Towards System Implementation and Data Analysis for Crowdsensing Based Outdoor RSS Maps

© 2013 IEEE. With the explosive usage of smart mobile devices, sustainable access to wireless networks (e.g., Wi-Fi) has become a pervasive demand. Most mobile users expect seamless network connection with low cost. Indeed, this can be achieved by using an accurate received signal strength (RSS) map of wireless access points. While existing methods are either costly or unscalable, the recently emerged mobile crowdsensing (MCS) paradigm is a promising technique for building RSS maps. MCS applications leverage pervasive mobile devices to collaboratively collect data. However, the heterogeneity of devices and the mobility of users could cause inherent noises and blank spots in collected data set. In this paper, we study how to: 1) tame the sensing noises from heterogenous mobile devices and 2) construct accurate and complete RSS maps with random mobility of crowdsensing participants. First, we build a mobile crowdsensing system called i Map to collect RSS measurements with heterogeneous mobile devices. Second, through observing experimental results, we build statistical models of sensing noises and derive different parameters for each kind of mobile device. Third, we present the signal transmission model with measurement error model, and we propose a novel signal recovery scheme to construct accurate and complete RSS maps. The evaluation results show that the proposed method can achieve 90% and 95% recovery rate in geographic coordinate system and polar coordinate system, respectively

Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing

Ubiquity of mobile devices with rich sensory capabilities has given rise to the mobile crowd-sensing (MCS) concept, in which a central authority (the platform) and its participants (mobile users) work collaboratively to acquire sensory data over a wide geographic area. Recent research in MCS highlights the following facts: 1) a utility metric can be defined for both the platform and the users, quantifying the value received by either side; 2) incentivizing the users to participate is a non-trivial challenge; 3) correctness and truthfulness of the acquired data must be verified, because the users might provide incorrect or inaccurate data, whether due to malicious intent or malfunctioning devices; and 4) an intricate relationship exists among platform utility, user utility, user reputation, and data trustworthiness, suggesting a co-quantification of these inter-related metrics. In this paper, we study two existing approaches that quantify crowd-sensed data trustworthiness, based on statistical and vote-based user reputation scores. We introduce a new metric - collaborative reputation scores - to expand this definition. Our simulation results show that collaborative reputation scores can provide an effective alternative to the previously proposed metrics and are able to extend crowd sensing to applications that are driven by a centralized as well as decentralized control

Conversational Sensing

Recent developments in sensing technologies, mobile devices and context-aware user interfaces have made it possible to represent information fusion and situational awareness as a conversational process among actors - human and machine agents - at or near the tactical edges of a network. Motivated by use cases in the domain of security, policing and emergency response, this paper presents an approach to information collection, fusion and sense-making based on the use of natural language (NL) and controlled natural language (CNL) to support richer forms of human-machine interaction. The approach uses a conversational protocol to facilitate a flow of collaborative messages from NL to CNL and back again in support of interactions such as: turning eyewitness reports from human observers into actionable information (from both trained and untrained sources); fusing information from humans and physical sensors (with associated quality metadata); and assisting human analysts to make the best use of available sensing assets in an area of interest (governed by management and security policies). CNL is used as a common formal knowledge representation for both machine and human agents to support reasoning, semantic information fusion and generation of rationale for inferences, in ways that remain transparent to human users. Examples are provided of various alternative styles for user feedback, including NL, CNL and graphical feedback. A pilot experiment with human subjects shows that a prototype conversational agent is able to gather usable CNL information from untrained human subjects

A Service-Oriented Approach to Crowdsensing for Accessible Smart Mobility Scenarios

This work presents an architecture to help designing and deploying smart mobility applications. The proposed solution builds on the experience already matured by the authors in different fields: crowdsourcing and sensing done by users to gather data related to urban barriers and facilities, computation of personalized paths for users with special needs, and integration of open data provided by bus companies to identify the actual accessibility features and estimate the real arrival time of vehicles at stops. In terms of functionality, the first "monolithic" prototype fulfilled the goal of composing the aforementioned pieces of information to support citizens with reduced mobility (users with disabilities and/or elderly people) in their urban movements. In this paper, we describe a service-oriented architecture that exploits the microservices orchestration paradigm to enable the creation of new services and to make the management of the various data sources easier and more effective. The proposed platform exposes standardized interfaces to access data, implements common services to manage metadata associated with them, such as trustworthiness and provenance, and provides an orchestration language to create complex services, naturally mapping their internal workflow to code. The manuscript demonstrates the effectiveness of the approach by means of some case studies

Multi-dimensional urban sensing in sparse mobile crowdsensing

International audienceSparse mobile crowdsensing (MCS) is a promising paradigm for the large-scale urban sensing, which allows us to collect data from only a few areas (cell selection) and infer the data of other areas (data inference). It can significantly reduce the sensing cost while ensuring high data quality. Recently, large urban sensing systems often require multiple types of sensing data (e.g., publish two tasks on temperature and humidity respectively) to form a multi-dimensional urban sensing map. These multiple types of sensing data hold some inherent correlations, which can be leveraged to further reduce the sensing cost and improve the accuracy of the inferred results. In this paper, we study the multi-dimensional urban sensing in sparse MCS to jointly address the data inference and cell selection for multi-task scenarios. We exploit the intra-and inter-task correlations in data inference to deduce the data of the unsensed cells through the multi-task compressive sensing and then learn and select the most effective cell, task pairs by using reinforcement learning. To effectively capture the intra-and inter-task correlations in cell selection, we design a network structure with multiple branches, where branches extract the intra-task correlations for each task, respectively, and then catenates the results from all branches to capture the inter-task correlations among the multiple tasks. In addition, we present a two-stage online framework for reinforcement learning in practical use, including training and running phases. The extensive experiments have been conducted on two real-world urban sensing datasets, each with two types of sensing data, which verify the effectiveness of our proposed algorithms on multi-dimensional urban sensing and achieve better performances than the state-of-the-art mechanisms

A Communication Architecture for Crowd Management in Emergency and Disruptive Scenarios

Crowd management aims to develop support infrastructures that can effectively manage crowds at any time. In emergency and disruptive scenarios this concept can minimize the risk to human life and to the infrastructure. We propose the Communication Architecture for Crowd Management (CACROM), which can support crowd management under emergency and disruptive scenarios. We identify, describe, and discuss the various components of the proposed architecture, and we briefly discuss open challenges in the design of crowd management systems for emergency and disruptive scenarios

Reinforcement learning-based cell selection in sparse mobile crowdsensing

International audienceSparse Mobile Crowdsensing (MCS) is a novel MCS paradigm which allows us to use the mobile devices to collect sensing data from only a small subset of cells (sub-areas) in the target sensing area while intelligently inferring the data of other cells with quality guarantee. Since selecting sensed data from different cell sets will probably lead to diverse levels of inference data quality, cell selection (i.e., choosing which cells in the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i.e., data collection costs) for ensuring a certain level of data quality. To address this issue, this paper proposes the reinforcement learning-based cell selection algorithm for Sparse MCS. First, we model the key concepts in reinforcement learning including state, action, and reward, and then propose a Q-learning based cell selection algorithm. To deal with the large state space, we employ the deep Q-network to learn the Q-function that can help decide which cell is a better choice under a certain state during cell selection. Then, we modify the Q-network to a deep recurrent Q-network with LSTM to catch the temporal patterns and handle partial observability. Furthermore, we leverage the transfer learning techniques to relieve the dependency on a large amount of training data. Experiments on various real-life sensing datasets verify the effectiveness of our proposed algorithms over the state-of-the-art mechanisms in Sparse MCS by reducing up to 20% of sensed cells with the same data inference quality guarantee