In recent years, in order to provide a better quality of service (QoS) to IoT devices, the cloud computing paradigm has shifted towards the edge. However, the resource capacity (e.g. bandwidth) in Fog network is limited and it is essential to efficiently bind the IoT applications with stringent QoS requirements with the available network infrastructure. In this paper, we formulate a joint user association and resource allocation problem in the downlink of the Fog Network considering the evergrowing demand of QoS requirements imposed by the Ultra- Reliable Low Latency Communications (URLLC) and enhanced Mobile Broadband (eMBB) services. First, we determine the priority of different QoS requirements of heterogeneous IoT applications at the Fog Network by enforcing the analytical framework using an analytic hierarchy process (AHP). Using the AHP, we then formulate a two-sided matching game to initiate stable association between the Fog Network infrastructure (i.e., Fog devices) and IoT devices. Subsequently, we consider the externalities in the matching game which occurs due to job delay and solve the network resource allocation problem by applying the best-fit resource allocation strategy during matching. The simulation results illustrate the stability of the user association and efficiency of resource allocation with higher utility gain.

In this paper, we design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed to improve the data freshness and connectivity to the Internet of Things (IoT) devices. First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy. We also incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. Second, we propose an agile deep reinforcement learning with experience replay model to solve the formulated problem concerning the contextual constraints for the UAV-BS navigation. Moreover, the proposed approach is well-suited for solving the problem, since the state space of the problem is extremely large and finding the best trajectory policy with useful contextual features is too complex for the UAV-BSs. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time. Finally, the simulation results illustrate the proposed approach is 3.6 % and 3.13 % more energy efficient than those of the greedy and baseline deep Q Network (DQN) approaches.

Anomaly-based In-Vehicle Intrusion Detection System (IV-IDS) is one of the protection mechanisms to detect cyber attacks on automotive vehicles. Using artificial intelligence (AI) for anomaly detection to thwart cyber attacks is promising but suffers from generating false alarms and making decisions that are hard to interpret. Consequently, this issue leads to uncertainty and distrust towards such IDS design unless it can explain its behavior, e.g., by using eXplainable AI (XAI). In this paper, we consider the XAI-powered design of such an IV-IDS using CAN bus data from a public dataset, named “Survival”. Novel features are engineered, and a Deep Neural Network (DNN) is trained over the dataset. A visualization-based explanation, “VisExp”, is created to explain the behavior of the AI-based IV-IDS, which is evaluated by experts in a survey, in relation to a rule-based explanation. Our results show that experts’ trust in the AI-based IV-IDS is significantly increased when they are provided with VisExp (more so than the rule-based explanation). These findings confirm the effect, and by extension the need, of explainability in automated systems, and VisExp, being a source of increased explainability, shows promise in helping involved parties gain trust in such systems.

With the ubiquitous growth of Internet-of-things (IoT) devices, current low-power wide-area network (LPWAN) technologies will inevitably face performance degradation due to congestion and interference. The rule-based approaches to assign and adapt the device parameters are insufficient in dynamic massive IoT scenarios. For example, the adaptive data rate (ADR) algorithm in LoRaWAN has been proven inefficient and outdated for large-scale IoT networks. New solutions involving machine learning (ML) and reinforcement learning (RL) techniques are shown to be very effective in solving resource allocation in dense IoT networks. In this article, we propose a new concept of using two independent learning approaches for allocating spreading factor (SF) and transmission power to the devices using a combination of a decentralized and centralized approach. SF is allocated to the devices using RL for contextual bandit problem, while transmission power is assigned centrally by treating it as a supervised ML problem. We compare our approach with existing state-of-the-art algorithms, showing a significant improvement in both network level goodput and energy consumption, especially for large and highly congested networks.

Spectrum sharing is an important aspect of 5G new radio, as it plays a complementary role for fulfilling diversified service requirements. This paper studies unlicensed spectrum sharing, namely, local thermal equilibrium (LTE) over unlicensed bands (LTE-U), for providing a better quality of experience (QoE) in 5G networks. Specifically, unlicensed band selection and resource allocation (time, licensed, and unlicensed) are jointly designed, and an optimization problem is formulated with the objective of maximizing LTE users' QoE [measured in mean opinion score (MOS)] while protecting incumbent wireless systems such as Wi-Fi in the unlicensed spectrum. To solve the multi-player interaction in this spectrum space fairly, we employ a game-theoretic approach. A virtual coalition formation game (VCFG) is used to solve the unlicensed band selection problem. The outcome of the VCFG defines the optimization problem within each coalition. This optimization problem is then decomposed into two sub-problems: 1) time-sharing problem between the LTE-U and Wi-Fi systems and 2) a resource allocation problem for the LTE-U system. The cooperative Kalai-Smorodinsky bargaining solution is used for solving the first sub-problem, whereas the Q-learning algorithm is used for solving the second. VCFG and Q-learning-based resource allocation algorithms are proposed in this paper. In addition, the stability of VCFG and optimal sharing time are also proved in this paper. Simulation results show the advantages of the proposed approach over other baseline methods in terms of the MOS, percentage of unsatisfied users, and fairness. The results also show that the proposed approach can better protect the performance of Wi-Fi users compared to the conventional listen-before-talk scheme.

The computational tasks at multi-access edge computing (MEC) are unpredictable in nature, which raises uneven energy demand for MEC networks. Thus, to handle this problem, microgrid has the potentiality to provides seamless energy supply from its energy sources (i.e., renewable, non-renewable, and storage). However, supplying energy from the microgrid faces challenges due to the high uncertainty and irregularity of the renewable energy generation over the time horizon. Therefore, in this paper, we study about the microgrid-enabled MEC networks’ energy supply plan, where we first formulate an optimization problem and the objective is to minimize the energy consumption of microgrid-enabled MEC networks. The problem is a mixed integer nonlinear optimization with computational and latency constraints for tasks fulfillment, and also coupled with the dependencies of uncertainty for both energy consumption and generation. Therefore, we show that the problem is an NP-hard problem. As a result, second, we decompose our formulated problem into two subproblems: 1) energy-efficient tasks assignment problem for MEC into community discovery problem and 2) energy supply plan problem into Markov Decision Process (MDP). Third, we apply a low complexity density-based spatial clustering of applications with noise (DBSCAN) to solve the first subproblem for each BS distributedly. Sequentially, we use the output of the first subproblem as a input for solving the second subproblem, where we apply a model-based deep reinforcement learning (MDRL). Finally, the simulation results demonstrate the significant performance gain of the proposed model with a high accuracy energy supply plan.