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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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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.

Abstract The maximum amount of sensor energy is consumed during broadcasting. Hence in order to make delay with the network lifetime of a wireless sensor network (WSN), there is a need of optimum utilization of sensor energy. Scheduling in media access control (MAC) layer plays a critical role in the designing of WSN to avoid collision and conserve more energy. The Distributed energy aware MAC (DE-MAC) is one such efficient and feasible MAC protocol that addresses the energy management issues in WSN. This work proposes an extension of DE-MAC framework by allocating varying period slots to the nodes based on their residual energy level. Energy of all the nodes is exchanged at regular intervals. Accordingly, the nodes with low energy are allocated less time slots for broadcast and are made to sleep for more time. Simulation is carried out using NS2 (network simulator 2) and the efficiency and performance results are compared with DE-MAC for analysis. Improvement in performance for various parameters such as residual energy, throughput, and packet delivery ratio can be observed.

The maximum amount of sensor energy is consumed during broadcasting. Hence in order to make delay with the network lifetime of a wireless sensor network (WSN), there is a need of optimum utilization of sensor energy. Scheduling in media access control (MAC) layer plays a critical role in the designing of WSN to avoid collision and conserve more energy. The Distributed energy aware MAC (DE-MAC) is one such efficient and feasible MAC protocol that addresses the energy management issues in WSN. This work proposes an extension of DE-MAC framework by allocating varying period slots to the nodes based on their residual energy level. Energy of all the nodes is exchanged at regular intervals. Accordingly, the nodes with low energy are allocated less time slots for broadcast and are made to sleep for more time. Simulation is carried out using NS2 (network simulator 2) and the efficiency and performance results are compared with DE-MAC for analysis. Improvement in performance for various parameters such as residual energy, throughput, and packet delivery ratio can be observed. [ABSTRACT FROM AUTHOR]

An essential concern in the modern period is the growing energy consumption in the Internet of Things (IoT) networks, particularly when connecting resource-constrained devices. Prolonging the network's lifespan under these circumstances poses a significant challenge. Clustering is a prominent strategy to enhance the network's operational duration. However, poor cluster heads (CH) selection rapidly drains energy from network nodes. In multi-hop clustering systems, CH consumes more power by forwarding the data from other CHs and its data, creating "hot spots" in the network. We propose a novel evolutionary multi-objective clustering protocol (EMCP) for heterogeneous IoT networks to prolong the network lifetime. In the initial stage, the genetic algorithm optimizes the CH selection process by considering the residual energy, communication radius, the distance between the edge node and CH, and the distance between CHs as fitness parameters. The distance between IoT nodes and CHs determines the clustering process. The energy monitoring module also keeps track of the CHs' power consumption following each data transmission. If the CH's remaining power drops to a threshold value, the new cluster head is one of the IoT nodes close to the previous cluster head with the highest residual energy. In addition, the simulation is performed with multiple edge servers to solve the hot spot problem. The suggested protocol's performance is compared against state-of-the-art protocols for various parameters, including network stability, network longevity, the number of alive nodes, residual energy, and throughput. According to the simulation findings, the EMCP protocol achieves 15,347, 19,493, 24,187, 41,833 rounds, and 1,901,246 packets, respectively, for metrics such as network stability, half nodes dead, three-quarter node dead, last node dead, and throughput. Similarly, EMCP-MS has 17,565, 25,585, 30,410, 45,567 rounds, and 1,957,942 packets for the same metrics. Thus, the EMCP protocol has 94.55%, 80.54%, and 26.15% more life than the GAOC, DDMPEAwithANUM, and OptGACHE protocols. EMCP-MS has 75.49%, 56.55%, and 26.51% more longevity than multi-sink versions of the above-mentioned state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model. [ABSTRACT FROM AUTHOR]

Quality of Service (QoS) is a crucial aspect of Mobile Ad Hoc Networks (MANET) that needs examination to demonstrate optimal performance. The scientific community is increasingly concerned with the challenge of developing an energy-aware clustering method for MANETs. This is owing to the fact that the battery-operated sensor devices that form the backbone of these wireless networks cannot be recharged. The selection of a cluster's leader is a difficult problem in MANET. Additionally, the research focuses on Optimal Route Selection (ORS) within the MANET context, acknowledging the significance of establishing efficient communication paths between cluster heads and member nodes. Through the integration of a reliability pair factor and node energy considerations, the proposed ORS algorithm generates optimal paths based on maximizing energy efficiency while minimizing the sum of hops between nodes. The study suggests a new algorithm based on the way waterwheel plants move and change their places as they explore and exploit new territory in the quest for food. The suggested method is named Binary Waterwheel Plant Algorithm (BWPA). In this method, a novel model is used to represent both the binary search space and the mapping from continuous to discrete spaces. Particularly, mathematical models of the fitness and cost functions used by the algorithm are constructed. Through the use of a reliability pair factor and node energy, the proposed research paper on Optimal Route Selection (ORS) generates the optimal path between the cluster head and member node and establishes the path based on the maximum energy and sum of hops among the nodes. By fusing the reactive power of differential evolution with the exhaustive search efficiency of the BWPA, the proposed method extends the life of networks. The recommended method increases node lifetime by compounding the dynamic capabilities of discrepancy evolution with the high effectiveness of search. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) have the potential to revolutionize the field of biomedical monitoring. However, the design of Body Area Networks (BANs) faces numerous challenges, such as limited energy resources, efficient sensor node deployment, sensor node miniaturization, collaborative communication, customized application development, and maintaining network security against different attacks that target secrecy, integrity, and network availability. Traditional security mechanisms with high computation and communication overhead are infeasible in these networks due to resource constraints in the sensor nodes. In this work, we propose an energy-efficient framework to mitigate Denial of Sleep (DoS) attacks in WBANs. This framework incorporates three parameters: efficient energy utilization, repetitive event detection, and controlled transmissions. The aim is to increase network lifetime under DoS attacks. The results have shown that the proposed framework increases the network lifetime of Pulsed Medium Access Control (MAC) from 16 minutes to 50 minutes under complete DoS attacks. Furthermore, when 20 percent of the network is under attack, the network lifetime is extended to 6.5 days. Similarly, the network lifetime of the Medical Emergency Body (MEB) MAC has increased from 6 minutes to 35 minutes under severe DoS attacks. When only 20 percent of the network is under attack, the network lifetime is extended to 6 days.

The versatility and diverse applications of IoT-based Heterogeneous WSN (HWSN) technologies make them valuable tools for achieving sustainability goals in Sustainable Smart Cities (SSCs). Proper management of underlying heterogeneous architecture is crucial for the successful operation of smart applications. To prolong the WSN’s lifespan and avoid failures, effective energy management is essential. Although researchers are continually exploring heterogeneity in WSN, it gets more and more important to create cost-effective paradigms that cover multiple facets of SSC while ensuring their stability and reliability. The concept of Dominating Sets (DS) in a graph can be leveraged to minimize resource utilization in WSNs by arranging nodes into disjoint DS, with only one set executing duties at any given time. In this work, we propose a novel technique for IoT-based HWSNs-enabled SSC, called EADDSA, utilizing the DS concept to plan the sleep-and-awake scheme for heterogeneous nodes, based on their resource capabilities. We propose a new algorithm, called the Energy Attentive Algorithm (EAA), to find disjoint DSs that are energy-aware. EAA algorithm attentively tries to form the set that maximizes lifespan while adhering to DS conditions in each iteration. EADDSA further incorporates an effective DS scheduling strategy to enhance the HWSN lifetime by establishing operational guidelines for each round, taking into account the estimated lifetimes and the designated number of working rounds for each DS. This enables efficient allocation of data sensing and gathering tasks across the network minimizes resource usage, and extends network lifetime.

A wireless sensor network (WSN) uses sensor nodes, which have an integrated processor for managing and monitoring the environment in a certain area. They are connected to the base station (BS), which functions as the central processing unit (CPU) of the WSN system. WSN has several difficulties, such as low processing power and a brief network lifespan. To solve the problem of network lifetime, we present a hybrid meta-heuristic (MH) optimization algorithm called the chaotic reptile search algorithm (CRSA) for energy conservation in WSNs. The proposed algorithm is an improvement on the original Reptile Search Algorithm (RSA) by combining the RSA algorithm and a chaotic map. RSA, like other meta-heuristic algorithms, suffers from trapping in local minima. Invoking the chaotic maps in the proposed algorithm can boost diversity and prevent becoming stuck in local minima. In the WSN, selecting the optimal cluster heads (CHs) helps save energy consumption. The proposed CRSA is used for selecting an optimum set of cluster heads amongst the other sensing nodes via the sensing field in a WSN. The experiments have evaluated the proposed algorithm under different conditions against five meta-heuristic algorithms and three versions of the low-energy adaptive clustering hierarchy (LEACH) algorithm. The proposed CRSA has verified the effectiveness of the mentioned algorithms in terms of the total consumed energy, the number of operating nodes, the packet reception by the base station, and the network lifetime. The findings of the experiment indicate that the suggested CRSA is a promising algorithm, and it achieves improvements against all these previously mentioned algorithms.

Wireless sensor networks are a collection of tiny sensor devices powered by energy-scarce batteries. Depending on the nature of the application, those tiny devices might be deployed in areas that make it difficult to recharge or change their battery. Therefore, it is important to optimize the energy consumption of these devices to prolong their lifetime for as long as possible. Clustering is regarded as the most efficient approach to extend the lifetime of wireless sensor networks. This study proposes a novel method integrating cluster head selection and cluster formation to obtain as long a network lifetime as is possible. We employ a linear programming model to resolve the issue of cluster head selection and a Hierarchical Agglomerative Clustering (HAC) method to form clusters in the network. We evaluated HAC on different sets of inputs where we varied the position of the sink from the corner of the network to the center. HAC outperformed other existing clustering techniques for the first node death (FND) and half node death (HND) lifetime definitions in the cases where the sink is at the corner of the network. In the cases where the sink is located at the center of the network, HAC outperformed the other existing techniques for all definitions of network lifetime.

Wireless Sensor Networks (WSNs) are widely studied for their data collection and monitoring capabilities across diverse applications. However, the limited energy resources of sensor nodes present a significant challenge in extending the network's lifespan. To overcome this, we introduce a Deep Learning based Grouping Model Approach (DL-GMA) that optimizes energy usage in WSNs. DL-GMA employs advanced deep learning techniques, particularly Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), to enhance energy efficiency through effective cluster formation, Cluster Head (CH) selection, and CH maintenance. Evaluation using key metrics—Energy Efficiency (88.7 %), Network Stability (90.8 %), Network Scalability (87.1 %), Congestion Level (18.3 %), and Quality of Service (QoS) (93.4 %)—demonstrates the effectiveness of DL-GMA in energy utilization optimization and overall network performance. Incorporating deep learning and intelligent grouping, our approach extends WSN lifespan and improves data transmission efficiency. DL-GMA represents a significant advancement in energy optimization for WSNs, addressing the challenges of limited energy resources and maximizing the network's potential while improving data transmission efficiency.

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.

Enhanced source location privacy and prolonged network lifetime are imperative for WSNs—the skin of IoT. To address these issues, a novel technique named source location privacy with enhanced privacy and network lifetime (SLP-E) is proposed. It employs a reverse random walk followed by a walk on annular rings, to create divergent routing paths in the network, and finally, min-hop routing together with the walk on dynamic rings to send the packets to the base station (BS). The existing random walk-based SLP approaches have either focused on enhancing only privacy at the cost of network lifetime (NLT) or have aimed at improving the amount of privacy without degrading the network lifetime performance. Unlike these schemes, the objectives of the proposed work are to simultaneously improve the safety period and network lifetime along with achieving uniform privacy. This combination of improvements has not been considered so far in a single SLP random walk-based scheme. Additionally, this study investigates for the first time the impact of the sensors' radio range on both privacy strength and network lifetime metrics in the context of SLP within WSNs. The performance measurements conducted using the proposed analytical models and the simulation results indicate an improvement in the safety period and network lifespan. The safety period in SLP-E increased by 26.5%, 97%, 123%, and 15.7% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. Similarly, the network lifetime of SLP-E increased by 17.36%, 0.2%, 83.41%, and 13.42% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. No matter where a source node is located within a network, the SLP-E provides uniform and improved privacy and network lifetime. Further, the simulation results demonstrate that the sensors' radio range has an impact on the safety period, capture ratio, and the network lifetime. [ABSTRACT FROM AUTHOR]

Energy constraint has become the major challenge for designing wireless sensor networks. Network lifetime is considered as the most substantial metric in these networks. Routing technique is one of the best choices for maintaining network lifetime. This paper demonstrates implementation of new methodology of routing in WSN using firefly swarm intelligence. Energy consumption is the dominant issue in wireless sensor networks routing. For network cutoff avoidance while maximize net lifetime energy exhaustion must be balanced. Balancing energy consumption is the key feature for rising nets lifetime of WSNs. This routing technique involves determination of optimal route from node toward sink to make energy exhaustion balance in network and in the same time maximize network throughput and lifetime. The proposed technique show that it is better than other some routing techniques like Dijkstra routing, Fuzzy routing, and ant colony (ACO) routing technique. Results demonstrate that the proposed routing technique has beat the three routing techniques in throughput and extend net lifetime. [ABSTRACT FROM AUTHOR]

With the development of Internet-of-Things (IoT) technology, industries such as smart agriculture, smart health, smart buildings, and smart cities are attracting attention. As a core wireless communication technology, Bluetooth Low Energy (BLE) is gaining a lot of interest as a highly reliable low-power communication technology. In particular, BLE enables a connectionless mesh network that propagates data in a flooding manner using advertising channels. In this paper, we aim to optimize the energy consumption of the network by minimizing the scanning time while preserving the reliability of the network. Maximizing network lifetime requires various optimizing algorithms, including exhaustive searching and gradient descent searching. However, they are involved with excessive computational complexity and high implementation costs. To reduce computational complexity of network optimization, we mathematically model the energy consumption of BLE networks and formulate maximizing network lifetime as an optimization problem. We first present an analytical approach to solve the optimization problem and show that finding the minima from the complicated objective function of the optimization problem does not guarantee a valid solution to the problem. As a low-complexity solution, we approximate the complicated objective function into a convex form and derive a closed-form expression of the suboptimal solution. Our simulation results show that the proposed suboptimal solution provides almost equivalent performance compared to the optimal solution in terms of network lifetime. With very low computational complexity, the proposed suboptimal solution can extensively reduce implementation costs. [ABSTRACT FROM AUTHOR]

One of the major significant problems in the existing techniques in Wireless Sensor Networks (WSNs) is Energy Efficiency (EE) because sensor nodes are battery-powered devices. The energy-efficient data transmission and routing to the sink are critical challenges because WSNs have inherent resource limitations. On the other hand, the clustering process is a crucial strategy that can rapidly increase network lifetime. As a result, WSNs require an energy-efficient routing strategy with optimum route election. These issues are overcome by using Tasmanian Fully Recurrent Deep Learning Network with Pelican Variable Marine Predators Algorithm for Data Aggregation and Cluster-Based Routing in WSN (TFR-DLN-PMPOA-WSN) which is proposed to expand the network lifetime. Initially, Tasmanian Fully Recurrent Deep Learning Network (TFR-DLN) is proposed to elect the Optimal Cluster Head (OCH). After OCH selection, the three parameters, trust, connectivity, and QoS, are optimized for secure routing with the help of the Pelican Variable Marine Predators Optimization Algorithm (PMPOA). Finally, the proposed method finds the minimum distance among the nodes and selects the best routing to increase energy efficiency. The proposed approach will be activated in MATLAB. The efficacy of the TFR-DLN- PMPOA-WSN approach is assessed in terms of several performances. It achieves higher throughput, higher packet delivery ratio, higher detection rate, lower delay, lower energy utilization, and higher network lifespan than the existing methods. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs), which are facilitated by the internet of things (IoTs), can be difficult to improve the lifespan of the network target area. Although the hotspot issue (i.e., the cluster head closest to the base station fails quickly) is mitigated by the clustered-based routing technique, it still has an important effect on the network's lifespan and target area. However, improper distribution of load between cluster heads has been shown to negatively impact network lifespan efficiency, so even though unequal clusters have been utilized successfully to tackle the hotspot issue, further work is needed. This study provides an energy-aware reliable routing (EARR) model for resolving the hotspot as well as load balancing issues simultaneously. To extend the lifespan of the network, the EARR model effectively minimizes energy consumption by the cluster heads using enhanced multi-objective optimization parameters. Further, EARR provides improved routing optimization metrics to improve data delivery with energy efficiency, less delay, and packet loss. The results of the experiments demonstrate that the EARR model provides excellent throughput and lifespan efficiency with low delay and communication overhead. [ABSTRACT FROM AUTHOR]

Designing effective clustering algorithms plays a very important role in improving network lifetime target coverage (NLTC) in heterogeneous wireless sensor networks (HWSNs). However, the current clustering mechanism failed to address hotspot problem resulting in poor coverage and network lifetime performance. Recently, unequal clustering has been used for addressing hotspot problem in HWSNs. However, when performing inter-cluster routing, load balancing is not efficient and the energy degradation is high, which affects the overall network performance. This work introduces an effective model namely adaptive traffic aware clustering and routing (ATACR) model to address hotspot and load balancing issues. The ATACR introduces a novel unequal clustering and ideal distribution of load in different cluster levels. The ATACR model improves network lifetime, reduces control channel overhead, and attains better throughput in comparison with existing routing models. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are widely studied for their data collection and monitoring capabilities across diverse applications. However, the limited energy resources of sensor nodes present a significant challenge in extending the network's lifespan. To overcome this, we introduce a Deep Learning based Grouping Model Approach (DL-GMA) that optimizes energy usage in WSNs. DL-GMA employs advanced deep learning techniques, particularly Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), to enhance energy efficiency through effective cluster formation, Cluster Head (CH) selection, and CH maintenance. Evaluation using key metrics—Energy Efficiency (88.7 %), Network Stability (90.8 %), Network Scalability (87.1 %), Congestion Level (18.3 %), and Quality of Service (QoS) (93.4 %)—demonstrates the effectiveness of DL-GMA in energy utilization optimization and overall network performance. Incorporating deep learning and intelligent grouping, our approach extends WSN lifespan and improves data transmission efficiency. DL-GMA represents a significant advancement in energy optimization for WSNs, addressing the challenges of limited energy resources and maximizing the network's potential while improving data transmission efficiency. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has transformed human life by improving quality of life and revolutionizing all business sectors. The sensor nodes in IoT are interconnected to ensure data transfer to the sink node over the network. Owing to limited battery power, the energy in the nodes is conserved with the help of the clustering technique in IoT. Cluster head (CH) selection is essential for extending network lifetime and throughput in clustering. In recent years, many existing optimization algorithms have been adapted to select the optimal CH to improve energy usage in network nodes. Hence, improper CH selection approaches require more extended convergence and drain sensor batteries quickly. To solve this problem, this paper proposed a coati optimization algorithm (EACH-COA) to improve network longevity and throughput by evaluating the fitness function over the residual energy (RER) and distance constraints. The proposed EACH-COA simulation was conducted in MATLAB 2019a. The potency of the EACH-COA approach was compared with those of the energy-efficient rabbit optimization algorithm (EECHS-ARO), improved sparrow optimization technique (EECHS-ISSADE), and hybrid sea lion algorithm (PDU-SLno). The proposed EACH-COA improved the network lifetime by 8–15% and throughput by 5–10%. [ABSTRACT FROM AUTHOR]

This research work presents an algorithm for extending network lifetime in multi-hop wireless sensor networks (WSN). WSNs face energy gap issues around sink nodes due to the transmission of large amounts of data through nearby sensor nodes. The limited power supply to the nodes limits the lifetime of the network, which makes energy efficiency crucial. Multi-hop communication has been proposed as an efficient strategy, but its power consumption remains a research challenge. In this study, an algorithm is developed to mitigate energy holes around the sink nodes by using a modified ultra-low-power wake-up receiver and an energy scheduling technique. Efficient power scheduling reduces the power consumption of the relay node, and when the residual power of the sensor node falls below a defined threshold, the power emitters charge the nodes to eliminate energy-hole problems. The modified wake-up receiver improves sensor sensitivity while staying within the micro-power budget. This study's simulations showed that the developed RF energy harvesting algorithm outperformed previous work, achieving a 30% improvement in average charged energy (AEC), a 0.41% improvement in average energy (AEH), an 8.39% improvement in the number of energy transmitters, an 8.59% improvement in throughput, and a 0.19 decrease in outage probability compared to the existing network lifetime enhancement of multi-hop wireless sensor networks by RF Energy Harvesting algorithm. Overall, the enhanced power efficiency technique significantly improves the performance of WSNs.

Among the different challenges related to the improvement of wireless sensor network (WSN) performance, the optimization of energy consumption takes an important place. Basically, sensor nodes in WSN are powered by limited battery. These batteries can allow sensor nodes to be functional for a limited period of time then sensor nodes turn out to be dead. Enhancing energy consumption and finding new manners to save sensor nodes' energy is becoming essential and more challenging. Improving energy consumption for each sensor nodes leads to enhance the lifetime of WSN. Clustering is among the outstanding strategies to reach this goal. In this paper, a new scheme of routing protocol in WSN will be presented in order to minimize the energy consumption and improve WSN lifetime. The new scheme is proposed in order to find the best selection of cluster head (CH) by taking in consideration the energy at each sensor node. It has been proposed based on Firefly Algorithm. We have compared the proposed scheme with LEACH, EAMMH, SEP, E-SEP, BRE, NEAHC and WEB protocol. Experimental results show that the proposed scheme yields better performance than the compared routing protocols in terms of better energy consumption and packet delivery between sensor nodes and base station. Results obviously prove that the proposed scheme could improve the WSN lifetime.

While sending data from a submerged wireless sensor network, more power is necessary due to the additional distance the signal must travel as well as the higher amount of energy lost along the route. Also, the acoustic medium generated by the shadow and the acoustic animals alters the transmission between the source and destination nodes. Multipath fading, significant path loss, and various interferences are some of the difficulties that come with communication at larger depths. The connection strength was variable, there was a low bandwidth, a high salt content, high pressure, high temperature, a sinkhole, and the route curved. These variables influence a variety of network aspects, including energy efficiency, link quality, and packet delivery ratio (PDR). The Energy Balanced Reliable and Effective Clustering (EBREC) method was created to ensure that data packets reach at their destination intact. This was done in order to avoid the complications caused by these considerations. PDR and transmission loss reduction are other targets of the presented effort. The anticipated EBREC’s performance is assessed by comparing it to many already defined benchmarks. According to the evaluation report, the simulation results reveal that EBREC performs very well when compared to the other methods in terms of Residual Energy of 0.615 J, Consumed Energy of 2.3 mJ, Throughput of 10Mbps, PDR of 97.6 %, Network Lifetime of 1750 sec, Number of Alive Nodes of 91%. Hence, the simulation results reveal that the EBREC technique beats competing approaches.

Environmental wireless sensor networks (EWSNs) are essential in environmental monitoring and are widely used in gas monitoring, soil monitoring, natural disaster early warning and other fields. EWSNs are limited by the sensor battery capacity and data collection range, and the usual deployment method is to deploy many sensor nodes in the monitoring zone. This deployment method improves the robustness of EWSNs, but introduces many redundant nodes, resulting in a problem of duty cycle design, which can be effectively solved by duty cycle optimization. However, the duty cycle optimization in EWSNs is an NP-Hard problem, and the complexity of the problem increases exponentially with the number of sensor nodes. In this way, non-heuristic algorithms often fail to obtain a deployment solution that meets the requirements in reasonable time. Therefore, this paper proposes a novel heuristic algorithm, the Quantum Evolutionary Golden Jackal Optimization Algorithm (QEGJOA), to solve the duty cycle optimization problem. Specifically, QEGJOA can effectively prolong the lifetime of EWSNs by duty cycle optimization and can quickly get a deployment solution in the face of multi-sensor nodes. New quantum exploration and exploitation operators are designed, which greatly improves the global search ability of the algorithm and enables the algorithm to effectively solve the problem of excessive complexity in duty cycle optimization. In addition, this paper designs a new sensor duty cycle model, which has the advantages of high accuracy and low complexity. The simulation shows that the QEGJOA proposed in this paper improves by 18.69, 20.15 and 26.55 compared to the Golden Jackal Optimization (GJO), Whale Optimization Algorithm (WOA) and the Simulated Annealing Algorithm (SA).

Nowadays, the number of Internet of things (IoT) devices has rapidly increased due to their increasing use in different real-world applications. The sensor devices represent the basic element of the IoT network because they gather data from various environments and situations, while the sink node serves as the network's brain because it processes the data and makes decisions. However, the large amount of data that the sensor devices gather and send to the gateway toward the sink, on the one hand, causes the sensor's limited energy to be depleted and, on the other hand, makes it more difficult to achieve the decisions using these data at the sink. Therefore, before sending data to the gateway, it is important to get rid of any duplicate data while maintaining a high level of data quality. In this paper, a distributed prediction–compression-based mechanism (DiPCoM) for saving power in IoT networks is suggested. DiPCoM makes periodic decisions on sending the data to the gateway. It uses the autoregressive integrated moving average prediction method in each period to predict the next period's data and decide whether the current data should be sent to the gateway. When the decision is made to send the data to the gateway, an effective compression approach is used by DiPCoM to get rid of the duplicate data. It combines different data transmission reduction techniques such as adaptive piecewise constant approximation, differential encoding, symbolic aggregate approximation, and Lempel–Ziv–Welch. Simulation results based on real-world data show that the DiPCoM method is better than other techniques in terms of energy consumption, data reduction ratio, transferred data size, and data accuracy. [ABSTRACT FROM AUTHOR]

Among the different challenges related to the improvement of wireless sensor network (WSN) performance, the optimization of energy consumption takes an important place. Basically, sensor nodes in WSN are powered by limited battery. These batteries can allow sensor nodes to be functional for a limited period of time then sensor nodes turn out to be dead. Enhancing energy consumption and finding new manners to save sensor nodes' energy is becoming essential and more challenging. Improving energy consumption for each sensor nodes leads to enhance the lifetime of WSN. Clustering is among the outstanding strategies to reach this goal. In this paper, a new scheme of routing protocol in WSN will be presented in order to minimize the energy consumption and improve WSN lifetime. The new scheme is proposed in order to find the best selection of cluster head (CH) by taking in consideration the energy at each sensor node. It has been proposed based on Firefly Algorithm. We have compared the proposed scheme with LEACH, EAMMH, SEP, E-SEP, BRE, NEAHC and WEB protocol. Experimental results show that the proposed scheme yields better performance than the compared routing protocols in terms of better energy consumption and packet delivery between sensor nodes and base station. Results obviously prove that the proposed scheme could improve the WSN lifetime. • A new scheme of routing protocol has been proposed in order to optimize the energy consumption by sensor nodes in WSN. • The proposed scheme has been proposed based on Fireflies optimization algorithm. • Results of simulation showed the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]