A survey on subjecting electronic product code and non-ID objects to IP identification

Over the last decade, both research on the Internet of Things (IoT) and real-world IoT applications have grown exponentially. The IoT provides us with smarter cities, intelligent homes, and generally more comfortable lives. However, the introduction of these devices has led to several new challenges that must be addressed. One of the critical challenges facing interacting with IoT devices is to address billions of devices (things) around the world, including computers, tablets, smartphones, wearable devices, sensors, and embedded computers, and so on. This article provides a survey on subjecting Electronic Product Code and non-ID objects to IP identification for IoT devices, including their advantages and disadvantages thereof. Different metrics are here proposed and used for evaluating these methods. In particular, the main methods are evaluated in terms of their: (i) computational overhead, (ii) scalability, (iii) adaptability, (iv) implementation cost, and (v) whether applicable to already ID-based objects and presented in tabular format. Finally, the article proves that this field of research will still be ongoing, but any new technique must favorably offer the mentioned five evaluative parameters.Comment: 112 references, 8 figures, 6 tables, Journal of Engineering Reports, Wiley, 2020 (Open Access

Analysing Crowd Behaviours using Mobile Sensing

PhDResearchers have examined crowd behaviour in the past by employing a variety of methods including ethnographic studies, computer vision techniques and manual annotation-based data analysis. However, because of the resources to collect, process and analyse data, it remains difficult to obtain large data sets for study. Mobile phones offer easier means for data collection that is easy to analyse and can preserve the user’s privacy. The aim of this thesis is to identify and model different qualities of social interactions inside crowds using mobile sensing technology. This Ph.D. research makes three main contributions centred around the mobile sensing and crowd sensing area. Firstly, an open-source licensed mobile sensing framework is developed, named SensingKit, that is capable of collecting mobile sensor data from iOS and Android devices, supporting most sensors available in modern smartphones. The framework has been evaluated in a case study that investigates the pedestrian gait synchronisation phenomenon. Secondly, a novel algorithm based on graph theory is proposed capable of detecting stationary social interactions within crowds. It uses sensor data available in a modern smartphone device, such as the Bluetooth Smart (BLE) sensor, as an indication of user proximity, and accelerometer sensor, as an indication of each user’s motion state. Finally, a machine learning model is introduced that uses multi-modal mobile sensor data extracted from Bluetooth Smart, accelerometer and gyroscope sensors. The validation was performed using a relatively large dataset with 24 participants, where they were asked to socialise with each other for 45 minutes. By using supervised machine learning based on gradient-boosted trees, a performance increase of 26.7% was achieved over a proximity-based approach. Such model can be beneficial to the design and implementation of in-the-wild crowd behavioural analysis, design of influence strategies, and algorithms for crowd reconfiguration.UK Defence Science & Technology Laboratory (DSTL

Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01

RTST Trend Report: lead theme Contextualisation

Specht, M., Börner, D., Tabuenca, B., Ternier, S., De Vries, F., Kalz, M., Drachsler, H., & Schmitz, B. (2012). RTST Trend Report: lead theme Contextualisation. Deliverable 1.7 of STELLAR network of excellence. Heerlen, The Netherlands.In summary this trend-scouting report highlights different design dimensions of contextualizing learning. On the one hand designing educational context: the components and constituents of the educational setting, which also have to be orchestrated in an instructional design or the process of orchestration (Luckin, 2010, Specht, 2009) on the other hand bridging and linking learning contexts for seamless learning support: Wong et al. define design dimensions of seamless learning experiences and which gaps they identify and what challenges must be tackled to create seamless learning experiences (Wong, 2011).STELLAR Network of Excellence, Grant 23191

A cost-effective, mobile platform-based, photogrammetric approach for continuous structural deformation monitoring

PhD ThesisWith the evolution of construction techniques and materials technology, the design of modern civil engineering infrastructure has become increasingly advanced and complex. In parallel to this, the development and application of appropriate and efficient monitoring technologies has become essential. Improvement in the performance of structural monitoring systems, reduction of labour and total implementation costs have therefore become important issues that scientists and engineers are committed to solving. In this research, a non-intrusive structural monitoring system was developed based on close-range photogrammetric principles. This research aimed to combine the merits of photogrammetry and latest mobile phone technology to propose a cost-effective, compact (portable) and precise solution for structural monitoring applications. By combining the use of low-cost imaging devices (two or more mobile phone handsets) with in-house control software, a monitoring project can be undertaken within a relatively low budget when compared to conventional methods. The system uses programmable smart phones (Google Android v.2.2 OS) to replace conventional in-situ photogrammetric imaging stations. The developed software suite is able to control multiple handsets to continuously capture high-quality, synchronized image sequences for short or long-term structural monitoring purposes. The operations are fully automatic and the system can be remotely controlled, exempting the operator from having to attend the site, and thus saving considerable labour expense in long-term monitoring tasks. In order to prevent the system from crashing during a long-term monitoring scheme, an automatic system state monitoring program and a system recovery module were developed to enhance the stability. In considering that the image resolution for current mobile phone cameras is relatively low (in comparison to contemporary digital SLR cameras), a target detection algorithm was developed for the mobile platform that, when combined with dedicated target patterns, was found to improve the quality of photogrammetric target measurement. Comparing the photogrammetric results with physical measurements, which were measured using a Zeiss P3 analytical plotter, the returned accuracy achieved was 1/67,000. The feasibility of the system has been proven through the implementation of an indoor simulation test and an outdoor experiment. In terms of using this system for actual structural monitoring applications, the optimal relative accuracy of distance measurement was determined to be approximately 1/28,000 under laboratory conditions, and the outdoor experiment returned a relative accuracy of approximately 1/16,400

Handling Emergent Conflicts in Adaptable Rule-based Sensor Networks

This thesis presents a study into conflicts that emerge amongst sensor device rules when such devices are formed into networks. It describes conflicting patterns of communication and computation that can disturb the monitoring of subjects, and lower the quality of service. Such conflicts can negatively affect the lifetimes of the devices and cause incorrect information to be reported. A novel approach to detecting and resolving conflicts is presented. The approach is considered within the context of home-based psychiatric Ambulatory Assessment (AA). Rules are considered that can be used to control the behaviours of devices in a sensor network for AA. The research provides examples of rule conflict that can be found for AA sensor networks. Sensor networks and AA are active areas of research and many questions remain open regarding collaboration amongst collections of heterogeneous devices to collect data, process information in-network, and report personalised findings. This thesis presents an investigation into reliable rule-based service provisioning for a variety of stakeholders, including care providers, patients and technicians. It contributes a collection of rules for controlling AA sensor networks. This research makes a number of contributions to the field of rule-based sensor networks, including areas of knowledge representation, heterogeneous device support, system personalisation, and in particular, system reliability. This thesis provides evidence to support the conclusion that conflicts can be detected and resolved in adaptable rule-based sensor networks