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The development of smart vehicles and rich cloud services have led to the emergence of vehicular edge computing. To perform the distributed computation tasks efficiently, Coded Distributed Computing (CDC) was proposed to reduce communication costs and mitigate the straggler effects through the use of coding techniques. In this paper, we propose a double auction mechanism to allocate the resources of the edge servers to the vehicles in order to complete the CDC tasks. Specifically, the vehicles use the PolyDot codes to manage the tradeoff between communication costs and recovery threshold. Given the requirements of various vehicles, the double auction mechanism matches the edge servers with the required resources to the vehicles. Besides, the double auction mechanism also determines the prices that the vehicles need to pay for the resources of the edge servers. The analyses show that the double auction mechanism satisfies the properties of individual rationality, incentive compatibility and budget-balance. From the simulation, the utility of auctioneer increases when the number of vehicles and edge servers increases. Ministry of Education (MOE) National Research Foundation (NRF) This work was supported in part by the Programme DesCartes, in part by the National Research Foundation, Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise Programme, in part by Alibaba Group through Alibaba Innovative Research Program and Alibaba-NTU Singapore Joint Research Institute, in part by the National Research Foundation, Singapore, under the AI Singapore Programme under Grant AISG2-RP-2020-019, in part by WASP/NTU under Grant M4082187 (4080), in part by the Singapore Ministry of Education under Grant Tier 1 (RG16/20), in part by the SUTD SRG-ISTD-2021-165, and in part by the SUTD-ZJU IDEA under Grant (SUTD-ZJU (VP) 202102).

In this paper, we propose a Peer-to-Peer (P2P) energy trading scheme between EVs and the SPs to manage the demand response in V2G environment. Unlike the traditional schemes, which consists of complex energy-transportation meshes, the proposed scheme achieves a balance between demand and response by providing incentives to EVs out of their self-interests. However, the online transactions security and privacy protection of EVs poses challenges with respect to confidentiality and integrity preservation. To cope up with the issues, we design a consortium blockchain-based scheme to ensure secure energy transactions between EVs and the SPs without trusted third-party intervention. Moreover, the energy pricing and the amount of traded energy problems for demand response are solved by a double auction mechanism to maximize the social welfare. Numerical results based on a real-time implementation on a private Ethereum network having smart contract demonstrated that the auction-based mechanism achieves social welfare maximization with privacy protection of online transactions between EVs and the SPs. The results obtained show that the rate of convergence, scalability metric average latency, and standard deviation of the energy transactions have been improved by approximately 18.18%, 15%, and 28.1%, respectively in comparison to the existing schemes. Moreover, security and privacy analysis of the proposed scheme shows that it improves the transaction security substantially in comparison to the existing state-of-the-art proposals.

Peer-to-Peer (P2P) resource sharing promotes local resource-hungry task offloading to other mobile devices and balances the resource consumption between mobile devices. Most of existing P2P task offloading systems aims to solve the resource sharing between one pair exclusively without considering the cost of resource supply and the strategic behaviors of mobile users. In this paper, we propose two user models for the P2P task offloading system: honest user model and strategy user model. For the honest user model, we formulate the resource allocation maximization problem with latency and energy consumption constraints as an Integer Linear Programming. We show that the solution for honest user model can output 189% resource transactions of that for the strategic users. For the strategy user model, we propose a double auction-based P2P task offloading system, and design a truthful multi-resource transaction mechanism to maximize the number of resource transactions. We first group the mobile users based on the connected components to improve the efficiency of double auction. Then we utilize the McAfee Double Auction to price the resource transactions. Finally, we split each winning mobile user of double auction into multiple virtual mobile users, and use the matching approach to calculate the resource allocation. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the designed multi-resource transaction mechanism satisfies the desirable properties of computational efficiency, individual rationality, budget balance, truthfulness for resource request/supply, and general truthfulness for bid/ask price.

Blockchain technology is developing rapidly and has been applied in various aspects, among which there are broad prospects in Internet of Things (IoT). However, IoT mobile devices are restricted in communication and computation due to mobility and portability, so that they can’t afford the high computing cost for blockchain mining process. To solve it, the free resources displayed on non-mining-devices and edge cloud are selected to construct collaborative mining network(CMN) to execute mining tasks for mobile blockchain. Miners can offload their mining tasks to non-mining-devices within a CMN or the edge cloud when there are insufficient resources. Considering competition for resource of non-mining-devices, resource allocation problem in a CMN is formulated as a double auction game, among which Bayes-Nash Equilibrium (BNE) is analyzed to figure out the optimal auction price. When offloading to edge cloud, Stackelberg game is adopted to model interactions between edge cloud operator and different CMNs to obtain the optimal resource price and devices’ resource demands. The mechanism realizes improving the mining utility in mining networks while ensuring the maximum profit of edge cloud operator. Finally, profits of mining networks are compared with an existing mode which only considers offloading to edge cloud. Under the proposed mechanism, mining networks obtain 6.86% more profits on average.

In this paper, we propose a secure and efficient blockchain-based data trading approach for the Internet of Vehicles (IoV). First, we apply consortium blockchain technologies to ensure secure and truthful data trading, and propose a general blockchain-based data trading framework for IoV. Second, to improve the efficiency of data trading and encourage more participants to trade data, we propose an iterative double auction mechanism with the purpose of achieving social welfare maximization, in which pricing rules of buyers and sellers are designed to induce participants to submit bids and to decide the amount of traded data and its price among buyers and sellers. In particular, in our algorithm, the hidden information of individuals can be extracted gradually so that the privacy of participants in data trading can be protected well. Finally, the experimental results show the efficiency of our proposed algorithm. Moreover, the correctness of social welfare maximization, incentive compatibility, individually rationality, and weakly budget balance of our auction mechanism are verified in the experiments.

Autonomous electric vehicles (AEVs) are gaining ground around the world. They are equipped with intelligent decision-making capabilities that allow them to optimize on their battery usage and engage in energy trading for profit maximization. This paper envisions a market model, where energy aggregators considers buying and selling electricity from AEVs. In this system, the aggregators reside on cloudlets at the edge of a cloud computing system for prompt communication and negotiation with AEVs. Whether they are parked or en-route, AEVs can be crowdsourced to provide energy to consumers at needed times. This study proposes double auction models to incentivize AEV agents to participate in the market based on their time preferences and energy pricing. The proposed system implements dynamic pricing structures, which are applicable to various scenarios of energy trading. To evaluate the proposed system, this paper provides extensive theoretical analysis and simulation experiments to demonstrate that the proposed auctioning models are computationally efficient, truthful, individually rational, and budget balanced.

This paper investigates the secure communication issue for both static and dynamic cognitive radio networks (CRNs), where multiple nonaltruistic primary users (PUs), secondary users (SUs), and eavesdroppers exist. The design objective is to provide secure communications for PUs and, meanwhile, to ease the starvation of transmission opportunities for SUs. To achieve this goal, we propose a barter-like trading model to incentivize the cooperation among nonaltruistic users. Specifically, PUs leverage the assistance of SUs in the form of cooperative relaying or friendly jamming and, in return, yield certain licensed spectrum accessing time to the aided SUs. We propose a truthful nonmonetary double auction framework (FONDA) toward secure communications for static CRN where PUs and SUs interact in a single round. Then, we extend our framework to d-FONDA for dynamic CRN, where SUs who have patience (tolerant of traffic delay) arrive and leave the network dynamically. We prove that both FONDA and d-FONDA preserve nice economic properties, including truthfulness, individual rationality, and budget balance. Simulation results reveal that the proposed frameworks provide substantial performance gains compared with the baseline scheme and suffer acceptable performance degradation over ideal schemes.

In this paper, we study the power allocation problem in a multiuser cooperative network, where users help each other's transmissions to an access point. We use iterative double auction (IDA) game to model the interaction among the users and the access point. In each iteration of this game, the users first submit bids for buying other users' power and asks for selling its own power, and then, the access point determines the power allocation based on users' bids and asks. We propose a distributed algorithm for the implementation of the IDA-based power allocation. We also show that the proposed algorithm achieves a weighted sum signal-to-noise ratio (sum-SNR)-optimal solution. Simulation results are conducted to verify the performance of the proposed algorithm.

For the purpose of enhancing the accuracy of primary user (PU) transmission detection, using multiple secondary users (SUs) to sense cooperatively, instead of sensing individually, has recently been suggested. However, there are some issues: The first is whether the SUs are willing to sense the PU. It is better to give SUs incentives, or they may not behave cooperatively. The second is the system energy cost. It will be unnecessary and would be a waste of energy to let all the SUs sense together if some of them can work just as well. The third is the fusion time. If there are too many sensing SUs, it will take too long to collect the results and fuse them. To solve these issues, we propose the Secondary User stiMulating (SUM) mechanism. SUM has two parts: sensing auctions and report fusion. In sensing auctions, we introduce double auction into cooperative sensing, which is a pioneer work. We design a truthful winner determination and pricing mechanism. In report fusion, we propose a greedy algorithm to first satisfy the PU throughput demand and then maximize the SU throughput; this algorithm further stimulates the SU to use cooperative sensing. We prove that our auction mechanism is truthful and that the fusion problem is NP-hard. The simulation results show that SUM is effective in terms of saving the system energy cost, reducing the report fusion time, and maintaining the system throughput.

Recently, as it can greatly increase the capacity of cellular networks through spatial diversity, cooperative communications has been obtaining more and more attention. As a widely used schema, user (user equipment, UE) cooperation presents its advantage. However, due to the users' selfish nature, UE may be unwilling to serve as a relay node if they cannot obtain a corresponding reward. On the contrary, to save energy for longer standby time, particularly when there is energy shortage, many source users would like to purchase the relay service from other idle UE. Therefore, reconciling the demand of both sides is a challenging problem to be resolved. In this paper, a realistic double-auction scenario is constructed between multiple source UE and multiple idle UE in a cellular network. We take the avaricious psychology of network entities into consideration by determining the ask and bid prices through a markup, which is the function of their residual energy. To solve this double-auction-based optimal relay assignment problem, we transform it into maximum matching (MM) and maximum weighted matching (MWM) problems, respectively, and we solve them by corresponding algorithms. Experimental results show that the proposed algorithms dramatically maximize the system performance involving successful source–relay pairs, system throughput, and social welfare (SW), compared with other idealized truthful double-auction scheme that only consider the system throughput.