A Discrete event model for multiple inhabitants location tracking

6 pagesInternational audienceSmart Home technologies are aiming to improve the comfort and safety of the inhabitants into their houses. To achieve this goal, online indoor location tracking of the inhabitants is often used to monitor the air conditioning, to detect dangerous situations and for many other applications. In this paper, it is proposed an approach to build a model allowing dynamic tracking of several persons in their house. A method to construct such a model by using finite automata and Discrete Event System (DES) paradigms is presented. An approach to reduce the size of the model is also introduced. Finally, an efficient algorithm for location tracking is proposed. For the sake of better understanding, an illustrative example is used throughout the paper

Towards optimal sensor deployment for location tracking in smart home

International audienceAmbient Assisted Living (AAL) aims to ease the daily living and working environmentfor disabled/elderly peopleat home. AAL use information and communication technology based on sensors data. These sensors are generally placed randomly without taking into account the layout of buildings and rooms. In this paper, we develop a mathematical model foroptimal sensor placement in order (i) to optimize the sensor number with regard to room features, (ii) to ensure a reliability level in sensor networkconsidering a sensor failure rate. This placement ensures the targettracking in smart home sinceoptimizing sensorplacement allow us to distinguish different zonesand consequently, to identify the target location, according to the activated sensors

Sustainable crowdshipping using public transport: a case study evaluation in Rome

Abstract The paper analyses the willingness to act as a crowdshipper in the case of a last mile B2C e-commerce for pick up/delivery. Specifically, it focuses on crowdshipping services deployed using the public transport network and considering passengers as crowdshippers already moving for other reasons. In fact, this is the most environmental-friendly type of service one can develop given it avoids performing dedicated trips. The paper uses stated preference to identify the most important features associated with the choice of acting as a crowdshipper and discrete choice models to study the underlying behavior. The implementation case study refers to the city of Rome, Italy, and addresses its metro lines, thus understanding and quantifying the effects of this freight transport strategy for e-commerce in an urban context and providing local policy makers a good knowledge base for its future development

Safe Beacon: A Bluetooth Based Solution to Monitor Egress of Dementia Sufferers within a Residential Setting

The global population is ageing, as a consequence of this there will be a greater incidence of ageing related illnesses which cause cognitive impairment–such as Alzheimer’s disease. Within residential care homes, such cognitive impairment can lead to wandering of individuals beyond the boundaries of safety provided. This wandering, particularly in urban areas can be life threatening. This study introduces a novel solution to detect, and alert caregivers of, egress of at-risk inhabitants of a care home. This solution operates through a combination of wearable Bluetooth beacons and beam-formed listening devices. In an evaluation process involving 275 egress events, this solution proved to offer accurate operation with no incidence of false positives. Notably, this solution has been deployed within a real residential care home environment for over 12 months. Proposed future work discusses improvements to this solution

Occupancy monitoring and prediction in ambient intelligent environment

Occupancy monitoring and prediction as an influential factor in the extraction of occupants' behavioural patterns for the realisation of ambient intelligent environments is addressed in this research. The proposed occupancy monitoring technique uses occupancy detection sensors with unobtrusive features to monitor occupancy in the environment. Initially the occupancy detection is conducted for a purely single-occupant environment. Then, it is extended to the multipleoccupant environment and associated problems are investigated. Along with the occupancy monitoring, it is aimed to supply prediction techniques with a suitable occupancy signal as the input which can enhance efforts in developing ambient intelligent environments. By predicting the occupancy pattern of monitored occupants, safety, security, the convenience of occupants, and energy saving can be improved. Elderly care and supporting people with health problems like dementia and Alzheimer disease are amongst the applications of such an environment. In the research, environments are considered in different scenarios based on the complexity of the problem including single-occupant and multiple-occupant scenarios. Using simple sensory devices instead of visual equipment without any impact on privacy and her/his normal daily activity, an occupant is monitored in a living or working environment in the single-occupant scenario. ZigBee wireless communication technology is used to collect signals from sensory devices such as motion detection sensors and door contact sensors. All these technologies together including sensors, wireless communication, and tagging are integrated as a wireless sensory agent

Robust, fine-grained occupancy estimation via combined camera & WiFi indoor localization

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Private car O-D flow estimation based on automated vehicle monitoring data: theoretical issues and empirical evidence

Data on the daily activity of private cars form the basis of many studies in the field of transportation engineering. In the past, in order to obtain such data, a large number of collection techniques based on travel diaries and driver interviews were used. Telematics applied to vehicles and to a broad range of economic activities has opened up new opportunities for transportation engineers, allowing a significant increase in the volume and detail level of data collected. One of the options for obtaining information on the daily activity of private cars now consists of processing data from automated vehicle monitoring (AVM). Therefore, in this context, and in order to explore the opportunity offered by telematics, this paper presents a methodology for obtaining origin–destination flows through basic info extracted from AVM/floating car data (FCD). Then, the benefits of such a procedure are evaluated through its implementation in a real test case, i.e., the Veneto region in northern Italy where full-day AVM/FCD data were available with about 30,000 vehicles surveyed and more than 388,000 trips identified. Then, the goodness of the proposed methodology for O-D flow estimation is validated through assignment to the road network and comparison with traffic count data. Taking into account aspects of vehicle-sampling observations, this paper also points out issues related to sample representativeness, both in terms of daily activities and spatial coverage. A preliminary descriptive analysis of the O-D flows was carried out, and the analysis of the revealed trip patterns is presented