A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> Wireless Body Area Networks (WBANs) provide efficient communication solutions to the ubiquitous healthcare systems. Health monitoring, telemedicine, military, interactive entertainment, and portable audio/video systems are some of the applications where WBANs can be used. The miniaturized sensors together with advance micro-electro-mechanical systems (MEMS) technology create a WBAN that continuously monitors the health condition of a patient. This paper presents a comprehensive discussion on the applications of WBANs in smart healthcare systems. We highlight a number of projects that enable WBANs to provide unobtrusive long-term healthcare monitoring with real-time updates to the health center. In addition, we list many potential medical applications of a WBAN including epileptic seizure warning, glucose monitoring, and cancer detection. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> Medical body area networks will employ both implantable and bodyworn devices to support a diverse range of applications with throughputs ranging from several bits per hour up to 10 Mbps. The challenge is to accommodate this range of applications within a single wireless network based on a suitably flexible and power efficient medium access control protocol. To this end, we present a Medical Medium Access Control (MedMAC) protocol for energy efficient and adaptable channel access in body area networks. The MedMAC incorporates a novel synchronisation mechanism and initial power efficiency simulations show that the MedMAC protocol outperforms the IEEE 802.15.4 protocol for two classes of medical applications. <s> BIB002 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> In this paper, we present a portable intelligent electrocardiogram (ECG) monitoring system using the Zigbee wireless network technology. The system consist of both hardware and software development. For the hardware development, a Field Programmable Gate Arrays (FPGA), ECG sensor, preprocessing circuit, Zigbee transmission module, and external memory are used. And the FPGA with a Nios II soft-core CPU is the heart of the system, which is used to control all other units. As for the software development, a real-time system called μC/OS-II is used and some related application programs is designed. The objective of this system is to continuously monitor cardiac patients. <s> BIB003 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> Crowd behavior research gets more and more important, because crowd has a matter of common livelihood. However, estimation of looking at crowd behavior in a large field of view hasn't had adequate study done. To solve above problem, this paper presents the fluctuation of ZigBee's Received Signal Strength Indicator (RSSI) effected crowd behavior indoor space and the possibility of estimation of crowd behavior using ZigBee's RSSI from the macro standpoint. To begin with, we define the crowd behavior in indoor space. Next, we measure ZigBee's RSSI in those of environment. Finally, according to the given result about environment, we analyze time-series of RSSI, including the possibility of knowledge for crowd behavior from ZigBee's RSSI. <s> BIB004 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <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> BIB005 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> Acquisition of data to capture human mobility and interactions during large-scale events is a challenging task. In this paper we discuss a mobile sensing method for mapping the mobility of crowds at large scale events using a participatory Bluetooth sensing approach. This non-invasive technique for collecting spatio-temporal data about participant mobility and social interactions uses the capabilities of Bluetooth capable smartphones carried by participants. As a proof-of-concept we present a field study with deployment of the method in a large music festival with 130000 participants where a small subset of participants installed Bluetooth sensing apps on their personal smartphones. Our software module uses location and Bluetooth scans to utilize smartphones as provisional scanners that are present with higher frequency in regions with high density of participants. We discuss the initial results obtained and outline opportunities and challenges introduced by this methodology along with opportunities for future pervasive systems and applications. <s> BIB006 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different physiological brain states. In this paper, we propose an efficient framework for evaluating the power-accuracy trade-off for EEG-based compressive sensing and classification techniques in the context of epileptic seizure detection in wireless tele-monitoring. The framework incorporates compressive sensing-based energy-efficient compression, and noisy wireless communication channel to study the effect on the application accuracy. Discrete cosine transform (DCT) and compressive sensing are used for EEG signals acquisition and compression. To obtain low-complexity energy-efficient, the best data accuracy with higher compression ratio is sought. A reconstructed algorithm derived from DCT of daubechie's wavelet 6 is used to decompose the EEG signal at different levels. DCT is combined with the best basis function neural networks for EEG signals classification. Extensive experimental work is conducted, utilizing four classification models. The obtained results show an improvement in classification accuracies and an optimal classification rate of about 95% is achieved when using NN classifier at 85% of CR in the case of no SNR value. The satisfying results demonstrate the effect of efficient compression on maximizing the sensor lifetime without affecting the application's accuracy. <s> BIB007 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> This survey aims to encourage the multidisciplinary communities to join forces for innovation in the mobile health monitoring area. Specifically, multidisciplinary innovations in medical emergency scenarios can have a significant impact on the effectiveness and quality of the procedures and practices in the delivery of medical care. Wireless body sensor networks (WBSNs) are a promising technology capable of improving the existing practices in condition assessment and care delivery for a patient in a medical emergency. This technology can also facilitate the early interventions of a specialist physician during the pre-hospital period. WBSNs make possible these early interventions by establishing remote communication links with video/audio support and by providing medical information such as vital signs, electrocardiograms, etc. in real time. This survey focuses on relevant issues needed to understand how to setup a WBSN for medical emergencies. These issues are: monitoring vital signs and video transmission, energy efficient protocols, scheduling, optimization and energy consumption on a WBSN. <s> BIB008 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> Thank you for downloading simulation and software radio for mobile communications. Maybe you have knowledge that, people have look hundreds times for their chosen books like this simulation and software radio for mobile communications, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some harmful bugs inside their computer. <s> BIB009 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> Recently, mobile crowd sensing (MCS) is an important approach for collecting data from the participants in the urban streets. The main problem in collecting data process is how to cover all segments the street sides and select a minimal number of participants in each street segment that preserves the energy of the mobile devices and prolongs the MCS network lifetime. To solve this problem, a new flow coverage scheme is proposed to cover a specific street and achieve the coverage requirements. The proposed scheme is based on using a modified localization method that uses a minimal usage of GPS and utilizes the Zig-bee technology to communicate with the neighbor nodes and estimate the distance between nodes by using the Time of Arrival method. Experimental results by using a real data show that the proposed localization and coverage scheme can achieve high localization accuracy, reduce the usage of location sensors, and prove that the proposed street coverage scheme achieves the coverage requirements. <s> BIB010 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> The unprecedented growth of today’s cities together with increased population mobility are fueling the avalanche in the numbers of vehicles on the roads. This development led to the new challenges for the traffic management, including the mitigation of road congestion, accidents, and air pollution. Over the last decade, researchers have been focusing their efforts on leveraging the recent advances in sensing, communications, and dynamic adaptive technologies to prepare the deployed road traffic management systems (TMS) for resolving these important challenges in future smart cities. However, the existing solutions may still be insufficient to construct a reliable and secure TMS that is capable of handling the anticipated influx of the population and vehicles in urban areas. Along these lines, this work systematically outlines a perspective on a novel modular environment for traffic modeling, which allows to recreate the examined road networks in their full resemblance. Our developed solution is targeted to incorporate the progress in the Internet of Things (IoT) technologies, where low-power, embedded devices integrate as part of a next-generation TMS. To mimic the real traffic conditions, we recreated and evaluated a practical traffic scenario built after a complex road intersection within a large European city. <s> BIB011 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> The plummeting cost of Bluetooth tags and the ubiquity of mobile devices are revolutionizing the traditional lost-and-found service. This paper presents SecureFind, a secure and privacy-preserving object-finding system via mobile crowdsourcing. In SecureFind, a unique Bluetooth tag is attached to every valuable object, and the owner of a lost object submits an object-finding request to many mobile users via the SecureFind service provider. Each mobile user involved searches his vicinity for the lost object on behalf of the object owner who can infer the location of his lost object based on the responses from mobile users. SecureFind is designed to ensure strong object security such that only the object owner can discover the location of his lost object as well as offering location privacy to mobile users involved. The high efficacy and efficiency of SecureFind are confirmed by extensive simulations. <s> BIB012 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> In recent years, there have been efforts to collect human contact traces during social events (e.g., conferences) using Bluetooth devices (e.g., mobile phones, iMotes). The results of these studies have enabled the ability to do the crowd-sourcing task from within the crowd, in order to answer questions, such as: what is the current density of the crowd, or how many people are attending the event? However, in those studies, the sensing devices are usually distributed and configured in a certain manner. For example, the number of devices is fixed, people register for the devices on a volunteering basis. In this paper, we treat the above problem as an optimization problem and draw the connection to the vertex cover problem in graph theory. Since finding the optimal solution for minimum vertex cover problem is NP-complete, approximation algorithms have to be used. However, we will show that the well-known approximation algorithms do not perform well with the crowd-sensing task. In this paper, we propose the notions of node observability and coverage utility score and design a new context-aware approximation algorithm to find vertex cover that is tailored for crowd-sensing task. In addition, we design human-centric bootstrapping strategies to make initial assignment of sensing devices based on meta information about the participants (e.g., interests, friendship). The motivation is to assign the sensing task to a more "socialized" device to obtain better sensing coverage. We perform comprehensive experiments on real-world data traces obtained from previous experimental studies in conference and academic social context. The results show that our proposed approach significantly outperforms the baseline approximation algorithms in terms of sensing coverage. <s> BIB013 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> In this paper, we propose a Mobile Crowd Sensing application based on Community Information-Centric Networking to collect opportunistic sensing data in limited area where it restricts radius of Bluetooth Low Energy beacon. The application has more valuable features by comparing with common Mobile Crowd Sensing one. First is to support data integrity and utilize simple communication model according to IP-less communication through common CICN features. Secondly, to gather sensing data by using a name including BLE beacon Identifier. The application inherently supports user privacy and data integrity. We deployed several environmental sensors with Raspberry Pis and BLE beacons together in different places across a building. The developed MCS application collects sensing data as the user walks around in a smart building and draws building sensor map. We show implementation result as well as simple analysis work. <s> BIB014 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> Cyber-physical systems (CPS) help create new services and applications by revolutionising our world in different fields through their tight interactions and automated decisions. This is especially true with the ongoing increase in the number of physical things (sensors, actuators, smartphones, tablets, and so on) along with the explosive increase in the usage of online networking services and applications. Future fifth generation (5G) cellular networks will facilitate the enabling of CPS communications over current network infrastructure through different technologies such as device-to-device (D2D) communications. In this study, the authors discuss about the main challenges that cellular providers will face as the massive number of CPS devices attempt to access the cellular spectrum. A case study is presented on how to ease the spectrum access of these devices through D2D spatial spectrum sensing. Furthermore, the authors discuss about protecting these D2D links from eavesdropping, since security is becoming a critical aspect in the cyber-physical space, especially with the large amount of traffic that is constantly flowing through the network. <s> BIB015 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> Existing Location-based social networks (LBSNs), e.g., Foursquare, depend mainly on GPS or cellular-based localization to infer users’ locations. However, GPS is unavailable indoors and cellular-based localization provides coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor environments, where people spend 89 percent of their time. This in turn affects the user experience, in terms of the accuracy of the ranked list of venues, especially for the small screens of mobile devices, misses business opportunities, and leads to reduced venues coverage. In this paper, we present CheckInside: a system that can provide a fine-grained indoor location-based social network. CheckInside leverages the crowd-sensed data collected from users’ mobile devices during the check-in operation and knowledge extracted from current LBSNs to associate a place with a logical name and a semantic fingerprint. This semantic fingerprint is used to obtain a more accurate list of nearby places as well as to automatically detect new places with similar signature. A novel algorithm for detecting fake check-ins and inferring a semantically-enriched floorplan is proposed as well as an algorithm for enhancing the system performance based on the user implicit feedback. Furthermore, CheckInside encompasses a coverage extender module to automatically predict names of new venues increasing the coverage of current LBSNs. Experimental evaluation of CheckInside in four malls over the course of six weeks with 20 participants shows that it can infer the actual user place within the top five venues 99 percent of the time. This is compared to 17 percent only in the case of current LBSNs. In addition, it increases the coverage of existing LBSNs by more than 37 percent. <s> BIB016 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> Evidence shows that Smart Cities are starting to materialise in our lives through the gradual introduction of the Internet of Things (IoT) paradigm. In this scope, crowdsensing emerges as a powerful solution to address environmental monitoring, allowing to control air pollution levels in crowded urban areas in a distributed, collaborative, inexpensive and accurate manner. However, even though technology is already available, such environmental sensing devices have not yet reached consumers. In this paper, we present an analysis of candidate technologies for crowdsensing architectures, along with the requirements for empowering users with air monitoring capabilities. Specifically, we start by providing an overview of the most relevant IoT architectures and protocols. Then, we present the general design of an off-the-shelf mobile environmental sensor able to cope with air quality monitoring requirements; we explore different hardware options to develop the desired sensing unit using readily available devices, discussing the main technical issues associated with each option, thereby opening new opportunities in terms of environmental monitoring programs. <s> BIB017 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> It is becoming increasingly important to accurately detect a user's presence at certain locations during certain times of the day, e.g., to study the user's patterns with respect to mobility, behavior, or social interactions and to enable the delivery of targeted services. However, instead of geographic locations, it is often more important to determine a locale that is important to the user, e.g., the place of work, home, homes of family and friends, social gathering places, etc. These significant personal places can be determined through analysis, e.g., via segmentation of location traces into a discrete sequence of places. However, segmentation of traces with many gaps (e.g., due to loss of network or GPS signal) results in a large number of small segments, where many of these segments actually belong together. This work proposes a new segmentation approach that opportunistically fills gaps in location traces with the help of data from other (co-located) users. Using data from 195 users, collected over a 2-year period, we show that this approach yields fewer and larger segments, where each segment accurately represents the presence of a user at a significant personal place. <s> BIB018 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> 1) ACCESS COMMUNICATION NETWORK <s> As mobile crowd sensing techniques are steering many smart-city applications, an incentive scheme that motivates the crowd to actively participate becomes a key to the success of such city-scale applications. This paper presents a crowd sensing WiFi advisory system called WiFiScout, which helps smartphone users to find good quality WiFi hotspots. The quality information is defined in terms of user experience and hence the system requires users to contribute information of their experience with WiFi hotspots. To motivate people to contribute such information, we design and implement a gamification-based incentive scheme in WiFiScout. It allows a user to "conquer WiFi territories" by becoming the top contributor for WiFi hotspots at different locations. The contribution is based on the diversity and amount of data a user submits, for which he will be rewarded accordingly. WiFiScout has been implemented on Android and it facilitates the collection of city-wide WiFi advisory information provided by real users according to their actual experience. <s> BIB019
In access communication networks, the communication media have different types of wireless protocols such as ZigBee (IEEE 802.1.5.4), Bluetooth (IEEE 802.15.1), and Wi-Fi (IEEE 802.11b) as well as Global system for mobile communications (GSM)/general packet radio service (GPRS). In the following, we will present a brief description of ZigBee, Bluetooth, and Wi-Fi, as well as 3G/4G/5G as sensor communications media Zigbee is one option enabling the crowd-sensing connectivity through technology based on IEEE 802.15.4 and operates on 2.4 GHz BIB001 . ZigBee is typically used for low data transfer rate, low power consumption due to low physical data rate, and low-cost wireless applications. It can accommodate up to 264 nodes in the network BIB009 . Cai and Liang BIB003 used ZigBee to design and implement an intelligent system for remote health monitoring electroencephalograph (ECG) analysis and diagnosis. ZigBee is designed for selfrecovery network acknowledgements, and it can automatically interpret data messages routed in different areas of a network with different radios without user intervention. Gad-ElRab et al. BIB010 propose a flow coverage scheme based on using a modified localization method that relies on less GPS usage and employs ZigBee technology to cover a specific street and complete the coverage requirements. The scheme uses ZigBee technology in order to communicate with the neighbor nodes and estimate the distance between these nodes using Time of Arrival method. The experimental result shows that the flow coverage scheme and localization reduce the usage of location sensors and demonstrates that the proposed coverage scheme reaches the coverage requirements, and finally achieve high localization accuracy. The ZigBee technology is simpler and less expensive than Bluetooth technology. Arai et al. BIB004 defined and measured ZigBee's Received Signal Strength Indicator (RSSI). They analyzed a time-series of RSSI in indoor space to acquire information about crowd behavior (CB). They showed three CB features, which are: density, velocity, and specific patterns. Despite the extended area covered when using the ZigBee technology, it is not supported by modern smartphones BIB017 . Bluetooth is a wireless technology designed to connect different wireless devices such as telephones, notebooks, PDAs, printers, and computers. Bluetooth provides a short range of 10, which can be increased up to 100 meters, and operates in the 2.4 GHz band with a transmission speed (data rate) of 800 kb/s [73] . Stopczynski et al. BIB006 express the structural design of the Android mobile operating system that allows the Bluetooth sensor approach to obtain participatory data. The approach is used to map the mobility of crowds in large-scale events. The approach is deployed in a large music festival with 130,000 attendees, where a small subset of participants installed Bluetooth sensing apps on their own smartphones. However, this approach has limited scalability and limited spatial coverage. More recent utilization of the Bluetooth technology in crowd-sensing can be found in BIB013 where the authors have proposed a new contextaware approximation algorithm to find vertex cover that is tailored for crowd-sensing tasks. Authors design humancentric preface strategies to collect information about the participants using sensors. The sensing task refers to the task of collecting opportunistic Bluetooth contacts, or wireless contacts, in mobile ad hoc networks. You et al. BIB014 proposed an MCS application to collect opportunistic sensing data in a limited area using Bluetooth based on Community Information-Centric Networking technology. The application feature supports data integrity and uses IP-less communication as a simple communication model. Several sensors have been deployed with Raspberry Pis and Bluetooth across a building to collect data when a participant walks and draws a map for sensors' location in the building. The application fundamentally supports privacy and data integrity of participants. The work in BIB018 proposed an opportunistic location discovery method that fills the gaps in a user's location trace by deriving location data from other users employing the power of mobile crowd-sensing. Authors used a hierarchical cluster merging approach, which looks for other users using Bluetooth and Wi-Fi scans to detect closeness of users. However, Bluetooth has a very short range that requires higher participants' density for same sensing accuracy requirements. Wireless Fidelity (Wi-Fi) refers to certain types of wireless network protocol 802.11b standards that enable devices to communicate with each other without cords or cables. Wi-Fi enables a better range from the base station, a faster connection, and better security than Bluetooth. Wi-Fi uses the super ultra-low noise S-band (2.4 GHz) to extend the range BIB019 , uses frequency-hopping techniques to connect multiple devices together, and has a range of approximately 1000 feet outdoors BIB002 Average data transmission rates is 54 Mb/s. Wi-Fi differs from Bluetooth in that it covers greater distances and provides higher throughput, but it requires more expensive hardware and may present higher power consumption BIB005 , BIB007 . Wu and Luo BIB019 present a WiFiScout advisory system that integrates a gamificationbased incentive system to compensate users who give the most important data based on variety and amount of the contributed data. The proposed system supports three ways: 1) offline search, which allows a user to search around for WiFi, 2) online review, user can use his smartphone to submit a review about his experience on that WiFi, and 3) gamification-based WiFi map, which shows all WiFi access points on a city map. Using Wi-Fi in the MCS is very important, an iSense novel framework has been proposed in  for decreasing the unnecessary energy overhead on participatory devices. iSense entirely offloads the localization burden to the crowd-sensing servers. Thus, it reduces the consumed energy at the mobile devices. iSense employs the existing network signaling. Wi-Fi is the most common technology available on mobile devices; however, the infrastructure mode is more technologically developed compared to the Ad Hoc mode. Whereas Ad Hoc mode is typically more suitable for MCS especially in areas not covered by Wi-Fi access points. Cellular communication technology, GPRS supports mobile data service on the 2G (GSM) and 3G cellular communication systems BIB007 , BIB002 Moreover, 4G offers joint services such as voice, data, and multimedia at data rates of up to 100 Mbps as well as pervasive mobile access to a wide variety of user devices and independent networks. Foremski et al.  propose a location-tracking algorithm that improve crowd-sensing data for modeling cellular networks. To measure human location and signal strength in cellular networks, a practical application has been developed to perform the measurements of human mobility and signal levels without user interference and with minimum power utilization. However, authors mention that many users are not willing to participate in crowd-sensing experiments. They also mentioned that their system decreased the battery lifetime by around 20%. Another research in BIB015 discusses the challenges that face cellular providers as the number of cyberphysical system (CPS) devices trying to access the cellular spectrum increases dramatically. This work presents a deviceto-device (D2D) communication technology for CPS communication over current network infrastructure through the use of fifth generation cellular networks. Masek et al. BIB011 develop a next-generation traffic management system in a smart city environment that incorporates the Internet of Things (IoT) technologies with low-power and long-range 5G embedded devices. They started by data sensing that utilizes heterogeneous road monitoring tools that measure, and send the traffic information, vehicle speed, etc. to the traffic management entity. The results show that the use of modern devices, such as Raspberry Pi2, satisfies the requirements for future traffic management systems in smart cities. Sun et al. BIB012 present a secure and privacy protective object finding application via mobile crowdsourcing using 4G/5G. They proposed an approach called SecureFind that obtains the finding request from the service provider. Hence, 4G and 5G cellular networks authentication and privacy schemes are required to protect user identity, and location privacy among others. Another interesting system utilizes location-based social network (LBSN) on Cellular systems is called CheckInside BIB016 . This system provides a fine grained indoor localization. It leverages the crowd-sensing data collected from mobile devices during the check-in operation. In addition, it extracts knowledge from the LBSN to connect a location with a logical name and a footprint. The system uses Wi-Fi in VOLUME 7, 2019 two modes: ON and OFF. When the Wi-Fi is ON, this leads to a higher accuracy compared to the use of cellular localization. Experimental results show that the CheckInside system can achieve the actual participant's location. Even though cellular is costly, it is the most widely used technology. Costs are expected to drop with the advent of 5G and its support for IoT through the massive machine type communications (mMTC) paradigm. Table 3 and Table 4 show the comparison between ZigBee, Bluetooth, Wi-Fi, and Cellular in terms of impact on MCS and technology characteristics BIB005 , BIB015 ,  - BIB008 .