Secure Data Collection for Wireless Sensor Network

Ifzarne, Samir; Hafidi, Imad; Idrissi, Nadia

doi:10.1007/978-3-030-53440-0_26

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Equivalent energy is shared between the fitness assessment function and the entire sensor network. This is exhibited in Equation (2),…”

Section: Energy Efficiencymentioning

confidence: 94%

“…To maximize the lifespan of network, cluster base routing is significant in WSN. 2,3 WSN cluster gathers large amount of clusters as well as drop nodes. Every cluster has cluster members together with cluster heads (CHs).…”

Section: Introductionmentioning

confidence: 99%

“…The nodes on sensor have some benefits, such as low power, low computation ability, and low storage capacity. To maximize the lifespan of network, cluster base routing is significant in WSN 2,3 . WSN cluster gathers large amount of clusters as well as drop nodes.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

Janarthanan,

Srinivasan

2023

Int J Communication

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SummaryThe data are combined and transmitted through wireless sensor network (WSN) in internet of things applications. Multi‐Objective Cluster Head Based Energy Aware Routing using Auto‐Metric Graph Neural Network with Hybrid Balancing Composite Motion and Border Collie Optimization is proposed in this article for secured data aggregation (SDA) in WSN. The proposed method activates routing procedure via cluster head (CH). Hence, data aggregation basis Auto‐Metric Graph Neural Network selects CH depending upon multi‐objective fitness function. This fitness function deems factors, such as energy, delay, throughput, distance amid the nodes, capacity, collision, traffic rate, and cluster density. After CH selection, malicious node is present in the cluster. The optimum path is depending on three parameters: trust, connectivity, and quality of service (QoS). These three parameters have optimized under Hybrid Balancing Composite Motion and Border Collie Optimization method for ideal path selection. The ideal path is employed to transmit the gathered data to base station. The proposed technique is executed in network simulator, and its efficiency is evaluated under performance metrics, like network lifetime, packet delivery ratio, and delay. The proposed technique achieves 19.67%, 22.38%, and 27.45% better network lifetime; 28.92%, 25.83%, and 19.46% better packet delivery ratio; and 12.32%, 34.54%, and 22.33% lower delay while compared to the existing models: Taylor‐spotted hyena optimization for dependable and energy‐efficient CH selection‐based safe data routing and fault tolerance in WSN (T‐SHO‐DAWSN), multi‐objective cluster head utilizing SAPGAN for secured data aggregation (SAPGAN‐DAWSN), and energy efficient data accumulation design for protected routing protocol in WSN (MDAS‐DAWSN) methods, respectively.

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“…Equivalent energy is shared between the fitness assessment function and the entire sensor network. This is exhibited in Equation (2),…”

Section: Energy Efficiencymentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

Janarthanan,

Srinivasan

2023

Int J Communication

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“…A lot of work has been proposed by utilizing CS as a security scheme regardless of its security degree to achieve data privacy, security, energy and efficiency [12][13][14][15][16].…”

Section: Literature Reviewmentioning

confidence: 99%

Survey of Compressive Sensing Techniques Based on Secure Date Aggregation in WSNs

madkour,

Soliman,

Dessouky

et al. 2023

Preprint

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A key function of wireless sensor networks (WSN) is data collection. Due to the hot spot issue and the limited energy supply, developing data gathering techniques is complicated. The WSN faces three main challenges: security, data routing, and processing a lot of data. Since compressive sensing can achieve simultaneous sampling and compression, it is widely used in signal processing technique. Due to resource limitations and computational limitations, WSN security solutions are different from those in traditional networks. Compressive sensing (CS) and Elliptical curve Diffie-Hellman key exchange are used to solve these problems. The measurement matrix is configured to be as a public key that is understood by both the sensor node and the base station in order to achieve high safety and efficiency for data gathering in wireless sensor networks. Security and effective data collecting are the main study goals. A prime-numbered address strategy for TPID (tree path identifier) routing and cluster head selection is used. Comparison between seven types of CS algorithms is introduced over different data sparsity levels. The network parameters is being tested are Network life time, throughput, residual node energy and total energy dissipated. The results revels that the compression system can reduce the size of the transmitted data and consequently the energy consumption while still maintains the data security.

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“…Compared to the ubiquitous mobile ad hoc networks [18], WSNs are susceptible to several attacks due to their deployment and operation in open or unsecured communication channels. It is worth mentioning that a typical WSN domain could aggregate several sensor nodes and wireless base stations [19].…”

Section: Introductionmentioning

confidence: 99%

IBOOST: A Lightweight Provably Secure Identity-Based Online/Offline Signature Technique Based on FCM for Massive Devices in 5G Wireless Sensor Networks

Meshram¹,

Imoize²,

Elhassouny

et al. 2021

IEEE Access

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The fifth-generation (5G) wireless network is commercialized. The need to integrate massive devices in 5G and wireless sensor networks (WSN) to provide several convenient services for network users becomes imperative. However, there are growing concerns that 5G-WSNs pose new security threats to sensitive user information. User authentication and key agreement have been provided for secure end-to-end communication. However, stricter security techniques are required as billions of massive devices are being networked to collect and process complex user data in real-time. Therefore, anonymous authentication and authorization are highly coveted for privacy preservation and prevention of unlawful exploitation of user data. However, guaranteeing data integrity, authentication, and non-repudiation require special-purpose identity-based signature techniques that are quite difficult to design in practice. In order to address this problem, this paper proposes a lightweight, provably secure identity-based online/offline signature technique (IBOOST) and its extension for massive devices in 5G-WSNs using fractional chaotic maps. The IBOOST scheme achieves multi-time use of offline storage at a lower processing time. Therefore, the signer can reuse the offline pre-stored information in a polynomial time. This makes our IBOOST superior to the existing online/offline signature techniques that allow only a single signature. Additionally, the new technique enables the pre-registration process with a secret key, and no secret key is required in the offline stage. Also, the proposed IBOOST proves to be secure in the random oracle unforgeability under the chosen message attack (UF-IBS-CMA). Finally, the IBOOST and its enhanced version (A-IBOOST) give the lowest computational costs compared to several contending techniques. Therefore, the proposed IBOOST shows superior security and performance with better computational overhead than the preliminary techniques.

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Secure Data Collection for Wireless Sensor Network

Cited by 8 publications

References 17 publications

Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

Survey of Compressive Sensing Techniques Based on Secure Date Aggregation in WSNs

IBOOST: A Lightweight Provably Secure Identity-Based Online/Offline Signature Technique Based on FCM for Massive Devices in 5G Wireless Sensor Networks

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