Game Theoretic Approaches in Vehicular Networks: A Survey

In the era of the Internet of Things (IoT), vehicles and other intelligent components in Intelligent Transportation System (ITS) are connected, forming the Vehicular Networks (VNs) that provide efficient and secure traffic, ubiquitous access to information, and various applications. However, as the number of connected nodes keeps increasing, it is challenging to satisfy various and large amounts of service requests with different Quality of Service (QoS ) and security requirements in the highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources so that either an individual or group objectives can be achieved. Game theory, a theoretical framework designed for strategic interactions among rational decision-makers who faced with scarce resources, can be used to model and analyze individual or group behaviors of communication entities in VNs. This paper primarily surveys the recent advantages of GT used in solving various challenges in VNs. As VNs and GT have been extensively investigate34d, this survey starts with a brief introduction of the basic concept and classification of GT used in VNs. Then, a comprehensive review of applications of GT in VNs is presented, which primarily covers the aspects of QoS and security. Moreover, with the development of fifth-generation (5G) wireless communication, recent contributions of GT to diverse emerging technologies of 5G integrated into VNs are surveyed in this paper. Finally, several key research challenges and possible solutions for applying GT in VNs are outlined

Dynamic Pricing for Revenue Maximization in Mobile Social Data Market with Network Effects

Mobile data demand is increasing tremendously in wireless social networks, and thus an efficient pricing scheme for social-enabled services is urgently needed. Though static pricing is dominant in the actual data market, price intuitively ought to be dynamically changed to yield greater revenue. The critical question is how to design the optimal dynamic pricing scheme, with prospects for maximizing the expected long-term revenue. In this paper, we study the sequential dynamic pricing scheme of a monopoly mobile network operator in the social data market. In the market, the operator, i.e., the seller, individually offers each mobile user, i.e., the buyer, a certain price in multiple time periods dynamically and repeatedly. The proposed scheme exploits the network effects in the mobile users' behaviors that boost the social data demand. Furthermore, due to limited radio resource, the impact of wireless network congestion is taken into account in the pricing scheme. Thereafter, we propose a modified sequential pricing policy in order to ensure social fairness among mobile users in terms of their individual utilities. We analytically demonstrate that the proposed sequential dynamic pricing scheme can help the operator gain greater revenue and mobile users achieve higher total utilities than those of the baseline static pricing scheme. To gain more insights, we further study a simultaneous dynamic pricing scheme in which the operator determines the pricing strategy at the beginning of each time period. Mobile users decide on their individual data demand in each time period simultaneously, considering the network effects in the social domain and the congestion effects in the network domain. We construct the social graph using Erd\H{o}s-R\'enyi (ER) model and the real dataset based social network for performance evaluation.Comment: 31 pages, submitted for possible journal publicatio

Mean-Field-Type Games in Engineering

A mean-field-type game is a game in which the instantaneous payoffs and/or the state dynamics functions involve not only the state and the action profile but also the joint distributions of state-action pairs. This article presents some engineering applications of mean-field-type games including road traffic networks, multi-level building evacuation, millimeter wave wireless communications, distributed power networks, virus spread over networks, virtual machine resource management in cloud networks, synchronization of oscillators, energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201

Edge User Allocation with Dynamic Quality of Service

In edge computing, edge servers are placed in close proximity to end-users. App vendors can deploy their services on edge servers to reduce network latency experienced by their app users. The edge user allocation (EUA) problem challenges service providers with the objective to maximize the number of allocated app users with hired computing resources on edge servers while ensuring their fixed quality of service (QoS), e.g., the amount of computing resources allocated to an app user. In this paper, we take a step forward to consider dynamic QoS levels for app users, which generalizes but further complicates the EUA problem, turning it into a dynamic QoS EUA problem. This enables flexible levels of quality of experience (QoE) for app users. We propose an optimal approach for finding a solution that maximizes app users' overall QoE. We also propose a heuristic approach for quickly finding sub-optimal solutions to large-scale instances of the dynamic QoS EUA problem. Experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approaches against a baseline approach and the state of the art.Comment: This manuscript has been accepted for publication at the 17th International Conference on Service-Oriented Computing and may be published in the book series Lecture Notes in Computer Science. All copyrights reserved to Springer Nature Switzerland AG, Gewerbestrasse 11, 6330 Cham, Switzerlan

A Generic Framework for Task Offloading in mmWave MEC Backhaul Networks

With the emergence of millimeter-Wave (mmWave) communication technology, the capacity of mobile backhaul networks can be significantly increased. On the other hand, Mobile Edge Computing (MEC) provides an appropriate infrastructure to offload latency-sensitive tasks. However, the amount of resources in MEC servers is typically limited. Therefore, it is important to intelligently manage the MEC task offloading by optimizing the backhaul bandwidth and edge server resource allocation in order to decrease the overall latency of the offloaded tasks. This paper investigates the task allocation problem in MEC environment, where the mmWave technology is used in the backhaul network. We formulate a Mixed Integer NonLinear Programming (MINLP) problem with the goal to minimize the total task serving time. Its objective is to determine an optimized network topology, identify which server is used to process a given offloaded task, find the path of each user task, and determine the allocated bandwidth to each task on mmWave backhaul links. Because the problem is difficult to solve, we develop a two-step approach. First, a Mixed Integer Linear Program (MILP) determining the network topology and the routing paths is optimally solved. Then, the fractions of bandwidth allocated to each user task are optimized by solving a quasi-convex problem. Numerical results illustrate the obtained topology and routing paths for selected scenarios and show that optimizing the bandwidth allocation significantly improves the total serving time, particularly for bandwidth-intensive tasks

Applications of Game Theory in Vehicular Networks: A Survey

In the Internet of Things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming Vehicular Networks (VNs) that provide efficient and secure traffic and ubiquitous access to various applications. However, as the number of nodes in ITS increases, it is challenging to satisfy a varied and large number of service requests with different Quality of Service and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve either an individual or a group's objectives. Game Theory (GT), a theoretical framework designed for strategic interactions among rational decision-makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including its basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs integrating with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions for applying GT in VNs are outlined.Comment: It has been submitted to "IEEE communication surveys and tutorials".This is the revised versio

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with a huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML- and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML- and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.Comment: 21 pages, 3 figure

A Comprehensive Insight into Game Theory in relevance to Cyber Security

The progressively ubiquitous connectivity in the present information systems pose newer challenges tosecurity. The conventional security mechanisms have come a long way in securing the well-definedobjectives of confidentiality, integrity, authenticity and availability. Nevertheless, with the growth in thesystem complexities and attack sophistication, providing security via traditional means can beunaffordable. A novel theoretical perspective and an innovative approach are thus required forunderstanding security from decision-making and strategic viewpoint. One of the analytical tools whichmay assist the researchers in designing security protocols for computer networks is game theory. Thegame-theoretic concept finds extensive applications in security at different levels, including thecyberspace and is generally categorized under security games. It can be utilized as a robust mathematicaltool for modelling and analyzing contemporary security issues. Game theory offers a natural frameworkfor capturing the defensive as well as adversarial interactions between the defenders and the attackers.Furthermore, defenders can attain a deep understanding of the potential attack threats and the strategiesof attackers by equilibrium evaluation of the security games. In this paper, the concept of game theoryhas been presented, followed by game-theoretic applications in cybersecurity including cryptography.Different types of games, particularly those focused on securing the cyberspace, have been analysed andvaried game-theoretic methodologies including mechanism design theories have been outlined foroffering a modern foundation of the science of cybersecurity

Resource Management and Quality of Service Provisioning in 5G Cellular Networks

With the commercial launch of 5G technologies and fast pace of expansion of cellular network infrastructure, it is expected that cellular and mobile networks traffic will exponentially increase. In addition, new services are expected to spread widely, such as the Internet of Things connected to mobile networks. This will add additional burden in terms of traffic load. As a result, some studies suggest that mobile traffic may increase more than 1000 times compared to the amount of traffic that is generated nowadays. This means that network resources for mobile services must be managed and controlled in a smart way, because resources are always limited, but the demand for services and the need for keeping user equipment always connected to mobile networks can be considered unlimited, leaving gap between huge service demands and available resources. In order to narrow this gap, major consideration should be given to the management of network resources to avoid network congestion and performance degradation during peak hour/s and traffic spikes, and allow access to network services to more customers when demand is high. On the other hand, guaranteeing quality of service requirements for the wide range of new services is another challenge that must be met in 5G networks. In this paper we will review 5G networks characteristics and specifications, then carry out a survey on resource management and QoS provisioning to improve and manage resource utilization in 5G networks.Comment: 21 pages, 8 figures, 3 table