In this paper, we investigate controllable mobile text-to-image generation at scale, considering diverse user preferences. In particular, we observe that, by incorporating visual conditions (e.g.,Canny maps and depth maps) as supplementary inputs alongside text prompts, fine-grained and controllable image generation could be achieved. To this end, we propose a system design for distributed and controllable mobile text-to-image generation by leveraging edge computing. This system can satisfy diverse user-specified quality preferences at reduced transmission cost through effective cooperation of mobile and edge computing. In particular, the proposed system consists of aVisual Condition Engineeringmodule and aDistributed Denoising Controlmodule. Since extensive profiling reveals that different visual conditions affect both generation quality and sensitivity to image encoding parameters, the first module selects the optimal configuration of user-specific visual condition on mobile devices. Key to this module is a Pareto Frontier-based model which subtly balances user-preferred generation quality and transmission efficiency. The second module enables collaborative generation by adaptively distributing denoising tasks between mobile devices and the edge server, according to their available computing resources. At the core of this module is an efficient deep reinforcement learning algorithm designed to optimize the dynamic distribution of denoising tasks. By integrating the deep diffusion model, this algorithm achieves superior action space exploration capabilities while maintaining fast convergence and reliable execution, thereby facilitating enhanced adaptability under variable computing resource scenarios. Extensive experimental results reveal that, the designed system can achieve a reduction in transmission cost by over 90% and enhance user satisfaction by up to 18%, with consistent performance across various diffusion models under diverse resource constraints.

As microservice architecture has become the mainstream of application development, microservice deployment in edge networks is seen as a promising technology to improve the performance of applications requested by users. Most prior work assumes that every application requests totally different microservices, and ignore microservice instances can be shared among user requests. Moreover, these work studies either microservice deployment strategy or task scheduling strategy of user requests to pre-deployed microservices independently, which does not jointly optimize microservice deployment and users’ task scheduling decisions. To address this issue, our work models the mapping between tasks of user requests to microservices, and investigates the joint optimization problem of microservice deployment and task scheduling to reduce the delay of user requests. Since this problem is NP-hard, a two-loop iterative algorithm is proposed to obtain the suboptimal solution efficiently. Numerical results show that our proposed algorithm obtains the lowest sum of delay of requests compared with the other baselines.

In this work, we analyze outgoing longwave radiation (OLR) data from the National Oceanic and Atmospheric Administration (NOAA) to extract thermal infrared (TIR) anomalies over mainland China from 2007 to 2019. These anomalies are statistically significantly correlated with earthquakes of magnitude$\geq 4.5$, 5.0 and 5.5. We then calculate the distance ($d/^{\circ } $) and time lag$(t/\text {days})$between each TIR anomaly and the corresponding earthquake. By examining the distribution of earthquakes in$t \times d$space, our results show that the normalized frequency of earthquakes as a function of distance and time follows a power-law distribution:$f(d,t,M)=\phi (M) *t^{-d_{t}(M)}*d^{-d_{d}(M)}$, where$f(d,t,M)$represents the frequency of earthquakes with magnitude${\geq }M$occurring within the time-space unit$(t, d)$around TIR anomalies. The coefficient$\phi (M)$is negatively correlated with magnitude$[\phi (4.5)=0.31,\phi (5.0)=0.30,\phi (5.5)=0.28]$, while$d_{d}(M)$and$d_{t}(M)$are two positive coefficients$[d_{t}(4.5)=1.59,d_{t}(5.0)=1.58,d_{t}(5.5)=1.50;d_{d}(4.5)=2.76,d_{d}(5.0)=2.66,d_{d}(5.5)=2.59]$. A larger$\phi $suggests that earthquakes are more spatially and temporally concentrated around TIR anomalies, while$d_{d}$and$d_{t}$represent the decay rates of frequency with distance and time, respectively. These findings imply that earthquakes are more likely to occur near TIR anomalies, and the model$f(d,t,M)$could potentially be used to construct a probabilistic earthquake forecasting system based on TIR anomalies.

Microservice deployment strategy gains increasing attentions due to its benefits for reducing the overall computation and transmission delay of applications. Most prior work models the dependencies between microservices of an application as service function chain or directed acyclic graph, and neglects the interaction relationship and the different communication overhead between two interactive microservices. To characterize the features of applications, our work considers the interaction dependency between microservices, and models the communication frequency over each dependency edge. In this paper, we address the joint optimization of microservice deployment and resource allocation decisions to minimize the sum of delay of multiple applications in the distributed edge networks. Since this problem is NP-hard, a coalition game-based algorithm is proposed to obtain the dependency-aware microservice deployment and resource allocation scheme. Numerical results validate the advantages of our approach particularly when microservices have strong interaction.

In large-scale Internet of Thing (IoT), ambient backscatter communication has become a new green technology of concern. At present, the research scenario on ambient backscatter communication mainly focuses on single-transmitter and single-receiver, and a few studies have multiple receivers. And backscatter system is always only allowing one transmitter-receiver pair active. However, the condition multiple transmitters communicate with multiple receivers is essential to achieve giant connection in Internet of Everything. Besides, the system with active multipair can transmit more byte and use energy more effectively. So there is an urgent need of research on multi-transmitter multi-receiver system. We propose an ambient back scatter communication system which allows multi-transmitter and multi-receiver active. This paper is devoted to studying the user association problem in such system. When studying the ambient backscatter communication system, we pay attention to the power limitation. Because the energy collected by the device is relatively small and limit communication performance. In the case of limited link budget, this paper maximized system communication capacity. A priority-based access strategy is proposed in this paper. It arranges priority to the receiver according to the power threshold and link budgets. Then, the strategy handles association problem according to receiver's priority from high to low. Simulation results show that the proposed access strategy has better convergence than the random access strategy. It achieves maximum communication capacity and suboptimal bit rate. What's more, it has low complexity.

With emerging requirement of local low-latency services, Mobile Edge Computing (MEC) is a promising solution to tackle the challenge between urgent demands for computation capability and limited battery energy of mobile devices. Moreover, the sharing property of applications costs waste as for the processing of redundant data, which derives an imperative need for the collaboration among users. In this paper, by leveraging these features, we design a D2D-assisted MEC system for energy efficiency of devices with the consideration of task delay. For sake of energy minimization, a strategy that jointly optimizes resource allocation and tasks offloading assignment is proposed. Further, a low-complexity algorithm is developed to decompose the original problem into two subproblems and get the sub-optimal solution efficiently. Simulation results present the efficient and effective performance of the proposed algorithm with different application parameters. Particularly, it is shown that our proposed algorithm gets 48.41%~90.58% and 37.33%~96.63% improvement of energy consumption than those of non-collaboratively offloading scheme and randomly offloading scheme, respectively.

The main requirement of the computation offloading service is the low service delay, which would correspond to a high Quality of Service (QoS). Recently, many works show that placing virtual resources (e.g., computing resource) in the Mobile Edge Network (MEN) to form a Mobile Edge Computing (MEC) system contributes to a lower service delay. However, due to the limited virtual resource in the MEN and the constraints of the wireless channel condition, only part of users can be served with low enough service delay. Moreover, considering the independent virtual machine (VM) environment on the MEC server in MEN for each user, efficient virtual resources management is needed. In this paper, we propose two computation offloading strategies combined with the virtual resources management to minimize the average service delay, where the virtual resources consists of the computing resources and the storage resources. One of the proposed strategies guarantees the fairness of users while the other one not. Then the optimal deployments of VMs with different strategies are obtained through the simulations. We also find and calculate the optimal proportion of these two kinds of virtual resource when there is only a limited budget for them.