IoT Solution for AI-Enabled PRIVACY-PREServing with Big Data Transferring: An Application for Healthcare Using Blockchain

Elhoseny, Mohamed; Haseeb, Khalid; Shah, Asghar Ali; Ahmad, Irshad; Alghamdi, Mohammed I.

doi:10.3390/en14175364

Cited by 32 publications

(15 citation statements)

References 43 publications

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“…However, smart devices [17,18] offer real-time data aggregation and improve performance in a distributed manner. However, many solutions often fail to meet remote users' rising demands, especially for big data management with the least computing power and security for constraint nodes [19,20]. Thus, many recent solutions explore network edges for manipulating operations and attaining a high response time [21][22][23].…”

Section: Related Workmentioning

confidence: 99%

Cloud-edge load balancing distributed protocol for IoE services using swarm intelligence

et al. 2023

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Rapid development of the Internet of Everything (IoE) and cloud services offer a vital role in the growth of smart applications. It provides scalability with the collaboration of cloud servers and copes with a big amount of collected data for network systems. Although, edge computing supports efficient utilization of communication bandwidth, and latency requirements to facilitate smart embedded systems. However, it faces significant research issues regarding data aggregation among heterogeneous network services and objects. Moreover, distributed systems are more precise for data access and storage, thus machine-to-machine is needed to be secured from unpredictable events. As a result, this research proposed secured data management with distributed load balancing protocol using particle swarm optimization, which aims to decrease the response time for cloud users and effectively maintain the integrity of network communication. It combines distributed computing and shift high cost computations closer to the requesting node to reduce latency and transmission overhead. Moreover, the proposed work also protects the communicating machines from malicious devices by evaluating the trust in a controlled manner. Simulation results revealed a significant performance of the proposed protocol in comparison to other solutions in terms of energy consumption by 20%, success rate by 17%, end-to-end delay by 14%, and network cost by 19% as average in the light of various performance metrics.

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Section: Related Workmentioning

confidence: 99%

Cloud-edge load balancing distributed protocol for IoE services using swarm intelligence

et al. 2023

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“…In contrast, the technique manages various encryption methods and keys for every feature level. Elhoseny et al [16] aim to devise an IoT solution for AI-assisted privacy preservation with big data transmitting utilizing BC. Initially, the presented method employs a graph-modeling to advance a reliable and scalable system to collect and transmit data.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Metaheuristics with Trust Aware Route Selection for Wireless Sensor Networks

Devaraj¹,

Murthy²,

Alenezi³

et al. 2023

Computer Systems Science and Engineering

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Recently, a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks (WSN). In routing, the trust system includes or avoids nodes related to the estimated trust values in the routing function. This article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol (EMTA-SRSP) for WSN. The presented EMTA-SRSP technique majorly involves the optimal selection of routes in WSN. To accomplish this, the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data communication. For the clustering process, the nodes with maximum residual energy will be considered cluster heads (CHs). In addition, the OAOA technique gets executed to choose optimal routes based on objective functions with multiple parameters such as energy, distance, and trust degree. The experimental validation of the EMTA-SRSP technique is tested, and the results exhibited a better performance of the EMTA-SRSP technique over other approaches.

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“…Deep learning lowers the cost function and executes the learning process by altering the weight values using a backpropagation technique. First, the system gives an input vector and weights, and the error rate is calculated by comparing the output to the desired output [47,48].…”

Section: Deep Learning (Dl) Techniques For Idssmentioning

confidence: 99%

Deep Learning for Intrusion Detection and Security of Internet of Things (IoT): Current Analysis, Challenges, and Possible Solutions

Khan

Kashif

Jhaveri

et al. 2022

Security and Communication Networks

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In the last decade, huge growth is recorded globally in computer networks and Internet of Things (IoT) networks due to the exponential data generation, approximately zettabyte to a petabyte. Consequently, security issues have also been arisen with the network growth. However, intrusion detection in such big data is challenging. Smart homes, cities, grids, devices, objects, e-commerce, e-banking, e-government, etc., are different advanced applications of the evolving networks. Many Intrusion Detection Systems (IDS) have been developed recently due to most computer networks’ exposure to security and privacy threats. Data confidentiality, integrity, and availability damage will occur in case of IDS prevention failure. Conventional techniques are not effective enough to cope the advanced attacks. Advanced deep learning techniques have been proposed for automatic intrusion detection and abnormal behavior identification of networks. This research aims to provide an inclusive analysis of intrusion detection based on deep learning techniques followed by different intrusion detection systems. In this review, public network-based datasets of IDS are fully explored and analyzed. Deep learning techniques for IDS have been critically evaluated based on different performance metrics (accuracy, precision, recall, f-1 score, false alarm rate, and detection rate). Furthermore, existing challenges and possible solutions for networks security and privacy have been discussed.

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IoT Solution for AI-Enabled PRIVACY-PREServing with Big Data Transferring: An Application for Healthcare Using Blockchain

Cited by 32 publications

References 43 publications

Cloud-edge load balancing distributed protocol for IoE services using swarm intelligence

Cloud-edge load balancing distributed protocol for IoE services using swarm intelligence

Enhanced Metaheuristics with Trust Aware Route Selection for Wireless Sensor Networks

Deep Learning for Intrusion Detection and Security of Internet of Things (IoT): Current Analysis, Challenges, and Possible Solutions

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