Anchor-based void detouring routing protocol in three dimensional IoT networks

Gupta, Naveen Kumar; Yadav, Rama Shankar; Nagaria, R. K.; Gupta, Deepak; Tripathi, Achyut Mani; Pandey, Om Jee

doi:10.1016/j.comnet.2023.109691

Cited by 4 publications

(1 citation statement)

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“…Real-world applications, scalability, and the challenges posed by diverse urban infrastructures are areas that require deeper exploration. Additionally, issues related to data privacy, the potential for system hacks, and the reliability of IoT devices in varying conditions are concerns that are yet to be comprehensively addressed in the literature [22,23]. In conclusion, while the current trajectory of transport-management solutions, bolstered by IoT and ML, is promising, it's evident that the field remains ripe for further innovation and research.…”

Section: Introductionmentioning

confidence: 99%

Traffic Congestion Detection and Alternative Route Provision Using Machine Learning and IoT-Based Surveillance

et al. 2023

JMC

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The Automated Dynamic Traffic Assignment (ADTA) system introduces a novel approach to urban traffic management, merging the power of IoT with machine learning. This research assessed the system's performance in comparison to traditional traffic management strategies across various real-world scenarios. Findings consistently showcased the ADTA's superior efficiency: during peak traffic, it reduced vehicle wait times by half, and in scenarios with unexpected road closures, congestion detection was almost five times quicker, rerouting traffic with a remarkable 95% efficiency. The system's adaptability was further highlighted during weather challenges, ensuring safer vehicle speeds and substantially reducing weather-induced incidents. Large-scale public events, known disruptors of traffic flow, witnessed significantly reduced backlogs under the ADTA. Moreover, emergency situations benefitted from the system's rapid response, ensuring minimal delays for critical vehicles. This research underscores the potential of the ADTA system as a transformative solution for urban traffic woes, emphasizing its scalability and real-world applicability. With its integration of innovative technology and adaptive mechanisms, the ADTA offers a blueprint for the future of intelligent urban transport management.

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Section: Introductionmentioning

confidence: 99%

Traffic Congestion Detection and Alternative Route Provision Using Machine Learning and IoT-Based Surveillance

et al. 2023

JMC

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Fault‐tolerant independent gradient forwarding routing for wireless sensor network

Singh,

Prakash

2024

Int J Communication

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SummaryFor the sensor network, greedy forwarding is thought to be the best energy‐efficient routing method. There are two restrictions on greedy forwarding: a minimum local scenario and an intersection problem in which it is unable to locate an existing route. The proposed independent gradient forward (IGF) proves that the Euclidean equation used by greedy forwarding is less capable of dealing with the problems mentioned above. IGF considers each dimension independently to enhance the efficiency of greedy forwarding, sustaining its simplicity. It contributes to strengthening the suggested IGF's ability to manage holes and get around obstructions in the wireless sensor network. We have demonstrated that it travels a shorter potential path and avoids stuck nodes well in advance. It is more energy‐efficient since it can predict the void node and never visits a stalled node. We evaluated the suggested IGF's performance against other traditional boundary traversal‐based methods on both rare and dense networks. In comparison with other boundary traversal‐based algorithms, the simulation results indicate that it generates a 10% smaller route to bypass. The IGF average energy consumption increases slowly, rising by just 7% and 12.5% for dense and rare networks, respectively, with a fivefold increase in void size (from 100 to 500 m).

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