A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> The first decade of the century witnessed a proliferation of devices with sensing and communication capabilities in the possession of the average individual. Examples range from camera phones and wireless global positioning system units to sensor-equipped, networked fitness devices and entertainment platforms (such as Wii). Social networking platforms emerged, such as Twitter, that allow sharing information in real time. The unprecedented deployment scale of such sensors and connectivity options ushers in an era of novel data-driven applications that rely on inputs collected by networks of humans or measured by sensors acting on their behalf. These applications will impact domains as diverse as health, transportation, energy, disaster recovery, intelligence and warfare. This paper surveys the important opportunities in human-centric sensing, identifies challenges brought about by such opportunities and describes emerging solutions to these challenges. <s> BIB002 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> Who texts, and with whom do they text? This article examines the use of texting using metered traffic data from a large dataset (nearly 400 million anonymous text messages). We ask: 1) How much do different age groups use mobile phone based texting (SMS)? 2) How wide is the circle of texting partners for different age groups? 3) To what degree are texting relationships characterized by age and gender homophily? We find that texting is hugely popular among teens compared to other age groups. Further, the number of persons with whom people text is quite small. About half of all text messages go to only five other persons. Finally, we find that there is pronounced homophily in terms of age and gender in texting relationships. These findings support previous claims that texting is an important element of teen culture and is an element in the construction of a bounded solidarity. <s> BIB003 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> Reputation systems rate the contributions to participatory sensing campaigns from each user by associating a reputation score. The reputation scores are used to weed out incorrect sensor readings. However, an adversary can deanonmyize the users even when they use pseudonyms by linking the reputation scores associated with multiple contributions. Since the contributed readings are usually annotated with spatiotemporal information, this poses a serious breach of privacy for the users. In this paper, we address this privacy threat by proposing a framework called IncogniSense. Our system utilizes periodic pseudonyms generated using blind signature and relies on reputation transfer between these pseudonyms. The reputation transfer process has an inherent trade-off between anonymity protection and loss in reputation. We investigate by means of extensive simulations several reputation cloaking schemes that address this tradeoff in different ways. Our system is robust against reputation corruption and a prototype implementation demonstrates that the associated overheads are minimal. <s> BIB004 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms. <s> BIB005 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> This article investigates how and to what extent the power of collective although imprecise intelligence can be employed in smart cities. The main visionary goal is to automate the organization of spontaneous and impromptu collaborations of large groups of people participating in collective actions (i.e., participAct), such as in the notable case of urban crowdsensing. In a crowdsensing environment, people or their mobile devices act as both sensors that collect urban data and actuators that take actions in the city, possibly upon request. Managing the crowdsensing process is a challenging task spanning several socio-technical issues: from the characterization of the regions under control to the quantification of the sensing density needed to obtain a certain accuracy; from the evaluation of a good balance between sensing accuracy and resource usage (number of people involved, network bandwidth, battery usage, etc.) to the selection of good incentives for people to participAct (monetary, social, etc.). To tackle these problems, this article proposes a crowdsensing platform with three main original technical aspects: an innovative geo-social model to profile users along different variables, such as time, location, social interaction, service usage, and human activities; a matching algorithm to autonomously choose people to involve in participActions and to quantify the performance of their sensing; and a new Android-based platform to collect sensing data from smart phones, automatically or with user help, and to deliver sensing/actuation tasks to users. <s> BIB006 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> With the proliferation of sensor-embedded mobile computing devices, participatory sensing is becoming popular to collect information from and outsource tasks to participating users. These applications deal with a lot of personal information, e.g., users' identities and locations at a specific time. Therefore, we need to pay a deeper attention to privacy and anonymity. However, from a data consumer's point of view, we want to know the source of the sensing data, i.e., the identity of the sender, in order to evaluate how much the data can be trusted. “Anonymity” and “trust” are two conflicting objectives in participatory sensing networks, and there are no existing research efforts which investigated the possibility of achieving both of them at the same time. In this paper, we propose ARTSense, a framework to solve the problem of “trust without identity” in participatory sensing networks. Our solution consists of a privacy-preserving provenance model, a data trust assessment scheme and an anonymous reputation management protocol. We have shown that ARTSense achieves the anonymity and security requirements. Validations are done to show that we can capture the trust of information and reputation of participants accurately. <s> BIB007 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> This survey provides a structured and comprehensive overview of research on security and privacy in computer and communication networks that use game-theoretic approaches. We present a selected set of works to highlight the application of game theory in addressing different forms of security and privacy problems in computer networks and mobile applications. We organize the presented works in six main categories: security of the physical and MAC layers, security of self-organizing networks, intrusion detection systems, anonymity and privacy, economics of network security, and cryptography. In each category, we identify security problems, players, and game models. We summarize the main results of selected works, such as equilibrium analysis and security mechanism designs. In addition, we provide a discussion on the advantages, drawbacks, and future direction of using game theory in this field. In this survey, our goal is to instill in the reader an enhanced understanding of different research approaches in applying game-theoretic methods to network security. This survey can also help researchers from various fields develop game-theoretic solutions to current and emerging security problems in computer networking. <s> BIB008 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> Participatory sensing has emerged as a novel paradigm for data collection and collective knowledge formation about a state or condition of interest, sometimes linked to a geographic area. In this paper, we address the problem of incentive mechanism design for data contributors for participatory sensing applications. The service provider receives service queries in an area from service requesters and initiates an auction for user participation. Upon request, each user reports its perceived cost per unit of amount of participation, which essentially maps to a requested amount of compensation for participation. The participation cost quantifies the dissatisfaction caused to user due to participation. This cost is considered to be private information for each device, as it strongly depends on various factors inherent to it, such as the energy cost for sensing, data processing and transmission to the closest point of wireless access, the residual battery level, the number of concurrent jobs at the device processor, the required bandwidth to transmit data and the related charges of the mobile network operator, or even the user discomfort due to manual effort to submit data. Hence, participants have strong motive to mis-report their cost, i.e. declare a higher cost that the actual one, so as to obtain higher payment. We seek a mechanism for user participation level determination and payment allocation which is most viable for the provider, that is, it minimizes the total cost of compensating participants, while delivering a certain quality of experience to service requesters. We cast the problem in the context of optimal reverse auction design, and we show how the different quality of submitted information by participants can be tracked by the service provider and used in the participation level and payment selection procedures. We derive a mechanism that optimally solves the problem above, and at the same time it is individually rational (i.e., it motivates users to participate) and incentive-compatible (i.e. it motivates truthful cost reporting by participants). Finally, a representative participatory sensing case study involving parameter estimation is presented, which exemplifies the incentive mechanism above. <s> BIB009 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> With the continuous emergence of smart portable devices, like TabletPC or smartphones, crowdsensing is becoming an active research topic. While exploiting the wisdom of the crowd provides huge benefits to pervasive applications, one of the key challenges remains to motivate people to contribute to flow of data by sharing some of the capabilities of their device. In this paper, we therefore report on the APISENSE platform, a participative platform that help scientists to collect realistic datasets from a population of voluntary participants. More specifically, we provide a preliminary investigation of how user incentives can be integrated in such a platform to encourage people to contribute while leaving enough flexibility to the scientist for choosing the model that fits their requirements. <s> BIB010 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> Abstract Mobile sensing has become a promising paradigm for mobile users to obtain information by task crowdsourcing. However, due to the social preferences of mobile users, the quality of sensing reports may be impacted by the underlying social attributes and selfishness of individuals. Therefore, it is crucial to consider the social impacts and trustworthiness of mobile users when selecting task participants in mobile sensing. In this paper, we propose a S ocial A ware C rowdsourcing with R eputation M anagement (SACRM) scheme to select the well-suited participants and allocate the task rewards in mobile sensing. Specifically, we consider the social attributes, task delay and reputation in crowdsourcing and propose a participant selection scheme to choose the well-suited participants for the sensing task under a fixed task budget. A report assessment and rewarding scheme is also introduced to measure the quality of the sensing reports and allocate the task rewards based the assessed report quality. In addition, we develop a reputation management scheme to evaluate the trustworthiness and cost performance ratio of mobile users for participant selection. Theoretical analysis and extensive simulations demonstrate that SACRM can efficiently improve the crowdsourcing utility and effectively stimulate the participants to improve the quality of their sensing reports. <s> BIB011 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> Crowdsourcing-based mobile applications are becoming more and more prevalent in recent years, as smartphones equipped with various built-in sensors are proliferating rapidly. The large quantity of crowdsourced sensing data stimulates researchers to accomplish some tasks that used to be costly or impossible, yet the quality of the crowdsourced data, which is of great importance, has not received sufficient attention. In reality, the low-quality crowdsourced data are prone to containing outliers that may severely impair the crowdsourcing applications. Thus in this work, we conduct pioneer investigation considering crowdsourced data quality. Specifically, we focus on estimating user motion trajectory information, which plays an essential role in multiple crowdsourcing applications, such as indoor localization, context recognition, indoor navigation, etc. We resort to the family of robust statistics and design a robust trajectory estimation scheme, name TrMCD, which is capable of alleviating the negative influence of abnormal crowdsourced user trajectories, differentiating normal users from abnormal users, and overcoming the challenge brought by spatial unbalance of crowdsourced trajectories. Two real field experiments are conducted and the results show that TrMCD is robust and effective in estimating user motion trajectories and mapping fingerprints to physical locations. <s> BIB012 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues. <s> BIB013 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> The recent paradigm of mobile crowd sensing (MCS) enables a broad range of mobile applications. A critical challenge for the paradigm is to incentivize phone users to be workers providing sensing services. While some theoretical incentive mechanisms for general-purpose crowdsourcing have been proposed, it is still an open issue as to how to incorporate the theoretical framework into the practical MCS system. In this paper, we propose an incentive mechanism based on a quality-driven auction (QDA). The mechanism is specifically for the MCS system, where the worker is paid off based on the quality of sensed data instead of working time, as adopted in the literature. We theoretically prove that the mechanism is truthful, individual rational, platform profitable, and social-welfare optimal. Moreover, we incorporate our incentive mechanism into a Wi-Fi fingerprint-based indoor localization system to incentivize the MCS-based fingerprint collection. We present a probabilistic model to evaluate the reliability of the submitted data, which resolves the issue that the ground truth for the data reliability is unavailable. We realize and deploy an indoor localization system to evaluate our proposed incentive mechanism and present extensive experimental results. <s> BIB014 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> Retaining an existing customer than acquiring a new one is less expensive in terms of marketing cost, bonus, and incentives offered etc. Telecommunication industry across the globe is stiff competitive environment where market is almost saturated and main focus of customer service becomes retaining existing customers and snatching others' customers to increase market share as well as profit. At the same time telecom industry facing the problem of churn or attrition more than anything especially for prepaid customers as the customers could switch the service providers easily. Telecom operators generate huge volume of call detail records every day for every call, SMS or internet access made by its customers. Telecom operators use these huge operational data for business processing to understand customer behavior. In this paper a mechanism is developed to store CDR data in a suitable Data Warehouse (DW) schema and analytically process these using OLAP tools to understand the prepaid customers usage, spending and propensity to marketing offers. Depending on the usage pattern proper segmentation of the customers has been done and they have been categorized for different types of targeted marketing offers and benefits to retain as well as acquire new customers. <s> BIB015 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PARTICIPANT SELECTION <s> In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development. <s> BIB016
To perfectly select the participants in mobile crowd-sensing paradigm, several points need to be covered such as participant location, participant privacy, participant incentive, and participant connectivity Nowadays, a huge amount of research has been initiated to appropriately consider the following concerns regarding participant location, privacy, incentives and connectivity: 1-Participant location: The work-organizer selects some participants whose locations are almost independent, assuming that there is sufficiently number of participants available [34] 2-Participant privacy: Several privacy techniques require the locations of various participants to be uncorrelated to protect the participant location BIB006 3-Participant incentive: Mechanism in an MCS architecture framework needs incentives in order to increase the human participant's motivation to take part in the MCS tasks and cover their mobile data cost. Without the incentives mechanism, the participants will be reluctant to collect and deliver the high-quality data BIB001 . 4-Participant connectivity: Usually mobile devices are equipped with numerous wireless communication interfaces such as Bluetooth, Cellular, ZigBee, WiFi, and other interfaces maintained by several wireless technologies. Cellular data provides long-range communication infrastructure, while WiFi is the mid-range of communication, and ZigBee and Bluetooth provide short-range communication  . For example, to obtain information for participant selection, crowdsourcing application that uses CDR collected from every user on the communication network can be used. CDR includes a detailed record containing extensive spectrum of related information: the telephone numbers, tower 3858 VOLUME 7, 2019 FIGURE 2. MCS paradigm for participatory and opportunistic sensing BIB002 . location, calling start time, calling duration, and calling and receiving terminals as well as an international standardized unique number to identify a mobile subscriber called International Mobile Subscriber Identity (IMSI) BIB013 , BIB015 . CDRs may contain a large amount of information on how, when, and with whom one communicates, hence increase the charges from the mobile phone operators. In addition, most CDRs contain location, and customer's external data such as age or gender, and both information on voice calls and text messages. However, to combine both portions of information into one simple measure is not yet clear. Moreover, it seems that there is a difference in the use of messages or priority between texts and voice calls contained in measures, which takes into account one type of communication BIB016 , BIB003 . Such collection of personal data makes CDRs a very rich source of data for researchers. Finally, the structure of a social network from CDRs should be continuous, taking into consideration that there is no one-size-fits-all technique available. CDRs are generated in real-time so it can exist almost instantaneously for mining, whereas billing data is available only monthly. Typical CDR data fields are shown in Table 1 . Ren et al. BIB011 propose a Social Aware Crowdsourcing with Reputation Management (SACRM) scheme for participant location. To choose the perfect participant location for the sensing task based on a fixed task budget, the authors propose a participant location selection scheme that is used to evaluate the confidence and cost performance ratio of mobile users for participant location selection. Social elements, task delay, and reputation are all considered. The results show that the proposed reputation management scheme reduces the crowdsourcing cost using the cost performance ratio of the participants for reputation evaluation. MCS brings some concerns about participating users' privacy. Thus, data privacy mechanisms should be enforced on both the mobile devices and the monitoring center to protect private data, such as the user location, the presence of some people at a given place, and the driving patterns of vehicle drivers. As demonstrated in Figure 2 , data privacy enforcement step is applied after data collection regardless of whether participatory or opportunistic MCS. Wang et al. BIB007 proposed a privacy-preserving reputation framework based on blind signatures. While, Christin et al. BIB004 proposed an IncogniSense anonymous reputation framework, which generates a repeat alias by blind signature and then transfers the reputation between these aliases. For participant incentives, Yang et al. BIB005 described two incentive mechanisms to stimulate mobile user participation in platform-centric and user-centric mobile sensing, respectively. To exploit the utility of the platform, the authors presented a Stackelberg game in BIB008 based on an incentive mechanism to represent the platform-centric model. For the user-centric model, the authors design an auction-based incentive mechanism that is demonstrated to be computationally efficient, individually balanced, profitable, and truthful. Meanwhile, Wen et al. BIB014 proposed an incentive approach based on a quality-driven auction with a Wi-Fi fingerprintbased indoor localization system. This model incorporates a theoretical framework into the practical MCS system. Because MCS allows a broad range of mobile applications, the authors introduce a probability model to evaluate the reliability of the provided data. In the best approach of MCS, the worker is paid off based on the quality of data sensed instead of the working time. The authors presented extensive experimental results and proved that the approach is true, reasonable, and public. Moreover, Yang et al. BIB005 addressed the problem of motivating people to participate in a crowdsensing experiment, with a game theoretic analysis of human behavior and the suggestion of an auction-based approach for incentives. This approach is extended by Koutsopoulos BIB009 to provide the intention of an ideal compensation portion that reduces the cost of the incentive provided to participants. However, the incentives are not always possible, and a large number of research applications of the crowdsourcing idea rely on people voluntarily helping in the research project BIB010 or being given non-financial incentives. For participant connectivity, there exist several communication media that have different types of wireless protocols such as ZigBee (IEEE 802.1.5.4), Bluetooth (IEEE 802.15.1), and WiFi (IEEE 802.11b) as well as Cellular data. Details of some of these protocols are presented later in the paper. Some research have been done on optimizing these protocols, for example Zhang et al. BIB012 propose TrMCD, which is a robust route estimation strategy to extenuate the negative impact of abnormal crowdsourced user routes and recognize normal and abnormal users, as well as to attenuate the effect of the location-unbalanced crowdsourced routes.