MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications

Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper proposes a mobile collaborative platform called MOSDEN that enables and supports opportunistic sensing at run time. MOSDEN captures and shares sensor data across multiple apps, smartphones and users. MOSDEN supports the emerging trend of separating sensors from application-specific processing, storing and sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing the efforts in developing novel opportunistic sensing applications. MOSDEN has been implemented on Android-based smartphones and tablets. Experimental evaluations validate the scalability and energy efficiency of MOSDEN and its suitability towards real world applications. The results of evaluation and lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing, 2014. arXiv admin note: substantial text overlap with arXiv:1310.405

Fighting Pandemics with Augmented Reality and Smart Sensing-based Social Distancing

In a postpandemic world, remaining vigilant and maintaining social distancing are still crucial so societies can contain the virus and the public can avoid disproportionate health impacts. Augmented reality (AR) can visually assist users in understanding the distances in social distancing. However, integrating external sensing and analysis is required for social distancing beyond the users’ local environment. We present DistAR, an android-based application for social distancing leveraging AR and smart sensing using on-device analysis of optical images and environment crowdedness from smart campus data. Our prototype is one of the first efforts to combine AR and smart sensing technologies to create a real-time social distancing application.Peer reviewe

MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices

The Internet of Things (IoT) is part of Future Internet and will comprise many billions of Internet Connected Objects (ICO) or `things' where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/ virtual identities and attributes. Collecting data from these objects is an important task as it allows software systems to understand the environment better. Many different hardware devices may involve in the process of collecting and uploading sensor data to the cloud where complex processing can occur. Further, we cannot expect all these objects to be connected to the computers due to technical and economical reasons. Therefore, we should be able to utilize resource constrained devices to collect data from these ICOs. On the other hand, it is critical to process the collected sensor data before sending them to the cloud to make sure the sustainability of the infrastructure due to energy constraints. This requires to move the sensor data processing tasks towards the resource constrained computational devices (e.g. mobile phones). In this paper, we propose Mobile Sensor Data Processing Engine (MOSDEN), an plug-in-based IoT middleware for mobile devices, that allows to collect and process sensor data without programming efforts. Our architecture also supports sensing as a service model. We present the results of the evaluations that demonstrate its suitability towards real world deployments. Our proposed middleware is built on Android platform

An Android-Based Mechanism for Energy Efficient Localization Depending on Indoor/Outdoor Context

Today, there is widespread use of mobile applications that take advantage of a user\u27s location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user\u27s behalf can significantly improve a smartphone\u27s battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy

Smartphone sensing platform for emergency management

The increasingly sophisticated sensors supported by modern smartphones open up novel research opportunities, such as mobile phone sensing. One of the most challenging of these research areas is context-aware and activity recognition. The SmartRescue project takes advantage of smartphone sensing, processing and communication capabilities to monitor hazards and track people in a disaster. The goal is to help crisis managers and members of the public in early hazard detection, prediction, and in devising risk-minimizing evacuation plans when disaster strikes. In this paper we suggest a novel smartphone-based communication framework. It uses specific machine learning techniques that intelligently process sensor readings into useful information for the crisis responders. Core to the framework is a content-based publish-subscribe mechanism that allows flexible sharing of sensor data and computation results. We also evaluate a preliminary implementation of the platform, involving a smartphone app that reads and shares mobile phone sensor data for activity recognition.Comment: 11th International Conference on Information Systems for Crisis Response and Management ISCRAM2014 (2014

Context-aware Dynamic Discovery and Configuration of 'Things' in Smart Environments

The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Currently, such Internet-connected objects or `things' outnumber both people and computers connected to the Internet and their population is expected to grow to 50 billion in the next 5 to 10 years. To be able to develop IoT applications, such `things' must become dynamically integrated into emerging information networks supported by architecturally scalable and economically feasible Internet service delivery models, such as cloud computing. Achieving such integration through discovery and configuration of `things' is a challenging task. Towards this end, we propose a Context-Aware Dynamic Discovery of {Things} (CADDOT) model. We have developed a tool SmartLink, that is capable of discovering sensors deployed in a particular location despite their heterogeneity. SmartLink helps to establish the direct communication between sensor hardware and cloud-based IoT middleware platforms. We address the challenge of heterogeneity using a plug in architecture. Our prototype tool is developed on an Android platform. Further, we employ the Global Sensor Network (GSN) as the IoT middleware for the proof of concept validation. The significance of the proposed solution is validated using a test-bed that comprises 52 Arduino-based Libelium sensors.Comment: Big Data and Internet of Things: A Roadmap for Smart Environments, Studies in Computational Intelligence book series, Springer Berlin Heidelberg, 201