AI Based Energy Efficient Routing Protocol for Intelligent Transportation System

Goswami, Pratik; Mukherjee, Amrit; Hazra, Ranjay; Yang, Lixia; Ghosh, Uttam; Qi, Yong; Wang, Hongjin

doi:10.1109/tits.2021.3107527

Cited by 60 publications

(26 citation statements)

References 39 publications

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“…This work also proposes new method for energy efficiency by implementation of hybrid approach of DAI and selforganizing Map (SOM). It also save the overall network energy and reduce the computation challenges [62].…”

Section: Discussionmentioning

confidence: 99%

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

et al. 2022

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The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).

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Section: Discussionmentioning

confidence: 99%

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

et al. 2022

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“…In [19], the authors developed an energy-efficient non-cooperative game for distributed CRN over interference channels. For CRNs and IoT, a noncooperative game based on power allocation is proposed in [20], which investigates a mesh adaptive search technique for device to device-assisted CRN; [21] proposes a gradient adaptation optimization for power allocation as well as EE in CRNs. Although gradient techniques are reliable, they can fail to accomplish global optimization.…”

Section: Related Workmentioning

confidence: 99%

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

Ahmed

Garg

Rai³

et al. 2022

Electronics

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For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.

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“…Recently, much AI-related wireless communication work has been discussed by researchers. To realize the rapid response of node communication and meet the requirements of the bandwidth and response time of network transmission for a large amount of data and applications, Pratik Goswami et al [29] proposed a method for implementing distributed artificial intelligence using neural networks. The method realizes node prediction by considering the number of observations of node power consumption when executing a specific application and finally effectively saves the response time of routing.…”

Section: Related Workmentioning

confidence: 99%

Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

Gou

Long

et al. 2022

Wireless Communications and Mobile Computing

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Billions of people around the world send and receive data over online networks daily. Sufficient and redundant data are transmitted over social platforms with AI-assisted in 5G networks. In opportunistic social networks, the main challenge faced by traditional methods is that numerous user nodes participate in data transmission, causing a lot of message copy redundancy and node cache consumption. As a result, the transmission delay of the algorithm is high, the node energy consumption is too large, and even information is lost. To solve these problems, this study establishes an artificial intelligence-based optimization multiple evaluation method. The main purpose of this method is to avoid information loss caused by data loss when reducing data noise, reasonably select communication nodes in opportunistic social network scenarios, optimize data transmission performance, and avoid network congestion. Moreover, our method can effectively identify and exclude potential malicious nodes, reducing the situation that packets are intercepted and discarded. The experiment confirms that the optimized transmission evaluation scheme can effectively reduce routing overheads and energy consumption of a user node, improve the delivery ratio of node data transmission, and ensure the reliability and security of data transmission.

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AI Based Energy Efficient Routing Protocol for Intelligent Transportation System

Cited by 60 publications

References 39 publications

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

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