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Vehicular crowdsensing is anticipated to become a key catalyst for data-driven optimization in the Intelligent Transportation System (ITS) domain. Yet, the expected growth in massive Machine-type Communication (mMTC) caused by…
The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication…
While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly up-to-date information for crowdsensing-enabled big data services in a smart city…
Next-generation cellular networks will evolve into more complex and virtualized systems, employing machine learning for enhanced optimization and leveraging higher frequency bands and denser deployments to meet varied service demands. This…
as a novel client-based method for resource-efficient opportunistic transmission of delay-tolerant vehicular sensor data. BS-CB applies a hybrid approach which brings together all major machine learning disciplines - supervised,…
The full future of the sixth generation will develop a fully data-driven that provide terabit rate per second, and adopt an average of 1000+ massive number of connections per person in 10 years 2030 virtually instantaneously. Data-driven…
Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of…
Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Internet of Things (IoT) systems. Cars acting as mobile sensors…
As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional…
Multi-Mobile Network Operator (MNO) networking is a promising method to exploit the joint force of multiple available cellular data connections within vehicular networks. By applying anticipatory communication principles, data transmissions…
In order to satisfy the ever-growing Quality of Service (QoS) requirements of innovative services, cellular communication networks are constantly evolving. Recently, the 5G NonStandalone (NSA) mode has been deployed as an intermediate…
With recent advancements in the field of communications and the Internet of Things, vehicles are becoming more aware of their environment and are evolving towards full autonomy. Vehicular communication opens up the possibility for…
Connected vehicles disseminate detailed data, including their position and speed, at a very high frequency. Such data can be used for accurate real-time analysis, prediction and control of transportation systems. The outstanding challenge…
Machine learning methods are increasingly adopted in communications problems, particularly those arising in next generation wireless settings. Though seen as a key climate mitigation and societal adaptation enabler, communications related…
In recent years, mobile devices have gained increasing development with stronger computation capability and larger storage space. Some of the computation-intensive machine learning tasks can now be run on mobile devices. To exploit the…
Efficient data transmission scheduling within vehicular environments poses a significant challenge due to the high mobility of such networks. Contemporary research predominantly centers on crafting cooperative scheduling algorithms tailored…
Efficient data offloading plays a pivotal role in computational-intensive platforms as data rate over wireless channels is fundamentally limited. On top of that, high mobility adds an extra burden in vehicular edge networks (VENs),…
Human-centric applications such as virtual reality and immersive gaming will be central to the future wireless networks. Common features of such services include: a) their dependence on the human user's behavior and state, and b) their need…
Sixth-generation (6G) wireless networks are expected to support immersive and mission-critical applications requiring ultra-reliable communication, sub-second responsiveness, and multi-Gbps data rates. Dense small-cell deployments are a key…
Machine learning-based data rate prediction is one of the key drivers for anticipatory mobile networking with applications such as dynamic Radio Access Technology (RAT) selection, opportunistic data transfer, and predictive caching. User…