RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained

Non-personal Data Collection for Toy User Interfaces

Toy-user-interfaces (ToyUI) are computing devices or peripherals that leverage interactivity and connectivity with other devices to promote physical and social play. ToyUI products may collect both personal and non-personal data (NPD) on their users. We propose nine data patterns for NPD collection as part of ToyUI design based on the study of 297 ToyUI items from both the literature and industry. In addition, we introduce a printed circuit board (PCB) used for rapid prototyping that enabled NPD data collection concerning both objects and users by gathering non-personal identification, positioning system, and motion tracking. We demonstrate the effectiveness of our hardware architecture by embedding it into two design scenarios, namely, closed rules and open-ended rules solutions. The objectives here are to assist the ToyUI makers in creating more meaningful play experiences while ensuring the privacy of children’s and their parents’ data

IoT4Fun Rapid Prototyping Toolkit for Smart Toys

Rapid prototyping tools turn the design of smart toys faster and easier for creative teams. Appropriate tools for smart toys should meet a list of requirements, which include distributed data collection and adaptability for assorted toy shapes and size. The IoT4Fun toolkit innovates by mixing the embedded, modular, and plug-and-play approaches. It supports motion tracking data, wireless communication, and contactless identification. IoT4Fun demonstrates its effectiveness to design a variety of smart toy solutions by fitting into a hula-hoop toy until spherical, cubic, and wearable shapes. Solutions connect with either mobile applications or other toys and play rules range from open-ended to closed behaviors. End-users exhaustively tested developed solutions, and technical assessment evaluates their integrity after playtesting sessions. Results show comparative data on battery consumption and vulnerabilities threats for data security and privacy of each design. Future versions of IoT4Fun can benefit from miniaturization, robustness, and reliability improvements

Quality assessment technique for ubiquitous software and middleware

The new paradigm of computing or information systems is ubiquitous computing systems. The technology-oriented issues of ubiquitous computing systems have made researchers pay much attention to the feasibility study of the technologies rather than building quality assurance indices or guidelines. In this context, measuring quality is the key to developing high-quality ubiquitous computing products. For this reason, various quality models have been defined, adopted and enhanced over the years, for example, the need for one recognised standard quality model (ISO/IEC 9126) is the result of a consensus for a software quality model on three levels: characteristics, sub-characteristics, and metrics. However, it is very much unlikely that this scheme will be directly applicable to ubiquitous computing environments which are considerably different to conventional software, trailing a big concern which is being given to reformulate existing methods, and especially to elaborate new assessment techniques for ubiquitous computing environments. This paper selects appropriate quality characteristics for the ubiquitous computing environment, which can be used as the quality target for both ubiquitous computing product evaluation processes ad development processes. Further, each of the quality characteristics has been expanded with evaluation questions and metrics, in some cases with measures. In addition, this quality model has been applied to the industrial setting of the ubiquitous computing environment. These have revealed that while the approach was sound, there are some parts to be more developed in the future

'HighChest': An augmented freezer designed for smart food management and promotion of eco-efficient behaviour

This paper introduces HighChest, an innovative smart freezer designed to promote energy efficient behavior and the responsible use of food. Introducing a novel humanâmachine interface (HMI) design developed through assessment phases and a user involvement stage, HighChest is state of the art, featuring smart services that exploit embedded sensors and Internet of things functionalities, which enhance the local capabilities of the appliance. The industrial design thinking approach followed for the advanced HMI is intended to maximize the social impact of the food management service, enhancing both the user experience of the product and the userâs willingness to adopt eco- and energy-friendly behaviors. The sensor equipment realizes automatic recognition of food by learning from the users, as well as automatic localization inside the deposit space. Moreover, it provides monitoring of the applianceâs usage, avoiding temperature and humidity issues related to improper use. Experimental tests were conducted to evaluate the localization system, and the results showed 100% accuracy for weights greater or equal to 0.5 kg. Drifts due to the lid opening and prolonged usage time were also measured, to implement automatic reset corrections

RFID Applications for Sanitary Environments

Healthcare represents one of the most significant sectors where the diffusion of RFID technology is growing day by day. Many different applications have already been studied and developed, with both active and passive devices working at all the available operative frequencies. Sanitary environments are nowadays extremely complex structures employing several thousands of people with very strict safety requirements: in emergency situations for example 5 minutes can make the difference for a patient between survive and die. RFID is especially indicated to be employed in these scenarios for two main reasons: first of all because it’s a particularly reliable technology, with good performances, few errors and fast interaction, and secondly because, due to the presence of many different technological systems, ad-hoc solutions can be designed on the specific requirements of the application to be realized. At present the most common RFID applications in healthcare can be divided into two main categories: the items tracking and the tracking and identification of people, patients and sanitary operators. The items tracking is performed in order to avoid the loss of expensive devices and to reduce wasting of time during assistance operations: systems studied and realized for this purpose cover all the range of RFID systems and provide different services according to their different performances. RFID systems for the identification of people probably represent the most interesting sector, due to the variety of different applications that can be studied and realized. The most common systems foresee the use of RFID for the tracking of sanitary operators or patients during their assistance operations. Along with this many other applications have been implemented, including systems operating the unambiguous matching between the patient and his treatments (for example the medicine or the blood sack) or between the mother and the child in the paediatrics departments. Finally the availability of a memory on the transponders fostered to the use of RFID technology also as a mean to promptly store and retrieve patient related information: for example electronic case history or electronic medical prescription applications have been studied and developed

OSEM : occupant-specific energy monitoring.

Electricity has become prevalent in modern day lives. Almost all the comforts people enjoy today, like home heating and cooling, indoor and outdoor lighting, computers, home and office appliances, depend on electricity. Moreover, the demand for electricity is increasing across the globe. The increasing demand for electricity and the increased awareness about carbon footprints have raised interest in the implementation of energy efficiency measures. A feasible remedy to conserve energy is to provide energy consumption feedback. This approach has suggested the possibility of considerable reduction in the energy consumption, which is in the range of 3.8% to 12%. Currently, research is on-going to monitor energy consumption of individual appliances. However, various approaches studied so far are limited to group-level feedback. The limitation of this approach is that the occupant of a house/building is unaware of his/her energy consumption pattern and has no information regarding how his/her energy-related behavior is affecting the overall energy consumption of a house/building. Energy consumption of a house/building largely depends on the energy-related behavior of individual occupants. Therefore, research in the area of individualized energy-usage feedback is essential. The OSEM (Occupant-Specific Energy Monitoring) system presented in this work is capable of monitoring individualized energy usage. OSEM system uses the electromagnetic field (EMF) radiated by appliances as a signature for appliance identification. An EMF sensor was designed and fabricated to collect the EMF radiated by appliances. OSEM uses proximity sensing to confirm the energy-related activity. Once confirmed, this activity is attributed to the occupant who initiated it. Bluetooth Low Energy technology was used for proximity sensing. This OSEM system would provide a detailed energy consumption report of individual occupants, which would help the occupants understand their energy consumption patterns and in turn encourage them to undertake energy conservation measures

Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment