We analyze the betweenness centrality (BC) of nodes in large complex networks. In general, the BC is increasing with connectivity as a power law with an exponent $\eta$. We find that for trees or networks with a small loop density $\eta=2$ while a larger density of loops leads to $\eta<2$. For scale-free networks characterized by an exponent $\gamma$ which describes the connectivity distribution decay, the BC is also distributed according to a power law with a non universal exponent $\delta$. We show that this exponent $\delta$ must satisfy the exact bound $\delta\geq (\gamma+1)/2$. If the scale free network is a tree, then we have the equality $\delta=(\gamma+1)/2$.

Betweenness Centrality in Large Complex Networks

The development of vehicular Internet of Things (IoT) applications, such as E-Transport, Augmented Reality, and Virtual Reality are growing progressively. The mobility aware services and network-based security are fundamental requirements of these applications. However, multi-side offloading enabling blockchain and cost-efficient scheduling in heterogeneous vehicular fog cloud nodes network become a challenging task. The study formulates this problem as a convex optimization problem, where all constraints are the convex set. The goal of the study is to minimize communication cost and computation cost of applications under mobility, security, deadline, and resource constraints. Initially, we propose a novel vehicular fog cloud network (VFCN) which consists of different components and heterogeneous computing nodes. The ensure mobility privacy, the study devises Mobility Aware Blockchain-Enabled offloading scheme (MABOS). It extends blockchain enable multi-side offloading (e.g., offline offloading and online offloading) with proof of work (PoW), proof of creditability (PoC) and fault-tolerant techniques. The purpose is to offload all tasks under the secure network without any violation. Furthermore, to ensure Quality of Service (QoS) of applications, this work suggests linear search based task scheduling (LSBTS) method, which maps all tasks onto appropriate computing nodes. The experimental results show that devise schemes outperform all existing baseline approaches to the considered problem.

Mobility Aware Blockchain Enabled Offloading and Scheduling in Vehicular Fog Cloud Computing

Edge-cloud computing, combining the benefits of both edge computing and cloud computing, is one of the most promising ways to address the resource insufficiency of smart devices. Task offloading is an important challenge must be addressed for edge-cloud computing in practice, which decides the place and the time for performing each task. Even though there is existing research focusing on the task offloading in edge-cloud computing, a lot of problems should be solved before the application of these offloading technologies. Thus, in this article, we first propose a taxonomy of task offloading in edge-cloud environments to investigate and classify related research articles, and then summarize several challenges which have not been addressed for future research directions on this area to promote the development of edge-cloud market.

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A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

A home hospitalization system based on the Internet of things, Fog computing and cloud computing

In the current scenario, Cloud computing is providing services to IoT-sensor based applications in task offloading. In time-sensitive real-time applications, latency is a major problem in cloud computing. Due to exponential growth in IoT-sensor applications huge amount of multimedia data is produced and only the use of cloud computing decreases the efficiency of quality of service (QoS) in IoT-sensor applications. Fog computing uses to resolve the aforementioned issues in cloud computing. Fog computing accomplishes the low-latency requirement of QoS in time-sensitive real-time IoT-sensor applications. Thus the tasks of IoT-sensor applications are computed by various fog nodes. In this paper, a meta-heuristic scheduler Smart Ant Colony Optimization (SACO) task offloading algorithm inspired by nature is proposed to offload the IoT-sensor applications tasks in a fog environment. The proposed algorithm results are compared with Round Robin (RR), throttled scheduler algorithm and two bio-inspired algorithms such as modified particle swarm optimization (MPSO) and Bee life algorithm (BLA). Numerical result shows the significant improvement in latency by the proposed Smart Ant Colony Optimization (SACO) algorithm in task offloading of IoT-sensor applications comparison to Round Robin (RR), throttled, and MPSO and BLA. Proposed technique reduces the task offloading time by 12.88, 6.98, 5.91 and 3.53% in comparison to Round Robin (RR), throttled, MPSO, and BLA.

Task Offloading in Fog Computing for Using Smart Ant Colony Optimization

With the exponential increase in the number of IoT devices and the amount of emitted data from these devices, it is expensive and inefficient to offload all tasks to the remote data center. How to optimize the energy consumption of application requests from IoT devices meeting the deadline constraint is also a challenge. Fog computing adjacent to users has the feature of lower service delay but less resource than the remote cloud. Fog does not appear to replace cloud, they are complementary to each other, cooperation between them is worth studying. This paper proposes a general IoT-fog-cloud architecture that fully exploits the advantages of fog and cloud. Then, the energy and time efficient computation offloading and resource allocation is formulated into the energy and time cost minimization problem. We then propose an ETCORA algorithm to solve the problem, improving the energy consumption and completion time of application requests. Finally, extensive simulations are carried out to verify that the proposed method indeed outperforms the other two methods in reducing energy consumption and completion time of requests.

Energy and time efficient task offloading and resource allocation on the generic IoT-fog-cloud architecture

Vehicular ad hoc network (VANET) is an emerging technology for the future intelligent transportation systems (ITSs). The current researches are intensely focusing on the problems of routing protocol reliability and scalability across the urban VANETs. Vehicle clustering is testified to be a promising approach to improve routing reliability and scalability by grouping vehicles together to serve as the foundation for ITS applications. However, some prominent characteristics, like high mobility and uneven spatial distribution of vehicles, may affect the clustering performance. Therefore, how to establish and maintain stable clusters has become a challenging problem in VANETs. This paper proposes a link reliability-based clustering algorithm (LRCA) to provide efficient and reliable data transmission in VANETs. Before clustering, a novel link lifetime-based (LLT-based) neighbor sampling strategy is put forward to filter out the redundant unstable neighbors. The proposed clustering scheme mainly composes of three parts: cluster head selection, cluster formation, and cluster maintenance. Furthermore, we propose a routing protocol of LRCA to serve the infotainment applications in VANET. To make routing decisions appropriate, we nominate special nodes at intersections to evaluate the network condition by assigning weights to the road segments. Routes with the lowest weights are then selected as the optimal data forwarding paths. We evaluate clustering stability and routing performance of the proposed approach by comparing with some existing schemes. The extensive simulation results show that our approach outperforms in both cluster stability and data transmission.

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Efficient and Reliable Cluster-Based Data Transmission for Vehicular Ad Hoc Networks

Fog computing as an extension of the cloud based infrastructure, provides a better computing platform than cloud computing for mobile computing, Internet of Things, etc. One of the problems is how to make full use of the resources of the fog so that more requests of applications can be executed on the edge, reducing the pressure on the network and ensuring the time requirement of tasks. The high mobility of fog nodes also has a great impact on the task completion time and user satisfaction. Thus, a general IoT-Fog-Cloud computing architecture with a contract-based resource sharing mechanism is proposed in this paper. The contract establishment problem of resource sharing mechanism among fog clusters is modeled as a sealed-bid bilateral auction in order to take full advantage of the fog resources and ensure that more tasks could be executed on the fog. Then, we propose a scheduling method based on functional domain construction to mitigate the influence of mobility of fog nodes. It includes the selection of critical fog nodes and the construction of fog function domains based on spectral clustering. The selection of critical fog nodes is used to find the best fog nodes in each fog cluster with respect to the betweenness centrality, computing performance and communication delay to the IoT nodes. The critical nodes are responsible for building the functional domains of the remaining fog nodes in each fog cluster. Functional domain construction is used to determine the set of fog nodes contained in the corresponding functional domain. Finally, through extensive simulation experiments, the performance difference between the proposed method and the other four methods in terms of average service time, average utilization of fog nodes, success rate of tasks, average WLAN delay and the average cost of successful tasks are evaluated. Results show that our method generally outperforms the other four methods in these metrics.

Contract-Based Resource Sharing for Time Effective Task Scheduling in Fog-Cloud Environment

The geographically dispersed resources and ever-changing context incur unique heterogeneity, potential fragility, and vulnerability of an edge-cloud system. Thus, the reliability guarantee of services in the edge-cloud is critical. This paper firstly proposes a QoS-aware scheduling model with fault-tolerance in the edge-cloud, which extends the traditional primary-backup (PB) fault-tolerant model to improve the service reliability in the edge-cloud with the time constraints of tasks being satisfied. Then, a QoS-aware fault-tolerant scheduling algorithm including primary copy placement, backup copy placement and an adjustment mechanism is proposed to improve the QoS levels of tasks in the edge-cloud. The primary copy placement is to guarantee the earlier execution of the primary copy of a task to better satisfy the time requirements of tasks. The backup copy placement is to ensure the later execution of the backup copy of a task, reducing the overlapping of the two copies of a task, realizing the improvement of the resource utilization in the edge-cloud under the condition of redundancy and deadline requirements of tasks. The adjustment mechanism is triggered to rearrange the task copies of a computing node of the edge-cloud after the deallocation of a backup copy on the node, to better assist the goal-achievement of the primary and backup copy scheduling. Finally, through extensive simulation experiments with the real world taxi traces, the performance difference between the proposed method and the other four methods are evaluated. Results show that the proposed method generally outperforms the other methods in terms of guarantee ratio, average QoS level, and reliability cost.

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QoS-Aware Task Placement With Fault-Tolerance in the Edge-Cloud

As the number of IoT devices with limited resources and the corresponding observed data grow exponentially, the method of offloading all tasks to a remote data center becomes expensive, even inefficient. How to optimize the energy consumption of application requests from IoT devices satisfying the deadline constraint is also a challenge. Fog computing is closer to users, featuring the lower service delay but less resource than the remote cloud. Fog does not mean to replace cloud. They are complementary to each other, and cooperation between them is worth studying. The main points of this paper are: (1) Proposing a general IoT-fog-cloud computing architecture that fully exploits the advantages of fog and cloud. (2) Formulating the energy efficient computation offloading and dynamic resource scheduling (eoDS) problem, then proposing an eoDS algorithm to solve the problem, reducing the energy consumption and completion time of application requests (3) Compared with cloud nodes, the mobility of fog nodes is higher. For this, we propose the fog functional areas reconstruction method to adaptively deal with the changing environment, improving the resource utilization of fog.

Huaiying Sun

Papers

Energy and time efficient task offloading and resource allocation on the generic IoT-fog-cloud architecture

Efficient and Reliable Cluster-Based Data Transmission for Vehicular Ad Hoc Networks

Contract-Based Resource Sharing for Time Effective Task Scheduling in Fog-Cloud Environment

QoS-Aware Task Placement With Fault-Tolerance in the Edge-Cloud

Towards Energy and Time Efficient Resource Allocation in IoT-Fog-Cloud Environment