Routing Research Articles

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.

PKCA: A priori‐knowledge and congestion‐awareness method for adaptive routing algorithms in mesh architectures

AbstractAdaptive routing algorithms have been widely utilized in Network‐on‐Chip (NoC) architectures and have shown to enhance overall throughput in numerous studies. The adaptive routing algorithms can effectively detect network congestion. On the one hand, one‐hop awareness or local awareness can easily detect network congestion but may also result in local greed. On the other hand, global awareness is better for load balancing, but it is difficult to be aware of the network congestion status. This article proposes a lightweight adaptive on‐chip routing algorithm based on the concentric circles theory and prior knowledge derived from real‐life observations. The algorithm, named the Priori‐Knowledge and Congestion‐Awareness method (PKCA), aims to optimize the routing efficiency within the chip. PKCA is designed to be not only simple but also to have low time complexity, allowing it to calculate paths to destinations without local greed. We performed evaluations, and the results demonstrated that our design surpasses one‐hop awareness, two‐hop awareness, and global awareness by 31%, 25%, and 20%, respectively, in terms of latency, and by 22%, 14%, and 22%, respectively, in terms of throughput. Furthermore, the time complexity is only in an 2D mesh.

Intelligent Routing in Directional Ad Hoc Networks Through Predictive Directional Heat Map From Spatio-Temporal Deep Learning

By applying a simple shortest/minimum-cost routing algorithm, the mobile ad-hoc network (MANET) with heavy data transmissions may be easily congested if multiple routing paths meet at the same relay node. A path may be unable to provide high throughput since some of its path nodes are helping other paths to forward the heavy traffic. Therefore, those busy nodes should be avoided when a new path is to be established. The task of optimal path seeking becomes more challenging when a MANET is equipped with directional antennas that may cause directional interference with neighboring receivers. The motivation of our research is to build an intelligent proactive routing scheme for MANETs with directional antennas. Our directional routing protocol considers not only the global traffic distribution in different areas of the MANET, but also the properties of directional antennas. It uses spatio-temporal deep learning algorithm to predict the next-time snapshot of directional heat map (DHM), which shows the traffic density distribution in each network location as well as the coverage of each directional antenna. Such a DHM will be used to identify the optimal path that can avoid congested areas as well as the interference from all neighboring directional links. Furthermore, an optimization algorithm is designed to perform optimal path selection. It is able to automatically split a single path into multiple ones that later on get converged again into one path, if the path needs to go around a congested area. Therefore, our routing scheme achieves better quality-of-service (QoS) performance than traditional routing mechanisms such as AODV and OLSR.

Routing and Scheduling in Multigraphs With Time Constraints—A Memetic Approach for Airport Ground Movement

Routing and scheduling problems with increasingly realistic modelling approaches often entail the consideration of multiple objectives, time constraints, and modelling the system as a multigraph. This detailed modelling approach has increased computational complexity and may also lead to violation of the additivity property of the costs. In the worst scenario, increased complexity makes the problem intractable for exact algorithms. Even when the problem is solvable, exact algorithms may not provide solutions within the given time budget, and the found solutions are not guaranteed to be optimal due to the additivity property violation. Approximate solution methods become more suitable in this case. This paper focuses on one particular real-world application, the Airport Ground Movement Problem, where both time constraints and parallel arcs are involved. We introduce a novel Memetic Algorithm for Routing in Multigraphs with Time constraints (MARMT) and present a comprehensive study of its different variants based on diverse genetic representation methods. We propose a local search operator that enhances search efficiency and effectiveness. MARMT is tested on real data based on two airports of different sizes. Our results show that MARMT does not suffer from the non-additivity property problem as it outperforms the state-of-the-art exact algorithm when allowed to converge. When a time budget of 10 seconds is imposed on MARMT, it is able to provide solutions with quality comparable (within 1-5% degradation) to the ones given by the exact algorithm with respect to the aggregated objective values. MARMT can be adapted for other applications, such as train operations.

Research on Secure Community Opportunity Network Based on Trust Model

With the innovation of wireless communication technology and the surge of data in mobile networks, traditional routing strategies need to be improved. Given the shortcomings of existing opportunistic routing strategies in transmission performance and security, this paper proposes a community opportunistic routing decision-making method based on the trust model. This algorithm calculates the node’s trust value through the node’s historical forwarding behavior and then calculates the node’s trust value based on the trust model. Thresholds and trust attenuation divide dynamic security communities. For message forwarding, nodes in the security community are prioritized as next-hop relay nodes, thus ensuring that message delivery is always in a safe and reliable environment. On this basis, better relay nodes are further selected for message forwarding based on the node centrality, remaining cache space, and remaining energy, effectively improving the message forwarding efficiency. Through node trust value and community cooperation, safe and efficient data transmission is achieved, thereby improving the transmission performance and security of the network. Through comparison of simulation and opportunistic network routing algorithms, compared with traditional methods, this strategy has the highest transmission success rate of 81% with slightly increased routing overhead, and this algorithm has the lowest average transmission delay.

Reliability Optimization of Traffic Routing in Train Communication Networks Based on Faulty Link Diagnosis

With the increasing adoption of industrial Ethernet in train communication network (TCN), the limitations of insufficient reliability and incompatibility between different Ethernet technologies have become increasingly apparent. To address these challenges, time‐sensitive networking (TSN) has emerged as a solution, enabling multi‐network convergence and enhancing data transmission determinism and reliability through techniques such as frame replication and elimination for reliability (FRER). However, the utilization of these policies will adversely affect schedulability due to increased network load. To address the issue of link failures in TCN topologies, a routing policy based on faulty link diagnosis is proposed. The proposed policy utilizes an improved max‐min ant system (IMMAS) for diagnosing and resolving faulty links. Furthermore, a routing optimization algorithm is introduced to enhance transmission reliability by planning alternative paths for traffic. To evaluate the performance of the proposed routing algorithm, multiple sets of test cases are conducted separately under different failure scenarios to compare them with other routing policies. The results demonstrate the superior reliability of the proposed routing policy over alternative approaches. Additionally, the traffic utilizing the proposed routing policy show improved schedulability following integer linear programming (ILP) scheduling. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

SMART: Secured and Mobility Aware Routing Technique for Opportunistic IoT Network in Smart Cities

Transferring data between nodes in the Opportunistic Internet of Things (OppIoT) network may lead to the transmission of multiple copies of each message, which can increase communication costs and jeopardise network security. This necessitates a routing method that is effective and can address both problems. To protect transmitted data and reduce communication overhead, this study suggests a Secured and Mobility Aware Routing Method (SMART) routing algorithm for OppIoT networks in smart cities. With a buffer size of 30 MB and an overhead ratio of 27.9, the delivery probability can be increased by more than 50%. The simulation’s findings demonstrate that, in terms of delivery probability, overhead ratio, and reports, the proposed SMART protocol outperforms more traditional routing methods.

Enhancing Energy Efficiency in Wireless Sensor Networks through I-LEACH: A Data Clustering and Routing Protocol

The inclusion of innovative technology into sensor networks has resulted in notable advancements in several industries, including mining, healthcare, military surveillance, and more. Using a multi-hop approach, nodes are placed strategically across the Region of Interest (RoI) in order to transfer data to the Base Station (BS). In order to address issues with energy consumption, effective cluster head selection, packet loss, routing algorithms, and energy efficiency, the Wireless Sensor Network (WSN) has become a key field of study. The objectives of this research are to increase nodes' Residual Energy, and lengthen the network lifetime. Using an effective clustering mechanism and Cluster Head election procedure is the suggested method. The goal of choosing a CH is to minimize the distance from the base station while optimizing the node's remaining energy. The protocol acts in two stages: firstly, computing a new Threshold value for the cluster head election process, and secondly, applying a data fusion mechanism based energy model is used in the network to reduce redundant energy transfer.

Athena: Smart order routing on centralized crypto exchanges using a unified order book

SummaryMost cryptocurrency spot trading occurs on centralized crypto exchanges, where offers for buying and selling are organized via an order book. In liquid markets, the price achieved for buying and selling deviates only slightly from the assumed reference price, that is, trading is associated with low implicit costs. However, compared to traditional finance, crypto markets are still illiquid, and consequently, the reduction of implicit costs is crucial for any trading strategy and of high interest, especially for institutional investors. This paper describes the design and implementation of Athena, a system that automatically splits orders across multiple exchanges to minimize implicit costs. For this purpose, order books are collected from several centralized crypto exchanges and merged into an internal unified order book. In addition to price and quantity, the entries in the unified order book are enriched with information about the exchange. This enables a smart order routing algorithm to split an order into several slices and execute these on several exchanges to reduce implicit costs and achieve a better price. An extensive evaluation shows the savings of using the smart order routing algorithm.

A comparative analysis on Relibale routing models in wireless sensor in smart applications

Researchers are drawn to Wireless Sensor Networks (WSNs) due to its versatility and possible use cases in a variety of fields, including target tracking and identification environmental sensing, industrial process tracking, and tactical systems. Recently, there has been a lot of focus on the particular issues that arise from implementing routing in sensor networks as opposed to the more conventional data routing in wireless connections. Due to the importance of energy conservation in the design of WSNs, numerous protocols for routing, power management, and data transmission have been developed for these networks. When it comes to environmental monitoring, traffic monitoring, etc., routing in WSNs is crucial. The primary objective of this study is to analyse routing issues and to analyse routing-related optimization issues. Features pertaining to routing issues like energy consumption, security, speed, and dependability are explored later. Attention has been paid to the routing protocols, which might vary by use case and network design. In this study, a comprehensive review is performed on the cutting-edge approaches to routing in WSNs. A brief introduction to WSNs and a discussion of the obstacles that routing protocols must overcome throughout their creation before moving on to a thorough examination of the various routing approaches now in use is considered. The comparative analysis is performed different routing techniques like Energy-Aware Routing for Software-Defined Multihop Wireless Sensor Networks (EAR-SDMWSN), DORA: A Destination-Oriented Routing Algorithm for Energy-Balanced Wireless Sensor Networks (DORA) and Energy Proficient Load Balancing Routing Scheme for Wireless Sensor Networks (EP-LBR). The benefits and drawbacks of each routing method, with an emphasis on performance is also performed.

Energy efficient multipath routing in space division multiplexed elastic optical networks

This paper introduces a novel dynamic multipath routing, modulation level, spatial and spectrum assignment algorithm for space division multiplexing (SDM) enabled elastic optical networks (EON) with the aim of minimizing the blocking probability and the energy consumed by bandwidth variable transponders (BVTs). The adopted multipath routing strategy allows the splitting of the demand into several sublightpaths using different fiber cores but ensuring that all of them utilize the same set of fibers in order to avoid differential delay. The method also imposes continuity constraints in both spectrum and core location in order to use cost-effective SDM Reconfigurable Optical Add and Drop Multiplexers (ROADMs) without lane change support. The complete usage of multi-core fibers (MCFs) in this kind of networks is restricted due to inter-core crosstalk (XT), which can reduce the quality of received signals. Therefore, the method besides using the most effective modulation format, also ensures that the XT of the lightpaths (or sublightpaths) does not exceed the threshold for each modulation format. A simulation study comparing our method with another similar proposal from the literature is presented for different types of topologies in terms of link distances. Simulation results demonstrate that the proposed multipath routing algorithm in networks including links close to or beyond 1000 km significantly boost the dynamic performance in terms of blocking probability, energy consumption, and latency.

An Intelligent Energy-Efficient Data Routing Scheme for Wireless Sensor Networks Utilizing Mobile Sink

Data collection and energy consumption are critical concerns in Wireless sensor networks (WSNs). To address these issues, both clustering and routing algorithms are utilized. Therefore, this paper proposes an intelligent energy-efficient data routing scheme for WSNs utilizing a mobile sink (MS) to save energy and prolong network lifetime. The proposed scheme operates in two major modes: configure and operational modes. During the configure mode, a novel clustering mechanism is applied once, and a prescheduling cluster head (CH) selection is introduced to ensure uniform energy expenditure among sensor nodes (SNs). The scheduling technique selects successive CHs for each cluster throughout the WSNs’ lifetime rounds, managed at the base station (BS) to minimize SN energy consumption. In the operational mode, two main objectives are achieved: sensing and gathering data by each CH with minimal message overhead, and establishing an optimal path for the MS using the genetic algorithm. Finally, the MS uploads the gathered data to the BS. Extensive simulations are conducted to verify the efficiency of the proposed scheme in terms of stability period, network lifetime, average energy consumption, data transmission latency, message overhead, and throughput. The results demonstrate that the proposed scheme outperforms the most recent state-of-the-art methods significantly. The results are substantiated through statistical validation via hypothesis testing utilizing ANOVA, as well as post hoc analysis.

Assessing Routing Algorithms for Payment Channel Networks

Payment Channel Networks (PCNs) are a promising approach to overcome scalability issues of blockchains. To achieve efficient payments in PCNs, it is necessary to route transactions between a payer and a payee. Especially in large-scale PCNs, multi-hop routing becomes necessary, since transactions need to be relayed by nodes. For this, a scalable routing algorithm is needed, which fits the individual objectives of PCN users. In this article, we study whether routing protocols from the field of Wireless Sensor Networks can be applied in PCNs. To this end, we first derive requirements for routing in PCNs, select suitable approaches, and analyze to which degrees they perform well and meet the requirements. We adapt selected protocols and evaluate them with regard to the lengths of payment paths, fees, and success ratio.

Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning.

Spiking neural networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.

Energy optimized artificial hummingbird algorithm for routing in IoT‐based software‐defined WSN

SummaryThe Internet of Things (IoT) has become widely used in applications such as smart homes, industrial automation, and transportation due to its affordable hardware and fast internet connectivity. However, the increase in IoT‐enabled gadgets, particularly those running on batteries or connected to other sources, is putting strain on the world's energy requirements. Therefore, this study focuses on a green routing solution for battery‐powered IoT‐enabled Software‐defined Wireless Sensor Networks (IoT‐SDWSN). Finding green solutions for IoT‐based networks to address this energy challenge has become crucial. This study focuses on developing a green routing solution for battery‐powered IoT‐SDWSN. Energy efficiency in IoT‐SDWSN is attained by the process of clustering nodes. The network is partitioned into small clusters, and a Control Node (CN) is set up by a Control Server (CS) to transmit the data packets sent by sensor nodes. Choosing a CN in these networks is a critical concern due to the substantial energy consumption involved in delivering data to the CS. This research focuses on the problem of energy‐efficient cluster routing in IoT‐based SD‐WSN. It introduces the Energy‐optimized Artificial Hummingbird Algorithm (EOAHA) as a green routing technique. EOAHA aims to extend the lifespan of IoT‐based SD‐WSNs by intelligently selecting (based on a new fitness function) CNs to distribute the network load and increase its overall longevity. To evaluate the performance of EOAHA, a comparative analysis is conducted against other state‐of‐the‐art algorithms. The results demonstrate that EOAHA outperforms these algorithms by a minimum of 13.5% in terms of network longevity.

TTDCapsNet: Tri Texton-Dense Capsule Network for complex and medical image recognition.

Convolutional Neural Networks (CNNs) are frequently used algorithms because of their propensity to learn relevant and hierarchical features through their feature extraction technique. However, the availability of enormous volumes of data in various variations is crucial for their performance. Capsule networks (CapsNets) perform well on a small amount of data but perform poorly on complex images. To address this, we proposed a new Capsule Network architecture called Tri Texton-Dense CapsNet (TTDCapsNet) for better complex and medical image classification. The TTDCapsNet is made up of three hierarchic blocks of Texton-Dense CapsNet (TDCapsNet) models. A single TDCapsNet is a CapsNet architecture composed of a texton detection layer to extract essential features, which are passed onto an eight-layered block of dense convolution that further extracts features, and then the output feature map is given as input to a Primary Capsule (PC), and then to a Class Capsule (CC) layer for classification. The resulting feature map from the first PC serves as input into the second-level TDCapsNet, and that from the second PC serves as input into the third-level TDCapsNet. The routing algorithm receives feature maps from each PC for the various CCs. Routing the concatenation of the three PCs creates an additional CC layer. All these four feature maps combined, help to achieve better classification. On fashion-MNIST, CIFAR-10, Breast Cancer, and Brain Tumor datasets, the proposed model is evaluated and achieved validation accuracies of 94.90%, 89.09%, 95.01%, and 97.71% respectively. Findings from this work indicate that TTDCapsNet outperforms the baseline and performs comparatively well with the state-of-the-art CapsNet models using different performance metrics. This work clarifies the viability of using Capsule Network on complex tasks in the real world. Thus, the proposed model can be used as an intelligent system, to help oncologists in diagnosing cancerous diseases and administering treatment required.

A novel intelligent fault diagnosis method of rolling bearings based on capsule network with Fast Routing algorithm

AbstractFault diagnosis is a novel technology crucial for monitoring the proper functioning and ensuring the stability of mechanical devices and components. Nevertheless, most existing data‐driven methods for rolling bearing fault diagnosis exhibit limited diagnostic capabilities in scenarios characterized by noise interference and inadequate training data. To address this issue, this paper proposes a novel intelligent fault diagnosis method for rolling bearings based on Capsule Network with Fast Routing algorithm (FCN). Firstly, the vibration signal is transformed into a time‐frequency map through continuous wavelet transform (CWT), and the transformed time‐frequency map is input into the network model to enable the network to learn features more fully. Subsequently, this paper introduces FCNs into capsule networks, effectively mitigating the extended training times typically associated with capsule networks and reducing the demands on training equipment. Extensive experiments are conducted utilizing two distinct bearing datasets to assess the method's stability and generalization. The results of these experiments demonstrate the proposed approach's ability to maintain robust fault diagnosis capabilities, even in the presence of noise interference and limited training data. This innovative method lays the foundation for intelligent rolling bearing diagnosis and is readily adaptable to other rotating components.

Routing networking technology based on improved ant colony algorithm in space-air-ground integrated network

Space-air-ground integrated networks comprise a multi-level heterogeneous integrated network that combines satellite-based, aerial, and terrestrial networks. With the increasing human exploration of space and growing demands for internet applications, space-air-ground integrated networks have gradually emerged as the direction for communication network development. These networks face various challenges such as extensive coverage, diverse communication node types, low-quality communication links, and simultaneous operation of multiple network protocols. However, the rapid development and widespread application of artificial intelligence and machine learning technologies in recent years have offered new perspectives and solutions for the communication architecture and routing algorithm research within space-air-ground integrated networks. In these networks, not all nodes can typically communicate directly with satellites; instead, a specific set of specialized communication nodes facilitates data communication between aerial and satellite networks due to their superior communication capabilities. Consequently, in contrast to traditional communication architectures, space-air-ground integrated networks, particularly in the terrestrial layer, often need to address challenges related to the diversity of communication node types and low-quality communication links. A well-designed routing approach becomes crucial in addressing these issues. Therefore, this paper proposes an AODV routing network protocol based on an improved ant colony algorithm (AC-AODV), specifically designed for the terrestrial layer within the space-air-ground integrated networks. By integrating information such as the type, energy, and location of communication nodes, this protocol aims to facilitate network communication. The objective is to guide information flow through nodes that are more suitable for communication, either by relaying communication or by connecting with satellites through specialized nodes. This approach alleviates the burden on ordinary nodes within the terrestrial communication network, thereby enhancing the overall network performance. In this protocol, specialized nodes hold a higher forwarding priority than regular nodes. When a source node needs to transmit data, it enters the route discovery phase, utilizing its own type, location, and energy information as heuristic data to calculate forwarding probabilities. Subsequently, it broadcasts route request (RREQ) messages to find the path. Upon receiving the RREQ message, the destination node sends an RREP message for updating information elements and selects the optimal path based on these information elements. Compared to AODV, AC-AODV shows significant improvements in performance metrics such as transmission latency, throughput, energy conversion rate, and packet loss rate.

HOEEACR: Hybrid Optimized Energy-Efficient Adaptive Clustered Routing for WSN

Wireless Sensor Networks have been used for sensing and gathering data about an environment from a remote location for many years in a variety of engineering applications. In WSN, nodes must overcome energy consumption to function efficiently. To resolve these issues and extend the usefulness of the network, clustering and routing algorithms are promising. One of the main issues with WSNs is that they lack restricted energy sources. To overcome this issue, a Hybrid Optimized Energy-Efficient Adaptive Clustered Routing approach (HOEEACR) has been proposed for WSNs. This paper presents the Genetic Bee Colony (GBC) Algorithm for Cluster Head Selection by considering distances to neighbors, residual energy, node degrees, and node centralities. Furthermore, an optimal routing path for the cluster heads is found by utilizing the Aquila with African Vulture Optimization (AAVO) algorithm. The AAVO optimizes network performance using residual energy, node degree, and distance. The proposed HOEEACR method has been extensively tested to ensure network lifetime and energy efficiency. According to experimental results, the proposed HOEEACR method consistently outperforms existing techniques on a variety of performance metrics.

SECURE and energy-efficient routing protocol based on micro-segmentation and batch authentication

An effective technique in designing routing algorithms for Wireless Sensor Networks (WSNs) is named clustering nodes, which augments the network lifespan. More tasks are performed by the Cluster Heads (CHs) of the clustered WSNs, thereby consuming more energy. Hence, a secure energy-efficient Routing Protocol (RP) centered on micro-segmentation and batch authentication is proposed here. The proposed method begins by initializing the WSN nodes. After that, a random key is engendered for every node centered on their Node ID. The nodes undergo a Micro-segmentation process; in addition, they are securely grouped utilizing the Supremum Distance based K-Prototype (SD-KP) algorithm. Now, a fog layer with a number of fog nodes is initialized; also, every single micro-segmented group is assigned with an optimal fog node. Then, by using the Quasi Deterministic Sequence-Black Widow Optimization (QDS-BWO) algorithm, the optimal fog nodes are selected. By using the Root Squared- Diffie Hellman (R2-DH) technique, a key agreement is created for each group centered on their Node IDs. By using the Bitwise Cyclic Shift –BLAKE 512 (BCS-BLAKE-512) algorithm, the created keys are converted into hashcode. Now, for the purpose of performing batch authentication, the hashcode of each group is sent to the trusted authority. Subsequent to verification, data sensing occurs. Lastly, by using the Elliptic Curve Cryptography (ECC) algorithm, the data is securely encrypted and it is transmitted via the routes selected utilizing the Learned Gradient Weight Initialization-based Geography and Energy Aware Routing (LGWI-GEAR) algorithm. The proposed framework's efficacy is proved by the experimental outcomes.