Increase Network Lifetime Research Articles

An Improved Coverage Hole Finding System for Critical Applications Based on Computational Geometric Techniques

Wireless Sensor Networks (WSNs) contain coverage holes caused by both random node sensor deployment and malfunctioning nodes. Because fixing the battery is challenging, collaborative discovery and assessment of coverage shortfalls, as well as getting rid of these holes, has been recognized as critical in WSNs. While placing nodes for sensors in a large-scale WSN is challenging. This research provides a cost-effective coverage hole detection approach based on collaborative distributed point placement. Create a polygon first by employing an angle estimate approach and neighbor data. Following that, a based on points hole identification technique is used to assess if a coverage issue appears in a large-scale WSN's supplied ROI. Furthermore, the region of the coverage hole is estimated using computational geometry-based polygonal triangulation methods. The accuracy of the method is tested here using statistical data. The results show that it outperforms earlier coverage hole-detecting algorithms. In particular, the method improves coverage rate by 75% when compared to conventional methodologies. It also lowers energy usage by 90%, adding to increased network lifetime. The quantitative favourable results demonstrate the effectiveness of the collaborative distributed point placement technique in detecting and successfully resolving coverage gaps in WSNs. In regards to coverage rate, energy consumption, and network longevity, the system being proposed beats previous coverage hole-detecting techniques.

DEVELOPMENT OF WIRELESS SENSOR NETWORKS EFFICIENT CLUSTERIZATION METHOD

Wireless sensor networks are a critical element of the Internet of Things and are widely used in various fields. Effective clustering is key to energy efficiency and network stability in wireless sensor networks. Optimize the density of clusters, their number and size to increase the stability and service life of the network – It is one of the tasks of clustering wireless sensor networks. Another problem – effectively organize the network topology in order to balance the load and extend the network lifetime. Clustering has proven to be an effective approach for organizing a network into a cohesive hierarchy. However, there are several key issues that affect the practical application of clustering methods in sensor network applications. These issues include the rationale for developing different clustering approaches, classification of proposed approaches based on their goals and design principles, and problems associated with clustering. K-means algorithm is one of the unsupervised machine learning methods for clustering tasks and is widely used in wireless sensor networks. This article proposes a modified K-means clustering algorithm that eliminates the shortcomings of the basic K-means algorithm. The modified algorithm increases network lifetime, reduces power consumption and improves network stability.

Improvement of Low Energy Adaptive Clustering Hierarchical Protocol Based on Genetic Algorithm to Increase Network Lifetime of Wireless Sensor Network

Improvement of Low Energy Adaptive Clustering Hierarchical Protocol Based on Genetic Algorithm to Increase Network Lifetime of Wireless Sensor Network

Optimal data transmission for decentralized IoT and WSN based on Type-2 Fuzzy Harris Hawks Optimization

Due to the widespread availability of services and smart devices, the decentralized internet of things and wireless sensor network-based systems (DIoT and WSN) have drawn the attention of academics and researchers and are still under constant development. Many challenges face the DIoT and WSN networks and need to be solved. The main issue among these challenges is reducing energy consumption in order to increase network lifetime. Energy-efficient routing protocols and the clustering method are the best solutions to this problem. In this paper, Type-2 Fuzzy Harris Hawks Optimization (T2FHHO) is used to choose the best path (route) from the source (cluster head (CH)) to the destination nodes (base station (BS)). Along with this, we propose a new fitness function to find the next hop based on residual energy, distance, traffic, and buffer size. Then, we apply the T2FHHO method to get the best CH. This protocol seeks to increase the long-term reward earned by each node. The best path in terms of the minimum cost obtained using the proposed method is found. The two main contributions of this study are as follows: firstly, we present a new efficient routing approach that minimizes costs and maximizes efficiency. Secondly, we apply the T2FHHO to the network parameters of energy consumption, network lifetime, and convergence curves. The results indicate that T2FHHO outperforms competing methods. The proposed method holds the promise of significantly advancing the state of the art in energy-efficient routing for DIoT and WSN networks, with significant implications in various sectors and applications, ensuring extended network lifetimes and sustainable IoT implementations.

A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis

A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors.

Energy‐efficient adaptive clustering (EEAC) with rendezvous nodes and mobile sink

SummaryWireless sensor networks are an indispensable part of the present industrial and environmental scenario, with the everlasting challenges to increase network lifetime and reduce energy consumption. This paper presents an energy‐efficient adaptive clustering (EEAC) model, which is built upon the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with mobile sink and rendezvous nodes. The nodes are energy aware and participate in cluster head selection only if their current energy is higher than the average energy for the round. The idle nodes in the rendezvous region, which do not act as rendezvous nodes undergo clustering within themselves for the efficient routing of data. The EEAC inspires local clustering successfully conserves the energy of continuous long‐range transmission and enhances network lifetime. The proposed scheme proves to be remarkably better than the previous schemes, especially in large network cross‐sections. It improves alive nodes' performance for around 50% more rounds than the optimizing‐LEACH and the remaining energy curves improve around 500 rounds for 250 mx250 m network. For 350 mx350 m and 450 mx450 m networks, the results improved much in EEAC. In addition, the improvement in stability period is 450%, 578%, and 198% for the network cross‐section 150 mx150 m, 200 mx200m, and 250 mx250 m, respectively.

Energy-efficient mobile sink data collection in wireless sensor networks using LEACH with rendezvous nodes

In Wireless Sensor Networks (WSNs), energy consumption management poses a formidable challenge due to the energy-intensive nature of data transmission and processing. WSNs consist of densely deployed sensor nodes, and data collection is carried out by cluster heads. To address the issue of transmitting data to the mobile sink (MS) and extend the network's lifetime, rendezvous nodes(RNs) are utilized. However, existing approaches have not sufficiently improved network longevity. To tackle this problem, a novel protocol called Energy Efficient Mobile Sink Data Collection (EEMSDC) is proposed. This protocol aims to balance energy usage, prevent sensor node overload, and facilitate data delivery to the MS through RNs. The proposed approach optimizes the MS's tour in WSNs by employing predetermined TDMA slots for efficient data transmission with RNs. The proposed protocol outperforms existing protocols such as LEACH, EAMMH, LEACH-C, MOD-LEACH, MGEAR, and TDEEC in terms of network lifetime, energy consumption, and data loss. By reducing network overhead and ensuring balanced energy consumption among sensor nodes, the proposed approach significantly extends the network's lifetime. The effectiveness of the proposed protocol is validated through mathematical analysis and simulations conducted using MATLAB 2021a. The simulations show a remarkable reduction in energy consumption by up to 68% and an impressive increase in network lifetime by 73% compared to LEACH. These findings underscore the substantial advantages of the proposed approach in enhancing the performance of WSNs.

A Cross-layer Method for Scheduling and Routing Real-time Traffic Flow in Industrial IoT

In the Industrial Internet of Things, a wide variety of sensors are distributed all over the environment to monitor data collection, thereby allowing industrial processes to be monitored more efficiently. One of the fundamental goals of IIoT is to provide the highest level of reliability while simultaneously increasing network lifetime, reducing power consumption, and preventing delays. 6TiSCH is a popular communication standard relied upon in IIoT. The aim of the present study is to propose an inter-layer method that simultaneously considers network scheduling and routing processes based on TSCH and RPL approaches in multi-sink environments. The proposed method is intended to address the limitations of IIoT and meet the requirements of field-specific applications.

A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks.

Clustering is considered to be one of the most effective ways for energy preservation and lifetime maximization in wireless sensor networks (WSNs) because the sensor nodes are equipped with limited energy. Thus, energy efficiency and energy balance have always been the main challenges faced by clustering approaches. To overcome these, a distributed particle swarm optimization-based fuzzy clustering protocol called DPFCP is proposed in this paper to reduce and balance energy consumption, to thereby extend the network lifetime as long as possible. To this end, in DPFCP cluster heads (CHs) are nominated by a Mamdani fuzzy logic system with descriptors' residual energy, node degree, distance to the base station (BS), and distance to the centroid. Moreover, a particle swarm optimization (PSO) algorithm is applied to optimize the fuzzy rules, instead of conventional manual design. Thus, the best nodes are ensured to be selected as CHs for energy reduction. Once the CHs are selected, distance to the CH, residual energy, and deviation in the CH's number of members are considered for the non-CH joining cluster in order to form energy-balanced clusters. Finally, an on-demand mechanism, instead of periodic re-clustering, is utilized to maintain clusters locally and globally based on local information, so as to further reduce computation and message overheads, thereby saving energy consumption. Compared with the existing relevant protocols, the performance of DPFCP was verified by extensive simulation experiments. The results show that, on average, DPFCP improves energy consumption by 38.20%, 15.85%, 21.15%, and 13.06% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, and increases network lifetime by 46.19%, 20.69%, 20.44%, and 10.99% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, respectively. Moreover, the standard deviation of the residual network was reduced by 61.88%, 55.36%, 54.02%, and 19.39% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO. It is thus clear that the proposed DPFCP protocol efficiently balances energy consumption to improve the overall network performance and maximize the network lifetime.

Hybrid Marine Predators and Border Collie Optimization algorithm for multipath routing in IoT

SummaryInternet of Things (IoT) is a network of smart things that connects people and objects for gathering and exchanging information by means of embedded sensor. IoT‐based wireless sensor network (WSN) nodes are monitored as a resource parameter at the variety of methods, including resource calculation, energy resource, and storage resource, with multipath routing protocols necessary to enable extended network lifespan and achieve high energy utilization. The existing routing protocols did not provide optimal path from the multipath route. Hence, in this manuscript, a Hybrid Marine Predators and Border Collie Optimization algorithm is proposed for multipath routing in IoTs. The main aim of this endeavor is to “increase network lifetime.” IoT is simulated initially and multipath routing initiates in internet of things network. Multipath routing is achieved by proposed Hyb‐MP‐BCOA; this is the combination of Hybrid Marine Predators Algorithm and Border Collie Optimization algorithm. The proposed Hyb‐MP‐BCOA algorithm chooses the optimum path from the multiple path obtainable for routing, depending upon fitness parameter, like context awareness, network lifetime, residual energy, trust, and delay. Based on the fitness parameter, the optimal path is selected by using Hyb‐MP‐BCOA algorithm. Finally, the proposed Hyb‐MP‐BCOA‐MR‐IoT model is applied in MATLAB, and efficacy of proposed technique is estimated by using various performance metrics, like drop, normalized network energy, lifetime of network, delay, throughput, energy consumed, packet delivery ratio, and number of sensor nodes versus aggregation level. The proposed method attains lower delay of 14.285%, 50%, 53.846%, and 57.142%, lower packet drop of 14.285%, 50%, 53.846%, and 57.142%, lower energy consumption compared with existing methods such as multiobjective sun flower optimization with gray wolf optimizer (GWO) for multipath routing in IoT (SFO‐GWO‐MR‐IoT), multiobjective Whale Lion Fireworks optimization algorithm and fitness function for multipath routing in IoT (WLFA‐MR‐IoT), multipath routing in IoT using shuffled frog leaping algorithm (SFLA‐MR‐IoT), and multipath routing in IoT using hybrid Whale Optimization Algorithm‐Moth Flame Optimization (MFO)(hyb‐WOA‐MFO‐MR‐IoT). Finally, the safe communication is attained in the IoT network.

Investigation and Analysis on Energy Efficient Stable Election Protocol (SEP) for WSN

Abstract: We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources—this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes. Sensor nodes usually have limited energy supply and they are impractical to recharge. How to balance traffic load in sensors in order to increase network lifetime is a very challenging research issue. Many clustering algorithms have been proposed recently for wireless sensor networks (WSNs). The use of mobile sinks has been shown to be an effective technique to enhance network performance features such as latency, energy efficiency, network lifetime, etc. In this paper, a modified Stable Election Protocol (SEP), which employs a mobile sink, has been proposed for WSNs with non-uniform node distribution. The decision of selecting cluster heads by the sink is based on the minimization of the associated additional energy and residual energy at each node. Besides, the cluster head selects the shortest path to reach the sink between the direct approach and the indirect approach with the use of the nearest cluster head. Simulation results demonstrate that our algorithm has better performance than traditional routing algorithms, such as LEACH and SEP.

A serious gaming approach for optimization of energy allocation in CubeSats

Energy consumption remains an open challenge in aerial systems such as CubeSats and therefore optimization of its allocation is a top priority for maximizing operational capacity. Our research review reveals a plethora of approaches for optimization of energy allocation and all achieving varying degrees of success and not without any compromises. In this paper, we exploit the use of serious gaming in a novel energy allocation algorithm that aims at minimizing energy consumption to maximize the utilities of both CubeSats and terrestrial sensors. To demonstrate this, we use Stackelberg for serious gaming and standalone topology for CubeSat configuration. The experimental results show that the use of a Stackelberg game approach for optimization has led to reduction in the required transmission energy in sensors, an improved link performance between the CubeSat and ground sensors, and an increase in network lifetime and performance without resorting into sensor power enhancements or other external power sources. The overall average operational capacity improvement predictions range between 22 to 27% across all performance indicators of energy efficiency across RF chains of link budgets.

A fuzzy model for content-centric routing in Zigbee-based wireless sensor networks (WSNs).

WSN is one of the most efficient technologies in intelligent communication and because of its advantages, this technology has been utilized in various applications. By using WSNs, different types of data can be collected and analyzed in wide environments. The high variety of applications and types of data available in this network can cause several challenges about heterogeneous data routing. This research, presents a Fuzzy Model for Content-Centric Routing (FMCCR) in WSN to solve these challenges. The performance of FMCCR is based on two basic steps: "topology control", and "data transmission through content-centric and fuzzy logic-based routing algorithm". In the first step of FMCCR, the network topology is constructed. In the second step of the proposed method, data transmission paths are determined based on network topology and content type, and data transmission is performed. The performance of FMCCR has been evaluated in a simulation environment and the results have been compared with previous algorithms. The results show that FMCCR reduce energy consumption and improve the traffic load distribution in the network in addition to increasing the network lifetime. According to the results, FMCCR can increase network lifetime at least 10.74% and at the same time, deliver at least 88.1% more packets through the network, compared to previous methods. These results, prove the efficiency of the proposed method for using in real-world scenarios.

Horizontal trajectory based mobile multi-sink routing in underwater sensor networks

Scientific, commercial, exploration, and monitoring applications of underwater sensor networks have drawn the attention of researchers toward the investigation of routing protocols that are robust, scalable, and energy efficient. This has brought significant research in network layer routing protocols. Irrespective of the field of application it is desirable to increase network lifetime by reducing energy consumed by sensor nodes in the network or by balancing energy in the entire network. Energy balancing refers to the uniform distribution of the network’s residual energy such that all nodes remain alive for a long time. It requires uniform energy consumption by each sensor node in the network instead of the same node being involved in every transmission. In this paper, we discuss two routing methods for three-dimensional environments in which the water region under monitor is divided into subregions of equal height and each subregion has a sink. Nodes in the subregion send data to the sink designated for that subregion. The first method called static multi-sink routing uses static sinks and the second method called horizontal trajectory-based mobile multi-sink routing (HT-MMR) uses mobile sinks with a horizontal trajectory. Simulation results show that the proposed HT-MMR reduces average energy consumption and average energy tax by 16.69% and 16.44% respectively. HT-MMR is energy efficient as it enhances network lifetime by 11.11%.

A multipath routing protocol for secure energy efficient communication in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is comprised of a number of sensor nodes (SNs) that are randomly placed in an open, harsh environment for many applications. Due to the resource-constrained nature of SNs and hostile deployment environments, energy efficiency and security are considered two key factors in designing WSN routing protocols. This paper proposes an Energy Efficient Secure Multipath (EESM) routing protocol to securely construct efficient routes and transmit data packets between SNs and the base station (BS). EESM achieves energy efficiency through minimal task allocation among SNs whereas all computation-intensive tasks such as network information collection, routing table generation, and network maintenance are performed by the BS. The proposed protocol incorporates lightweight security mechanisms including a one-way hash chain, message authentication code, encryption, and clique-based coordinator selection and monitoring schemes to defend against numerous security attacks. Simulation results show that EESM can successfully detect and protect the network against various security attacks such as replay attacks, sybil attacks, sinkhole attacks, spoofing attacks, compromised node attacks, and so on. In terms of energy efficiency, the proposed protocol achieves an up to 37% increase in network lifetime and a 6% increase in throughput over Secure and Energy Efficient Multipath (SEEM) routing, Secure and Reliable Multipath Routing (SRMR), and Reliable and Multipath Encounter Routing (RMER) protocols. The paper implements the protocol in a real environment using Arduino motes to analyze security overheads and network setup time.

Energy-Efficient Data Aggregation and Cluster-Based Routing in Wireless Sensor Networks Using Tasmanian Fully Recurrent Deep Learning Network with Pelican Variable Marine Predators Algorithm

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.

Data Acquisition Control for UAV-Enabled Wireless Rechargeable Sensor Networks

In the realm of Internet of Things (IoT), wireless sensor networks (WSNs) have been the subject of ongoing research into the use of energy harvesting to capture ambient energy, and wireless power transfer (WPT) via a mobile charger to overcome the energy limitations of sensors. Moreover, to mitigate energy imbalance and reduce the number of hops, strategies have been developed to leverage cars or unmanned aerial vehicles (UAVs) as mobile sinks. The primary objective of this work is to increase network lifetime by reducing energy consumption of hotspot nodes and increasing the amount of data acquired from all sensors in an environment that combines the methods mentioned above.To achieve this objective, the proposed method involves developing multiple minimum depth trees (MDTs) for all nodes, considering the energy of the UAV and sensor nodes. Parent nodes prevent their own energy depletion and ensure data transmission without imbalance by adaptively controlling the data sensed at the nodes and their child nodes. Consequently, the energy depletion of nodes in hotspots is prevented, more sensory data is acquired, and balanced data collection from all nodes is achieved. Simulation results demonstrate that the proposed scheme outperforms other state-of-the-art methods in terms of reduced energy depletion, increased network connectivity, and the amount of data collected at the sink node. This scheme will be applied to applications that collect environmental data outdoors, such as climate measurement, to collect data uniformly and increase the lifespan of the network, thereby reducing network maintenance costs while collecting data effectively.

Optimization of Energy-Efficient Algorithms for Real-Time Data Administering in Wireless Sensor Networks for Precision Agriculture

Precision agriculture relies heavily on wireless sensor networks (WSNs) to monitor environmental conditions and manage resources efficiently. However, traditional WSN algorithms face significant challenges in energy management, resulting in frequent battery replacements, high maintenance costs, and reduced network reliability. Additionally, issues with high data latency and short network lifetimes hinder real-time decision-making and continuous monitoring. This study aims to address these challenges by developing energy-efficient algorithms that enhance the performance and longevity of WSNs in precision agriculture. The objectives include creating adaptive sampling, dynamic power management, and sleep scheduling algorithms to optimize energy consumption; improving data transmission efficiency and reducing latency through advanced data aggregation techniques; and extending network lifetime with balanced energy load distribution and efficient power usage strategies. The study introduces innovative algorithms that significantly reduce energy consumption and extend network lifetime, contributing to the field with adaptive and dynamic methodologies. Extensive simulations demonstrate that the proposed algorithms achieve a 30% reduction in energy consumption, a 20% decrease in data latency, and a 40% increase in network lifetime compared to traditional methods. Additionally, the packet delivery ratio improved by 10%, and computational overhead was reduced by 30%, highlighting the efficiency and reliability of the proposed solutions. These results are consistent with recent advancements in WSN optimization techniques, such as those utilizing cognitive radio and deep learning, validating the potential of the proposed algorithms for real-world application.

Optimal relaying nodes selection for IEEE 802.15.6-based two-hop star topology WBAN

Wireless body area networks (WBANs) are a promising communication technology that supports various types of medical and non-medical applications with heterogeneous requirements. Generally, IEEE 802.15.6-based WBAN use one-hop star topology to establish direct communication between the hub and body sensors. The standard also supports two-hop star topology to establish communication where direct transmission between the hub and sensor nodes is not possible due to poor link quality caused by body shadowing. Data transmission reliability and network lifetime are key factors in WBANs. Therefore, we propose an optimal relaying nodes selection mechanism as an alternative to the two-hop star topology extension of IEEE 802.15.6 to enhance the packet delivery ratio, network lifetime, and throughput. In this work, while choosing a relaying node for a disconnected node, we consider the fitness value of all the relaying capable nodes and select the fittest node as relaying node. We calculate the fitness value of all the relaying capable nodes based on their link quality, residual energy, and current traffic load using the multi-criteria decision making method “Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)”, which was originally developed by Ching-Lai Hwang and Yoon in 1981. We use the Mamdani fuzzy inference system (FIS) to calculate the link quality index of relaying capable nodes based on various link quality parameters such as Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Packet Error Rate (PER). Moreover, we also designed a schedule slot allocation mechanism for all the sensor nodes based on their packet generation rate (PGR). The proposed scheme significantly improved packet delivery ratio, throughput, and network lifetime compared to the IEEE 802.15.6 standard’s two-hop extension of star topology and other relay selection mechanisms. The simulation result of the proposed mechanism shows that the packet delivery ratio and network lifetime increase by (5–30)% and 50% respectively, compared to the IEEE 802.15.6 two-hop star topology.

Enhanced clustering based routing protocol in vehicular ad‐hoc networks

AbstractA vehicular ad‐hoc network (VANET) is derived from a mobile ad‐hoc network that is a part of less infrastructure network design. Vehicular communication in VANET can be achieved using vehicle‐to‐infrastructure (V2I) and vehicle‐to‐vehicle (V2V) communication. A vehicle communicates with other vehicles through onboard units while communicating with roadside units in an infrastructure mode. Secure clustering is required for the communication between nodes in the whole network. The fundamental problem with the VANET is the instability of the network that occurs due to vehicles' mobile nature, which decreases the network's efficiency. This research proposes an enhanced cluster‐based lifetime protocol ECBLTR that focuses on maximising the network's stability of routing and average throughput. The Sugeno model fuzzy inference system is used for assessing the cluster head (CH) that takes residual energy, local distance, node degree, concentration, and distance from the base station as input parameters. Our enhanced routing protocol shows that the proper channel model with an efficient routing protocol enhances the link throughput of the VANET for fixed network size. Our results show an efficient selection method of CH through the fuzzy system and a 10% increase in network lifetime. Furthermore, performance evaluation also demonstrates the impact of network sizes and routing protocols on packet delivery ratio and packet loss, average end‐to‐end delay, and overhead transmission.