Collaborative Edge Research Articles

Collaborative Edge Mobile Model for IoT Emergency Management

Traffic congestion has resulted in the loss of human lives globally as a result of failing to transfer accident victims, critical patients, medical equipment, and medications on time. With the ever-increasing volume of vehicular traffic, the convergence of Edge-based approaches has emerged as a potential platform for a Collaborative Edge Traffic Management Model. The collaborative edge model focuses on three objectives reducing the latency of the ambulance arrival to the accident, delivering the ambulance to the closest hospital and taking into consideration the patient case severity by a set of collaborating edges communicating to facilitate and manage this process.

Incentive-Driven Task Allocation for Collaborative Edge Computing in Industrial Internet of Things

Residing in the proximity of end devices, edge computing (EC) holds great potential to provide low-latency, energy-efficient, and secure services, which has become an essential part of the Industrial Internet of Things (IIoT). To future accelerate task processing and reduce service latency, this work proposes an online incentive-driven task allocation scheme to stimulate collaborative computing among EC servers and IIoT devices. To better serve dynamic and heterogeneous tasks in terms of profiles and importance, EC servers (including neighboring servers) and IIoT devices with available resources can cooperatively process the tasks. Considering the heterogeneity of computing resources in edge servers and industrial IoT devices, we formulate a task allocation problem, which is NP hard. An online incentive-driven task allocation algorithm is proposed to this NP-hard problem, which will optimize task assignment strategies to maximize system utility, promote faster computing, and stimulate collaborative computing. Theoretical analyses show that the online incentive algorithm can satisfy incentive compatibility, individual rationality, computational efficiency, and feasibility. The results demonstrate that the proposed task allocation scheme with collaborative EC achieves superior performance and effectiveness.

Personalized Vehicular Edge Computing in 6G

Sixth-generation (6G) space-air-ground integrated vehicular networks (SAGIVNs) are expected to provide customized edge computing services for moving vehicles (MVs). In this article, by integrating 6G SAGIVNs and edge computing, we propose a secure and personalized vehicular edge computing framework in 6G to satisfy the diversified requirements of MVs. In the framework, we first develop a collaborative edge computing mechanism, where each 6G infrastructure (e.g., satellite, drone, and base station) cooperates with parked vehicles to provide services for MVs in order to improve the efficiency of the edge computing services. Then, considering the security issues, a smart-contract-based secure collaboration mechanism is designed to establish a reliable transaction environment for edge computing in 6G. Next, based on the personalized service requirements of MVs and the available resources in 6G SAGIVNs, the resource allocation strategy of each infrastructure and the competitiveness of different infrastructures are discussed to provide MVs with the optimal personalized service. After that, compared with the conventional schemes, a case is studied to evaluate the effectiveness of the proposed framework. Finally, we outline open research topics to identify future research opportunities and directions.

Deadline-Aware Task Scheduling for IoT Applications in Collaborative Edge Computing

Collaborative edge computing (CEC) is a promising technology for supporting latency-sensitive Internet of Things (IoT) applications by distributing computation tasks among edge nodes. However, the edge resources are limited compared to the traditional cloud. Therefore, it is important to decide when and where to execute tasks from IoT applications to meet the latency requirements of the applications. In this study, we propose a task scheduling algorithm in the CEC environment, which considers the priority (i.e., deadline) of tasks and network flows with the aim of reducing the deadline miss ratio (DMR) of IoT applications. The simulation results demonstrate that the proposed algorithm can significantly reduce the DMR of other benchmark methods.

Constrained Multiobjective Optimization for IoT-Enabled Computation Offloading in Collaborative Edge and Cloud Computing

Internet-of-Things (IoT) applications are becoming more resource-hungry and latency-sensitive, which are severely constrained by limited resources of current mobile hardware. Mobile cloud computing (MCC) can provide abundant computation resources, while mobile-edge computing (MEC) aims to reduce the transmission latency by offloading complex tasks from IoT devices to nearby edge servers. It is still challenging to satisfy the quality of service with different constraints of IoT devices in a collaborative MCC and MEC environment. In this article, we propose three constrained multiobjective evolutionary algorithms (CMOEAs) for solving IoT-enabled computation offloading problems in collaborative edge and cloud computing networks. First of all, a constrained multiobjective computation offloading model considering time and energy consumption is established in the mobile environment. Inspired by the push and pull search framework, three CMOEAs are developed by combing the advantages of population-based search algorithms with flexible constraint handling mechanisms. On one hand, three popular and challenging constrained benchmark suites are selected to test the performance of the proposed algorithms by comparing them to the other seven state-of-the-art CMOEAs. On the other hand, a multiserver multiuser multitask computation offloading experimental scenario with a different number of IoT devices is used to evaluate the performance of three proposed algorithms and other compared algorithms as well as representative offloading schemes. The experimental results of the benchmark suites and computation offloading problems demonstrate the effectiveness and superiority of the proposed algorithms.

The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT)

The Internet of Things (IoT) tsunami, public embracement, and the ubiquitous adoption of smart devices are affecting virtually every industry, directly or indirectly. The success of the current and future landscape of IoT and connected devices requires service provision characterized by scalability, ubiquity, reliability, and high-performance, among others. In order to achieve this attribution, the integration of IoT and Cloud Computing (CC), known as cloud IoT, has emerged as a new paradigm providing advanced services specific to aggregating, storing, and processing data generated by IoT. While the convergence of IoT and Cloud brings opportunities, it suffers from specific limitations such as bandwidth, latency, and connectivity. The increasing need for supporting interaction between cloud and IoT led to Edge and Fog Computing (FC) in which computing and storage resources are located not only in the cloud but also at the edges near the source of data. The hierarchical and collaborative edge–fog–cloud architecture brings tremendous benefits as it enables us to distribute the intelligence and computation – including Artificial Intelligence (AI), Machine Learning (ML), and big data analytics – to achieve an optimal solution while satisfying the given constraints e.g., delay-energy tradeoff. Due to the hierarchical, cross-layer, and distributed nature of this model, achieving an osmotic and effective convergence of IoT, edge, fog, and cloud computing requires overcoming many challenges with respect to design and implementation, as well as deployment and evaluation. This paper provides a comprehensive insight into the edge-fog-cloud computing paradigm by providing a blend of discussions on all important aspects of the underlying technologies to offer opportunities for more holistic studies and to accelerate knowledge acquisition. To gain a deep understanding of edge–fog–cloud, we will begin this paper by providing an in-depth tutorial and presenting the main requirements, state-of-the-art reference architectures, building blocks, components, protocols, applications, and other similar computing paradigms, including their similarities and differences. Following this, a holistic reference architecture for edge–fog–cloud IoT is presented and the major corresponding design and deployment considerations (e.g., service models, infrastructure design, provisioning, resource allocation, offloading, service migration, performance evaluation, and security concerns) are discussed. Next, we will take a look at the role of privacy-preserving, distributed, and collaborative analytics as well as the interaction between edge, fog, and cloud computing. Finally, we will overview the main challenges in the field of edge–fog–cloud computing that need to be tackled to realize the full potential of IoT.

Blockchain-Based Trust Edge Knowledge Inference of Multi-Robot Systems for Collaborative Tasks

Collaborative inference helps robots to complete large tasks with mutual collaboration in edge-assisted multi-robot systems. It is challenging to provide trusted edge collaborative inference in the presence of malicious nodes. In this article, we propose a blockchain-based collaborative edge knowledge inference (BCEI) framework for edge-assisted multi-robot systems. First, we formulate the inference process at the edge as the collaborative knowledge graph construction and sharing model. Second, to guarantee the trust of knowledge sharing, an efficient knowledge-based blockchain consensus method is presented. Finally, we conduct a case study on the emergency rescue application to evaluate the proposed framework. The experiment results demonstrate the efficiency of the proposed framework in terms of latency and accuracy.

Distributed Learning-Based Cache Replacement in Collaborative Edge Networks

In this letter, distributed content caching is considered in a collaborative edge caching system where a central infostation broadcasts information about the content migration to all edge nodes. Each edge node is equipped with a small base station for fetching the requested contents from its neighbouring edge nodes and with a storage unit for caching the contents. To achieve efficient content caching and collaboration, an online decision-making problem of maximizing the cache-hit-ratio whilst ensuring the end users’ quality-of-experience (QoE) is formulated. Furthermore, it is assumed that the content popularity knowledge is not available in advance and has to be leaned regularly over time in an online manner. To this end, we propose a distributed online content-popularity leaning algorithm based on Thompson sampling for updating the cache storage units in real-time. Simulation results demonstrate that the proposed algorithm outperforms the benchmarks in terms of the cache-hit-ratio and QoE in the long run.

Collaborative Edge Computing for Social Internet of Vehicles to Alleviate Traffic Congestion

Edge computing in vehicles is emerging as an essential candidate for the Internet of Vehicles (IoV) to improve traffic efficiency. The proliferation of IoV pushes the horizon of edge computing. The social features and connections among vehicles are significant for traffic efficiency solutions. However, it is quite challenging to perform collaborative edge computing (CEC) for social IoV systems because of network heterogeneity, vehicle mobility, user selfishness, privacy, and so on. This article focuses on the CEC, in the social IoV system to alleviate urban traffic congestion. Recent research reveals that intelligent traffic lights control through city-wide mobile edge computing (MEC) servers can reduce vehicles’ average waiting time at signal intersections. This article has focused on a CEC-based traffic management system (CEC-TMS) to reduce the average waiting time. It utilizes multiagent-based deep reinforcement learning (DRL) for the MEC servers that interact with IoV and traffic lights to generate dynamic green waves at congested intersections. Results demonstrate the effectiveness of the proposed system under the paradigm of multiagent DRL.

A-DECS: Enhanced collaborative edge–edge data storage service for edge computing with adaptive prediction

Due to the rapid development of IoT and 5G technologies, end-users of the Internet are generating a sea of data, which burdens the cloud cores and data centers. The urgent traffic demand causes a bad quality of experience in the traditional reactive networks. With the improvement of hardware, the edge of the Internet can share some responsibility for cloud servers. In small-scale scenarios, offload computing and storage tasks to edge nodes can help improve the user experience and utilize resources of the edge networks. In this paper, we propose a collaborative edge–edge data storage service with adaptive prediction called A-DECS based on our previous work DECS (Zhou and Chen, 2020) in local area scenarios. It manages nodes in the edge network and fully utilizes the limited resources in the cluster. A-DECS picks the most appropriate node to offload tasks. It can also proactively replicate popular data and generate forwarding rules in advance. A-DECS can reduce the read latency of cold-start and sudden peak flow situations. We compare it to state-of-the-art research. This experiment result proves that A-DECS is more suitable for edge clusters due to its lower latency and better resource utilization.

A trusted and collaborative framework for deep learning in IoT

More and more Internet of Things (IoT) applications provide intelligent services, with the development of artificial intelligence algorithms, such as deep reinforcement learning. However, along with the trend of utilizing a large model with high accuracy in AI-enabled IoT, resource-limited IoT devices are difficult to handle these large-scale models with high response latency. By collaborating with edge nodes, the devices could respond quickly. However, IoT applications contain a large amount of user privacy, and pushing data to others might lead to privacy leakage. Inspired by the trusted execution environment technology, we propose a framework that enables trusted collaboration for future AI-enabled IoTs, in terms of computation security and transmission security, where the data could be processed in an isolated environment, and two approaches are proposed to ensure the security in data transmission. Experimental results show that our framework provides flexible and dynamic collaboration with low overhead and can effectively support collaborative edge intelligence.

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

The Social Internet of Things (SIoT) now penetrates our daily lives. As a strategy to alleviate the escalation of resource congestion, collaborative edge computing (CEC) has become a new paradigm for solving the needs of the Internet of Things (IoT). CEC can provide computing, storage, and network connection resources for remote devices. Because the edge network is closer to the connected devices, it involves a large amount of users’ privacy. This also makes edge networks face more and more security issues, such as Denial-of-Service (DoS) attacks, unauthorized access, packet sniffing, and man-in-the-middle attacks. To combat these issues and enhance the security of edge networks, we propose a deep learning-based intrusion detection algorithm. Based on the generative adversarial network (GAN), we designed a powerful intrusion detection method. Our intrusion detection method includes three phases. First, we use the feature selection module to process the collaborative edge network traffic. Second, a deep learning architecture based on GAN is designed for intrusion detection aiming at a single attack. Finally, we propose a new intrusion detection model by combining several intrusion detection models that aim at a single attack. Intrusion detection aiming at multiple attacks is realized through the designed GAN-based deep learning architecture. Besides, we provide a comprehensive evaluation to verify the effectiveness of the proposed method.

3-D-SIS: A 3-D-Social Identifier Structure for Collaborative Edge Computing Based Social IoT

The social Internet of Things (IoT) (SIoT) helps to enable an autonomous interaction between the two architectures that have already been established: social networks and the IoT. SIoT also integrates the concepts of social networking and IoT into collaborative edge computing (CEC), the so-called CEC-based SIoT architecture. In closer proximity, IoT devices self-organize into a CEC-based SIoT computing cluster and provide social device-to-device (S-D2D) services, such as computation offloading, service discovery, and content delivery. In the CEC-based SIoT, however, cooperation based on social connections leads to a problem called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">social and spatial physical trade-off</i> . This problem is also referred to as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mismatch</i> problem, which arises because the spatial neighbors in the social layer cannot always be related. The spatial distance thus calls for additional multi-hop transmissions. This work presents a novel solution called 3-D-social identifier structure <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(3-D-SIS)</i> model. The 3-D-SIS model is based on 3-D social space (3-D-SS) and considers social ties and physical connections (i.e., intra-neighbor) of the SIoT devices and utilizes a 3-D structure to evaluate that relationship. Moreover, it minimizes the end-to-end delay and communication cost to address the mismatch problem. To validate the performance of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(3-D-SIS)</i> model, we use the real traces of social networks <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(INFOCOM06)</i> . The results show that the 3-D-SIS selects the best neighbor in S-D2D communication and improves performance in terms of end-to-end delay and throughput.

FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles

Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes network resources. To fill these gaps, this article proposes a vehicle cooperative positioning (CP) system based on federated learning (FedVCP), which makes full use of the potential of social Internet of Things (IoT) and collaborative edge computing (CEC) to provide high-precision positioning correction while ensuring user privacy. To the best of our knowledge, this article is the first attempt to solve the privacy of CP from a perspective of federated learning. In addition, we take the advantages of local cooperation through vehicle-to-vehicle (V2V) communications in data augmentation. For individual differences in vehicle positioning, we utilize transfer learning to eliminate the impact of such differences. Extensive experiments on real data demonstrate that our proposed model is superior to the baseline method in terms of effectiveness and convergence speed.

Multihop Offloading of Multiple DAG Tasks in Collaborative Edge Computing

Collaborative edge computing (CEC) is a recently popular paradigm enabling sharing of data and computation resources among different edge devices. Task offloading is an important problem to address in CEC as we need to decide when and where each task is executed. However, it is challenging to solve task offloading in CEC as tasks can be offloaded to a multihop neighboring device leading to bandwidth contention among network flows. Most existing works do not jointly consider network flow scheduling that can lead to network congestion and inefficient performance in terms of completion time. Another challenge is to formulate and solve the problem considering the dependencies among dependent tasks and conflicting network flows. Few recent works have considered multihop computation offloading; however, these works focus on independent tasks and do not jointly consider the dependencies with network flows. In this work, we mathematically formulate the problem of jointly offloading multiple tasks consisting of dependent subtasks and network flow scheduling in CEC to minimize the average completion time of tasks. We have proposed a joint dependent task offloading and flow scheduling heuristic (JDOFH) that considers both dependencies in task directed acyclic graph and start time of network flows. Performance comparison done using simulation for both real application task graph and simulated task graphs shows that JDOFH leads to up to 85% improvement in average completion time compared to benchmark solutions which do not make a joint decision.

A Blockchain-Based Mutual Authentication Scheme for Collaborative Edge Computing

With the ever-increasing requirements of delay-sensitive and mission-critical applications, it becomes a popular research trend to incorporate edge computing in the Internet of Things (IoT) to mitigate the pressure of traditional cloud-based IoT architecture. Edge computing delivers real-time computations and communications for IoT devices by leveraging edge servers deployed close to users, which creates a collaborative edge computing (CEC) paradigm. The capacity of edge servers is beneficial but risky, as vulnerable servers can be exploited to conduct surveillance or perform other nefarious activities. Besides, fake IoT devices would bring security threats and compromise the IoT system. This highlights the necessity of designing a secure and efficient mutual authentication scheme for CEC. In this direction, related works have proposed various authentication mechanisms, but most of them are found unfit due to the absence of decentralization, anonymity, and mobility. Motivated by this fact, we propose a blockchain-based mutual authentication scheme that bridges these gaps. Specifically, blockchain, certificateless cryptography, elliptic curve cryptography, and pseudonym-based cryptography are integrated into our scheme to provide mutual authentication between edge servers and IoT devices. Except for static conditions, both intraedge and interedge authentication are considered. Besides, we elaborate on the key generation procedures and design a session key negotiation mechanism. Extensive experiments and security analyses have been conducted to show the feasibility of the proposed scheme.

Social Prediction-Based Handover in Collaborative-Edge-Computing-Enabled Vehicular Networks

Collaborative edge computing (CEC) can realize the cooperation and integration of heterogeneous resources distributed in adjacent areas, increasing the overall resource utilization efficiency. In a CEC-supported heterogeneous vehicular network composed of different access solutions, including cellular vehicle-to-everything (C-V2X) and dedicated short-range communications (DSRC), good network connections can guarantee timely access to edge resources. How to maintain stable and high-quality network connections for vehicles is a crucial issue. With traditional received signal strength (RSS)-based handover schemes, vehicles may encounter severe ping-pong effects and even direct handover failures leading to data packet loss. In this article, to overcome the frequent handover problem caused by vehicles' high-speed motion and the ever-changing network environment, we propose a trajectory prediction-based handover scheme. In this scheme, the sojourn time of a vehicle staying in each candidate network's coverage can be obtained through a social long short-term memory (social-LSTM)-based prediction model. Together with the signal strength, available bandwidth, and cost, the sojourn time is also taken as a handover decision attribute parameter. Simulation results show that our proposed scheme can reduce the number of handovers effectively.

Trustworthy Blockchain-Empowered Collaborative Edge Computing-as-a-Service Scheduling and Data Sharing in the IIoE

Owing to the technology of 5G and beyond, collaborative edge computing-as-a-service has enabled trillions of interconnected edge applications. It has also become a prospective paradigm for providing computing services by offloading computationally intensive assignments to mobile-edge servers or fog nodes due to terminals constrained computing and caching resources. Nevertheless, in this process, trust of computing-as-a-service scheduling and edge data sharing in heterogeneous systems is an unavoidable challenge of paramount importance. As a powerful tool that addresses security issues, blockchains can ensure the trustworthiness and irreversibility of computing data by consensus mechanisms. However, in the Industrial Internet of Energy (IIoE), the storage burden of a single blockchain has increased. Therefore, from the perspective of a stable real-time operation, we propose a multiedgechain structure that accommodates thousands of edge data and promotes on-chain data efficiency to achieve cross-chain edge data sharing for heterogeneous blockchain systems. Moreover, aiming at the profits of computing resource scheduling in the IIoE, a two-stage Stackelberg game strategy with an optimal scheduling demand and reward is provided considering the edge user’s preferences and risk factors. Finally, the simulation results verify the superiority of the proposed scheme, regarding the game equilibrium, utility optimization, and data sharing efficiency of cloud–edge collaboration.

Collaborative Edge Computing for Smart Villages [Energy and Security

The term "collaborative edge computing" is new. Edge computing is a crucial technology for the deployment of smart villages. Due to edge devices' resource constraint features, collaborative computing is a key challenge to borrow computational resources to meet demands of smart villages. This article gives first-hand experiences on collaborative edge computing and its use for smart villages.

A Priority-Based Multicast Flow Scheduling Method for a Collaborative Edge Storage Datacenter Network

Edge storage, as a supplement to cloud storage, reduces latency by providing services in a timely and efficient manner near the source. In a collaborative edge storage datacenter network (CESN), not only does the edge storage datacenter (ESDC) that is closest to the user provide services, but multiple ESDCs work together to provide better services. In this collaborative work mechanism, different application session requests create large persistent multicast flows with diverse performance requirements. Existing multicast scheduling methods such as unicast shortest path (USP) and static single tree (SST) do not consider flow characteristics or performance requirements. In this paper, we first modeled the multicast flow scheduling problem in a CESN. The model is based on different types of flows with diverse network requirements. Then, we tailored a multicast flow scheduling method based on multiple-attribute decision-making and a genetic algorithm (MDGA). MDGA selects appropriate multicast routing paths for flows in a CESN by considering the requested flow types and network status. The experimental results show that the proposed MDGA method can balance network loads and reduce the average transmission delay for high-priority flows better than USP and SST.