John Herbert

Web-based real-time remote monitoring for pervasive healthcare

The goal of the CARA (Context-Aware Real-time Assistant) healthcare architecture is to enable improved healthcare through the intelligent use of wireless remote monitoring of patient vital signs, supplemented by rich contextual information. One of its applications currently being deployed is the remote live monitoring of a patient by a healthcare professional. The vital signs are monitored using a wireless BAN based on sensors that can monitor position in space, ECG, blood pressure, and blood oxygenation. A design goal of ubiquitous access means that all communications are performed using recent web technologies, thereby minimizing issues with firewalls and facilitating remote ease of access. The only tool required for this application is a web browser with the commonly-available Adobe Flash plug-in installed. Thus remote monitoring, independent of geographic location, is possible form any computer or suitable smartphone. Important aspects of this application include: inter-visibility between patient and caregiver; real-time interactive medical consultation; and replay, review and annotation of the remote consultation by the medical professional. The annotation of significant parts of the multi-modal monitored signals by the medical professional provides the basis for the automated intelligent analysis of the CARA system. The paper discusses the application in the context of the overall CARA healthcare architecture, and presents results of some experiments using the application.

Adding preemption to TinyOS

Event-driven operating systems such as TinyOS are the preferred choice for wireless sensor networks. Alternative designs such as MANTIS following a classical multi-threaded approach are also available. Event-based systems are generally more energy efficient than multi-threaded systems. However, multi-threaded systems are more capable than event-based systems of supporting time critical tasks as task preemption is supported. Timeliness can be traded for energy efficiency by choosing the appropriate operating system. In our recent work we have shown that the multi-threaded system MANTIS can be modified to be as energy efficient as TinyOS. As a result, the modified MANTIS can be used to fit both sensor network design goals of energy efficiency and timeliness. This solution is not considered optimal as most existing sensor network applications and software libraries are developed for TinyOS. Therefore, we present a TinyOS modification that adds preemption while retaining the existing TinyOS structure and features.

Context-aware hybrid reasoning framework for pervasive healthcare

Pervasive computing has emerged as a viable solution capable of providing technology-driven assistive living for elderly. The pervasive healthcare system, Context-Aware Real-time Assistant (CARA), is designed to provide personalized healthcare services for elderly in a timely and appropriate manner by adapting the healthcare technology to fit in with normal activities of the elderly and working practices of the caregivers. The work in this paper introduces a personalized, flexible, and extensible hybrid reasoning framework for CARA system in a smart home environment which provides context-aware sensor data fusion as well as anomaly detection mechanisms that supports activity of daily living analysis and alert generation. We study how the incorporation of rule-based and case-based reasoning enables CARA to become more robust and to adapt to a changing environment by continuously retraining with new cases. Noteworthy about the work is the use of case-based reasoning to detect conditional anomalies for home automation, and the use of hierarchical fuzzy rule-based reasoning to deal with exceptions and to achieve query-sensitive case retrieval and case adaptation. Case study for evaluation of this hybrid reasoning framework is carried out under simulated but realistic smart home scenarios. The results indicate the feasibility of the framework for effective at-home monitoring.

Fuzzy CARA - A Fuzzy-Based Context Reasoning System For Pervasive Healthcare

Abstract Pervasive computing is allowing healthcare to move from care by professionals in hospital to self-care, mobile care, and at-home care. The pervasive healthcare system, CARA(Context Aware Real-time Assistant), is designed to provide personalized healthcare services for chronic patients in a timely and appropriate manner by adapting the healthcare technology to fit in with normal activities of the elderly and working practices of the caregivers. This paper presents a fuzzy-logic based context model and a related context-aware reasoning middleware that provides a personalized, flexible and extensible reasoning framework for CARA. It provides context-aware data fusion and representation as well as inference mechanisms that support remote patient monitoring and caregiver notification. Noteworthy about the work is the use of fuzzy-logic to deal with the imperfections of the data, and the use of both structure and hierarchy to control the application of rules in the context reasoning system. Results are shown for the evaluation of the fuzzy-logic based context reasoning middleware under simulated but realistic scenarios of patient monitoring. The results indicate the feasibility of the system for e_ective at-home monitoring.

An Experimental Comparison of Event Driven and Multi-Threaded Sensor Node Operating Systems

Two different operating system types are currently considered for sensor networks: event driven and multi-threaded. This paper compares the two well-known operating systems TinyOS (event driven) and MANTIS (multi-threaded) regarding their memory usage, power consumption and processing capabilities. TinyOS and MANTIS are both ported to the DSYS25 sensor platform. Both operating systems are used to execute the same sensor network application and the aforementioned parameters of interest are measured. The results presented in this paper show for which set of applications each operating system is preferable

Multilevel Security and Quality of Protection

Constraining how information may flow within a system is at the heart of many protection mechanisms and many security policies have direct interpretations in terms of information flow and multilevel security style controls. However, while conceptually simple, multilevel security controls have been difficult to achieve in practice. In this paper we explore how the traditional assurance measures that are used in the network multilevel security model can be re-interpreted and generalised to provide the basis of a framework for reasoning about the quality of protection provided by a secure system configuration.

Energy Efficient VM Placement Supported by Data Analytic Service

The popularity and commercial use of cloud computing has prompted an increased concern among cloud service providers for energy efficiency while still maintaining quality of service. One of the key techniques used for the efficient use of cloud server resources is virtual machine placement. This work introduces a precise VM placement algorithm that ensures energy efficiency and also prevents Service Level Agreement (SLA) violation. The mathematical model of the algorithm is supported by a sophisticated data analytic system implemented as a service. The precision of the algorithm is achieved by allowing each individual VM to build its own data model on demand over an appropriate time horizon. Thus the data model can reflect accurately the characteristics of resource usage of the VM. The algorithm can communicate synchronously or asynchronously with the data analytic service which is deployed as a cloud-based solution. In the experiments, several advanced data modelling and use forecasting techniques were evaluated. Results from simulation-based experiments show that the VM placement algorithm (supported by the data analytic service) can effectively reduce power consumption, the number of VM migrations, and prevent SLA violation, it also compares very favourably with other placement algorithms.

Data Management within mHealth Environments: Patient Sensors, Mobile Devices, and Databases

Pervasive environments generate large quantities of data, originating from backend servers, portable devices, and wireless mobile sensors. Pervasive sensing devices that monitor properties of the environment (including human beings) can be a large data source. Unprocessed datasets may include data that is faulty and irrelevant, and data that is important and useful. If not managed correctly the large amount of data from a data-rich pervasive environment may result in information overload or delivery of incorrect information. Context-sensitive quality data management aims to gather, verify, process, and manage the multiple data sources in a pervasive environment in order to deliver high quality, relevant information to the end-user. Managing the quality of data from different sources, correlating related data, and making use of context, are all essential in providing end users with accurate and meaningful data in real time. This requirement is especially true for critical applications such as in a medical environment. This article presents the Data Management System (DMS) architecture. It is designed to deliver quality data service to its users. The DMS architecture employs an agent-based middleware to intelligently and effectively manage all pervasive data sources, and to make use of context to deliver relevant information to the end-user. Two of the DMS components are presented: (1) data validation and (2) data consistency. The DMS components have been rigorously evaluated using various medical-based test cases. This article demonstrates a careful, precise approach to data based on the quality of the data and the context of its use. It emphasises the DMS architecture and the role of software agents in providing quality data management.

Mobile Agent Architecture Integration for a Wireless Sensor Medical Application

Wireless sensor nodes are used to monitor patient vital signs in a medical application. To ensure proper patient care is provided, real-time patient data must be managed correctly in the context of relevant patient information and medical knowledge. The data management system (DMS) is an agent-based architecture that aims to provide flexible, effective data management within a wireless patient sensor network (WPSN). The DMS is built primarily on the sophisticated JADE agent platform. JADE runs on resource-rich platforms such as servers, PCs, PDAs and high-end mobile phones. The lightweight Agilla agent platform can run on resource constrained sensor nodes. An integrated mobile agent based architecture combining Jade and Agilla is presented. This makes best use of the more sophisticated agent platform for high-level functionality and the lighter agent middleware for low-level sensor data collection. The resulting system is a unified agent architecture that runs on heterogeneous platforms on a wireless network

An Embedding of Timed Transition Systems in HOL

The theory of Timed Transition Systems developed by Henzinger, Manna, and Pnueli provides a formal framework for specifying and reasoning about real-time systems. In this paper, we report on some preliminary investigations into the mechanization of this theory using the HOL theorem prover.We review the main ideas of the theory and describe how it has been formally embedded in HOL. A graphical notation of timed transition diagrams and a real-time temporal logic for requirements have also been embedded in HOL using the embedding of timed transition systems. The proof rules proposed by Henzinger et al have been verified formally and we illustrate their use, as well as some problems we have encountered, by reference to a small example. More work is required on interfaces and proof methods to have a generally usable system.