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

Block-Based Development of Mobile Learning Experiences for the Internet of Things

The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor's standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks.Spanish National Research Agency (AEI) - ERDF fund

Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN

Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper

Autonomy Operating System for UAVs: Pilot-in-a-Box

The Autonomy Operating System (AOS) is an open flight software platform with Artificial Intelligence for smart UAVs. It is built to be extendable with new apps, similar to smartphones, to enable an expanding set of missions and capabilities. AOS has as its foundations NASAs core flight executive and core flight software (cFEcFS). Pilot-in-a-Box (PIB) is an expanding collection of interacting AOS apps that provide the knowledge and intelligence onboard a UAV to safely and autonomously fly in the National Air Space, eventually without a remote human ground crew. Longer-term, the goal of PIB is to provide the capability for pilotless air vehicles such as air taxis that will be key for new transportation concepts such as mobility-on-demand. PIB provides the procedural knowledge, situational awareness, and anticipatory planning (thinking ahead of the plane) that comprises pilot competencies. These competencies together with a natural language interface will enable Pilot-in-a-Box to dialogue directly with Air Traffic Management from takeoff through landing. This paper describes the overall AOS architecture, Artificial Intelligence reasoning engines, Pilot-in-a-box competencies, and selected experimental flight tests to date

IoT enabled aquatic drone for environment monitoring

This thesis presents a platform that tackles environment monitoring by using air and water quality sensors to provide data for the user to know what is happening in that surveilled area. The hardware is incorporated in a sensing module in order to be used with an Unmanned Surface Vehicle (USV). It presents a monitoring system based on Raspberry Pi platform and a multichannel sensing module associated with water quality and air quality measurement parameters. Thus, the temperature, relative humidity and gas concentration are measured as well as the underwater acoustic signals using a hydrophone. The data is stored on the memory of the drone’s computational platform (Raspberry Pi), and synchronized with a remote server database. Audio streaming capabilities were implemented in the server side. Additionally, a mobile application was developed to be used by people working in the field for data visualization, audio streaming playback and statistical analysis (by showing plotted data).O intuito desta dissertação é apresentar uma plataforma de monitorização ambiental através da instalação de sensores de qualidade do ar e da água de forma a fornecer dados ao utilizador daquela área vigiada. O hardware é apresentado num módulo onde estão presentes todos os componentes por forma a poder ser usado num drone aquático. É apresentado um sistema de monitorização baseado no sistema de processamento Raspberry Pi e um módulo multicanal de sensores de medição de qualidade do ar e qualidade da água. Sensores esses de medição da temperatura, humidade relativa e concentrações de gases tal como a medição de sinais de áudio debaixo de água com o uso de um hidrofone. Os dados estão alojados na memória do sistema computacional do drone (Raspberry Pi) e estão sincronizados com uma base de dados remota alojada num servidor cloud. Um sistema de streaming de áudio foi também implementado do lado do servidor. Adicionalmente, foi desenvolvida uma aplicação móvel que permite visualizar os dados provenientes dos sensores, reproduzir a stream de áudio e também analise de estatísticas (com apresentação gráfica dos dados)