Introduction to the Special Section on Edge Computing AI-IoT Integrated Energy Efficient Intelligent Transportation System for Smart Cities

Most countries around the world are trying to make the idea of “smart cities” a reality by building the basicinfrastructure needed to use the technology. In this case, edge computing (EC) is very important for faster data processing and faster responses at the edges of the network. In recent years, smart cities have started using EC to improve building security, home automation, urban parking systems, and traffic and city management. Traditional IoT networks collect data and send it to a central cloud for further processing. EC devices, in contrast, can process and analyze the data themselves, as well as reduce the load on the network. In addition, mobile crowdsensing (MCS) and mobile edge computing (MEC) provide the crowdsensing services needed for a smart city in a densely populated area. These techniques provide a specific service at a specific location and for a specific period of time. However, they are better suited to support technical communication services with static edges than the human side. This leads to a dynamic extension of MEC called human-enabled edge computing (HEC), which combines people, devices, the Internet, and information with the architecture of MEC and the ability to sense MCS.

In general, a traditional sensor network does not take context into account as well as HEC because it uses smart devices such as smartphones and wearables that people carry with them. It also uses data from mobile devices to obtain crowd intelligence and provide services based on what people want. Edge computing involves both people and things and therefore requires intelligent methods for classification and decision making, such as machine learning, data mining, and cognitive intelligence. Edge intelligence is used in the smart city to leverage data from different parts of the smart city. This is done by running analytics algorithms at the edge of the city. This speeds up the time it takes networked devices to make decisions and improves the quality of the data. Smart cities are taking advantage of HEC and next-generation wireless technology to connect things to people and the Internet of Things (IoT), resulting in powerful services and automation in the creation of dense and changing data sets. A successful edge computing infrastructure requires a local server, AI, and connections to computing systems in mobile devices, cars, and the Internet of Things (IoT).

The research community has responded with enthusiasm. Only research articles that meet the journal's requirements are accepted for publication after peer review. In this special issue, we have received 34 articles, and we finally include only one article, which has been fairly peer-reviewed and accepted for publication. The following points highlight the remarkable scientific achievements of the accepted article.

In the paper entitled, “ Edge Computing AI-IoT Integrated Energy Efficient Intelligent Transportation System for Smart Cities”, the authors, Suresh Chavhan, Deepak Gupta, Sarada Prasad Gochhayat, Chandana B. N., Ashish Khanna, K. Shankar, and Joel J. P. C. Rodrigues, propose a novel edge-based AI-IoT integrated energy-efficient intelligent transportation system for smart cities using a distributed multi-agent system. First, an urban area is divided into a large number of regions and each region is divided into a finite number of zones. After that, for each zone, an optimal number of RSUs (Roadside Units) are installed along with edge computing devices. The MAS (Mobile Agent Server) is installed in each RSU, which can be used to collect huge amounts of data from the various sensors, devices, and infrastructures. The edge computing device uses the raw data collected from MAS to process, analyse, and predict. The predicted information is communicated to the neighbouring RSUs, vehicles, and the cloud using the IoT via MAS. From the extensively evaluated simulation results, the experiments demonstrated the effectiveness of the proposed system. Thus, the predicted information can be used by the cargo vehicles to maintain smooth and steady movement; it can thus be used to reduce greenhouse gas emissions and energy consumption, and eventually improve the mileage of cargo vehicles by reducing traffic congestion in urban areas.

We thank all authors and reviewers for their prompt and effective contributions. We are very grateful to the Editor-in-Chief of the journal for giving us the opportunity to run a special issue in this journal. We also hope that this special issue will be of interest to the scientific world.

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