MOBILE CROWD SENSING IN URBAN SPACES USING SEAMLESS INDOOR-OUTDOOR LOCALIZATION

Abstract

Smart city integrates various intelligent devices, infrastructures, and services to monitor and control human activities with efficiency. Smart city management requires aggregation of urban informatics for sustainable cities. Some conventional sensing techniques, such as sensor networks are used to gather real-world data. Sensor network deployment is non-trivial because of high installation cost, insufficient space coverage. Therefore, to handle these issues, researchers have proposed a new large-scale sensing paradigm, called Mobile Crowd Sensing, based on the power of user-companioned devices, such as smartphones, smart vehicles, wearable devices. Mobile crowd sensing collects users’ local knowledge, such as local information, ambient context, noise level, and traffic conditions, using sensor-enabled devices participatory and/or non-participatory. The collected information is further aggregated and transferred to the cloud for data processing. It can be used to find the mobility patterns, traffic analysis and planning, public safety, environmental monitoring, and mobile social recommendation. In order to monitor and track the movement patterns of one or more persons in a densely populated area, the persons must be uniquely identified. Human location tracking/monitoring can allow the authorities to find/identify a lost person among thousands in the crowd, to evacuate people during emergencies, to manage crowd movements, to predict crowd in the future and to plan the resources accordingly. Existing wireless tracking based systems either use the packet analyzer software, such as Tcpdump, Wireshark, Kismet, or extra hardware which incurs high cost and makes the system complex. Some systems require RFID/BLE/Bluetooth tags to be provided to each person which is quite challenging and significantly expensive. Moreover, it is not feasible to distribute tags in case of emergencies or disasters. Some systems use expensive tag readers, so the number of tag readers to be deployed in the area should be limited. In addition, several commercial tag readers or scanners use proprietary technology and software which make it difficult to modify and integrate it with other systems. Recently, researchers have started utilizing sensor-enabled smartphones as a tag for large scale human sensing. As the usage of smartphones is increasing in the world, more persons can be tracked without providing any tag in the future. Some of the smartphone-based location tracking systems require an application to be installed on the client’s smartphone. The installed application obtains the location using GPS sensor of smartphone and continuously updates the location to the remote server using Internet connection. ii However, it is rare that users in a large crowd and in remote locations will have the Internet connection all the time. There are many operating systems and versions for smartphones, which makes the development and distribution of application a difficult task. In additions, at many indoor and urban locations, GPS does not work well. Most of the existing systems use two-step process for intercepting the MAC id (first step is to capture the probes from wireless signal then second is to process them). On the other hand, many research works are focusing on single positioning/wireless technology, such as RFID, Wi-Fi enabled devices, BT/BLE tags, and smartphone's GPS/General Packet Radio Service (GPRS) to track human in either indoor or outdoor environment. There is a need to design a portable, low-cost and easy-to-deploy system for tracking a large number of individuals using efficient wireless technology. The proposed system should be able to find a person’s current location as accurately as possible, as well as, upload the current position of a uniquely identified person with minimum delay, power, and network bandwidth. We explore that human identification and monitoring are critical in many applications, such as surveillance, evacuation planning. Human identification and monitoring are non-trivial tasks in the case of a large and densely populated crowd. However, none of the existing solutions consider seamless identification, tracking, and localization of the crowd in both indoor and outdoor environments with significant accuracy. In this dissertation, we propose a unmodified smartphone-based non-participatory human identification, tracking, and monitoring system to monitor the movement patterns of individual(s) in a densely crowded environment. The proposed system uses the smartphone as a sensing unit without any hardware modification to extract the MAC ids from the wireless probe requests emitted from the users’ wireless devices. Our proposed system employs hybrid localization technique (Google location API), a combination of multiple positioning technologies, GPS-Wi-Fi-Cellular to track individual(s) seamlessly in both indoor and outdoor stretches. MAC ids are stored and processed locally for short-term analysis, and then the filtered data is uploaded to the cloud server for extensive analysis and visualization. We also develop a real-time testbed for exploiting location analytics and to identify, track and find mobility patterns and visiting sequences of individual(s) in the data collected from the IIT Roorkee Institute campus and Har-ki-Pauri, Haridwar, India. Further, we develop a fast and scalable human trajectory tracking system. In the proposed system, we enhance the capability of sensing units, where these sensing units can communicate with each other and retrieve the data in real-time. Further, sensing units can iii track an individual with smart devices and can provide a complete analysis of his visited locations, such as stay time, trajectory in real-time. We use the Redis in-memory database and XMPP at the sensing units for fast data retrieval and exchange, respectively. When an individual move to a new location, WebSocket server updates that person’s new location automatically among all sensing units to make the system analysis in real-time. On the other hand, we have explored the access points locations in and around the IIT Roorkee campus and use access points data for localization and trajectory formulation of individuals with smartphones. The IIT campus provided a privileged environment for this research. To find the usability of our proposed system, we develop and deploy a real prototype testbed in IIT Roorkee campus. In the next step of our research work, we propose a real-time surveillance system which can identify, track and monitor a suspicious person (i.e., outlier) in the large-scale crowd where abnormal activities of individuals are considered as an anomaly/outlier. The proposed system handles the MAC randomization through the association/authentication frames and discards the locally assigned MAC addresses. We further propose an optimal sensing units’ selection algorithm to find the latest trajectory of the detected outlier(s). To validate and to show the usability of our proposed system, we develop a real prototype testbed and evaluate it extensively on a real-world dataset collected at IIT Roorkee, India. Optimal sensing units’ selection algorithm selects sensing units with an average selection accuracy of 95.3%. Individuals sharing similar location traces and performing same activities in their daily life over a long/short period of time may have similar interests and lifestyles. The correlation among users’ locations and activities can be used further for finding friends having similar lifestyles. We develop a users’ interaction framework based on recurrent neural networks for users having similar lifestyles/daily routine. By learning from historical users’ daily routine and preferences data, our proposed solution can predict the user’s schedule and suggests friends accordingly. We collect records from 50 users for the time period of six months in real-time to train the model. Further, data is processed and stored in the cloud for finding the users’ working patterns and their location coordinates within a time span. Experimental results show that our prediction module can get a good accuracy of around 92.8% which is well commensurate with the high variation in the user’s daily routine In short, the objective of our research is threefold. First is, to design and develop a portable, low-cost, and easy-to-deploy smartphone-based human identification, tracking, iv and monitoring system. Second is, to analyze the human mobility behavior patterns in real-time (e.g., frequency, order, and periodicity of visits, suspicious mobility pattern) and location analytics (e.g., Number of individuals at given location, arrival and departure from a location over time, stay time at location). Third is, to develop the SmartCST platform for easy prototyping of various MCS-based applications, such as crowd monitoring, human mobility and behavior, modelling human interactions, pilgrim safety, etc., with extensive analysis and visualization of localization data through the cloud server.