Higher Network Lifetime Research Articles

DQ-NN and phantom routing for enhanced source location privacy for IoT under multiple source and destination

The Internet of Things (IoT) is now an essential component of our day-to-day lives. In any case, the association of various devices presents numerous security challenges in IoT. In some cases, ubiquitous data or traffic may be collected by certain smart devices which threatens the privacy of a source node location. To address this issue, a hybrid DL technique named Deep Q Learning Neural network (DQ-NN) is proposed for the Source Location Privacy (SLP) in IoT networks based on phantom routing. Here, an IoT network with multiple sources and destinations is considered first, and then the phantom node is chosen by analyzing neighbor list, energy, distance, and trust heterogeneity parameters. After that, multiple routes are created from the source node to the sink node via the phantom node. Finally, path selection is performed by the proposed DQ-NN. Moreover, DQ-NN is obtained by merging the Deep Q Learning Network (DQN) and Deep Neural Network (DNN). A simulation environment consisting of 150 nodes is created to study the effectiveness of performance and scalability. The proposed novel DQ-NN outperforms other existing algorithms, by recording a high network lifetime is 111.912, a safety period of 664970.7 m, an energy is 0.034 J, and a distance is 56.594 m.

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.

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.

Energy efficient data dissemination in wireless sensor network enabled IoT using domain‐adaptive message passing graph neural network

SummaryIn the past few years, restricted wireless sensor networks (WSNs) enabled the Internet of Things (IoT) have attracted significant attention and expansion to enhance service delivery and resource efficiency. Dissemination is a service offered by WSN that uses radio transmission and over‐the‐air programming for updating the deployed sensor nodes through online. The centralized data dissemination methods are replaced by the distributed approaches because they affect the drawbacks of a single point of failure, no scalability, and insecurity. Therefore, an Energy Efficient Protocol for Data Dissemination in Wireless Sensor network‐enabled IoT using Domain‐Adaptive Message Passing Graph Neural Network (EEP‐WSN‐IoT‐DMPGNN) is proposed in this paper. The nodes are formed as clusters utilizing the Deep Fuzzy Curriculum Clustering (DFCC) technique that rewards nodes belonging to a given cluster. By using the Crayfish Optimization Algorithm (COA), the Cluster Head (CH) selection optimally chose the ideal CH and satisfies the multiple objective functions, such as energy, delay, traffic density, and distance. Afterward, domain‐adaptive Message Passing Graph Neural Network (DMPGNN) based routing protocol is developed, the input given to the routing protocol includes a sink, action history, future node, and maximum‐distance node, which attains enhanced data transfer in the chosen path. The proposed technique attains a lower no. of dead nodes, lower energy consumption, and higher Network Lifetime while analyzed with existing techniques, such as routing technique depending on deep learning for effectual data transmission in 5G WSN communication (DL‐RPDT‐WSN), Reinforcement‐Learning base energy effectual optimized routing protocol in WSN (RL‐EERP‐WSN), and Energy‐efficient intellectual routing method for IoT‐enabled WSN (EIR‐IoT‐WSN), respectively.

Energy Efficient Spiking Deep Residual Network and Binary Horse Herd Optimization Espoused clustering Protocol for Wireless Sensor Networks

Nowadays, Wireless Sensor Networks (WSNs) plays several important application fields, specially monitoring events without any human interference. The sensor nodes (SNs) have lesser life time in wireless sensor network owing to its continual sensing with battery drains very rapidly. As a result of the heavy traffic, sensors nearby quickly expire and energy hole issues start to appear. To overcome these issues, an Energy Efficient Spiking Deep Residual Network and Binary Horse Herd Optimization Espoused Clustering Protocol for Wireless Sensor Networks (EE-SDRN-BHHO-CP-WSN) is proposed in this manuscript for sustaining the energy efficiency in the wireless sensor network. One of the main issues in wireless sensor network assisted applications is making use of the available energy. An optimum path selection is a key process of saving energy from sensor nodes to the sink. Spiking Deep Residual Network (SDRN) and Binary Horse Herd Optimization (BHHO) optimize energy efficiency in WSN. The cluster head (CH) is designated by effectual fitness function generated by multi-objectives. It benefits in low power consuming and decreases a count of idle sensor nodes. After CH selection, Binary horse herd optimization algorithm is considered for optimal route selection and data transmission by sink node. The EE-SDRN-BHHO-CP-WSN method is implemented in MATLAB and the efficiency of the proposed method is analyzed with different metrics, like network lifetime, number of alive nodes, number of dead nodes, throughput, energy consumption, packet delivery ratio (PDR). The proposed EE-SDRN-BHHO-CP-WSN method attains 32.35 %, 42.34 %, 49.27 % higher network lifetime, 26.34 %, 31.94 % and 39.35 % higher number of alive nodes and 46.26 %, 38.64 % and 27.83 % lower number of dead nodes compared with other existing models, such as Multi-objective CH based Energy-aware Optimized Routing Algorithm in WSN (MCH-EOR-WSN), Energy efficient CH selection utilizing improved Sparrow Search Algorithm in WSN (EECH-ISSA-WSN), and New Energy Aware CH Selection Algorithm for WSN (NEA-CHSA-WSN respectively).

IoT-based Deep Learning Neural Network (DLNN) algorithm for voltage stability control and monitoring of solar power generation

Today, Solar Photovoltaic (SPV) energy, an advancing and attractive clean technology with zero carbon emissions, is widely used. It is crucial to pay serious attention to the maintenance and application of Solar Power Generation (SPG) to harness it effectively. The design was more costly, and the automatic monitoring is not precise. The main objective of the work related to designed and built up the Internet of Things (IoT) platform to monitor the SPV Power Plants (SPVPP) to solve the issue. IoT platform designing and Data Analytics (DA) are the two phases of the proposed methodology. For building the IoT device in the IoT platform designing phase, diverse lower-cost sensors with higher end-to-end delivery ratio, higher network lifetime, throughput, residual energy, and better energy consumption are considered. Then, Sigfox communication technology is employed at the Low-Power Wireless Area Network (LPWAN) communication layer for lower-cost communication. Therefore, in the DA phase, the sensor monitored values are evaluated. In the analysis phase, which is the most significant part of the work, the input data are first pre-processed to avoid errors. Next, to monitor the Energy Loss (EL), the fault, and Potential Energy (PE), the solar features are extracted as of the pre-processed data. The significance of utilizing the Transformation Search centered Seagull Optimization (TSSO) algorithm, the significant features are chosen as of the extracted features. Therefore, the computational time of the solar monitoring has been decreased by the Feature Selection (FS). Next, the features are input into the Gaussian Kernelized Deep Learning Neural Network (GKDLNN) algorithm, which predicts the faults, PE, and EL. In the experimental evaluation, solar generation is assessed based on Wind Speed (WS), temperature, time, and Global Solar Radiation (GSR). The systems are satisfactory and produce more power during the time interval from 12:00 PM to 1:00 PM. The performance of the proposed method is evaluated based on performance metrics and compared with existing research techniques. When compared to these techniques, the proposed framework achieves superior results with improved precision, accuracy, F-measure, and recall.

An Optimized Hierarchal Cluster Formation Approach for Management of Smart Cities

A smart city is a metropolis technology that employs information technology with several internet of things (IoT) devices to enhance the quality of services for citizens, such as the traffic system, energy consumption, and waste collection. In fact, the quality of service (QoS) of these daily routine services are based on an assistive observation system. Wireless sensor networks (WSNs), as the key component of IoT, are used here to gather data into surveillance subsystems for supporting the decision making. To enhance the collected data management of the surveillance subsystems, many clustering techniques are introduced. The low-energy adaptive clustering hierarchy protocol (LEACH) is a key clustering technique of WSN. However, this protocol has deterring limitations, especially in the cluster formation step, which negatively impacts the residual power of many nodes. In fact, a limited number of efforts that try to optimize the clustering formation step represent the main motivation of this work. Considering this problem, the current research proposes an optimized approach to enhance the cluster formation phase of LEACH. The proposed approach depends on the suitability of the residual energy in the nodes to cover the communication energy, with CHs (cluster heads) as a key factor when allocating the node clusters in the first competition. The remaining power and the density of CHs are employed to weigh the accepted CHs and adjust the optimized size of the clusters in the secondary competition. The third competition helps each cluster to select the optimal members from the candidate members according to the impact of each. The advantages and efficiency of the ICSI (intelligent cluster selection approach for IoT) are observed via the ratio of surviving nodes increasing by 21%, residual energy increasing in 32% of the nodes, and a 34% higher network lifetime.

A Novel Hybridized Cluster-Based Geographical Opportunistic Routing Protocol for Effective Data Routing in Underwater Wireless Sensor Networks

Underwater wireless sensor nodes comprise hundreds to thousands of battery-operated sensor nodes with limited bandwidth. These networks are employed to transmit the data with enhanced quality of service (QoS). However, efficient data routing is the most challenging obstacle in many underwater applications. To solve the issues in underwater sensor nodes, the hybridized cluster-based geographical opportunistic routing protocol with distance vector establishment has been proposed to transmit the data efficiently. Primarily, the proposed methodology finds out the shortest path with minimal hop count whereas the void node can be updated with infinite hop count. Thereafter, the sleep/wake scheduling and waiting mechanism and periodic beaconing algorithm are incorporated into the proposed model to attain a higher packet delivery ratio with minimal energy consumption. This proper scheduling and optimal cluster routing enhance the continuous data transmission in underwater applications. The simulation result reveals that the proposed method achieves better energy efficiency and higher network lifetime when compared with the existing clustering methods.

Dual-discriminator Conditional Generative Adversarial Network Optimized with Hybrid Momentum Search Algorithm and Giza Pyramids Construction Algorithm for Cluster-based Routing in WSN Assisted IoT

Wireless sensor network (WSN) efficiently sends and receives the data on the internet of things (IoT) environment. As a large-scale WSN's nodes are powered by batteries, it is essential to create an energy-efficient system to decrease energy consumption and increase the network's lifespan. The existing methods not present effectual cluster head (CH) selection and trust node computation. Therefore, dual-discriminator conditional generative adversarial network optimized with a hybrid Momentum search algorithm and Giza Pyramids Construction algorithm for Cluster Based Routing in WSN Assisted IoT is proposed in this manuscript, for securing data transmission by identifying the optimum CH in the network (DDcGAN-MSA-GPCA-CBR-WSN-IoT). Initially, the proposed method is acting routing process via cluster head. Therefore, Dual-Discriminator conditional Generative Adversarial Network (DDcGAN) is considered to select the CH depending on multi-objective fitness function. The multi-objective fitness function, such as energy, delay, throughput, distance among the nodes, cluster density, capacity, collision, traffic rate, and cluster density. Based on fitness function, CH is selected. After cluster head selection, a malicious node depends on three parameters: trust, delay, and distance. These three parameters are optimized by hyb MSA-GPCA for ideal trust path selection. The proposed DDcGAN-MSA-GPCA-WSN-IoT technique is activated in PYTHON and network simulator (NS2) tool. Its effectiveness is analyzed under performance metrics, such as number of alive nodes, dead nodes, delay, energy consumption, packet delivery ratio, a lifetime of sensor nodes, and total residual energy. The simulation outcomes display that the proposed method attains lower delay, higher packet delivery ratio and high network lifetime when comparing to the existing models.

Fuzzy‐based unequal clustering protocol for underwater wireless sensor networks

SummaryUnderwater wireless sensor network (UWSN) is a network made up of underwater sensor nodes, anchor nodes, surface sink nodes or surface stations, and the offshore sink node. Energy consumption, limited bandwidth, propagation delay, high bit error rate, stability, scalability, and network lifetime are the key challenges related to underwater wireless sensor networks. Clustering is used to mitigate these issues. In this work, fuzzy‐based unequal clustering protocol (FBUCP) is proposed that does cluster head selection using fuzzy logic as it can deal with the uncertainties of the harsh atmosphere in the water. Cluster heads are selected using linguistic input variables like distance to the surface sink node, residual energy, and node density and linguistic output variables like cluster head advertisement radius and rank of underwater sensor nodes. Unequal clustering is used to have an unequal size of the cluster which deals with the problem of excess energy usage of the underwater sensor nodes near the surface sink node, called the hot spot problem. Data gathered by the cluster heads are transmitted to the surface sink node using neighboring cluster heads in the direction of the surface sink node. Dijkstra's shortest path algorithm is used for multi‐hop and inter‐cluster routing. The FBUCP is compared with the LEACH‐UWSN, CDBR, and FBCA protocols for underwater wireless sensor networks. A comparative analysis shows that in first node dies, the FBUCP is up to 80% better, has 64.86% more network lifetime, has 91% more number of packets transmitted to the surface sink node, and is up to 58.81% more energy efficient than LEACH‐UWSN, CDBR, and FBCA.

Energy Efficient Region based Source Distributed Routing Algorithm for Sink Mobility in Underwater Sensor Network

The residual nodes energy consumption is a primary factor to extend the lifespan of network. Utilizing a sensor node's very low-powerbattery in Underwater Wireless Sensor Networks (UWSNs) is still a difficult task. To increase the network lifespan, this paper proposes an Energy Efficient Region based Source Distributed Routing Algorithm for Sink Mobility in Underwater Sensor Network (EERSDRA-GCOP-UWSN). The proposed work consists of three Phases that are initialization phase, Routing Phase and Data transmission. For initialization phase, sinks and the sensor nodes are initialized. For Routing phase, Energy EERSDRA with sink mobility is employed to identify the region of sensor nodes. For data transmission phase, Geographic and Cooperative Opportunistic Protocol is proposed to select the best relay node on the basis of depth fitness factor. The proposed EERSDRA-GCOP-UWSN is evaluated with the performance metrics. Then performance of the EERSDRA-GCOP-UWSN proposed method provides 18.409%, 13.843% and 23.768% high Network lifetime, 27.744%, 24.881% and 19.703% low energy consumption, 17.346%, 20.852% and 26.347% high throughput and 16.385%, 20.416% and 22.907% high PDR when compared to the existing methods such as, Energy-efficient regional based cooperative routing protocol with sink mobility for UWSN (EERCRP-UWSN), a safe energy efficient and cooperative routing protocol for UWSN (SEECRP-UWSN), geo-opportunistic routing protocols for improving the residual energy range (EEGORP-UWSN) methods respectively. Finally, the performance of the proposed method achieves better result while compared to the existing methods.

Analysis of Life Time Improvement Energy Efficient Risk-Free Protocol (ERP) for Data Transmission in Wireless Sensor Networks

Recently, wireless sensor networks (WSNs) have come to be seen as the fundamental architecture that paved the way for the development of the Internet of Things (IoT). However, a challenging problem develops when WSNs are connected to the IoT because of their nodes' high energy consumption and low network lifetime. As a result, the core concepts of WSNs include energy limits in sensor nodes, sensor data exchange, and routing protocols. The enhanced smart-energy-efficient routing protocol (ESEERP) presented in this study addresses the aforementioned shortcomings by extending the network's lifespan and enhancing connectivity. Since the Internet of Things (IoT) demands a significant amount of energy for sensing, processing, and data transport, the IoT application in wireless sensor networks (WSNs) poses substantial issues in maintaining network durability. As a result, multiple traditional algorithms incorporate different optimization techniques to improve the performance of WSN networks. We utilize IPv4 in wireless sensor networks to transmit data (packets), however, this strategy prevents users from accessing big amounts of data over long distances since it may need a lot of power to transmit large amounts of data using IPv4. The Internet of Things (IoT) can be used to solve this problem because it connects small battery-operated devices like sensors as well as consumer electronics and home appliances. One of the most often utilised wireless methods for sensor network connectivity is the IEEE 802.11 standard.

Secure and Energy-Efficient Network Topology Obfuscation for Software-Defined WSNs

Network topology obfuscation (NTO) is generally considered as a promising proactive mechanism to mitigate traffic analysis attacks. The main challenge is to strike a balance among energy consumption, reliable routing, and security levels due to resource constraints in sensor nodes. Furthermore, software-defined wireless sensor networks (WSNs) are more vulnerable to traffic analysis attacks due to the uncovered pattern of control traffic between the controller and the nodes. In this article, a new energy-aware NTO mechanism is proposed, which maximizes the attack costs and is efficient and practical to be deployed. Specifically, first, a route obfuscation method is proposed by utilizing ranking-based route mutation, based on four different critical criteria: 1) route overlapping; 2) energy consumption; 3) link costs; and 4) node reliability. Then, a sink node obfuscation method is introduced by selecting several fake sink nodes that are indistinguishable from actual sink nodes, according to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -anonymity model. As a result, the most suitable routes and sink nodes can be selected, and a highest obfuscation level can be reached without sacrificing energy efficiency. Finally, extensive simulation results demonstrate that the proposed methods can strongly mitigate traffic analysis attacks and achieve effective NTO for software-defined WSNs. In addition, the proposed methods can reduce the success rate of the attacks while achieving lower energy consumption and higher network lifetime.

ICRA: An Intelligent Clustering Routing Approach for UAV Ad Hoc Networks

As an important means of obtaining information of marine situation, the marine monitoring system relying on UAV has been paid more and more attention by all countries in the world, and the demand for tasks is growing continually. In UAV ad hoc networks, routing protocols with immutable routing policies that lack flexibility are generally incapable of maintaining effective performance due to the complicated and rapidly changing environmental situation and application requirements. In this paper, we propose an intelligent clustering routing approach (ICRA) for UANETs. The ICRA is composed of three components: the clustering module, the clustering strategy adjustment module and the routing module. In the clustering process, each node needs to calculate its utility. In order to maintain high topology stability and long network lifetime in different network states, the reinforcement learning based clustering strategy adjustment module needs continuous learning the benefits brought by adopting different strategies to calculate the nodes utility in a specific network state. With the learned knowledge, clustering strategy adjustment module could determine the optimal clustering strategy according to the current network state. In the routing phase, the proposed scheme can reduce the end-to-end delay and improve the packet delivery rate by introducing inter-cluster forwarding nodes to forward messages among different clusters. Extensive experiments have been conducted to verify ICRA’s robustness and superiority over existing schemes. The results demonstrate that ICRA could achieve better performance than its state-of-the-art counterparts with regard to the clustering efficiency, topology stability, energy efficiency and quality of service.

Enabling security in MANETs using an efficient cluster based group key management with elliptical curve cryptography in consort with sail fish optimization algorithm

AbstractIn MANET, group key management is a vital part of multicast security. But distribution of keys in an authenticated manner is a difficult task in group key management. The existing methods provide low security with high processing time during group key management resulting does not provide sufficient results. Therefore, enabling security in MANETs using an efficient cluster based group key management with elliptical curve cryptography in consort with sail fish optimization algorithm is proposed in this article for two‐level security with reduced computational overhead. At first, all the nodes in the cluster are structured in hierarchy method. The key server creates public key utilizing private key of the group node. Here, elliptical curve cryptography based meta‐heuristic sail fish optimization algorithm is used to select the optimal private key for better secure communication. After selecting this optimum private key, the key server creates the public key, and a common group key is created using this generated public keys. If the nodes joint or exit from the subgroup, the reset process is executed in group key management process. Finally, this process reduces the computational overhead of rekeying method. The proposed method is simulated by Python programming and network simulator‐3. The proposed elliptical curve cryptography based sail fish optimization algorithm attains 10.9%, 22.21%, and 11.43% low computational overhead, 19.34%, 13.45%, and 42.13% low latency, 43.45%, 22.21%, and 12.22% high packet delivery rate, 11.23%, 13.41%, and 21.11% high network life time than the existing methods.

An enhanced routing algorithm based on a re-position particle swarm optimization (RA-RPSO) for wireless sensor network

Energy saving is a major requirement in the design of routing algorithms to maximize the throughput and lifetime of wireless sensor networks. To address this requirement, this work proposes a routing algorithm based on a novel Reposition Particle Swarm Optimization (RPSO) algorithm and a new fitness function. The role of the RPSO algorithm is to salvage particles that may have fallen into local minima, if any. The algorithm is evaluated using three types of benchmark functions, namely unimodal, multimodal, and rotated multimodal. The evaluation results demonstrate that RPSO outperforms native Particle Swarm Optimization and Particle Swarm Optimization with Levy Flight in terms of convergence speed and global optimum identification. The proposed RPSO is then employed to develop an efficient routing algorithm. The routing algorithm is tested in terms network lifetime, number of dead sensor nodes, energy consumption, and number of packets delivered to base station, against four competitive Meta-Heuristic algorithms. The test results clearly indicate that the routing algorithm outperforms all the competitive algorithms for all the performance metrics. In particular, it achieves 14% to 29% higher network lifetime, 52% to 58% less dead sensor nodes, 61% to 70% less energy consumption, and 13% to 36% more throughput.

Blockchain enabled joint trust (MF‐WWO‐WO) algorithm for clustered‐based energy efficient routing protocol in wireless sensor network

AbstractIn this manuscript, a blockchain enabled joint trust (MF‐WWO‐WO) algorithm for clustered based energy efficient routing protocol in wireless sensor network (WSN) is proposed for secure data transmission by finding the optimum cluster head (CH) in the network. Here, MayflyWater Wave Optimization (MF‐WWO) algorithm is used for CH selection accurately. After the selection of CH, the malicious node occurs in the cluster. Hence, the multi objective whale optimization algorithm is proposed to find the trust path from the several paths. At last, the optimized selected trust paths are given to the blockchain for communications in the network that is safe and more trustworthy. The simulation is done in MATLAB. The proposed MF‐WWO‐WO achieves high throughput, high efficiency, high network life time, high packet delivery ratio, low delay, low energy consumption, and low packet loss ratio. The outcomes shows the better performance of the proposed approach when compared to the existing approaches, like blockchain and clustered based secured data aware energy efficient using fitness averaged rider optimization algorithm FA‐ROA blockchain and clustered based secured data aware energy efficient using trusted moth flame optimization and genetic optimization algorithm TMFO‐GOA. Finally, the proposed approach attains better energy efficiency and security.

Network layer communication protocol design for underwater agriculture farming

SummaryIn recent times, underwater agriculture farming is seen as a viable alternative to land‐based farming by agricultural scientists due to increasing limitations in land‐based farming areas around the world. But farming underwater is not as easy as it is said. Continuous monitoring of different parameters of the water inside the biosphere where the herbs, crops, and plants are cultivated is one of the highest priorities for the effective outcome in underwater farming. To achieve such precise monitoring, sensor‐based data collection from the underwater biosphere is the only way. Employing human divers for monitoring of water parameters has many limitations such as diving time, accidents due to harsh underwater environment. Alternatively, adopting sensor‐based monitoring provides spatial and temporal flexibility. But one of the significant challenges of such an underwater wireless sensor network (UWSN) is ensuring reliable communication between the underwater sensors and surface station. To ensure reliable delivery of sensed information network layer operation is vital. The underwater environment has unique challenges such as high interference, harsh environment, limited lifetime, and short network lifetime the network layer protocols of terrestrial networks can't be adopted into the underwater environment. So this research focuses on the development of a mathematical model for network layer communication using stochastic network calculus (SNC) taking into consideration the requirements of underwater agriculture farming application. In this research, the delay and backlog bounds of the proposed model are derived. The proposed mathematical model is also evaluated for its correctness using the discrete event simulation.

An optimal communication in WSN enabled by fuzzy clustering and improved meta-heuristic model

PurposeWireless sensor networks (WSN) have been widely adopted for various applications due to their properties of pervasive computing. It is necessary to prolong the WSN lifetime; it avails its benefit for a long time. WSN lifetime may vary according to the applications, and in most cases, it is considered as the time to the death of the first node in the module. Clustering has been one of the successful strategies for increasing the effectiveness of the network, as it selects the appropriate cluster head (CH) for communication. However, most clustering protocols are based on probabilistic schemes, which may create two CH for a single cluster group, leading to cause more energy consumption. Hence, it is necessary to build up a clustering strategy with the improved properties for the CH selection. The purpose of this paper is to provide better convergence for large simulation space and to use it for optimizing the communication path of WSN.Design/methodology/approachThis paper plans to develop a new clustering protocol in WSN using fuzzy clustering and an improved meta-heuristic algorithm. The fuzzy clustering approach is adopted for performing the clustering of nodes with respective fuzzy centroid by using the input constraints such as signal-to-interference-plus-noise ratio (SINR), load and residual energy, between the CHs and nodes. After the cluster formation, the combined utility function is used to refine the CH selection. The CH is determined based on computing the combined utility function, in which the node attaining the maximum combined utility function is selected as the CH. After the clustering and CH formation, the optimal communication between the CH and the nodes is induced by a new meta-heuristic algorithm called Fitness updated Crow Search Algorithm (FU-CSA). This optimal communication is accomplished by concerning a multi-objective function with constraints with residual energy and the distance between the nodes. Finally, the simulation results show that the proposed technique enhances the network lifetime and energy efficiency when compared to the state-of-the-art techniques.FindingsThe proposed Fuzzy+FU-CSA algorithm has achieved low-cost function values of 48% to Fuzzy+Particle Swarm Optimization (PSO), 60% to Fuzzy+Grey Wolf Optimizer (GWO), 40% to Fuzzy+Whale Optimization Algorithm (WOA) and 25% to Fuzzy+CSA, respectively. Thus, the results prove that the proposed Fuzzy+FU-CSA has the optimal performance than the other algorithms, and thus provides a high network lifetime and energy.Originality/valueFor the efficient clustering and the CH selection, a combined utility function was developed by using the network parameters such as energy, load, SINR and distance. The fuzzy clustering uses the constraint inputs such as residual energy, load and SINR for clustering the nodes of WSN. This work had developed an FU-CSA algorithm for the selection of the optimal communication path for the WSN.

Empowered Hybrid Parent Selection for Improving Network Lifetime, PDR, and Latency in Smart Grid

To support the constraints of smart meters—low power and memory—of AMI network, RPL is considered as the most suitable routing protocol to be implemented in practice. Network lifetime, PDR, and latency are the critical issues to be focused on and addressed. Generally, single parent selection scheme cannot satisfy all expected performance requirements of RPL based on AMI network due to tradeoff between workload balancing and transmission performance, PDR and latency. Moreover, the single parent also suffers from the package size and transmission range. Then, multiparent solution is proposed to overcome these demerits using multipath transmission strategy. Although the existing multiparent solutions, MELT and MAHP, overcome the issue of transmission performance, they present low network lifetime since multiparent solution consumes high energy in data transmission. In this paper, we propose an “empowered hybrid parent selection (EHPS)” that exploits the merits of multiparent solution and the single parent with cognitive radio technology in a hybridizing scheme. To split the data packet efficiently under multipath transmission strategy, a fuzzy AHP (FAHP) is adopted; therefore, EHPS balances the workload effectively and maximizes the network lifetime over long transmission range and large data size. Moreover, by exploiting cognitive radio, EHPS is flexible to the transmission range and data size since it achieves the highest transmission performance, highest PDR, and lowest latency among others, while maintaining high network lifetime.