Human Sensing Meets People Crowd Detection – A Case of Developing Countries
DOI:
https://doi.org/10.47672/ejt.984Keywords:
People Crowd Detection, Human Sensing, Sensor Networks.Abstract
Purpose: The main purpose of this study was to examine the application of sensors and sensor networks for detection of people crowds in developing cities. This paper discusses unique challenges associated with people crowd detection especially in the urban towns of developing countries and gives a comparative review and analysis of popular human sensing approaches in the detection of people crowds.
Methodology: This study provides a survey and categorization of popular human sensing approaches using literature especially published within the past two decades. The paper then analyzes current human sensing technologies vis-à-vis people crowd detection in developing cities. The respective strengths and shortfalls of various approaches are highlighted. Finally, by means of examples, a comparative analysis of different human sensing categories is carried out.
Findings: The spontaneous, dynamic and chaotic nature of people crowds, together with the poor infrastructural development characteristic of developing economies pose unique challenges to the effectiveness of people crowd detection systems. Although there are advances in crowd detection, most of these are in the area of non-people crowds, while most of the research done on people crowd detection have been on indoor crowd settings. In addition, challenges unique to people crowd detection in developing countries include: scalability and cost of crowd detection systems, security of the detection system infrastructure, confidentiality of subjects being monitored, requirements for incentives and the ability to support passive and real time people crowd detection.
Unique contribution to theory, practice and policy: This study emphasizes the need for both indoor and outdoor people crowd detection systems appropriate for the needs of developing cities. The study contributes to the body of knowledge since people crowds unlike other types of crowds present a unique set of challenges that call for special attention.
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Gong, S., Loy, C. C., & Xiang, T. (2011). Security and surveillance. In Visual analysis of humans (pp. 455-472). Springer, London.
Farhan, L., Shukur, S. T., Alissa, A. E., Alrweg, M., Raza, U., & Kharel, R. (2017, December). A survey on the challenges and opportunities of the Internet of Things (IoT). In 2017 Eleventh International Conference on Sensing Technology (ICST) (pp. 1-5). IEEE.
Arjun, D., Indukala, P. K., & Menon, K. U. (2017, April). Border surveillance and intruder detection using wireless sensor networks: A brief survey. In 2017 International Conference on Communication and Signal Processing (ICCSP) (pp. 1125-1130). IEEE.
Bhadwal, N., Madaan, V., Agrawal, P., Shukla, A., & Kakran, A. (2019, April). Smart border surveillance system using wireless sensor network and computer vision. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM) (pp. 183-190). IEEE.
Ez-Zaidi, A., & Rakrak, S. (2016). A comparative study of target tracking approaches in wireless sensor networks. Journal of Sensors, 2016.
Darabkh, K. A., Albtoush, W. Y., & Jafar, I. F. (2017). Improved clustering algorithms for target tracking in wireless sensor networks. The Journal of Supercomputing, 73(5), 1952-1977.
Yang, D., Sheng, W., & Zeng, R. (2015, June). Indoor human localization using PIR sensors and accessibility map. In 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) (pp. 577-581). IEEE.
Teixeira, T., Dublon, G., & Savvides, A. (2010). A survey of human-sensing: Methods for detecting presence, count, location, track, and identity. ACM Computing Surveys, 5(1), 59-69.
Rashid, B., & Rehmani, M. H. (2016). Applications of wireless sensor networks for urban areas: A survey. Journal of network and computer applications, 60, 192-219.
Bouchabou, D., Nguyen, S. M., Lohr, C., LeDuc, B., & Kanellos, I. (2021). A survey of human activity recognition in smart homes based on IoT sensors algorithms: Taxonomies, challenges, and opportunities with deep learning. Sensors, 21(18), 6037.
Liu, Z., Liu, X., Zhang, J., & Li, K. (2019). Opportunities and challenges of wireless human sensing for the smart IoT world: A survey. IEEE Network, 33(5), 104-110.
Carreño, P., Gutierrez, F., Ochoa, S. F., & Fortino, G. (2013, October). Using human-centric wireless sensor networks to support personal security. In International Conference on Internet and Distributed Computing Systems (pp. 51-64). Springer, Berlin, Heidelberg.
Kiehl, Z. A., Durkee, K. T., Halverson, K. C., Christensen, J. C., & Hellstern, G. F. (2020). Transforming work through human sensing: a confined space monitoring application. Structural Health Monitoring, 19(1), 186-201.
Wu, C., Yang, Z., Zhou, Z., Liu, X., Liu, Y., & Cao, J. (2015). Non-invasive detection of moving and stationary human with WiFi. IEEE Journal on Selected Areas in Communications, 33(11), 2329-2342.
Zhao, Y., & You, Y. (2021). Design and data analysis of wearable sports posture measurement system based on Internet of Things. Alexandria Engineering Journal, 60(1), 691-701.
Erol, B., Amin, M. G., & Boashash, B. (2017, May). Range-Doppler radar sensor fusion for fall detection. In 2017 IEEE Radar Conference (RadarConf) (pp. 0819-0824). IEEE
Shibata, K., & Yamamoto, H. (2019, February). People crowd density estimation system using deep learning for radio wave sensing of cellular communication. In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 143-148). IEEE.
Kostoff, R. N., Heroux, P., Aschner, M., & Tsatsakis, A. (2020). Adverse health effects of 5G mobile networking technology under real-life conditions. Toxicology Letters, 323, 35-40.
Liu, S., Zhao, Y., Xue, F., Chen, B., & Chen, X. (2019). DeepCount: Crowd counting with Wi-Fi via deep learning. arXiv preprint arXiv:1903.05316
Wang, Y., Liu, J., Chen, Y., Gruteser, M., Yang, J., & Liu, H. (2014, September). E-eyes: device-free location-oriented activity identification using fine-grained Wi-Fi signatures. In Proceedings of the 20th annual international conference on Mobile computing and networking (pp. 617-628)
Depatla, S., Muralidharan, A., & Mostofi, Y. (2015). Occupancy estimation using only Wi-Fi power measurements. IEEE Journal on Selected Areas in Communications, 33(7), 1381-1393.
Kurkcu, A., & Ozbay, K. (2017). Estimating pedestrian densities, wait times, and flows with Wi-Fi and Bluetooth sensors. Transportation Research Record, 2644(1), 72-82.
Wan, J., O’grady, M. J., & O’Hare, G. M. (2015). Dynamic sensor event segmentation for real-time activity recognition in a smart home context. Personal and Ubiquitous Computing, 19(2), 287-301.
Guillén-Pérez, A., & Baños, M. D. C. (2018, October). A Wi-Fi-based method to count and locate pedestrians in urban traffic scenarios. In 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 123-130). IEEE.
Determe, J. F., Singh, U., Horlin, F., & De Doncker, P. (2020). Forecasting Crowd Counts with Wi-Fi Systems: Univariate, Non-Seasonal Models. IEEE Transactions on Intelligent Transportation Systems.
Mu, M. (2020). Wi-Fi-based Crowd Monitoring and Workspace Planning for COVID-19 Recovery. arXiv preprint arXiv:2007.12250.
Andión, J., Navarro, J. M., López, G., Álvarez-Campana, M., & Dueñas, J. C. (2018). Smart behavioral analytics over a low-cost IoT Wi-Fi tracking real deployment. Wireless Communications and Mobile Computing, 2018.
Turk, A. M. (2010). URL https://www. mturk. com/mturk/welcome.
Ma, Y., Zhou, G., & Wang, S. (2019). Wi-Fi sensing with channel state information: A survey. ACM Computing Surveys (CSUR), 52(3), 1-36
Dasari, V. S., Kantarci, B., Pouryazdan, M., Foschini, L., & Girolami, M. (2020). Game theory in mobile crowdsensing: A comprehensive survey. Sensors, 20(7), 2055.
Guo, B., Chen, H., Yu, Z., Xie, X., Huangfu, S., & Zhang, D. (2014). FlierMeet: a mobile crowdsensing system for cross-space public information reposting, tagging, and sharing. IEEE Transactions on Mobile Computing, 14(10), 2020-2033.
Koutsopoulos, I. (2013, April). Optimal incentive-driven design of participatory sensing systems. In 2013 Proceedings IEEE INFOCOM (pp. 1402-1410). IEEE.
Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019). A survey on mobile crowdsensing systems: Challenges, solutions, and opportunities. IEEE communications surveys & tutorials, 21(3), 2419-2465.
Musa, A. B. M., & Eriksson, J. (2012, November). Tracking unmodified smartphones using wi-fi monitors. In Proceedings of the 10th ACM conference on embedded network sensor systems (pp. 281-294).
Mohandes, M. (2011, June). Pilgrim tracking and identification using the mobile phone. In 2011 IEEE 15th International Symposium on Consumer Electronics (ISCE) (pp. 196-199). IEEE.
Bolic, M., Rostamian, M., & Djuric, P. M. (2015). Proximity detection with RFID: A step toward the internet of things. IEEE Pervasive Computing, 14(2), 70-76.
Mohandes, M. (2008, May). An RFID-based pilgrim identification system (a pilot study). In 2008 11th International Conference on Optimization of Electrical and Electronic Equipment (pp. 107-112). IEEE.
Ghosh, A., & Das, S. K. (2008). Coverage and connectivity issues in wireless sensor networks: A survey. Pervasive and Mobile Computing, 4(3), 303-334.
Zou, Y., & Chakrabarty, K. (2003, March). Sensor deployment and target localization based on virtual forces. In IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No. 03CH37428) (Vol. 2, pp. 1293-1303). IEEE.
Tsai, Y. R. (2008). Sensing coverage for randomly distributed wireless sensor networks in shadowed environments. IEEE Transactions on Vehicular Technology, 57(1), 556-564.
Elfes, A. (2013). Occupancy grids: A stochastic spatial representation for active robot perception. arXiv preprint arXiv:1304.1098.
Yuan, Y., Qiu, C., Xi, W., & Zhao, J. (2011, December). Crowd density estimation using wireless sensor networks. In 2011 seventh international conference on mobile Ad-hoc and sensor networks (pp. 138-145). IEEE.
Yang, T., Mu, D., Hu, W., & Zhang, H. (2014). Energy-efficient border intrusion detection using wireless sensors network. EURASIP Journal on Wireless Communications and Networking, 2014(1), 1-12.
Tang, X., Huang, M. C., & Mandal, S. (2017, October). An “Internet of Ears” for crowd-aware smart buildings based on sparse sensor networks. In 2017 IEEE SENSORS (pp. 1-3). IEEE.
Kasudiya, J., Bhavsar, A., & Arolkar, H. (2020). Wireless Sensor Network: A Possible Solution for Crowd Management. In Smart Systems and IoT: Innovations in Computing (pp. 23-31). Springer, Singapore.
Hu, Y., Meng, Z., Zabihi, M., Shan, Y., Fu, S., Wang, F., ... & Zeng, B. (2019). Performance enhancement methods for the distributed acoustic sensors based on frequency division multiplexing. Electronics, 8(6), 617.
Toffoli, G. (2008). The Definitive Guide to IReport. Apress.
Schmidt, G. B., & Jettinghoff, W. M. (2016). Using Amazon Mechanical Turk and other compensated crowdsourcing sites. Business Horizons, 59(4), 391-400.
Eisenman, S. B., Lane, N. D., Miluzzo, E., Peterson, R. A., Ahn, G. S., & Campbell, A. T. (2006, October). Metrosense project: People-centric sensing at scale. In Workshop on World-Sensor-Web (WSW 2006), Boulder.
Yu, Z., Xu, H., Yang, Z., & Guo, B. (2015). Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE Transactions on Human-Machine Systems, 46(1), 151-158.
Yang, D., Xue, G., Fang, X., & Tang, J. (2012, August). Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing. In Proceedings of the 18th annual international conference on Mobile computing and networking (pp. 173-184).
Lin, J., Yang, D., Li, M., Xu, J., & Xue, G. (2017). Frameworks for privacy-preserving mobile crowdsensing incentive mechanisms. IEEE Transactions on Mobile Computing, 17(8), 1851-1864.
Jaimes, L. G., Vergara-Laurens, I. J., & Raij, A. (2015). A survey of incentive techniques for mobile crowd sensing. IEEE Internet of Things Journal, 2(5), 370-380.
Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., & Zhou, X. (2015). Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM computing surveys (CSUR), 48(1), 1-31.
Sun, K., Zhao, Q., & Zou, J. (2020). A review of building occupancy measurement systems. Energy and Buildings, 216, 109965.
Schulz, D., Fox, D., & Hightower, J. (2003, August). People tracking with anonymous and id-sensors using rao-blackwellised particle filters. In IJCAI (pp. 921-928).
Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., & Zhou, X. (2015). Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM computing surveys (CSUR), 48(1), 1-31.
Deif, D. S., & Gadallah, Y. (2013). Classification of wireless sensor networks deployment techniques. IEEE Communications Surveys & Tutorials, 16(2), 834-855.
Snigdh, I., & Gosain, D. (2016). Analysis of scalability for routing protocols in wireless sensor networks. Optik, 127(5), 2535-2538.
Sajwan, M., Sharma, A. K., & Verma, K. (2020). Analysis of scalability for hierarchical routing protocols in wireless sensor networks. In Proceedings of ICETIT 2019 (pp. 107-116). Springer, Cham.
Yu, R., Sun, Z., & Mei, S. (2007, March). Scalable topology and energy management in wireless sensor networks. In 2007 IEEE Wireless Communications and Networking Conference (pp. 3448-3453). IEEE.
Google, n.d. [Satellite image showing Gikondo Transit Center, Kigali, Rwanda.]. Retrieved January 19, 2022 from https://www.hrw.org/report/2020/01/27/long-we-live-streets-they-will-beat-us/rwandas-abusive-detention-children
Singh, J. P. (2019, May). Rwanda: Imminent Kigali city master plan to reveal development roadmap for 30 years. Devdiscourse News Desk. https://www.devdiscourse.com/article/other/533242-rwanda-imminent-kigali-city-master-plan-to-reveal-development-roadmap-for-30-years
Bhushan, B., & Sahoo, G. (2020). Requirements, protocols, and security challenges in wireless sensor networks: An industrial perspective. In Handbook of computer networks and cyber security (pp. 683-713). Springer, Cham.
Zhang, X., Yang, Z., Sun, W., Liu, Y., Tang, S., Xing, K., & Mao, X. (2015). Incentives for mobile crowd sensing: A survey. IEEE Communications Surveys & Tutorials, 18(1), 54-67.
Broll, G., & Benford, S. (2005, September). Seamful design for location-based mobile games. In International Conference on Entertainment Computing (pp. 155-166). Springer, Berlin, Heidelberg.
Barkhuus, L., Chalmers, M., Tennent, P., Hall, M., Bell, M., Sherwood, S., & Brown, B. (2005, September). Picking pockets on the lawn: the development of tactics and strategies in a mobile game. In International Conference on Ubiquitous Computing (pp. 358-374). Springer, Berlin, Heidelberg.
Hoh, B., Yan, T., Ganesan, D., Tracton, K., Iwuchukwu, T., & Lee, J. S. (2012, September). TruCentive: A game-theoretic incentive platform for trustworthy mobile crowdsourcing parking services. In 2012 15th International IEEE Conference on Intelligent Transportation Systems (pp. 160-166). IEEE.
Antin, J., & Shaw, A. (2012, May). Social desirability bias and self-reports of motivation: a study of Amazon Mechanical Turk in the US and India. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2925-2934).
Álvarez, R., Díez-González, J., Alonso, E., Fernández-Robles, L., Castejón-Limas, M., & Perez, H. (2019). Accuracy analysis in sensor networks for asynchronous positioning methods. Sensors, 19(13), 3024.
Menard, T., Miller, J., Nowak, M., & Norris, D. (2011, October). Comparing the GPS capabilities of the Samsung Galaxy S, Motorola Droid X, and the Apple iPhone for vehicle tracking using FreeSim_Mobile. In 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) (pp. 985-990). IEEE.
Hess, B., Farahani, A. Z., Tschirschnitz, F., & von Reischach, F. (2012, November). Evaluation of fine-granular GPS tracking on smartphones. In Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems (pp. 33-40).
Zandbergen, P. A. (2009). Accuracy of iPhone locations: A comparison of assisted GPS, Wi-Fi and cellular positioning. Transactions in GIS, 13, 5-25.Merry, K., & Bettinger, P. (2019). Smartphone GPS accuracy study in an urban environment. PloS one, 14(7), e0219890.
Merry, K., & Bettinger, P. (2019). Smartphone GPS accuracy study in an urban environment. PloS one, 14(7), e0219890.
Nurlan, Z., Kokenovna, T. Z., Othman, M., & Adamova, A. (2021). Resource Allocation Approach for Optimal Routing in IoT Wireless Mesh Networks. IEEE Access, 9, 153926-153942.
Wietfeld, C., Ide, C., & Dusza, B. (2014, June). Resource efficient mobile communications for crowd-sensing. In 2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC) (pp. 1-6). IEEE.
Ali, M. U., Hur, S., & Park, Y. (2019). Wi-Fi-based effortless indoor positioning system using IoT sensors. Sensors, 19(7), 1496.
Shibata, K., & Yamamoto, H. (2019, February). People crowd density estimation system using deep learning for radio wave sensing of cellular communication. In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 143-148). IEEE.
de Brito Guerra, T. C., de Santana, P. M., de Medeiros Campos, M. M., de Oliveira Mattos, M., de Medeiros, A. A., & de Sousa, V. A. (2019). RF-Driven Crowd-Size Classification via Machine Learning. IEEE Antennas and Wireless Propagation Letters, 18(11), 2321-2324.
Ding, H., Han, J., Liu, A. X., Xi, W., Zhao, J., Yang, P., & Jiang, Z. (2018). Counting human objects using backscattered radio frequency signals. IEEE Transactions on Mobile Computing, 18(5), 1054-1067.
Xi, W., Zhao, J., Li, X. Y., Zhao, K., Tang, S., Liu, X., & Jiang, Z. (2014, April). Electronic frog eye: Counting crowd using WiFi. In IEEE INFOCOM 2014-IEEE Conference on Computer Communications (pp. 361-369). IEEE.
Yang, Y., Cao, J., Liu, X., & Liu, X. (2018, July). Wi-Count: Passing people counting with COTS WiFi devices. In 2018 27th International Conference on Computer Communication and Networks (ICCCN) (pp. 1-9). IEEE.
Zou, H., Zhou, Y., Yang, J., Gu, W., Xie, L., & Spanos, C. (2017, December). Freecount: Device-free crowd counting with commodity wifi. In GLOBECOM 2017-2017 IEEE Global Communications Conference (pp. 1-6). IEEE.
Zhao, Y., Liu, S., Xue, F., Chen, B., & Chen, X. (2019). DeepCount: Crowd counting with Wi-Fi using deep learning. Journal of Communications and Information Networks, 4(3), 38-52.
Hong, J., & Ohtsuki, T. (2015). Signal eigenvector-based device-free passive localization using array sensor. IEEE Transactions on Vehicular Technology, 64(4), 1354-1363.
Nishimori, K., Koide, Y., Kuwahara, D., Honmay, N., Yamada, H., & Hideo, M. (2011, April). MIMO sensor-evaluation on antenna arrangement. In Proceedings of the 5th European Conference on Antennas and Propagation (EUCAP) (pp. 2771-2775). IEEE.
Sun, Y., Song, H., Jara, A. J., & Bie, R. (2016). Internet of things and big data analytics for smart and connected communities. IEEE access, 4, 766-773.
Weppner, J., & Lukowicz, P. (2013, March). Bluetooth based collaborative crowd density estimation with mobile phones. In 2013 IEEE international conference on pervasive computing and communications (PerCom) (pp. 193-200). IEEE.
Alhmiedat, T., & Aborokbah, M. (2021). Social distance monitoring approach using wearable smart tags. Electronics, 10(19), 2435.
Galinina, O., Mikhaylov, K., Huang, K., Andreev, S., & Koucheryavy, Y. (2018). Wirelessly powered urban crowd sensing over wearables: Trading energy for data. IEEE Wireless Communications, 25(2), 140-149.
Arora, A., Ramnath, R., Ertin, E., Sinha, P., Bapat, S., Naik, V., ... & Parker, K. (2005, August). Exscal: Elements of an extreme scale wireless sensor network. In 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA'05) (pp. 102-108). IEEE.
Ludwig, T., Reuter, C., Siebigteroth, T., & Pipek, V. (2015, April). CrowdMonitor: Mobile crowd sensing for assessing physical and digital activities of citizens during emergencies. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 4083-4092).
Yan, T., Marzilli, M., Holmes, R., Ganesan, D., & Corner, M. (2009, November). mCrowd: a platform for mobile crowdsourcing. In Proceedings of the 7th ACM conference on embedded networked sensor systems (pp. 347-348).
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