A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. MCS DATA COLLECTION <s> Victims, volunteers, and relief organizations are increasingly using social media to report and act on large-scale events, as witnessed in the extensive coverage of the 2010-2012 Arab Spring uprisings and 2011 Japanese tsunami and nuclear disasters. Twitter® feeds consist of short messages, often in a nonstandard local language, requiring novel techniques to extract relevant situation awareness data. Existing approaches to mining social media are aimed at searching for specific information, or identifying aggregate trends, rather than providing narratives. We present CrisisTracker, an online system that in real time efficiently captures distributed situation awareness reports based on social media activity during large-scale events, such as natural disasters. CrisisTracker automatically tracks sets of keywords on Twitter and constructs stories by clustering related tweets on the basis of their lexical similarity. It integrates crowdsourcing techniques, enabling users to verify and analyze stories. We report our experiences from an 8-day CrisisTracker pilot deployment during 2012 focused on the Syrian civil war, which processed, on average, 446,000 tweets daily and reduced them to consumable stories through analytics and crowdsourcing. We discuss the effectiveness of CrisisTracker based on the usage and feedback from 48 domain experts and volunteer curators. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. MCS DATA COLLECTION <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> BIB002 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. MCS DATA COLLECTION <s> We here investigate the potential of participatory sensor networks derived from location sharing systems, such as Foursquare, to understand the human dynamics of cities. We propose the City Image visualization technique, which builds a transition graph mapping people's movements between location categories, and demonstrate its use to identify similarities and differences of human dynamics across cities by clustering cities according to their citizens' routines. We also analyze centrality metrics of the transition graphs built for different cities, considering transitions between specific venues. We show that these metrics complement the City Image technique, contributing to a deeper understanding of city dynamics. <s> BIB003 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. MCS DATA COLLECTION <s> In recent times, emergencies such as the 2013 flood in mid Europe have clearly shown that besides the professional emergency services and authorities, citizens get a more and more active role in crisis response work. They organize themselves and coordinate private relief activities. Those activities can be found in (physical) groups of affected local citizens, but also within (digital) social media groups. To detect and use this civil potential by professional emergency services, approaches are needed that support the instructing of citizens and coordinating of their actions to avoid needless duplications or conflicts. In this paper we present a concept, based on a mobile crowd sensing approach, which was designed as well as implemented as the system prototype CrowdMonitor and facilitates the monitoring of physical and digital activities of and the assignment of specific tasks to citizens. <s> BIB004 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. MCS DATA COLLECTION <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> BIB005 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. MCS DATA COLLECTION <s> As the competition in mobile markets becomes fiercer, telecom operators have devoted themselves to serving subscribers better. Among large number of subscribers, there are some anomalous users who should be paid more attention to, such as those issuing a lot of unsolicited calls to normal subscribers or making calls to one single phone number continuously. Our study is based on the data of Call Detail Records(CDR) of 10,000 subscribers provided by a Chinese telecom operator, who are randomly sampled from a high-tech industry district for one month. In this paper, we distinguish the abnormal users from the normal ones using agglomerative hierarchical clustering, and divide the anomalous users into telecom marketers or frauds, mobile hot lines and testers. We find that all the anomalous users are distributed in urban areas. In addition, testers and telecom marketers or frauds have relatively weak mobility, and are located in the centralized areas. On the contrary, users being recognized as mobile hot lines show the characteristics of high mobility and relatively decentralized distribution in geographical aspects. <s> BIB006 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. MCS DATA COLLECTION <s> Nowadays, there are two significant tendencies, how to process the enormous amount of data, big data, and how to deal with the green issues related to sustainability and environmental concerns. An interesting question is whether there are inherent correlations between the two tendencies in general. To answer this question, this paper firstly makes a comprehensive literature survey on how to green big data systems in terms of the whole life cycle of big data processing, and then this paper studies the relevance between big data and green metrics and proposes two new metrics, effective energy efficiency and effective resource efficiency in order to bring new views and potentials of green metrics for the future times of big data. <s> BIB007
In MCS, there are two different data source types: mobile sensing and user-generated data in mobile social network services. Mobile sensing is a method of data collection using participatory sensing, while mobile social networks rapidly bridge the gap between online interactions and physical elements BIB002 . Maji and Sen BIB005 developed a mechanism to store CDR data in appropriate data warehouse schematic and analytically process the data using On-Line Analytical Processing (OLAP) server tools to understand the prepaid customer's usage and spending and provide appropriate marketing offers. The system also analyzes the telecom data, such as CDR billing, and proposes customer reporting criteria based on ON net and OFF net call frequency to classify appropriate cutomers for different promotional action types. Authors mentioned that the result shows a percentage of retention and attracts other customers from competitors. Wang and Zhang BIB006 used approach of agglomerative hierarchical clustering to distinguish the abnormal users from normal ones. Authors studied the proposed scheme based on the data of CDR for 10,000 subscribers provided by Chinese telecom operator, which are sampled randomly for one month. The result shows that the abnormal users are distributed in an urban area and normal centralized area. Silva et al. BIB003 proposed a visualization technique called City Image that captures typical transition between Points of Interest (PoI) in a city using publicly available data. Based on the people mobility, the technique provides a visual summary of the city dynamics. Moreover, it explores urban transition graphs to user's movements between city locations. Eventually, City Image is a promising technique that allows for a better understanding of the city dynamics, and helps to visualize the common routine of its citizens. Social networks have become extremely popular in information technology in recent years because of the proliferation of online social networks sources such as Facebook, LinkedIn and MySpace. These sources can provide information about human mobility, air quality, traffic patterns, and geographical data. The data are gathered by smartphones, vehicles, machinery, medical equipment, and other machines and then sent to the remote server. In this regard, the large-scale user-contributed data opens a new window to understand the dynamics of the city and society, which constitutes the other data source for mobile crowd-sensing There exist several approaches that attempt to use crowdsourcing utilizing diverse data collection techniques. Some of these approaches focus on actions of the services and actions of the citizens in very critical or different time situations. CrisisTracker (CT) is an online system that captures the distribution state of awareness reports based on social media activities throughout largescale events such as natural disasters. It tracks a huge pool of keywords on Twitter social media and builds stories by gathering tweets that contain these keywords. CT system is used for exploring Twitter with pre-filters based on tweets related to specific disaster and location to provide social awareness from public tweets. It is an open-source web platform and does not use any other social media sources or civil reactions for its sensing BIB001 . Another open-source crisis-mapping platform called Ushahidi was used on January 12, 2010, when a 7.0-degree magnitude earthquake struck Haiti. Ushahidi provides a way to capture, organize, and share information about dangers coming immediately from Haitians. Ushahidi collects information from two sources, text messages received via mobiles phones, and social media (e.g., Twitter, Facebook, blogs). Eventually, it tries to serve expert organizations with possibilities for demanding citizens or digital supporters to collect important data or to share information  . Wu et al.  present an innovative framework for collecting critical information in a particular disaster area from endusers and mobile devices in order to support timely suitable contextual reconstruction and rescue operations. Both crowdsourcing and crowd-sensing pose the challenge of handling excessive amount of data which requires leveraging big data processing techniques BIB007 -  . Ludwig et al. BIB004 prototyped a crowd monitoring system based on the concept of mobile phone crowd-sensing approach. The design and implementation of the concept of crowd monitoring combine monitoring of physical activities of local citizens with digital social media activities. In addition, it assigns tasks to citizens for emergency services. The tasks can vary from filling sandbags for example to collecting crisis-related information. The authors explored the impact of citizen-generated content on social media during a specific emergency event. They also explored on-site and off-site citizen involvement. Eventually, it led to the implementation of a web-based application called ''CrowdMonitor'' to use the observed findings to support assessment and collaboration between citizens and emergency services. Finally, with the help of this approach, emergency services can collect information from social media or from local citizens and could obtain a better overview of the event.