In order to solve the problem that high-priority tasks can not be processed timely and reliably due to the disorder of multi-task and dynamicity in Power Internet of Things(PIoTs), a high-reliability small-area task offloading mechanism with trust evaluation and fuzzy logic(HRSATF) is proposed. First, considering task priority, preemptive priority queue is introduced to ensure high-priority tasks processed preferentially, and minimum resource allocation coefficients(MRACs) of tasks are solved to ensure the effectiveness of offloading. Second, the trust model between smart device(SD) and edge server(ES) is established, and ESs are divided into three priorities based on trust value and computing power by fast non-dominated sorting. Thirdly, fuzzy logic is applied to select target ES when the priorities of task and ES do not match or the ES is offline, and MRAC is used to schedule tasks between SD and ES. Finally, NSGA2 is modified (MNSGA2) to verify the effectiveness of HRSATF in terms of success rate, time, power consumption and load balancing, where success rate is increased by$102.3\%$, and time and power consumption are decreased by$90.7\%$,$89.3\%$at most, respectively.

Smart firefighting utilizes advanced information technologies to enhance disaster prevention and emergency response capabilities. The deployment of Service Function Chains (SFCs) in smart firefighting involves orchestrating a series of Virtual Network Functions (VNFs) as software instances on edge servers, enabling flexible and efficient service provisioning for firefighting applications. Security-enhancing VNFs, such as data encryption modules and firewalls, are deployed to strengthen the security of the smart firefighting system. A significant challenge in smart firefighting SFCs is ensuring strict low-delay and reliability constraints while optimizing energy efficiency. Traditional sequential SFCs introduce substantial end-to-end delays, rendering them unsuitable for delay-sensitive applications. Parallelized SFC addresses this issue by multiple independent VNFs in an SFC to run in parallel. In this paper, the deployment of parallelized SFCs for smart firefighting is formulated as an Integer Linear Programming (ILP) model. Since the NP-hard nature of the ILP model, we proposed a heuristic scheme named EADRC to minimize energy consumption while satisfying reliability and end-to-end delay constraints. Extensive simulations demonstrate the effectiveness of EADRC, achieving significant reductions in energy consumption, as well as improved SFC acceptance ratio and reliability compared to baseline approaches.

Abstract As vehicular technology advances, intelligent vehicles generate numerous computation‐intensive tasks, challenging the computational resources of both the vehicles and the Internet of Vehicles (IoV). Traditional IoV struggles with fixed network structures and limited scalability, unable to meet the growing computational demands and next‐generation mobile communication technologies. In congested areas, near‐end Mobile Edge Computing (MEC) resources are often overtaxed, while far‐end MEC servers are underused, resulting in poor service quality. A novel network framework utilizing sixth‐generation mobile communication (6G) and digital twin technologies, combined with task migration, promises to alleviate these inefficiencies. To address these challenges, a task migration and re‐offloading model based on task attribute classification is introduced, employing a hybrid deep reinforcement learning (DRL) algorithm—Dueling Double Q Network DDPG (QDPG). This algorithm merges the strengths of the Deep Deterministic Policy Gradient (DDPG) and the Dueling Double Deep Q‐Network (D3QN), effectively handling continuous and discrete action domains to optimize task migration and re‐offloading in IoV. The inclusion of the Mini Batch K‐Means algorithm enhances learning efficiency and optimization in the DRL algorithm. Experimental results show that QDPG significantly boosts task efficiency and computational performance, providing a robust solution for resource allocation in IoV.

Although smart healthcare has achieved rapid development and has gained considerable attention from academia and industry, pressing challenges exist, such as resource limitation and stringent security demand. To tackle the issues, a blockchain-based and trust-aware task-offloading strategy (BBTAS) is proposed in this work, whereby task-offloading requests are published as blockchain transactions that are automatically yet securely performed by smart contracts (SCs). An SC is a set of codes implementing predefined rules agreed by the participants involved. When a rule meets certain criteria or triggered by some events or transactions, an SC can perform by itself and generate verifiable results, which can be validated and securely stored in the blockchain. The proposed recommendation filtering method (RFM) and trust penalty measure (TPM)-based trust mechanism is powerful in resisting network insider attacks, and the inherent time-inefficiency problem of blockchain can be alleviated by integrating trust factor, which is theoretically analyzed in this work. To optimize the selection of service node, a utility-based decision-making method (UB-DMM) is proposed, which can maximize the system utility on the basis of fully considering multiple significant performance metrics of the system. Extensive simulations have been carried out to validate the effectiveness and superiority of the proposed measures.

Base station networks are a crucial component of fifth-generation communication systems. Faced with increasing traffic demands and energy consumption, connecting base stations to microgrids is essential for optimizing resource management within green base station networks and reducing their energy consumption and environmental impact. In this situation, existing methods for renewable energy base station resource management lack flexibility and intelligent optimization for energy trading involving multiple base stations. Therefore, this paper proposes an energy trading method based on software-defined networking (SDN) and the nonlinear tangent perturbation-multiagent proximal policy optimization (NTP-MAPPO) algorithm that improves the economic efficiency and renewable energy utilization rate of multi-base-station microgrids. Specifically, we propose a reference scenario for energy trading within a multibase-station microgrid based on SDN, and then model it using game theory to account for energy sharing among different base station microgrids. We express this model as a Markov decision process and solve it using the improved NTP-MAPPO algorithm, which offers an enhanced exploratory performance. Numerical analysis is subsequently used to validate the effectiveness of the proposed method.

Abstract With more and more interconnected smart devices (ISDs) accessing the Internet of Things (IoT), massive and diverse tasks need to be transformed and computed. Mobile edge computing enables the offloading of tasks to nearby servers to enhance processing efficiency, which makes ISDs idle, causing resource waste and failing to satisfy the high real‐time requirements of tasks. Besides, when tasks with different priorities are processed in the order they are generated, it will be difficult for IoT to guarantee a timely response to high‐priority tasks. To address the aforementioned issues, we establish an edge‐terminal‐local architecture by software‐defined networking to centrally manage idle ISD resource (2ISDR). Then the proposed two‐step scheduling mechanism with preemptive priority queue ensures the real‐time responses to high‐priority tasks, and the minimum resource allocation coefficients make offloading effective. Finally, we also propose a modified NSGA‐III algorithm named MNSGA‐III, which is designed to make decisions about offloading and solve resource allocation for tasks, and we correct infeasible solutions by a two‐step correction function to ensure the feasibility of MNSGA‐III. Experimental results show that the method can ensure a timely response to high‐priority tasks and optimize processing time, energy consumption, and economic cost through the utilization of 2ISDR.

Abstract In recent years, mobile edge computing (MEC) has become a promising solution to solve the shortage of technical resources in wireless body area networks (WBANs). However, the existing research work has not fully utilized the communication and computing resources in WBANs scenarios. To solve this problem, a task offloading framework that combined with cellular, WiFi networks and device‐to‐device communications is proposed, that makes full use of resources to improve system reliability. Considering that a single MEC server may be overloaded by a large number of patients, the total task offloading cost and load variance is formulated into a multi‐objective optimization problem (MOOP). A non‐dominated sorting genetic algorithm with smart mobile device ‐ patient connection matrix (NSGA ‐SPCM) to solve the MOOP. In view that an SDM may connect multiple patients at the same time during chromosome crossing, the SPCM can quickly detect the unfeasible gene location and mutate it into viable. Simulation results show that the proposed framework and algorithm have good performance.

Abstract Recently, with the fast development of IoT, Internet of medical things (IoMT) has drawn wide attention from both industry and academia. However, pressing challenges exist in practical implementation of IoMT, such as service provision with stringent latency. To address the challenges, fog computing is generally employed in IoMT systems. However, it raises additional concerns of trust and security. To tackle the issue, the authors introduce the security measure of trust into this work, and a superior heterogeneous stacking ensemble learning measure for trustworthiness prediction (SEM‐TP) of fog services is proposed. Besides, to reduce unnecessary time cost incurred by unimportant features, an efficient voting‐based feature selection (FS) strategy called voting‐based feature selection method is proposed to select significant features, which is based on diverse FS measures. Extensive experiments are conducted and the results show that the proposed framework outperforms commonly used single classifiers and competing stacking models in terms of Accuracy, Precision, Recall, F1‐score, Kappa coefficient, and Hamming distance under different conditions, validating the effectiveness, robustness, and superiority of the proposed trustworthiness prediction and FS methods.

Simultaneous localization and mapping (SLAM) serves as a cornerstone in autonomous systems and has seen exponential growth in its roles, particularly in facilitating advanced path planning solutions. One emerging avenue of research that is rapidly evolving is the incorporation of multi-sensor fusion techniques to enhance SLAM-based path planning. The paper initiates with a thorough review of various sensor types and their attributes before covering a broad spectrum of both traditional and contemporary algorithms for multi-sensor fusion within SLAM. Performance evaluation metrics pertinent to SLAM and sensor fusion are explored. A special focus is laid on the interconnected roles and applications of multi-sensor fusion in SLAM-based path planning, discussing its significance in navigation scenarios as well as addressing challenges such as computational burden and real-time implementation. This paper sets the stage for future developments in creating more robust, resilient, and efficient SLAM-based path planning systems enabled by multi-sensor fusion.

Abstract In recent years, large‐scale satellite networks have been studied emphatically due to its advantages in high throughput and low latency. Many companies and institutions are keen to build large‐scale low earth orbit satellite constellations to provide space‐based Internet services, which poses a great demand for the design of efficient and reliable routing schemes. Three attribute indexes to evaluate the performance of the satellites are proposed. With the Dempster–Shafer theory, the attributes can be fused for the routing decision‐making process. Based on the Message Identification divergence (M‐I divergence) measure method, a Belief Message Importance divergence based Routing algorithm is proposed. By adjusting the characteristic parameter, it can amplify the divergence between distance‐based evidence and others properly. In this way, Belief Message Importance divergence‐based Routing attempts to find the optimal shortest path according to the status of intermediate nodes. Compared with existing satellite routing schemes, the algorithm that is proposed can approach the optimal routing performance, increasing the throughput by 47.2% on average, and decreasing the total delay and packet drop rate by 59.5% and 42.1% on average, respectively.

Convolutional neural network (CNN)-based color image demosaicking methods have achieved great success recently. However, in many applications where the computation resource is highly limited, it is not practical to deploy large-scale networks. This paper proposes a lightweight CNN for color image demosaicking. Firstly, to effectively extract shallow features, a multi-core feature extraction module, which takes the Bayer sampling positions into consideration, is proposed. Secondly, by taking advantage of inter-channel correlation, an attention-aware fusion module is presented to efficiently reconstruct the full color image. Moreover, a feature enhancement module, which contains several cascading attention-aware enhancement blocks, is designed to further refine the initial reconstructed image. To demonstrate the effectiveness of the proposed network, several state-of-the-art demosaicking methods are compared. Experimental results show that with the smallest number of parameters, the proposed network outperforms the other compared methods in terms of both objective and subjective qualities.

Medical sensors are usually attached to or implanted inside patient body. Since sensing results of Body Area Networks (BAN) can directly impact the control of medical equipment, the authenticity and integrity of sensing data is essential for safety of patients. Restricted by the limited resources available to BAN sensors, researchers have referred to Physical Unclonable Function of the nodes to achieve authentication. Existing approaches focus on the authentication between control unit and sensors. Mutual authentication among body sensors has not been carefully studied. In this paper, we propose to design a lightweight mutual authentication mechanism for BAN sensors with physical unclonable functions (PUF). Using control unit as a middle point, a pair of body sensors can establish shared secrets so that authenticity of exchanged data can be protected. The proposed approach does not require sensors to conduct any encryption operations, which suits the restricted resources available to BAN nodes. The analysis shows that the proposed approach has very low overhead and does not introduce new vulnerabilities into the system.

This paper proposes a novel algorithm for compressive sensing (CS) reconstruction of color images. First of all, to better describe color image characteristics, we take inter-channel correlation into consideration and present two types of regularization, including inter-channel correlation-based nonlocal low-rank (ICNL) regularization and inter-channel correlation-based total variation (ICTV) regularization. Afterwards, both regularization terms are incorporated into the minimization problem, and an efficient algorithm is proposed to solve the joint formulation, by using a split-Bregman-based technique. To demonstrate the effectiveness of the proposed approach, four benchmark methods are compared, and the experiments are carried out on several color images with different subrates.

In cloud computing, quality of services is often enforced through Service Level Agreement (SLA) between end users and cloud providers. While SLAs on hardware resources such as CPU cycles or bandwidth can be monitored by low layer sensors, the enforcement of security SLAs stays a very challenging problem. Several high level architectures for security SLAs have been proposed. However, details still need to be filled before they can be deployed. In this paper, we propose to design mechanisms to detect violations of security SLAs. Specifically, we focus on unauthorized accesses to memory pages of a virtual machine and violation of the memory deduplication policies. Through measuring the accumulated memory access latency, we try to derive out whether or not the memory pages have been swapped out and the order of accesses to them. These events will then be compared to access commands issued by the local VM. In this way, unauthorized memory accesses or violation of deduplication policies can be detected. Compared to existing approaches, our mechanisms do not need explicit help from the hypervisor or third parties. Therefore, it can detect SLA violations even when they are initiated by the hypervisor. We implement our approaches under VMWare with Windows virtual machines. Our experiment results show that the VM can effectively detect the violations with small increases in overhead.

This paper introduces a novel reconstruction model with compound regularization to recover compressed-sensed video sequences. For a target frame, the compound regularization consists of total variation (TV) norm of the frame, l1norm of the frame in a certain transform domain, and TV norm of the residual between the frame and its prediction. The first two terms in the compound regularization are used to describe image characteristics, while the third term exploits inter-frame correlation within video sequences. To solve the minimization problem, a new splitting objective function is considered, and it is divided into sub-problems that are easy to solve. In addition, bivariate shrinkage method is integrated into the proposed algorithm so that high quality of reconstruction results can be guaranteed. Experimental results show that the proposed algorithms are substantially superior to state-of-the-art reconstruction methods.

As compressed sensing can capture signal at sub-Nyquist rate, it is suitable to apply multi-view compressed imaging framework in vision sensor networks. The image views in such networks are correlated with each other, and therefore the performance of independent view reconstruction can be further improved by joint reconstruction. In this paper, we propose a joint reconstruction algorithm, where disparity estimation and disparity compensation are used to exploit the correlation between views. The target optimization problem is divided into two sub-problems and they are solved alternately by proximal-gradient method. We show by experiments that, for a given sub-rate, the proposed joint reconstruction scheme outperforms the independent reconstruction in terms of image quality.

H.264 Scalable Video Coding (SVC) extension has spatial scalability which is able to provide various resolution sequences for a single encoded bit-stream. In order to reduce redundancies between different layers, for spatial scalable intra-coded frames, co-located reconstructed 8×8 sub-macroblock in base layer (BL) is up-sampled to predict the marcoblock (MB) in enhancement layer (EL). Unfortunately, simple 1-D poly-phase up-sampling filter used in current SVC isn't cable of achieving ideal result, which limits the performance of inter-layer intra prediction (ILIP). This paper proposes an adaptive optimization method for inter-layer texture up-sampling by applying wiener filter and controlling it at block level. Working as an additional part of ILIP, the proposed method can greatly reduce the prediction error between the original EL signals and the up-sampled BL signals. Experimental results show that, the proposed method achieves bit rate reduction up to 14.25% and PSNR increment up to 0.97 dB when compared with the traditional method in current SVC.

As sensor nodes are typically powered by nonrenewable batteries, energy efficiency is a critical factor in wireless sensor networks (WSNs). Orthogonal modulations appropriate for the energy-limited WSN setup have been investigated under the assumption that batteries are linear and ideal, but their effectiveness is not guaranteed when more realistic nonlinear battery models are considered. In this paper, based on a general model that integrates typical WSN transmission and reception modules with realistic battery models, we derive two battery power-conserving schemes for two M-ary orthogonal modulations, namely pulse position modulation (PPM) and frequency shift keying (FSK), both tailored for WSNs. Then we analyze and compare the battery power efficiency of PPM and FSK over various wireless channel models. Our results reveal that FSK is more power-efficient than PPM in sparse WSNs, while PPM may outperform FSK in dense WSNs. We also show that in sparse WSNs, the power advantage of FSK over PPM is no more than 3 dB; whereas in very dense WSNs, the power advantage of PPM over FSK can be much more significant as the constellation size M increases.