SAC-Based Computation Offloading for Reconfigurable Intelligent Surface-Aided Mobile Edge Networks

In this paper,we concentrate on a reconfigurable intelligent surface(RIS)-aided mobile edge computing(MEC)system to improve the offload efficiency with moving user equipments(UEs).We aim to minimize the energy consumption of all UEs by jointly optimizing the discrete phase shift of RIS,UEs’transmitting power,computing resources allocation,and the UEs’task offloading strategies for local computing and offloading.The formulated problem is a sequential decision making across multiple coherent time slots.Furthermore,the mobility of UEs brings uncertainties into the decision-making process.To cope with this challenging problem,the deep reinforcement learning-based Soft Actor-Critic(SAC)algorithm is first proposed to effectively optimize the discrete phase of RIS and the UEs’task offloading strategies.Then,the transmitting power and computing resource allocation can be determined based on the action.Numerical results demonstrate that the proposed algorithm can be trained more stably and perform approximately 14%lower than the deep deterministic policy gradient benchmark in terms of energy consumption.

An intelligent active probing and trace-back scheme for IoT anomaly detection

Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data collection.An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.

Mobile user forecast and power-law acceleration invariance of scale-free networks

This paper studies and predicts the number growth of China＇s mobile users by using the power-law regression. We find that the number growth of the mobile users follows a power law. Motivated by the data on the evolution of the mobile users, we consider scenarios of self-organization of accelerating growth networks into scale-free structures and propose a directed network model, in which the nodes grow following a power-law acceleration. The expressions for the transient and the stationary average degree distributions are obtained by using the Poisson process. This result shows that the model generates appropriate power-law connectivity distributions. Therefore, we find a power-law acceleration invariance of the scale-free networks. The numerical simulations of the models agree with the analytical results well.

Context-Based Mobile User Interface

Context awareness is increasingly gaining applicability in interactive ubiquitous mobile computing systems. Each context-aware application has its own set of behaviors to react to context modifications. Hence, every software engineer needs to clearly understand the goal of the development and to categorize the context in the application. We incorporate context-based modifications into the appearance or the behavior of the interface, either at the design time or at the run time. In this paper, we present application behavior adaption to the context modification via a context-based user interface in a mobile application. We are interested in a context-based user interface in a mobile device that is automatically adapted based on the context information. We use the adaption tree, named in our methodology, to represent the adaption of mobile device user interface to various context information. The context includes the user’s domain information and dynamic environment changes. Each path in the adaption tree, from the root to the leaf, presents an adaption rule. An e-commerce application is chosen to illustrate our approach. This mobile application was developed based on the adaption tree in the Android platform. The automatic adaption to the context information has enhanced human-computer interactions.

Workload Allocation Based on User Mobility in Mobile Edge Computing

Mobile Edge Computing(MEC)has become the most possible network architecture to realize the vision of interconnection of all things.By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations(sBSs),the execution latency and device power consumption can be reduced on resource-constrained mobile devices.However,computation delay of Mobile Edge Network(MEN)tasks are neglected while the unloading decision-making is studied in depth.In this paper,we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user.We obtain the next possible location through the user's past location information,and receive the next access server according to the grid matrix.Furthermore,the next time task sequence is calculated on the base of the historical time task sequence,and the server is chosen to preload the task.In the experiments,the results demonstrate a high accuracy of our proposed model.

Resource Management in UAV Enabled MEC Networks

Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things(IoT)devices to nearby mobile edge servers,thereby lowering energy consumption and response time for ground mobile users or IoT devices.Integration of Unmanned Aerial Vehicles(UAVs)and the mobile edge computing(MEC)server will significantly benefit small,battery-powered,and energy-constrained devices in 5G and future wireless networks.We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator(OI),the computational capacity(CC),the power consumption,the time duration,and the optimal location planning simultaneously.It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users(MUs)locally.This paper utilizes the k-means clustering algorithm,the interior point method,and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem.According to simulation results,both local and offloading schemes give optimal solution.

Using UAV to Detect Truth for Clean Data Collection in Sensor⁃Cloud Systems

Mobile edge users(MEUs)collect data from sensor devices and report to cloud systems,which can facilitate numerous applications in sensor‑cloud systems(SCS).However,because there is no effective way to access the ground truth to verify the quality of sensing devices’data or MEUs’reports,malicious sensing devices or MEUs may report false data and cause damage to the platform.It is critical for selecting sensing devices and MEUs to report truthful data.To tackle this challenge,a novel scheme that uses unmanned aerial vehicles(UAV)to detect the truth of sensing devices and MEUs(UAV‑DT)is proposed to construct a clean data collection platform for SCS.In the UAV‑DT scheme,the UAV delivers check codes to sensor devices and requires them to provide routes to the specified destination node.Then,the UAV flies along the path that enables maximal truth detection and collects the information of the sensing devices forwarding data packets to the cloud during this period.The information collected by the UAV will be checked in two aspects to verify the credibility of the sensor devices.The first is to check whether there is an abnormality in the received and sent data packets of the sensing devices and an evaluation of the degree of trust is given;the second is to compare the data packets submitted by the sensing devices to MEUs with the data packets submitted by the MEUs to the platform to verify the credibility of MEUs.Then,based on the verified trust value,an incentive mechanism is proposed to select credible MEUs for data collection,so as to create a clean data collection sensor‑cloud network.The simulation results show that the proposed UAV‑DT scheme can identify the trust of sensing devices and MEUs well.As a result,the proportion of clean data collected is greatly improved.

Mobile Phone Users Worse Than Drunk Drivers

在手机逐渐成为现代通讯不可缺少的工具的今天,一边开车一边打手机也日益成为一种时髦。但有多少人认真考虑过这样做的危险呢?万一撞了人是否要追究打手机的责任呢?一些严谨的英国人为今后的官司提供了确凿的依据:手机之猛,猛于酒后驾车。

Learning to detect subway arrivals for passengers on a train

The use of traditional positioning technologies, such as GPS and wireless local positioning, rely on un- derlying infrastructure. However, in a subway environment, such positioning systems are not available for the position- ing tasks, such as the detection of the train arrivals for the passengers in the train. An alternative approach is to exploit the contextual information available in the mobile devices of subway riders to detect train arrivals. To this end, we pro- pose to exploit multiple contextual features extracted from the mobile devices of subway riders to precisely detecting train arrivals. Following this line, we first investigate poten- tial contextual features which may be effective to detect train arrivals according to the observations from 3D accelerome- ters and GSM radio. Furthermore, we propose to explore the maximum entropy （MaxEnt） model for training a train ar- rival detector by learning the correlation between contextual features and train arrivals. Finally, we perform extensive ex- periments on several real-world data sets collected from two major subway lines in the Beijing subway system. Experi- mental results validate both the effectiveness and efficiency of the proposed approach.

Analysis of subscription characteristics and user mobility in different types of MMORPGs

Massively multiplayer online role-playing game（MMORPG） is one of the fastest-growing segments of the online game industry.MMORPG subscription refers to game accounts logged in during a certain period of time.MMORPG user mobility explains the dynamics of subscriber size change.This article explores the subscription characteristics and user mobility in different types of MMORPGs.It is found that subscription characteristics in different types of MMORPGs are marked by dissimilarity.On one hand,the curve of competition-based game subscription is linear,and the stability period is short,therefore it is necessary for the game operators to introduce new versions timely.On the other hand,the gradient rate of subscription in the community-based game is slow at first and then fast,and the stability period is long,hence it will take the game operator a long cycle to launch a new version.The difference of subscription curve is caused by the fact that user mobility in competition-based game is higher than in the community-based game,as attractiveness of community-based game can maintain a longer period due to network externality.The purpose of the study is to help the game operator to understand the development stage and features of the game and to make effective decisions to attract more players.