In current Data-as-a-Service (DaaS) platforms, data marketplaces are the primary mechanism by which data owners deliver data products and services to users. However, data owners incur substantial costs to collect or produce data before selling it, exposing them to economic risks due to market price fluctuations. Options trading, used in traditional commodity markets, can alleviate sellers' financial burdens with forward contracts at fixed option prices and pre-paid option premiums. To our knowledge, the application of option trading in data marketplaces has not been thoroughly studied or implemented. In this work, we advocate for the first data options marketplace on a DaaS platform that mitigates price fluctuation risks and reduces market entry barriers. Designing such a marketplace involves several key challenges, including the easy replication of data and the difficulty in assessing data quality before production. To address these challenges, we first design a data quality prediction method based on sellers' reputation. Following this, we model and quantify the competitive relationships among buyers. On this basis, we model the interactions between sellers and buyers in the data options market as a two-stage Stackelberg game, focusing on maximizing sellers' profit. We formally derive the perfect subgame equilibrium for option trading and derive each seller's optimal pricing strategy. Numerical experiments demonstrate the superiority of data options trading over conventional data trading methods in DaaS platforms.

Mobile devices within the Mobile Edge Computing (MEC) system can offload tasks from the near server to reduce the retrieval latency. Combining collaborative learning for MEC will make progress in both speeding up the convergence of the task-offloading algorithms applied at the end devices side and relieving resources lacking pressure on servers at edge server or cloud server sides. However, stochastic bandit algorithms are incompetent in the presence of complex environments and rarely consider device-to-device communication among users, lacking flexibility under a centralized system. Thus, in this paper, we propose an Upper Confidence Bound (UCB) basedAdaptiveProbabilityUpdating algorithm (APU) and apply it at users side guiding them offloading tasks efficiently. APU could let users manage their own probability-based distributed task offloading strategy and based on it select the most adaptive server. Drawing on a long line of research in network communication and traditional MAB frameworks, we build a multi-agent variant of APU and its corresponding MEC system. Furthermore, we take the regret analysis of the proposed algorithms by rigorous mathematical proof of the sub-linearity convergence, and at the end of this paper, we do some simulation experiments to demonstrate the effectiveness.

As mobile devices proliferate and mobile applications diversify, Mobile Edge Computing (MEC) has become widely adopted to efficiently allocate computing resources at the network edge and alleviate network congestion. In the MEC initial phase, the absence of vital information presents challenges in devising task-offloading policies, and identifying malicious devices responsible for providing inaccurate feedback is complex. To fill in such gaps, we introduce a consortium blockchain-enabledCommitteeVoting basedTaskOffloadingModel (CVTOM) to collaboratively formulate resource allocation policies and establish deterrence against malicious servers producing erroneous results intentionally. Different voting principle mechanisms of each committee member are first designed in a Blockchain-enabled system which helps to represent the system's resource status. Additionally, we propose a Multi-armed Bandits relatedThompsonSampling basedAdaptivePreferenceOptimization (TSAPO) algorithm for task-offloading policy, enhancing the timely identification of potent edge servers to improve computing resource utilization which first considers dynamic edge server space and parallel computing scenarios. The solid proof process greatly contributes to the theoretical analysis of the TSAPO. The simulation experiments demonstrate the delay and budget can be reduced by around 25% and 10% respectively, showcasing the superior performance of our approach.

With the development of the fifth generation (5G) technology, Mobile Edge Computing (MEC) is becoming a useful architecture, which is envisioned as a cloud extension version. Users within the MEC system could save more time on data transmission, in which the tasks can be executed on the edge side. Multi-armed bandits (MAB) are powerful tools that help users offload tasks to their best servers in the MEC system. However, as the system scale grows, the traditional MAB algorithms are weak and the channel condition deteriorates rapidly, which will lead to an increasing time cost of edge tasks offloading and computing. Therefore, in this paper, we propose aBatch-basedMulti-userServerElimination (BMSE) to solve such a problem. BMSE contains two sub-algorithms, BMSE inUserLevel (BMSE-UL) and BMSE inSystemLevel (BMSE-SL). BMSE-UL is applied among users for the initial stage which can help them discard servers with obvious bad performance. BMSE-SL is applied in the whole MEC system which can guide users offloading tasks collectively. Furthermore, we establish the optimality of the proposed algorithms by proving the sub-linearity convergence of their regrets and demonstrate the effectiveness through extensive experiments.

Federated learning (FL) can train a global model from clients' local data set, which can make full use of the computing resources of clients and performs more extensive and efficient machine learning on clients with protecting user information requirements. Many existing works have focused on optimizing FL accuracy within the resource constrained in each individual round, however there are few works comprehensively consider the optimization for latency, accuracy and energy consumption over all rounds in wireless federated learning. Inspired by this, in this paper, we investigate FL in wireless networks where client selection and bandwidth allocation are two crucial factors which significantly affect the latency, accuracy and energy consumption of clients. We formulate the optimization problem as a mixed-integer problem, which is to minimize the cost of time and accuracy within the long-term energy constrained over all rounds. To address this optimization problem, we propose a per-round energy drift plus cost (PEDPC) algorithm from an online perspective, and the performance of the PEDPC algorithm is verified in simulation results in terms of latency, accuracy and energy consumption in IID and NON-IID data distributions.