Mobile crowdsensing (MC), an excellent solution to large-scale spatiotemporal data sensing problems, has recently received lots of attention from both industry and academia. In the MC system, any requester can acquire the sensing data for his points of interest (PoIs) by offering some payments to attract a group of mobile users capable of completing these PoI-related sensing tasks. However, the current MC work neglected three vital factors, more or less. First, they assume that these distributed users are mutually independent in MC, ignoring the social effects. Actually, the sensing data collected by one user may be corroborated by others’ sensing data, so-called information corroboration. Second, all rational and selfish users are inclined to gather to perform these tasks due to information corroboration. Meanwhile, they may be strategic about their participation levels to maximize profits. However, more similar sensing data will undoubtedly lower the information value, so any user has a tradeoff between gather and scatter. Third, although mobile users can obtain some payments, privacy issues may still prevent them from participating in MC. In this article, we propose a secure blockchain-assisted socially-aware MC framework by adopting the smart contract technique of Ethereum. For this framework, we further devise a two-stage Stackelberg game model to assist the requester (i.e., the leader in the game) in properly pricing each PoI-related sensing task, so that mobile users (i.e., the followers in the game) can exactly select their tasks and determine their participation levels. To analyze the game equilibrium, we extend the traditional Hessian matrix method to a multidimension case involving the multiuser multitask hyperspace setting. We conduct extensive experiments to prove the equilibrium and effectiveness of the proposed solution. We also implement a prototype and deploy the smart contract to an official Ethereum test network to demonstrate the practicability of the proposed framework.

Multi-Armed Bandit (MAB) that solves the sequential decision-making to the prior-unknown settings has been extensively studied and adopted in various applications such as online recommendation, transmission rate allocation, etc. Although some recent work has investigated the multi-agent MAB model, they supposed that agents share their bandit information based on social networks but neglected the incentives and arm-pulling budget for heterogeneous agents. In this paper, we propose a transaction-based multi-agent MAB framework, where agents can trade their bandit experience with each other to improve their total individual rewards. Agents not only face the dilemma between exploitation and exploration, but also decide to post a suitable price for their bandit experience. Meanwhile, as a buyer, the agent accepts another agent whose experience will help her the most, according to the posted price and her risk-tolerance level. The key challenge lies in that the arm-pulling and experience-trading decisions affect each other. To this end, we design the transaction-based upper confidence bound to estimate the prior-unknown rewards of arms, based on which the agents pull arms or trade their experience. We prove the regret bound of the proposed algorithm for each independent agent and conduct extensive experiments to verify the performance of our solution.

The multi-armed bandit (MAB) models have always received lots of attention from multiple research communities due to their broad application domains. The optimal selection problem with unknown rewards in advance, such as ad recommendation in social networks, spectrum access in the cognitive radio field, etc., can be efficiently solved by using MAB models. In an MAB model, given$N$arms whose rewards are unknown in advance, the player selects exactly one arm in each round, and his goal is to maximize the cumulative rewards over a fixed horizon. Further, a more general model called combinatorial MAB (i.e., CMAB), where$K$arms can be played simultaneously in each round, is put forward. However, the existing CMAB models neglect the strategic behaviors of the$N$arms, which indicates that one arm might report false information to increase its own profits. In fact, in many applications such as user selection in crowdsensing, the arms are not the feelingless machines but the rational individuals. To this end, we combine the upper confidence bound (UCB) with auction theory to develop a new algorithm called auction-based UCB (AUCB). We divide the auction-based CMAB problem into two sub-problems: winning arm selection and payment computation problems. For AUCB, we derive an upper bound on regret and prove the truthfulness in one round, individual rationality, and computational efficiency. In addition, we consider an extended situation that some arms may be unavailable in some rounds and the arms will bid inconsistently in different rounds. We devise another algorithm called eAUCB to solve this problem. Extensive simulations are conducted to show the significant performance of the proposed algorithms.

Blockchain came to prominence as the distributed ledger underneath Bitcoin, which protects the transaction histories in a fully-connected, peer-to-peer network. The blockchain mining process requires high computing power to solve a Proof-of-Work (PoW) puzzle, which is hard to implement on users’ mobile devices. So these miners may leverage the edge/cloud service providers (ESPs/CSP) to calculate the PoW puzzle. The existing edge-assisted blockchain networks assumed that all ESPs have a uniform propagation delay, which is unrealistic. In this article, we consider a more practical scene where ESPs locate in diverse positions of the blockchain network, which causes different propagation delays when supporting the computation of the PoW puzzle. Additionally, these ESPs connect to a remote CSP for resource scheduling when the computing tasks exceed their maximum capacity. The blockchain mining process generally involves complicated competition and games among CSP, ESPs, and miners. Each service provider focuses on how to determine his resource price so that he can maximize his utility. According to the set resource price, each miner concentrates on scheduling his resource requests for each ESP to maximize individual personal utility, which depends on ESPs’ resource price and propagation delays. We first model such a resource pricing and scheduling problem as a three-stage multi-leader multi-follower Stackelberg game and aim at finding the Stackelberg equilibrium. Then, we analyze the subgame optimization problem in each stage and propose an iterative algorithm based on backward induction to achieve the Nash equilibrium of the Stackelberg game. Finally, extensive simulations are conducted to verify the significant performance of the proposed solution.

Currently, the blockchain, as a key enabling technology of digital currency, has attracted lots of attention from both industry and academia. The blockchain mining process requires high computing power to solve a Proof-of-Work (PoW) puzzle, which is hard to implement on users’ mobile devices. So these miners may leverage the resources of the edge/cloud service providers (ESPs/CSP) to calculate the PoW puzzle. The existing edge-assisted blockchain networks simply assumed that all ESPs have a uniform propagation delay, which is not realistic. In this paper, we consider a more practical scene where ESPs with distributed geographic locations have diverse propagation delays when supporting the computation of the PoW puzzle. Additionally, the blockchain mining process generally involves the complicated competition and game among these ESPs and miners. Each ESP focuses on how to determine his resource price and to select the requests from the miners, so that he can maximize his utility. According to the set resource price, each miner concentrates on scheduling his resource requests for each ESP to maximize his individual utility which depends on ESPs’ resource price and propagation delays. We model such a resource pricing and scheduling problem as a multi-leader multi-follower Stackelberg game and aim at finding the joint maximization of the utilities of each ESP and each individual miner. We prove the existence and uniqueness of the Stackelberg equilibrium (SE) and meanwhile propose an algorithm to achieve the corresponding SE. Finally, extensive simulations are conducted to verify the significant performance of the proposed solution.