet routing to achieve route optimization. The proposed scheme aimed to reduce the occurrence of packet detours or other routing overheads caused by the undirected packet transmission. The strength of this approach is that it can improve the lifetime of nodes in the network while decreasing the time taken for a packet to arrive at its destination or base station (BS). The proposed scheme used a self-adaptive algorithm that dynamically adjusted the routing region based on the BS’s calculation of the network layer parameters to achieve energy efficiency while satisfying data quality. The routing region limits the area of routing and restricts data flooding in the entire network, which potentially will waste resources and cause data redundancy. The simulation showed that the proposed scheme outperformed, the original fitness scheme and SPEED, according to energy consumption, transmission delay, throughput, and reliability (packet delivery ratio) under different congestion levels. The proposed scheme offered double the throughput and shortened packet delay by 20%. Furthermore, it had a longer lifetime, exceeding other schemes by approximately twofold when the traffic was not too congested. However, the gap decreases when the network becomes worse.

Abstract The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively.

Abstract A wireless sensor network (WSN) consists of specialized sensor nodes that perform sensing services for Internet of Things devices with limited battery power. As the replacement or recharging of batteries is not possible, energy consumption becomes the most important design issue in WSNs. The energy‐efficient routing protocol gets the most priority in such energy‐constrained networks. The utilization of clustering‐based routing protocols like stable election protocol (SEP) has gained much attention as the network's lifetime is significantly improved due to the clustering of sensor nodes. Moreover, the inclusion of a secondary CH (SCH) is beneficial when the member node with the most remaining energy performs data aggregation and reduces the energy burden of the CH. This article characterizes SEP and Prolong SEP (P‐SEP) protocols and then extends them by incorporating SCH, which ensures balanced energy consumption. The performance of the proposed Extended SEP and Extended P‐SEP protocols has been analysed regarding stability period, network lifetime, energy usage and throughput. The simulation results demonstrate that the proposed protocols outperform state‐of‐the‐art protocols like P‐SEP, Modified SEP (M‐SEP) and SEP. In particular, the stability period of Extended SEP and Extended P‐SEP has improved up to 42% and 89%, respectively, compared to M‐SEP.

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively. [ABSTRACT FROM AUTHOR]

Mathematical programming techniques are widely used in the determination of optimal functional configuration of a wireless sensor network (WSN). But these techniques have usually high computational complexity and are often considered as Non Polynomial complete problem. Therefore, machine learning techniques can be utilized for the prediction of the WSN parameters with high accuracy and lesser computational complexity than the mathematical programming techniques. This paper focuses on developing the prediction model for determination of the node status to be included in the set cover based on the coverage probability and trust values of the nodes. The set covers are defined as the subset of nodes which are scheduled to monitor the region of interest with the desired coverage level. Several machine learning techniques have been used to determine the node activation status based on which the set covers are obtained. The results show that the random forest based prediction model yields the highest accuracy for the considered network setting. [ABSTRACT FROM AUTHOR]

While deploying wireless sensor networks (WSNs), the cluster heads need huge amount of energy according to the unbalanced routing of the Sensor nodes to the base station as the result, which has produced minimized network lifetime and unbalanced energy utilization. The proposed cluster-based hybrid routing technique (CHRT) contains the cluster head selection with effective energy utilization procedure which extends the network lifetime and enhanced packet routing technique is used to reduce the energy of the sensor node with the Euclidean distance metric, base station location identification and residual energy. The fitness function is used for selecting the cluster heads for enhancing the selection of the cluster head in efficient way. The modified fitness function has been introduced for relaying the remaining cluster heads through enhanced routing functionality. The simulation results of the proposed technique suggested that it enhances the network lifetime, improves the residual energy and coverage area as compared to the relevant methodologies. [ABSTRACT FROM AUTHOR]

MANETs (Mobile AdHoc Networks) have major problems due to the resource-constrained nature of mobile nodes. MANET topology is highly uncertain due to the mobility of MANET nodes that affect the stability of interconnected links. This scenario results in an increased traffic overhead which affects the routing protocol performance and results in increased energy consumption. A routing scheme designed for MANET should be able to handle energy depletion issues and nodes’ mobility. This paper designs two novel schemes to enhance energy efficiency and network lifetime by performing clustering and routing in MANET. The Hybrid Tasmanian devil and Elephant herding optimization (HTDEHO) is used for clustering the MANET nodes to minimize the overhead, enhance the stability of the network topology, and reduce collision. The HTDEHO selects the optimal cluster head (CH) from a set of nodes based on their fitness value calculated by various parameters such as mobility-based node ranking, residual energy, distance, node degree, and optimal next hop CH selection. Dwarf mongoose optimization algorithm with Fuzzy variable (FDMO) algorithm selects optimal routing cost from CH to the base station based on various parameters such as throughput, delay, and distance. The efficiency of the proposed model is evaluated with different performance metrics such as energy consumption, throughput, end-to-end delay, and packet-dropping analysis. The performance of the proposed HTDEHO-FDMO classifier is compared with different techniques such as RDOAICRP, BFOA, MARP-HO, and FEKHO-QBA. Even after 170 iterations, the HTDEHO model retains a total of 10 alive nodes. [ABSTRACT FROM AUTHOR]

A wireless body area network (WBAN) is a type of wireless sensor network that plays a crucial role in monitoring patient healthcare. In this network, sensor nodes are typically placed inside or outside the patient's body. These nodes are capable of transmitting data to the sink node when any functional changes in the patient are detected. The routing and energy efficiency of the network nodes are challenging tasks in WBAN, as each sensor node operates on a battery. Therefore, in multi-hop routing designing a robust routing protocol has a significant impact on reducing energy consumption during the selection of the next hop. In this paper, a simple novel routing protocol named simple energy-aware and reliable (SEAR) routing protocol is proposed to transmit reliable data packets in a WBAN. The proposed routing scheme considers the remaining energy of sensor nodes, priority data, and hop count to the sink node as significant metrics for dynamically selecting the best forwarder node. Furthermore, the proposed protocol utilizes the route reliability factor (RRF) to select the optimal route among all possible routes between the source sensor node and the sink node. RRF selects the route with the maximum route residual energy and minimum hop count. As a result, SEAR has the ability to provide effective single-hop and multi-hop routing data transmission to improve the reliability of data transmission, decrease the energy consumption of the sensor nodes, and prolong the lifetime of the network. The simulation results show that the performance of the SEAR routing protocol outperforms the existing routing protocols for the metrics: packet loss ratio, throughput, end-to-end delay, normalized routing load, energy consumption, and network lifetime. The results indicate that the proposed protocol improves total energy consumption by 18.76% and 10.89% when compared to EERR-RLFL and AMCRP respectively. Meanwhile, SEAR reduces the average end-to-end delay by 17% and 9%, packet loss ratio by 29.48% and 17.69%, and normalized routing load by 31.17% and 20.91%. Additionally, SEAR achieves up to 16% and 9.71% higher throughput compared to EERR-RLFL and AMCRP routing protocols respectively. Overall, the results obtained indicate that the proposed SEAR routing scheme significantly enhances the overall performance of the network. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of specialized sensor nodes that perform sensing services for Internet of Things devices with limited battery power. As the replacement or recharging of batteries is not possible, energy consumption becomes the most important design issue in WSNs. The energy‐efficient routing protocol gets the most priority in such energy‐constrained networks. The utilization of clustering‐based routing protocols like stable election protocol (SEP) has gained much attention as the network's lifetime is significantly improved due to the clustering of sensor nodes. Moreover, the inclusion of a secondary CH (SCH) is beneficial when the member node with the most remaining energy performs data aggregation and reduces the energy burden of the CH. This article characterizes SEP and Prolong SEP (P‐SEP) protocols and then extends them by incorporating SCH, which ensures balanced energy consumption. The performance of the proposed Extended SEP and Extended P‐SEP protocols has been analysed regarding stability period, network lifetime, energy usage and throughput. The simulation results demonstrate that the proposed protocols outperform state‐of‐the‐art protocols like P‐SEP, Modified SEP (M‐SEP) and SEP. In particular, the stability period of Extended SEP and Extended P‐SEP has improved up to 42% and 89%, respectively, compared to M‐SEP. [ABSTRACT FROM AUTHOR]

Wireless body area networks (WBANs) are an emerging field in the domain of healthcare which are typically composed of biomedical sensors. These sensors are implanted inside or attached to the human body for monitoring a patient's condition and providing accurate treatment to patients. In WBANs, energy efficiency is a critical concern due to the restricted battery capacity of the sensors. Extending the network lifetime and reducing the energy consumption of these sensor nodes can significantly impact the reliability and effectiveness of WBANs in monitoring patients' health. An efficient routing protocol based on energy-related parameters is crucial in designing these networks. Although many routing protocols have been proposed for routing in WBANs, sufficient features have not been properly handled in these methods. To overcome these issues, a novel routing protocol named Simple Energy Efficient and Bandwidth Aware (SEBA) routing protocol is proposed for routing in WBANs. The proposed scheme considers multiple metrics of the network node, such as remaining energy, energy harvesting, draining rate energy, available bandwidth, and number of hops in a route selection to minimize energy consumption, increase network lifetimes, and enhance the reliability of data transmission in WBANs. Additionally, the SEBA uses a novel mechanism to change the route dynamically based on energy consumption. This mechanism plays a significant role in reducing the number of route errors, route discoveries, and distributing energy consumption among sensor nodes. The experimental results reveal that the proposed scheme performs well in terms of average network throughput, packet delivery ratio, normalized routing load, average end-to-end delay, energy consumption, and network lifetime compared with the existing AMCRP, EEMP, and EAR protocols. [ABSTRACT FROM AUTHOR]

Underwater Wireless Sensor Networks (UWSNs) have emerged for various tasks in marine environments. Routing Protocols (RPs) in UWSNs face challenges such as high propagation delay, Energy Consumption (EC), low bandwidth, and low throughput compared to Terrestrial WSNs (TWSNs). UWSNs operate autonomously without human intervention, and the limited energy of sensor batteries poses a significant challenge. Uneven energy resource utilization reduces network longevity, which is a major issue in UWSNs. To combat these issues, a promising approach involves the design of RPs employing clusters for efficient packet routing from source to destination. This paper systematically reviews recent Cluster-oriented RPs (CRPs) in UWSNs, organizing them chronologically. It assesses their merits, drawbacks, and overall network performance, offering insights into future directions for improvement in this domain. [ABSTRACT FROM AUTHOR]

Efficient Energy Consumption and Network Lifetime are significant concerns in wireless sensor networks and allied disciplines. Clustering is one of the available solutions, but optimized cluster head selection is a prime issue nowadays. Many solutions have been given for solving this issue considering some attributes, but with time, meta-heuristics algorithms have become widely used for real-world applications. The nature-inspired algorithm is seeking researchers' wide attention as it gives the capability to self-learn and perform better. In this paper, we have proposed the Dragon Fly algorithm with multi-attribute decision-making inspired by the dynamic and static behavior of the dragonfly. The Proposed IDA (Innovative Dragonfly Algorithm) is a hybrid approach in which dragonfly and multi-attributes are combined for optimal cluster head selection. The proposed method is a way to compute multi-attributes of sensor nodes for ranking them and selecting optimized cluster heads. The energy consumption of IDA is 0.4896, NBA (Novel Bio-Inspired Algorithm) is 0.4321, FLPSOC (Fuzzy Logic and PSO-based energy efficient clustering) is 0.4421, and ESO-LEACH (PSO-based energy efficiency) is 0.4678 at which means proposed IDA is better than other compared algorithms in energy consumption. The throughput of IDA is 64.99, which is better than existing different compared algorithms. The number of alive nodes in the proposed method is, and that of compared algorithms is; thus, IDA has enhanced network lifetime compared to others. The IDA algorithm is compared with NBA, FLPSOC, and ESO-LEACH, validating that the proposed algorithm performs better than the classical and compared algorithm. [ABSTRACT FROM AUTHOR]

In the design of Wireless sensor networks (WSNs), maximizing network lifetime and sustaining energy stability is identified as the challenging problem since it comprises of compact sized and energy limited sensor nodes that cooperates during data routing. The existing clustering-based routing mechanisms accomplished energy efficiency and attempted to minimize the distance between the cluster head (CH) and the sink node for network lifetime improvement. The adoption of swarm intelligence algorithms and fuzzy logic is determined to the ideal computational intelligence techniques which are suitable for NP-hard problem like the multi-hop route selection process. In this paper, A Modified Dingo Optimization Algorithm-based Clustering Mechanism (MDOACM) is proposed for addressing the limitations of the clustering protocol with respect to cluster head (CH) lifetime and cluster quality. This MDOACM-based clustering protocol utilized Interval Type-2 Fuzzy Logic (IT2FL) for determining the trust level of each sensor node since the existence of an untrustworthy node introduces adverse impact on the data quality and reliability. It specifically used MDOA for achieving better clustering with balanced trade-off between the rate of exploration and exploitation such that frequent re-clustering is prevented. It effectively prevented malicious nodes with minimized energy consumption and enhanced network lifetime. It also adopted a communication system that supports the sensors in attaining the objective with reduced energy and maximized confidence level during the transmission of full exploration data. The results of MDOACM confirmed an average improvement in network lifetime of 23.18% and 25.16% with respect to different energy levels and density of sensor nodes. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have recently been viewed as the foundation infrastructure that has paved the way for the emergence of the Internet of Things (IoT). However, there is a challenge when integrating WSNs into the IoT due to high-energy consumption in their nodes and poor network lifespan. As a result, the fundamental issues in WSN are sensor node energy scarcity, sensor data exchange, and routing protocols. To address the aforementioned shortcomings, this paper proposes an optimized energy-efficient path-planning approach that increases network lifetime and connectivity. These applications employ large amounts of data and need a data that are more effective sensing and data transfer method. This study presents Improvised DSHR (Dual Step Hybrid Routing) - a methodology for cluster-based routing efficiency. It consists of a two-phase method designed to identify the best route taking clustering into account. In addition, it consists of cluster head selection, optimum route building, node integration with the cluster head, and sensing range optimization. Furthermore, an efficient data transmission. Improvised DSHR is evaluated considering network lifetime, communication overhead, no active nodes, and Route Length evaluated, and then the model is compared to the current Leach protocol to demonstrate its efficiency. [ABSTRACT FROM AUTHOR]

Industrial applications and automation controls are closely connected with heterogeneous Wireless Sensor Network (WSN); thus, Industrial Internet-of Thing (IIoT) enhances the productivity, scalability, and flexibility of the operations. However, in many cases such as tracking a device or service localization becomes very crucial to state back the operations at times. Existing literature show a various localization solutions, but they face the drawbacks of high energy consumption, and non-coherence in heterogeneity. Besides, in a WSN-based IIoT, non-cooperativeness exists among nodes due to various system and environmental parameters, which make the system resource-exhaustive. In this paper, we introduce a novel localization algorithm that addresses the above-mentioned problems. We call our proposed algorithm RAnge-free Power efficient COoperative Localization (RAPCOL). Specifically, the use of co-operative beacon nodes to broadcast the information to Base Station and low energy consumption highlights the novelty of the work. RAPCOL uses weight metrics for selecting optimal cooperative beacon nodes to prolong the network lifetime. We also introduce an Improvised Particle Swarm Optimization (IPSO) that credits in the contribution in RAPCOL. We run a overall nine sets of experiments to analyze the localization accuracy, effect of beacon nodes, sensor nodes, network connectivity, and sensing field, error frequency, residual energy, time, and network lifetime. A comparative study with the existing localization models shows that RAPCOL is 30 % better than the existing models in terms of accuracy and resource consumption. We observe stable performance of RAPCOL with a differentiated effect of beacon nodes and sensor nodes. We also observe that our proposed IPSO 20 % better in fast convergence to the optimal solution. Though RAPCOL localization time is 12 % higher than other existing protocols, RAPCOL's accuracy and energy saving mechanism make it efficient for IIoTs. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) play a crucial role in monitoring and gathering data from remote environments. Maximizing network lifetime is paramount due to constrained sensor node energy. This study addresses the challenge of efficient cluster head selection to prolong network operation. The problem focuses on utilizing the Harris Hawk Optimization (HHO) algorithm for selecting optimal cluster heads in WSNs. HHO mimics the hunting behavior of Harris hawks to iteratively refine the selection process, aiming to minimize energy consumption while maintaining network coverage. The method involves initializing Harris hawks (representing potential cluster heads) within the sensor field, where their movements simulate search and convergence towards optimal locations. Through computational simulations, the effectiveness of HHO is evaluated against traditional methods like LEACH and PSO. Results indicate that HHO outperforms competitors by extending network lifetime up to 30%, with an average reduction in energy consumption by 15%. Specifically, numerical values show an increase in network lifetime from 3000 hours to 3900 hours, while reducing energy consumption from 2000 J/bit to 1700 J/bit. This research underscores the efficacy of HHO in enhancing WSN efficiency through optimized cluster head selection, promising sustainable operation in resource-constrained environments. [ABSTRACT FROM AUTHOR]

This study examines energy consumption and the impact on network lifetime using ANODR and DYMO routing protocols in MANETs with the QualNet simulator. Both ANODR and DYMO are designed for MANETs but employ different approaches for establishing and maintaining routes among mobile nodes. ANODR adapts routing decisions based on neighborhood information, while DYMO operates on-demand, establishing routes only when necessary. To investigate energy consumption and network lifetime, simulations or experiments should be conducted to compare the performance of these protocols under various scenarios. Analyzing these QoS metrics provides insights into the energy efficiency and impact on network lifetime of ANODR and DYMO in MANETs. [ABSTRACT FROM AUTHOR]

Our study aims to develop more energy-efficient mobile communication systems through the exploration of the 6th generation (6G) technology that is expected to be implemented in 2033. We focus on the impact of device-todevice (D2D) communication on power efficiency, which is a crucial need in this domain. To achieve this, we conducted a pioneering experiment using an in-house testbed and K-means clustering to classify locations as D2D enabled or disabled. Our findings show that there is a dynamic clustering mechanism that enables certain nodes to sustain D2D functionality around temporary base stations, resulting in a remarkable 5% improvement in network lifetime per second. This research not only enhances our understanding of 6G networks but also provides a practical methodology for optimizing energy consumption, which holds significant implications for society in advancing sustainable and efficient communication. [ABSTRACT FROM AUTHOR]

Mobile Ad hoc Network (MANET) is an infrastructure-less network of mobile nodes which used in military, rescue operations, disaster relief, and rural areas for communications. To process the data packets, MANET nodes consume a lot of energy during real time data transmission. The conventional protocol selects the random route path in the network. Due to overhead in the random route path the power consumptions may exhaust the energy of nodes which causes the communication delay or may path failure. To address these issues an optimized energy efficient routing protocol could be used which prevents excessive consumption of node energy and enhances the network lifespan. In our research work, a novel nature-inspired Energy Efficient Optimized Sleep Scheduling (EEOSS) protocol is proposed to enhance the effectiveness and increase lifetime of ad hoc network. EEOSS protocol is the hybridization of ad hoc on-demand multi-path distance vector (AOMDV), Ant colony optimization (ACO), Particle swarm optimization (PSO) and sleep scheduling (SS) algorithms. AOMDV selects the multiple paths for data transmission whereas, ACO and PSO are used to identify the optimum route path based on number of nodes, packet size and node speed. To save the energy of nodes the SS algorithm puts nodes into sleep state when nodes are not participating actively in the network. The suggested protocol is compared experimentally with other existing routing protocols and measures performance based on throughput, energy consumption, end-to-end delay (E2ED), and network lifetime. The experiments are simulated on NS 2.35 and the results find that the EEOSS protocol has 12% improvement in throughput, 18% improvement in network lifetime, 12% improvement in E2ED and 9% improvement for energy consumption as compare to other state of art. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) sense and collect information from a desired phenomenon with the help of sensor nodes that have limited computational power, battery, and memory. Several data aggregation approaches are proposed to make the sensor networks energy-efficient, increasing the network's lifetime by controlling data redundancy at aggregator nodes. Redundant data is suppressed before transmission to the sink. In this work, our aim is to enhance the network lifetime by efficiently utilizing the network's energy through controlled data redundancy and minimizing data transmission to the sink. Data aggregation occurs in two steps: firstly, within clusters where the cluster-head serves as the aggregation point, and secondly, at a central point in the network where the gateway node acts as the aggregation point. Experiments demonstrate that our proposed approach yields better results compared to a benchmark clustering protocol in terms of network stability, the number of data packets transferred to the destination, energy dissipation of nodes, and overall network lifetime. [ABSTRACT FROM AUTHOR]

Data aggregation using the shortest path is a challenging issue in wireless sensor networks. Data aggregation slows the communication process and consumes many resources available to sensor nodes. So, while transmitting the aggregated data, it is crucial to identify the shortest and most reliable route for energy-efficient data aggregation from the source to the sink node. However, the nodes closer to the sink node are heavily utilized in multi-hop transmission and succumb to early death, which causes energy imbalance and an energy hole problem in the network. To overcome this problem, we propose an innovative node utilization index-based data routing and aggregation (NUIDRA) protocol. The NUIDRA protocol is designed and implemented in two phases. The first phase is to determine the shortest path from the source node to the sink node based on the amount of bandwidth utilized by each sensor node, minimum hop count, node's residual energy, and data aggregation factor. In the second phase, the selected shortest path is used for data transmission and aggregation using the dynamic selection of the aggregator node. The choice of aggregator node is based on the node's utilization index (UI) and adjacent node count from which it receives the data. The NUIDRA protocol is compared and analysed with I-LEACH and QADA protocols. The extensive simulation results show that in the proposed NUIDRA protocol, there is an increase in the average throughput of 70%, packet delivery ratio by 41.93%, and the average latency is reduced by 58.15% as compared to the I-LEACH protocol. Further, there is an increase in the average throughput of 24%, packet delivery ratio by 7.31%, and the average latency is reduced by 53.23% as compared to the QADA protocol for a data packet size of 512 bytes. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are important for applications like environmental monitoring and industrial automation. However, the limited energy resources of sensor nodes pose a significant challenge to the network’s longevity. Energy imbalances among nodes often result in premature failures and reduced overall network lifespan. Current solutions have not adequately addressed this issue due to network dynamics, varying energy consumption rates, and uneven node distribution. To tackle this, we propose a novel method using Prim’s algorithm to construct minimum spanning trees (MSTs) that enhance energy balance in WSNs. Prim’s algorithm effectively identifies optimal connections among network nodes to minimize energy consumption. Our methodology includes several key steps: network initialization, energy consumption modeling, MST construction using Prim’s algorithm, and optimizing the movement of mobile sink nodes. Extensive experiments with diverse datasets show that our approach significantly improves energy equilibrium, demonstrating high sensitivity and moderate complexity. This research underscores the potential of Prim’s algorithm to extend the lifespan of WSNs and enhance energy efficiency, contributing to sustainable and effective network deployments.

Data compression and optimal path discovery for data communication are vital for enhancing energy efficiency in wireless sensor networks (WSNs), crucial for their sustainability due to limited power resources. This study proposes a strategy that combines data compression and routing to optimize energy in WSNs. It is based on a Bio-Inspired Ant-Cuckoo optimized Relay-based Energy Efficient Data Aggregation (BACREED) algorithm which leverages both Ant Colony Optimization (ACO) and Cuckoo Search (CS) algorithms to enhance communication efficiency between cluster leads (CLs) and the destination node through a forwarding node. Comparative evaluation against Low Energy Adaptive Clustering Hierarchy (LEACH) and its variants, Genetic Algorithm Data Aggregation, Ant optimized using Energy Efficient Data aggregation, and Bio-inspired Ant Cuckoo Energy Efficient Data aggregation demonstrate superior performance in energy efficiency, throughput, and network longevity. This work integrates ‘Fast and Efficient Lossless Adaptive Compression Scheme with Outlier Detection and Replacement (FELACS-ODR) data compression algorithm with CS to improve network performance in terms of energy efficiency and optimal path discovery. Simulation results using MATLAB exhibit a path length reduction of 84 % for the proposed algorithm compared to a 73 % rate for the baseline algorithm with an optimal cluster-lead count between 40 to 80. Energy consumption increases slowly with data compression, significantly outperforming rapid increase scenarios without compression, particularly evident as the node count increases from 1100 and 200 nodes respectively. This research underscores the potential of leveraging FELACS-ODR and CS techniques for substantial enhancements in WSN energy efficiency.

Abstract Internet of Things (IoT)-assisted Wireless Sensor Networks (WSNs) integrate traditional WSNs with the expansive ecosystem of IoT devices. This integration enables sensor nodes (SNs) to connect to the internet, facilitating seamless data exchange, remote monitoring, and real-time control of physical environments. IoT-assisted WSNs are crucial in various fields, including industrial automation, smart cities, healthcare, and environmental monitoring. In these networks, sensor nodes near the base station (BS) are responsible for relaying data to nearby nodes and the BS itself, a process that consumes significant energy. This issue, known as the "hotspot problem," arises when certain nodes deplete their energy faster than others. Unequal clustering techniques address this challenge by distributing the energy load more effectively, allowing nodes with higher energy reserves to take on more tasks while conserving the energy of nodes with lower reserves. This study introduces the Tuna Swarm Algorithm-based Energy Efficient Unequal Clustering Approach (TSA-EEUCA) to enhance the performance of IoT-assisted WSNs. The proposed method aims to improve energy efficiency and extend network lifetime by organizing nodes into clusters of unequal sizes. The core of this approach is the Tuna Swarm Algorithm (TSA), inspired by the cooperative foraging behavior of tuna swarms. Unequal cluster formation and cluster head selection are determined by a fitness function that considers both energy levels and distance metrics. To validate the effectiveness of the proposed method, a series of simulations were conducted. The results showed that the proposed method outperforms existing techniques, offering a more efficient and longer-lasting solution for IoT-assisted WSNs.