Enhance Network Lifetime Research Articles

Elderly care and health monitoring using smart healthcare technology: An improved routing scheme for wireless body area networks

AbstractHypertensive patients need regular checkups and constant monitoring for taking time critical decisions by the medical experts. Unfortunately, it is hard to maintain uninterrupted patient health surveillance due to limited medical staff resulting in an increasing mortality rate annually. Thanks to recent developments in wireless sensor networking, we can monitor constantly and efficiently diverse parameters of a network. Similarly, Wireless Body Area Networks (WBANs) have become a well‐known sub‐branch of Wireless Sensor Networks. Such sensor networks can be leveraged for patient health monitoring, minimising the medical staff workload. Wireless Body Area Networks require tiny sensor nodes with limited battery power. Therefore, it is always desirable to design effective routing schemes that can enhance network lifetime, and reduce packet drop ratio. In this paper, we re‐simulate and explain in detail the results of a selected published journal article for WBANs and provide some modifications to improve the network's overall performance. Based on these amendments, the modified protocol successfully extends the operational time of the network than the original. Our performance evaluation parameters are dead nodes, throughput, residual energy, and path loss versus the number of rounds. These analyses support effective solutions that improve network performance and data delivery ratio.

Fuzzy based Energy Efficient Rider Remora Routing protocol for secured communication in WSN network

Due to the resource constraint nature of WSN, ensuring secured communication in WSN is a challenging problem. Moreover, enhancing the network lifetime is one of the major issues faced by the existing studies. So, in order to secure the communication between WSNs and achieve improved network lifetime, a novel trust enabled routing protocol is proposed in this study. Initially, the clusters are constructed using Direct, Indirect, and Total Trust evaluations, which helps to identify the faulty nodes. After, an Improved Fuzzy-based Balanced Cost Cluster Head Selection (IFBECS) method is used to choose the cluster head (CH). Finally, to determine the best path from source to destination, a hybrid bionic energy-efficient routing model known as an Energy Efficient Rider Remora Routing (EERRR) protocol is introduced. To improve the network lifetime and throughput, the parameters like remaining energy of the CH, sensor node space, CH, etc., are considered by the utilized protocol. The proposed mechanism is implemented in NS-2 programming tool. The simulation results show that the proposed routing protocol has attained improved PDR of 97.92 % at the time period of 50 ms, reduced energy consumption of 3.336 at the time period of 100 ms, higher throughput of 86262.7 at the time period of 250 ms, and enhanced network lifetime of 1028.08 rounds in 200 nodes. Therefore, by attaining better results as compared with other existing protocols, it is clearly revealed that the proposed routing protocol is highly suitable for secured energy efficient WSN communication.

Retraction Note: A hybrid soft computing: Sgp clustering methodology for enhancing network lifetime in wireless multimedia sensor networks

Retraction Note: A hybrid soft computing: Sgp clustering methodology for enhancing network lifetime in wireless multimedia sensor networks

Optimizing Energy Efficiency in 6G Communication Networks Based on Data Transmission Rate Allocation

To progress communication and sensing in recent years, the Sixth-Generation (6G) network was developed. To transform the technological landscape of model transmission networks we use the 6G network, and it also used to enhance the speed, decrease the latency and improve the connectivity of network. Yet, in the 6G infrastructure with these developments it has severe challenge, that is the 6G network does not negate the benefits of energy consumption associated with network. In 6G communication the sensing performance of the smart device only relay on the available power of the network, and in the 6G communication energy efficiency becomes crucial. To progress the energy efficiency of data transmission in 6G communication networks, we deployed a Data Transmission Rate Allocation (DTRA) method. Moreover, to progress the data transmission of efficient nodes we deploy a Residual Energy Cluster Head (RECH) method. In addition, to improve the reliability and speed in the 6G network we use Dynamic Multipath Routing Protocol (DMRP) method, and it also mitigate the channel defects. Atlast, in the 6G communication networks we certify QoS through optimizing the transmission rate of the user equipment through low energy consumption, and quality of channel by the use of DTRA method. Finally, in this paper we evaluate the metrices performance of the existing methods and proposed method, for instance: efficiency, energy consumption, network lifetime, and transmission speed. After evaluate the performance metrices the DTRA method enhance network lifetime and energy efficiency of 95.3% based on 6G communication networks.

Enhancing network lifetime in wireless sensor networks through coverage-aware optimized sensor activation

Wireless sensor networks (WSNs) are a pivotal technology in the modern era, enabling the monitoring and sensing of environmental conditions across vast areas with unprecedented precision and flexibility. At the heart of WSNs lie crucial challenges such as optimizing coverage, extending network lifetime, and strategizing node deployment to ensure efficient operation while conserving energy. This paper introduces the coverage-aware optimized sensor activation and deployment (CAOSAD) Strategy, a novel methodology designed to address these challenges. By integrating advanced node placement algorithms and scheduling techniques, the EcoNet lifespan maximization (ELM) strategy significantly enhances area and target coverage, minimizes energy consumption, and thereby prolongs the network’s operational lifespan. We present a comprehensive framework that dynamically adjusts node activity based on a predictive model, ensuring robust coverage and connectivity with minimal energy expenditure. Through a series of simulations, the ELM strategy demonstrates a substantial improvement in network sustainability compared to existing methodologies, offering a promising approach for the development of future WSNs. By focusing on the synergy between coverage optimization, energy-efficient node deployment, and innovative scheduling algorithms, this paper contributes a ground-breaking perspective to the research and application of WSNs, setting a new benchmark for the design of eco-friendly and durable sensing infrastructures.

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

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

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 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.

Energy-efficient quad tree-based clustering using edge-assisted UAV-relay to enhance network lifetime in WSN

Wireless sensor networks' most prominent concern is energy optimization. It faces significant problems like high energy consumption, data loss, delay, and low network lifetime. To improve, it uses clustering. However, during clustering, coverage holes are most likely to appear near the network's edge, within the cluster, and between clusters. As a result, there are more energy holes and dead nodes; therefore, the goal of this work is to maximize node network lifetime and minimize energy consumption during data transmission in the wireless sensor network (WSN). The proposed work includes three entities: sensor nodes, an edge-assisted unmanned aerial vehicle (UAV), and a base station. It uses an edge-assisted unmanned aerial vehicle to provide additional resources to the UAV, which helps reduce energy consumption during data transmission. This research proposes using communication to enhance the speed and bandwidth of data transmission and reduce transmission latency. This work attempts to improve performance by increasing throughput.

Dual step hybrid routing protocol for network lifetime enhancement in WSN-IoT environment

Recent development in internet of things (IoT) has been generating huge data due to the large number of nodes deployed and utilized for different applications. In addition, these applications utilize big data and require a more efficient mechanism for data sensing and data transmission. This research work proposes dual step hybrid routing (DSHR) protocol for efficient cluster-based routing. It comprises a two-phase algorithm, which aims at finding the optimal path considering clustering. It further comprises several processes such as cluster head selection, optimal path construction, integrating of nodes to cluster head and sensing range optimization. DSHR is evaluated considering the network lifetime; thereafter model is compared with the existing low energy adaptive clustering hierarchy (LEACH) protocol to prove the efficiency.

Hierarchical Routing with Optimization Algorithm for SDN: Enhancing Network Lifetime and QoS

Hierarchical Routing with Optimization Algorithm for SDN: Enhancing Network Lifetime and QoS

Cooperative MAC Protocol based on Best Data Rate (CMAC-DR)

As wireless signals are broadcast in nature, which implies that, a broadcast communication purposive to a predetermined destination may be received by a non-intended intermediate station. Cooperative transference, which employ interposed stations to pass on the eavesdropped data to attain the contrast gains, has a substantial capability to revamp the channeling effectiveness in wireless systems. In this it is evident that having cooperation amid stations in a wireless system can accomplish higher throughput with enhanced network lifetime. Proffered work bestows a model for medium access layer called Cooperative MAC protocol based on optimal Data Rate (CMAC-DR). In the proffered work, stations with more data rate aid stations having lesser data rate in their communication by redirecting their congestion. In CMAC-DR model, utilizing the conveyance of eavesdropped information, potential helper stations with more data rate Send out Helper Ready to Send (HRTS), the stations with less data rate maintains a table, called Co-op table of potential helper stations, that can aid in its transmissions. During communication, the source station with low data rate chooses either transmitting by the way of a helper station, so that it lowers the end to end transference delay and increases the throughput or opt only direct transmission, if no potential helper is found or if CMAC-DR becomes an overhead. By analyzing varied simulated scenarios, CMAC-DR evaluates the elevation in the overall network lifetime, throughput and minimization of delay. The CMAC-DR protocol is unambiguous and in accordant with legacy 802.11 also when compared to this, we find improved performance in terms of delay throughput and network lifetime since data rate is considered as relay selection condition.

Energy‐efficient clustering algorithm using distributed fuzzy‐logic to prolong the survivability of wireless sensor networks

AbstractEnergy efficiency is critical for prolonging the survivability of wireless sensor networks (WSNs), and clustering algorithms play a significant role in achieving this goal. An application‐specific wireless sensor network requires adapted methods and techniques to meet its requirements. A vast amount of research has been done on optimizing energy consumption and enhancing network lifetime of sensor nodes. To increase the lifetime of WSNs, we present and evaluate an energy‐efficient clustering algorithm based on distributed fuzzy logic (EECADFL). High reliability, low error rates during clustering, and its ability to perform well in large‐scale networks with many nodes are some of the main benefits of this method. In wireless sensor networks, simulation results showed that the scheme provided better lifetime performance while limiting dead nodes and improving cluster head selection.

BS-SCRM: a novel approach to secure wireless sensor networks via blockchain and swarm intelligence techniques

In this paper, we present a novel Secure Clustering Routing Method based on Blockchain and Swarm Intelligence (BS-SCRM) for Wireless Sensor Networks (WSNs), which serves as a cornerstone in the Internet of Things (IoT) infrastructure. Recognizing the limitations of existing clustering routing methods in addressing security threats, our approach integrates blockchain technology to fortify WSNs against vulnerabilities such as man-in-the-middle attacks. The proposed BS-SCRM method is structured in two phases: (1) an enhanced cluster head (CH) election utilizing an elite strategy-enhanced Whale Optimization Algorithm (WOA) that considers node energy and proximity to the base station, and (2) a secure data on-chain phase where blockchain comes into play, encrypting and validating cluster data to safeguard integrity and prevent tampering. We further tackle the challenge of implementing blockchain in resource-constrained WSNs by assigning distinct roles to devices, i.e., ordinary nodes with data viewing permissions and accounting nodes entrusted with both data viewing and consensus algorithm execution. Extensive simulations confirm that BS-SCRM not only improves clustering quality but also provides a more secure and energy-efficient routing solution compared to contemporary methods. More specifically, simulation results in different scenarios demonstrate that BS-SCRM enhances network lifetime by 24–73% compared to other clustering methods when facing attacks.

Energy and delay aware routing model for smart crop monitoring application using internet of things

The adoption of modern technology into farming has seen significant expansion to enhance the quality and quantity of crop production. In this research work, wireless sensor networks (WSNs) and the Internet of Things (IoTs) are integrated to provide a real-time crop monitoring system to farmers. Usually, battery-powered IoT devices equipped with sensors like temperature, humidity, motion sensors, etc. are placed across agriculture areas for real-time monitoring of crops and cattle. In improving energy efficiency and reducing delay clustering multi-hop routing has been used with good effect; However, the existing methods failed to enhance network lifetime and target coverage due to failure in addressing load-balancing and hotspot problems. In addressing research problems this paper presents an Energy and Delay Aware Routing (EDAR) scheme for heterogenous WSN-IoTs leveraging unequal grouping of networks and adoption of multipath routing for provisioning IoT applications. The simulation outcome shows the EDAR model achieves very good performance in terms of throughput and lifetime performance with minimal delay and communication overhead.

Energy-Efficient Dynamic MAC Protocol for M2M Communication

In the Internet of Things, which is vital for smart city applications, a vast network of battery-operated Machine Type Communication Devices (MTCDs) requires Machine Type Communication. These MTCDs connect directly to a network with a base station, facilitating a wide range of smart city functionalities. The development of energy-efficient communication protocols is essential to support the deployment of energy-constrained battery-operated devices. These protocols aim to minimize energy consumption, thereby enhancing network lifetime. Additionally, they must address scalability, reduce collisions and delays, and ensure a high Quality of Service (QoS) to effectively manage the vast number of devices involved. Clustering is a viable strategy to enhance scalability and energy efficiency in network communications. However, within this framework, particularly in the proposed Power Efficient Dynamic MAC protocol for M2M Communication (PMAC), there's still a need for energy-efficient communication between the Machine Type Communication Devices (MTCDs). This requirement is crucial to optimize the overall performance and sustainability of the network. In this paper, we analyses the Energy Efficient CSMA/CA (E-CSMA/CA) protocol, specifically developed for facilitating communication between Machine Type Communication Devices (MTCDs) in short-range communications. This analysis is set within the context of smart city applications, with a particular focus on smart metering. This approach underscores the importance of efficient, reliable communication protocols in modern urban infrastructure, especially for applications like smart metering that are integral to the smart city ecosystem.

Dynamic load balancing in IoT-enabled WSNs using fuzzy logic-based control

The rapid expansion of Internet of Things (IoT) applications and the increasing complexity of Wireless Sensor Networks (WSNs) have created a critical need for efficient load balancing strategies. This paper proposes a dynamic load balancing approach for IoT-enabled WSNs using a fuzzy logic-based control mechanism. The proposed method aims to optimize energy consumption, reduce latency, and enhance network lifetime by intelligently distributing the workload among sensor nodes. The fuzzy logic controller takes into account various parameters, such as energy levels, communication distances, and node density, to make adaptive load balancing decisions. The control mechanism allocates tasks to the most suitable nodes, ensuring efficient utilization of resources and preventing overloading of individual nodes. Simulations are conducted in diverse network scenarios to validate the performance of the proposed approach. Results demonstrate significant improvements in energy efficiency, latency reduction, and overall network lifetime compared to traditional load balancing techniques. The fuzzy logic-based control mechanism proves to be a promising solution for addressing the dynamic and resource-constrained nature of IoT-enabled WSNs, paving the way for more robust and resilient networks in various IoT applications.

A Hybrid Heuristic Model for Duty Cycle Framework Optimization

This paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversification of varying WSNs. The proposed HSMO-DC primarily has two parts, where the first part takes care of the online cluster head selection and network communication using the seagull algorithm while the second part performs parameter optimization using the mayfly algorithm. The seagull is aimed at improving the energy distribution in the network through an effective bandwidth allocation procedure while reducing the total energy dissipation. Comparatively, with other clustering protocols, our proposed methods reveal an enhanced network lifetime with an improved network throughput and adaptability based on selected standard metric of performance measurement.