An improved optimal energy aware data availability approach for secure clustering and routing in wireless sensor networks

Roberts, Michaelraj Kingston; Thangavel, Jayapratha

doi:10.1002/ett.4711

Cited by 12 publications

(8 citation statements)

References 33 publications

(47 reference statements)

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“…Reducing the packet delivery rate (PDR) REDAA [39] Considering energy saving and data availability, forming the most stable paths in the network, finding best cluster heads, improving network lifetime Not designing a security mechanism SecAODV [40] Clustering, creating a trade-off between energy efficiency and network security, Considering the residual energy of node when finding new paths, the use of symmeric and asymmetric keys to secure communication links High routing overhead, high latency in the routing process https://doi.org/10.1371/journal.pone.0290119.t001…”

Section: Scheme Advantages Disadvantagesmentioning

confidence: 99%

A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare

Hosseinzadeh,

Mohammed,

Rahmani

et al. 2023

PLoS ONE

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Patients must always communicate with their doctor for checking their health status. In recent years, wireless body sensor networks (WBSNs) has an important contribution in Healthcare. In these applications, energy-efficient and secure routing is really critical because health data of individuals must be forwarded to the destination securely to avoid unauthorized access by malicious nodes. However, biosensors have limited resources, especially energy. Recently, energy-efficient solutions have been proposed. Nevertheless, designing lightweight security mechanisms has not been stated in many schemes. In this paper, we propose a secure routing approach based on the league championship algorithm (LCA) for wireless body sensor networks in healthcare. The purpose of this scheme is to create a tradeoff between energy consumption and security. Our approach involves two important algorithms: routing process and communication security. In the first algorithm, each cluster head node (CH) applies the league championship algorithm to choose the most suitable next-hop CH. The proposed fitness function includes parameters like distance from CHs to the sink node, remaining energy, and link quality. In the second algorithm, we employs a symmetric encryption strategy to build secure connection links within a cluster. Also, we utilize an asymmetric cryptography scheme for forming secure inter-cluster connections. Network simulator version 2 (NS2) is used to implement the proposed approach. The simulation results show that our method is efficient in terms of consumed energy and delay. In addition, our scheme has good throughput, high packet delivery rate, and low packet loss rate.

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Section: Scheme Advantages Disadvantagesmentioning

confidence: 99%

A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare

Hosseinzadeh,

Mohammed,

Rahmani

et al. 2023

PLoS ONE

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“…Monisha et al [7] investigated an efficient relay node selection scheme for mission critical communication using machine learning, the temporary database learning and control (t-DLC) unit during an emergency is attached to minimize communication overhead in the medium. Kingston Roberts et al [8] designed an improved optimal energy-aware data availability approach for secure clustering and routing in wireless sensor networks, it can increase the network lifetime by focusing on the selection of stable routing paths and cluster heads. So et al [9] analyzed the probability of visibility limitation of autonomous vehicle detection performance according to various road geometry settings, and the reliable autonomous driving system will improve traffic safety based on the enhanced detection and recognition capability of autonomous vehicle sensors.…”

Section: Related Researchmentioning

confidence: 99%

“…Driverless technology of intelligent vehicles has received extensive attention and research in recent years [1][2][3][4][5][6][7][8][9]. According to United Nations statistics, road accidents kill 1.3 million people and injure 50 million people worldwide every year; the emergence of autonomous driving technology can effectively avoid some accidents and improve road safety.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Robust LiDAR-Based Vehicle Detection for On-Road Autonomous Driving

Jin

Yang

et al. 2023

Remote Sensing

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The stable detection and tracking of high-speed vehicles on the road by using LiDAR can input accurate information for the decision-making module and improve the driving safety of smart cars. This paper proposed a novel LiDAR-based robust vehicle detection method including three parts: point cloud clustering, bounding box fitting and point cloud recognition. Firstly, aiming at the problem of clustering quality degradation caused by the uneven distribution of LiDAR point clouds and the difference in clustering radius between point cloud clusters in traditional DBSCAN (TDBSCAN) obstacle clustering algorithms, an improved DBSCAN algorithm based on distance-adaptive clustering radius (ADBSCAN) is designed, and a point cloud KD-Tree data structure is constructed to speed up the traversal of the algorithm; meanwhile, the OPTICS algorithm is introduced to enhance the performance of the proposed algorithm. Then, by adopting different fitting strategies for vehicle contour points in various states, the adaptability of the bounding box fitting algorithm is improved; Moreover, in view of the shortcomings of the poor robustness of the L-shape algorithm, the principal component analysis method (PCA) is introduced to obtain stable bounding box fitting results. Finally, considering the time-consuming and low-accuracy training of traditional machine learning algorithms, advanced PointNet in deep learning technique is built to send the point cloud within the bounding box of a high-confidence vehicle into PointNet to complete vehicle recognition. Experiments based on our autonomous driving perception platform and the KITTI dataset prove that the proposed method can stably complete vehicle target recognition and achieve a good balance between time-consuming and accuracy.

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“…Furthermore, a novel cluster head selection algorithm based on residual energy and optimal distance for efficient energy consumption has been proposed. In Reference 58 author has presented a residual energy‐based data availability approach for enhanced network lifetime by focusing on stable routing paths and clustering methods.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption

Srivastava,

Mishra

2023

Concurrency and Computation

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SummarySensors plays an important role in day‐to‐day life as they sense and transfer data on the cloud and servers. These sensors have limited battery power due to which their optimized use is preferred with efficient energy consumption. As sensors were deployed almost everywhere under‐earth, underwater, electronic devices, and mobiles thus their enhanced performance is really important. In WSN, clustering is considered to be an essential approach that gives various advantages such as efficient energy, stability period, network lifetime, less delay, and scalability but has an issue of hot‐spot or energy‐hole problems. For this Un‐Equal Clustering is proposed where the size of clusters varies directly to the distance of the B.S. (Base Station). The main objective of Un‐Equal clustering is energy consumption, hot‐spot problem, and load balance among the cluster heads. In this paper, we have proposed an improved fuzzy‐based multi‐attributes un‐equal clustering to overcome the problem of hot‐spot problem. Earlier, researchers considered two attributes for selecting cluster heads (CHs), but later some of the MADM approaches were used that considered multiple attributes for optimal cluster head selection. In this paper, we have applied the fuzzy‐based TOPSIS method for optimized Un‐Equal clustering where the optimal node deployments with load balance among sensor nodes having optimal coverage and connectivity is considered. The results of the proposed method validate that it is one of the effective ways for selecting optimal cluster heads using fuzzy‐based multi‐attributes. Fuzzy‐based TOPSIS for cluster heads selection method shows 37% enhanced network performance with other compared existing algorithms. The performance of the proposed method is evaluated in terms of network stability, packet delivery, energy consumption, and network lifetime and F‐MAUC (Fuzzy based multi‐attributes un‐equal clustering) outperforms all other compared algorithms.

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An improved optimal energy aware data availability approach for secure clustering and routing in wireless sensor networks

Cited by 12 publications

References 33 publications

A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare

A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare

Robust LiDAR-Based Vehicle Detection for On-Road Autonomous Driving

Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption

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