Dual Secure Optimal Trusted Routing for Sensitive Data Transfer to Ensure Accurate Patient Healthcare State Prediction Using IoT-Enabled Wireless Sensor Networks

The utilization of Digital Twin technology allows for the simulation of network behavior, anticipating traffic surges, and implementing efficient traffic routing strategies to prevent congestion. This enhances network efficiency and improves overall speed. However, VANETs (Vehicular Ad-Hoc Networks) pose unique challenges due to their dynamic nature and frequent network disconnects. Developing and implementing effective VANET routing protocols becomes complex considering these factors. To address these challenges, a novel hybrid optimization model is proposed in this research. The model comprises optimal Cluster Head (CH) selection for data transmission. The clustering of mobile nodes is initially performed, but ensuring consistency in fast-paced environments remains a significant challenge. Therefore, the selection of the most suitable node as the CH is crucial. This research introduces a novel route selection mechanism that focuses on optimal CH selection. Multiple objectives such as mean routing load, packet delivery ratio, throughput, End-to-End Delay, and Control packet overhead are considered in the CH selection process. To determine the ideal CH from a pool of potential candidates, a new hybrid optimization model called Hunger's Foraging Behavior Customized Honey Badger Optimization (HFCHBO) is introduced. The HFCHBO combines the standard Honey Badger Algorithm (HBA) with Hunger Games Search (HGS). This hybrid model effectively formulates successful routing paths for data transmission between vehicles and the CH to the Base Station (BS). By combining these two approaches, the HFCHBO model aims to overcome the limitations of traditional clustering algorithms in ensuring consistent performance in dynamic environments. The proposed route selection mechanism incorporates multiple objectives to evaluate the performance of potential CHs, including mean routing load, packet delivery ratio, throughput, End-to-End Delay, and Control packet overhead. To facilitate data transmission between vehicles and the CH to the Base Station (BS), the HFCHBO model formulates successful routing paths. By utilizing the simulation capabilities of the Digital Twin technology, the model analyzes the network behavior, predicts traffic patterns, and makes informed decisions on routing strategies.

Wireless sensor networks are battery-powered ad hoc networks in which sensor nodes that are scattered over a region connect to each other and form multi-hop networks. Since these networks consist of sensors that are battery operated, care has to be taken so that these sensors use energy efficiently. This paper proposes an optimized hierarchical routing technique which aims to reduce the energy consumption and prolong network lifetime. In this technique, the selection of optimal cluster head (CHs) locations is based on artificial fish swarm algorithm that applies various behaviors such as preying, swarming, and following to the formulated clusters and then uses a fitness function to compare the outputs of these behaviors to select the best CHs locations. To prove the efficiency of the proposed technique, its performance is analyzed and compared to two other well-known energy efficient routing techniques: low-energy adaptive clustering hierarchy (LEACH) technique and particle swarm optimized (PSO) routing technique. Simulation results show the stability and efficiency of the proposed technique. Simulation results show that the proposed method outperforms both LEACH and PSO in terms of energy consumption, number of alive nodes, first node die, network lifetime, and total data packets received by the base station. This may be due to considering residual energies of nodes and their distance from base station , and alternating the CH role among cluster's members. Alternating the CH role balances energy consumption and saves more energy in nodes.

This paper introduces an integrated methodology that enhances both the efficiency and security of wireless sensor networks (WSNs) against various active attacks. A two-fold strategy is proposed that incorporates an advanced cluster head (CH) selection and a customized, lightweight encryption protocol. The CH selection process is optimized through a multi-phase approach using fuzzy logic, local and global network qualifiers, and a trust index to ensure the election of CHs that are not only energy-efficient but also reliable. To complement the robust CH selection, the study introduces a hybrid yet lightweight encryption scheme customized Rivest-Shamir-Adleman (c-RSA) and customized advanced encryption standard (c-AES) algorithms. This scheme is customized for WSNs with limited computational resources, maintaining strong encryption standards while significantly reducing energy consumption and computational overhead. Experimental results demonstrate that the proposed system substantially enhances network performance, exhibiting a 34.15% improvement in energy efficiency and a 30.95% increase in reliability over existing methods such as LEACH and its modified versions. This comprehensive approach underscores the potential for a synergistic design in WSNs that does not compromise on security while optimizing operational efficiency.

SummaryWireless sensor networks are significantly used for data sensing and aggregating dusts from a remote area environment in order to utilize them in a diversified number of engineering applications. The data transfer among the sensor nodes is attained through the inclusion of energy efficient routing protocols. These energy efficient routing necessitates optimal cluster head selection procedure for handling the challenge of energy consumption to extend the stability and lifetime in the sensor networks. The implementation of energy efficient routing is still complicated even when the process of clustering is enhanced through the cluster head selection. The majority of the existing cluster head selection schemes suffer from the issues of poor selection accuracy, increased computation, and duplicate nodes' selection. In this paper, hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm (HSFLBOA) for optimal cluster head selection is proposed for resolving issues that are common in cluster head selection schemes. This proposed HSFLBOA used the objective function that used the parameters of node energy, data packet transmission delay, cluster traffic density, and internode distance in the cluster. The simulation results of the proposed HSFLBOA is determined to be significant in achieving superior throughput and network energy compared to benchmarked metaheuristic optimal cluster head schemes.

SummaryIn this paper, improved bat and enhanced artificial bee colony optimization algorithm‐based cluster routing (IBEABCCR) scheme is proposed for optimal cluster head (CH) selection with the merits of global diversity and improved convergence rate. It is proposed for achieving optimal CH selection by balancing the tradeoff between the phases of exploration and exploitation. It specifically targeted on the formulation of an ideal CH selection scheme using improved bat optimization algorithm (IBOA) for minimizing the energy depletion rate. It also focuses on the design of an enhanced artificial bee colony (EABC)‐based sink node mobility scheme for determining the optimal points of deployment over which sink nodes can be moved to achieve better delivery of packets from CH to sink node. This CH selection and sink node mobility schemes are contributed for extending the network lifespan using the fitness function, which adopted the factors of node centrality, node degree, distance amid CH and base station (BS), distance among sensor nodes, and residual energy during CH selection process. The simulation experiments were performed using MATLAB version 2018, which confirmed that the number of alive nodes realized in the network is enhanced by 39.21% with the location of BS positioned at (100, 100). The number of rounds (network lifetime) is enhanced by 23.84% with different BS locations in the network. Furthermore, the packets received at the BS are also realized to be enhanced by 26.32% on an average in contrast to the baseline CH schemes used for investigation.

SummaryClustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes.

Energy efficiency has become a primary issue in wireless sensor networks (WSN). The sensor networks are powered by battery and thus they turn out to be dead after a particular interval. Hence, enhancing the data dissipation in energy efficient manner remains to be more challenging for increasing the life span of sensor devices. It has been already proved that the clustering method could improve or enhance the life span of WSNs. In the clustering model, the selection of cluster head (CH) in each cluster regards as the capable method for energy efficient routing, which minimizes the transmission delay in WSN. However, the main problem dealt with the selection of optimal CH that makes the network service prompt. Till now, more research works have been processing on solving this issue by considering different constraints. Under this scenario, this paper attempts to develop a new clustering model with optimal cluster head selection by considering four major criteria like energy, delay, distance, and security. Further, for selecting the optimal CHs, this paper proposes a new hybrid algorithm that hybridizes the concept of dragon fly and firefly algorithm algorithms, termed fire fly replaced position update in dragonfly. Finally, the performance of the proposed work is carried out by comparing with other conventional models in terms of number of alive nodes, network energy, delay and risk probability.

Wireless Sensor Networks (WSNs) play an indispensable role in the lives of human beings in the fields of environment monitoring, manufacturing, education, agriculture etc., However, the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless existence. In this context, several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival time. However, there still exists a room for improvement in Cluster Head (CH) selection based on the integration of critical parameters. The meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are predominant. In this paper, a hybrid Marine Predators Optimization and Improved Particle Swarm Optimization-based Optimal Cluster Routing (MPO-IPSO-OCR) is proposed for ensuring both efficient CH selection and data transmission. The robust characteristic of MPOA is used in optimized CH selection, while improved PSO is used for determining the optimized route to ensure sink mobility. In specific, a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA. The high-speed ratio, unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality point. The simulation investigation and statistical results confirm that the proposed MPO-IPSO-OCR is capable of improving the energy stability by 21.28%, prolonging network lifetime by 18.62% and offering maximum throughput by 16.79% when compared to the benchmarked cluster-based routing schemes.

Wireless Sensor Networks (WSNs) are challenged by the need for optimized Energy Consumption (EC), efficient Data Aggregation (DA), and reliable routing due to their dynamic topologies and limited resources. Existing solutions like TEAMR and DDQNDA address these concerns but face significant drawbacks—TEAMR lacks adaptability to rapidly changing topologies, while DDQNDA suffers from high computational overhead and delayed convergence, hindering its effectiveness in real-time scenarios. To overcome these limitations, this paper introduces the Adaptive Reinforcement Learning (RL)-Based DA and Routing Optimization (ARL-DARO) algorithm. The proposed methodology follows a systematic approach, beginning with cluster formation and Cluster Head (CH) selection (CHS) using the Grey Wolf Optimizer (GWO), which ensures Energy-Efficient (EE) clustering and optimal CH selection. In the next step, trust factors such as Node Connectivity (NC), Residual Trust (RT), and Cooperation Rate (CR) are integrated into Quality of Service (QoS) metrics as part of the Fitness Function(FF) to enhance route reliability and security. Finally, the ARL-DARO algorithm is employed to dynamically optimize both data aggregation and routing. It leverages Q-learning to select optimal routes based on energy efficiency, security, and link reliability, further reducing data redundancy and improving adaptability to real-time network changes. Performance is assessed using parameters such EC, packet delivery ratio (PDR), end-to-end latency (E2E delay), throughput, and network lifetime (NL) across networks with 100, 200, 300, 400, and 500 nodes. Results show that ARL-DARO significantly reduces energy consumption by up to 45%, increases throughput by 30%, and extends network lifetime, proving its effectiveness over existing methods.

With the increasing use of the internet for transferring data, the security of this data has been a serious concern since the very beginning. There has been an ever-increasing number of cyber-attacks happening all over the internet. Hackers, after getting access to the end user's personal computer, have complete control over all the data flowing in and out of the computer. In this case, if any sensitive data gets in the hands of the hacker, it might create a great catastrophe for that person and the party he wants to communicate with. Hence, there is a need for creating an encryption system for data transfer that is extremely sensitive such as Criminal Data, Banking data and it can extend to a person's private details such as banking details and account passwords. For any such sensitive data transfer, we need a very strong encryption system, which ensures that the data being transferred is safe and is only accessible to the person who is authenticated to view that data. This paper discusses the various methodologies, algorithms, and proposes a solution to securely transfer sensitive data over the internet.

Clustering is one of the fundamental techniques for prolonging the life expectancy of Wireless Sensor Networks (WSNs). However, cluster head selection remains the major challenge in WSN concerning energy stabilisation. This paper intends to propose the Firefly Cyclic Grey Wolf Optimisation (FCGWO) to simulate the optimal cluster head selection framework. The main objective of this paper is to select the cluster head optimally by focusing on the stabilisation of energy, minimisation of distance between nodes and minimisation of delay. It hybridises the Firefly (FF) and Grey Wolf Optimisation (GWO) algorithms to attain the best performance. After the simulation, it compares the performance of the FCGWO-based cluster head selection with the traditional algorithms like Genetic Algorithm (GA), Group Search Optimisation (GSO), Artificial Bee Colony (ABC), Fractional Artificial Bee Colony (FABC), Firefly (FF) with Cyclic Randomisation (FCR) and GWO based cluster head selection. The performance comparison appears to analyse the network lifetime, energy efficiency and statistics of dead nodes. The simulation outcomes show that the proposed cluster head selection model is more efficient to prolong the lifetime of the network.

A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks

Clustering-based routing is preferred to support fault tolerance, load balancing, reliable communication, and to prolong the network lifetime in a wireless sensor network (WSN). The low-energy adaptive clustering hierarchy (LEACH) is the most popular routing technique, introduced for the first time for homogeneous WSNs. However, the random selection of cluster heads (CHs) in LEACH protocols results in poor performance in real network deployments due to the faster rate of energy depletion at CHs. The dynamic selection of CHs based on a heuristic approach can minimize the energy consumption at CHs and enhance the network lifetime. In this paper, a metaheuristic algorithm called grey wolf optimization (GWO) and its enhanced versions are proposed in selecting the optimal CH. The fitness function is defined based on sink distance to CH and residual energy at the sensor node. The optimal values of fitness function give an efficient CH selection and cost-effective routing. The primary goal of this paper is to maximize the network lifetime of WSNs by optimal selection of CHs using the improved GWO (IGWO) algorithm. The proposed IGWO-based LEACH protocol confirmed the optimal selection of CH with minimum energy consumption, resolved premature convergence, and enhanced the network lifetime by balancing the number of alive and dead nodes in WSN.

In wireless sensor network (WSN), limited energy resources with the nodes is a complex challenge as far as data routing, collecting and aggregating the data is concerned as all these processes are energy demanding. Network lifetime, stability period, and potential of the WSN are some of the parameters which are to be maximized subject to the constraints. The cluster head selection in the heterogeneous wireless sensor network has not been explored much and needs to be improved further to discover the potential of WSN in this area. In this study, optimal cluster head selection in heterogeneous wireless sensor network through Diversity-Driven Multi-Parent Evolutionary Algorithm with Adaptive Non-Uniform Mutation has been suggested. The efficacy of the proposed technique is tested on Classical Benchmark Functions, and obtained results are compared with the state of the art of algorithms. This algorithm is also validated on a heterogeneous wireless sensor network with cluster head selection as a multi-objective optimization problem. The residual energy of sensor node and distance travelled are to be optimized in order to minimize the fitness function. Simulation suggested that the proposed algorithm is found to be reliable and outperforms in terms of remaining energy of nodes, alive nodes versus round, dead nodes versus rounds, the lifespan of network, throughput, and stability period.

Currently, analysts in a variety of nations have developed various WSN clustering protocols. The major characteristic is the Low Energy Adaptive Clustering Hierarchy (LEACH), which attained the objective of energy balance by sporadically varying the Cluster Heads (CHs) in the region. Nevertheless, because it implements an arbitrary number system, the appropriateness of CH is complete with suspicions. In this paper, an optimal cluster head selection (CHS) model is developed regarding secure and energy-aware routing in the Wireless Sensor Network (WSN). Here, optimal CH is preferred based on distance, energy, security (risk probability), delay, trust evaluation (direct and indirect trust), and Received Signal Strength Indicator (RSSI). Here, the energy level is predicted using an improved Deep Convolutional Neural Network (DCNN). To choose the finest CH in WSN, Bald Eagle Assisted SSA (BEA-SSA) is employed in this work. Finally, the results authenticate the effectiveness of BEA-SSA linked to trust, RSSI, security, etc. The Packet Delivery Ratio (PDR) for 100 nodes is 0.98 at 500 rounds, which is high when compared to Grey Wolf Optimization (GWO), Multi-Objective Fractional Particle Lion Algorithm (MOFPL), Sparrow Search Algorithm (SSA), Bald Eagle Search optimization (BES), Rider Optimization (ROA), Hunger Games Search (HGS), Shark Smell Optimization (SSO), Rider-Cat Swarm Optimization (RCSO), and Firefly Cyclic Randomization (FCR) methods.