Fast track articleA novel localization and coverage framework for real-time participatory urban monitoring
Introduction
Over the last decade, we have witnessed tremendous advancement in the smartphone technology. Mobile phone has evolved from basic communication device to a powerful sensing platform with a rich set of built in sensors. Examples include microphone for capturing audio, camera for capturing video, RGB light sensor for measuring intensity of light, gesture sensor for detecting hand movement, Global Positioning System (GPS) for retrieving location, barometer for measuring atmospheric pressure, accelerometer for measuring acceleration, gyro sensor for determining rotation state of the device, fingerprint sensor for identifying user fingerprint, pulse sensor for monitoring heart rate, and so on. Moreover, modern smartphones provide convenient interfaces (e.g., Bluetooth, WiFi, NFC) to connect with external sensors and devices, hence giving birth to a host of wearable gears such as Apple Watch [1], Samsung Gear [2], iHealth Edge [3], etc., which stay connected with the smartphones while monitoring various phenomena surrounding the user. As a result, a new paradigm of applications exploiting the sensing platform of the mobile devices emerged in the last decade and gained significant attention both in the industry and research community.
Due to the ubiquitous nature of smartphone and its extremely large user base, it has been leveraged to design applications in different domains of our life. For example, applications were designed where the smartphone sensors are used for assisting and monitoring individuals, such as activity, sleep, and overall health and well-being monitoring [4], [5], [6]. By taking it a step further, data collected from individuals can be compiled by healthcare providers or government agencies and associated with environmental factors to analyze not only individual but also community-wide activity, habit and exposure [7]. Another important class of applications aims to monitor different environmental and urban scenarios by collecting data from the smartphone sensors. Similar to monitoring environmental or urban scenarios with Wireless Sensor Networks (WSNs), a network of smartphones can be conceived for such tasks where a smartphone is treated as a location-aware multi-modal sensor node, and participates in the data collection process by sampling the required sensor. The notion of sensory data collection through the participation of a group of smartphone users to create knowledge is known as participatory sensing [8], [9], [10], [11], [12] in general.
Depending on the use case, there are several design choices for a participatory sensing application. For example, based on the feature to monitor, it can be designed for monitoring urban/environmental feature or personal (smartphone user’s) scenario. Based on user involvement for data collection, an application may notify the user each time to collect data sample, whereas, an application can be designed to automatically collect data samples as required, once authorized by the user. Some application can be designed for monitoring only indoor events, while some other may target outdoor scenarios. Based on the activity or motion state of the smartphone user, there can be variations too—some application can be designed to collect data samples from pedestrians, while some other may collect data from smartphone users moving in vehicle. Finally, based on the update frequency requirement of the underlying monitored phenomenon, an application could be designed to collect data continuously or at a much lower frequency. Adopting each of these design choices poses its own research challenges that must be solved for successful deployment of a participatory sensing application. In our work, we primarily address the challenges of participatory sensing applications designed for monitoring urban scenarios automatically and continuously, also referred as real-time participatory urban monitoring application.
One important challenge in the data collection process is how to ensure the coverage of the collected data? For traditional Wireless Sensor Networks (WSNs), this problem has been extensively investigated where sensors are either static and deployed in a known area, or moving with predefined trajectories [13], [14], [15], [16], [17]. Hence, sensors in WSNs can be scheduled to collect data samples as required to achieve the coverage. In case of participatory sensing applications, data collection is performed by mobile devices (smartphones) carried by human users with uncontrolled mobility. Another important challenge is the localization of the smartphones especially when the location information is required continuously. And lastly an important challenge is to ensure energy efficiency in the data collection process. For WSNs, energy efficiency in the data collection process has been widely investigated from different aspects [18], [19], [20], [21]. Similar to wireless sensors, the mobile devices are battery powered and hence energy efficiency in the data collection process is also very important [22], [23], [24]. The existing approaches that address these challenges broadly fall into two categories. In the first category, the server collects data samples from all participating mobile devices at a predefined frequency (as low as 1 s) [25], resulting in redundant data sample collection from densely populated urban area. In the second category, each participant informs its location to the server. In turn, the server selects the mobile devices as required to ensure different criteria (e.g., coverage, battery life, etc.) [26], [23]. However, for a real-time continuous monitoring applications by pedestrians, location needs to be frequently updated and informed to the server, which can take significant amount of battery life.
In this paper, we propose a novel framework called Energy Efficient Framework for Localization and Coverage in Participatory Urban Sensing (PLUS) [27], where a mobile device is not required to send its location information to the server. PLUS divides the monitored area into smaller blocks, and uses a metric called desired sensing coverage (DSC) [28], to ensure partial coverage of the collected data associated with each block. A participating mobile device executes a novel energy efficient localization scheme to determine the block level location (i.e., the block where a user currently is) of the user. The sLoc scheme maintains individual mobility history and uses a Markov Predictor Model to predict his future block transitions (i.e., the block where the user is likely to move from the current block). Moreover, to activate the GPS effectively to determine a block transition, sLoc learns from previous behavior of the user in each block. After determining the block level location, a mobile device probabilistically performs the sensing task and sends the collected data to the server to ensure the DSC requirement. The major contributions of this paper can be summarized as follows.
- •
We propose PLUS, a framework for continuous monitoring applications using participatory sensing. PLUS is able to guarantee partial coverage of the collected data in an energy efficient way without knowing the location of the participating mobile devices.
- •
We formally define the block transition detection problem to continuously determine the block level location of a user with smartphone while minimizing the usage of the location sensor (GPS). Then we propose our localization scheme sLoc to solve it.
- •
We implemented sLoc in Android devices. Through real world experiments, we show that sLoc can reduce GPS usage as much as 85% when a mobile user follows his regular routes, as compared to a method that continuously uses GPS.
- •
We emulated a participatory sensing application to continuously monitor WiFi signal strength in our university campus using the PLUS framework for different DSC (a partial coverage metric) requirements. Experimental results demonstrate that our framework can significantly reduce energy consumption as compared to the traditional approaches.
- •
We discuss the impact of data collection frequency on the sLoc scheme and its applicability on participatory monitoring applications. We further show the effect of data collection frequency on the energy savings of the data collection process.
- •
We derive conservative condition on the applicability of our framework to perform data collection in an energy efficient manner.
Section snippets
Related work
In the recent years, a plethora of applications in the field of mobile sensing and participatory sensing have been proposed. Detail surveys on these types of applications can be found in [29], [30]. Both mobile sensing applications and participatory sensing applications undergo several common research challenges, e.g., energy efficient data collection, duty cycle GPS usage. Additionally, participatory sensing applications experience some extra challenges because of the collaborative nature of
PLUS framework
In the proposed PLUS framework, there is a server (or cluster of servers), offering web services to receive data samples from participating mobile devices. A participant is required to install a software package developed for PLUS on his mobile device that includes a sensory data sampling component shown in Fig. 1(a). This component takes help from sample requirement calculator and localization scheme that are described later in this section. Each participating mobile device has one or more
Performance evaluation
In this section, we evaluate the performance of the proposed participatory sensing framework. For that, we first evaluate the performance of sLoc in terms of GPS usage and accuracy in continuous block detection implemented on Android based smartphones with the help of real experiments. Then we discuss the effectiveness of the data collection process by our framework in terms of energy efficiency. However, real experiments of the data collection process would require access to many test subjects
Discussion
The design of the PLUS framework has several interesting observation and challenges for its applicability that are discussed below.
Conclusion
In this paper, we presented PLUS, an energy efficient framework for localization and coverage in participatory urban sensing. Using PLUS, a continuous monitoring application can specify a desired partial coverage requirement of data. The framework performs data collection intelligently to achieve the required coverage while minimizing the energy consumption for localization and communication with the server. We also presented a localization scheme called sLoc to determine the block level
References (58)
- et al.
Coverage and connectivity issues in wireless sensor networks: A survey
Pervasive Mob. Comput.
(2008) - Apple Watch, https://www.apple.com/watch/ (retrieved March,...
- Samsung Gear, http://www.samsung.com/us/mobile/wearable-tech (retrieved March,...
- iHealth Edge, http://www.ihealthlabs.com/fitness-devices/ihealth-edge/ (retrieved March,...
- et al.
A review and taxonomy of activity recognition on mobile phones
BioNanoScience
(2013) - et al.
Toss’n’turn: smartphone as sleep and sleep quality detector
- N.D. Lane, M. Mohammod, M. Lin, X. Yang, H. Lu, S. Ali, A. Doryab, E. Berke, T. Choudhury, A. Campbell, BeWell: A...
- et al.
Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype
- J.A. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, M.B. Srivastava, Participatory sensing, in: ACM...
- et al.
Mobiscopes for human spaces
IEEE Pervasive Comput.
(2007)
A distributed activity scheduling algorithm for wireless sensor networks with partial coverage
Wirel. Netw.
Data collection in wireless sensor networks with mobile elements: A survey
ACM Trans. Sensor Netw. (TOSN)
A novel framework for energy-efficient data gathering with random coverage in wireless sensor networks
ACM Trans. Sensor Netw. (TOSN)
Data collection in wireless sensor networks with mobile elements: A survey
ACM Trans. Sensor Netw. (TOSN)
Energy-efficient randomized switching for maximizing lifetime in tree-based wireless sensor networks
IEEE/ACM Trans. Netw.
Minimum cost collaborative sensing network with mobile phones
Mobile phone sensing systems: A survey
IEEE Commun. Surv. Tutor.
Cited by (20)
Computation and communication efficient approach for federated learning based urban sensing applications against inference attacks
2024, Pervasive and Mobile ComputingThe Internet of People (IoP): A new wave in pervasive mobile computing
2017, Pervasive and Mobile ComputingCitation Excerpt :The people-centric sensing (also known as crowdsensing) paradigm represents the first step in this evolution [201]. According to this paradigm, which combines wireless communications and sensor networks with human daily life activities, people with their smart devices, willingly or unwillingly, represent potential sensing devices distributed across the physical space, [202–215]. A mobile phone, though not built specifically for sensing, can in fact readily function as sophisticated sensors by exploiting the camera (as video and image sensors), the microphone (as an acoustic sensor), the embedded GPS receivers (to sense location information), etc.
D-Log: A WiFi Log-based differential scheme for enhanced indoor localization with single RSSI source and infrequent sampling rate
2017, Pervasive and Mobile ComputingCitation Excerpt :In [21], in-device recorded RSSI from a single access point is used, however, the technique relies on dead reckoning to provide a perceived triangulation on the device. Khan et al. improved the coverage of localization through active participation of users [24]. Other localization techniques employ the use of ZigBee networks (e.g. [25,26]), RFID tags [27], or propagation model and autonomous crowdsourcing [28].