Internet of Things (IoT) allows billions of physical objects to be connected to collect and exchange data for offering various applications, such as environmental monitoring, infrastructure management, and home automation. On the other hand, IoT has unsupported features (e.g., low latency, location awareness, and geographic distribution) that are critical for some IoT applications, including smart traffic lights, home energy management and augmented reality. To support these features, fog computing is integrated into IoT to extend computing, storage and networking resources to the network edge. Unfortunately, it is confronted with various security and privacy risks, which raise serious concerns towards users. In this survey, we review the architecture and features of fog computing and study critical roles of fog nodes, including real-time services, transient storage, data dissemination and decentralized computation. We also examine fog-assisted IoT applications based on different roles of fog nodes. Then, we present security and privacy threats towards IoT applications and discuss the security and privacy requirements in fog computing. Further, we demonstrate potential challenges to secure fog computing and review the state-of-the-art solutions used to address security and privacy issues in fog computing for IoT applications. Finally, by defining several open research issues, it is expected to draw more attention and efforts into this new architecture.

Securing Fog Computing for Internet of Things Applications: Challenges and Solutions

More particularly, calculations are developed and examined to reduce the entire quantity of Wireless access points as well as their locations in almost any given atmosphere while with the throughput needs and the necessity to ensure every place in the area can achieve a minimum of k APs. This paper concentrates on using Wireless for interacting with and localizing the robot. We've carried out thorough studies of Wireless signal propagation qualities both in indoor and outside conditions, which forms the foundation for Wireless AP deployment and communication to be able to augment how human operators communicate with this atmosphere, a mobile automatic platform is developed. Gas and oil refineries could be a harmful atmosphere for various reasons, including heat, toxic gasses, and unpredicted catastrophic failures. When multiple Wireless APs are close together, there's a possible for interference. A graph-coloring heuristic can be used to find out AP funnel allocation. Additionally, Wireless fingerprinting based localization is developed. All of the calculations implemented are examined in real life situations using the robot developed and answers are promising. For example, within the gas and oil industry, during inspection, maintenance, or repair of facilities inside a refinery, people might be uncovered to seriously high temps to have a long time, to toxic gasses including methane and H2S, and also to unpredicted catastrophic failures.

/pdf/internet-of-vehicles-from-intelligent-grid-to-autonomous-pma0tn3ofd.pdf

Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds

Fog computing paradigm extends the storage, networking, and computing facilities of the cloud computing toward the edge of the networks while offloading the cloud data centers and reducing service latency to the end users. However, the characteristics of fog computing arise new security and privacy challenges. The existing security and privacy measurements for cloud computing cannot be directly applied to the fog computing due to its features, such as mobility, heterogeneity, and large-scale geo-distribution. This paper provides an overview of existing security and privacy concerns, particularly for the fog computing. Afterward, this survey highlights ongoing research effort, open challenges, and research trends in privacy and security issues for fog computing.

Security and Privacy in Fog Computing: Challenges

Security and trust issues in Fog computing: A survey

Cloud computing plays a vital role in processing a large amount of data. However, with the arrival of the Internet of Things, huge data are generated from these devices. Thus, there is the need for bringing characteristics of cloud closer to the request generator, so that processing of these huge data takes place at one-hop distance closer to that end user. This led to the emergence of fog computing with the aim to provide storage and computation at the edge of the network that reduces network traffic and overcomes many cloud computing drawbacks. Fog computing technology helps to overcome challenges of big data processing. The paper discusses the taxonomy of fog computing, how this is different from cloud computing and edge computing technologies, its applications, emerging key technologies (i.e., communication technologies and storage technologies) and various challenges involved in fog technology.

Fog computing: from architecture to edge computing and big data processing

Secure intelligent traffic light control using fog computing

Vehicular cloud (VC) extends cloud computing to vehicles participating in vehicular ad hoc networks, aiming to provide computing and storage services at low cost to vehicles, improve traffic efficiency and safety, ensure real-time services, etc. Due to the highly dynamic nature of VC, it is challenging to efficiently form a dynamic VC securely and anonymously or to securely deliver messages to the dynamic VC without potentially violating the privacy of cloud users. In this paper, we present a concrete secure and privacy-preserving communication scheme for VC establishment and data dissemination. Our scheme allows a group of vehicles that are geographically close to each other to form a VC securely, anonymously and dynamically. This allows vehicle resources to be integrated and shared securely. Once a VC is formed, any cloud user may deliver messages to be securely and anonymously processed in the VC.

Privacy-Preserving Cloud Establishment and Data Dissemination Scheme for Vehicular Cloud

Industrial wireless networks (IWNs) have applications in areas such as critical infrastructure sectors and manufacturing industries such as car manufacturing. In car manufacturing, IWNs can facilitate manufacturers to improve the design of the vehicles by collecting vehicular status and other related data (such an IWN is also known as vehicular ad hoc networks—VANETs). Vehicle positioning is a key functionality in VANETs. Most existing vehicle positioning systems are capable of providing accurate 2D positioning, but the demand for accurate 3D positioning has increased sharply in recent times (e.g., due to the building of more elevated roads). There are, however, security and privacy concerns relating to 3D positioning systems in VANET. In this paper, we propose two secure and privacy-preserving 3D positioning schemes based on vehicle-to-roadside (V2R) and vehicle-to-vehicle (V2V) communications, respectively. Our schemes are based on the round trip time ranging technique which is used to achieve 3D position. The security and the privacy of vehicles in our schemes are guaranteed through a newly designed one-pass authenticated key agreement protocol. Using experiments, we show that a vehicle can determine whether it is on or under an elevated road in a short period of time.

https://link.springer.com/content/pdf/10.1186%2Fs13638-018-1289-9.pdf

Secure and privacy-preserving 3D vehicle positioning schemes for vehicular ad hoc network

The invention discloses a vehicle information secure collection method applicable to fog computing in an intelligent traffic light system. The method comprises a setting step, a registering step, a difficulty distribution step, a certificate generation step and an information collection step. The method is characterized in that locations and the number of vehicles cannot be counterfeited; a fog computing device does not need too much computing and storage cost; the identity and location information of the vehicles will not be leaked to other vehicles and fog computing devices; the method is easy to deploy; and the vehicle information statistics is accurate.

Vehicle information secure collection method applicable to fog computing in intelligent traffic light system

Vehicular ad-hoc network (VANET) is a core component of intelligent traffic management system which could provide various of applications such as accident prediction, route reporting, etc. Due to the problems caused by traffic congestion, route reporting becomes a prospective application which can help a driver to get optimal route to save her travel time. Before enjoying the convenience of route reporting, security and privacy-preserving issues need to be concerned. In this paper, we propose a new secure and privacy-preserving route reporting scheme for VANETs. In our scheme, only an authenticated vehicle can use the route reporting service provided by the traffic management center. Further, a vehicle may receive the response from the traffic management center with low latency and without violating the privacy of the vehicle. Experiment results show that our scheme is much more efficiency than the existing one.

https://iopscience.iop.org/article/10.1088/1742-6596/910/1/012070/pdf

Yuanfei Zhang

Papers

Secure intelligent traffic light control using fog computing

Privacy-Preserving Cloud Establishment and Data Dissemination Scheme for Vehicular Cloud

Secure and privacy-preserving 3D vehicle positioning schemes for vehicular ad hoc network

Vehicle information secure collection method applicable to fog computing in intelligent traffic light system

Efficient Secure and Privacy-Preserving Route Reporting Scheme for VANETs