Unmanned aerial vehicle (UAV) as an aerial base station is a promising technology to rapidly provide wireless connectivity to ground users. Given UAV’s agility and mobility, a key question is how to adapt UAV deployment to best cater to the instantaneous wireless traffic in a territory. In this paper, we propose an adaptive deployment scheme for a UAV-aided communication network, where the UAV adapts its displacement direction and distance to serve randomly moving users’ instantaneous traffic in the target cell. In our adaptive scheme, the UAV does not need to learn users’ exact locations in real time, but chooses its displacement direction based on a simple majority rule by flying to the spatial sector with the greatest number of users in the cell. To balance the service qualities of the users in different sectors, we further optimize the UAV’s displacement distance in the chosen sector to maximize the average throughput and the successful transmission probability, respectively. We prove that the optimal displacement distance for average throughput maximization decreases with the user density: the UAV moves to the center of the chosen sector when the user density is small and the UAV displacement becomes mild when the user density is large. In contrast, the optimal displacement distance for success probability maximization does not necessarily decrease with the user density and further depends on the target signal-to-noise ratio (SNR) threshold. Extensive simulations show that the proposed adaptive deployment scheme outperforms the traditional non-adaptive scheme, especially when the user density is not large.

Building upon the prevailing concept of edge computing (EC), a distributed EC market requires decentralized and verified transaction management to trade computing resources. Towards this goal, we study a blockchain-aided EC market wherein each data service operator (DSO) rents a group of edge computing nodes (ECNs) and leases the ECNs to the user terminals (UTs) to provide computation offloading services. A trustworthiness model is introduced to evaluate the quality of each network entity throughout the transactions. We develop a two-level trading mechanism over smart contract to enable the automatic and efficient transactions among the network entities and provide high quality services. First, we propose a smart contract based matching mechanism to establish the renting association between the DSOs and ECNs with the aim of maximizing the social welfare. Second, we propose a social welfare improved double auction (SWIDA) mechanism to build up the leasing association between the DSOs and UTs, and determine the pricing of the winners. We show that the proposed double auction mechanism can achieve individual rationality, balanced budget, truthfulness in expectation, and an improved social welfare than the benchmark mechanisms. Moreover, we put forth a trustworthiness driven Proof-of-Stake (PoS) consensus mechanism to enable verified transaction and fair allocation of block generation reward. Following the principle of PoS, we formulate the block generation as a coalitional game, wherein each stakeholder votes according to its trustworthiness and coinage, and shares the reward among the coalition according to the Shapley values. The simulation results show that the proposed PoS consensus mechanism can reduce the wealth inequality among the network entities compared with the conventional consensus mechanisms.

Unmanned aerial vehicle (UAV) technology is a promising solution for rapidly providing wireless communication services to ground users. When the users demands dynamically change over time, the key challenge is how to adapt the UAV deployment strategy to the partial and even outdated observations on the users' activities given the UAV's flying speed limit. In this paper, we study dynamic UAV deployment to learn and adapt to the time-varying user activities, where the activity pattern of a user (if out of the UAV service coverage) is hidden from the UAV and follows a time-slotted Markov chain that switches between active and idle states. We formulate the learning-and-adaption based UAV deployment problem as a partially observable Markov decision process (POMDP) to maximize the total discounted hit rate of active users. We show there is a fundamental delay-reward tradeoff, and prove that the UAV will optimally follow a threshold-based policy by waiting at an idle user for a time threshold before moving to another user. Furthermore, we extend to a more general scenario where the UAV does not even know the parameters of each user's temporal activity distribution, and apply Q-learning to develop another threshold-based deployment policy for a multi-user scenario.

With the development of federated learning (FL), mobile devices (MDs) are able to train their local models with private data and send them to a central server for aggregation, thereby preventing leakage of sensitive raw data. In this paper, we aim to improve the training performance of FL systems in the context of wireless channels and stochastic energy arrivals of each MD. To this purpose, we dynamically optimize MDs transmission power and training task scheduling. We first model this dynamic programming problem as a constrained Markov decision process (CMDP). Due to high dimensions of the proposed CMDP problem, we propose online stochastic learning methods to simplify the CMDP and design online algorithms to obtain an efficient policy for all MDs. Since there are long-term constraints in our CMDP, we utilize a Lagrange multipliers approach to tackle this issue. Furthermore, we prove the convergence of the proposed online stochastic learning algorithm. Numerical results indicate that the proposed algorithms can achieve better performance than the benchmark algorithms.

As a shared and distributed database, blockchain empowers distributed edge computing (EC) networks with decentralization, integrity, and auditability of trust management. However, a bottleneck issue of blockchain-enabled decentralized trust management (B-DTM) lies in off-chain trust verification, since blockchain is a guarantee of on-chain trustworthiness rather than the reliability of off-chain trust. To orchestrate the off-chain and on-chain trust, we develop a novel B-DTM framework for EC networks that features off-chain trust verification and on-chain trust based consensus mechanisms. In the off-chain environment of EC, each edge node gains its verifiable trust according to Quality of Task Execution (QoTE). Furthermore, we justify that verified off-chain trust can be used as stake for the on-chain consensus. In particular, we design a novel consensus mechanism called Proof-of-Trusted-Work (PoTW), wherein the difficulty adjustable mining work and chain structure of trust data are introduced to promote the on-chain trust security and the trust based mining difficulty adjustment is employed to improve the on-chain trust update efficiency. Simulation results demonstrate the efficiency and security performances of the proposed B-DTM against various on-chain and off-chain malicious attacks.

Blockchain-assisted federated learning (BFL) can achieve decentralized storage and management of model data without relying on a central server. However, security issues caused by deliberate attacks in distributed systems and efficiency issues induced by heterogeneous computing consumption in resource-limited systems need to be urgently addressed in BFL. To address these issues, we propose a decentralized reputation management (DRM) mechanism for a trustworthy BFL (T-BFL) network, that explores, stores, and utilizes the endogenous reputation of distributed nodes to promote system security and efficiency. The proposed DRM includes three core modules, i.e., decentralized reputation evaluation, reputation-based model aggregation, and reputation-based blockchain consensus. Specifically, in the off-chain phase of T-BFL, the reputation value of each node is evaluated based on model quality, which other peer nodes can verify. This reputation value further determines the weight of global aggregation at each node. In the on-chain phase, the reputation of each node serves as the stake to dynamically adjust its consensus difficulty. Furthermore, we investigate the convergence rate of the T-BFL network under the poisoning attack, and dynamically optimize the energy allocation of local training, consensus, and communications by minimizing the upper bound of the global loss function. Extensive experiments are conducted to evaluate the performance of T-BFL on MNIST, Fashion-MNIST, and Cifar-10 datasets. The experimental results demonstrate that, compared with traditional BFL, T-BFL can achieve up to 56.12% accuracy improvement and $8.6\times $ acceleration for reaching the target learning accuracy under the poisoning attack.