Network Lifetime Research Articles

Energy-efficient routing protocol for WSN based on relay node selection and A-star algorithm

Due to miniaturization constraints and manufacturing costs, wireless sensor networks (WSN) sensor nodes are generally outfitted with restricted resources concerning computing capability, energy, data storage capacity, and data delivery rate. These constraints encourage a significant portion of the research issues in WSNs, particularly the energy constraint, which is a fundamental issue. This paper suggests an innovative routing protocol that optimizes energy use called RNS_A-star, which combines a routing algorithm using a relay node and an improved heuristic for the A-star algorithm. The RNS_A-star protocol first applies a data routing algorithm using a relay node, which allows either using a relay node or simply sending the intra-cluster data to the CH. Then, the A-star algorithm is used to construct a multi-hop routing route between the CHs and the base station (BS) in order to find the best path for reducing energy consumption during data transmission. Simulation results show that the suggested RNS_A-star protocol performs better than ECO-BAT, LEACH-IACA, and LEACH protocols regarding energy conservation and extending network lifetime.

Optimise Energy Consumption of Wireless Sensor Networks by using modified Ant Colony Optimization

Routing represents a pivotal concern in the context of Wireless Sensor Networks (WSN) owing to its divergence from traditional network routing paradigms. The inherent dynamism of the WSN environment, coupled with the scarcity of available resources, engenders considerable challenges for industry and academia alike in devising efficient routing strategies. Addressing these challenges, a viable recourse is applying heuristic search methodologies to ascertain the best path in WSNs. Ant Colony Optimization (ACO) is a well-established heuristic algorithm that has demonstrated notable advancements in routing contexts. This paper presents an altered routing protocol that is based on ant colony optimization. In this protocol, we incorporate the inverse of the distance between nodes and their neighbours in the probability equations of ACO, along with considering pheromone levels and residual energy. These formulation modifications facilitate the selection of the most suitable candidate for the subsequent hop, effectively minimising the average energy consumption across all nodes in each iteration. Furthermore, in this protocol, we iteratively fine-tune ACO's parameter values based on the outcomes of several experimental trials. The experimental analysis is conducted through a diverse set of network topologies, and the results are compared against well-established ACO algorithms and routing protocols. The efficacy of the proposed protocol is assessed based on various performance metrics, encompassing throughput, energy consumption, network lifetime, energy consumption, the extent of data transferred over the network, and the length of paths traversed by packets. These metrics collectively provide a comprehensive evaluation of the performance attainments of the routing protocols.

Retraction Note: Enhancement of network lifetime using fuzzy clustering and multidirectional routing for wireless sensor networks

Retraction Note: Enhancement of network lifetime using fuzzy clustering and multidirectional routing for wireless sensor networks

ATENA: Adaptive TEchniques for Network Area Coverage and Routing in IoT-Based Edge Computing

The Internet of Things (IoT) and Edge Computing (EC) are now pervasive. IoT networks are made up of several objects, deployed in an area of interest (AoI), that can communicate with each other and with a remote computing centre for decision-making. EC reduces latency and data congestion by bringing data processing closer to the source. In this paper, we address the problems of network coverage and data collection in IoT-based EC networks. Several solutions exist designed to solve these problems unfortunately, they are either not energy-efficient or do not consider connectivity and they do not cover AoI. The proposed routing mechanisms are often not suited for AoI coverage schemes and lead to poor data routing delay or high packet losses. To address these shortcomings, we propose ATENA, a periodic, lightweight and energy-efficient protocol that aims to improve network coverage based on the two new schemes used to define a few number of objects to be kept awake at each period it also uses an adaptive routing scheme to send the collected data to the computing centre. This protocol is designed to take into account the limited resources of objects and ensures a longer network lifetime. A comparison of ATENA’s simulation results with recent existing protocols shows that it significantly improves network coverage, network lifetime and end-to-end delay to the computing centre.

Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks

Wireless sensor networks are used to monitor the environment, to detect anomalies or any other problems and risks in the system. If used in the transportation network, they can monitor traffic and detect traffic risks. In wireless sensor networks, energy constraints must be handled to enable continuous environmental monitoring and surveillance data gathering and communication. Energy-aware path planning of autonomous ground vehicle charging for sensor nodes can solve energy and battery replacement problems. This paper uses the Nearest Neighbour algorithm for the energy-aware path planning problem with an autonomous ground vehicle. Path planning simulations show that the Nearest Neighbour algorithm converges faster and produces a better solution than the genetic algorithm. We offer robust and energy-efficient path planning algorithms to swiftly collect sensor data with less energy, allowing the monitoring system to respond faster to anomalies. Positioning communicating sensors closer minimizes their energy usage and improves the network lifetime. This study also considers the scenario in which it is recommended to avoid taking direct travelling pathways between particular node pairs for a variety of different reasons. To address this more challenging scenario, we provide an Obstacle-Avoided Nearest Neighbour-based approach that has been adapted from the Nearest Neighbour approach. Within the context of this technique, the direct paths that connect the nodes are restricted. Even in this case, the Obstacle-Avoided Nearest Neighbour-based approach achieves almost the same performance as the the Neighbour-based approach.

Mobility and Temperature Sensitive Energy Efficient Routing (MTS-EER) protocol in a wireless body area network

Sensor technology advancements have provided the platform to implement wireless body area networks, thanks to the nanosized sensor units capable of sensing, aggregating, and forwarding physiological information. The collected information is routed to the desired destination unit for data analysis and decision-making in remote healthcare. However, improving energy utilization remains a brain-teasing problem for the research community, especially considering imbalanced energy consumption and postural movements. Mobility contributes to disconnectivity issues, high energy drainage, and retransmission delays. In addition, the sensor node’s thermal level also poses a challenge in maintaining safer and reliable data transmission. To overcome these issues, a novel Mobility and Temperature Sensitive Energy-Efficient Routing ¬(MTS-EER) algorithm has been proposed that includes a two-step process. In step 1, an Intelligent Path Estimation Function (IPEF) is designed considering the sensor’s mobility, temperature, and energy level. IPEF depends on crucial parameters i.e.: - residual energy, signal-to-noise ratio (SNR), distance, total energy, and most importantly temperature of the sensor unit. The sensor node with the least IPEF is selected as the Cluster Head (CH). In step 2, an optimized and sustainable energy conservation model (OSECM) is implemented based on the Adaptive Transmission Power (ATP) and the Power Management Module (PMM). The ATP conserves the energy via intelligently varying the transmission power and PMM manages the sleep pattern of sensor nodes to yield a high network lifetime and efficient energy utilization. The algorithm includes a clustering approach with dual sink nodes to conserve energy and improve reliability. Finally, the results are compared with the recent state-of-the-art research work. The proposed algorithm provides better results considering residual energy, throughput, network lifetime, and end-to-end delay.

A survey on network lifetime maximization using data aggregation trees

A survey on network lifetime maximization using data aggregation trees

Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks

Researchers are currently exploring the anticipated sixth-generation (6G) wireless communication network, poised to deliver minimal latency, reduced power consumption, extensive coverage, high-level security, cost-effectiveness, and sustainability. Quality of Service (QoS) improvements can be attained through effective resource management facilitated by Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper proposes two models for enhancing QoS through efficient and sustainable resource allocation and optimization of base stations. The first model, a Hybrid Quantum Deep Learning approach, incorporates Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). CNNs handle resource allocation, network reconfiguration, and slice aggregation tasks, while RNNs are employed for functions like load balancing and error detection. The second model introduces a novel neural network named the Base Station Optimizer net. This network includes various parameters as input and output information about the condition of the base station within the network. Node coverage, number of users, node count and user locations, operating frequency, etc., are different parametric inputs considered for evaluation, providing a binary decision (ON or SLEEP) for each base station. A dynamic allocation strategy aims for network lifetime maximization, ensuring sustainable operations and power consumption are minimized across the network by 2 dB. The QoS performance of the Hybrid Quantum Deep Learning model is evaluated for many devices based on slice characteristics and congestion scenarios to attain an impressive overall accuracy of 98%.

A novel differentiated coverage-based lifetime metric for wireless sensor networks

This paper delves into optimizing network lifetime (NL) subject to connected-coverage requirement, a pivotal issue for realistic wireless sensor network (WSN) design. A key challenge in designing WSNs consisting of energy-limited sensors is maximizing NL, the time a network remains functional by providing the desired service quality. To this end, we introduce a novel NL metric addressing target-specific coverage requirements that remedies the shortcomings imposed by conventional definitions like first node die (FND) and last node die (LND). In this context, while we want targets to be sensed by multiple sensors for a portion of the network lifetime, we let the periods, during which cells are monitored by at least one sensor, vary. We also allow the ratios of multiple and single tracking times to differ depending on the target and incorporate target-based prioritization in coverage. Moreover, we address role assignment to sensors and propose a selective target-sensor assignment strategy. As such, we aim to reduce redundant data transmissions and hence overall energy consumption in WSNs. We first propose a unique 0-1 mixed integer programming (MIP) model, to analyze the impact of our proposal on optimal WSN performance, precisely. Next, we present comprehensive comparative studies of WSN performance for alternative NL metrics regarding different coverage requirements and priorities across a wide range of parameters. Our test results reveal that by utilizing our novel NL metric total coverage time can be improved significantly, while facilitating more reliable sensing of the target region.

Adaptive quorum based scheduling and interference-free routing for edge enabled UAV assisted software-define WSN using AI

Wireless Sensor Networks (WSN) faces numerous problems. The deployment of the sensor nodes in faraway places makes battery replacement a difficult process. As a result, there are energy constraints and security issues. So, the fusion of SDN with WSN is SDWSN, which flexibly brings network management. The main issue here is controller placement, which has an impact on communication dependability, latency, and throughput. The major drawbacks in existing work include high energy consumption, inefficient data collection, increased response time, and poor packet delivery ratio. This paper presents a unique Artificial Intelligence-based method for adaptive quorum-based scheduling and interference-free routing in edge-enabled UAV-assisted Software-Defined Wireless Sensor Networks (SDWSNs). It combines energy-efficient clustering with E-DBSCAN, multiple attribute-based software node authentication, and interference-free routing with the IDEE algorithm. The NS-3 tool was utilized to conduct simulations using a setup consisting of 4 edge-assisted UAVs, 4 SDN controllers, and 100 wireless sensor nodes. The findings show considerable improvements in different performance measures when compared to previous techniques. In particular, this suggested approach reduces latency by 64.77 % when compared to DGRL and 66.30 % when compared to ESRA. Additionally, it uses less energy than DGRL and ESRA by 45.74 % and 51.89 %, respectively. In comparison to DGRL and SRA, the packet delivery ratio is enhanced by 27.27 % and 32.43 %, respectively. Furthermore, the Node Network Lifetime is increased by 24.29 % in comparison to ESRA and 12.99 % in comparison to DGRL. Additionally, our method increases throughput by 22.08 % compared to DGRL and 30.56 % compared to SRA, while reducing the Controller Response Time by 2.39 % compared to ESRA and 6.46 % compared to DGRL. These findings demonstrate how well our method improves the effectiveness and efficiency of SDWSNs, making it a viable option for practical use.

An efficient hybrid bat sand cat swarm optimization‐based node localization for data quality improvement in wireless sensor networks

SummaryNode localization in wireless sensor networks (WSNs) ensures that the collected data is contextually accurate, enabling effective monitoring and management of various applications. Recently, there has been a surge in research focused on addressing node localization within WSNs. Emerging trends in this field involve the application of metaheuristic optimization techniques to refine node location determination accuracy. However, existing techniques often struggle with balancing accuracy, energy consumption, network lifetime, and computational efficiency, particularly in challenging WSN environments. Therefore, this research introduces a novel approach called efficient hybrid bat sand cat swarm optimization (EHBSCSO) to address node localization within WSNs. The hybrid method leverages the exploration capabilities of the bat optimization algorithm and the exploitation strengths of the sand cat swarm optimization algorithm. This combination allows for efficient determination of node positions, significantly improving localization accuracy while minimizing energy consumption. The EHBSCSO utilizes the received signal strength indicator (RSSI) and time of flight (ToF) approaches to assess distances among nodes accurately. Accurate node localization directly improves data quality by ensuring spatially precise data collection, reducing communication overhead, and enhancing the overall reliability of the collected data. Compared to conventional methods, the proposed EHBSCSO algorithm demonstrates superior performance, with a mean localization error of 0.18%, energy consumption of 7.2 J, computational time of 8.9 s, and localization time of 0.19 s. These metrics underscore its efficiency and precision. The research indicates that EHBSCSO not only optimizes localization accuracy but also contributes to energy efficiency and faster computational times, addressing key challenges in WSN node localization.

Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios.

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10-20% compared to the benchmark algorithms.

Retraction Note: An evolutionary multipath energy-efficient routing protocol (EMEER) for network lifetime enhancement in wireless sensor networks

Retraction Note: An evolutionary multipath energy-efficient routing protocol (EMEER) for network lifetime enhancement in wireless sensor networks

Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

Energy efficient data communication for WSN based resource constrained IoT devices

In the Internet of Things (IoTs) and wireless sensor networks (WSNs), improving security and energy efficiency are key concerns. Clustering, which involves managing cluster heads, plays a pivotal role in extending network lifetime. The selection of a cluster head, responsible for data transfer between nodes, is a key aspect of network management. This paper proposes two variants of a novel algorithm designed for energy efficient communication in a resource constrained IoT environments. One variant considers remaining energy, distance, and node degree for cluster head selection, while the other focuses on remaining energy and distance only. Including node degree ensures cluster heads do not waste energy by remaining idle or performing unnecessary tasks such as the cluster head selection process in every round. The authors tested these variants against several well known algorithms using MATLAB simulation environment, evaluating factors such as operating nodes, number of clusters, transmission energy, and remaining energy. The proposed algorithm extends network lifetime by maintaining more operating nodes for longer, not changing clusters or cluster heads frequently, minimizing energy consumption for transmission, and conserving more remaining energy. Consequently, the proposed algorithm outperforms existing approaches by addressing issues like zero cluster head selection, compulsory cluster head selection in every round, avoiding cluster heads that connect to no nodes, and preventing network destabilization due to unnecessary re-elections.

ECMSH: An Energy-efficient and Cost-effective data harvesting protocol for Mobile Sink-based Heterogeneous WSNs using PSO-TVAC

Efficient energy consumption is crucial in Wireless Sensor Networks (WSNs). Uncontrolled energy usage can lead to the hotspot issue, hindering network lifetime and successful packet delivery. Sink mobility has been suggested as a solution, but it comes with challenges such as high data gathering delay and poor packet reception. These problems stem from the short contact time of nodes with the Mobile Sink (MS). To tackle these issues, we present an MS-based heterogeneous WSN with super and normal nodes. Most previous studies only considered the energy heterogeneity of sensors. These methods also suffered from different issues such as fixed MS tours, including inappropriate criteria in cluster construction, proposing greedy schemes, and employing basic metaheuristic algorithms. In our proposed model, super nodes are richer in initial energy and transmission range than normal sensors. In each round, the nodes are organized into clusters, and the MS visits the Cluster Heads (CHs) to gather data packets. Super nodes, owing to their elevated initial energy, are more adept at executing energy-sensitive tasks compared to normal sensors. Additionally, as CH, super nodes extend the contact time with the MS due to their longer transmission range, delivering more packets. The clusters are constructed using a variant of Particle Swarm Optimization (PSO), namely PSO-TVAC. We empower this method with effective initialization and decoding methods. Furthermore, we propose a heuristic intra-cluster multi-hop routing algorithm to enhance network lifetime. Our other contribution is to propose an efficient algorithm to determine the time to reconfigure the network, while the other algorithms mainly reconfigure the WSN periodically. Simulation results demonstrate superior performance compared to state-of-the-art algorithms, showcasing lower energy consumption, higher energy efficiency, higher lifetime, reduced packet delivery delay, and higher number of received packets by 30%, 38.2%, four times, 20.6%, and 22.6%, respectively.

Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks

Clustering and routing protocols play a pivotal role in reducing energy consumption and extending the lifespan of wireless sensor networks. However, optimizing energy efficiency to maximize network longevity remains a primary challenge for these protocols. This paper introduces QPSOFL, a clustering and routing protocol that integrates quantum particle swarm optimization and a fuzzy logic system to enhance energy efficiency and prolong network lifespan. QPSOFL employs an enhanced quantum particle swarm optimization algorithm to select optimal cluster heads, utilizing Sobol sequences for population diversification during initialization. Additionally, it incorporates Lévy flight and Gaussian perturbation-based position updates to prevent trapping in local optima. Benchmark experiments validate QPSOFL's efficacy compared to Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (QPSO), focusing on accuracy, search capability, and convergence speed. Within QPSOFL, a fuzzy logic system determines the best next-hop cluster head based on descriptors such as residual energy, energy deviation, and relay distance. Extensive simulations compare QPSOFL's performance in terms of network lifetime, throughput, energy consumption, and scalability against existing protocols E-FUCA, IHHO-F, F-GWO, and FLPSOC, demonstrating its superior performance over these counterparts.

A Fuzzy Logic-Based Directional Charging Scheme for Wireless Rechargeable Sensor Networks.

Wireless Power Transfer (WPT) has become a key technology to extend network lifetime in Wireless Rechargeable Sensor Networks (WRSNs). The traditional omnidirectional recharging method has a wider range of energy radiation, but it inevitably results in more energy waste. By contrast, the directional recharging mode enables most of the energy to be focused in a predetermined direction that achieves higher recharging efficiency. However, the MC (Mobile Charger) in this mode can only supply energy to a few nodes in each direction. Thus, how to set the location of staying points of the MC, its service sequence and its charging orientation are all important issues related to the benefit of energy replenishment. To address these problems, we propose a Fuzzy Logic-based Directional Charging (FLDC) scheme for Wireless Rechargeable Sensor Networks. Firstly, the network is divided into adjacent regular hexagonal grids which are exactly the charging regions for the MC. Then, with the help of a double-layer fuzzy logic system, a priority of nodes and grids is obtained that dynamically determines the trajectory of the MC during each round of service, i.e., the charging sequence. Next, the location of the MC's staying points is optimized to minimize the sum of charging distances between MC and nodes in the same grid. Finally, the discretized charging directions of the MC at each staying point are adjusted to further improve the charging efficiency. Simulation results show that FLDC performs well in both the charging benefit of nodes and the energy efficiency of the MC.

A mobile data collection method for balancing energy consumption and delay in strip-shaped wireless sensor networks with branches

Strip-shaped Wireless Sensor Networks (WSNs) with branches are commonly used in various long and narrow applications, such as mines, factories, subways, and pipelines, and they face serious energy hole problems caused by multi-hop communication. The Mobile Data Collector (MDC) can alleviate the energy hole problem. Current solutions have two limitations: one is the balance between energy consumption and delay, and the other is the overly ideal network model, e.g., the square region or circular area. This paper focuses on strip-shaped networks and proposes a novel mobile data collection method to find a trade-off between energy preservation and data delivery delay. Firstly, the MDC path is planned by solving the diameter of the tree in the network, resulting in reduced delay. Secondly, the network energy consumption is further reduced by clustering and optimal transmission distance adjustment. Then, a network lifetime balancing mechanism is designed to balance network energy between backbone and branches. Finally, the performance of the algorithm proposed in this paper has been studied in four types of strip-shaped WSNs and compared with four existing MDC methods with evaluation metrics of maximum node energy consumption, network delay and weighted sum of both. The simulation results demonstrate that the proposed algorithm is applicable to different types of strip-shaped WSNs with branches and achieves excellent network performance, which can effectively balance network energy consumption and data collection delay.

Energy efficient routing protocol for enhancing the network lifetime in wireless sensor network

Wireless sensor networks (WSNs) confront significant challenges related to battery capacity, as sensor nodes operate on limited energy resources. To address this issue, low energy adaptive clustering hierarchy (LEACH) protocol is commonly employed for power management in WSNs. LEACH is commonly used for power management. Here, sensing region is divided into clusters and sectors, placing a gateway node at the center to minimize energy consumption during data transmission. It employs one-hop, two-hop, or three-hop pathways based on node proximity to the base station (BS) to optimize energy usage. Network performance is assessed using rounds, throughput, and energy usage. MATLAB simulations compare the proposed approach with dual layer LEACH (DL-LEACH) and LEACH, showing significant improvements in network lifetime. The proposed scheme outperforms LEACH by 515% and 347% for 20% and 50% node depletion, respectively. Compared to DL-LEACH, it extends network lifetime by 27% and 59% under similar scenarios. Sectoring, clustering, and multi-hop communication reduce energy consumption, enhancing network lifetime and addressing WSN challenges effectively.