Remote data integrity checking (RDIC) enables a storage server, say cloud to prove verifier that it is actually storing owner's honestly. To date, number of RDIC protocols have been proposed in the literature, but most constructions suffer from issue complex key management, is, they rely on expensive public infrastructure (PKI), which might hinder deployment practice. In this paper, we propose new construction identity-based (ID-based) protocol by making use key-homomorphic cryptographic...

The rapidly expanding number of Internet Things (IoT) devices is generating huge quantities data, but public concern over data privacy means users are apprehensive to send a central server for machine learning (ML) purposes. easily changed behaviors edge infrastructure that software-defined networking (SDN) provides makes it possible collate IoT at servers and gateways, where federated (FL) can be performed: building model without uploading the server. FedAvg an FL algorithm which has been...

Autonomous driving has so far received numerous attention from academia and industry. However, the inevitable occlusion is a great menace to safety reliable driving. Existing works have primarily focused on improving perception ability of single autonomous vehicle (AV), but problem brought by occlusions remains unanswered. In this paper, we propose multi-tier task offloading framework with collaborative computing approach, where an AV able achieve comprehensive concerned region-of-interest...

Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy increasing demands computation-intensive and latency-sensitive applications for intelligent transportation. The existing content caching schemes, when used in networks, face two distinct challenges: 1) Vehicles connected an server keep moving, making popularity varying hard predict. 2) Cached is easily out-of-date since each vehicle stays area short duration. To address these...

Vehicular edge computing (VEC) is a new paradigm that has great potential to enhance the capability of vehicle terminals (VTs) support resource-hungry in-vehicle applications with low latency and high energy efficiency. In this article, we investigate an important computation offloading scheduling problem in typical VEC scenario, where VT traveling along expressway intends schedule its tasks waiting queue minimize long-term cost terms tradeoff between task consumption. Due diverse...

MEC is an emerging paradigm that utilizes computing resources at the network edge to deploy heterogeneous applications and services. In system, mobile users enterprises can offload computation-intensive tasks nearby reduce latency save energy. When make offloading decisions, task dependency needs be considered. Due NP-hardness of problem, existing solutions are mainly heuristic, therefore have difficulties in adapting increasingly complex dynamic applications. To address challenges...

Data integrity, a core security issue in reliable cloud storage, has received much attention. auditing protocols enable verifier to efficiently check the integrity of outsourced data without downloading data. A key research challenge associated with existing designs is complexity management. In this paper, we seek address complex management checking by introducing fuzzy identity-based auditing, first such an approach, best our knowledge. More specifically, present primitive where user's...

Edge computing has great potential to address the challenges in mobile vehicular networks by transferring partial storage and functions network edges. However, it is still a challenge efficiently utilize heterogeneous edge architectures deploy large-scale IoV systems. In this article, we focus on collaborations among different anchors propose novel collaborative framework, called CVEC. Specifically, CVEC can support more scalable services applications both horizontal vertical collaborations....

Federated Learning (FL) is an emerging approach for collaboratively training Deep Neural Networks (DNNs) on mobile devices, without private user data leaving the devices. Previous works have shown that non-Independent and Identically Distributed (non-IID) harms convergence speed of FL algorithms. Furthermore, most existing work measures global-model accuracy, but in many cases, such as content-recommendation, improving individual User model Accuracy (UA) real objective. To address these...

Fog computing (FC) is an emerging distributed platform aimed at bringing computation close to its data sources, which can reduce the latency and cost of delivering a remote cloud. This feature related advantages are desirable for many Internet-of-Things applications, especially sensitive mission intensive services. With comparisons other technologies, definition architecture FC presented in this paper. The framework resource allocation reduction combined with reliability, fault tolerance,...

Cyber-physical social systems (CPSS) is an emerging complicated topic which a combination of cyberspace, physical space, and space. Many problems in CPSS can be mathematically modelled as optimization problems, some them are multi-objective (MOO) (MOPs). In general, the MOPs difficult to solve by traditional mathematical programming methods. High performance computing with much faster speed required address these issues. this paper, kind high approaches, evolutionary (EMO) algorithms, used...

Driven by the tremendous application demands, Internet of Things (IoT) systems are expected to fulfill computation-intensive and latency-sensitive sensing computational tasks, which pose a significant challenge for IoT devices with limited ability battery capacity. To address this problem, edge computing is promising architecture where can offload their tasks servers. Current works on task offloading often overlook unique topologies schedules from devices, leading degraded performance...

Mobile Edge Computing (MEC) is a new computing paradigm with great potential to enhance the performance of user equipment (UE) by offloading resource-hungry computation tasks lightweight and ubiquitously deployed MEC servers. In this paper, we investigate problem decision resource allocation among multiple users served one base station achieve optimal system-wide utility, which defined as trade-off between task latency energy consumption. Mobility in process considered optimization. We prove...

Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks cloud or edges, computation offers a feasible solution execute DNNs. However, energy-efficient DNN based systems with deadline constraints cloud-edge environments is still open...

Network slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses huge challenge management due virtualized infrastructure stringent quality-of-service requirements. Digital twin (DT) technology paves way achieving cost-efficient performance-optimal management, through creating virtual representation of slicing-enabled networks digitally simulate its behaviors...

The last few decades have witnessed an explosive growth of the Internet-of-Things (IoT) systems, which provide ubiquitous sensing and computing services. When adopted in industrial manufacturing environments, IoT is referred to as (IIoT), has attracted increasing research attention. Energy efficiency one most important topics green IIoT, 1) limited resource can significantly affect lifetime IIoT systems 2) massive sensors, devices, machines keep consuming a considerable amount energy, carbon...

Edge computing is an emerging promising paradigm that brings computation and storage resources to the network edge, hence significantly reducing service latency traffic. In edge computing, many applications are composed of dependent tasks where outputs some inputs others. How offload these a vital challenging problem which aims determine placement each running task in order maximize Quality-of-Service (QoS). Most existing studies either design heuristic algorithms lack strong adaptivity or...

In device to (D2D) aided mobile edge computing (MEC) networks, by implementing content caching and D2D links, the server nearby devices can provide task offloading platforms. For parallel tasks, proper decisions on help reduce delay energy consumption. However, what is often ignored in previous works joint optimization of caching. this paper, we aim find optimal strategies, so as minimize The minimization problem formulated a multi-objective problem, concerning both offloading. an integer...

Internet-of-Things (IoT) are increasingly operating in the zero-trust environments where any devices and systems may be compromised hence untrusted. In addition, data collected by sent from IoT shared with processed edge computing systems, order to reduce reliance on centralized (cloud) servers, leading further security privacy issues. To cope these challenges, this paper proposes an innovative blockchain-enabled information sharing solution context guarantee anonymity yet entity...

Edge caching is a promising approach to reduce duplicate content transmission in Internet-of-Vehicles (IoVs). Several Reinforcement Learning (RL) based edge methods have been proposed improve the resource utilization and backhaul traffic load. However, they only obtain local sub-optimal solution, as neglect influence from environments by other agents. This paper investigates strategies with consideration of delivery cache replacement exploiting distributed Multi-Agent (MARL). A hierarchical...

Vehicular Edge Computing (VEC) is the transportation version of Mobile (MEC) in road scenarios. One key technology VEC task offloading, which allows vehicles to send their computation tasks surrounding Roadside Units (RSUs) or other for execution, thereby reducing delay and energy consumption. However, existing offloading schemes still have various gaps face challenges that should be addressed because with time-varying trajectories need process massive data high complexity diversity. In this...

With the potential of reshaping future mobility, connected and autonomous vehicles (CAVs) offer a opportunity to transform world with significant social, industrial, economic benefits. One major challenge CAVs is driving safety while meeting occlusions. To resolve this problem, researches on various types sensor technologies inference models have been advanced in recent years. However, sensory data collected by an individual vehicle insufficient occlusion-aware driving. Alternatively, as...