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

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

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

A Time-Sensitive IoT Data Analysis Framework

This paper proposes a Time-Sensitive IoT Data Analysis (TIDA) framework that meets the time-bound requirements of time-sensitive IoT applications. The proposed framework includes a novel task sizing and dynamic distribution technique that performs the following: 1) measures the computing and network resources required by the data analysis tasks of a time-sensitive IoT application when executed on available IoT devices, edge computers and cloud, and 2) distributes the data analysis tasks in a way that it meets the time-bound requirement of the IoT application. The TIDA framework includes a TIDA platform that implements the above techniques using Microsoft’s Orleans framework. The paper also presents an experimental evaluation that validates the TIDA framework’s ability to meet the time-bound requirements of IoT applications in the smart cities domain. Evaluation results show that TIDA outperforms traditional cloud-based IoT data processing approaches in meeting IoT application time-bounds and reduces the total IoT data analysis execution time by 46.96%

ConTaaS: An Approach to Internet-Scale Contextualisation for Developing Efficient Internet of Things Applications

The Internet of Things (IoT) is a new internet evolution that involves connecting billions of sensors and other devices to the Internet. Such IoT devices or IoT things can communicate directly. They also allow Internet users and applications to access and distil their data, control their functions, and harness the information and functionality provided by multiple IoT devices to offer novel smart services. IoT devices collectively generate massive amounts of data with an incredible velocity. Processing IoT device data and distilling high-value information from them presents an Internet-scale computational challenge. Contextualisation of IoT data can help improve the value of information extracted from IoT. However, existing contextualisation techniques can only handle small datasets from a modest number of IoT devices. In this paper, we propose a general-purpose architecture and related techniques for the contextualisation of IoT data. In particular, we introduce a Contextualisation-as-a-Service (ConTaaS) architecture that incorporates scalability improving techniques, as well as a proof-of-concept implementation of all these that utilises elastic cloud-based infrastructure to achieve near real-time contextualisation of IoT data. Experimental evaluations validating the efficiency of ConTaaS are also provided in this paper

City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed Systems and Technologies (IJDST), 201

HalleyAssist: A Personalised Internet of Things Technology to Assist the Elderly in Daily Living

Ambient Assisted Living (AAL) research has received extensive attention in recent years. AAL applications combine aspects of Internet of Things (IoT), smart platform design and machine learning to produce an intelligent system. In this paper we describe a personalised IoT-based AAL system that enables an independent and safe life for elderly people within their own home via real-time monitoring and intervention. The system, HalleyAssist underpinned by smart home automation functions includes a novel approach for monitoring the wellbeing and detecting abnormal changes in behavioral patterns of an elderly person. The significance of the approach is in the use of machine learning models to automatically learn normal behavioral pattern for the person from IoT sensor data and using the models derived to detect significant changes in behavioral pattern should they occur. The architecture and developed proof of concept of the proposed system is presented along with discussion of how privacy and security concerns are addressed. We also report on outcomes of real-world in-home trials of an early version of the system where it was installed in four older people\u27s home for a period of six weeks. The response from the older people to the deployed system was very positive. Finally, the paper presents a discussion and an analysis of the results using the data collected during the in-home trials

Design and Development of a Diabetes Self-Management Platform: A Case for Responsible Information System Development

Diabetes Mellitus a prevalent chronic disease that affects people from all genders and ages, continues to grow exponentially with predictions of nearly 578 million people affected by 2030. Self-management, known to be an essential aspect of any care program can help patients with diabetics to control blood glucose and thereby reduce the impact and likely complications. However, self-management to date has included the development of digital health solutions have poor sustained uptake. This is primarily since such digital solutions have a poor fit with patient and clinician needs. In this paper we propose a digital platform for supporting patients with diabetes. The proposed platform is a work-in-progress research and has been co-designed and co-developed (jointly with patients and clinicians) based on design science principles and includes key aspects of task-technology fit information system theory for further evaluation

The role of big data analytics in industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well