Search

The Internet of Things (IoT) networks have become the infrastructure to enable the detection and reaction of anomalies in various domains, where an efficient sensory data gathering mechanism is fundamental since IoT nodes are typically constrained in their energy and computational capacities. Besides, anomalies may occur occasionally in most applications, while the majority of time durations may correspond to a healthy situation. In this setting, the range, rather than an accurate value of sensory data, should be more interesting to domain applications, and the range is represented as the category of sensory data in this paper. To decrease the energy consumption of IoT networks, this paper proposes an energy-efficient sensory data gathering mechanism, where the category of sensory data is processed by adopting the compressed sensing algorithm. The sensory data are forecasted through a data prediction model in the cloud, and sensory data of an IoT node is necessary to be routed to the cloud for the synchronization purpose, only when the category provided by this IoT node is different from the category of the forecasted value in the cloud. Experimental results demonstrate that our approach performs better than state-of-the-art techniques, in terms of the network traffic and energy consumption. [ABSTRACT FROM AUTHOR]

Multi-access Edge Computing (MEC) offers cloud computing capabilities at the edge of the network. Growing demand for low-latency services requires Service Function Chains (SFCs) to be scaled up beyond MEC network to core network. To adapt to network dynamics and provide low-latency services, being able to migrate SFCs when needed is of paramount importance. However, migration of SFCs among edge and core networks such that average latency is optimized as well as considering resource consumption is an intractable challenge because improper migration of Virtual Network Functions (VNFs) results in failure of meeting the requirements of network policies. In this paper, we investigate SFCs in edge-core networks and model the Latency-aware Edge-Core SFCs Migration problem based on open Jackson networks. Two SFC migration algorithms, i.e., Profit-driven Heuristic Search (PHS) and Average Utilization Based (AUB), are proposed to efficiently optimize average latency of all SFCs in edge-core networks. Extensive evaluation results show that PHS optimizes average latency by 19.5%, while AUB can further reduce average latency by up to 36.9% by allowing a marginally higher number of VNF migrations. [ABSTRACT FROM AUTHOR]

• Smart secured transportation is a pre-requisite for sustainable smart cities. • PKI-based authentication incurs high computation and communication overhead. • Successive authentication can reduce overhead in PKI-based authentication. • Edge computing concepts can enhance security and privacy in smart transportation system. Vehicular networks are getting huge attention among the research community due to its tremendous potential to offer diversified applications and services to the drivers and passengers towards the realization of intelligent transportation system (ITS) for connected living and sustainable smart cities. Cloud computing (CC), which possesses large capacity in terms of computing and storage resource, may not be suitable for many vehicular applications like safety and emergency services due to high bandwidth requirement and large response time. Edge or fog computing can be a promising technology that can be integrated with vehicular networks to meet the requirements of less bandwidth usage and lower response time. Vehicular cloud network (VCN) extends cloud computing concepts among the vehicles in the vehicular networks to enable vehicles with excess computing, storage or bandwidth capacities to share the resources with other vehicles, who are willing to use that extra resource to fulfill their service requirements. Security poses a great challenge in a vehicular network due to high mobility, dynamic and frequent changes in topology and sudden variation in vehicle density in the roads. Privacy of the vehicular users must be ensured to encourage mass participation for making the system trustworthy. Both security and privacy are pre-requisites of a sustainable smart city. For secured service provisioning, before transmitting a message, connected vehicles must be authenticated. In this paper, we have proposed a system to authenticate vehicles for secured service provisioning in edge-enabled vehicular cloud networks. A novel technique has been developed to avoid redundant authentication to validate a vehicle successively across various RSUs (road side units). Extensive simulation works have been done and the outputs demonstrate efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in cases of sensitive and critical data through secure communication is vital. However, due to the relatively long distance between remote cloud and end nodes, cloud computing cannot provide effective and direct management for end nodes, which leads to security vulnerabilities. In this paper, we propose a novel trust evaluation model based on the trust transitivity on a chain assisted by mobile edge nodes, which is used to ensure the reliability of nodes in the Internet of Things and prevent malicious attacks. The mobile edge nodes offer a new solution to solve the above problems with relatively strong computing and storage abilities. Firstly, we design calculation approaches to different trust chains to measure their trust degrees. Secondly, we propose an improved Dijkstra's algorithm for collecting trust information of sensor nodes by mobile edge nodes. Finally, the experimental results show that our trust model based on mobile edge nodes can evaluate sensor nodes more precisely and enhance the security on the Internet of Things. • An efficient trust chain evaluation model assisted by mobile edge nodes is proposed. • The model provides calculation methods of the value of the different trust chains. • Trust information of sensor nodes are collected by an improved Dijkstra algorithm. [ABSTRACT FROM AUTHOR]

Abstract 5G networks are anticipated to provide service for vertical industries. Mobile Edge Computing (MEC) framework is a physical infrastructure improvement to host mobile applications for the introduction of new service provisioning in 5G networks. Application Orchestration is responsible for scheduling user sessions to application instances. In this paper, we propose four scheduling algorithms to assign sessions in a system with multiple MEC clouds. Results show that scheduling based on the occupancy level of MECs is quite robust in various scenarios and can be applied with the fractional guard channel policy to provide service. [ABSTRACT FROM AUTHOR]

The uncertainties of IoT-edges system and environment make challenges to applications in reliability, stability, and total latency, etc. With edge devices increasingly able to connect to cloud servers from anywhere, applications can potentially perform deployment changing at runtime to improve performance. In this paper, we propose a dynamic-programming based algorithm, named E-ODD , for the on-demand deployment of applications at runtime. When the device does not have enough computational resources, it offloads some movable tasks to the cloud to occupy additional resources. However, when it is difficult to occupy cloud resources or computational resources on the device become excessive, the device migrates tasks back. Besides, we present MODE, a generic and flexible middleware for edge cloud on-demand deployment. We propose a context model and detect corresponding complex events which trigger the deployment changes. Based on the event detection model, middleware can acquire changing information real-timely. We have successfully applied our middleware for ventricular fibrillation monitoring. Finally, experiments prove that our on-demand deployment model outperforms other selected models both in total latency and throughput, especially in dynamic environments. [ABSTRACT FROM AUTHOR]

• A cloud-edge-terminal hierarchical fault monitoring and control system is constructed. • A novel traveling wave-based line selection and fault location algorithm based on the time difference is presented. • A least squares model is introduced to eliminate the calibration error of the initial traveling wave-front arrival time. • A low-cost, low-power embedded TWAM and a special traveling wave sensor especially used in distribution networks are development by the author's R&D team. The topology structure of a distribution network is complex, and the amount of fault data is considerable, which induces strong fault data processing pressure on traditional cloud service platforms. Edge computing can address the distribution network autonomously and improve the efficiency of distribution network fault data processing. Therefore, in this paper, a novel method is proposed for line selection and fault location in a distribution network based on a cloud-edge-terminal hierarchical fault monitoring and control system. An edge computing gateway is installed at the front end of each main feeder. According to the transmission characteristics of the traveling wave-front, the time characteristic matrices before and after the fault are constructed, and the element variation rules of the matrices under different fault locations are analyzed. Line selection is realized in the cloud service platform; then, the fault location is accurately calculated at the edge computing gateway. To eliminate the calibration error of the initial traveling wave-front arrival time, a least squares model is introduced. The accuracy and reliability of line selection and fault location in the distribution network are improved by fitting the front and end terminal time characteristic matrices before and after the fault. The simulation analysis and field test results show that the proposed method is not affected by the line parameters or the topology structure, and it effectively eliminates the influence of the wave-front calibration error. The proposed method is simple to implement and has high accuracy, reliability and speed. [ABSTRACT FROM AUTHOR]