Attribute-Based Secure Data Aggregation for Isolated IoT-Enabled Maritime Transportation Systems

Wang, Anxi; Shen, Jian; Vijayakumar, Pandi; Gupta, Brij B.

doi:10.1109/tits.2021.3127436

Cited by 25 publications

(8 citation statements)

References 38 publications

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“…As the number of devices on an Internet of Things (IoT) network increases, so do the transmission bandwidth and transmission power consumption. In IoT networks, compressed data aggregation (CDA) has been proposed as an effective approach to reduce the amount of data collected [1][2][3]. In the WSN system, with regard to the time of packet transfer from one node to another node, the possibility of security risks was generated.…”

Section: Literature Surveymentioning

confidence: 99%

Aggregation Technique Using Dynamic Cross-Propagation Clustering Algorithm in Wireless Body Sensor Networks

Arulprakash

Baalamurugan

Dhanaraj

et al. 2022

Wireless Communications and Mobile Computing

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Wireless body sensor networks (WBSNs) are characterized by a large number of battery-powered wireless sensor nodes, and the most challenging aspects of WBSNs are sensor node energy consumption, delay, and security (communication and data) while maintaining regular wireless sensor network (WSN) capabilities. Data aggregation, as a common procedure in data gathering applications, can waste a lot of energy since sensor nodes may stay in the listen to state even when they are not receiving data during the data collection process. In this research work, introducing the Self-Executing-Dynamic Cross-Propagation Clustering (SE-DCPC) algorithm helps to improve the node energy consumption positively by turning nodes to the accessible state when not in use and waking them up when necessary. The algorithm is energy-based and uses a self-executing-based dynamic cross-propagation clustering system to send/receive scheduling data in the WSN platform. The energy level of the nodes is the most essential component in constructing network communication, in contrast to earlier clustering algorithms. The purpose of this proposed algorithm is to enhance the traditional notion of the clustering algorithm (location-based clustering), leading to the primary goal of enhancing the permanence of the wireless sensor network, which is to conserve network coverage, using self-executing DCPC clustering technology for location and power. The result of performance analysis of the SE-DCPC is achieved by simulation using two different communication processes of clustering and intelligence decision-making methods. The numerical results shows that SE-DCPC can effectively handle and maintain a high rate of network node energy consumption. The simulation result shows that the proposed approaches securely obtain the high throughput and very minimal delay at the client side, compared to existing clustering algorithm approaches. This SE-DCPC algorithm increased by 21.89% the communication medium lifetime and by 37% the energy consumption and reduced by 23.27% the overhead compared with existing clustering algorithms.

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Section: Literature Surveymentioning

confidence: 99%

Aggregation Technique Using Dynamic Cross-Propagation Clustering Algorithm in Wireless Body Sensor Networks

Arulprakash

Baalamurugan

Dhanaraj

et al. 2022

Wireless Communications and Mobile Computing

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“…Ports are involved with various emissions originating from many sources [14], [15]. The emissions encompass marine vessels, vehicles, and cargo-handling systems, and they significantly impact the ecosystem [16]- [18]. Furthermore, it is crucial to acknowledge that activities such as container drayage and interterminal transit significantly contribute to the emissions generated by ports [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning for Green Shipping: Navigating towards Sustainable Maritime Practices

Nguyen,

Tran

et al. 2024

JOIV : Int. J. Inform. Visualization

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This paper aims to investigate the role that artificial intelligence (AI) plays in promoting sustainability in the marine industry. The report demonstrates the potential of AI-driven technology to improve vessel operations, decrease emissions, and promote environmental stewardship. This potential is shown by detailed examination of existing trends, problems, and possibilities. Several vital studies highlight the significance of policy interventions that encourage the use of artificial intelligence. These interventions include financial incentives, legal frameworks, and programs to increase capability. Throughout this work, the importance of the role that artificial intelligence plays in driving efficiency, safety, and sustainability is emphasized. This work also highlights the urgent need for action to address climate change and environmental degradation in the marine sector. The marine industry can lessen its carbon footprint, decrease pollution, and improve ecosystem health if it shifts to various alternative fuels, renewable energy sources, and technologies powered by artificial intelligence. At the end of this work, an appeal is made to policymakers, industry stakeholders, and technology providers, urging them to prioritize investments in artificial intelligence research and development and to create collaboration to speed up the transition to a marine sector that is more sustainable and resilient.

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“…With the emergence of big data and its applications [1][2][3][4][5], cloud computing is becoming increasingly important and popular for the Smart Device Client (SDC). Since sensitive user information can be easily hacked, such information is encrypted on the SDC side and then outsourced to the cloud.…”

Section: Introductionmentioning

confidence: 99%

A provably lightweight and secure DSSE scheme, with a constant storage cost for a smart device client

Bulbul,

Abduljabbar,

Mohammed

et al. 2024

PLoS ONE

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Outsourcing data to remote cloud providers is becoming increasingly popular amongst organizations and individuals. A semi-trusted server uses Searchable Symmetric Encryption (SSE) to keep the search information under acceptable leakage levels whilst searching an encrypted database. A dynamic SSE (DSSE) scheme enables the adding and removing of documents by performing update queries, where some information is leaked to the server each time a record is added or removed. The complexity of structures and cryptographic primitives in most existing DSSE schemes makes them inefficient, in terms of storage, and query requests generate overhead costs on the Smart Device Client (SDC) side. Achieving constant storage cost for SDCs enhances the viability, efficiency, and easy user experience of smart devices, promoting their widespread adoption in various applications while upholding robust privacy and security standards. DSSE schemes must address two important privacy requirements: forward and backward privacy. Due to the increasing number of keywords, the cost of storage on the client side is also increasing at a linear rate. This article introduces an innovative, secure, and lightweight Dynamic Searchable Symmetric Encryption (DSSE) scheme, ensuring Type-II backward and forward privacy without incurring ongoing storage costs and high-cost query generation for the SDC. The proposed scheme, based on an inverted index structure, merges the hash table with linked nodes, linking encrypted keywords in all hash tables. Achieving a one-time O(1) storage cost without keyword counters on the SDC side, the scheme enhances security by generating a fresh key for each update. Experimental results show low-cost query generation on the SDC side (6,460 nanoseconds), making it compatible with resource-limited devices. The scheme outperforms existing ones, reducing server-side search costs significantly.

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Attribute-Based Secure Data Aggregation for Isolated IoT-Enabled Maritime Transportation Systems

Cited by 25 publications

References 38 publications

Aggregation Technique Using Dynamic Cross-Propagation Clustering Algorithm in Wireless Body Sensor Networks

Aggregation Technique Using Dynamic Cross-Propagation Clustering Algorithm in Wireless Body Sensor Networks

Artificial Intelligence and Machine Learning for Green Shipping: Navigating towards Sustainable Maritime Practices

A provably lightweight and secure DSSE scheme, with a constant storage cost for a smart device client

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