Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

Abstract

Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications.

Federated Learning Enabled SDN for Routing Emergency Safety Messages (ESMs) in IoV Under 5G Environment

“…Short-range V2V and V2R communications [51] are basic vehicular communications that are enabled through the IEEE 802.11p protocol/WAVE [52], [53]. The dedicated spectrum for this protocol is 75 MHz in the range of 5.850-5.925 GHz [54], [55]. One of the main problems using IEEE 802.11p technology in emergency traffic situations is broadcast storms, which have been addressed in the literature [56], [57], [58], [59], and [60] using various mechanisms.…”

Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN

“…Subsequently, the resulting products are fed into a linear layer with an activation function. For a given content embedding x c i and a user embedding y u j , the output is computed as follows: mi,j = σ(w T (x c i y u j )) (2) in this equation, σ denotes the sigmoid activation function, w represents a trainable matrix, and signifies the element-wise product. To train the matrix factorization model, we minimize the binary cross-entropy (BCE) loss between the ground truth values m i,j and the predicted values mi,j .…”

“…Researchers have explored the potential applications of ICN in diverse areas such as the Internet of Things (IoTs), where the efficient dissemination and retrieval of data is crucial for IoT devices to interact and exchange information [1]. In the context of the Internet of Vehicles (IoVs), ICN can enable efficient content delivery, facilitate real-time communication, and support intelligent transportation systems [2]. Furthermore, in the realm of 5G networks, ICN has been investigated for its potential to enhance content delivery, reduce latency, and improve overall network performance [3].…”

Applying Artificial Intelligence in Networks Automation—Theoretical Analysis and Future Research Areas

“…This section explores the application areas of network automation versus AI [5][6][7][8]. The rule-based and patternbased AI is differentiated in Table 1.…”