A framework for data quality and feedback in participatory sensing

The rapid adoption of mobile phones by society over the last decade and the increasing ability to capture, classifying, and transmit a wide variety of data (image, audio, and location) have enabled a new sensing paradigm - where humans carrying mobile devices can act as sensor systems. Human-in-the-loop sensor systems raise many new challenges in areas of sensor data quality assessment, mobility and sampling coordination, and user interaction procedures. To this end, we outline our initial steps in designing an incentive based reputation system for assessing data quality, provide an overview of techniques to obtain fine grained contextual information in mobile situations, and detail feedback mechanisms to enable more interactive and informative sensor systems

Multimodal identification of journeys

Due to the ubiquity of localisation technology, users now have the ability to keep a record of their own location, as a kind of ‘location diary’. Such a large collection of data can become unmanageable without some way to structure that data to make it useful and searchable. We address this problem of structuring location data by proposing a framework for classifying the data into often-traversed routes. In this work, commonly traversed routes are identified with clusters based on sensed data. Our framework does not rely on any one source of location information, but can fuse data from multimodal localisation sources. We demonstrate the effectiveness of our algorithm by examining the combination of GPS, wireless signal strength readings and image-matching on very challenging data in a variety of environmental conditions. By fusing these three modalities we obtained better performance than any individual or combination of two modalities. As it can be orientated towards the needs and capabilities of the user based on context, this method becomes useful for some ambient assisted living applications

iDiary: from GPS signals to a text-searchable diary

This paper describes a system that takes as input GPS data streams generated by users' phones and creates a searchable database of locations and activities. The system is called iDiary and turns large GPS signals collected from smartphones into textual descriptions of the trajectories. The system features a user interface similar to Google Search that allows users to type text queries on their activities (e.g., "Where did I buy books?") and receive textual answers based on their GPS signals. iDiary uses novel algorithms for semantic compression (known as coresets) and trajectory clustering of massive GPS signals in parallel to compute the critical locations of a user. Using an external database, we then map these locations to textual descriptions and activities so that we can apply text mining techniques on the resulting data (e.g. LSA or transportation mode recognition). We provide experimental results for both the system and algorithms and compare them to existing commercial and academic state-of-the-art. This is the first GPS system that enables text-searchable activities from GPS data.United States. Office of Naval Research (Grant ONR-MURI Award N00014-12-1-1000)Hon Hai/Foxconn International Holdings Ltd.Singapore-MIT Alliance for Research and Technology CenterSingapore. National Research FoundationUnited States. Dept. of Defense. National Defense Science & Engineering Graduate Fellowship Progra

A crowdsensing method for water resource monitoring in smart communities

Crowdsensing aims to empower a large group of individuals to collect large amounts of data using their mobile devices, with the goal of sharing the collected data. Existing crowdsensing studies do not consider all the activities and methods of the crowdsensing process and the key success factors related to the process. Nor do they investigate the profile and behaviour of potential participants. The aim of this study was to design a crowdsensing method for water resource monitoring in smart communities. This study opted for an exploratory study using the Engaged Scholarship approach, which allows the study of complex real-world problems based on the different perspectives of key stakeholders. The proposed Crowdsensing Method considers the social, technical and programme design components. The study proposes a programme design for the Crowdsensing Methodwhich is crowdsensing ReferenceFrameworkthat includes Crowdsensing Processwith key success factors and guidelines that should be considered in each phase of the process. The method also uses the Theory of Planned Behaviour (TPB) to investigate citizens’intention to participate in crowdsensing for water resource monitoring and explores their attitudes, norms and perceived behavioural control on these intentions. Understanding the profiles of potential participants can assist with designing crowdsensing systems with appropriate incentive mechanisms to achieve adequate user participation and good service quality. A survey was conducted to validate the theoretical TB model in a real-world context. Regression and correlation analyses demonstrated that the attitudes, norms and perceived behavioural control can be used to predict participants’ intention to participate in crowdsensing for water resource monitoring. The survey results assisted with the development of an Incentive Mechanism as part of the Crowdsensing Method. This mechanism incorporates recruitment and incentive policies, as well as guidelines derived from the literature review and extant system analysis. The policies, called the OverSensepolicies, provide guidance for recruitment and rewarding of participants using the popular Stackelberg technique. The policies were evaluated using simulation experiments with a data set provided by the case study, the Nelson Mandela Bay Municipality. The results of the simulation experiments illustrated that the OverSenserecruitmentpolicycan reduce the computing resources required for the recruitment of participants and that the recruitment policy performs better than random or naïve recruitment policies. The proposed Crowdsensing Method was evaluated using an ecosystem of success factors for mobile-based interventions identified in the literature and the Crowdsensing Method adhered to a majority (90%) of the success factors. This study also contributes information systems design theory by proposing several sets of guidelines for crowdsensing projects and the development of crowdsensing systems. This study fulfils an identified need to study the applicability of crowdsensing for water resource monitoring and explores how a crowdsensing method can create a smart community

Digital life stories: Semi-automatic (auto)biographies within lifelog collections

Our life stories enable us to reflect upon and share our personal histories. Through emerging digital technologies the possibility of collecting life experiences digitally is increasingly feasible; consequently so is the potential to create a digital counterpart to our personal narratives. In this work, lifelogging tools are used to collect digital artifacts continuously and passively throughout our day. These include images, documents, emails and webpages accessed; texts messages and mobile activity. This range of data when brought together is known as a lifelog. Given the complexity, volume and multimodal nature of such collections, it is clear that there are significant challenges to be addressed in order to achieve coherent and meaningful digital narratives of our events from our life histories. This work investigates the construction of personal digital narratives from lifelog collections. It examines the underlying questions, issues and challenges relating to construction of personal digital narratives from lifelogs. Fundamentally, it addresses how to organize and transform data sampled from an individual’s day-to-day activities into a coherent narrative account. This enquiry is enabled by three 20-month long-term lifelogs collected by participants and produces a narrative system which enables the semi-automatic construction of digital stories from lifelog content. Inspired by probative studies conducted into current practices of curation, from which a set of fundamental requirements are established, this solution employs a 2-dimensional spatial framework for storytelling. It delivers integrated support for the structuring of lifelog content and its distillation into storyform through information retrieval approaches. We describe and contribute flexible algorithmic approaches to achieve both. Finally, this research inquiry yields qualitative and quantitative insights into such digital narratives and their generation, composition and construction. The opportunities for such personal narrative accounts to enable recollection, reminiscence and reflection with the collection owners are established and its benefit in sharing past personal experience experiences is outlined. Finally, in a novel investigation with motivated third parties we demonstrate the opportunities such narrative accounts may have beyond the scope of the collection owner in: personal, societal and cultural explorations, artistic endeavours and as a generational heirloom

A Framework for Data Quality and Feedback in Participatory Sensing

The rapid adoption of mobile phones by society over the last decade and the increasing ability to capture, classifying, and transmit a wide variety of data (image, audio, and location) have enabled a new sensing paradigm - where humans carrying mobile devices can act as sensor systems. Human-in-the-loop sensor systems raise many new challenges in areas of sensor data quality assessment, mobility and sampling coordination, and user interaction procedures. To this end, we outline our initial steps in designing an incentive based reputation system for assessing data quality, provide an overview of techniques to obtain fine grained contextual information in mobile situations, and detail feedback mechanisms to enable more interactive and informative sensor systems