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NORS: An Open Source Platform to Facilitate Participatory Sensing with Mobile Phones
Abstract
With more than two billion phone subscribers worldwide and even more sold mobile phones, we can say that mobile phones are the most pervasive portable device today. Over the years their functionalities developed rapidly and today they can be used for performing tasks beyond just calling and messaging: they can provide an increasingly available intelligence at the edge of the network. This intelligence provides a fertile ground for the devices to act as sensor sinks in localized sensor networks and, furthermore, bridge between widely dispersed sensor networks through wide area connectivity. Currently, there are many sensor networks that have been deployed all over the world for various purposes. It is only natural that mobile phones would start, more and more, connecting to this available sensor infrastructure in order to use the data for customizing applications and services or collect, aggregate and provide the data to application servers. This paper presents a platform solution that allows for exploiting this very intelligence residing in everyone's mobile device to implement, among others, participatory sensing scenarios of various kinds. The platform is available under an open source license to facilitate innovation while preserving openness of key components of the platform.
... The importance of mobile devices and their capabilities has long been recognized within research projects such as [1][2][3][4]22] as well as commercial solutions such as [5,6]. This is due to mobile devices becoming increasingly more powerful in recent years. ...
... While other mobile-based sensing platforms have been developed during the years (e.g., [1] [3] [4][29]), we think there is value in presenting our platform, which can be immediately downloaded and used by the research community, therefore minimizing the time it takes to deal with sensing-specific issues and, instead, focusing on developing advanced algorithms that make use of such collected information. Our motivation behind creating a mobile-based sensing platform and gateway started a long time ago, with a Symbian-based platform [2], when it became clear to us that mobiles will become more pervasive computing devices, with ever-increasing capabilities for collecting, processing and interacting with end users. However, the more recent developments of mobile devices, software development environments and even user attitudes towards sensing, allowed us to greatly improve the platform by making it easier to add new sensors, functionalities and user interaction means. ...
... The area of lifestyle monitoring is very well represented both in research [1][2][3][4] as well as in the commercial space [5][6] [10][11][12][13][14][15], with mobile phones providing means for data collection, processing and remote access. Utilizing mobile devices for such scenarios, however, comes with challenges, in particular since the devices are not dedicated sensor platforms but they are primarily meant for personal or professional use [3]. ...
Utilizing mobile devices for gaining a better understanding of one’s surrounding, physiological state and overall behavior has been argued for in many previous works. Despite the increasing usage of mobile devices for research in this space, few platforms developed are readily available for supporting the wider research community. This paper presents a mobile sensing platform that allows for exploiting the latest and ever-increasing capabilities residing in mobile devices. While we highlight the main design and implementation characteristics of this solution, we also outline our experiences with this platform for typical usage scenarios in lifestyle management.
... Machine-to-machine (M2M) communications describes a system where multiple electronic devices communicate autonomously to enable the sharing of information [11]. Among the millions of M2M devices that will be deployed world-wide in the coming years, smartphones will be the most mobile, versatile and powerful devices that can be used as sensors and M2M gateways [12]. Therefore much research has been done on smartphone sensing applications in recent years [12,13,14,15,16,17,18,19,20,21,22,23]. ...
... Among the millions of M2M devices that will be deployed world-wide in the coming years, smartphones will be the most mobile, versatile and powerful devices that can be used as sensors and M2M gateways [12]. Therefore much research has been done on smartphone sensing applications in recent years [12,13,14,15,16,17,18,19,20,21,22,23]. ...
Road crashes are a growing concern of governments and is rising to become one of the leading preventable causes of death, especially in developing countries. The ubiquitous presence of smartphones provides a new platform on which to implement sensor networks and driver assistance systems, as well as other ITS applications. In this paper, existing approaches of using smartphones for ITS applications are analyzed and compared. Particular focus is placed on vehicle-based monitoring systems, such as driving behavior and style recognition, accident detection and road condition monitoring systems. Further opportunities for use of smartphones in ITS systems are highlighted, and remaining challenges in this emerging field of research are identified.
... 13: A plot of the processed 2Hz accelerometer measurement over the chosen, 390 to 430 seconds, interval showing two consecutive Speed Bump events (S1 and S2). ...
... NORS [36] is an open source platform that enables participatory sensing using mobile phones. It mainly focuses on collecting data instead of processing. ...
The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data has become easier than ever. However, large scale data management tasks
still require significant amounts of resources that can be expensive regardless of whether they are purchased or rented (e.g. pay-as-you-go infrastructure). Further, not everyone is interested in such large scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data. Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called C-MOSDEN ( Context-aware Mobile Sensor Data ENgine) for the IoT domain. We evaluated the proposed platform using three real-world scenarios that highlight the importance of 'selective sensing'. The computational effectiveness and efficiency of the proposed platform are investigated and is used to highlight the advantages of context-aware selective sensing.
... Trossen et al. [20] presented a platform, NORS, that allows mobile devices to be the part of sensing frameworks and work as gateways to forward sensor data. NORS is based on the publish-subscribe communication paradigm. ...
Participatory sensing, which enables citizens to collect and share data, can be helpful to design and develop useful applications in the domain of environmental monitoring, transportation, and health-care. However, the data collection process for such applications involves dealing with a variety of data sources, ranging from fixed environmental sensors to mobile human sensors, generating and presenting data in different formats. The citizens' engagement in sensing campaigns creates additional requirements to take care of their privacy concerns and motivate them to share data. Applications also need to compute the trustworthiness of the source of information to ensure the high quality of the collected data. Consequently, successfully dealing with these issues and deploying an application in an urban environment becomes a challenging task. In this paper we present a framework that uses fixed and participatory sensing to collect data from heterogeneous sources and presents a uniform interface to disseminate data to concerned applications according to their data requirements. We used our framework to develop a participatory sensing based smart phone application enabling and motivating the citizens to report positive and negative urban environmental behaviours. We report the results of our ongoing field trial. One of our findings is that different users contribute information about different environmental issues with different intensity. Moreover, we find that the current user participation to collect and share data follows the power law.
... Mobile Crowdsensing is related to Participatory and Opportunistic Sensing since the sensing task can require various amounts of manual user interaction on mobile phones but, in addition, necessitates a common server that coordinates the sensing tasks on all mobile devices [21]. A number of crowdsensing architectures have been proposed in the literature [49,43,59,15]. In Mobile Crowdsensing, the mobile client runs an application that is connected to a server component in the cloud. ...
In recent years, with tremendous advances in areas like mobile devices, algorithms for distributed systems, communication technology or protocols, all basic technological pieces to realise a Smart City are at hand. Missing, however, is a mechanism that bridges these pieces to ease the creation of Smart Cities at a larger scale. In this visionary paper, we discuss challenges of Smart Cities and propose enabling technologies to bridge the above mentioned pieces for their actual realisation. In particular, we introduce the concepts of Social Opportunistic Sensing (SOS) and Social Centric Networking (SCN). While the former is an enabling technology to interconnect all parties in a Smart City, the latter has the potential to enhance offline social networks in Internet of Things (IoT) enhanced Smart Cities by connecting individuals based on their automatically updated profile via context-based routing.
... Being personal devices, users will likely be unwilling to allow their phones to participate in studies without personal interest in the project and minimal impact on phone performance and battery life. The issue of minimal impact on the user's phone has been addressed by managing network transmission and sending updates [2]. ...
This paper presents a method to select appropriate users for participatory sensing applications.
... Trossen and Pavel presented a platform, Nokia Remote Sensing, which allows mobile devices to be the part of sensing frameworks and works as gateways to forward sensor data [34]. Nokia Remote Sensing is based on the publish-subscribe communication paradigm. ...
Persistent urbanisation of our planet places a continuous strain on cities’ resources and the quality of service delivery. While increasing city infrastructure might help alleviate this problem, the scale and complexity of future cities mean that this approach is unsustainable. Cities, however, are becoming increasingly instrumented with a myriad of sensors, both fixed and mobile. While a number of systems aim to exploit such sensors to gather information and to provide a real-time view of the city, existing approaches are application-specific, hindering their scalability and reuse. Using the city of Dublin (Ireland) as a testbed, this paper describes our iterative consultation process with city stakeholders to design CityWatch, an urban-scale data sensing and dissemination framework. In particular, it presents the resulting design of two prototype applications, the requirements on the overall framework, an initial implementation, and discusses the early results of ongoing trials. Copyright © 2014 John Wiley & Sons, Ltd.
... Our solution can provide an important piece in addressing a major concern for solutions in this space. Another area where we see potential for realizing our architecture is gateway-based sensor systems, such as those presented in [8], enabling anonymity for the sensor queries being issued by interested applications. The proposed architecture can also be mapped onto many topic-based publish-subscribe systems. ...
Revealing one's interests in communication has been recognized as a growing problem in the Internet. We postulate that it is desirable for future information retrieval systems to provide privacy in both what information is requested and what information is received, without raising obstacles to the deployment of accounting and access control mechanisms. This paper outlines a solution that fulfills this requirement in the context of broker-based systems, that is, systems in which brokers facilitate the communication between a consumer and a provider (of information). Broker-assisted communication is a common paradigm used in many settings, including contemporary information-centric networking approaches. We present the design and the evaluation of a solution that conceals consumers' interests, without hiding consumer identity or location. The developed solution is applied over a system of hierarchically organized brokers; similar systems are used in many information lookup services. Because in these systems, information is distributed in various locations, traditional private information retrieval (PIR) protocols exhibit significant communication overhead. Our solution achieves up to 97% less communication overhead compared with a PIR protocol, without additional computational overhead
... Actually, people-centric wireless sensing platforms (e.g., SenseWeb [10], CarTel [11], NORS [12], CenceMe [13] or AnonySense [14]) are based on a centralized architecture to process raw context information collected by mobile devices in a participatory or opportunistic manner. Therefore, application developers make connections to dedicated servers to checkout data, e.g., via a Web-based interface. ...
The proliferation of mobile computing devices and wireless sensors into our daily life promotes new spontaneous applications. Associated network offer an increasing capacity of sensing surrounding context information and communicating without any pre-existing infrastructure. However, for such envi- ronments, application developers still have to cope with lot of heterogeneous devices and unpredictable disconnections as well as dealing with a large amount of shareable distributed context information. In order to facilitate the construction of context- aware software applications, this paper proposes a new middle- ware service for cooperative context management in spontaneous networks. First, relying on a component-based design approach to handle heterogeneity, context information sensing and processing operations of the service are systematically controlled by devices according to their local sensor connections. Second, our service defends autonomous context information sharing between devices through an adapted gossiping algorithm in a peer-to-peer manner. The benefits of our proposal for application development are justified through a simple context-aware application case study and the first service's performance and scalability properties are evaluated through some detailed simulation results. Index Terms—context management, context sharing, peer-to- peer, middleware, spontaneous networks
... Mobile phone data is collected through the NORS platform [15], a mobile Java-based sensing platform that implements various sensor handlers allowing data collection of phone data as well as from attached BTenabled sensors. The NORS platform collects data in its own file format. ...
As computing devices become more pervasive, our daily activities start generating a vast amount of information that could be exploited for helping us better understand ourselves. In this paper we present a system that uses easily available data correlated into a story-based representation aimed at providing users with a better understanding of their lifestyles. While this is still work in progress, we believe that it provides valuable insights into the design of such systems. Our initial findings show that user data generated through a person's daily activities can reveal a wealth of valuable information which they can use to adjust and improve their lifestyles.
... Systems such as [13] provide some support for people with depression, by using an interactive questionnaire and prompting for a periodic recording of blood pressure. Some research platforms, built to enable well-being applications, are described in [14][15] [16]. However, very few of the available lifestyle management systems support users in understanding why something happened. ...
Even though mental health is an important part of our wellbeing we believe that, so far, it has been overlooked in favour of physical health by most of the existing self-monitoring solutions. Our goal is to utilise context aware technologies in order to support people in understanding how various aspects of their lives influence their wellbeing, including their mental health. For that, we need to gain a deeper insight into the challenges of designing such solutions, from sensing to interaction paradigms. This paper describes our system, the design challenges we have encountered, the decisions we have made and our ongoing work in terms of system design as well as usage experiments.
... The system can collect information from both physical (raw data) and logical sensors (interpreted data). As shown, we currently use: a Garmin ForeRunner 305 wristwatch-like device used for fitness-related monitoring, providing heart rate and GPS-related information as raw as well as interpreted data; a mobile phone running the NORS platform [9], a Java-based sensing platform that allows collection of data from the phone and from attached BT- enabled sensors, and an Alive Heart Monitor -small wearable sensing device developed by Alive Technologies. PC data collection is performed through a commercial activity monitoring platform. ...
The burden on our healthcare systems is ever increasing with people living longer and requiring more support. Technologies can help ease the strain. One important aspect in health management is prevention enabled by self-monitoring and, eventually, self-understanding one's lifestyle through building supportive, interactive and engaging systems. This paper outlines a system aimed at empowering users with a better understanding of how their lifestyles can impact their wellbeing by using a context-based story-telling approach. While this work is still in its early phases, we believe that a lot of promise is shown through the current development status as well as our system design. Our initial findings show that available user data generated through our daily activities can reveal a wealth of information related to our lifestyles and provide support in implementing changes.
... We are also using the wireless Alive heart monitor [14] that provides ECG and 3D axis accelerometer. A Nokia N95 phone provides us with messaging and calling activity, images, audio and video recordings, GPS data, WLAN activity, and is also used as a sensing gateway for attached sensors, utilizing the sensor platform described by Trossen and Pavel [13]. From computers, we have been looking at various available activity monitoring platforms that collect information about messaging, communication, web browsing, etc. ...
Almost all technology supported activities we perform today involve a device with computational power able to collect data that can give information about our activities as well as, with the appropriate sensors, about our physiological status. While recording user data is not particularly novel, in our work, we concern ourselves with methods that people can use to gather and analyze data about themselves as they go about everyday work and leisure activities in order to better support self-monitoring and self-understanding. Our aim is to enable people to detect causality relationships in their behaviours either for serving their curiosity or, as we hope, for really empowering them with a tool for self-changes. In this 'work in progress' paper we describe ongoing work towards these ends. We start out by a motivation and short description of our work, followed by an exemplar scenario on 'preventive healthcare' for the system we envision. Then we describe some of the most relevant and influential related work before describing the system design and the main challenges, followed by an overview of the current status and our future vision.
... NORS [14] is an open source platform to facilitate participatory sensing with mobile phones. However, NORS transmits the collected data to specific application servers instead of peer-to-peer decentralized data sharing among mobile devices as in our work. ...
Abstract- Today, people often go around with different personal portable or wearable computing devices. They can interact wirelessly with static and mobile sensors gradually integrated in the surrounding environments that provide them with various context information characterizing their current situation. Therefore, context-based spontaneous services help applications provide users with the most relevant information without users specific intervention. This paper illustrates the interest of such services in mobile and ubiquitous environments through several examples in which nearby heterogeneous mobile devices and sensors communicate in an autonomous fashion. Their proof of concept prototypes have shown the benefits of our previously proposed middleware for context management.
A cyber-physical system (CPS) is a system of collaborating computational elements controlling physical entities. CPS represents the next stage on the road to the creation of smart cities through the creation of an Internet of Things, data and services. Mobility is one of the major characteristic of both CPS and IoT. In this Chapter, we discuss mobile sensing platforms and their applications towards different but interrelated paradigms such as IoT, sensing as a service, and smart cities. We highlight and briefly discuss different types of mobile sensing platforms and functionalities they offer. Mobile sensing platforms are more oftenly integrated with smart phones and tablet devices. The resource constrained nature of the mobile devices requires different types of designs and architectural implementations. We proposed a software-based mobile sensing platform called Mobile Sensor Data Engine (MOSDEN). It is a plug-in-based scalable and extendible IoT middleware for mobile devices that provide an easy way to collect sensor data from both internal and external sensors. MOSDEN act as intermediary device that collects data from external sensors and upload to the cloud in real-time or on demand. We evaluate MOSDEN in both stand-alone and collaborative environments. The proof of concept is developed on Android platform.
Ambient factors such as temperature, lighting, and air quality influence occupants' productivity and behavior. Although these factors are regulated by industry standards and monitored by the facilities management groups, occupants' perceptions vary from actual values due to various factors such as building schedules and occupancy, occupant activity and preferences, weather and climate, and the placement of sensors. While occupant comfort surveys are sometimes conducted, they are generally limited to one-time or periodic assessments that do not fully represent occupant experiences throughout building operations. This study proposes a new methodology for gathering real time data on a continuous basis through participatory sensing of occupant ambient comfort in indoor environments based on a smart phone application. The developed application is presented and validated by a pilot study in a university building. Occupant perceptions of temperature are compared to actual temperature records. No correlation i
Pervasive healthcare is gaining attention outside the research community. But real-life deployment of many solutions faces the realities that come with everyday life, namely the existence of boundaries between applications, networks and devices. These often impair the end user experience through failing along these boundaries or enforcing particular end user experiences at them, e.g., by providing single-device or single-network solutions. We intend to address these shortcomings with an approach that places information at the centre of the platform design, including the low-level communication framework. We believe that this approach alone can address the various governance and provenance conflicts that emerge in real-life deployments. This paper describes early work by outlining our design vision as well as early findings on its realization.
An emerging category of devices at the edge of the Internet are consumer-centric mobile sensing and computing devices, such as smartphones, music players, and in-vehicle sensors. These devices will fuel the evolution of the Internet of Things as they feed sensor data to the Internet at a societal scale. In this article, we examine a category of applications that we term mobile crowdsensing, where individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest. We present a brief overview of existing mobile crowdsensing applications, explain their unique characteristics, illustrate various research challenges, and discuss possible solutions. Finally, we argue the need for a unified architecture and envision the requirements it must satisfy.
The growing number of deployed heterogeneous networks and applications is resulting in a sporadic isolated embedded sensor network environment. The TinyML project addresses the need for an embedded sensor network standardized "markup language" for intra-network, as well as inter-network, communication. The GML-based SensorML (SML) markup language provides a sensor-centric approach for satellite-based sensor coordination for global location services. However, SML relies on characteristics that differ from embedded wireless sensor networks, such as scale, hardware, energy, and infrastructure. TinyML focuses exclusively on embedded sensor network features, constraints, and capabilities. TinyML is capable of leveraging the flexibility of XML data structures with embedded sensor network reprogrammability. We present the concept of virtual sensors and actuators and implemented the necessary components to overcome the SML deficiencies. TinyML is a step forward in making sensor networks more accessible to the non-expert user and for archiving data retrieved from senor networks in a self-documenting manner.
This document defines formally the semantics of XQuery 1.0 [XQuery 1.0: A Query
Language for XML] and XPath 2.0 [XML Path Language (XPath) 2.0].
SenSay is a context-aware mobile phone that adapts to dynamically changing environmental and physiological states. In addition to manipulating ringer volume, vibration, and phone alerts, SenSay can provide remote callers with the ability to communicate the urgency of their calls, make call suggestions to users when they are idle, and provide the caller with feedback on the current status of the SenSay user. A number of sensors including accelerometers, light, and microphones are mounted at various points on the body to provide data about the user's context. A decision module uses a set of rules to analyze the sensor data and manage a state machine composed of uninterruptible, idle, active and normal states. Results from our threshold analyses show a clear delineation can be made among several user states by examining sensor data trends. SenSay augments its contextual knowledge by tapping into applications such as electronic calendars, address books, and task lists.
Smart phones are a particularly tempting platform for building context-aware applications because they're programmable and often use well-known operating systems. There's a gap, however, between the operating systems' functionality and the features that application developers need. To fill this gap, we've designed and developed ContextPhone, a software platform consisting of four interconnected modules provided as a set of open source C++ libraries and source code components. ContextPhone runs on off-the-shelf mobile phones using Symbian OS and the Nokia Series 60 Smartphone platform. ContextPhone was developed using an iterative, human-centered design strategy. It thus helps developers more easily create applications that integrate into both existing technologies and users' everyday lives.
SSI: Simple Sensor Interface
- J Hyyrylainen
Machine-to-Machine solution
- Aplicom
Wireless Sensor Networks
- Crossbow