Mobile edge computing (MEC) is expected to provide cloud-like capacities for mobile users (MUs) at the edge of wireless networks. However, deploying MEC systems faces many challenges, one of which is to achieve an efficient distributed offloading mechanism for multiple users in time-varying wireless environments. In this paper, we study a multi-user multi-edge-node computation offloading problem. Since edge nodes' communication and computing capacities are limited which leads resource contention when many MUs offload to the same edge node at the same time, we formulate this problem as a non-cooperative exact potential game (EPG), where each MU, in each time slot, selfishly maximizes its number of processed central processor unit (CPU) cycles and reduces its energy consumption. Assuming that channel information is static and available to MUs, we show that MUs could achieve a Nash equilibrium via a best response-based offloading mechanism. Next, we extend the problem to a practical scenario, where the number of processed CPU cycles is time-varying and unknown to MUs because of the uncertain channel information. In this case, we adopt an unknown payoff game framework and prove that the EPG properties still hold. Then, we propose a model-free reinforcement learning offloading mechanism which helps MUs learn their long-term offloading strategies to maximize their long-term utilities. Numerical results illustrate that our proposed algorithm for unknown CSI outperforms other schemes, such as local processing and random assignment, and achieves up to 87.87% average long-term payoffs compared to the perfect CSI case.

The benefit of having a complementary network such as device-to-device (D2D) communications underlaying cellular systems lies in alleviating the traffic overloading burdens at base stations (BSs). This is critical in modern-day scenarios, e.g., in offloading and caching of mobile data traffic, and delivery of popular Internet contents to users. In this paper, we are chiefly interested in the establishment of D2D networks and how such a network evolves dynamically over time as D2D links are continuously formed and broken. Another important yet challenging research opportunity, i.e., the leverage of human users' social ties for D2D communications, will also be addressed in this paper. To investigate the D2D network formation problem, we look at a game-theoretic framework where the utility function is designed to balance the physical and social domains-where we systematically extract social tie strengths from a real-world data set. To study the evolution of such D2D networks through time in a dynamic stochastic context, we propose a dynamic potential game and show that its equilibrium behaviors can be achieved through some strategy and payoff learning mechanisms. Simulation results verify our analysis. Using a content delivery application to assess the performance of our proposed scheme, it can be shown that major improvements can be obtained in terms of network delay, content accessibility, and BS load reduction.

In wireless body area network (BAN) applications such as wearable computing, healthcare and sports, Bluetooth Low Energy (BLE) is a new and promising technology, which uses the unlicensed 2.4-GHz spectrum band for data transmission. Since there exist many wireless technologies operating in this frequency band, the issues of cross-technology interference and coexistence present a major challenge. In this work, we develop a testbed to conduct our experimental studies, focusing on BLE and its coexistence capabilities when being deployed in a dense environment, under possible interference from WiFi and ZigBee/IEEE 802.15.4. One scenario of interest is a network of several co-located BLE-based BANs, each of which is designed in a star topology with one gateway and multiple BLE sensor nodes. The second scenario represents a highly heterogeneous network where each BAN now carries both BLE and ZigBee sensors, while being exposed to interference from external WiFi transmission. Our results show that the performance of BLE is relatively robust to interference from other BLE transmission as well as those from nearby ZigBee and WiFi devices.

We propose a new framework combining dynamic sampling rates for healthcare sensors driven by user behavior, and an adaptive MAC scheduling scheme applied to the time-slotted channel hopping (TSCH) protocol in IEEE 802.15.4, which provides high throughput and reliable communications. First, we introduce a system software architecture for machine-learning-assisted healthcare monitoring that detects the user's behavior using edge computing and adjusts the sampling rates of the healthcare sensors accordingly. Second, we propose an adaptive MAC scheduling scheme for TSCH based on a state machine model that reacts to the dynamic traffic generated by the healthcare sensors. In case of an urgent state, the MAC scheduler automatically allocates extra timeslots to the appropriate sensors so as to enable the reliable transfer of high-resolution sensor data for further analysis. Experimental results from our testbed, implemented in Contiki-OS on the OpenMote-CC2538 platform, show that the proposed adaptive scheduling scheme can respond quickly to changes in user behavior and ensure the reliable transfer of sensor data in emergency situations.

The energy consumption and applications' execution latency of mobile devices (MDs) can be improved by migrating application tasks to a nearby edge device. In this paper, we propose an optimization framework to investigate the scenario when a MD can offload tasks to multiple access points (APs) and scale its central process unit (CPU) frequency. Firstly, the optimal solution is derived from an exhaustive search based approach; and then a semidefinite relaxation (SDR) based approach is proposed to efficiently solve the problem. The obtained results from our simulation indicate that the SDR-based algorithm is able to achieve close-to-optimal performance. We also show that our proposed scheme can reduce the MD's energy consumption and tasks' execution latency, by taking advantage of having multiple APs and flexible CPU frequency.

Device-to-device (D2D) communication is a promising technology to improve spectrum efficiency, energy efficiency, and transmission delay due to proximity of user equipments (UEs). With cooperative D2D communications, a UE needs to select an optimal peer to act as a relay to forward data to the base station (BS). Therefore, it is essential to take into account the social relationships among UEs during peer selection so as to enhance data privacy. In this paper, we investigate a dynamic social-aware peer selection problem by formulating it as a dynamic optimization problem and proposing the drift-plus-penalty ratio algorithm to solve it. Simulation results show that the proposed peer selection scheme outperforms other existing schemes while keeping the handover frequency lower than others.

In this paper, we propose an optimization framework of offloading from a single mobile device (MD) to multiple edge devices. We aim to minimize both total tasks' execution latency and the MD's energy consumption by jointly optimizing the task allocation decision and the MD's central process unit (CPU) frequency. This paper considers two cases for the MD, i.e., fixed CPU frequency and elastic CPU frequency. Since these problems are NP-hard, we propose a linear relaxation-based approach and a semidefinite relaxation (SDR)-based approach for the fixed CPU frequency case, and an exhaustive search-based approach and an SDR-based approach for the elastic CPU frequency case. Our simulation results show that the SDR-based algorithms achieve near optimal performance. Performance improvement can be obtained with the proposed scheme in terms of energy consumption and tasks' execution latency when multiple edge devices and elastic CPU frequency are considered. Finally, we show that the MD's flexible CPU range can have an impact on the task allocation.

Honeypots have been considered as one of the methods to ensure security for networks in the Internet of Things (IoT) realm. In this paper, we study the problem of defending against attacks in honeypot-enabled networks by looking at a game-theoretic model of deception involving an attacker and a defender. The attacker may try to deceive the defender by employing different types of attacks ranging from a suspicious to a seemingly normal activity, while the defender in turn can make use of honeypots as a tool of deception to trap attackers. The problem is modeled as a Bayesian game of incomplete information, where equilibria are identified for both the one-shot game and the repeated game versions. Our results showed that there is a threshold for the frequency of active attackers, above which both players will take deceptive actions and below which the defender can mix up his/her strategy while keeping the attackers success rate low.

In modern days, breakthroughs in information and communications technologies lead to more and more devices of every imaginable type being connected to the Internet. This also strengthens the need for protection against cyber-attacks, as virtually any devices with a wireless connection could be vulnerable to malicious hacking attempts. Meanwhile, honeypot-based deception mechanism has been considered as one of the methods to ensure security for modern networks in the Internet of Things (IoT). In this paper, we address the problem of defending against attacks in honeypot-enabled networks by looking at a game-theoretic model of deception involving an attacker and a defender. The attacker may try to deceive the defender by employing different types of attacks ranging from a suspicious to a seemingly normal activity, while the defender in turn can make use of honeypots as a tool of deception to trap attackers. The problem is modeled as a Bayesian game of incomplete information, where equilibria are identified for both the one-shot game and the repeated game versions. Our results show that there is a threshold for the frequency of active attackers, above which both players will take deceptive actions and below which the defender can mix up his/her strategy while keeping the attacker's success rate low.

Distributed Energy Resources (DER) are one of the distinguished features of Smart Grid. A combination of small-scale energy generator and storage unit can produce energy to serve the associated load, while at the same time store or sell excessive energy. Assuming there is an energy surplus, the system can choose to sell a portion of its available energy to the market. In this work, an oligopoly model is developed in order to study dynamic pricing in such a scenario. The problem can be characterized as a dynamic N-player differential game, where the optimal solutions correspond to the equilibria of the game. We provide a mathematical analysis for the solution of the game, where a complete characterization of the steady-state price and optimal strategies of the players can be obtained in the symmetric case. Extensive numerical studies are provided to demonstrate the behaviors of the proposed market model and to analyze the impacts of various market parameters on the system.

In this paper, a non-coherent base station cooperation (NC-BSC) scheme is examined for the downlink OFDMA wireless cellular networks. We formulate the resource allocation in the scheme as a subcarrier allocation optimization problem with equal power allocated on each subcarrier. To balance the tradeoff between system throughput and fairness, a multi-cell scenario controllable fairness is extended from single-cell scenario mentioned in [1], based on which the controllable global fairness (GF) scheme in the NC-BSC system is proposed. Two heuristic iterative algorithms are proposed to realize the subcarrier allocation in the NC-BSC system with and without the controllable fairness in a distributed manner. The performance is compared with traditional dynamic subcarrier allocation in noncooperative multi-cell networks. Simulation results show the performance enhancement through implementation of NC-BSC and the effectiveness of multi-cell controllable fairness in the NC-BSC system.

Fair and efficient subcarrier assignment for an infrastructure-based cellular OFDMA network is a challenging problem due to interferences from neighboring cells. In this paper, we investigate this problem using potential game theory, where the utility function adopted is meant for minimizing the co-channel interferences among different players. Each of these players is considered to be a cell with a central base station (BS) and its multiple mobile stations (MS) in a downlink scenario. Under such formulation, we establish the potential game. Hence, a Nash equilibrium (NE) will be guaranteed for the proposed game, which can be achieved via certain strategy dynamics such as the best/better responses. We propose our iterative algorithm for obtaining the NEs and present numerical results to evaluate the performance of our game. Improvement in efficiency and fairness is observed in this study.

This paper investigates the subcarrier allocation problem for a downlink multi-cell multiuser OFDMA network using potential game theory. Each player is considered to be a central base station together with all the mobiles distributed within its coverage area. In such a system, co-channel interferences (CCI), if left uncontrolled, could hinder the transmissions and limit the throughputs of the users, especially those near the cell-edge area. Certain remedies, including power control with pricing, did not seem to solve the problem completely. We specifically address this issue from an interference-minimizing approach, where the utility function adopted is meant to minimize the total CCI among players. Under such formulation, we show that the formulated game can be mathematically described by a potential game. Hence, a Nash equilibrium (NE) will be guaranteed for the proposed game and stable solutions can be achieved via myopic gameplays such as the best/better responses. We propose our iterative algorithm for obtaining the NEs and address several performance issues such as fairness for edge-users and the price of anarchy. Numerical results show the improvement in efficiency and fairness using this approach.

In this paper, we consider a scenario where distributed users in an OFDM-based system share the spectrum for transmission. Each network user (player) comprises a pair of transmit and receive nodes and will be allocated a given number of subcarriers. We propose a utility function that aims at efficiently allocating subcarriers to all players by minimizing the amount of interference generated. We first show that for the utility function used in the subcarrier assignment problem, it is possible to find a network potential function for the game. This guarantees the existence of at least one pure Nash equilibrium if each player is assigned with a predetermined number of subcarriers for transmission. Hence, convergence to Nash equilibrium can be achieved by iteratively playing the best-response game (also known as myopic game) among the players. Our simulation results show that distributing power over a number of subcarriers can achieve better per user capacity compared to the case where transmission is made through only a single subcarrier.