Meandered-slot antennas for sensor-RFID tags

This letter introduces a planar antenna layout suited to Sensor-RFID fabrication. The geometry is based on a meandered-slot profile on a suspended patch and permits to host sensors and electronics in a small space. The available geometrical parameters are optimized by means of a Genetic Algorithm (GA) procedure aimed to maximize the antenna realized gain. The antenna performances are discussed through examples and prototypes

Energy Cooperation in Battery-Free Wireless Communications with Radio Frequency Energy Harvesting

Radio frequency (RF) energy harvesting techniques are becoming a potential method to power battery-free wireless networks. In RF energy harvesting communications, energy cooperation enables shaping and optimization of the energy arrivals at the energy-receiving node to improve the overall system performance. In this article, we propose an energy cooperation scheme that enables energy cooperation in battery-free wireless networks with RF harvesting. We first study the battery-free wireless network with RF energy harvesting and then state the problem that optimizing the system performance with limited harvesting energy through new energy cooperation protocol. Finally, from the extensive simulation results, our energy cooperation protocol performs better than the original battery-free wireless network solution

Multiport sensor RFIDs for wireless passive sensing of objects - Basic theory and early results

A new family of passive sensor radio-frequency identification devices is here proposed for applications in the context of wireless sensor networks. The new tags, working in the ultra-high frequency band, are able to detect the value or the change of some features of the tagged body without using any specific sensor. Such tags are provided with multiple chips embedded either within a cluster of cooperating antennas or in a single multiport antenna, and exploit the natural mismatch of the antenna input impedance caused by the change of the tagged object. A basic theory of multiport sensor tags is formulated with the purpose to describe the possible classification and detection performances in a unitary context. Some numerical examples and a first experiment corroborate the feasibility of the idea

Activities of daily life recognition using process representation modelling to support intention analysis

Purpose – This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. Design/methodology/approach – This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients. Findings – A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches. Originality/value – The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features

A Framework to Recognise Daily Life Activities with Wireless Proximity and Object Usage Data

The profusion of wireless enabled mobile devices in daily life routine and advancement in pervasive computing has opened new horizons to analyse and model the contextual information. This contextual information (for example, proximity data and location information) can be very helpful in analysing the human behaviours. Wireless proximity data can provide important information about the behaviour and daily life routines of an individual. In this paper, we used Bluetooth proximity data to validate this concept by detecting repeated activity patterns and behaviour of low entropy mobile people by using n-gram and correlative matrix techniques. Primary purpose is to find out whether contextual information obtained from Bluetooth proximity data is useful for activities and behaviour detection of individuals. Repeated patterns found in Bluetooth proximity data can also show the long term routines such as, monthly or yearly patterns in an individual's daily life that can further help to analyse more complex and abnormal routines of human behaviour