Improving the Reliability of Pervasive Computing Applications By Continuous Checking of Sensor Readings

International audienceThis paper shows that context-aware applications commonly make implicit assumptions about a sensor infrastructure. Because context-awareness critically relies on these assumptions, the developer typically need to ensure their validity by encoding them in the application code, polluting it with non-functional concerns. This defensive programming approach can be avoided by formulating these assumptions aside from the application, thus factorizing them as an explicit model of the sensor infrastructure. This model can be expressed as a set of rules and can be checked automatically and continuously to ensure the reliability of a sensor infrastructure, both at installation time and during normal functioning. The usefulness of our approach is demonstrated in the domain of assisted living for seniors. We applied it to sensor data collected in the context of a 9-month field study of an assisted living platform, deployed at the home of 24 seniors. We show that several kinds of sensor malfunctions could have been identified upon their occurrence, thanks for our continuous checking, and resolved

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use

NoiseSPY: a real-time mobile phone platform for urban noise monitoring and mapping

In this paper we present the design, implementation, evaluation, and user experiences of the NoiseSpy application, our sound sensing system that turns the mobile phone into a low-cost data logger for monitoring environmental noise. It allows users to explore a city area while collaboratively visualizing noise levels in real-time. The software combines the sound levels with GPS data in order to generate a map of sound levels that were encountered during a journey. We report early findings from the trials which have been carried out by cycling couriers who were given Nokia mobile phones equipped with the NoiseSpy software to collect noise data around Cambridge city. Indications are that, not only is the functionality of this personal environmental sensing tool engaging for users, but aspects such as personalization of data, contextual information, and reflection upon both the data and its collection, are important factors in obtaining and retaining their interest

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Implementation of MD5 Framework for Privacy-Preserving Support for Mobile Healthcare

The improvement of science and technology has made life so easy and fast that smartphones and other touch-screen minicomputers have become the most trusted personal storage and communication devices for individuals. Comparable to the rich enhancement in wireless body sensor networks, it is valuable to the development of medical treatment to be exceptionally adaptable and become very flexible by means of smartphones through 2G and 3G system bearers. This has made treatment simple even to the common individual in the general public with less payable cash. In this paper, we introduce privacy-preserving support for mobile healthcare using message digest where we have used an MD5 algorithm instead of AES, which can certainly achieve an efficient way and minimizes the memory consumed and the large amount of PHI data of the medical user (patient) is reduced to a fixed amount of size compared to AES which in parallel increases the speed of the data to be sent to TA without any delay which in-turn. This study implements a secure and privacy-preserving opportunistic computing framework (SPOC) for mobile-health care emergency. Utilizing smartphones and SPOC, assets like computing power and energy can be gathered to reliably to take care of intensive personal health information (PHI) of the medicinal client when he/she is in critical situation with minimal privacy disclosure. With these, the healthcare authorities can treat the patients (restorative clients) remotely, where the patients live at home or at different spots they run. This sort of a treatment can be done under mHealth (Mobile-Healthcare). In malice of the fact that in them-medicinal services administration, there are numerous security and information protection issues to be succeed. The main aim of this paper is to bring medical health to patients in remote locations by providing the basic triage of an emergency to increase the patient’s body acceptance until they can reach a proper medical facility, in addition to providing emergency care in minimal payable cash

A Survey on Wireless Sensor Network Security

Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. This paper discusses the current state of the art in security mechanisms for WSNs. Various types of attacks are discussed and their countermeasures presented. A brief discussion on the future direction of research in WSN security is also included.Comment: 24 pages, 4 figures, 2 table

Wireless sensor data processing for on-site emergency response

This thesis is concerned with the problem of processing data from Wireless Sensor Networks (WSNs) to meet the requirements of emergency responders (e.g. Fire and Rescue Services). A WSN typically consists of spatially distributed sensor nodes to cooperatively monitor the physical or environmental conditions. Sensor data about the physical or environmental conditions can then be used as part of the input to predict, detect, and monitor emergencies. Although WSNs have demonstrated their great potential in facilitating Emergency Response, sensor data cannot be interpreted directly due to its large volume, noise, and redundancy. In addition, emergency responders are not interested in raw data, they are interested in the meaning it conveys. This thesis presents research on processing and combining data from multiple types of sensors, and combining sensor data with other relevant data, for the purpose of obtaining data of greater quality and information of greater relevance to emergency responders. The current theory and practice in Emergency Response and the existing technology aids were reviewed to identify the requirements from both application and technology perspectives (Chapter 2). The detailed process of information extraction from sensor data and sensor data fusion techniques were reviewed to identify what constitutes suitable sensor data fusion techniques and challenges presented in sensor data processing (Chapter 3). A study of Incident Commanders’ requirements utilised a goal-driven task analysis method to identify gaps in current means of obtaining relevant information during response to fire emergencies and a list of opportunities for WSN technology to fill those gaps (Chapter 4). A high-level Emergency Information Management System Architecture was proposed, including the main components that are needed, the interaction between components, and system function specification at different incident stages (Chapter 5). A set of state-awareness rules was proposed, and integrated with Kalman Filter to improve the performance of filtering. The proposed data pre-processing approach achieved both improved outlier removal and quick detection of real events (Chapter 6). A data storage mechanism was proposed to support timely response to queries regardless of the increase in volume of data (Chapter 7). What can be considered as “meaning” (e.g. events) for emergency responders were identified and a generic emergency event detection model was proposed to identify patterns presenting in sensor data and associate patterns with events (Chapter 8). In conclusion, the added benefits that the technical work can provide to the current Emergency Response is discussed and specific contributions and future work are highlighted (Chapter 9)