Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) can provide mobile users (MU) with additional computing services and a wide range of connectivity. This paper investigates the joint optimization strategy of task offloading and resource allocation for UAV-assisted MEC systems in complex scenarios with the goal of reducing the total system cost, consisting of task execution latency and energy consumption. We adopt a game theoretic approach to model the interaction process between the MEC server and the MU Stackelberg bilayer game model. Then, the original problem with complex multi-constraints is transformed into a duality problem using the Lagrangian duality method. Furthermore, we prove that the modeled Stackelberg bilayer game has a unique Nash equilibrium solution. In order to obtain an approximate optimal solution to the proposed problem, we propose a two-stage alternating iteration (TASR) algorithm based on the subgradient method and the marginal revenue optimization method. We evaluate the effective performance of the proposed algorithm through detailed simulation experiments. The simulation results show that the proposed algorithm is superior and robust compared to other benchmark methods and can effectively reduce the task execution latency and total system cost in different scenarios.

Synthesis problems on multiplayer non-zero-sum games (MG) with multiple environment players that behave rationally are the problems to find a good strategy of the system and have been extensively studied. This paper concerns the synthesis problems on stochastic MG (SMG), where a special controller other than players, called nature, which chooses a move in its turn randomly, may exist. Two types of synthesis problems on SMG exist: cooperative rational synthesis problem (CRSP) and non-cooperative rational synthesis problem (NCRSP). The rationality of environment players is modeled by Nash equilibria, and CRSP is the problem to decide whether there exists a Nash equilibrium that gives the system a payoff not less than a given threshold. Ummels et al. studied the complexity of CRSP for various classes of objectives and strategies of players. CRSP fits the situation where the system can make a suggestion of a strategy profile (a tuple of strategies of all players) to the environment players. However, in real applications, the system may rarely have an opportunity to make suggestions to the environment, and thus CRSP is optimistic. NCRSP is the problem to decide whether there exists a strategy σ0 of the system satisfying that for every strategy profile of the environment players that forms a 0-fixed Nash equilibrium (a Nash equilibrium where the system's strategy is fixed to σ0), the system obtains a payoff not less than a given threshold. In this paper, we investigate the complexity of NCRSP for positional (i.e. pure memoryless) strategies. We consider ω-regular objectives as the model of players' objectives, and show the complexity results of the problem for several subclasses of ω-regular objectives. In particular, the problem for terminal reachability (TR) objectives is shown to be Σp2-complete.

The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.

This paper investigates the spectrum allocation problem. Under the current spectrum management mode, large amount of spectrum resource is wasted due to uncertainty of user's demand. To reduce the impact of uncertainty, a presale mechanism is designed based on spectrum pool. In this mechanism, the spectrum manager provides spectrum resource at a favorable price for presale aiming at sharing with user the risk caused by uncertainty of demand. Because of the hierarchical characteristic, we build a spectrum market Stackelberg game, in which the manager acts as leader and user as follower. Then proof of the uniqueness and optimality of Stackelberg Equilibrium is given. Simulation results show the presale mechanism can promote profits for both sides and reduce temporary scheduling.

We investigate the uplink channel selection problem of unmanned aerial vehicle (UAV)-aided data collection system in delay-sensitive sensor networks. In the studied model, the fixed-wing UAV is dispatched to gather sensing information from terrestrial sensor nodes (SNs) and they contend for uplink channels for transmission. With the goal of minimizing the system-wide delay, we formulate a resource allocation problem. Encountered with the challenge that the flight trajectory of UAV is unknown to SNs and the wireless channel is time-varying, we solve the problem by stochastic game approach and further propose a fully distributed channel selection algorithm which is proved to converge to a pure strategy Nash Equilibrium (NE). Simulation results are presented to show that our proposed algorithm has good performance.

We discuss Nash equilibria in combinatorial auctions with item bidding. Specifically, we give a characterization for the existence of a Nash equilibrium in a combinatorial auction with item bidding when valuations by n bidders satisfy symmetric and subadditive properties. By this characterization, we can obtain an algorithm for deciding whether a Nash equilibrium exists in such a combinatorial auction.

Recently, multihop wireless sensor networks (WSNs) are widely developed and applied to energy efficient data collections from environments by establishing reliable transmission radio links and employing data aggregation algorithms, which can eliminate redundant transmissions and provide fusion information. In this paper, energy efficiency which consists of not only energy consumptions but also the amount of received data by the base station, as the performance metric to evaluate network utilities is presented for achieving energy efficient data collections. In order to optimize energy efficiency for improvements of network utilization, we firstly establish a graphical game theoretic model for energy efficiency in multihop WSNs, considering message length, practical energy consumptions and packet success probabilities. Afterwards, we propose a graphical protocol for performance optimization from Nash equilibrium of the graphical game theory. The approach also consists of the distributed protocol for generating optimum tree networks in practical WSNs. The experimental results show energy efficient multihop communications can be achieved by optimum tree networks of the approach. The quantitative evaluation and comparisons with related work are presented for the metric with respect to network energy consumptions and the amount of received data by the base station. The performances of our proposal are improved in all experiments. As an example, our proposal can achieve up to about 52% energy efficiency more than collection tree protocol (CTP). The corresponding tree structure is provided for the experiment.

For break arcs occurring between Ag and Ag/SnO$_2$ 12,wt% electrical contact pairs, the electrical conductivity, viscosity and specific heat at constant pressure are calculated as thermodynamic and transport properties. Mixture rates of contact material vapor are 0%, 1%, 10% and 100%. Influence of the contact material on the properties is investigated. Temperature for the calculation ranges from 2000,K to 20000,K. Following results are shown. When the mixture rate is changed, the electrical conductivity varies at lower temperature (< 10000,K), and the viscosity and specific heat vary widely at all temperature range. The electrical conductivity is independent of the mixture rate when the temperature is exceeding 10000,K. The thermodynamic and transport properties are independent of the kind of the contact materials.

This paper focuses on learning the economic behaviour of the access point (AP) and users in wireless local area networks (WLANs), and using a game theoretic approach to analyze the interactions among them. Recent studies have shown that the AP would adopt a simple, yet optimal, fixed rate pricing strategy when the AP has an unlimited uplink bandwidth to the Internet and the channel capacity of WLAN is unlimited. However, the fixed rate strategy fails to be optimal if a more realistic model with limited capacity is considered. A substitute pricing scheme for access service provisioning is hence proposed. In particular, the AP first estimates the probable utility degradation of existing users consequent upon the admission of an incoming user. Second, the AP decides: (i) whether the incoming user should be accepted; and (ii) the price to be announced in order to try to maximize the overall revenue. The condition, under which the proposed scheme results in a perfect Bayesian equilibrium (PBE), is investigated.

Dynamic spectrum leasing (DSL) is regarded as a promising dynamic spectrum sharing (DSS) scheme both to improve the spectrum revenue of primary users (PUs) and to guarantee the QoS of secondary users (SUs). A pricing-based DSL termed PBDSL is formulated as a Stackelberg DSL game model, where PUs as players entering the interacting game with multiple SUs. The strategic design contains both optimal spectrum pricing schemes (including unit spectrum/interference price and interference sensitivity distributed adjustments) of PUs for the specific shared/leased spectrum and optimal transmission strategies (e.g., transmit power and bandwidth) of SUs. To capture two types of competition relationships among multiple SUs and between SUs and PUs, we investigate two intra-game models of multiple PUs and SUs, respectively, which interact with each other to constitute the final Stackelberg DSL game. The existence and uniqueness of Stackelberg equilibrium solution (SES) are analyzed and proved for presented games, based on which a joint multi-stage PBDSL algorithm is presented to approximate the optimal equilibrium strategies. Numerical results demonstrate the convergence property of the interactive decision-making process, and verify the effectiveness of the proposed algorithm, in a comparison with the Nash equilibrium solution (NES)-based approach.

Multihoming is widely used by Internet service providers (ISPs) to obtain improved performance and reliability when connecting to the Internet. Recently, the use of overlay routing for network application traffic is rapidly increasing. As a source of both routing oscillation and cost increases, overlay routing is known to bring challenges to ISPs. In this paper, we study the interaction between overlay routing and a multihomed ISP's routing strategy with a Nash game model, and propose a routing strategy for the multihomed ISP to alleviate the negative impact of overlay traffic. We prove that with the proposed routing strategy, the network routing game can always converge to a stable state, and the ISP can reduce costs to a relatively low level. From numerical simulations, we show the efficiency and convergence resulting from the proposed routing strategy. We also discuss the conditions under which the multihomed ISP can realize minimum cost by the proposed strategy.

Non-homogeneous Poisson processes (NHPPs) have gained much popularity in actual software testing phases to estimate the software reliability, the number of remaining faults in software and the software release timing. In this paper, we propose a new modeling approach for the NHPP-based software reliability models (SRMs) to describe the stochastic behavior of software fault-detection processes. The fundamental idea is to apply the equilibrium distribution to the fault-detection time distribution in NHPP-based modeling. We also develop efficient parameter estimation procedures for the proposed NHPP-based SRMs. Through numerical experiments, it can be concluded that the proposed NHPP-based SRMs outperform the existing ones in many data sets from the perspective of goodness-of-fit and prediction performance.

One important issue in cognitive transmission is for multiple secondary users to dynamically acquire spare spectrum from the single primary user. The existing spectrum sharing scheme adopts a deterministic Cournot game to formulate this problem, of which the solution is the Nash equilibrium. This formulation is based on two implicit assumptions. First, each secondary user is willing to fully exchange transmission parameters with all others and hence knows their complete information. Second, the unused spectrum of the primary user for spectrum sharing is always larger than the total frequency demand of all secondary users at the Nash equilibrium. However, both assumptions may not be true in general. To remedy this, the present paper considers a more realistic assumption of incomplete information, i.e., each secondary user may choose to conceal their private information for achieving higher transmission benefit. Following this assumption and given that the unused bandwidth of the primary user is large enough, we adopt a probabilistic Cournot game to formulate an opportunistic spectrum sharing scheme for maximizing the total benefit of all secondary users. Bayesian equilibrium is considered as the solution of this game. Moreover, we prove that a secondary user can improve their expected benefit by actively hiding its transmission parameters and increasing their variance. On the other hand, when the unused spectrum of the primary user is smaller than the maximal total frequency demand of all secondary users at the Bayesian equilibrium, we formulate a constrained optimization problem for the primary user to maximize its profit in spectrum sharing and revise the proposed spectrum sharing scheme to solve this problem heuristically. This provides a unified approach to overcome the aforementioned two limitations of the existing spectrum sharing scheme.

With the proliferation of IEEE 802.11 wireless local area networks, large numbers of wireless access points have been deployed, and it is often the case that a user can detect several access points simultaneously in dense metropolitan areas. Most owners, however, encrypt their networks to prevent the public from accessing them due to the increased traffic and security risk. In this work, we use pricing as an incentive mechanism to motivate the owners to share their networks with the public, while at the same time satisfying users' service demand. Specifically, we propose a “federated network” concept, in which radio resources of various wireless local area networks are managed together. Our algorithm identifies two candidate access points with the lowest price being offered (if available) to each user. We then model the price announcements of access points as a game, and characterize the Nash Equilibrium of the system. The efficiency of the Nash Equilibrium solution is evaluated via simulation studies as well.

In this paper, a decentralized concurrent transmission strategy in shared channel in Ad Hoc networks is proposed based on game theory. Firstly, a static concurrent transmissions game is used to determine the candidates for transmitting by channel quality threshold and to maximize the overall throughput with consideration of channel quality variation. To achieve NES (Nash Equilibrium Solution), the selfish behaviors of node to attempt to improve the channel gain unilaterally are evaluated. Therefore, this game allows each node to be distributed and to decide whether to transmit concurrently with others or not depending on NES. Secondly, as there are always some nodes with lower channel gain than NES, which are defined as hunger nodes in this paper, a hunger suppression scheme is proposed by adjusting the price function with interferences reservation and forward relay, to fairly give hunger nodes transmission opportunities. Finally, inspired by stock trading, a dynamic concurrent transmission threshold determination scheme is implemented to make the static game practical. Numerical results show that the proposed scheme is feasible to increase concurrent transmission opportunities for active nodes, and at the same time, the number of hunger nodes is greatly reduced with the least increase of threshold by interferences reservation. Also, the good performance on network goodput of the proposed model can be seen from the results.

We investigate the problem of joint frequency and power allocation in wireless mesh networks, using a self-pricing game based solution. In traditional pricing game models, the price factor is determined from the global information of the network, which causes heavy communication overhead. To overcome this problem, we propose a self-pricing game model, in which the price factor is determined by the distributed access points processing their individual information; moreover, it is implemented in an autonomous and distributed fashion. The existence and the efficiency of Nash equilibrium (NE) of the proposed game are studied. It is shown that the proposed game based solution achieves near cooperative network throughput while it reduces the communication overhead significantly. Also, a forcing convergence algorithm is proposed to counter the vibration of channel selection. Simulation results verify the effectiveness and robustness of the proposed scheme.

In this paper, a new power control scheme is proposed to maximize the network throughput with fairness provisioning. Based on the Stackelberg game model, the proposed scheme consists of two control mechanisms; user-level and system-level mechanisms. Control decisions in each mechanism act cooperatively and collaborate with each other to satisfy efficiency and fairness requirements. Simulation results demonstrate that the proposed scheme has excellent network performance, while other schemes cannot offer such an attractive performance balance.

The idea of using molecules and molecular structures as functional electronic device, promises to substantially decrease the size and improve the performance of electronic devices. In this paper, nonequilibrium Green's function formalism (NEGF) combined with extended Huckel theory (EHT), a semiempirical approach is used to study the electron transport phenomenon in single molecular junction systems. Benzene diamine molecule is studied to investigate the bonding of amine group to gold electrodes and the electron transport across the junction. The results are compared with that of benzene dithiol molecule with thiol end groups. Furthermore, the influence of charging and torsion angle on the transport characteristics is emphasized.

A semi-distributed resource allocation scheme based on multihop equilibrium is proposed for OFDM-relay networks. This method aims to reduce the amount of feedback information from the relay nodes (RNs). Moreover, it utilizes radio resource by striking an efficient balance between the capacities of the BS-RN link and RN-MS link. Simulation results show that the proposed semi-distributed scheme achieves good performances in terms of throughputs and fraction of satisfied users.

This paper introduces a theory for fast optimization of the circuit topology and parameters in sinusoidal oscillators. The theory starts from a system model composed of standard active and passive elements. We then include even the output load in the circuit, so that there is no longer any interaction with the outside of the system through the port. This model is thus called no-input-no-output (NINO) oscillator. The circuit is cut at an arbitrary branch, and is characterized in terms of the scalar impedance from the cut point. This is called active impedance because it is a function of not only the stimulating frequency but also the active device gain. The oscillation frequency and necessary device gain are estimated by solving impedance-domain Barkhausen equilibrium equations. This estimation works for the adjustment of circuit elements to meet the specified oscillation frequency. The estimation of necessary device gain enables us to maximize the oscillation amplitude, thanks to the inherent negative-slope nonlinearity of active devices. The active impedance is also used to derive the oscillation Q (quality) factor, which serves as a key criterion for sideband noise minimization i.e. frequency spectrum purification. As an alternative measure to active impedance, we also introduce branch admittance matrix determinant. This has the same numerical effect as the scalar impedance but can be used to formulate oscillator characteristics in a more elegant fashion, and provides a lucent picture of the physical behavior of each element in the circuit. Based on the proposed theory, we provide the tabled formulas of oscillation frequency, necessary device gain, active Q factor for a variety of typical Colpitts, Hartley, and cross-coupled twin-FET (field-effect transistor) oscillators.