Link Lifetime Research Articles

Deep learning-based energy prediction and tangent search remora optimization-based secure multi-path data communication mechanism in WSN

ABSTRACT Wireless Sensor Network (WSN) has been exploited in numerous regions which can be hardly accessed by humans. However, it is essential to convey the information accumulated by the sensing devices or nodes to the Base Station (BS) for further processing. Multipath routing protocols are found to address these challenges and provide reliable communication. This paper aims to find an optimal path to the gateway with minimum energy consumption and reduced error rate while meeting the end-to-end delay requirements. In this research, an effective multipath routing based on energy prediction and hybrid optimization is developed. Here, a Deep Q-Network (DQN) is applied to predict the energy, and the process is augmented by the usage of a proposed Tangent Search Remora Optimization (TSRO) algorithm. Further, the multipath routing is executed using the TSRO algorithm, considering a fitness function formulated using various factors, like residual energy, distance, throughput, reliability, trust factors, predicted energy, Link Life Time (LLT), delay, and traffic intensity. The devised TSRO-routing is scrutinized for its competence based on trust, throughput, energy, distance, and delay and has achieved superior values of energy of 0.402 J, throughput at 25.056Mbps, trust at 84.975, and minimal distance of 29.964 m, and delay of 0.750 ms.

A novel fractional rat hawk optimization–enabled routing with deep learning–based energy prediction in wireless sensor networks

SummaryWireless sensor networks (WSNs) contain different sensors, which collect various data in the monitoring area. In general, one of the significant resources in WSNs is energy, which prolongs the network's lifetime. The energy‐efficient routing algorithms reduce energy consumption and enhance the survival cycle of WSNs. Thus, this work developed the optimization‐based WSN routing and deep learning (DL)–enabled energy prediction scheme for efficient routing in WSNs. Initially, the WSN simulation is carried out, and then, the node with minimum energy consumption is chosen as the cluster head (CH). Here, the proposed rat hawk optimization (RHO) algorithm is established for finding the best CH, and the RHO is the integration of rat swarm optimization (RSO) and fire hawk optimization (FHO). Furthermore, the routing is accomplished by the developed fractional rat hawk optimization (FRHO) using the fitness function includes delay, distance, link lifetime, and predicted energy of a network for predicting the finest route. Here, the fractional calculus (FC) is incorporated with the RHO to form the FRHO. The energy prediction is achieved by deep recurrent neural network (DRNN). The energy, delay, and throughput evaluation metrics are considered for revealing the efficiency of the proposed system, and the proposed system achieves the best results of 0.246 J, 0.190 s, and 67.13 Mbps, respectively.

Network Stability Based Multicriteria Weighted MPRs Selection Algorithm for Mobile Ad Hoc Networks

Manets are self-governing networks using several mobile nodes, which communicate together through wireless media, without the necessity of prior infrastructure. Therefore, due to highly dynamic and unconfident environments, Manets are weak to guarantee link lifetime, security, reliability and stability. Since, mobility, energy and security are the main factors for improved performance in the network. Paths and relay devices with enhanced stability and security are considered as critical subjects. The study suggests an improved process based on a Multicriteria purpose consistent with numerous systems of measurement. These systems of measurement are energy level, trust measure and mobility of mobile nodes. The concept is to select improved Multipoint relays (MPRs) able to transmit data efficiently, and increase lifetime and reachability. The proposed improvement supports the routing and the selection of paths. The selection of the greatest effective relay nodes can enhance the classification performance. The scheme is based on the OLSR protocol and performance results are defined using the network simulator (NS3) with different movement models. The results showed significant improvements in MCW_OLSR. The proposed version enhances network proficiency for different mobile environments.

A smart filtering-based adaptive optimized link state routing protocol in flying ad hoc networks for traffic monitoring

Nowadays, the use of drones as a fundamental element of smart cities has attracted the attention of many researchers to monitor and control the traffic of vehicles. Because of the high flexibility of multi-drone systems, like flying ad hoc networks (FANETs), they provide various services and improve modern life in smart cities. However, due to the unique features of FANET, especially the high speed of drones and rapid changes in network topology, communication reliability is a serious challenge in this network. Hence, traditional routing protocols, such as optimized link state routing (OLSR) scheme, cannot work well in these networks. In this paper, a smart filtering-based adaptive optimized link state routing (SFA-OLSR) scheme is proposed in FANETs. To increase adaptability to the FANET environment, SFA-OLSR provides a new solution to adjust the hello broadcast period so that each flying node specifies its broadcast period based on a new scale called cosine similarity between real and predicted positions. Furthermore, in SFA-OLSR, each flying node develops a filtering algorithm based on two parameters, namely link lifetime and remaining energy. The purpose of this algorithm is to reduce the size of the single-hop neighboring set of each flying node and minimize the search space when finding multi-point relays (MPRs). This increases the convergence speed of the algorithm. Then, SFA-OLSR exploits the sparrow search algorithm (SSA) to single out the best MPRs. This algorithm introduces a multi-objective function by focusing on three components, including energy, link lifespan, and neighbor degree. Lastly, the simulation process of SFA-OLSR is performed by the NS3 simulator. This process evaluates the performance of the proposed method and three schemes, namely Gangopadhyay et al., P-OLSR, and OLSR-ETX. These evaluations show that SFA-OLSR has a good performance in terms of three scales, namely packet delivery ratio, delay, and throughput, but its overhead is more than other methods.

A communication link lifetime prediction-supported V2V partial computation offloading scheme for autonomous driving

Vehicle Edge Computing (VEC) is a promising technique to improve the quality of service (QoS) and quality of experience (QoE) in autonomous driving by exploiting the resources at the network edge. However, the high mobility of the vehicles leads to stochastic communication link duration, and the tasks generated by various applications in autonomous driving incur fierce competition for resources. These challenges cause excessive task completion delays. In this paper, we propose a vehicle-to-vehicle (V2V) partial computation offloading scheme that leverages the prediction results of the communication link lifetime between vehicles. A History track, Current interactions and Future planning trajectory-aware Gated Recurrent Units (HCF-GRU) network is built to capture the essential factors to improve the prediction accuracy. Then, we design a GRU-based Proximal Policy Optimization (GRU-PPO) algorithm to obtain an optimal one-to-many offloading decision to minimize the task execution cost. The HCF-GRU prediction algorithm is evaluated on a real world vehicle trajectory dataset, and the performance of the GRU-PPO algorithm is analyzed on extensive numerical simulations. Experimental results demonstrate that our prediction network and offloading decision algorithm outperform the baseline methods in terms of prediction accuracy and task execution cost.

Taylor political optimizer‐based cluster head selection in IOT‐assisted WSN networks

SummaryIoT‐assisted WSN contains various nodes, which are placed on a huge scale that increases complications. Thus, the challenges and issues of these networks fluctuate as compared to WSNs. Hence, sensor nodes are imperative unit that runs on less energy resources. Hence, devising a robust and energy‐effective protocol for increasing network lifetime is a complex task. This paper devises a novel hybrid optimization‐driven approach for selecting cluster head (CH) in IoT‐assisted WSNs. Initially, the simulation of IoT nodes is done by configuration. Thereafter, the Cluster Head selection is done using a newly devised optimization technique, namely the Taylor‐Political optimizer (Taylor‐PO). Thus, fitness is newly developed by adapting certain attributes like energy, delay, inter and intra‐cluster distance, Link Lifetime (LLT), predicted energy, and delay. Here, the multipath routing is accomplished using Tunicate Swarm gray wolf optimization (TSGWO). Thus, the proposed Taylor‐PO is offered for effective Cluster Head selection along with multipath routing using TSGWO. The proposed Taylor‐PO offered improved performance with smallest delay of 0.006 sec, highest energy of 2.368 J, highest throughput of 494.043 kbps.

MF‐DLB: Multimetric forwarding and directed acyclic graph‐based load balancing for geographic routing in FANETs

SummaryFlying ad hoc network (FANET) comprising unmanned aerial vehicles (UAVs) emerges as a promising solution for numerous military and civil applications. Transferring data collected from the environment to the ground station (GS) is a primary concern for meeting the communication demands of most of these applications. However, the highly mobile UAVs with limited communication range, resulting in frequent topology change and intermittent connectivity, make data routing challenging. In such scenarios, geographic routing is a viable solution due to its scalability and robustness. However, the basic forwarding mechanism of geographic routing favors the neighboring UAV nearest to the destination, impacted substantially by link failures and routing holes in a dynamic environment. Additionally, routing decisions ignoring the current load over UAVs contribute to performance degradation due to the high concentration of data traffic near the GS. Thus, to address these issues, a geographic routing protocol named MF‐DLB comprising multimetric forwarding (MF) and a directed acyclic graph‐based load balancing (DLB) scheme is proposed to enhance packet forwarding in FANETs. MF takes account of multiple metrics related to connectivity, geographic progress, link lifetime, and residual energy to select the next hop with a stable communication link while effectively bypassing the routing holes. The second scheme, DLB, focuses on proactively maintaining routing paths near GS for load distribution among underutilized nodes to address the congestion problem. Simulations performed in network simulator ns‐3 confirm the outperformance of MF‐DLB over other related routing schemes in terms of different performance metrics.

Cross-Layer Energy Efficient (CLEE) Routing Algorithm for Mobile Ad-Hoc Networks

Ad-hoc routing algorithms in Wireless Sensor Networks (WSN) rely on nodes’ position awareness by regularly updating routing data of neighbouring nodes. Meanwhile, the transmission energy usage is not optimised as a result of this repeated updates and routing table deployment. Therefore, it is very critical to consider techniques of optimising or conserving energy in the design of wireless sensor networks (WSNs) in order to prolong the lifetime of the individual nodes. One of these techniques is the Cross-layer design which, is considered as an efficient method for addressing this challenge with WSNs. In this paper, we propose a Cross-Layer Energy Efficient (CLEE) routing algorithm to establish an optimal route from the source node to the destination node by selecting candidate nodes from neighbouring nodes based on the distance between these nodes and the rate of energy consumption by a possible candidate node. Then to select a designated node from the candidate nodes, the algorithm further computes the Signal Strength Quality (SQS) and the Link Lifetime (LL) as well as the Throughput (TH) rate of selected nodes. The proposed algorithm was simulated with a network size of 600 X 600 on Network Simulator 3 (NS3) in order to analyse its performance. An evaluation of the performance of the proposed CLEE protocol with existing similar protocols such as Ad-hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) reveals that CLEE outperformes AODV and DSR by conserving about 44% of available energy.

Flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data communication

ABSTRACT Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main limitations of WSN, which generally affect the network life time. Hence, a dynamic clustering and routing model is designed to resolve this issue. In this research work, a deep-learning model is employed for the prediction of energy and an optimization algorithmic technique is designed for the determination of optimal routes. Initially, the dynamic cluster WSN is simulated using energy, mobility, trust, and Link Life Time (LLT) models. The deep neuro-fuzzy network (DNFN) is utilized for the prediction of residual energy of nodes and the cluster workloads are dynamically balanced by the dynamic clustering of data using a fuzzy system. The designed Flamingo Jellyfish Search Optimization (FJSO) model is used for tuning the weights of the fuzzy system by considering different fitness parameters. Moreover, routing is performed using FJSO model which is used for the identification of optimal path to transmit data. In addition, the experimentation is done using MATLAB tool and the results proved that the designed FJSO model attained maximum of 0.657J energy, a minimum of 0.739 m distance, 0.649 s delay, 0.849 trust, and 0.885 Mbps throughput.

Performance evaluation and comparative analysis of CrowWhale-energy and trust aware multicast routing algorithm

Multipath routing helps to establish various quality of service parameters, which is significant in helping multimedia broadcasting in the Internet of Things (IoT). Traditional multicast routing in IoT mainly concentrates on ad hoc sensor networking environments, which are not approachable and vigorous enough for assisting multimedia applications in an IoT environment. For resolving the challenging issues of multicast routing in IoT, CrowWhale-energy and trust-aware multicast routing (CrowWhale-ETR) have been devised. In this research, the routing performance of CrowWhale-ETR is analyzed by comparing it with optimization-based routing, routing protocols, and objective functions. Here, the optimization-based algorithm, namely the Spider Monkey Optimization algorithm (SMO), Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO) algorithm, Water Wave Optimization (WWO) algorithm, Crow Search Algorithm (CSA), and, routing protocols, like Ad hoc On-Demand Distance Vector (AODV), CTrust-RPL, Energy-Harvesting-Aware Routing Algorithm (EHARA), light-weight trust-based Quality of Service (QoS) routing, and Energy-awareness Load Balancing-Faster Local Repair (ELB-FLR) and the objective functions, such as energy, distance, delay, trust, link lifetime (LLT) and EDDTL (all objectives) are utilized for comparing the performance of CrowWhale-ETR. In addition, the performance of CrowWhale-ETR is analyzed in terms of delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput, and it achieved better values of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using EDDTL as fitness.

A Dynamic Addressing Hybrid Routing Mechanism Based on Static Configuration in Urban Rail Transit Ad Hoc Network

With the rapid development of urban rail transit, the traditional urban rail wireless network based on fixed infrastructure is not in a position to meet the increasingly complex communication demand. At the same time, Ad Hoc network, as a special wireless mobile network, is developing rapidly. Applying this self-organized networking architecture to the urban rail vehicle–ground communication network can overcome the problems existing in the traditional urban rail communication system. The routing protocols that can achieve low delay and highly reliable data transmission are important in the urban rail transit scenario. Therefore, combined with the wireless Ad Hoc network and the characteristics of the urban rail transit scenario, this paper proposes a dynamic addressing hybrid routing mechanism based on static configuration. Using an improved AODV routing discovery algorithm and then writing the routing table into the router in advance for static configuration not only reduces network overhead but also prolongs the network’s lifetime. It also saves the delay of routing discovery. Then, the cluster head node dynamically monitors the link status, dynamically finds the path when the link needs to be replaced, and selects different update paths according to different types of communication services. Finally, each algorithm’s network performance parameters, like the routing discovery overhead, residual link lifetime, packet delivery rate, throughput, and end-to-end delay, are analyzed and compared.

Soil Moisture and Heat Level Prediction for Plant Health Monitoring Using Deep Learning with Gannet Namib Beetle Optimization in IoT.

Plant health monitoring is crucial in ensuring a constant food supply to satisfy the growing demand for food. Hence, it is essential to monitor plant health to maximize the yield and minimize the risk of various diseases. Soil moisture and temperature are of critical importance in plant growth, and predicting them enables farmers to take preventive actions, thereby mitigating the issues affecting plant health. This work presents a plant health monitoring approach by forecasting soil moisture and heat levels by collecting data in an Internet of Things (IoT) environment. Here, for transmitting the soil data acquired by the IoT nodes, a cluster head (CH) selection and routing technique using Gannet Namib beetle optimization (GNBO) is used. The data is routed to a prediction module, wherein soil moisture and heat levels are predicted by Convolutional long short term memory (Conv-LSTM). Furthermore, the hyperparameters of the Conv-LSTM are optimized by the GNBO algorithm. The efficiency of the GNBO-Conv-LSTM is examined based on link life time (LLT), energy, delay, distance, negative predictive value (NPV), positive predictive value (PPV), and true negative rate (TNR) and is observed to have achieved values of 0.675, 0.478 J, 0.092 ms, 50.200 m, 0.885, 0.882, and 0.875, correspondingly.

A Machine Learning-Aided Network Contention-Aware Link Lifetime- and Delay-Based Hybrid Routing Framework for Software-Defined Vehicular Networks

The functionality of Vehicular Ad Hoc Networks (VANETs) is improved by the Software-Defined Vehicular Network (SDVN) paradigm. Routing is challenging in vehicular networks due to the dynamic network topology resulting from the high mobility of nodes. Existing approaches for routing in SDVN do not exploit both link lifetimes and link delays in finding routes, nor do they exploit the heterogeneity that exists in links in the vehicular network. Furthermore, most of the existing approaches compute parameters at the controller entirely using heuristic approaches, which are computationally inefficient and can increase the latency of SDVN as the network size grows. In this paper, we propose a novel hybrid algorithm for routing in SDVNs with two modes: the highest stable least delay mode and the highest stable shortest path mode, in which the mode is selected by estimating the network contention. We distinctly identify two communication channels in the vehicular network as wired and wireless, where network link entropy is formulated accordingly and is used in combination with pending transmissions to estimate collision probability and average network contention. We use the prospect of machine learning to predict the wireless link lifetimes and one-hop channel delays, which yield very low Root Mean Square Errors (RMSEs), depicting their very high accuracy, and the wireless link lifetime prediction using deep learning yields a much lower average computational time compared to an optimization-based approach. The proposed novel algorithm selects only stable links by comparing them with a link lifetime threshold whose optimum value is decided experimentally. We propose this routing framework to be compatible with the OpenFlow protocol, where we modify the flow table architecture to incorporate a route valid time and send a packet_in message to the controller when the route’s lifetime expires, requesting new flow rules. We further propose a flow table update algorithm to map computed routes to flow table entries, where we propose to incorporate an adaptive approach for route finding and flow rule updating upon reception of a packet_in message in order to minimize the computational burden at the controller and minimize communication overhead associated with control plane communication. This research contributes a novel hybrid routing framework for the existing SDVN paradigm, scrutinizing machine learning to predict the lifetime and delay of heterogeneity links, which can be readily integrated with the OpenFlow protocol for better routing applications, improving the performance of the SDVN. We performed realistic vehicular network simulations using the network simulator 3 by obtaining vehicular mobility traces using the Simulation of Urban Mobility (SUMO) tool, where we collected data sets for training the machine learning models using the simulated environment in order to test models in terms of RMSE and computational complexity. The proposed routing framework was comparatively assessed against existing routing techniques by evaluating the communication cost, latency, channel utilization, and packet delivery ratio. According to the results, the proposed routing framework results in the lowest communication cost, the highest packet delivery ratio, the least latency, and moderate channel utilization, on average, compared to routing in VANET using Ad Hoc On-demand Distance Vector (AODV) and routing in SDVN using Dijkstra; thus, the proposed routing framework improves routing in SDVN. Furthermore, results show that the proposed routing framework is enhanced with increasing routing frequency and network size, as well as at low vehicular speeds.

MWCRSF: Mobility-based weighted cluster routing scheme for FANETs

Flying Ad-Hoc Network (FANET) formed by a swarm of Unmanned Aerial Vehicles (UAVs) is currently a trending research topic because of its capability in performing complex tasks more rapidly and efficiently. However, the high-speed mobility of UAVs, limited energy, and dynamically changing topology have led to critical challenges for the reliability of communication between UAVs, especially when designing routing schemes in FANET. In this paper, we introduce a Mobility-based Weighted Cluster Routing Scheme (MWCRSF) for FANET performance enhancement within the constraints of network resources. MWCRSF utilizes the high-level search efficiency of the Sparrow Search Algorithm (SSA) and a new clustering algorithm based on SSA is first proposed to set up the initial UAV clusters. A weighted sum-based algorithm is then proposed for Cluster Head (CH) selection, where the residual energy, node correlation degree, relative mobility, distance to the Base Station (BS), and average distance are adopted as the selection criteria. To keep the UAV clusters updated, an efficient cluster maintenance algorithm is proposed as well. In order to provide timely and reliable data transmission to the BS, the optimal next-hop route selection is performed considering the link lifetime, residual energy, and distance between nodes and BS. We exploit the power of skyline operators in multi-criteria decision-making to reduce the search space and improve the efficiency of CH selection and next-hop routing. The simulation results reveal that the proposed MWCRSF scheme outperforms the existing methods under different metrics including cluster lifetime, energy efficiency, throughput, and network lifetime.

An Energy-Aware and Fault-Tolerant WSN Routing Approach Using Serial Exponential Newton Metaheuristic Algorithm for Agricultural Lands

Wireless Sensor Network (WSN) is becoming a promising platform, in which Sensor Nodes (SNs) can sense and monitor the physical or environmental conditions and send the data to the Base Station (BS) through multi-hop routing. Here, opting for the right Cluster Head Selection (CHS) is the key to enhancing the clustering routing protocol performance. Here, a new optimization model to prolong the lifetime of the network and progress the energy efficiency in the routing process is anticipated. The Serial Exponential Newton Meta heuristic algorithm (SExpNMA) is developed for CHS and routing. The parameters like energy, Link Lifetime (LLT), and delay, are considered as the fitness function of the proposed SExpNMA to select the optimal solution. Ultimately, the performance analysis clearly reveals that the SExpNMA technique attains the maximum residual energy of 0.515 J, LLT of 0.346, the throughput of 0.617, and minimum delay of 0.236 s, respectively.

MPIGA – Multipath Selection Using Improved Genetic Algorithm

The Wireless Multimedia Networks (WMNs) have developed due to the extensive applications of wireless devices and increasing availability of lower cost hardware. The WMNs are used to transmit the multimedia content like audio and video streaming and they can be deployed within a lower budget. These networks can also be used in real-time data applications that demand energy-efficient management and awareness of Quality of Service (QoS). The energy resources are limited in the wireless devices that lead to the significant threats on the QoS for WMNs. An energy-efficient routing technique is needed to handle the dynamic topology of WMN that includes a vital resource as energy. The energy-efficient routing method was proposed in this work for the purpose of data communication based on a cluster head selection from each cluster in addition to the multipath route selection to reduce the network overhead and energy consumption. The cluster heads for each cluster are selected based on Node Coverage & average residual energy parameters.In this work, the proposed energy efficient routing algorithm uses improved genetic algorithm (IGA)based on a cost function for dynamic selection of the best path. The proposed cost function uses link lifetime &average link delay parameters to estimate the link cost. The proposed algorithm’s performance compared with other previous routing methods based on extensive simulation analysis. The results showed that the proposed method achieves better performance over three other routing techniques.

CVFP: Energy and trust aware data routing protocol based on Competitive Verse Flower Pollination algorithm in IoT

Internet of Things (IoT) is a network of things that are interconnected to provide a variety of services. These things can include smartphones, actuators, sensors, computers, and wearables. The IoT enables the collection and exchange of data between these objects. The expansion of smart networks is what has given the Internet of Things its global influence. Routing is an important strategy that involves the creation of routes and the transmission of data packets from the source to the intended recipient in the network. Energy and trust are considered to be the two most important parts that provide secure data transmission in a network. However, secure routing in the highly distributed and dynamic environment of IoT still results in a great challenge due to the heterogeneity of devices. Hence, an efficient secure data routing mechanism is designed with the proposed Competitive Verse Flower Pollination (CVFP) algorithm that considers trust, energy, delay, distance, and link lifetime (LLT) parameters to find an optimal routing path. Moreover, the selection of optimal path is made by involving CH nodes such that nodes have higher energy are chosen in the routing strategy. The quality of the link is measured using the LLT constraint in order to decide whether data can be routed or to perform a route maintenance mechanism to ensure reliability in data routing. The performance of the proposed algorithm is analyzed using the simulated data. The proposed algorithm attains a lower delay of 0.204 s, higher residual energy of 0.093 J, a maximum throughput of 94.59%, and a higher trust of 0.713.

Fractional Competitive Fruitfly Optimized Secure Routing Protocol under Mobile Sink Based WSN with Deep QNN Based Prediction

Wireless Sensor Networks (WSN) consists of numerous of low cost and less-energy sensor nodes that are responsible to gather and transmit the data packets from one node to destination point. WSN has a wide range of applications over agriculture, military, traffic monitoring, instrument surveillance, and security monitoring. In WSN, the nodes are located in a specific region to create a wireless network. The effective data communication among sensors is a challenging task because of different complex parameters. Typically, clustering is a well-preferred methodology to provide the effective communication by partitioning the nodes into different clusters. Every cluster possesses individual cluster head that transmits the data to other sensor nodes. Therefore, it is substantial to choose optimal cluster head and optimal route for effective transmission with less energy consumption and less delay. To increase the network efficiency and sink utilization, an energy aware routing algorithm called Fractional Competitive Fruit Fly Optimizer (FrCFFO) is designed, which is an integration of Fractional concept into the Competitive Fruit Fly Optimizer (CFFO). Here, the energy prediction is performed using Deep Quantum Neural Network (QNN). Effective CH selection and routing is done using the proposed FrCFFO and the fitness parameter is considered depending upon the factors like energy, distance, link lifetime, trust, and delay. Moreover, the developed FrCFFO has achieved effective performance with minimum delay of 0.098sec, maximum energy of 0.233J, and maximum PDR of 90.81%.

Delay-aware relay node selection for cluster-based wireless sensor networks

Maximizing the network lifetime through balancing energy and lowering the delay is a primary design goal of wireless sensor networks (WSNs). In WSNs, the sensor nodes task is to acquire the data from the area or point of interest and transmit it to the sink node via relay nodes timely. Relay nodes must dissipate some energy to perform this task. So, the relay nodes and cluster heads face the challenge of energy balancing. Hence, choosing the optimal cluster heads and relay nodes is necessary to balance the energy among the sensor nodes. In this context, this paper presents a novel clustering algorithm to balance energy by considering different metrics of WSNs, including distance, energy, link lifetime, and delay characteristics—using a relay node, etc.In this paper, a Hybrid Heuristic Data Aggregation (HHDAP) Protocol is used by combining particle swarm optimization (PSO) and improved Ant colony optimization (ACO).Through this HHDAP algorithm, the relay nodes are chosen based on a sensor node's distance, energy, link lifetime, and delay metrics. This work is simulated using NS2, and the performance of proposed and existing works are evaluated. The results show the performance improvements of the proposed work over the existing algorithms in terms of different quality of service metrics.

Modularized hypergraph clustering scheme model for stable VANET

SummaryVANET transforms vehicles into a social environment, allowing for maximum security and cooperative data transmission. Each vehicle is assigned a cluster, which transfers data to the cluster head (CH). The efficiency of a VANET network is determined by two factors: efficient vehicle groupings and optimal CH selection. This article proposes a novel modularized hypergraph clustering scheme in which the VANET is represented as a hypergraph network clustered using tensor trace maximization with the modularity matrix as the feed. CH is chosen from each cluster based on four vehicle parameters: vehicle speed about neighbors, consensus trust score, link lifetime, and connectivity level. For the final CH selection, all parameters are converted to eigenvalues. The fuzzy decision‐making scheme selects the best CH for all vehicles in that cluster based on a set of four parameters. The proposed algorithm for selecting the stable CH for improved network performance has also been validated and significantly improved using state‐of‐the‐art schemes.