ReFIoV: a novel reputation framework for information-centric vehicular applications

In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time

Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists

A topology-oblivious routing protocol for NDN-VANETs

Vehicular Ad Hoc Networks (VANETs) are characterized by intermittent connectivity, which leads to failures of end-to-end paths between nodes. Named Data Networking (NDN) is a network paradigm that deals with such problems, since information is forwarded based on content and not on the location of the hosts. In this work, we propose an enhanced routing protocol of our previous topology-oblivious Multihop, Multipath, and Multichannel NDN for VANETs (MMM-VNDN) routing strategy that exploits several paths to achieve more efficient content retrieval. Our new enhanced protocol, i mproved MMM-VNDN (iMMM-VNDN), creates paths between a requester node and a provider by broadcasting Interest messages. When a provider responds with a Data message to a broadcast Interest message, we create unicast routes between nodes, by using the MAC address(es) as the distinct address(es) of each node. iMMM-VNDN extracts and thus creates routes based on the MAC addresses from the strategy layer of an NDN node. Simulation results show that our routing strategy performs better than other state of the art strategies in terms of Interest Satisfaction Rate, while keeping the latency and jitter of messages low

Named Data Networking in Vehicular Ad hoc Networks: State-of-the-Art and Challenges

International audienceInformation-Centric Networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today's Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, Named Data Networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is Intelligent Transportation System (ITS) realized through Vehicular Ad hoc NETwork (VANET) where vehicles exchange information and content with each other and with the infrastructure. To date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community

Recent Advances in Cellular D2D Communications

Device-to-device (D2D) communications have attracted a great deal of attention from researchers in recent years. It is a promising technique for offloading local traffic from cellular base stations by allowing local devices, in physical proximity, to communicate directly with each other. Furthermore, through relaying, D2D is also a promising approach to enhancing service coverage at cell edges or in black spots. However, there are many challenges to realizing the full benefits of D2D. For one, minimizing the interference between legacy cellular and D2D users operating in underlay mode is still an active research issue. With the 5th generation (5G) communication systems expected to be the main data carrier for the Internet-of-Things (IoT) paradigm, the potential role of D2D and its scalability to support massive IoT devices and their machine-centric (as opposed to human-centric) communications need to be investigated. New challenges have also arisen from new enabling technologies for D2D communications, such as non-orthogonal multiple access (NOMA) and blockchain technologies, which call for new solutions to be proposed. This edited book presents a collection of ten chapters, including one review and nine original research works on addressing many of the aforementioned challenges and beyond

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Cooperative Caching in Vehicular Networks - Distributed Cache Invalidation Using Information Freshness

Recent advances in vehicular communications has led to significant opportunities to deploy variety of applications and services improving road safety and traffic efficiency to road users. In regard to traffic management services in distributed vehicular networks, this thesis work evaluates managing storage at vehicles efficiently as cache for moderate cellular transmission costs while still achieving correct routing decision. Road status information was disseminated to oncoming traffic in the form of cellular notifications using a reporting mechanism. High transmission costs due to redundant notifications published by all vehicles following a basic reporting mechanism: Default-approach was overcome by implementing caching at every vehicle. A cooperative based reporting mechanism utilizing cache: Cooperative-approach, was proposed to notify road status while avoiding redundant notifications. In order to account those significantly relevant vehicles for decision-making process which did not actually publish, correspondingly virtual cache entries were implemented. To incorporate the real-world scenario of varying vehicular rate observed on any road, virtual cache entries based on varying vehicular rate was modeled as Adaptive Cache Management mechanism. The combinations of proposed mechanisms were evaluated for cellular transmission costs and accuracy achieved for making correct routing decision. Simulation case studies comprising varying vehicular densities and different false detection rates were conducted to demonstrate the performance of these mechanisms. Additionally, the proposed mechanisms were evaluated in different decision-making algorithms for both information freshness in changing road conditions and for robustness despite false detections. The simulation results demonstrated that the combination of proposed mechanisms was capable of achieving realistic information accuracy enough to make correct routing decision despite false readings while keeping network costs significantly low. Furthermore, using QoI-based decision algorithm in high density vehicular networks, fast adaptability to frequently changing road conditions as well as quick recovery from false notifications by invalidating them with correct notifications were indicated

Equivalence classes for named function networking

Named Function Networking (NFN) is a generalization of Content-Centric Networking (CCN) and Named Data Networking (NDN). Beyond mere content retrieval, NFN enables to ask for results of computations. Names are not just content identifiers but λ-expressions that allow an arbitrary composition of function calls and data accesses. λ-expressions are pure and deterministic. In other words, they do not have side effects and they always yield the same result. Both properties together are known to as referential transparency. Referentially transparent functions can be evaluated individually no matter where and in what order, e.g. geographically distributed and concurrently. This simplifies the distribution of computations in a network, an attractive feature in times of rising needs for edge computing. However, NFN is affected by a lacking awareness for referentially opaque expressions that are characterized by having changing results or side effects, i.e. expressions that depend on outer conditions or modify outer states. The fundamental motivation of this thesis is to retrofit NFN with a clearer notion of referentially opaque expressions. They are indispensable not only to many common use cases such as e-mail and database applications, but also to network technologies such as software defined networking. We observed that many protocol decisions are based on expression matching, i.e. the search for equivalent expressions. Driven by this observation, this thesis explores possibilities to adapt the determination of equivalences in dependence of crucial expression properties such as their ability for aggregation, concurrent evaluation or permanently cacheable results. This exploration results in a comprehensive set of equivalence classes that is used for explicit attribution of expressions, leading to a system that is aware of the true nature of handled expressions. Moreover, we deliver a solution to support referentially opaque expressions and mutable states in an architecture that bases upon uniquely named and immutable data packets. Altogether, the findings condense to an extended execution model. It summarizes how the attribution of expressions with equivalence classes influences specific protocol decisions in order to support referentially transparent as well as referentially opaque expressions. We believe that our approach captivates due to its generality and extensibility. Equivalence classes depend upon universal properties. Therefore, our approach is not bound to a specific elaboration like NFN. We evaluate the applicability of our approach in a few application scenarios. Overall, the proposed solutions and concepts are an important contribution towards name-based distributed computations in information-centric networks