Moisture Computing-Based Internet of Vehicles (IoV) Architecture for Smart Cities

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 11; p. 3785
• Main Authors: Tufail, Ali, Namoun, Abdallah, Abi Sen, Adnan Ahmed, Kim, Ki-Hyung, Alrehaili, Ahmed, Ali, Arshad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 30-05-2021; MDPI
• Subjects: Artificial intelligence; Automation; Automobile industry; Cloud computing; Communication; Commuting; Computer architecture; Data centers; Distributed processing; Embedded systems; Empowerment; Internet of Things; Internet of Vehicles; IoV; MEC; Moisture; Network latency; Paradigms; Response time; Safety management; Sensors; smart vehicles; Software; Supply chain management; Traffic congestion; Vehicles
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21113785

Recently, the concept of combining ‘things’ on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management issues. Moreover, the MC can be used to dispatch emergency and roadside assistance in case of incidents and accidents. In contrast to the cloud which covers a broader area, the MC provides smart vehicles with critical information with fewer delays. We argue that the MC can help reduce infrastructure costs efficiently since it requires a medium-scale data center with moderate resources to cover a wider area compared to small-scale data centers in edge computing and large-scale data centers in cloud computing. We performed mathematical analyses to demonstrate that the MC reduces network delays and enhances the response time in contrast to the edge and cloud infrastructure. Moreover, we present a simulation-based implementation to evaluate the computational performance of the MC. Our simulation results show that the total processing time (computation delay and communication delay) is optimized, and delays are minimized in the MC as apposed to the traditional approaches.