We present a novel game-theoretic (GT) and reinforce-ment learning (RL) framework for computational offloading in the mobile edge computing (MEC) network operated by multiple service providers (SPs). The network is formed by MEC servers installed at stationary base stations (BSs) and unmanned aerial vehicles (UAVs) deployed as quasi-stationary BSs. Since computing powers of MEC servers are limited, the BSs in proximity can form coalitions with shared data processing resources to serve their users more efficiently. However, as BSs can be privately-owned or controlled by different SPs, in any coalition, the BSs: i) take only the actions that maximize their long-term payoffs; ii) do not coordinate their actions with other BSs in the coalition. That is, inside each coalition, BSs act in an independent and self-interested manner. Therefore, the interactions among BSs cannot be described by conventional coalitional games. Instead, the network operation is modeled by a two-level hierarchical model. The upper level is a cooperative game that defines the process of coalition formation. The lower level comprises the set of non-cooperative sub-games to represent a self-interested and independent behavior of BSs in coalitions. To enable each BS to select a coalition and decide on its action maximizing its long-term payoff, we propose two algorithms that combine coalition formation with RL and prove that these algorithms converge to the states where the coalitional structure is strongly stable and the strategies of BSs are in the mixed-strategy Nash equilibrium (NE).

Distributed computing has become a common approach for large-scale computation tasks due to benefits such as high reliability, scalability, computation speed, and cost-effectiveness. However, distributed computing faces critical issues related to communication load and straggler effects. In particular, computing nodes need to exchange intermediate results with each other in order to calculate the final result, and this significantly increases communication overheads. Furthermore, a distributed computing network may include straggling nodes that run intermittently slower. This results in a longer overall time needed to execute the computation tasks, thereby limiting the performance of distributed computing. To address these issues, coded distributed computing (CDC), i.e., a combination of coding theoretic techniques and distributed computing, has been recently proposed as a promising solution. Coding theoretic techniques have proved effective in WiFi and cellular systems to deal with channel noise. Therefore, CDC may significantly reduce communication load, alleviate the effects of stragglers, provide fault-tolerance, privacy and security. In this survey, we first introduce the fundamentals of CDC, followed by basic CDC schemes. Then, we review and analyze a number of CDC approaches proposed to reduce the communication costs, mitigate the straggler effects, and guarantee privacy and security. Furthermore, we present and discuss applications of CDC in modern computer networks. Finally, we highlight important challenges and promising research directions related to CDC.

We present a novel game-theoretic (GT) and reinforce-ment learning (RL) framework for computational offloading in the mobile edge computing (MEC) network operated by multiple service providers (SPs). The network is formed by MEC servers installed at stationary base stations (BSs) and unmanned aerial vehicles (UAVs) deployed as quasi-stationary BSs. Since computing powers of MEC servers are limited, the BSs in proximity can form coalitions with shared data processing resources to serve their users more efficiently. However, as BSs can be privately-owned or controlled by different SPs, in any coalition, the BSs: i) take only the actions that maximize their long-term payoffs; ii) do not coordinate their actions with other BSs in the coalition. That is, inside each coalition, BSs act in an independent and self-interested manner. Therefore, the interactions among BSs cannot be described by conventional coalitional games. Instead, the network operation is modeled by a two-level hierarchical model. The upper level is a cooperative game that defines the process of coalition formation. The lower level comprises the set of non-cooperative sub-games to represent a self-interested and independent behavior of BSs in coalitions. To enable each BS to select a coalition and decide on its action maximizing its long-term payoff, we propose two algorithms that combine coalition formation with RL and prove that these algorithms converge to the states where the coalitional structure is strongly stable and the strategies of BSs are in the mixed-strategy Nash equilibrium (NE).

We study the problem of autonomous operation of the device-to-device (D2D) pairs in a heterogeneous cellular network with multiple base stations (BSs). The spectrum bands of the BSs (that may overlap with each other) comprise the sets of orthogonal wireless channels. We consider the following spectrum usage scenarios: 1) the D2D pairs transmit over the dedicated frequency bands and 2) the D2D pairs operate on the shared cellular/D2D channels. The goal of each device pair is to jointly select the wireless channel and power level to maximize its reward, defined as the difference between the achieved throughput and the cost of power consumption, constrained by its minimum tolerable signal-to-interference-plus-noise ratio requirements. We formulate this problem as a stochastic non-cooperative game with multiple players (D2D pairs) where each player becomes a learning agent whose task is to learn its best strategy (based on the locally observed information) and develop a fully autonomous multi-agent Q-learning algorithm converging to a mixed-strategy Nash equilibrium. The proposed learning method is implemented in a long term evolution-advanced network and evaluated via the OPNET-based simulations. The algorithm shows relatively fast convergence and near-optimal performance after a small number of iterations.

In this paper, Deep Neural Networks (DNN) with Bat Algorithms (BA) offer a dynamic form of traffic control in Vehicular Adhoc Networks (VANETs). The former is used to route vehicles across highly congested paths to enhance efficiency, with a lower average latency. The latter is combined with the Internet of Things (IoT) and it moves across the VANETs to analyze the traffic congestion status between the network nodes. The experimental analysis tests the effectiveness of DNN-IoT-BA in various machine or deep learning algorithms in VANETs. DNN-IoT-BA is validated through various network metrics, like packet delivery ratio, latency and packet error rate. The simulation results show that the proposed method provides lower energy consumption and latency than conventional methods to support real-time traffic conditions.

The paper considers a cloud-based Content Delivery Network (CDN) with Device-to-Device (D2D) and Unmanned Aerial Vehicle (UAV) enabled caching. The network is managed by the operator that offers content transfer services to the set of Content Providers (CPs). A subscriber of any CP can receive its content either by i) cellular content transfer via a terrestrial Base Station (BS) or a UAV, or by ii) D2D content transfer from another subscriber of this CP. Each CP aims to maximize its expected payoff defined as a reciprocal of the weighted sum of the expected content transfer cost and delay for its subscribers. The operator's objective is to maximize its long-term average revenue and stabilize the queuing system which represents the content transfer services. To model the interactions between the operator and the CPs, a novel framework is proposed that combines: i) cooperative game that enables the CPs to form coalitions in which all subscribers can exchange content via D2D links, thereby reducing content transfer costs and delays; ii) Lyapunov optimization based dynamic channel and UAVs' activity allocation policy of the operator. Through analytical and numerical evaluations, it is proven that if the operator and each CP are rational, the network reaches a state where all the CPs are in the stable coalitional structure, whereas, the dynamic policy of the operator is optimal with the trade-off in the total queue backlog.