This research reviews challenges in building sustainable relationships between the parties involved in the crowdfunding and crowdsourcing projects, which are running in extreme situations, such as the COVID-19 pandemic. This study aims to solve problems that generate the crowdsourcing concerns and to find better alternatives to increase trust for crowdfunding among donors, as this impacts their strategic sustainability in the conditions of turbulence and COVID-induced financial crisis. It was found that factors influence donor decisions in different ways, yet the common tendency for donor activity is non-monotonicity. Future development in the field of sustainable relationships should focus on creating a donor classification system.

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COVID-19 crisis impact on the stability between parties in crowdfunding and crowdsourcing.

Along with the generation of Internet of Things (IoT), the values of tremendous volumes of sensing data will be slowly unlocked. Thus, crowd-sensed data trading as a new business paradigm has recently attracted increasing attention. A typical data trading system contains a platform, data consumers, and crowd workers. The platform recruits crowd workers to collect data and then sells the data to consumers. In this article, we design a differentially private crowd-sensed data trading mechanism, called DPDT, to preserve the identity privacy of consumers and the task privacy against crowd workers during the data collection process, simultaneously. DPDT consists of a differentially private auction-based data pricing algorithm and a differentially private data collection algorithm. The data pricing algorithm achieves a good approximation to the maximum revenue. Meanwhile, it guarantees $(e^{2}-1)\epsilon $ -truthfulness and $2\epsilon $ -differential privacy, where $\epsilon >0$ is a small constant. The data collection algorithm is able to effectively protect the data collection task privacy against crowd workers. We prove that this data collection algorithm achieves $\delta $ -approximate $\epsilon $ -differential privacy, where $\delta is a small constant, and meanwhile guarantees a tight bound of the expected approximation ratio. At last, extensive simulations are conducted to verify the significant performance of DPDT.

DPDT: A Differentially Private Crowd-Sensed Data Trading Mechanism

Survey and taxonomy of clustering algorithms in 5G

Many future innovative computing services will use Fog Computing Systems (FCS), integrated with Internet of Things (IoT) resources. These new services, built on the convergence of several distinct technologies, need to fulfil time-sensitive functions, provide variable levels of integration with their environment, and incorporate data storage, computation, communications, sensing, and control. There are, however, significant problems to be solved before such systems can be considered fit for purpose. The high heterogeneity, complexity, and dynamics of these resource-constrained systems bring new challenges to their robust and reliable operation, which implies the need for integral resilience management strategies. This paper surveys the state of the art in the relevant fields, and discusses the research issues and future trends that are emerging. We envisage future applications that have very stringent requirements, notably high-precision latency and synchronization between a large set of flows, where FCSs are key to supporting them. Thus, we hope to provide new insights into the design and management of resilient FCSs that are formed by IoT devices, edge computer servers and wireless sensor networks; these systems can be modelled using Game Theory, and flexibly programmed with the latest software and virtualization platforms.

Fog Computing Systems: State of the Art, Research Issues and Future Trends, with a Focus on Resilience

The use of keystroke dynamics for user authentication has evolved over the years and has found its application in mobile phones. But the primary challenge with mobile phones is that they can be used in any position. Thus, it becomes critical to analyze the use of keystroke dynamics using the data collected in various typing positions. This research proposed a three-step authentication model that could be used to authenticate a user who is using the mobile in sitting, walking, and relaxing position. Furthermore, the mobile orientation (portrait and landscape) was considered while taking input from the user. Apart from using traditional keystroke features, accelerometer data were also combined for classification using Random Forest(RF) and K-Nearest Neighbour(KNN) classifiers. The three-step authentication method was able to authenticate a user with an EER of 2.9% for the relaxing landscape position. Finally, the model was optimized using Particle Swarm Optimization (PSO) to reduce the feature set and make the model more practical for mobile phones. Optimization helped to reduce the number of features from 55 to 17 and improved the EER to 2.2%. The research validated that relaxing and walking positions are the best positions to authenticate a user using keystroke dynamics.

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A Three-Step Authentication Model for Mobile Phone User Using Keystroke Dynamics

Security and Privacy for Next-Generation Wireless Networks

This chapter briefly introduces the formation and development of the much needed security requirements in VANETs and elaborates them, and then systematically describes the current methods and strategies being used, together with both the latest and more classical academic achievements. Finally, discussions and speculations on the future direction of research into the safety technology of the VANET are presented.

Connecting Things to Things in Physical-World: Security and Privacy Issues in Vehicular Ad-hoc Networks

From 1G to 4G, wireless networks are almost updated every 10 years. The next generation of wireless network (5G) is coming soon. The 5G network will be a complex heterogeneous network with wide coverage and ultra-dense small cell deployments. 5G networks will greatly promote the innovation of applications based on it, as well as the development of new-type computing models. However, 5G networks will also face some new security threats and challenges. This chapter will first introduce the history of wireless networks and the goals of next-generation wireless networks. It gives the architecture of next-generation wireless networks and introduces new networks architecture technologies such as NFV, SDN, D2D, and so on. Those new networks architecture technologies are derived from the combination of next-generation wireless networks and technologies like cloud computing and IoT. Sections 1.3 and 1.4 will review security and privacy issues cloud computing and embedded systems. Those systems and technologies play key roles in next-generation wireless networks. In the fifth section, the new computing model and unsolved security issues of 5G network are briefly introduced.

Networking Cyber-Physical Systems: Algorithm Fundamentals of Security and Privacy for Next-Generation Wireless Networks

The mobile crowdsourcing network (MCN) is a rising network architecture that comprises both crowdsensing and crowdsourcing computing. It has attracted broad attention in the world because of its powerful ability to deal with increasingly hard problems. Compared to traditional network, it is more vulnerable to be attacked for its generous payment. At the same time, an amount of input data which comes from various sources is delivered among the service providers, end users and participants, and the involved sensitive information may be revealed during the transmission. Moreover, as the characteristics of MCNs, including task crowdsourcing, human involvement, dynamic topology and heterogeneity, both security and privacy issues are more challenging. In this chapter, we review the current MCN architecture and new challenges of security and privacy issues at first. Then, we present some proposed approaches for security assurance and privacy protection in MCNs from three aspects: authentication, reputation and incentive mechanisms. Finally, possible research directions of security and privacy issues in MCNs and plenty of related reference are given.

Connecting Human to Cyber-World: Security and Privacy Issues in Mobile Crowdsourcing Networks

In order to sufficiently connect the physical-world to the cyber-world, the advances of Internet of Things (IOT) have significantly enhanced the capability of capturing data from pervasive space. However, a number of new challenges have risen in regard to the security and privacy issues, due to the characteristics of pervasive sensing in IOT. On one hand, the security and privacy issues not only appear in the cyber space but also appear in the physical space with new forms and approaches, which bring potential risks to personal safety in the cyber-physical space; on the other hand, the characteristics of the pervasive sensing also offer new opportunities to tackle the security and privacy issues with brand-new thoughts and methods, by leveraging the out-of-band sensing and communication techniques. In this chapter, we will introduce the new challenges of security and privacy in pervasive sensing, and investigate the state-of-the-art schemes to address security and privacy issues in cyber-physical space, finally, we will summarize and discuss the new thoughts and methods to tackle security and privacy issues in pervasive sensing, and further address the directions of future research.

Jin Shi

Papers

Security and Privacy for Next-Generation Wireless Networks

Connecting Things to Things in Physical-World: Security and Privacy Issues in Vehicular Ad-hoc Networks

Networking Cyber-Physical Systems: Algorithm Fundamentals of Security and Privacy for Next-Generation Wireless Networks

Connecting Human to Cyber-World: Security and Privacy Issues in Mobile Crowdsourcing Networks

Connecting Physical-World to Cyber-World: Security and Privacy Issues in Pervasive Sensing