Number Of Alive Nodes Research Articles

Robust AI Based Bio Inspired Protocol using GANs for Secure and Efficient Data Transmission in IoT to Minimize Data Loss

Objectives: To propose an AI-based protocol to enhance the reliability and security of IoT data transmission in a robust manner. Deep learning with bio-inspired methods is employed to address real-time challenges such as route optimization, data integrity, error recovery, and end-to-end delay in order to minimize data loss and maximize the transmission rate. Methods: Generative Adversarial Networks (GANs) are used to enhance the robustness of the protocol in combination with a bio-inspired Artificial Immune System (AIS) to detect IoT network anomalies and responds to malicious activities by using the adaptive learning capabilities of IS. Hybrid Automatic Repeat Request (HARQ), an error recovery method, is utilized to detect network errors in order to correct and retransmit data to the destination during error-prone network conditions. Queue learning action sets are used to discover the finest path for efficient data transmission. The OMNeT++ simulator is used to assess the performance of the proposed BIP-GANs protocol. The performance results are compared with the prevailing data transmission protocols, such as EAP-IFBA, DNN-CSO, and ALO-DHT. Findings: The suggested BIP-GANs data transmission protocol outperforms with promising results, with an energy consumption rate of 6%, 8 seconds data transmission speed, 6 second less delay rate, 98% robustness (security and confidentiality), 98.5% alive nodes, and 99% network life span, which is higher than the prevailing methods. Novelty: This research study provides a comprehensive solution to secure and efficient data transmission by integrating the AI-based bio-inspired BIP-GANs method to address the security and data transmission challenges of existing methods such as EAP-IFBA, DNN-CSO, and ALO-DHT in terms of energy consumption rate, transmission speed, delay rate, life span of the network, robustness, and number of alive nodes. Keywords: Artificial Intelligence, Generative Adversarial Networks, Error Recovery, Secured Data Transmission, Deep Learning, Bio­Inspired Algorithm

Optimized Adaptive Multi-Scale Dual An for Multi-Objective CHS and Energy-Aware Routing in 6G WC

In this research work, an Adaptive Multi-Scale Dual Attention Network with ZOA for Multi-Objective CHS with energy-aware routing in 6G wireless Communication (CHS-EAR-AM-SDAN-6G) is proposed to secure the data transmission by selecting optimum cluster heads in the 6G Wireless Communication network. Initially, the nodes are gathered together to form a cluster using an Adaptive Multi-Scale Dual Attention Network (AM-SDAN). The Zebra Optimization Algorithm (ZOA) strategically selects Cluster Heads (CHs) in wireless networks based on a multi-objective fitness function (MoFF) that minimizes energy consumption while considering factors, like distance, delay, and traffic density. The path and minimum value of fitness are recognized as the routing path and statistics are promoted to the sink node through the cluster head. The proposed scheme has been applied in Python and productivity of the proposed method is predictable with the help of several performances they are energy consumption, detection rate, computational time, packet delivery rate, number of alive nodes, and security. The performance of the proposed CHS-EAR-AMSDAN-6G method attains 25.93%, 24.81%, and 23.38% of alive nodes, 24.45%, 26.71% and 21.32% lower packet delivery rate, 27.56%, 26.43%, and 28.61% low computational period, related with three current methods, such as Generative Adversarial Learning for ITM in 6G Wireless Communication Networks (CHS-EAR-GAL-ITM-6G), ML Algorithms for the Future 6G WCN (CHS-EAR-ML-6G), Energy Efficient Distributed Federated Learning to the 6G Wireless Communication Networks (CHS-EAR-EEDFL-6G), respectively.

A DRL-based Partial Charging Algorithm for Wireless Rechargeable Sensor Networks

Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

A Novel Lie Hypergraph Based Lifetime Enhancement Routing Protocol for Environmental Monitoring in Wireless Sensor Networks

Wireless sensor network (WSN) is a rapid surging technology promising many fruitful innovations for the user community. The use of WSNs for continuous monitoring of environmental factors in severe situations, such as detection of volcanoes, forest fires, floods, and so on. Despite WSN’s various potential applications, energy and deployment are two major concerns influencing the network life span. The primary requirement is to monitor node energy consumption in order to improve network performance. Many approaches are proposed in recent times for energy efficiency, this article introduces an associated hypergraph with Lie algebra of upper triangular matrices for energy efficient clustering and routing. Initially, the sensor nodes are formulated as a hypergraph, and each hyperedge is treated as a cluster, from which cluster-head (CH) selection is exploited by minimum hypergraph transversal. The routing decision is employed by constructing the upper triangular matrix (UTM), and Lie commutators of the UTM Lie algebra identifies the best relay nodes for data forwarding. The performance of the proposed work is evaluated using simulation results which shows the effectiveness of the scheme over compared protocols in terms of, number of alive nodes, energy consumption and number of packets received in BS.

Energy Efficient Clustering Using Improved Particle Swarm Optimization in Wireless Sensor Networks

The Wireless Sensor Network (WSN) includes many low-cost nodes that have the capacity to perceive, operate, and communicate wirelessly. WSN can spread the information to all around through a cooperative node approach. It also has many advantages in terms of both cost and cooperative intelligence. In a wireless sensor network, nodes have limited energy resources, so their life cycle is considered as one of the main concerns about wireless sensor networks. Energy efficiency grouping and routing are two well-known issues in optimization that have been widely studied in order to increase the lifetime of wireless sensor networks. In this paper, an improved particle swarm optimization (IPSO) clustering algorithm for energy efficiency network management is introduced in order to find a route for creating optimal clusters. To evaluate the efficiency of the proposed clustering algorithm, this algorithm is simulated and compared with the particle swarm optimization(PSO) algorithm based on parameters such as network energy, number of live nodes and network life.

Energy aware optimal routing model for wireless multimedia sensor networks using modified Voronoi assisted prioritized double deep Q‐learning

SummaryThe energy hole problem is critical in Wireless Multimedia Sensor Network (WMSN), wherein the nodes closer to the sink node will expire sooner than the outer sub‐regions. Because they transmit their packet and forward the outer sub‐regions packet to the sink. Hence, a hole arises near the sink node after a very short time. Thus, an energy‐aware optimal routing model is introduced in this research using Modified Voronoi‐assisted prioritized double deep Q‐learning (PDDQL). Initially, the sensor nodes are deployed using the Voronoi cell structure that splits the entire region of interest into different Voronoi polygons. However, the Voronoi diagram will not always predict an efficient route because of the nodes at a Voronoi edge or vertex outside the transmission range. To tackle this issue, a new Voronoi diagram is proposed that depends on the maximum horizontal transmission range to remove the portion of Voronoi edges uncovered by the sensor node. After the deployment, a PDDQL algorithm is used to select the Relay Nodes based on their remaining energy. The proposed PDDQL determines an alternate route to transmit the packet if an energy hole problem has occurred in WMSN. The analysis of a proposed routing protocol based on the assessment measures like packet delivery ratio, average residual energy, number of alive nodes, and average end‐to‐end delay accomplished the value of 198.33 (ms), 2.05 (J), 100, and 0.85, respectively.

Design of ant lion optimization-based PEGASIS routing protocol for energy efficiency in networks

<p>In sensor networks the main problem facing by many researchers is regarding the energy efficiency. Different protocols are evaluated for communicating between the sensor nodes. The routing protocol when combined with evolutionary algorithms gives best optimal solution for the problem incurred in wireless sensor networks. In this paper, ant lion energy efficient-power efficient gathering in sensor information systems (ALEEPEGASIS) is used to develop the chain. This technique can achieve a global optimization solution by finding the best cluster head or the leader node for data communication. The techniques help in distributing the paths equally while the transmission of data process is performed. By performing this process, the power consumption near the sensor nodes can be reduced. The proposed technique is compared with other techniques like energy efficient PEGASIS and swarm energy efficient PEGASIS. The parameters used to compare are number of alive nodes, number of dead nodes and residual energy. The performance is observed using MATLAB simulation results.</p>

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.

UASN‐3D: An energy efficient localization based on LEACH‐BR algorithm (EELBL‐BR)

AbstractUnderwater acoustic sensor networks (UASN) research is gaining popularity as it has various applications such as military communication, oil rig maintenance, animal survival, linking submarines to land, gathering data for water monitoring, and other commercial fields. The two most critical requirements for the application's proper operation are the accurate knowledge of sensor node locations and the efficient transmission of accurate underwater sensor node information to the base station with efficient energy consumption. The proposed energy efficient localization based on the LEACH‐beacon and reinforced node (EELBL‐BR) algorithm satisfies both the requirements in 3D‐UASN. The proposed algorithm considers the deployment and computation of accurate location of sensor nodes in the underwater environment by applying I‐LASP (improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) (Yadav N, Khilar PM. Trans Emerg Telecommun Technol. 2023;34:e4772.) for 3D environment. It performs clustering of sensor nodes for enhancing network lifetime using three different types of nodes such as beacon, reinforced, and member nodes. The proposed clustering LEACH‐BR (low‐energy adaptive clustering hierarchy‐beacon and reinforced nodes) algorithm is based upon the LEACH algorithm which provides accurate location of all the sensor nodes, improves energy consumption and reliability in the underwater environment. The result shows that the proposed algorithm EELBL‐BR, considering both beacon and reinforced nodes, provides the improvement in the number of alive nodes, reduction in the number of dead nodes, reduction in energy consumption and enhances residual energy in the UASN by 68.90%, 51.91%, 51.47%, and 68.12% respectively with respect to the number of rounds as compared to that of the existing algorithm by Rizvi et al. (Wirel Pers Commun. 2022;124(4):3725–3741.) and thus outperforms the existing algorithm (Rizvi HH, Khan SA, Enam RN. Wirel Pers Commun. 2022;124(4):3725–3741.).

Three-stage fuzzy-metaheuristic algorithm for smart cities: Scheduling mobile charging and automatic rule tuning in WRSNs

Recent breakthroughs in wireless power transfer technology have been promising for empowering and enabling seamless operation of wireless sensors and developing sustainable systems such as wireless rechargeable sensor networks (WRSNs). Relying on this technology and focusing on WRSNs, this paper proposes an adaptive charging scheduling algorithm called the three-stage fuzzy metaheuristic algorithm (TSFM) by using the whale optimization algorithm (WOA) and a multiobjective function designed to meet the application requirements of WRSNs in smart cities. The TSFM algorithm benefits from the WOA for the automated configuration and optimization of the fuzzy rule-base table in a three-stage fuzzy inference system for clustering, routing, and scheduling mobile charging simultaneously. Unlike the other methods, the TSFM algorithm determines the ratio of effect of fuzzy input parameters of every stage based on application and environment in addition to considering effective parameters for every stage, especially for charging scheduling (e.g., the energy of a charge-request node, the distance between a charge-request node and a mobile charging (MC), the density of a charge-request node, the average energy consumption rate of a charge-request node, and the time elapsed after a charge request). Furthermore, for the proper distribution of energy in a network, better responsiveness, and optimal use of a charging robot, two energy thresholds are defined in the TSFM algorithm, namely as the charging request issue threshold and the charging operation completion threshold. The TSFM algorithm is adaptable to applications, environments, and requirements of WRSNs not only by using the features of nodes (as the fuzzy input parameters) but also by considering the application-specific features, environments, and infrastructure (e.g., network size, number of nodes, base station (BS) positions, and designer’s goals) within the process of optimizing tunable parameters. The TSFM algorithm was evaluated and compared with some common methods such as FCFS, NJNP, and ESS in terms of functional metrics such as energy utility, charging latency, survival rate, death rate of nodes, system stability, number of packets received by BS, number of request nodes, number of alive nodes, average residual energy, adaptability analysis, and statistical validation in two scenarios and six applications. According to the simulation results, the proposed TSFM algorithm improved the performance criteria and outperformed the other methods by far in terms of application. For instance, the TSFM algorithm improved the minimum system stability rate by 167%, 0.73%, and 123% compared with FCFS, NJNP, and ESS methods, respectively.

OQ‐IICA: Optimal QoS‐aware intra‐inter cluster data aggregation technique for IoT‐assisted WSNs using hybrid optimization techniques

SummaryThe wireless sensor network (WSN) contains sensor nodes for understanding, communicating, and storing battery capacity data. Data collection can be defined as the process that base stations use to eliminate unwanted transmissions and provide mixed information. This improves energy efficiency and extends the life of low‐energy WSNs enabled by IoT (IoT‐WSNs). In this article, we propose an optimal QoS‐aware intra‐inter cluster data aggregation technique for WSNs using hybrid optimization techniques (OQ‐IICA). First, we introduce a modified bowerbird optimization (MBO) algorithm for balanced clustering which improves energy efficiency. Second, we develop a multi‐objective seagull optimization‐based decision‐making (MSO‐DM) algorithm to estimate the CH of clusters in the network. Next, we introduce a teacher‐inspired cappuccino search algorithm to ensure the quality of data transfer between nodes by learning through internal and cluster routing. Finally, the proposed OQ‐IICA algorithm compares the latest technologies in ICA, Leach, and Leach‐C power consumption, latency, throughput, latency, number of live nodes, routing, and grid length.

A Novel Spider Monkey Optimized Fuzzy C-Means Algorithm (SMOFCM) for Energy-Based Cluster-Head Selection in WSNs

AI is getting increasingly complex as a result of its widespread deployment, making energy efficiency in Wireless Sensor Network (WSN)-based Internet of Things (IoT) systems a highly difficult problem to solve. In energy-constrained networks, cluster-based hierarchical routing protocols are a very efficient technique for transferring data between nodes. In this paper, a novel Spider Monkey Optimized Fuzzy C-Means Algorithm (SMOFCM) is proposed to improve the lifetime of the network and less energy consumption. The proposed SMOFCM technique makes use of the Fuzzy C-means clustering framework to build up the cluster formation, and the Spider Monkey Optimization technique to select the Cluster Head (CH). MATLAB was used to model the suggested SMOFCM. The suggested framework's network lifetime, number of alive nodes (NAN), energy consumption, throughput, and residual energy are compared to those of more established frameworks like LEACH, K-MEANS, DRESEP, and SMOTECP. SMOFCM technique improves the network lifetime by 11.95%, 7.59%, 4.97% and 3.83% better than LEACH, K-MEANS, DRESEP, and SMOTECP. According to experimental findings, the proposed SMOFCM technique outperforms the existing model.

Energy-Efficient Bi-Objective Optimization Based on the Moth–Flame Algorithm for Cluster Head Selection in a Wireless Sensor Network

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the Moth–Flame Optimization (MFO) algorithm and the Salp Swarm Algorithm (SSA), as well as the Whale Optimization Algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes’ residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed the other algorithms in terms of energy efficiency.

MOCRAW: A Meta-heuristic Optimized Cluster head selection based Routing Algorithm for WSNs

In Wireless Sensor Networks (WSNs), sensors are deployed in a specific region to sense the environment’s physical parameters. After sensing, data is processed and sent to the base station through a given route. Sensing and transmitting nodes consume a lot of energy; hence nodes die quickly; therefore, hot spot problems occur. Henceforth, data transmission is done by a single route; thus, WSNs experience network overhead problems. Nowadays, the enhancement of the energy of WSNs remains a challenging issue. Alternatively, efficient processes such as routing or clustering may be improved. Dynamic cluster head selection can be considered an important decision approach for optimal path selection and saving energy. This paper proposes a Meta-heuristic Optimized Cluster head selection-based Routing algorithm for WSNs (MOCRAW) to minimize node’s energy consumption and fast data transmission. MOCRAW removes isolated nodes or hot-spot problems and provides loop-free routing with the help of the Dragonfly Algorithm (DA), wherein the decision is based on Local Search Optimization (LSO) and Global Search Optimization (GSO). This protocol exploits two sub-processes: the optimal Cluster Head Selection Algorithm (CHSA) and Route Search Algorithm (RSA). CHSA uses Energy Level Matrix (ELM). ELM is based on node density, residual energy, the distance between Cluster Head (CH) and Base Station (BS), and inter-cluster formation. The inter-cluster discovers the optimum path between source to destination in RSA by levy distribution. MOCRAW performance is compared with other clustering and routing protocols on parameters such as the number of alive nodes, delay, packet delivery ratio, and average energy consumption. Simulation-based findings exhibit that the proposed methodology surpasses its peers and competitors in terms of energy efficiency.

Clustering based Dynamic Routing Algorithm for Efficient Energy Utilization in Wireless Sensor Networks (WSNs)

The significance of proper and efficient energy utilization is quite high in Wireless Sensor Networks (WSNs). As a result, network lifetime can be massively enhanced. Therefore, Graph enabled Energy Optimized Routing (GEOR) algorithm is adopted to extend the lifetime of the sensor network and minimize energy consumption in WSNs. An efficient solution for lifetime enhancement of sensor network and energy minimization are key objectives of the proposed GEOR algorithm. The proposed GEOR algorithm provides improved performance than the traditional Low Energy Adaptive Clustering Hierarchy (LEACH) approach. Here, the proposed GEOR algorithm enhances cluster size and the CH selection process is performed to elect CHs for multiple clusters present in the sensor network. A detailed solution for the optimization problem in WSNs is also presented. The mobility aspect of the sink node has a massive impact on energy consumption in WSNs. Experimental results are evaluated in terms of sensor network lifetime enhancement (number of alive nodes and number of dead nodes). Cluster formation and CH selection are performed based on the mobility cost function. Throughput and network lifetime results are obtained in terms of residual energy using the proposed GEOR algorithm. The result is compared against the traditional LEACH approach and the proposed GEOR algorithm shows superior performance.

Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) are emerging technology for disclosing multiple applications, such as oil, earth quick, and marine environments. All sensor nodes deployed in UWSNs operate through limited power batteries. Prolonging the network’s lifetime in such environments is an essential task and a hot topic among researchers. Multiple routing protocols have been designed to overcome the limited power issue and reduce energy consumption. Each routing protocol evaluated different parameters, but the issue is still unclear as to which one is better. In this study, we evaluated multiple routing protocols to investigate which is better in terms of parameters, such as packet delivery ratio (PDR), energy consumption, end-to-end delay, and the number of alive nodes. The simulation results indicate that Reliability and Adaptive Cooperation for Efficient UWSNs Using Sink Mobility (RACE-SM) performs better in all performance metrics than other routing schemes.

ARMS‐EGR—Adaptive ranking and mobile sink‐enabled energy‐efficient geographic routing protocol in flying ad hoc networks

SummaryUnmanned aerial vehicles (UAVs) have become widespread because of their involvement in a variety of applications. The task of designing the energy‐efficient routing between UAVs has been considered a matter of great interest due to the inherent challenges of controlling the dynamics exhibited by UAVs. Energy limitations are considered the main limitations of UAVs. This research paper proposes a novel routing protocol, adaptive ranking and mobile sink (MS)‐enabled energy‐efficient geographic routing (ARMS‐EGR) for flying ad hoc networks. In ARMS‐EGR, the whole network is partitioned into cells. The cell contains cell members (CM) and cell heads (CHs). The CH works as a cluster head. Additionally, two MSs have been used to collect data captured by CM. Multihop communication on the network leads to an increase in traffic and consumes the energy of the UAVs located near the base station (BS). MSs are used for power distribution and load balancing across the network. Adaptive ranking of forwarder UAVs and CHs is performed during intracell and intercell multihop communication, respectively, using adaptive ranking. A cell with one‐hop communication can directly send packets to the MS, but the ARMS‐EGR routing protocol has been proposed for multihop communication. The proposed approach is simulated in NS‐2.35 software. The results show that end‐to‐end latency and power consumption during packet transmission are greatly minimized. ARMS‐EGR also demonstrates improvements in message success rates, number of alive nodes, and packet delivery ratio, making ARMS‐EGR particularly suitable for flying ad hoc networks (FANETs).

A Formal Energy Consumption Analysis to Secure Cluster-Based WSN: A Case Study of Multi-Hop Clustering Algorithm Based on Spectral Classification Using Lightweight Blockchain.

Wireless Sensors Networks are integrating human daily life at a fast rate. Applications cover a wide range of fields, including home security, agriculture, climate change, fire prevention, and so on and so forth. If WSN were initially flat networks, hierarchical, or cluster-based networks have been introduced in order to achieve a better performance in terms of energy efficiency, topology management, delay minimization, load balancing, routing techniques, etc. As cluster-based algorithms proved to be efficient in terms of energy balancing, security has been of less importance in the field. Data shared by nodes in a WSN can be very sensitive depending on the field of application. Therefore, it is important to ensure security at various levels of WSN. This paper proposes a formal modeling of the energy consumed to secure communications in a cluster-based WSN in general. The concept is implemented using the Proof of Authentication (POAh) paradigm of blockchain and applied to a Multi-hop Clustering Algorithm based on spectral classification. The studied metrics are residual energy in the network, the number of alive nodes, first and last dead node.

DEDG: Cluster-Based Delay and Energy-Aware Data Gathering in 3D-UWSN with Optimal Movement of Multi-AUV

The monitoring of underwater aquatic habitats and pipeline leakages and disaster prevention are assisted by the construction of an underwater wireless sensor network (UWSN). The deployment of underwater sensors consumes energy and causes delay when transferring the gathered sensed data via multiple hops. The consumption of energy and delays are minimized by means of an autonomous unmanned vehicle (AUV). This work addresses the idea of reducing energy and delay by incorporating an AUVs-assisted, three-dimensional UWSN (3D-UWSN) called DEDG 3D-UWSN. Energy in the sensor nodes is saved by clustering and scheduling; on the other hand, the delay is minimized by the movement of the AUV and inter-cluster routing. In clustering, multi-objective spotted hyena optimization (MO-SHO) is applied for the selection of the best sensor for the cluster head, which is responsible for assigning sleep schedules for members. According to the total number of members, an equal half of the members is provided with sleep slots based on the energy and hop counts. The redundancy in the gathered data is eliminated by measuring the Hassanat distance. Then, the moving AUV is able to predict its movement by the di-factor actor–critic path prediction method. The mid-point among the four heads is determined so that the AUV can collect data from four heads at a time. In cases where the waiting time of the CH is exceeded, three-step, inter-cluster routing is executed. The three steps are the discovery of possible routes, ignoring the longest paths and validating the filtered path with a fuzzy–LeNet method. In this 3D-UWSN, the sensed data are not always normal, and, hence, a weighted method is presented to transfer emergency events by selecting forwarders. This work is implemented on Network Simulator version 3.26 to test the results. It achieves better efficiency in terms of data collection delay, end-to-end delay, AUV tour length, network lifetime, number of alive nodes and energy consumption.