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Intelligent applications based on machine learning are impacting many parts of our lives. They are required to operate under rigorous practical constraints in terms of service latency, network bandwidth overheads, and also privacy. Yet…
Efficient Vehicle-to-Everything enabling cooperation and enhanced decision-making for autonomous vehicles is essential for optimized and safe traffic. Real-time decision-making based on vehicle sensor data, other traffic data, and…
In recent years, there is an emerging trend that some computing services are moving from cloud to the edge of the networks. Compared to cloud computing, edge computing can provide services with faster response, lower expense, and more…
With the advancement of IoT technology, various domains such as smart factories, smart cities and smart cars use the IoT to provide value-added services. In addition, technologies such as MEC and network slicing provide another opportunity…
Nowadays, we are witnessing the advent of the Internet of Things (EC) with numerous devices performing interactions between them or with end users. The huge number of devices leads to huge volumes of collected data that demand the…
In an IoP environment, edge computing has been proposed to address the problems of resource limitations of edge devices such as smartphones as well as the high-latency, user privacy exposure and network bottleneck that the cloud computing…
This research focuses on real-time monitoring and analysis of track and field athletes, addressing the limitations of traditional monitoring systems in terms of real-time performance and accuracy. We propose an IoT-optimized system that…
Edge computing is a paradigm that shifts data processing services to the network edge, where data are generated. While such an architecture provides faster processing and response, among other benefits, it also raises critical security…
Mobile and Internet of Things devices are generating enormous amounts of multi-modal data due to their exponential growth and accessibility. As a result, these data sources must be directly analyzed in real time at the network edge rather…
Edge Computing is a new distributed Cloud Computing paradigm in which computing and storage capabilities are pushed to the topological edge of a network. However, various standards and implementations are promoted by different initiatives.…
Edge computing allows for the decentralization of computing resources. This decentralization is achieved through implementing microservice architectures, which require low latencies to meet stringent service level agreements (SLA) such as…
There are so many vehicles in the world and the number of vehicles is increasing rapidly. To alleviate the parking problems caused by that, the smart parking system has been developed. The parking planning is one of the most important parts…
The next generation networks offers significant potential to advance Intelligent Transportation Systems (ITS), particularly through the integration of Digital Twins (DTs). However, ensuring the uninterrupted operation of DTs through…
Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems.…
The concept of the Internet of Things (IoT) is a reality now. This paradigm shift has caught everyones attention in a large class of applications, including IoT-based video analytics using smart doorbells. Due to its growing application…
Gun violence is a severe problem in the world, particularly in the United States. Deep learning methods have been studied to detect guns in surveillance video cameras or smart IP cameras and to send a real-time alert to security personals.…
Expected to provide higher transportation efficiency and security, autonomous driving has attracted substantial attentions from both industry and academia. Meanwhile, the emergence of edge intelligence has further introduced significant…
The rapid technological advances in the Internet of Things (IoT) allows the blueprint of Smart Cities to become feasible by integrating heterogeneous cloud/fog/edge computing paradigms to collaboratively provide variant smart services in…
Heavy data load and wide cover range have always been crucial problems for internet of things (IoT). However, in mobile-edge computing (MEC) network, the huge data can be partly processed at the edge. In this paper, a MEC-based big data…
Devices with the ability to connect to the internet are growing in numbers day by day thus creating the need for a new way of manag-ing the way the produced traffic travels through data networks. Smart Cities, Vehicular Content Networks,…