Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network Similar to Cloud, Fog provides data, compute, storage, and application services to end-users In this article, we elaborate the motivation and advantages of Fog computing, and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks We discuss the state-of-the-art of Fog computing and similar work under the same umbrella Security and privacy issues are further disclosed according to current Fog computing paradigm As an example, we study a typical attack, man-in-the-middle attack, for the discussion of security in Fog computing We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device

https://annals-csis.org/Volume_2/pliks/503.pdf

The Fog computing paradigm: Scenarios and security issues

As new fabrication and integration technologies reduce the cost and size of micro-sensors and wireless interfaces, it becomes feasible to deploy densely distributed wireless networks of sensors and actuators. These systems promise to revolutionize biological, earth, and environmental monitoring applications, providing data at granularities unrealizable by other means. In addition to the challenges of miniaturization, new system architectures and new network algorithms must be developed to transform the vast quantity of raw sensor data into a manageable stream of high-level data. To address this, we propose a tiered system architecture in which data collected at numerous, inexpensive sensor nodes is filtered by local processing on its way through to larger, more capable and more expensive nodes.We briefly describe Habitat monitoring as our motivating application and introduce initial system building blocks designed to support this application. The remainder of the paper presents details of our experimental platform.

Habitat monitoring: Application driver for wireless communications technology

Fog computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage and application services to end users. In this article, we elaborate the motivation and advantages of Fog computing and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state of the art of Fog computing and similar work under the same umbrella. Distinguished from other reviewing work of Fog computing, this paper further discloses the security and privacy issues according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of system security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device. In addition, we discuss the authentication and authorization techniques that can be used in Fog computing. An example of authentication techniques is introduced to address the security scenario where the connection between Fog and Cloud is fragile. Copyright © 2015John Wiley & Sons, Ltd.

An overview of Fog computing and its security issues

Structural health monitoring (SHM) using wireless sensor networks (WSNs) has gained research interest due to its ability to reduce the costs associated with the installation and maintenance of SHM systems. SHM systems have been used to monitor critical infrastructure such as bridges, high-rise buildings, and stadiums and has the potential to improve structure lifespan and improve public safety. The high data collection rate of WSNs for SHM pose unique network design challenges. This paper presents a comprehensive survey of SHM using WSNs outlining the algorithms used in damage detection and localization, outlining network design challenges, and future research directions. Solutions to network design problems such as scalability, time synchronization, sensor placement, and data processing are compared and discussed. This survey also provides an overview of testbeds and real-world deployments of WSNs for SH.

Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey

Cloud services to smart things face latency and intermittent connectivity issues. Fog devices are positioned between cloud and smart devices. Their high speed Internet connection to the cloud, and physical proximity to users, enable real time applications and location based services, and mobility support. Cisco promoted fog computing concept in the areas of smart grid, connected vehicles and wireless sensor and actuator networks. This survey article expands this concept to the decentralized smart building control, recognizes cloudlets as special case of fog computing, and relates it to the software defined networks (SDN) scenarios. Our literature review identifies a handful number of articles. Cooperative data scheduling and adaptive traffic light problems in SDN based vehicular networks, and demand response management in macro station and micro-grid based smart grids are discussed. Security, privacy and trust issues, control information overhead and network control policies do not seem to be studied so far within the fog computing concept.

Fog computing: A cloud to the ground support for smart things and machine-to-machine networks

We investigate the problem of adaptive traffic light control using real-time traffic information collected by a wireless sensor network (WSN). Existing studies mainly focused on determining the green light length in a fixed sequence of traffic lights. In this paper, we propose an adaptive traffic light control algorithm that adjusts both the sequence and length of traffic lights in accordance with the real time traffic detected. Our algorithm considers a number of traffic factors such as traffic volume, waiting time, vehicle density, etc., to determine green light sequence and the optimal green light length. Simulation results demonstrate that our algorithm produces much higher throughput and lower vehicle's average waiting time, compared with a fixed-time control algorithm and an actuated control algorithm. We also implement proposed algorithm on our transportation testbed, iSensNet, and the result shows that our algorithm is effective and practical.

/pdf/adaptive-traffic-light-control-in-wireless-sensor-network-3ne5924z2r.pdf

Adaptive Traffic Light Control in Wireless Sensor Network-Based Intelligent Transportation System

Shortest path is a fundamental graph problem with numerous applications. However, the concept of classic shortest path is insufficient. In this paper, we study various concepts of “shortest” path in temporal graphs, called minimum temporal paths. Computing these minimum temporal paths is challenging as subpaths of a “shortest” path may not be “shortest” in a temporal graph. We propose efficient algorithms to compute minimum temporal paths and verified their efficiency using large real-world temporal graphs.

Efficient Algorithms for Temporal Path Computation

In recent years, research on using wireless sensor networks (WSNs) for structural health monitoring (SHM) has attracted increasing attention. Unlike other monitoring applications, detection of possible structure damage requires significant amount of domain knowledge that computer science researchers are usually unfamiliar with. As a result, most previous work in WSN-based SHM was done by researchers in civil engineering. However, civil researchers often tend to solve practical engineering problems but rarely consider designing a system in an optimal way, particularly when the limited wireless bandwidth and restricted resources of WSNs need to be addressed. Through the collaboration with civil researchers, we demonstrate that optimization design can significantly help improve the performance of a WSN-based SHM system. We consider a fundamental problem in SHM: modal analysis, which is used to obtain the dynamic structural vibration characteristics. Cluster-based modal analysis approach is adopted. In each cluster, the vibration characteristics are identified and then are assembled together. Different from other applications, clustering in this approach should meet some extra requirements of modal analysis. Moreover, cluster size should be optimized to minimize the total energy consumption. This clustering problem is formally formulated and proven to be NP complete. Two centralized and one distributed algorithms are proposed to solve the problem. The effectiveness and efficiency of the proposed cluster-based modal analysis along with the clustering algorithms are evaluated using both simulation and experiments.

Energy efficient clustering for WSN-based structural health monitoring

This paper investigates how to fuse grayscale and thermal video data for detecting foreground objects in challenging scenarios. To this end, we propose an intuitive yet effective method called weighted low-rank decomposition (WELD), which adaptively pursues the cross-modality low-rank representation. Specifically, we form two data matrices by accumulating sequential frames from the grayscale and the thermal videos, respectively. Within these two observing matrices, WELD detects moving foreground pixels as sparse outliers against the low-rank structure background and incorporates the weight variables to make the models of two modalities complementary to each other. The smoothness constraints of object motion are also introduced in WELD to further improve the robustness to noises. For optimization, we propose an iterative algorithm to efficiently solve the low-rank models with three subproblems. Moreover, we utilize an edge-preserving filtering-based method to substantially speed up WELD while preserving its accuracy. To provide a comprehensive evaluation benchmark of grayscale-thermal foreground detection, we create a new data set including 25 aligned grayscale-thermal video pairs with high diversity. Our extensive experiments on both the newly created data set and the public data set OSU3 suggest that WELD achieves superior performance and comparable efficiency against other state-of-the-art approaches.

Weighted Low-Rank Decomposition for Robust Grayscale-Thermal Foreground Detection

We investigate the problem of adaptive traffic light control of multiple intersections using real-time traffic data collected by a wireless sensor network (WSN). Previous studies mainly focused on optimizing the intervals of green lights in fixed sequences of traffic lights and ignored the traffic flow's characteristics and special traffic circumstances. In this paper, we propose an adaptive traffic light control scheme that adjusts the sequences of green lights in multiple intersections based on the real- time traffic data, including traffic volume, waiting time, number of stops, and vehicle density. Subsequently, the optimal green light length can be calculated from the local traffic data and traffic condition of neighbor intersections. Simulation results demonstrate that our scheme produces much higher throughput, lower average waiting time and fewer number of stops, compared with three control approaches: the optimal fixed-time control, an actuated control and an adaptive control.

Hejun Wu

Papers

Adaptive Traffic Light Control in Wireless Sensor Network-Based Intelligent Transportation System

Efficient Algorithms for Temporal Path Computation

Energy efficient clustering for WSN-based structural health monitoring

Weighted Low-Rank Decomposition for Robust Grayscale-Thermal Foreground Detection

Adaptive Traffic Light Control of Multiple Intersections in WSN-Based ITS