A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> D. DATA ACCURACY <s> Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users' mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users' privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> D. DATA ACCURACY <s> In the recent past, great attention has been directed toward road surface condition monitoring. As a matter of fact, this activity is of critical importance in transportation infrastructure management. In response, multiple solutions have been proposed which make use of mobile sensing, more specifically contemporary applications and architectures that are used in both crowdsensing and vehicle-based sensing. This has allowed for automated control as well as analysis of road surface quality. These innovations have thus encouraged and showed the importance of cloud to provide reliable transport services to clients. Nonetheless, these initiatives have not been without challenges that range from mobility support, locational awareness, low latency, as well as geo-distribution. As a result, a new term has been coined for this novel paradigm, called, fog computing. In this paper, we propose a privacy-preserving protocol for enhancing security in vehicular crowdsensing-based road surface condition monitoring system using fog computing. At the onset, this paper proposes a certificateless aggregate signcryption scheme that is highly efficient. On the basis of the proposed scheme, a data transmission protocol for monitoring road surface conditions is designed with security aspects such as information confidentiality, mutual authenticity, integrity, privacy, as well as anonymity. In analyzing the system, the ability of the proposed protocol to achieve the set objectives and exercise higher efficiency with respect to computational and communication abilities in comparison to existing systems is also considered. <s> BIB002
Although increasing the amount of collected data improves the accuracy of the monitored phenomenon or detected event, collecting sensed data from a large number of users may overload the communication network and remote servers. Therefore, it is required to determine clear and accurate relationships describing the dependence of the performance metric of MCS on the size of the collected data. Then, these dependencies and tradeoffs should be carefully considered to balance between the data accuracy from one side and the overloading of the communication network and servers on the other side. Mobile devices and smartphones are equipped with different types of sensors from different manufacturers that may vary significantly in their sensitivity and noise immunity. Thus, there is a need to improve the data accuracy by identifying devices that are likely to produce accurate sensing data, performing global centralized data aggregation, and taking into consideration the spatiotemporal mobility patterns of the users of the mobile devices and smartphones BIB001 , BIB002 . Although this challenge is common to both MCS and more ''traditional'' IoT networks, MCS faces additional issues. For example, accuracy in MCS can be compromised intentionally by malicious users. This issue was discussed in the paragraph related to trust in MCS in Section V.B. Another problem is that with MCS, there is less control on the type of used devices, and thus an increased degree of variety of devices is to be expected compared to IoT. This challenge is discussed in the previous paragraph.