This work concerns the design, testing, and comparison of approaches promoting the store-and-forwarding of messages in a Vehicular Delay-Tolerant Network (VDTN) while still making the network robust against attacks coming from ill-intentioned nodes that try to send large volumes of selfgenerated messages to cause congestion. Performance evaluation consisted of simulations with varying percentages of flooding and intermittent flooding attackers using the real-world road map of Salamanca on the Opportunistic Network Environment (ONE) simulator. Different performance evaluation metrics were considered, namely Message delivery ratio (MDR), latency, hop count, false positives in attacker detection, false negatives, reputation, and trust threshold. Evaluation metrics showed that the two novel approaches, designed using a reputation enforcement system that considers the previously known actions of opponents to isolate nodes that do not send messages from others, and also using concepts of Evolutionary Game Theory (EGT) to make the network adaptable to different states of the population of nodes, exceed the performance of the classic Tit-for-tat (TFT) and Tit-for-two-tats (TF2T) approaches for scenarios with or without attackers.

Academia and industry are constantly striving to improve the driving experience, where vehicles can now communicate, and different technologies can coexist and share the wireless medium to send and receive information. In terms of vehicle-to-vehicle (V2V) communication, vehicles can use Dedicated Short-Range Communication (DSRC) or Cellular Vehicle-to-Everything (C-V2X) sidelink technologies to communicate on the roads by sharing the 5.9 GHz band. However, coexistence between the two technologies remains challenging, as interference can occur when both technologies use co-channels to send vehicle messages in parallel, degrading the Packet Delivery Ratio (PDR) metric. In addition, malicious users can carry out various types of attacks to disrupt the vehicle connection, such as jamming attacks. In this paper, we transmitted packets using a constant bit rate (CBR). We considered Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios (i.e., in the DSRC communication) to understand how damaging the jamming attack can be. Based on our tests, the jamming attack caused by the C-V2X co-channel interference can cause an attenuation in the signal loss of up to 24 dB (i.e., using a packet size of 256 bytes) and up to 35 dB (i.e., using a packet size of 1,399 bytes), and signal-to-interference and noise ratio (SINR) by up to 26 dB (i.e., using a packet size of 256 bytes) and up to 40 dB (i.e., using a packet size of 1,399 bytes). Furthermore, we analyzed the interference effects in different data rates such as 3, 4.5, 6, 9, 12, 18, 24, and 27 Mbps.

The Cloud Computing (CC) paradigm has risen in recent years as a solution to a need for computation and battery-constrained User Equipment (UE) to run increasingly intensive computation tasks. Nevertheless, given the centralized nature of the CC paradigm, this option introduces significant network congestion problems and unpredictable communication delays unsuitable for real-time applications. In order to cope with these problems, the Mobile Edge Computing (MEC) concept has been introduced, which proposes to bring computation resources closer to the edge of the mobile networks in a distributed way. However, given that these edge computation resources are limited, this paradigm comes with its set of challenges that need to be solved in order to make it viable. This work proposes to innovate by presenting a network management agent capable of making offloading decisions from a heterogeneous network of UEs to a heterogeneous network of MEC servers. This agent is the orchestrator of a group of 5G Small Cells (SCeNBs), enhanced with computation and storage capabilities. In order to solve this high complexity problem, an Advantage Actor-Critic (A2C) agent is implemented and tested against several baselines. The proposed solution is shown to beat the baselines by making intelligent decisions taking into account computation, battery, delay and communication constraints ignored by the baselines. The solution is also shown to be scalable, data-efficient, robust, stable and adjustable to address not only the overall system performance but also to take into account the worst-case scenario.

The Internet of Vehicles (IoV) is the future of transportation. It will be present everywhere and will have a huge impact on our lives. However, there are plenty of aspects to consider while studying these networks, such as data dissemination, cybersecurity threats and vulnerabilities. For an IoV to work efficiently, data needs to spread through it efficiently. However, the dynamics of vehicular environments due to frequent node mobility and nodes' misbehavior poses many challenges to efficient data dissemination. Therefore, a deep learning-based monitoring system that is capable of detecting anomalies in the network and identifying known misbehavior is proposed. Performance evaluation shows that the monitoring system can identify well-known attacks with a very high success rate. Besides, the algorithm is also capable of detecting other types of misbehavior without labeling them.

The Internet of Vehicles (IoV) is undoubtedly at the core of the future of intelligent transportation. It will prevail over the road ecosystem, and it will have a huge impact on our lives throughout the provision of seamless connectivity among diverse transportation means. For the network to operate efficiently, the data needs to be quickly spread throughout the network, which requires low computational and bandwidth overheads. However, the dynamics of vehicular environments due to frequent node mobility poses many challenges to realize efficient data dissemination. This work addresses this type of problem by proposing a novel clustering algorithm at the edge of the network and an efficient message routing approach, which is known as Group’n Route (GnR). Both mechanisms resort to machine learning and graph metrics that reflect the social relationships between the nodes. Our performance evaluation reveals that the clustering algorithm yields stable results with varying road scenarios, which are becoming an advisable approach in the presence of mobile IoV nodes. Also, the designed routing protocol achieves two orders of magnitude smaller overhead and almost double the delivery rate when it is compared to traditional routing protocols, which thereby justify that the combination of our two proposed clustering and routing methods are a plausible alternative to support IoV communications in real-world setups.

The Software-Defined Networking (SDN) technology is a network management method that allows dynamic, programmatically efficient network planning to improve its performance and monitoring. Given that SDN at each passing day becomes more prominent, a framework that can ensure reliable communication and a global state among devices become more important. We propose a state consistency framework that leverages a state machine abstraction using network updates among adjacent switches through packet piggybacking. We also use a moving average that when a condition is met, P4's packet cloning is triggered, hence ensuring that all packets arrive at their destination in the presence of link failures.

The significant evolution of the Internet of Things (IoT) enabled the development of numerous devices able to improve many aspects in various fields in the industry for smart cities where machines have replaced humans. With the reduction in manual work and the adoption of automation, cities are getting more efficient and smarter. However, this evolution also made data even more sensitive, especially in the industrial segment. The latter has caught the attention of many hackers targeting Industrial IoT (IIoT) devices or networks, hence the number of malicious software, i.e., malware, has increased as well. In this article, we present the IIoT concept and applications for smart cities, besides also presenting the security challenges faced by this emerging area. We survey currently available deep learning (DL) techniques for IIoT in smart cities, mainly deep reinforcement learning, recurrent neural networks, and convolutional neural networks, and highlight the advantages and disadvantages of security-related methods. We also present insights, open issues, and future trends applying DL techniques to enhance IIoT security.

Modern Living, as we know it, has been impacted meaningfully by the Internet of Things (IoT). IoT consists of a network of things that collect data from machines (e.g., mobile devices) and people. Mobile application development is a flourishing tendency, given the increasing popularity of smartphones. Nowadays, users are accessing their desired services on the smartphone by means of dedicated applications as the latter offers a more customized and prompt service. In addition, companies are also looking to persuade users by offering interactive and effective mobile applications. Mobile application developers are using IoT to develop better applications. However, there is no generalized consensus on the selection of best architecture or even the most suitable communications protocols to be used on an IoT application development. Therefore, this article aims at presenting approaches, architectures, and methodologies relevant to the development of mobile IoT solutions.

Distributed reputation systems can be used to foster cooperation between nodes in decentralized and self-managed systems due to the nonexistence of a central entity. In this paper, a Robust and Distributed Reputation System for Delay-Tolerant Networks (REPSYS) is proposed. REPSYS is robust because despite taking into account first- and second-hand information, it is resilient against false accusations and praise, and distributed, as the decision to interact with another node depends entirely on each node. Simulation results show that the system is capable, while evaluating each node's participation in the network, to detect on the fly nodes that do not accept messages from other nodes and that disseminate false information even while colluding with others, and while evaluating how honest is each node in the reputation system, to classify correctly nodes in most cases.