A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> The vast majority of advances in sensor network research over the last five years have focused on the development of a series of small-scale (100s of nodes) testbeds and specialized applications (e.g., environmental monitoring, etc.) that are built on low-powered sensor devices that self-organize to form application-specific multihop wireless networks. We believe that sensor networks have reached an important crossroads in their development. The question we address in this paper is how to propel sensor networks from their small-scale application-specific network origins, into the commercial mainstream of people's every day lives; the challenge being: how do we develop large-scale general-purpose sensor networks for the general public (e.g., consumers) capable of supporting a wide variety of applications in urban settings (e.g., enterprises, hospitals, recreational areas, towns, cities, and the metropolis). We propose MetroSense, a new people-centric paradigm for urban sensing at the edge of the Internet, at very large scale. We discuss a number of challenges, interactions and characteristics in urban sensing applications, and then present the MetroSense architecture which is based fundamentally on three design principles: network symbiosis, asymmetric design, and localized interaction. The ability of MetroSense to scale to very large areas is based on the use of an opportunistic sensor networking approach. Opportunistic sensor networking leverages mobility-enabled interactions and provides coordination between people-centric mobile sensors, static sensors and edge wireless access nodes in support of opportunistic sensing, opportunistic tasking, and opportunistic data collection. We discuss architectural challenges including providing sensing coverage with sparse mobile sensors, how to hand off roles and responsibilities between sensors, improving network performance and connectivity using adaptive multihop, and importantly, providing security and privacy for people-centric sensors and data. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application's design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges. <s> BIB002 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> The development of sensing systems for urban deployments is still in its infancy. An interesting unresolved issue is the precise role assumed by people within such systems. This issue has significant implications as to where the complexity and the main challenges in building urban sensing systems will reside. This issue will also impact the scale and diversity of applications that are able to be supported. We contrast two end-points of the spectrum of conscious human involvement, namely participatory sensing, and opportunistic sensing. We develop an evaluation model and argue that opportunistic sensing more easily supports larger scale applications and broader diversity within such applications. In this paper, we provide preliminary analysis which supports this conjecture, and outline techniques we are developing in support of opportunistic sensing systems. <s> BIB003 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system. <s> BIB004 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity. <s> BIB005 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and/or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes. <s> BIB006 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone - a powerful sensor that is capable of making sophisticated inferences about human activity, location, and social events from sound. In this paper, we exploit this untapped sensor not in the context of human communications but as an enabler of new sensing applications. We propose SoundSense, a scalable framework for modeling sound events on mobile phones. SoundSense is implemented on the Apple iPhone and represents the first general purpose sound sensing system specifically designed to work on resource limited phones. The architecture and algorithms are designed for scalability and Soundsense uses a combination of supervised and unsupervised learning techniques to classify both general sound types (e.g., music, voice) and discover novel sound events specific to individual users. The system runs solely on the mobile phone with no back-end interactions. Through implementation and evaluation of two proof of concept people-centric sensing applications, we demostrate that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives. <s> BIB007 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> This paper describes the development and evaluation of wearable sensor earphones coupled with a mobile platform for comfortable assessment of cardiovascular function. Photoplethysmographic sensors were integrated into regular earphones to provide discreet and non-stigmatizing heart rate measurements. Preliminary results indicate that the proposed system has a high degree of accuracy at rest with an average error of 0.63%. <s> BIB008 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed. <s> BIB009 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research. <s> BIB010 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <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> BIB011 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Problem statement: The Wireless Body Area Sensor Networks (WBASNs) is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. This study surveys the state-of-the-art on Wireless Bo dy Area Networks, discussing the major components of research in this area including physiological sensin g and preprocessing, WBASNs communication techniques and data fusion for gathering data from sensors. In addition, data analysis and feedback will be presen ted including feature extraction, detection and classification of human related phenomena. Approach: Comparative studies of the technologies and techniques used in such systems will be provided in this study, using qualitative comparisons and use case analysis to give insight on potential uses for diff erent techniques. Results and Conclusion: Wireless Sensor Networks (WSNs) technologies are considered as one of the key of the research areas in computer scienc e and healthcare application industries. Sensor suppl y chain and communication technologies used within the system and power consumption therein, depend largely on the use case and the characteristics of the application. Authors conclude that Life-saving appl ications and thorough studies and tests should be conducted before WBANs can be widely applied to humans, particularly to address the challenges related to robust techniques for detection and classification to increase the accuracy and hence the confidence o f applying such techniques without physician intervention. Key word: Wireless body area sensor network, physiological s ensing, data preprocessing, wireless sensor communications, data fusion, classification algorithms, Chronic Disease (CD) <s> BIB012 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction. <s> BIB013 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Data and continuous data streams from mobile users/devices are becoming increasingly important for numerous applications including urban modelling, transportation, and more recently for mobile crowd-sensing to support citizen journalism and participatory sensing where sensor informatics and social networking meet. While significant efforts have focused towards the analysis of mobile user data, a key challenge that needs to be addressed in order to realize the full-potential is to address the scalability issues of real-time data collection and processing at run time. By scalability, we refer to both the challenges of data capture from a large number of users, as well as the issues of energy consumed on individual devices as a result of that capture. In this paper, we present mobile/on-board data stream mining as an effective approach to address the scalability issues of mobile data collection and run-time processing and as a significant component of mobile run-time analytics. We present experimental evaluation using the Nokia mobile data challenge open track dataset to show the significant energy and bandwidth savings that mobile data stream mining can achieve with no significant loss of useful information in this process. <s> BIB014 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Mobile crowd sensing is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous largescale applications. Human involvement is one of the most important features, and human mobility offers unprecedented opportunities for both sensing coverage and data transmission. In this article, we investigate the opportunistic characteristics of human mobility from the perspectives of both sensing and transmission, and discuss how to exploit these opportunities to collect data efficiently and effectively. We also outline various open issues brought by human involvement in this emerging research area. <s> BIB015 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Crowdsensing offers an efficient approach to meet the demand in large scale sensing applications. In crowdsensing, it is of great interest to find the optimal task allocation, which is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations and mobile users are constrained by time budgets. We show that the allocation problem is NP hard. We then focus on approximation algorithms, and devise an efficient local ratio based algorithm (LRBA). Our analysis shows that the approximation ratio of the aggregate rewards obtained by the optimal allocation to those by LRBA is 5. This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4. We also discuss about how to decide the fair prices of sensing tasks to provide incentives since mobile users tend to decline the tasks with low incentives. We design a pricing mechanism based on bargaining theory, in which the price of each task is determined by the performing cost and market demand (i.e., the number of mobile users who intend to perform the task). Extensive simulation results are provided to demonstrate the advantages of our proposed scheme. <s> BIB016 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS. <s> BIB017 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Monitoring and tracking people at home usually requires high cost hardware installations, which implies they are not affordable in many situations. This study/paper proposes a monitoring and tracking system for people with medical problems. A virtual organization of agents based on the PANGEA platform, which allows the easy integration of different devices, was created for this study. In this case, a virtual organization was implemented to track and monitor patients carrying a Holter monitor. The system includes the hardware and software required to perform: ECG measurements, monitoring through accelerometers and WiFi networks. Furthermore, the use of interactive television can moderate interactivity with the user. The system makes it possible to merge the information and facilitates patient tracking efficiently with low cost. <s> BIB018 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <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> BIB019 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Smartphone-based crowdsourcing fosters the rise of radically novel systems and applications in the context of network monitoring. This article discusses the most significant opportunities offered by this approach, and the major challenges that have to be faced. Our experience in building a smartphone-based crowdsourcing system, Portolan, is also included to provide a practical background to the discussion and to demonstrate the possible benefits. <s> BIB020 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Measurement of network performance in complex WiFi networks, such as in corporations and universities, is an important but challenging task, as wireless performance in networks with many access points is hard to measure and model. We demonstrate that crowdsourcing the task of measuring WiFi performance is an effective solution to this problem. By measuring performance directly with unmodified consumer mobile devices such as smartphones, it is possible to cheaply and easily detect problems that matter to users. Aggregated performance data across clients can then provide a global view of performance trends in an enterprise network. We demonstrate that periodic sampling allows the collection of representative data while keeping battery consumption low, and we leverage mobile sensor information to intelligently schedule these measurements. This system was deployed in two different large WLANs, where we discovered numerous previously undetected performance problems. <s> BIB021 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <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> BIB022 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Mobile crowdsensing serves as a critical building block for the emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy and storage), and may sacrifice the quality-of-service (QoS) of applications. Prior work has demonstrated that there is significant redundancy in the content of the sensed data. By judiciously reducing the redundant data, the data size and the load can be significantly reduced, thereby reducing resource cost, facilitating the timely delivery of unique, probably critical information and enhancing QoS. This paper presents a survey of existing works for the mobile crowdsensing strategies with emphasis on reducing the resource cost and achieving high QoS. We start by introducing the motivation for this survey, and present the necessary background of crowdsensing and IoT. We then present various mobile crowdsensing strategies and discuss their strengths and limitations. Finally, we discuss the future research directions for mobile crowdsensing. The survey addresses a broad range of techniques, methods, models, systems and applications related to mobile crowdsensing and IoT. Our goal is not only to analyze and compare the strategies proposed in the prior works but also to discuss their applicability towards the IoT, and provide the guidance on the future research direction of mobile crowdsensing. <s> BIB023 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> I. INTRODUCTION <s> Carpool commuting enables multiple individual travelers with similar schedules and itineraries to share a common vehicle during a trip, and travelers can split travel costs in gas, parking and tolls with each other. It emerges as an effective way to solve traffic congestion, parking space tension and air pollution resulting from vehicle emissions. One of the challenges that restrict widespread adoption of carpool commuting lies in matching carpoolers. Existing carpooler matching methods include building carpool lanes in main airports and bus stops, using centralized servers to identify carpoolers based on historical travel data or real time travel requests. However, these methods cannot be applied to large scale adoption or incur long matching latency. To overcome drawbacks of existing methods, we propose VShare, a dynamic carpool system that leverages the wireless social network characteristic and hierarchical cloud server architecture. VShare incorporates two design components: matching through the wireless social network and a hierarchical cloud server structure. Upon receiving a user travel request, VShare first identifies possible carpoolers from neighbors in nearby locations, which reduces latency of sending travel requests to remote servers. If no carpool is found within nearby locations, a hierarchical cloud server architecture is used to match the travel requests. We have implemented the design of VShare and conducted trace-driven experiments. Experimental results show the effectiveness of VShare in substantially reducing matching latency while providing high success rate in matching carpollers. <s> BIB024
Smart phones are ubiquitous mobile devices expected to proliferate rapidly, and their penetration is estimated to be in the order of billions worldwide. Delivery applications such as mobile application stores (Apple AppStore, Google Play Store, etc.) and social media have transformed mobile phones into intelligent computing devices using the instant download of applications BIB023 . Smartphone vendors are continuously increasing the number of built-in sensors, a fact that makes them an excellent contextual information provider. Thus, smartphones can be used for large scale sensing of the physical world at low cost by leveraging the available sensors on the phones. With the proliferation of smartphones, several sensing approaches have emerged such as mobile phone sensing BIB024 . To enhance the user experience, many of the applications that come installed or can be downloaded from the online application delivery platforms take advantage of sensors available on the phone. The fixed sensors on the smartphone offer the chance to develop innovative applications in many sectors such as environmental monitoring, healthcare, and transportation BIB015 . In such applications, smartphones play the role of base sensor nodes and gateways depending on the availability of the mobile phones within a region of interest. Similarly, sensors deployed in today's smartphones are witnessing a continuous improvement of their hardware and software capabilities Smartphones can gather process and transfer data between users. Eventually, smartphones, similar to static nodes are capable of sensing, computing, and communicating BIB002 , BIB013 . However, the main difference is that smartphones are moving and repositioning themselves in the network all the time. In such innovative sensory networks, smartphones can collaborate with traditional sensor nodes to form a global-scale network formed by billions of sensor nodes that are dynamically deployed, to support the needs of a variety of applications and services. The embedded sensors in mobile phones are leveraged for various sensing tasks for MCS applications of particular interest BIB016 . MCS takes advantage of large number of smartphone devices to collect data efficiently enabling several significant applications BIB023 . MCS refers to the use of smartphones owned by a diversity of participants to gather and share data of incidents of common interest. The basic idea of MCS is trying to consolidate the sensing idea with a ''collective'' sensing view The sensed data collected using fixed sensors in traditional sensing techniques is substituted by data collected from citizen-generated content within social media or applications using smartphones BIB014 , BIB017 . This creates a great amount of information, which complements sensor data with the opinions and experiences of citizens  . MCS allows a large number of smartphones to be used for activities that have a large public impact and the exchange of information between their users and can be supported by the cloud. With the support of the cloud, data fusion techniques can be applied on the information collected from the smartphones. This allows mobile sensing to be a multipurpose platform that can replace static sensing infrastructure to support a comprehensive range of applications from smart city applications and safety to monitoring the environment  . An emerging sensing paradigm is mobile crowd-sensing  which comes in a variety of modes: (i) opportunistic sensing BIB001 , BIB003 , (ii) participatory sensing BIB001 , and (iii) people/human-centric sensing BIB001 ,  , BIB011 . The traditional sensor network supports a single use case, while crowd-sensing data may contribute to various use cases [16] . Mobile sensing devices are extensively available and are a rich and inexpensive source of sensing data. Recently, varieties of sensors exist in most smartphones for different purpose. iPhone users represent 59.6 million in one database with embedded sensors including GPS, accelerometer, gyroscope, ambient light, proximity, microphone, and camera sensors. Light sensors are used for fine-tuning the screen brightness. Both proximity and light sensors permit the phone to complete simple forms of context recognition linked with the user interface. The GPS allows the phone to localize itself and enables regional or location aware searches, navigation, and mobile social networking applications BIB006 , BIB004 . Both compasses and gyroscopes determine direction and orientation, improving location based applications depending on the GPS BIB010 . The accelerometer has a different role, it can be used to identify several activities such as running, walking, and standing. The most powerful sensors found in any smartphone today are the camera and microphone, which are possibly the most global sensors on the planet BIB007 . The camera on the front of the phone can also be used for conventional tasks such as tracing the user's eye movement as an intermediary to start applications BIB008 , BIB012 . In addition to traditional purposes for the aforementioned sensors, more advanced applications are developed to further utilize the sensors. The mode of transportation of a user can be detected by a combination of accelerometer data and location from the GPS sensor This combination of sensor readings allows us to detect if the user is riding a bike, a car, a bus or even walking or running BIB018 . The sensors allow new applications in a wide diversity of domains, such as healthcare that uses accelerometer to measure the user's activities BIB005 ; safety and environmental monitoring which uses GPS for location data collection BIB009 ; transportation which also uses GPS BIB006 , [26] ; social networks based on Wi-Fi and GPS BIB006 These can open new areas of research called mobile phone sensing. The MCS research is focused on the possibility of growing pervasive urban and individual mobile technologies to support citizen daily life. The evolution of mobile phones has often been paired with the introduction of new sensors. For example, accelerometers have become more popular after being utilized to enhance photos captured by the camera and after using it in graphical user interface BIB010 . Sensors are used to automatically determine the orientation of the mobile phone screen and use that information to mechanically switch the display between landscape and portrait or properly orient the photos taken. Mobile crowd-sensing can be considered as crowdsourcing where the resource provided by the crowd is their sensing capabilities. Crowdsourcing is a group of outsourcing techniques that utilizes independent, volunteer, and paid human resources to complete a specific task BIB019 . It is also a process in which a task, a project, or a problem is performed by a group of private and geographically isolated participants. The participating members are compensated or provided with recognition once the problem is solved or the task is completed. Smartphone-based crowd-sensing take advantage of the tremendous growth in network-monitoring applications. Several smartphone based crowdsourcing applications use the call data records (CDR). The CDRs of social network refers to the information on communications between a large number of people at a certain time, which contains actual observations of communications between people and is stored by all telecom operators, though data semantics vary slightly among them BIB022 . Faggiani et al. BIB020 discuss the most important opportunities offered by crowdsourcing and the associated key challenges. Portolan, a smartphonebased crowdsourcing system, has been built to demonstrate possible benefits of crowdsourcing. For a large number of clients, the Portolan server is used to coordinate the activities of data collection. Portolan is able to build signal coverage maps and produce a graph of the Internet at the autonomous system (AS) level. Furthermore, Ren et al. BIB019 focus on the use of crowdsourcing for data collection to address numerous solid challenges in widespread computing systems such as participatory urbanism that encourages new methods and approaches for individual citizens to become active participants within their city, neighborhood, and urban selfreflexivity. Another example is the use of mobile phones as environmental sensing platforms that support community action to enforce positive societal change. Rosen et al. BIB021 proposed a Mobile Crowdsourcing Network for wireless network management (MCNet). This tool permits users to implement crowdsourcing of WiFi performance measurements. Wi-Fi was used to communicate between mobile devices such as smartphones or tablets and many access points that are fixed in a university or organization setting. Few hundred users used the MCNet application tool, where at a certain time at least 20 mobile devices should be connected to the network to guarantee an efficient crowdsourcing of measurements. They deployed this tool in one corporate and one university WLAN. They worked together with the network IT engineer to enhance the latency and throughput to as high as 37% and 38%, respectively. Their results showed that MCNet is an effective, practical system for crowdsourced Wi-Fi performance measurements in large and complex WLANs.