Text-to-image generation applications based on diffusion models face substantial challenges in computational efficiency and latency, particularly in time-sensitive scenarios, due to the inherently iterative denoising process. Although approximate caching techniques can reduce denoising iterations by reusing intermediate states of diffusion models, existing approaches fail to adequately capture user request behaviors. It is observed that users tend to issue a large number of prompt requests within short time intervals (bursty patterns), and that prompts from the same user often exhibit high similarity over short time periods (temporal locality). In this work, we define and formalize the Intermediate State Selection (ISS) problem to minimize denoising iterations. We further prove the NP-hardness of the ISS problem via a polynomial-time reduction from the Dominating Set problem. We then exploit both characteristics of prompt requests and present EdgeDiffusion, a novel edge-cloud cooperative framework in which the cloud retains image generation, while prompts caching and intermediate state selection are offloaded to edge servers. Specifically, we design an ISS algorithm that optimizes state reuse by leveraging temporal locality and an adaptive caching strategy tailored to bursty patterns. Experimental results on real-world datasets demonstrate that EdgeDiffusion achieves 18.3%-77.3% computational savings over baseline strategies (NIRVANA, qLRU-AC, LRU and LFU), while maintaining 98% quality of images.

The proliferation of Internet of Things applications has driven rapid Mobile Edge Computing (MEC) systems development by various Edge Service Providers (ESPs), creating a competitive computing market. Handling all received tasks from each ESP individually can significantly degrade the service performance of the MEC system. To enhance flexibility in network workload and service coverage, unmanned aerial vehicles (UAVs) have been employed in the MEC system. This paper proposes a potential game based trustful task offloading scheme for the multiple EPSs scenario in UAV-assisted MEC. Specifically, we formulate the Multiple ESPs Task Offloading (METO) problem into a potential game and prove the existence of Nash Equilibrium (NE). To guarantee the security of resource trading among different EPSs, we propose a blockchain-based sharing mechanism that converges to NE. Additionally, a reputation smart contract assesses ESPs' Quality of Service (QoS), influencing task allocation. Extensive simulations show our approach outperforms traditional baselines in maximizing each ESP's utility.

Edge computing has become a new paradigm in response to the increasing demand for time-sensitive and computation-intensive tasks, offering advantages over traditional cloud computing due to its proximity to terminal devices and low transmission latency. Container-based edge computing provides a powerful way to deploy applications and manage resources at the edge of the network. However, optimizing the caching strategy is crucial due to the limited capacity of the edge server, and the startup time of services on edge servers is a crucial consideration when making decisions regarding computation offloading and service caching. In this paper, taking into account container startup time, we formulate an optimization model for the task offloading, container caching, and image caching in the container-based edge computing architectures, which is a nonlinear integer programming (NLIP) problem that is NP-hard. We then propose an algorithm that finds the global optimal solution to this NLIP problem by transforming it into an equivalent linear integer programming problem. Our simulation experiments demonstrate that our proposed algorithm can effectively and fast find a globally optimal solution to the underlying problem and that our model outperforms the existing model without considering the container start-up time.

Mobile crowdsourcing‐based applications, widely popular, exploit the sensing data crowdsourced from smartphone users without putting any burden on the extra cost of data sensing and collection. However, user participation in crowdsourcing incurs resource cost, such as battery, bandwidth, thus it is critical to design incentive mechanisms for propelling user's participation. Previous diverse incentive mechanisms designed for crowdsourcing applications only focus on users' contribution for reward allocation, while ignore another important property, i.e. fairness, users' reward should be corresponding with their cost. In this study, the authors first introduce a new concept called rate of return (RoR), defined as the ratio of received reward and incurred cost for each user, to demonstrate the property of fairness. With the goal of guarantee, the fairness of reward allocation for each user in a multiple‐task system, three algorithms, consensus‐based reward allocation, consensus‐based balanced topology reward allocation and Gossip‐based reward allocation are proposed for the demands of various scenarios, in which the RoR values are synchronised by optimising the fairness function in either centralised or decentralised manner. Through rigorous theoretical analysis and extensive simulations, it is finally demonstrated that the proposed reward allocation algorithms have the good property of fairness with quick convergence.

Mobile opportunistic networks have been exploited for data forwarding and data offloading in many network scenarios, like the mobile edge networks, due to its low cost and high robustness. Existing data forwarding strategies exploit all available network resources to forward data in a ‘best‐effort’ manner. However, they ignore data's heterogeneous delay constraints and may ineffectively assign network resources, resulting in ineffective data forwarding. In this study, the authors improve the existing strategies by proposing a backbone‐based on‐demand data forwarding strategy, which assign network resources to data items on‐demand, according to their delay requirements. Specifically, they first propose an algorithm to extract a backbone structure in the network, where nodes in the backbone structure are responsible for the data forwarding in the whole network. Then, on‐demand data forwarding is formalised as an optimisation problem, which selects the minimum number of paths from the backbone to ensure data are delivered on time with high confidence. To address this problem, a path elimination process and a path selection algorithm are proposed to select highly‐independent paths according to the delay requirements of data. Evaluation results show that the proposed on‐demand strategy can significantly improve the performance of data forwarding in mobile opportunistic networks.

In the practical crowdsourcing systems, there exist many cooperative tasks, each of which requires a group of users to perform together, such as finding the shortest multi-hop path or obtaining the media resources from a set of hosts. In this paper, we tackle the problem of how to truthfully and fairly schedule or allocate sufficient users who join mobile crowd-sourcing applications with their smartphones. Moreover, the cooperation among users is taken into account. Thus, we present a novel Cooperative Crowdsourcing (C2) auction mechanism for crowdsourcing multiple cooperative tasks. C2 contains two parts: user selection and payment computation. In the first part, we first prove that users selection with the minimum social cost is NP hard problem and design a greedy algorithm to achieve near-optimal solution in polynomial time. The other part is that the server determines the payments of selected users to avoid the bidder's cheating behavior through a pricing algorithm that if and only if users honestly bid their cost, they can obtain the maximum utility. Both theoretical analysis and extensive simulations demonstrate that C2 auction achieves not only truthfulness, individual rationality and high computational efficiency, but also low overpayment ratio.

In low-duty-cycle wireless sensor networks, wireless nodes usually have two states: active state and dormant state. The necessary condition for a successful wireless transmission is that both the sender and the receiver are awake. In this paper, we study the problem: How fast can raw data be collected from all source nodes to a sink in low-duty-cycle WSNs? To address this, we define the Minimum Data Collection Delay (MDCD) problem, and give both the lower and upper tight bounds on the minimum delay for data collection when interfering links are eliminated. Furthermore, a novel concept, Virtual Grid Network (VGN) is introduced to successfully convert the MDCD problem into max-flow problem, and present a MDCD algorithm enlightened by the Ford-fulkerson max-flow method, which is able to find an optimal solution in polynomial time and achieves the lower bound. Extensive simulations are conducted and the results show that the proposed MDCD algorithm significantly outperforms the Shortest Path Routing algorithm (up to 32%) and achieves the lower bound.