In the early stages of federated learning (FL), clients were commonly assumed to perform an identical number of local updates per communication round, leading researchers to adopt a simple weighted aggregation method (e.g., FedAvg) across clients’ local models. However, in real-world edge computing scenarios, device heterogeneity and data heterogeneity often cause clients to perform varying numbers of local updates. Although numerous studies addressed the performance degradation due to heterogeneous local updates through algorithmic improvements (e.g., normalized aggregation methods like FedNova), these approaches do not inherently motivate clients to contribute sufficient computational effort. Designing effective incentives therefore requires a precise characterization of how clients’ local effort and data heterogeneity jointly affect global model convergence. To this end, we derive a generalized convergence bound for FedNova that explicitly accounts for both data heterogeneity and heterogeneous numbers of local updates. Building on this, we investigate the contract design problem for FL under clients’ two-dimensional private information on computational cost and data heterogeneity. We develop optimal contracts under complete, weakly incomplete, and strongly incomplete information scenarios. Under complete and weakly incomplete information, we derive a closedform solution showing that the server should only incentivize clients with both the lowest unit cost and the lowest data heterogeneity. Under strongly incomplete information, we transform the combinatorial problem into a convex optimization problem via a practical assumption of positive probabilities for client types. Our method achieves average cost reductions of 15% and 5.5% over the uniform contract and Stackelberg game benchmarks, respectively. © 2026 The Authors. Published by Elsevier B.V.

Blockchain-based energy trading (BBET) systems depend on prosumers to allocate energy betweentradingactivities andblockchain miningoperations. However, inadequate incentive structures lead prosumers to under-contribute to mining, creating throughput bottlenecks and system performance degradation. This paper introduces the Fee and Two-Piece Compensation (FTPC) mechanism to optimize energy allocation and enhance system throughput. We formulate the interaction between the system designer and prosumers as a three-stage Stackelberg game where the system designer establishes the incentive framework in Stage I, while prosumers determine energy allocation in Stage II and set transaction fees in Stage III. Our analysis demonstrates that prosumers' failure to internalize mining's positive externality results in suboptimal throughput investment. Counterintuitively, we show that impatient prosumers may exploit others' mining contributions as free riders. The FTPC mechanism resolves these issues by jointly optimizing transaction fees and compensation structures to align individual incentives with social welfare. We prove that FTPC achieves socially optimal outcomes through fully decentralized decision-making. Numerical evaluation shows FTPC improves social welfare and prosumer payoffs by 88.1% and 87.8%, respectively. Ethereum testbed implementation validates equilibrium convergence through iterative best-response dynamics.

With the rapid growth of the generative artificial intelligence (GenAI) applications and their increasing demand for computing resources, AI data centers have emerged as the key enablers of these large-model applications. Unfortunately, the traditional flow control schemes for data center networks cannot satisfy the synchronization requirements of multiple distributed machine learning (DML) applications. In this paper, we consider an AI data center with multiple DML applications sharing the computing resources of graphics processing units (GPUs). To efficiently utilize these resources under the stochasticity in a data center network, we formulate a stochastic optimization problem by optimizing the flow rate to minimize the average communication fraction. Through the Lyapunov optimization framework, we decompose the problem into an optimization problem for each time slot, and reformulate it as a mixed-integer linear programming (MILP) problem. As a result, we propose an online flow control algorithm and analyze its performance. Simulation results show that our proposed algorithm reduces the average communication fraction by 48% as compared with two classical flow control benchmarks.

In crowdfunding, a project creator maximizes revenue by jointly optimizing information disclosure and pricing strategies for contributors who arrive stochastically. Such a joint optimization problem is challenging to solve due to its online nature and intertwined strategies. Nevertheless, we propose an online algorithm (with low computational complexity$\mathcal{O}\left(N_{\textit{max}}^{2}\right)$) to address the problem in two steps. First, we show that it is optimal for the creator to charge the same price between two consecutive information disclosure instances. Second, we prove that a creator only needs to consider whether to disclose information immediately after a contributor arrives. We show that the creator increases the price as the campaign processes, which is consistent with the common observation. Our mechanism numerically outperforms the popular information disclosure policy used by leading platforms like Kickstarter and Indiegogo, yielding an average revenue gain of 150%.

Data protection policies (e.g., GDPR) enforce the right to be forgotten and require companies to perform machine unlearning once users request data removal. This process incurs costs for server and degrades model performance, impacting users' satisfaction. In this paper, we propose the first incentive mechanism for machine unlearning, where server compensates users to retain their data. We characterize server's major unlearning costs, accuracy degradation and consumed time, in data redemption amount through experiments on three datasets and two unlearning algorithms. We model server-users interaction as a two-stage Stackelberg game. In Stage I, server optimizes compensation unit prices to minimize costs. In Stage II, users jointly decide data redemption amounts as a non-cooperative game. By restricting the feasible set of Stage I to Nash Equilibrium of Stage II, we formulate a challenging non-convex bilevel optimization problem. We propose an iterative algorithm to compute optimal unit prices in Stage I and equilibrium data redemption amounts in Stage II by characterizing bilevel problem's convexity. We prove the distributed convergence of best response updates to the unique Nash equilibrium by showing Stage II is a submodular game. Experimental results show that our mechanism minimizes server cost and maximizes social welfare over two practical baselines

Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. We first analyze the impact of UAV deployment on the sensing quality and identify a threshold value for the sensing elevation angle that guarantees a satisfactory quality of data samples. Due to the non-ideal sensing channels, we consider the probabilistic sensing model, where the successful sensing probability of each UAV is determined by its position. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. Theoretical results suggest that the convergence rate can be improved if UAVs have a uniform successful sensing probability. Based on this analysis, we formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we apply the alternating optimization technique, and propose the bandwidth, batch size, and position optimization (BBPO) scheme to optimize these three decision variables alternately. Simulation results demonstrate that our BBPO scheme outperforms other baseline schemes regarding convergence rate and testing accuracy. The simulation implementation is available at https://github.com/TheaSherlock/ISCC-UAV.

In federated learning (FL), most existing works assumed that clients had a uniform number of local updates in each communication round, which might cause the straggler effect owing to the system and data heterogeneity. Adaptive federated learning (AFL) can address this issue by allowing an adaptive number of local updates among clients, thereby achieving a better convergence than the uniform FL. In this paper, we consider the contract design for AFL under clients’ two-dimensional private information regarding computation cost and contribution level. We aim to minimize the summation of model error and total payment to clients subjecting to Individual Rationality (IR) and Incentive Compatibility (IC) constraints. Through deriving the necessary and sufficient conditions for IR and IC constraints, we reduce the high dimension of the constraints, and obtain the closed-form optimal contract. Surprisingly, the optimal contract and the server’s cost in the incomplete information scenario are the same as in the complete information scenario. Moreover, we prove that the server should incentivize one type of client with the highest contribution-cost ratio to participate in the AFL training. Our simulation results demonstrate that our AFL-based contract scheme achieves the best trade-off regarding model error and total payment compared with the uniform FL.

Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for federated edge learning (FEEL) due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Due to the nonideal sensing channels, we consider the probabilistic sensing model. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. We formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we propose our algorithm based on the alternating optimization technique. Simulation results demonstrate that our algorithm outperforms other baselines regarding convergence rate and testing accuracy.

Nowadays, technology companies spend efforts col-lecting datasets from massive users and training machine-learning models to enable innovative artificial intelligence (AI) applications. However, current data protection policies, such as General Data Protection Regulation (GDPR), enforce the right to be forgotten and require the server to perform machine unlearning and eliminate the effect of users' data on the trained model once receiving the data redemption requests. Such privacy regulations are unfair to the server, as it incurs extra costs for unlearning but suffers from a degraded model. In this paper, we propose the first incentive mechanism in machine unlearning to compensate for the server's cost to the best of our knowledge. We first characterize the accuracy degradation and consumed time as a function of the unlearning ratio by conducting experiments on three popular datasets and two widely used unlearning algorithms. Then, we model the interaction between the server and users as a two-stage Stackelberg game. We propose an iterative algorithm to optimize the unit price for data redemption by characterizing the convexity of server's profit maximization problem. The experimental results on real datasets show that our mechanism can achieve the largest server's profit and social welfare, compared with the GDPR and no redemption schemes.

In this paper, we consider a blockchain-based energy trading (BBET) system with the proof-of-stake (PoS) protocol. The system designer aims to minimize system cost by considering the prosumers' strategic token allocation between blockchain staking and energy purchase for their applications. This is challenging as the system designer does not know prosumers' private information of impatience levels towards different applications. To this end, we propose an online learning mechanism (OLM), which includes incentive mechanisms to guide both prosumers' private information reporting and staking decisions in two phases. In the exploration phase, we design a randomized staking reward to encourage prosumers' truthful reporting of their private information for the learning of impatience level distributions. Based on the threshold structure of the prosumers' equilibrium staking strategies, in the exploitation phase, we propose a learning-error-based reward to minimize the system cost considering the finite-sample bias. By characterizing the optimal exploration duration, we prove that OLM achieves an asymptotic zero-regret against the complete information benchmark, with the regret bounded by [EQUATION] when operating for T time slots. We implement the corresponding smart contract in Ethereum to demonstrate the feasibility of our approach. Experiment results show that our mechanism reduces regret by an average of 74% compared to the state-of-art mechanism.

In a crowdfunding campaign, the project creator determines various campaign decisions, such as the pricing and information revelation strategy, to maximize the funding. Each contributor has a high or low valuation for the project. In this paper, we present a study on how the contributors’ random arrival and pledging process affect the creator’s campaign decisions. Specifically, a creator first determines and announces the pricing and information disclosure strategy before the campaign starts. Then contributors randomly arrive and choose their pledging decisions. Contributors make their decisions based on not only the disclosed status so far but also the estimation of the later contributors’ random arrival and pledging decisions. This randomness and complicated decision coupling among contributors render the analysis challenging. Nonetheless, we prove that contributors’ equilibrium decisions follow a threshold structure. Based on this, we propose an algorithm to solve the creator’s optimal campaign decisions. Our analysis on the creator’s strategic information disclosure shows that the contributors’ prior belief on the fraction of high-valuation contributors is critical. Specifically, when the prior belief is high, the creator withholds the pledging status information from the contributors until the campaign ends. When the prior belief is low, the creator should disclose the information update immediately once the first contributor arrives. Such an early disclosure increases the confidence of later contributors and motivates their pledging decisions. Our numerical results show that with strategic information disclosure, the creator can increase the funding by 100% on average, compared with the immediate-disclosure policy widely adopted by crowdfunding platforms.

In vertical federated learning (VFL), participants with different features of the same group of samples can train a global model cooperatively. Currently, no existing studies discuss the economic mechanism between the VFL participants. To fill this research gap, we study the incentive mechanism design with a linear reward scheme for VFL. Specifically, we model the interactions between the label owner and the data owner as a two-stage Stackelberg game. In Stage I, the label owner strategically chooses its processing speed and linear reward parameter for the data owner. In response to the label owner's decisions, the data owner will choose its optimal processing speed in Stage II. By characterizing the threshold structure of the reward parameter that incentivizes the maximum processing speed from a data owner in Stage II, we can derive the equilibrium of the two-stage Stackelberg game in closed-form. Finally, our simulation results show that as the cost coefficient of the data owner increases, the label owner will increase its reward but reduce its processing speed due to the linear cost growth but concave revenue growth.

To achieve the desirable funding target in a crowdfunding campaign, the project creator needs to accurately anticipate the pledging behaviors of contributors with practical cognitive limitations. In this paper, we present a study on how the contributors’ cognitive bounded rationality affects the creator’s campaign decisions. Specifically, we consider a two-stage crowdfunding model, where a creator first announces the project decisions (i.e., price and the minimum number of required contributors), and then the contributors choose their pledging behaviors (whether and how to contribute) as they arrive stochastically. We consider the cognitive hierarchy model, where contributors are classified into different levels according to their capability of anticipating other contributors’ behaviors. Surprisingly, we show that the cognitive limitation may improve the chance of project success, especially when the crowdfunding target is high. We prove that with a high average contributor cognitive level, a low funding target, and a low difficulty of motivating the lowest cognitive level contributors, the creator can achieve a close-to-optimal revenue by simply assuming that contributors are fully rational. Otherwise, ignoring the contributors’ cognitive limitations can lead to a significant revenue loss.

In current blockchain systems, the transaction fee is often not enough to cover the storage cost, jeopardizing blockchain sustainability in the long run. Such a storage sustainability issue is partially due to miners’ heterogeneous storage costs and users’ low-intensity fee competition. Motivated by these two observations, we propose a Fee and Transaction Expiration Time (FTET) mechanism to alleviate this issue. Specifically, we model the blockchain operation as a three-stage game. In Stage I, the system designer proposes the storage sustainability mechanism. In Stage II, each user decides whether to propose transactions and the corresponding transaction fees. In Stage III, each miner decides which transactions to include in the block. Although the analysis of the heterogeneous miner interaction is technically challenging, we fully solve it in closed-form motivated by how miners select transactions in practice. The equilibrium analysis reveals that high-storage-cost miners admit transactions with fees above a time-increasing threshold. Under the optimal FTET mechanism, the blockchain system can achieve the storage sustainability without any social welfare loss, comparing with the maximum achievable social welfare without the storage sustainability constraint. Moreover, the optimal FTET mechanism achieves a higher social welfare than the fee mechanism in current practice by selectively rejecting some transactions suffering high delays. Finally, we implement a blockchain prototype to compare the performance of the optimal FTET mechanism with the mining round time adjustment (MRTA) mechanism. The optimal FTET mechanism achieves higher social welfare (94.5% on average) and better storage sustainability. We find that more pending transactions may lead to lower transaction fees.

To exploit users’ heterogeneous data demands, several mobile network operators worldwide have launched the mobile data trading markets, where users can trade mobile data quota with each other. In this paper, we aim to understand the importance of data trading market (DTM) by studying the users’ operator selection and trading decisions, and analyzing the operator’s profit maximizing strategy. We model the interactions between the mobile operator and the users as a three-stage Stackelberg game. In Stage I, the operator chooses the operation fee imposed on sellers to maximize its profit. In Stage II, each user chooses his operator. In Stage III, each DTM user chooses his trading decisions. We derive the closed-form expression of the unique Nash equilibrium (NE) in Stages II and III, where every user proposes the same price such that the total demand matches with the total supply. We further show that the Stage I’s problem is convex and compute the optimal operation fee. Our analysis and numerical results show that an operator with a small initial market share can increase its profit by proposing a DTM, which is in line with the real-world situation in Hong Kong.

Miners in a blockchain system are suffering from ever-increasing storage costs, which in general have not been properly compensated by the users’ transaction fees. This reduces the incentives for the miners’ participation and may jeopardize the blockchain security. To mitigate this blockchain insufficient fee issue, we propose a Fee and Waiting Tax (FWT) mechanism, which explicitly considers the two types of negative externalities in the system. Specifically, we model the interactions between the protocol designer, users, and miners as a three-stage Stackelberg game. By characterizing the equilibrium of the game, we find that miners neglecting the negative externality in transaction selection cause they are willing to accept insufficient-fee transactions. This leads to the insufficient storage fee issue in the existing protocol (i.e., deployed in Bitcoin and Ethereum). Moreover, our proposed optimal FWT mechanism can motivate users to pay sufficient transaction fees to cover the storage costs and achieve the unconstrained social optimum. Numerical results show that the optimal FWT mechanism guarantees sufficient transaction fees and achieves an average social welfare improvement of 51.43% or more over the existing protocol. Furthermore, the optimal FWT mechanism reduces the average waiting time of low-fee transactions and all transactions by 68.49% and 61.56%, respectively.

The crowdfunding industry is expected to reach a volume of $90 billion per year. In crowdfunding, a creator needs to decide not only the pricing but also when and how frequent to disclose the campaign progress to the contributors, in order to maximize the project revenue. In this paper, we present a first analytical study on how the creator's pricing and information disclosure strategies affect the potential contributors' belief update process, hence the project success and creator's expected revenue. Specifically, we consider a multi-stage crowdfunding model, where a stage corresponds to the period between the creator's two information disclosures. At the beginning of the campaign, a creator announces her pricing decision and information disclosure strategy for revenue maximization. Then contributors coming in each following stage will choose whether to contribute, based on not only the disclosed pledging status so far but also the estimation of the impact of their decisions on later contributors. Such a model is challenging to optimize because of the coupling across multiple stages, especially with contributors' anticipations of future stages. Nevertheless, we are able to characterize the contributors' threshold-based equilibrium pledging decisions, and we incorporate such a structural result into the creator's mixed-integer revenue maximization problem. Through both analytical and numerical studies, we show that the contributors' prior belief of high-valuation contributor percentage plays a critical role in the creator's optimal strategic information disclosure decisions. When the contributors have a high prior belief, a creator should not announce the pledging history until all the contributors have made their pledging decisions. When the prior belief is low, the creator should disclose more often.

With the rich set of embedded sensors installed in smartphones, we are witnessing the emergence of many innovative commercial mobile crowdsensing applications, which combine the power of mobile technology with crowdsourcing to effectively collect time-sensitive and location-dependent information. Motivated by these real-world applications, we consider the distributed task selection problem for heterogeneous users with different initial locations, destinations, costs, speeds, and reputation levels. We design a Bayesian asynchronous task selection (BATS) algorithm to help the users plan their task selections based on the incomplete information of the task popularity statistics. We prove its convergence and characterize the computation time for the users' updates. As a performance benchmark, we consider the ideal case that the service provider centrally allocates the tasks to the users for social surplus maximization. We show that it is an NP-hard problem and propose a greedy centralized algorithm with a lower complexity as the benchmark performance. Simulation results suggest that the BATS scheme achieves the highest Jain's fairness index and coverage, while yielding a user payoff similar to that with the greedy centralized benchmark. Finally, we evaluate the schemes based on some real-world movement time and distance data from Google Maps.

A crowdfunding platform enables project creators to raise funding from a crowd of contributors to develop their projects. An accurate modeling and predication of contributors' behaviors is critical for a creator to achieve the project success. In this paper, we present a first study on a creator's optimal crowdfunding campaign parameter choices, considering the contributors' realistic cognitive limitations. Specfically, we consider a two-stage crowdfunding model: in Stage I a creator determines the project decisions (i.e., price and minimum number of contributors); in Stage II the contributors choose their pledging behaviors (whether to contribute and with what probabilities). We apply the cognitive hierarchy theory to model the cognitive limitations of the contributors, i.e., contributors are grouped based on their cognitive levels which reflect their capabilities of reasoning other contributors' decisions. Although the project decisions are highly coupled between contributors' pledging and creator's revenue maximization, we exploit the monotonic structure of the revenue maximization problem to compute creator's optimal solutions. We show that contributors with the same valuation exhibit the same pledging behaviors, independent of their cognitive levels (except those at the lowest cognitive levels). This explains the real-world phenomenon that projects either attract few contributors (often under 40% pledging rate) or are oversubscribed.

Miners in a blockchain system are suffering from the ever-increasing storage costs, which in general have not been properly compensated by the users' transaction fees. In the long run, this may lead to less participation of miners and jeopardize the blockchain security. In this paper, we study the economics of blockchain storage and identify the incentive issues related to this storage cost problem. More specifically, we model the interactions among users (who generate transactions) and miners in two stages, where the users set the transaction fees in Stage 1, and the miners select which transactions to include in Stage 2. Through characterizing the Nash equilibrium of the two-stage game, we find that the transaction fees indeed cannot cover the storage costs under the current practice in general, due to the negative externality and the unfair delay-based pricing. We also identify that a longer block interval can alleviate the concern by raising the transactions fees at the expense of larger delay.

Unmanned aerial vehicles (UAVs) can enhance the performance of cellular networks, due to their high mobility and efficient deployment. In this paper, we consider a single-UAV assisted wireless communication system, where the UAV is deployed as an aerial base station (BS) to serve ground users. We maximize the transmission rate of ground users in the downlink communication by optimizing the UAV trajectory. To account for the impact of the ground BS on the UAV trajectory design, we provide a higher reward for the UAV to serve at a cell edge position. The cost function takes into account both the energy consumption during moving and hovering. We formulate our problem as a route selection problem in an acyclic directed graph, where each vertex and each edge are associated with a reward and a cost, respectively. The shortest path (SP) scheme is used to determine the optimal trajectory. Simulation results show that the SP scheme achieves the highest payoff among the compared schemes. Finally, we provide an application scenario based on our campus map to illustrate how the UAV determines the optimal trajectory under the SP scheme.

In multimedia crowdsourcing, the requester's quality requirements and reward decisions will affect the workers' task selection strategies and the quality of their multimedia contributions. In this paper, we present a first study on how the workers' bounded cognitive rationality interacts with and affects the decisions and performance of a multimedia crowdsourcing system. Specifically, we consider a two-stage model, where a requester first determines the reward and the quality requirement for each task, and the workers select the tasks to accomplish accordingly. First, we consider the benchmark case where users are fully rational, and derive the requester's optimal rewards and quality requirements for the tasks. Furthermore, we focus on the more practical bounded rational case by modeling the workers' task selection behaviors using the cognitive hierarchy theory. Comparing with the fully rational benchmark, we show that the requester can increase her profit by taking advantage of the workers' bounded cognitive rationality, especially when the workers' population is large or the workers' average cognitive level is low. When the workers' average cognitive level is very high, however, the equilibrium under the practical bounded rational model converges to that under the benchmark fully rational model. It is because the workers at different levels make decisions sequentially and high cognitive level workers can accurately predict other users' strategies. Under both the fully and bounded rational models, we show that if workers are heterogeneous but one type of workers (either the high or the low quality) dominates the platform, the requester cannot make a higher profit by setting different quality requirements for different tasks.

Previous studies in crowdsourcing systems usually regard workers as fully rational players, who have infinite cognitive capabilities when reasoning about other players' decisions. However, recent psychological studies have revealed that humans are often bounded rational with cognitive reasoning limits. In this paper, we present a first study regarding the impact of such worker bounded rationality in a crowdsourcing system, and characterize how the result obtained from this more practical assumption deviates from the fully rational benchmark. Specifically, we consider a simple two-stage crowdsourcing model, where a requester first determines the rewards for workers completing the tasks, and then workers make their task choices accordingly. First, we show that such a model is non-trivial to analyze even in the fully rational case, due to the integer constraints on workers' choices. Nevertheless, we are able to characterize the closed-form solution of the optimal rewards and Nash equilibrium with full rationality by exploiting the special structure of the problem formulation. Next, we focus on the more practical bounded rational model, and apply the cognitive hierarchy theory from behavioral economics in the modeling of workers' decisions. Comparing with the fully rational benchmark, we show that in practice the requester can receive a higher profit when considering the workers' bounded rationality, especially when the number of workers is large or the workers' average cognitive level is low. When the workers' average cognitive level is high enough, however, the practical bounded rational model converges to the benchmark fully rational model.

In a delay-sensitive mobile crowdsensing (MCS) platform, a service provider offers monetary incentives to mobile users for participating in the data collection and reporting their obtained data by a deadline. One aspect missing from most prior literature in the incentive mechanism design is the consideration of the detailed data reporting process through cellular or Wi-Fi networks. In this paper, we consider the interactions between the service provider and the users in two stages. First, the service provider chooses a reward to maximize its expected profit under the incomplete information of the users' responses. Next, given the reward, each user makes his participation and reporting decisions, which are complicated due to his mobility and network heterogeneity. We propose an algorithm to compute the optimal user's decisions under the general setting using dynamic programming, and derive closed-form decision criteria for the special yet practical case of a non-discounted reward. We compute the optimal reward by characterizing the solution set and the discontinuity in the profit function. Simulation results show that our proposed algorithm achieves a significant gain in the user payoff over three benchmark heuristic schemes. In addition, a service provider's profit is sensitive to the estimation of the users' Wi-Fi availabilities.

In a mobile crowdsensing (MCS) application, user diversity and social effect are two important phenomena that determine its profitability, where the former improves the sensing quality, while the latter incentives the users' participation. In this paper, we consider a reward mechanism design for the service provider to achieve diversity in the collected data by exploiting the users' social relationship. Specifically, we formulate a two-stage decision problem, where the service provider first optimizes its rewards for profit maximization. The users then decide their effort levels through social network interactions as a participation game. The analysis is particularly challenging due to the users' interplay in both the diversity and social graphs, which leads to a non-convex bilevel optimization problem. Surprisingly, we find that the service provider can focus on one superimposed graph that incorporates the diversity and social relationship and compute the optimal reward as the Katz centrality in closed-form. Simulation results, based on the random graph and a real Facebook trace, show that the availability of network information improves both the service provider's profit and the users' social surplus over the incomplete information cases.

To exploit users' heterogeneous data demands, several mobile network operators worldwide have launched the mobile data trading markets, where users can trade mobile data quota with each other. In this work, we aim to understand the users' optimal trading decisions and the operator's revenue maximizing strategy. We model the interactions between the mobile operator and the users as a two-stage Stackelberg game. In Stage I, the operator chooses the operation fee imposed on sellers to maximize its revenue. In Stage II, each user decides whether to be a seller or a buyer and optimizes the corresponding trading price and quantity. We derive the closed-form expression of the unique Nash equilibrium (NE) in Stage II in closed-form, and prove that the users' decisions can converge to the NE through distributed best response updates. We show that at the NE, different types of sellers and buyers should propose the same price such that the total demand matches the total supply. We further show that the Stage I operation fee optimization problem is convex, and derive the optimal operation fee in closed-form. Our analysis and numerical results show that the users who have less uncertainty of their data usages can benefit more from data trading. We also show that an operation fee that is too high hurts both the users' payoffs and the operator's revenue.

Intelligent network selection plays an important role in achieving an effective data offloading in the integrated cellular and Wi-Fi networks. However, previously proposed network selection schemes mainly focused on offloading as much data traffic to Wi-Fi as possible, without systematically considering the Wi-Fi network congestion and the ping-pong effect, both of which may lead to a poor overall user quality of experience. Thus, in this paper, we study a more practical network selection problem by considering both the impacts of the network congestion and switching penalties. More specifically, we formulate the users' interactions as a Bayesian network selection game (NSG) under the incomplete information of the users' mobilities. We prove that it is a Bayesian potential game and show the existence of a pure Bayesian-Nash equilibrium that can be easily reached. We then propose a distributed network selection (DNS) algorithm based on the network congestion statistics obtained from the operator. Furthermore, we show that computing the optimal centralized network allocation is an NP-hard problem, which further justifies our distributed approach. Simulation results show that the DNS algorithm achieves the highest user utility and a good fairness among users, as compared with the on-the-spot offloading and cellular-only benchmark schemes.

Motivated by the recently launched mobile data trading markets (e.g., China Mobile Hong Kong's 2nd exChange Market), in this paper, the mobile data trading problem is studied under future data demand uncertainty. A brokerage-based market is introduced, in which sellers and buyers propose their selling and buying quantities, respectively, to the trading platform that matches the market supply and demand. To understand the users' realistic trading behaviors, a prospect theory (PT) model from behavioral economics is proposed, which includes the widely adopted expected utility theory (EUT) as a special case. Although the PT modeling leads to a challenging non-convex optimization problem, the optimal solution can be characterized by exploiting the unimodal structure of the objective function. Building upon this analysis, an algorithm is designed to help estimate the users' risk preference and provide trading recommendations dynamically, considering the latest market and usage information. It is shown via simulations that the risk preferences have a significant impact on a user's decision and outcome: a risk-averse dominant user can guarantee a higher minimum profit in the trading, while a risk-seeking dominant user can achieve a higher maximum profit. By comparing with the EUT benchmark, it is shown that a PT user with a low reference point is more willing to buy mobile data. Moreover, compared with an EUT user, a PT user is more willing to buy mobile data when the probability of large data demand is low.

We study the cooperation of the mobile network operator (MNO) and the venue owners (VOs) on the public Wi-Fi deployment. We consider aone-to-many bargainingframework, where the MNO bargains with VOs sequentially to determine where to deploy Wi-Fi and how much to pay. Taking into account the negative externalities among different steps of bargaining, we analyze the following two cases: for theexogenous bargaining sequencecase, we compute the optimal bargaining solution on the cooperation decisions and payments under a predetermined bargaining sequence; for theendogenous bargaining sequencecase, the MNO decides the bargaining sequence to maximize its payoff. Through exploring the structural property of the optimal bargaining sequence, we design a low-complexityOptimal VO Bargaining Sequencing(OVBS) algorithm to search the optimal sequence. More specifically, we categorize the VOs into three types based on the impact of the Wi-Fi deployment at their venues, and show that it is optimal for the MNO to bargain with these three types of VOs sequentially. Numerical results show that compared with the random and worst bargaining sequences, the optimal bargaining sequence improves the MNO's payoff by up to 14.8 and 45.3 percent, respectively.

The proliferation of public Wi-Fi hotspots has brought new business potentials for Wi-Fi networks, which carry a significant amount of global mobile data traffic today. In this paper, we propose a novelWi-Fi monetizationmodel for venue owners (VOs) deploying public Wi-Fi hotspots, where the VOs can generate revenue by providing two different Wi-Fi access schemes for mobile users (MUs): 1) thepremium access, in which MUs directly pay VOs for their Wi-Fi usage, and 2) theadvertising sponsored access, in which MUs watch advertisements in exchange of the free usage of Wi-Fi. VOs sell their ad spaces to advertisers (ADs) via an ad platform, and share the ADs’ payments with the ad platform. We formulate the economic interactions among the ad platform, VOs, MUs, and ADs as a three-stage Stackelberg game. In Stage I, the ad platform announces its advertising revenue sharing policy. In Stage II, VOs determine the Wi-Fi prices (for MUs) and advertising prices (for ADs). In Stage III, MUs make access choices and ADs purchase advertising spaces. We analyze the sub-game perfect equilibrium (SPE) of the proposed game systematically, and our analysis shows the following useful observations. First, the ad platform’s advertising revenue sharing policy in Stage I will affect only the VOs’ Wi-Fi prices but not the VOs’ advertising prices in Stage II. Second, both the VOs’ Wi-Fi prices and advertising prices are non-decreasing in the advertising concentration level and non-increasing in the MU visiting frequency. Numerical results further show that the VOs are capable of generating large revenues through mainly providing one type of Wi-Fi access (the premium access or advertising sponsored access), depending on their advertising concentration levels and MU visiting frequencies.

There has been a proliferation of public Wi-Fi hotspots that serve a significant amount of global mobile traffic today. In this paper, we propose a general Wi-Fi monetization model for public Wi-Fi hotspots deployed by venue owners (VOs), where VOs generate revenue from providing both the premium Wi-Fi access and the advertising sponsored Wi-Fi access to mobile users (MUs). With the premium access, MUs directly pay VOs for their Wi-Fi usage; while with the advertising sponsored access, MUs watch advertisements for the free usage of Wi-Fi. VOs sell their ad spaces to advertisers (ADs) via an ad platform, and share a proportion of the revenue with the ad platform. We formulate the economic interactions among the ad platform, VOs, MUs, and ADs as a three-stage Stackelberg game. By analyzing the equilibrium, we show that the ad platform's advertising revenue sharing policy affects a VO's Wi-Fi price but not the VO's advertising price. Moreover, we prove that a single term called equilibrium indicator determines whether a VO will fully rely on the premium access, or fully rely on the advertising sponsored access, or obtain revenue from both types of access. Numerical results show that the VO obtains a large revenue under a large advertising concentration level and a medium MU visiting frequency.

In this paper, we study a virtual wireless operator's spectrum investment problem under spectrum supply uncertainty. To obtain enough spectrum resources to meet its customer demands, the virtual operator can either sense for the temporarily unused spectrum in a licensed band or lease spectrum from a spectrum owner. Sensing is usually cheaper than leasing, but the amount of available spectrum obtained by sensing is uncertain due to the primary users' activities in the licensed band. Previous studies on spectrum investment problems mainly considered the expected profit maximization problem of a risk-neutral operator based on the expected utility theory (EUT). In reality, however, an operator's decision is influenced by not only the consideration of expected profit maximization, but also the level of its risk preference. To capture this tradeoff between these two considerations, we analyze the operator's optimal decision problem using the prospect theory from behavioral economics, which includes EUT as a special case. The sensing and leasing optimal problem under prospect theory is non-convex and challenging to solve. Nevertheless, by exploiting the unimodal structure of the problem, we are able to compute the unique global optimal solution. We show that, compared with an EUT operator, both the risk-averse and risk-seeking operator achieve a smaller expected profit. On the other hand, a risk-averse operator can guarantee a larger minimum possible profit, while a risk-seeking operator can achieve a larger maximum possible profit. Furthermore, the tradeoff between the expected profit and the minimum possible profit for a risk-averse operator is better when the sensing cost increases, while the tradeoff between the expected profit and the maximum possible profit for a risk-seeking operator is better when the sensing cost decreases.

The explosive growth of global mobile traffic has led to rapid growth in the energy consumption in communication networks. In this paper, we focus on the energy-aware design of the network selection, subchannel, and power allocation in cellular and Wi-Fi networks, while taking into account the traffic delay of mobile users. Based on the two-timescale Lyapunov optimization technique, we first design an online Energy-Aware Network Selection and Resource Allocation (ENSRA) algorithm, which yields a power consumption within$O\left({\frac{1}{V}} \right)$bound of the optimal value, and guarantees an$O\left(V \right)$traffic delay for any positive control parameter$V$. Motivated by the recent advancement in the accurate estimation and prediction of user mobility, channel conditions, and traffic demands, we further develop a novel predictive Lyapunov optimization technique to utilize the predictive information, and propose a Predictive Energy-Aware Network Selection and Resource Allocation (P-ENSRA) algorithm. We characterize the performance bounds of P-ENSRA in terms of the power-delay tradeoff theoretically. To reduce the computational complexity, we finally propose a Greedy Predictive Energy-Aware Network Selection and Resource Allocation (GP-ENSRA) algorithm, where the operator solves the problem in P-ENSRA approximately and iteratively. Numerical results show that GP-ENSRA significantly improves the power-delay performance over ENSRA in the large delay regime. For a wide range of system parameters, GP-ENSRA reduces the traffic delay over ENSRA by 20–30% under the same power consumption.

With the rich set of embedded sensors installed in smartphones and the large number of mobile users, we witness the emergence of many innovative commercial mobile crowdsensing applications that combine the power of mobile technology with crowdsourcing to deliver time-sensitive and location-dependent information to their customers. Motivated by these real-world applications, we consider the task selection problem for heterogeneous users with different initial locations, movement costs, movement speeds, and reputation levels. Computing the social surplus maximization task allocation turns out to be an NP-hard problem. Hence we focus on the distributed case, and propose an asynchronous and distributed task selection (ADTS) algorithm to help the users plan their task selections on their own. We prove the convergence of the algorithm, and further characterize the computation time for users' updates in the algorithm. Simulation results suggest that the ADTS scheme achieves the highest Jain's fairness index and coverage comparing with several benchmark algorithms, while yielding similar user payoff to a greedy centralized benchmark. Finally, we illustrate how mobile users coordinate under the ADTS scheme based on some practical movement time data derived from Google Maps.

In this paper, we study the cooperative Wi-Fi deployment problem, where the mobile network operator (MNO) cooperates with some venue owners (VOs) to deploy public Wi-Fi networks. The MNO negotiates with the VOs to determine where to deploy Wi-Fi and how much to pay. The MNO's objective is to maximize its payoff, which depends on the payments to VOs, the benefits due to data offloading and mobile advertising, and the costs due to deploying and operating Wi-Fi. We analyze the interactions among the MNO and VOs under the one-to-many bargaining framework, where the MNO bargains with VOs sequentially, taking into account the externalities among different steps of bargaining. We apply the Nash bargaining theory to analyze the cases with exogenous and endogenous bargaining sequences. For the former case, the bargaining sequence is predetermined, and we apply backward induction to compute the optimal bargaining solution related to the cooperation decisions and payments. For the latter case, the MNO can decide the bargaining sequence to maximize its payoff. We explore the structural property of the one-to-many bargaining, and design an Optimal VO Bargaining Sequencing (OVBS) algorithm that computes the optimal bargaining sequence. More precisely, we categorize VOs into three types based on the impact of the Wi-Fi deployment at their venues, and show that it is optimal for the MNO to bargain with these three types of VOs sequentially. Numerical results show that the optimal bargaining sequence improves the MNO's payoff over the random and worst bargaining sequences by up to 14.7% and 45.8%, respectively.

Motivated by the recently launched 2CM data trading platform of China Mobile Hong Kong, we study the optimal user mobile data trading problem under the future demand uncertainty. We consider a brokerage-based market, where sellers and buyers propose their selling and buying prices and quantities to the trading platform, respectively. The platform acts as a broker, which facilitates the trade by matching the supply and demand. To understand users' realistic trading behaviors, we use prospect theory (PT) from behavioral economics in the modeling, which leads to a challenging non-convex optimization problem. Nevertheless, we are able to determine the unique optimal solution in closed-form, by utilizing the unimodal structure of the objective function. When comparing with the benchmark expected utility theory (EUT), we show that a PT user with a low reference point is more willing to buy mobile data. Moreover, when the probability of high demand is low, comparing with an EUT user, a PT user is more willing to buy mobile data due to the probability distortion.

To accommodate the explosive growth in mobile data traffic, both mobile cellular operators and mobile users are increasingly interested in offloading the traffic from cellular networks to Wi-Fi networks. However, previously proposed offloading schemes mainly focus on reducing the cellular data usage, without paying too much attention on the quality of service (QoS) requirements of the applications. In this paper, we study the Wi-Fi offloading problem with delay-tolerant applications under usage-based pricing. We aim to achieve a good tradeoff between the user's payment and its QoS characterized by the file transfer deadline. We first propose a general Delay- Aware Wi-Fi Offloading and Network Selection (DAWN) algorithm for a general single-user decision scenario. We then analytically establish the sufficient conditions, under which the optimal policy exhibits a threshold structure in terms of both the time and file size. As a result, we propose a monotone DAWN algorithm that approximately solves the general offloading problem, and has a much lower computational complexity comparing to the optimal algorithm. Simulation results show that both the general and monotone DAWN schemes achieve a high probability of completing file transfer under a stringent deadline, and require the lowest payment under a non-stringent deadline as compared with three heuristic schemes.

Femtocell networks have the potential to satisfy the increasing demand of mobile data usage. The recently proposed concept of cognitive femtocell network provides an effective way to further improve the spectrum spatial and frequency reuse. In this paper, we study the subchannel allocation problem for orthogonal frequency division multiple access (OFDMA)-based hybrid overlay/underlay cognitive femtocell networks. While most of the previous related studies did not fully exploit the potential of spatial and frequency reuse of the network, we propose a hybrid overlay and underlay spectrum access mechanism to further improve the performance of cognitive femtocell networks. We formulate the subchannel allocation problem as a coalition formation game among femtocell users under the hybrid access scheme, and analyze the stability of the coalition structure. We propose an efficient algorithm based on the solution concept of recursive core, and achieve a stable and efficient allocation. Simulation results show that the proposed algorithm achieves an improvement in aggregate network throughput up to 72% comparing to the overlay only scheme, 35% comparing to the underlay only scheme, and 18% comparing to a recently proposed coalition formation algorithm in the literature.

To address the increasingly severe congestion problem in cellular networks, mobile operators are actively considering offloading the cellular traffic to other complementary networks. In this paper, we study the online network selection problem in operator-initiated data offloading with multiple mobile users, taking into account the operation cost, queueing delay, and traffic load in different access networks (e.g., cellular macrocell, femtocell, and Wi-Fi networks). We first design a Delay-Aware Network Selection (DNS) algorithm based on the Lyapunov optimization technique. The DNS algorithm yields an operation cost within O (1/V) bound of the optimal value, and guarantees an O (V) traffic delay for any control parameter V > 0. Next, we incorporate the prediction of users' mobilities and traffic arrivals into the network selection. Specifically, we assume that the users' locations and traffic arrivals in the next few time slots can be estimated accurately, and propose a Predictive Delay-Aware Network Selection (P-DNS) algorithm to utilize this information based on a novel frame-based design. We characterize the performance bounds of P-DNS in terms of cost-delay tradeoff theoretically. To further reduce the computational complexity, we propose a Greedy Predictive Delay-Aware Network Selection (GP-DNS) algorithm, where the operator solves the network selection problem approximately and iteratively. Numerical results show that GP-DNS improves the cost-delay performance over DNS, and reduces the queueing delay by roughly 40% with the same operation cost.

We study a secondary wireless operator's spectrum investment problem under spectrum supply uncertainty using prospect theory. In order to meet the demands of its users, the secondary operator can either sense for the unused spectrum in a licensed band, or lease spectrum from a spectrum owner. Sensing is usually cheaper than leasing, but the amount of spectrum obtained by sensing is uncertain. We formulate such a hybrid spectrum investment problem as a two-stage optimization problem, and compute the optimal sensing and leasing decisions using backward induction. To model the realistic investment behaviors under uncertainty, we apply prospect theory to overcome the limitations of the widely adopted expected utility theory. We show that the investment decision model based on prospect theory leads to a non-convex optimization problem, which is challenging to solve in closed-form. However, we characterize the uniqueness of the optimal solution analytically, and compute it through a simple line search. Comparing with the expected utility theory benchmark, the analysis based on prospect theory shows that the operator will be more conservative in sensing, in order to reduce the investment risk and to avoid a large possible loss. In other words, the operator is both risk averse and loss averse.

In participatory sensing (PS), users use smartphones to collect information related to a certain phenomenon of interest, and report their sensed data to the service provider through cellular or Wi-Fi networks. Previous studies on the incentive mechanism design for user participation often neglect the details of data reporting, which is non-trivial given the user mobility, location-dependent network availability, and transmission cost. In this paper, we study the decisions of the service provider and the users in PS applications that involve photo or video transmissions, where the reporting cost through the cellular network is non-negligible. The service provider uses a deadline reward scheme to motivate users to participate, and optimizes its reward to maximize its expected surplus. Users make their participation and reporting decisions based on the reward announced by the service provider. We jointly consider the user mobility and multiple access methods with different transmission costs and location heterogeneity in the problem formulation and analysis. For the general case with a time-discounted reward, we formulate a user's reporting decision problem as a sequential decision problem, and propose an optimal participation and reporting decisions (OPRD) algorithm using dynamic programming. For the special case with a fixed reward, we derive the closed-form participation and reporting decisions. Simulation results show that the OPRD algorithm improves the user payoff over the patient and impatient schemes by 9.8% and 13.2%, respectively.

Recently, the multimedia content delivery has replaced the traditional voice communication as the major source of traffic in wireless networks. The deployment of femtocells is promising in satisfying the requirements of these multimedia applications if the interference among the femtocell access points (FAPs) is well-managed. In this paper, we study the interference management problem of the FAPs in a cooperative multimedia femtocell network. We consider the network setting where the players (i.e., the FAPs) can coordinate their transmissions to reduce the level of interference within a coalition. We first formulate the interference management problem as a coalition formation game in partition form with negative externalities, where the payoff of a player depends on actions of other players in the same coalition and in different coalitions. Based on the solution concept of recursive core in coalitional games, we propose an efficient coalition formation algorithm, RECORD, to achieve a final stable coalition structure. Simulation results show that the RECORD algorithm results in a substantially higher flow throughput and aggregate utility than some previously proposed scheduling algorithms.

In this paper, we study the problem of random access with interference pricing in wireless ad hoc networks using non-cooperative game theory. While most of the previous works in random access games are based on the protocol model, we analyze the game under the more accurate signal-to-interference-plus-noise-ratio (SINR) model. First, under the setting with fixed interference linear pricing, we characterize the existence of the Nash equilibrium (NE) in the random access game. In particular, when the utility functions of all the players satisfy a risk aversion condition, we show that the game is a S-modular game and characterize the convergence of the strategy profile to the NE. Then, under the setting with adaptive interference linear pricing, we propose an iterative algorithm that aims to solve the network utility maximization (NUM) problem. Convergence of the solution to a Karush-Kuhn-Tucker (KKT) point of the NUM problem is studied. It can be shown that the solution obtained under the protocol model may result in starvation for some users due to the inaccurate interference pricing. Simulation results show that our proposed algorithm based on the SINR model achieves a higher average utility than the algorithm based on the protocol model and a carrier sense multiple access (CSMA) scheme implemented in a slotted time system.

With the use of energy harvesting technologies, the lifetime of a wireless sensor network (WSN) can be prolonged significantly. Unlike a traditional WSN powered by non-rechargeable batteries, the energy management policy of an energy harvesting WSN needs to take into account the energy replenishment process. In this paper, we study the energy allocation for sensing and transmission in an energy harvesting sensor node with a rechargeable battery and a finite data buffer. The sensor node aims to maximize the total throughput in a finite horizon subject to time-varying energy harvesting rate, energy availability in the battery, and channel fading. We formulate the energy allocation problem as a sequential decision problem and propose an optimal energy allocation (OEA) algorithm using dynamic programming. We conduct simulations to compare the performance between our proposed OEA algorithm and the channel-aware energy allocation (CAEA) algorithm from [1]. Simulation results show that the OEA algorithm achieves a higher throughput than the CAEA algorithm under different settings.

In this paper, we study random access in a drive-thru scenario, where roadside access points (APs) are installed on a highway to provide temporary Internet access for vehicles. We consider vehicle-to-roadside (V2R) communications for a vehicle that aims to upload a file when it is within the APs' coverage ranges, where both the channel contention level and transmission data rate vary over time. The vehicle will pay a fixed amount each time it tries to access the APs, and will incur a penalty if it cannot finish the file uploading when leaving the APs. First, we consider the problem of finding the optimal transmission policy with a single AP and random vehicular traffic arrivals. We formulate it as a finite-horizon sequential decision problem, solve it using dynamic programming (DP), and design a general dynamic optimal random access (DORA) algorithm. We derive the conditions under which the optimal transmission policy has a threshold structure, and propose a monotone DORA algorithm with a lower computational complexity for this special case. Next, we consider the problem of finding the optimal transmission policy with multiple APs and deterministic vehicular traffic arrivals thanks to perfect traffic estimation. We again obtain the optimal transmission policy using DP and propose a joint DORA algorithm. Simulation results based on a realistic vehicular traffic model show that our proposed algorithms achieve the minimal total cost and the highest upload ratio as compared with some other heuristic schemes. In particular, we show that the joint DORA scheme achieves an upload ratio 130% and 207% better than the heuristic schemes at low and high traffic densities, respectively.

Cooperative spectrum sensing is an effective technique to improve the sensing performance and increase the spectrum efficiency in cognitive radio networks (CRNs). In this paper, we consider a CRN with multiple primary users (PUs) and multiple secondary users (SUs). We first propose a cooperative spectrum sensing and access (CSSA) scheme for all the SUs, where the SUs cooperatively sense the licensed channels of the PUs in the sensing subframe. If a channel is determined to be idle, the SUs which have sensed that channel will have a chance to transmit packets in the data transmission subframe. We then formulate this multi-channel spectrum sensing and channel access problem as a hedonic coalition formation game, where a coalition corresponds to the SUs that have chosen to sense and access a particular channel. The value function of each coalition and the utility function of each SU take into account both the sensing accuracy and the energy consumption. We propose an algorithm for decision node selection in a coalition. Moreover, we propose an algorithm based on the switch rule to allow the SUs to make decisions on whether to join or leave a coalition. We prove analytically that the set with all the SUs converges to a final network partition, which is both Nash-stable and individually stable. Besides, the proposed algorithms are adaptive to changes in network conditions. Simulation results show that our proposed CSSA scheme achieves a better performance than the closest PU (CPU) scheme and the noncooperative spectrum sensing and access (NSSA) scheme in terms of the average utility of the SUs.

Spectrum sensing is one of the key technologies to realize spectrum reuse and increase the spectrum efficiency in cognitive radio networks (CRNs). In this paper, we study energy-efficient cooperative multi-channel spectrum sensing in CRNs. We first propose a cooperative spectrum sensing and accessing (CSSA) scheme for all the secondary users (SUs). The SUs cooperatively sense the licensed channels of the primary users (PUs) in the sensing slot. If a channel is determined to be idle, the SUs which have sensed that channel will have a chance to transmit packets in the data transmission slot. We then formulate this multi- channel spectrum sensing problem as a coalition formation game, where a coalition corresponds to the SUs that have chosen to sense and access a particular channel. The utility function of each coalition takes into account both the sensing accuracy and energy efficiency. We propose distributed algorithms to find the optimal partition that maximizes the aggregate utility of all the coalitions in the system. We prove analytically that the proposed algorithms terminate at a stable partition that achieves the optimal aggregate utility. Simulation results show that the proposed algorithms result in the self-organization of the SUs that achieves a higher aggregate utility after each iteration. Also, the convergence and optimality of the proposed algorithms are proved by simulation results.

In a drive-thru scenario where vehicles drive by a roadside access point (AP) to obtain temporary Internet access, it is important to design efficient resource allocation schemes to fully utilize the limited communication opportunities. In this paper, we study the random access problem in drive thru communications in a dynamic environment, where both the channel contention level and channel capacity vary over time. We assume that a vehicle has a file to upload when it is within the coverage range of the AP. The vehicle will pay a fixed amount each time it tries to access the AP, and will incur a penalty if it cannot finish the file uploading when leaving the AP. We first formulate the optimal transmission problem as a finite-horizon sequential decision problem. Then we solve the problem using dynamic programming, and design a dynamic optimal random access algorithm. Simulation results based on a realistic vehicular traffic model show that our algorithm achieves the minimal total cost, the highest probability of completing file upload, and the highest upload ratio as compared with two other heuristic schemes.

In cognitive radio networks, the secondary users (SUs) can be selected as the cooperative relays to assist the transmission of the primary user (PU). In order to increase the utility, the PU needs to consider whether it is beneficial to use cooperative transmission and which SU should be chosen as the cooperative relay. In addition, if the PU selects a secondary relay, it needs to allocate time resources for cooperative transmission. Then, the SUs need to determine their strategies of random access when the licensed spectrum of the PU is available. In this paper, we first establish a model for cooperative cognitive radio networks with one PU and multiple SUs. We then propose a cooperative transmission and random access (CTRA) scheme. Based on the sequential structure of the decision-making, we study the cooperative cognitive radio network and determine the equilibrium strategies for both the PU and the SUs using the Stackelberg game. Simulation results show that both the PU and the SUs obtain higher utilities when compared with the noncooperative transmission and random access (NTRA) scheme.

In this paper, we study the problem of multi-channel medium access control (MAC) in cognitive radio (CR) networks. While most of the previously proposed MAC protocols for CR networks are heuristic and are based on the simplistic protocol model, we design a distributed MAC protocol using the more accurate signal-to-interference-plus-noise-ratio (SINR) model. First, we assume that the secondary users are cooperative and formulate the problem of assigning transmission and listening probabilities for random access as a non-convex network utility maximization problem. We propose a three-phase algorithm that converges to a near-optimal solution after solving a number of convex optimization problems distributively. Simulation results show that our proposed algorithm based on the SINR model achieves a higher aggregate throughput than other schemes which are based on the protocol model. Then, we consider the case that the secondary users are rational. We use coalitional game theory to study the incentive issues of user cooperation in a given channel for the SINR model. In particular, we use the solution concept of the core to analyze the stability of the grand coalition, and the solution concept of the Shapley value to fairly divide the payoff among the users. We show that the Shapley value lies in the core when all the users are one-hop neighbours of each other. We illustrate the Shapley value and the core with a numerical example.

In this paper, we consider the problem of random access in wireless local area networks (WLANs) with each station generating either elastic or inelastic traffic. Elastic traffic is usually non-real-time, while inelastic traffic is usually coming from real-time applications. We formulate a network utility maximization (NUM) problem, where the optimization variables are the persistent probabilities of the stations and the utilities are either concave or sigmoidal functions. Sigmoidal utility functions can better represent inelastic traffic sources compared to concave utility functions commonly used in the existing random access literature. However, they lead to non-convex NUM problems which are not easy to solve in general. By applying the dual decomposition method, we propose a subgradient algorithm to solve the formulated NUM problem. We also develop closed-form solutions for the dual subproblems involving sigmoidal functions that have to be solved in each iteration of the proposed algorithm. Furthermore, we obtain a sufficient condition on the link capacities which guarantees achieving the global optimal solution when our proposed algorithm is being used. If this condition is not satisfied, then we can still guarantee that the optimal value of the objective function is within some lower and upper bounds. We perform various simulations to validate our analytical models when the available link capacities meet or do not meet the sufficient optimality condition.

In wireless local area networks (WLANs), quality of service (QoS) can be provided by mapping applications with different requirements (e.g., delay and throughput) into one of the available access categories (ACs), as is done in the IEEE 802.11e standard. With the increasing programmability of network adapters, a malicious user can strategically declare a higher AC for its application to gain an unfair share of resources. This can drastically degrade the network performance and avoid adequate service distinction among different ACs. In this paper, we use the technique of mechanism design in game theory to tackle this problem in WLANs with random access. We propose to use the Vickrey-Clarke-Groves (VCG) mechanism in order to motivate each station to inform the access point (AP) truthfully, about the required AC of its application. The AP will then inform each station about its persistent probability and the price it needs to pay for the offered service. The result of the allocation of the persistent probabilities can be used for admission control. Simulation results show that the use of mechanism design can lead to a higher aggregate utility and prevents malicious users from gaining an unfair share of the network bandwidth.

There is recently an increasing popularity in the use of wireless ad hoc networks, especially for sensor networks. However, these networks are susceptible to fading, interference and limited power supply. In this paper, we consider the issue of cooperative routing under the effect of both multi-user interference (MUI) and fading in ultra-wideband (UWB) networks. We first generate a single path route from any available routing algorithms. Based on this single path route, our cooperative routing algorithm is executed to see whether nodes which 'overhear' the information should cooperate to alleviate the effect of fading, and thus improve outage performance. From our result, it is shown that our cooperative routing algorithm reduces the average transmit energy by 8dB at 3% of outage.