With the development of edge computing and sharing economy, more services and contents are decentralized to the edge of the network. At present, most existing studies combine the content caching with service offloading mechanisms from the perspective of edge services to optimize network performance. However, few studies focus on what strategies the content providers (CPs) can implement to maximize their utilities. In order to motivate content providers to be more willing to share their contents, it is necessary to study the incentive mechanisms in the edge content market so that content audiences (CAs) can pay reasonable prices for high-quality contents. In this article, we first characterize the content supply and demand model, the CPs' cost and utilities in edge content market by considering both the time sensitivity of edge content and the heterogeneity of CAs. Furthermore, according to the edge content market environment characteristics, we divide the edge content market into a monopoly environment, where the content is only provided by a certain CP, and an open environment, where content services are provided by multiple CPs. In the monopoly environment, we establish a two-stage Stackelberg game to design the incentive mechanism. In the open environment, also we formulate the competitive behavior among CPs as a stochastic game. Since the CPs are not aware of each other's strategies and environmental uncertainty, the reinforcement learning-based algorithm (RLIMO) is used to derive the pricing strategy of CP. Finally, numerical results show that the proposed incentive mechanisms are reliable and effective.

The proliferation of portable embedded cameras makes it easy for mobile users to collaboratively collect environmental sensing videos in network-underdeveloped or disaster-affected zones. Compared with in other scenes, the video upload in disaster areas is more challenging due to intermittent wireless connections and moving transmission destinations. Deduplication (redundancy elimination) and cooperative video transmission are two effective ways to ensure timely video collection in damaged networks. However, deduplication in mobile crowd sensing (MSC) is primarily performed on texts and images. Furthermore, most of deduplication technologies require global information and are separated from video transmission routing. To solve such problems, this paper proposes a collaborative upload method of sensing videos, which performs the local video deduplication without excessive comparisons and feature exchanges. Also, we combine the two-round deduplication with the content-aware dynamic routing to avoid the propagation of redundant items caused by the content-free video routing. Besides, we integrate a novel mutual-assisted mechanism into our method to motivate relay cooperation and achieve load balance. We formulate the deduplication-supported collaborative video upload as a multi-stage decision problem. To tackle the time-varying destinations and the local deduplication during transmission in the decision problem, we develop a stepwise Mutual-Assisted Video Upload Algorithm (MAVUA) to route videos and remove duplicates. Extensive numerical validations are conducted to compare MAVUA with the existing algorithms. The numerical results demonstrate that MAVUA can save roughly 11% transmission time and achieve 80% load balancing improvement.

With the prosperity of cloud computing and video services, the demand for network resources has increased dramatically, leading to the remarkable growth in the amount of network energy consumption, a key factor restricting the development of data centers. Numerous existing works reduce network energy consumption by optimizing data transmission, but they ignore the energy consumption for data transmission preparation, such as activating devices and installing rules. In this paper, we jointly optimize device activation, rule installation and data transmission to minimize network energy consumption. Specifically, we first formulate the minimization problem of the energy consumption of device activation, rule installation, and data transmission. We then prove that it is NP-complete to get the optimal solution of the minimization problem, furthermore, we propose a heuristic algorithm to plan the path with minimum network energy consumption for each flow. The simulation results show that the energy consumption of our algorithm is close to the optimal solution solved by Gurobi, and our algorithm has lower complexity. Compared with the state-of-the-art algorithm, our algorithm always consumes less energy and has shorter flow completion time.

In recent years, the rapid development of urbanization has posed enormous challenges to transportation, security management, quality of life, and so on, which makes the research and development of smart city important. As an information-driven project, strong communication infrastructures are required for connecting smart objects, people, and sensors together. As a consequence, the optimization of the wireless network is the primary premise to support and improve the quality of smart services. The challenge lies in the difficulty to achieve the quantitative description of networks due to the complexity and variability of wireless environments. It is too coarse-grained to express characteristics of networks simply by the strength of received signals through mobile devices. To extract more fine-grained network characteristics, we dig into the PHY layer collecting CSI for network descriptions and extract three signal characteristics, i.e., Rician-K, delay spread and spectral width, from real-world wireless channels. The K-means clustering algorithm is implemented in this article for good performance of network partitioning, and experiments are conducted based on the data sets collected from real scenarios. Simulation results verify the feasibility of our scheme. © 1986-2012 IEEE.

Accurate objects detection in remote sensing images is very important, because security, transportation, and rescue applications in military and civilian fields require fully analyzing and using these images. To address the problem that many small-sized objects in remote sensing images are difficult to detect, this paper proposes an improved S(2)ANET-SR model based on S(2)A-NET network. In this paper, the original and reduced image are fed to the detection network at the same time, and then a super-resolution enhancement module for the reduced image is designed to enhance the feature extraction of small objects, after that, the perceptual loss and texture matching loss is proposed as supervision. Extensional experiments are conducted to evaluate the performance on the general remote sensing dataset DOTA, and the results show that our proposed method can achieve 74.47% mAP, which is 0.79% better than the accuracy of S(2)A-NET. (C) 2021 Elsevier B.V. All rights reserved.

Federated Learning (FL) has emerged as a privacy-preserving distributed machine learning paradigm. To motivate data owners to contribute towards FL, research on FL incentive mechanisms is gaining great interest. Existing monetary incentive mechanisms generally share the same FL model with all participants regardless of their contributions. Such an assumption can be unfair towards participants who contributed more and promote undesirable free-riding, especially when the final model is of great utility value to participants. In this paper, we propose a Fairness-Aware Incentive Mechanism for federated learning (FedFAIM) to address such problem. It satisfies two types of fairness notion: 1) aggregation fairness, which determines aggregation results according to data quality; 2) reward fairness, which assigns each participant a unique model with performance reflecting his contribution. Aggregation fairness is achieved through efficient gradient aggregation which examines local gradient quality and aggregates them based on data quality. Reward fairness is achieved through an efficient Shapley value-based contribution assessment method and a novel reward allocation method based on reputation and distribution of local and global gradients. We further prove reward fairness is theoretically guaranteed. Extensive experiments show that FedFAIM provides stronger incentives than similar non-monetary FL incentive mechanisms while achieving a high level of fairness.

With the rapid development of information technologies, industrial Internet has become more open, and security issues have become more challenging. The endogenous security mechanism can achieve the autonomous immune mechanism without prior knowledge. However, endogenous security lacks a scientific and formal definition in industrial Internet. Therefore, firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost analysis. Secondly, the endogenous security innovation mechanism is clearly defined. Thirdly, an improved clone selection algorithm based on federated learning is proposed. Then, we analyze the threat model of the industrial Internet identity authentication scenario, and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge proof. We conduct identity authentication experiments based on two types of blockchains and compare their experimental results. Based on the experimental analysis, Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial Internet. Internet of Things Application (IOTA) public blockchain can be used for data aggregation analysis of Internet of Things (IoT) edge nodes. Finally, we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.

The widespread use of the Internet of Things (IoTs) and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band settings. Within such systems, all participants related to commercial and industrial systems must communicate and generate data. However, due to the small storage capacities of IoT devices, they are required to store and transfer the generated data to third-party entity called "cloud", which creates one single point to store their data. However, as the number of participants increases, the size of generated data also increases. Therefore, such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security, privacy, and performance. To address these challenges, Federated Learning (FL) has been proposed as a reasonable decentralizing approach, in which clients no longer need to transfer and store real data in the central server. Instead, they only share updated training models that are trained over their private datasets. At the same time, FL enables clients in distributed systems to share their machine learning models collaboratively without their training data, thus reducing data privacy and security challeges. However, slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed system. Furthermore, these unnecessary communication rounds make the system vulnerable to security and privacy issues, because irrelevant model updates are sent between clients and servers. Thus, in this work, we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song (CKKS) to encrypt model parameters for their local information privacy-preserving function. The proposed solution uses the impetus term to speed up model convergence during the model training process. Furthermore, it establishes a secure communication channel between IoT devices and the server. We also use a lightweight secure transport protocol to mitigate the communication overhead, thereby improving communication security and efficiency with low communication latency between client and server.

With the rapid advance of wireless sensing techniques, it becomes possible to provide a fine-grained user activity tracking service at home and office. Such a technique is of broad applications in various domains such as personal activity diary, elderly care, and customized services. For example, several radio frequency (RF) based sensing systems were recently proposed for human activity recognition. However, most of them focused on specific scenarios and suffered from interference caused by other users and wireless devices. In this work, we propose Back-Guard, a backscattering-based sensing system that achieves accurate and non-intrusive user activity recognition and further user identification/authentication. Back-Guard carefully examines the backscatter spectrogram data and extracts high-level features from both spatial and temporal domains. Leveraging the parallel attention based deep learning model, our system can discriminate different motions and users accurately and robustly in various situations. We implemented a prototype system and collected data from 25 users for more than 2 months. Extensive experiments demonstrate that Back-Guard achieves 93.4% activity recognition accuracy and 91.5% user identification accuracy, respectively. In particular, Back-Guard can also tackle multiple user scenarios, which has little accuracy reduction when the users are separated, e.g., by around 2 meters.

Mechanical vibration monitoring plays a critical role in today's industrial Internet of Things (IoT) applications. Existing invasive solutions usually directly attach sensors to the target, which may affect the operations of delicate devices. Non-invasive video-based approaches incur poor performance in low light conditions, and laser-based ones have difficulties to monitor multiple objects simultaneously. In this work, we propose RF-Ear(+), a contactless vibration sensing system using Commercial off-the-shelf (COTS) RFID. RF-Ear(+) could accurately monitor the mechanical vibrations of multiple devices using a single tag: it can clearly tell which object is vibrating at what frequency without attaching tags on any device. RF-Ear(+) can measure the vibration with a frequency up to 987 Hz at a mean error rate of 0:4%. We further employ each device's unique vibration fingerprint to identify and differentiate devices of exactly the same model. What's more, RF-Ear(+) can detect the rotating machinery faults based on the constructed spectrogram, which achieves 98% accuracy on 6 types of states. To improve the computation efficiency, we optimize the input of model in both time and frequency domains, and thus enable deployment on low-cost edge devices successfully. Comprehensive experiments conducted in lab and wild demonstrate the effectiveness of our system.