For improving e-health services, we propose a context-aware framework to monitor the activities of daily living of dependent persons. We define a strategy for generating long-term realistic scenarios and a framework containing an adaptive monitoring algorithm based on three approaches for optimizing resource usage. The used approaches provide a deep knowledge about the person's context by considering: the person's profile, the activities and the relationships between activities. We evaluate the performances of our framework and show its adaptability and significant reduction in network, energy and processing usage over a traditional monitoring implementation.
Most of the previous studies and projects in HSH HIS operate in isolation from the real requirements of the healthcare institutions. Which in turn contributes to a high incidence of unsuccessful projects @cite_13 . The absence of such link leads to high uncertainty in the adoption of such projects. Therefore, it is desirable to improve the context-aware e-health systems and make easier the integration of the new proposed e-health systems into health institutions. The traditional health monitoring approaches tend to manage all sensed data with unconditional processing. Most of these approaches are aimed at continuous monitoring while maintaining the channels of transfer data available all the time. The adoption of such trend is causing many issues, such as collapse of the network, data transmission failure, energy consumption, important computational cost, and loss of priority in processing and making quick relevant decisions. The same situation happens with physical monitoring applications which assume uniform time-interval signal data.
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The basic requirement of wireless healthcare monitoring systems is to send physiological signals acquired from implantable or on-body sensor nodes to a remote location. Low-power consumption is required for wireless healthcare monitoring systems since most medical sensor nodes are battery powered. The emergence of new technologies in measuring physiological signals has increased the demand for high data rate transmission systems. Ultra-wide band (UWB) is a suitable wireless technology to achieve high data rates while keeping power consumption and form factors small. Although UWB transmitters are designed based on simple techniques, UWB receivers require complex hardware and consume comparatively higher power. In order to achieve reliable low power two-way communication, a sensor node can be constructed using a UWB transmitter and a narrow band receiver. This paper proposes a new medium access control (MAC) protocol based on a dual-band physical layer technology. Co-simulation models based on MATLAB and OPNET have been developed to analyze the performance of the proposed MAC protocol. We analyzed the performance of the MAC protocol for a realistic scenario where both implantable and wearable sensor nodes are involved in the data transmission. Priority-based packet transmission techniques have been used in the MAC protocol to serve different sensors according to their QoS requirements. Analysis is done with regard to important network parameters, such as packet loss ratio, packet delay, percentage throughput, and power consumption. The exponentially increasing stream of real time big data produced by Web 2.0 Internet and mobile networks created radically new interdisciplinary challenges for public health and computer science. Traditional public health disease surveillance systems have to utilize the potential created by new situation-aware realtime signals from social media, mobile sensor networks and citizens? participatory surveillance systems providing invaluable free realtime event-based signals for epidemic intelligence. However, rather than improving existing isolated systems, an integrated solution bringing together existing epidemic intelligence systems scanning news media (e.g., GPHIN, MedISys) with real-time social media intelligence (e.g., Twitter, participatory systems) is required to substantially improve and automate early warning, outbreak detection and preparedness operations. However, automatic monitoring and novel verification methods for these multichannel event-based real time signals has to be integrated with traditional case-based surveillance systems from microbiological laboratories and clinical reporting. Finally, the system needs effectively support coordination of epidemiological teams, risk communication with citizens and implementation of prevention measures. However, from computational perspective, signal detection, analysis and verification of very high noise realtime big data provide a number of interdisciplinary challenges for computer science. Novel approaches integrating current systems into a digital public health dashboard can enhance signal verification methods and automate the processes assisting public health experts in providing better informed and more timely response. In this paper, we describe the roadmap to such a system, components of an integrated public health surveillance services and computing challenges to be resolved to create an integrated real world solution. This paper presents the design, implementation, and characterization of a hardware platform applicable to wireless structural health monitoring (WSHM). The primary design goal is to devise a system capable of persistent operation for the duration of the life cycle of a target structure. It should be deployable during the construction phase and reconfigurable thereafter, suitable for continuous long-term monitoring. In addition to selecting the most energy efficient useful components to ensure the lowest possible power consumption, it is necessary to consider sources of energy other than, or complementary to, batteries. Thus, the platform incorporates multisource energy harvesting, electrochemical fuel cell (FC), energy storage, recharging capability, and intelligent operation through real-time energy information exchange with the primary controller. It is shown that, with appropriate integration, the device will have sufficient energy to operate perpetually in a distributed WSHM application. This conclusion is demonstrated through experimental results, simulations, and empirical measurements that demonstrate the high-efficiency energy conversion of the harvesters (up to 86 ) and low-power characteristics of the platform (less than 1 mW in sleep mode). It is shown that energy autonomy is comfortably achievable for duty cycles up to 0.75 , meeting the demands of the application, and up to 1.5 , invoking the FC. Limited energy supply is one of the major constraints in wireless sensor networks. A feasible strategy is to aggressively reduce the spatial sampling rate of sensors, that is, the density of the measure points in a field. By properly scheduling, we want to retain the high fidelity of data collection. In this paper, we propose a data collection method that is based on a careful analysis of the surveillance data reported by the sensors. By exploring the spatial correlation of sensing data, we dynamically partition the sensor nodes into clusters so that the sensors in the same cluster have similar surveillance time series. They can share the workload of data collection in the future since their future readings may likely be similar. Furthermore, during a short-time period, a sensor may report similar readings. Such a correlation in the data reported from the same sensor is called temporal correlation, which can be explored to further save energy. We develop a generic framework to address several important technical challenges, including how to partition the sensors into clusters, how to dynamically maintain the clusters in response to environmental changes, how to schedule the sensors in a cluster, how to explore temporal correlation, and how to restore the data in the sink with high fidelity. We conduct an extensive empirical study to test our method using both a real test bed system and a large-scale synthetic data set.