The rapid growth of IoT devices has led to an increase in computation-intensive and latency-sensitive tasks, making traditional cloud computing insufficient. Cloud-edge-terminal collaboration can enhance offload efficiency by optimizing processing latency, energy consumption, and price cost. However, in real-world networks, computational results often need to be transmitted to multiple users, increasing the offloading complexity. This paper proposes a three-tier collaborative offloading architecture for interacting users, considering constraints such as service caching and various types of resources. The optimization problem of computation latency and price cost is modeled as a Markov Decision Process. To address the problem, we propose a deep reinforcement learning algorithm based on the soft actor-critic framework. Given the discrete-continuous hybrid action space, the algorithm incorporates a dual-head mechanism. After that, transfer learning is incorporated into the training strategy to improve adaptability in dynamic environments. The simulation results demonstrate that the proposed approach outperforms existing performance, convergence, and adaptability methods.

Edge caching alleviates backhaul pressure and enhances video service quality by deploying video content near user devices. However, the limited storage capacity of edge servers struggles to cope with the exponential growth of video data, challenging the delivery of high-quality video services. While both Device-to-Device (D2D) caching and multi-bitrate video technology are promising solutions to relieve the pressure on edge servers, existing research suffers from a key limitation: studies on multi-bitrate caching are predominantly focused on the edge layer, while D2D caching is often limited to single-bitrate scenarios. This isolation neglects the significant benefits of integrating D2D caching with multi-bitrate technology and fails to develop a cross-layer caching strategy for multi-bitrate videos. To address this limitation, we propose a D2D-enhanced Multi-bitrate video cAching straTEgy (MATE) for cloud-edge-device collaborative networks. We formulate a joint service latency and caching replacement cost optimization problem, which can be modeled as a mixed-integer programming problem. To overcome the coupling between caching strategies at the edge layer and device layer, we employ an alternating iterative optimization approach to decouple the original problem into two subproblems. We design an edge-device double-layer joint caching strategy, i.e., a device-layer caching strategy based on greedy algorithm and Lagrange multipliers, and an edge-layer caching strategy based on multi-agent twin delayed deep deterministic policy gradient algorithm. Extensive simulations are conducted to demonstrate the effectiveness of the proposed MATE.

Virtual reality (VR) technology, as a latency-sensitive application, can achieve real-time response to enhance the user’s quality of experience (QoE) on edge devices. However, edge servers, unlike internally managed cloud servers, are prone to hardware failures, software abnormalities, and network attacks. Most prior studies have focused on reducing service delay and improving user coverage through service entity (SE) placement, often neglecting the critical impact of edge server malfunctions on user QoE. In this work, we design a stable service entity placement framework that connects users on faulty servers to collaborative edge servers, ensuring seamless task completion. This framework presents two primary challenges: determining the grouping of collaborative edge services and the placement of SEs. To address these challenges, we introduce a heuristic stable service entity placement (H-STEP) scheme. This scheme first determines the grouping of collaborative edge servers using an iterative search algorithm and then places SEs on suitable edge servers via a fast non-dominated sorting genetic placement algorithm. This approach balances stability benefits with total cost, enhancing the system’s economic benefits. We theoretically analyze the performance of H-STEP and derive the performance gap between H-STEP and the optimal scheme. Extensive real-data-driven simulations demonstrate that H-STEP’s performance closely approximates that of the optimal scheme and surpasses existing schemes.

The increasing prevalence of deep neural networks (DNNs) in industrial artificial intelligence systems (IAISs) promotes the development of industrial automation. However, the growing employment of DNNs also exposes them to various attacks. Recent studies have shown that the data preprocessing process of DNNs is vulnerable to image-scaling attack. Such attacks can craft an attack image, which looks like a given source image but becomes a different target image after being scaled to the target size. The attack images generated by existing image-scaling attacks are easily perceivable to the human visual system, significantly degrading the attack's stealthiness. In this paper, we investigate image-scaling attack from the perspective of signal processing. We unearth that the root cause of the weak deceiving effects of existing image-scaling attack images lies in the introduction of additional high-frequency signals during their construction. Thus, we propose an enhanced image-scaling attack (EIS), which employs adversarial images crafted based on the source (“clean”) images as the target images. Those adversarial images preserve the “clean” pixel information of source images, thereby significantly mitigating the emergence of additional high-frequency signals in the attack images. Specifically, we consider three realistic threat models covering deep models' training and inference phases. Correspondingly, we design three strategies tailored to generate adversarial images with vicious patterns. These patterns are subsequently integrated into the attack images, which can mislead a model with target input size after the necessary scaling operation. Extensive experiments validate the superior performance of the proposed image-scaling attack compared to the original one.

Federated learning (FL) is an effective privacy-preserving mechanism that collaboratively trains the global model in a distributed manner by solely sharing model parameters rather than data from local clients, like mobile devices, to a central server. Nevertheless, recent studies have illustrated that FL still suffers from gradient leakage as adversaries try to recover training data by analyzing shared parameters from local clients. To address this issue, differential privacy (DP) is adopted to add noise to the parameters of local models before aggregation occurs on the server. It, however, results in the poor performance of gradient-based interpretability, since some important weights capturing the salient region in feature maps will be perturbed. To overcome this problem, we propose a simple yet effective adaptive gradient protection (AGP) mechanism that selectively adds noisy perturbations to certain channels of each client model that have a relatively small impact on interpretability. We also offer a theoretical analysis of the convergence of FL using our method. The evaluation results on both IID and Non-IID data demonstrate that the proposed AGP can achieve a good trade-off between privacy protection and interpretability in FL. Furthermore, we verify the robustness of the proposed method against two different gradient leakage attacks.

The significance of RFID-based information collection is becoming increasingly visible as more and more sensor-augmented RFID systems are deployed. Tag information collection aims at efficiently and accurately collecting valuable information from target objects attached with RFID tags. Polling-based information collection can effectively avoid response collisions between RFID tags, and it is widely adopted to accurately inventory tags. However, in the traditional polling mode, a polling vector can only be used to query a tag at a time, which is inefficient. In this paper, we design a double polling mode to improve the utilization of polling vectors, which can simultaneously interrogate a pair of tags. Afterwards, several techniques are developed to reduce the polling vector length. Firstly, the Basic Double Polling-based protocol (BDP) employs double indexes to collect information, which greatly reduces the number of polling vectors. Secondly, the Segmented Double Polling-based protocol (SDP) divides the double indexes into several segments to cut the polling vector length down. Thirdly, the Partial Double Polling-based protocol (PDP) replaces the double index with the size of the empty segment between two adjacent non-zero indexes to further reduce the average polling vector length. Finally, the Differential Double Polling-based protocol (DDP) utilizes the size of the empty segment between two double indexes to improve the utilization of polling vectors. After that, extensive theoretical analyses and simulations are conducted, which demonstrate the feasibility and effectiveness of the proposed protocols.

The widespread deployment of Internet of Things (IoT) devices brings more and more computation intensive or delay sensitive tasks, causing a series of challenges to efficient services. Collaborative edge computing is an effective way to solve them, where the tasks will be processed in the devices, edge servers, and cloud server in parallel. However, the above collaborative paradigm requires dense deployment of base stations (BSs) and consumes lots of energy. To address this problem, in this paper, we introduce energy harvesting technology and construct a collaborative edge computing system powered by hybrid energy. Considering the highly variable task execution delay caused by the resource contention and the unstable energy state, we further introduce the Holt Linear Exponential Smoothing Prediction to predict the delay and then propose an Online Server Control schedule called OSC based on Lyapunov optimization to obtain the optimized offloading decision without the knowledge of the future system state. The extensive simulations illustrate that the proposed OSC outperforms other benchmark ones.

In recent years, wireless rechargeable sensor networks (WRSNs) have gained significant attention in the research community due to the current advancements in wireless power transfer technology. In mobile charger scheduling, previous works primarily emphasized the survival rate of sensor nodes. However, the primary task of a WRSN is to monitor targets in a given area. Therefore, the coverage of targets (CoT) maximization should be the primary objective of mobile charger scheduling. In this paper, we shift the focus to the CoT maximization on-demand charging scheduling problem, and formulate it as a multi-objective optimization problem, aiming to simultaneously enhance the average coverage and energy efficiency. We prove that the problem is NP-hard by reformulating it as a Multiple Travelling Salesman Problem with Deadline. We first propose the multiple chargers scheduling scheme for maximizing coverage of targets called MaxCov, which is designed to optimize the charging scheduling process and improve network performance in terms of coverage. Then, we further propose the multiple chargers scheduling scheme based on requests grouping called MaxCov-RG, which can well balance the trade-off between the performance and computational complexity. Finally, we validate the effectiveness of the proposed schemes via extensive simulations.

Federated learning (FL) is a distributed machine learning paradigm that addresses the challenges of privacy leakage and data silos by collaboratively training the global model through parameter exchange, rather than data, between the central server and local clients. However, recent researches highlight the vulnerability of FL to gradient leakage attacks where adversaries exploit shared parameters from clients to reconstruct sensitive training data. Differential privacy (DP) effectively mitigates this threat by adding noise to shared parameters, yet introduces a trade-off between privacy and accuracy in FL. To better balance the privacy and accuracy, in this paper we propose a staged noise perturbation strategy, called alternating noise permutation (ANP), from a novel perspective. ANP adds Gaussian-distributed random noise to model parameters during the critical learning period of FL, following DP principles. While in non-critical learning period, ANP alternately permutes the noise during odd and even communication rounds, achieving near mutual cancellation and mitigating the negative impact. Experimental results across three datasets and two neural networks under both independent identical distribution (IID) and NonIID scenarios demonstrate that ANP significantly improves classification accuracy and exhibits robustness against gradient leakage attack, ensuring the effectiveness of FL for secure and accurate collaborative model training.

As one of the significant supporting technologies for mobile virtual reality (MVR), computer vision is latency-sensitive and always requires real-time response and accurate object analysis. However, the limited computational resources of mobile devices lead to high service delay and low analysis quality, resulting in poor quality of service (QoS). By placing the edge service entities (SEs) of the video tasks on the edge server close to the mobile users, a satisfactory QoS can be obtained for MVR systems. Most of the previous works are restricted to optimizing QoS through service placement, while ignoring the key impact of the network access point and video frame resolution selections on QoS. In this paper, we propose an edge service entity placement model, which aims to jointly optimize service delay and analysis quality for MVR systems. Specifically, we design an efficient dynamic edge service entity placement scheme (EDSP-Edge) based on the block coordinate descent theory, which dynamically determines the selection strategies of network access points, service entities and video frame resolutions for users to effectively improve the QoS. We theoretically analyze the performance of EDSP-Edge and get the gap between EDSP-Edge and the optimal performance. Finally, extensive real-data driven simulations are conducted to show that the EDSP-Edge performs close to the optimal scheme and achieves at least 22% performance improvement compared with previous works.

With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more challenges to the biometric monitoring of drivers. Edge computing is a new paradigm to solve these challenges by offloading tasks from the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs). However, most of the traditional offloading schedules for vehicular networks concentrate on the edge, while some tasks may be too complex for ESs to process. To this end, we consider a collaborative vehicular network in which the cloud, edge and terminal can cooperate with each other to accomplish the tasks. The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge. We further construct the virtual resource pool which can integrate the resource of multiple ESs since some regions may be covered by multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule for biomedical data processing and analysis in Cloud-Edge-Terminal collaborative vehicular networks called MSCET. The parameters of the proposed MSCET are optimized to maximize the system utility. We also conduct extensive simulations to evaluate the proposed MSCET and the results illustrate that MSCET outperforms other existing schedules.

Convolutional neural network (CNN)-based recommender systems are playing an increasingly significant role in the vigorous development of Industrial Internet of Things, and have made great contributions to analyzing and mining a large amount of data to provide various services for terminal users. However, as the lack of explainability in deep learning, users often have low trust in the system due to their incomprehension of recommendation results. In addition, recommender systems have been facing a serious sparsity problem, and relying only on sparse rating data to learn user preferences and similarities may face malicious recommendation attacks. The abovementioned problems have been hindering the further improvement of recommendation performance. Therefore, in order to effectively alleviate the sparsity problem and meanwhile enhance the trustworthiness, an auxiliary review-based personalized attentional CNN (ARPCNN) is proposed in this article. By applying the proposed personalized word-level attention mechanism and personalized review-level attention mechanism in parallel CNNs, critical words and informative reviews are given high attention weights. Moreover, a user auxiliary network is proposed, which regards the reviews written by kindred spirits who have a trust relationship with the user as auxiliary reviews, and effectively extracts the user’s auxiliary review features, thereby achieving more accurate user modeling to improve the recommendation performance. Extensive experiments are conducted on four real-world datasets, and the results show that the performance of the proposed model is better than that of baselines, which verifies the effectiveness of ARPCNN.

Nowadays, Radio Frequency IDentification (RFID) technology is profoundly affecting all walks of life. Unknown tag identification, as an important service for RFID-enabled applications, aims to exactly collect all EPCs (Electronic Product Code) of unknown tags that are not recorded by the back-end server in the RFID systems. Efficient unknown tag identification is significant to accurately discover the unregistered or newly entering tags in many scenarios, such as warehouse management and retail industry. However, the replies of known tags and the unpredictable behaviors of unknown tags bring serious challenges for accurate and efficient identification of unknown tags. To handle these tough issues, we propose a Compact Unknown Tag identification protocol (CUT) to collect unknown tag EPCs in large-scale RFID systems. Firstly, we introduce a compact indicator vector to simultaneously label unknown tags and deactivate known tags. Then the unknown tags are instructed to reply their EPCs via another compact reply based indicator vector. In each indicator vector, the amount of expected empty and singleton slots is increased to greatly improve the labeling, deactivation and collection efficiency. After that, we validate the effectiveness of proposed CUT protocol by extensive theoretical analyses and simulations. The simulation results demonstrate that CUT protocol outperforms the state-of-the-art one.

Mobile crowdsensing (MCS) is an emerging paradigm that leverages pervasive smart terminals equipped with various embedded sensors to collect sensory data for wide applications. As the sensing scale increases in MCS, the design of efficient task allocation becomes crucial. However, many prior task allocation schemes, which ignore the time for task-performing, are not applicable to the scenario where mobile users with limited time budgets are able to undertake multiple sensing tasks. In this article, we focus on the task allocation in time dependent crowdsensing systems and formulate the time dependent task allocation problem, in which both the sensing duration and the user's sensing capacity are considered. We prove that the task allocation problem is NP-hard and propose an efficient task allocation algorithm called optimized allocation scheme of time-dependent tasks (OPAT), which can maximize the sensing capacity of each mobile user. The extensive simulations are conducted to demonstrate the effectiveness of the proposed OPAT scheme.