On account of chronic neurocognitive disorders, many people progressively lose their autonomy and become more dependent on others, finally reaching the stage when they need round-the-clock care from caregivers. Over time, as patients' needs increase with the evolution of their diseases, caregivers experience increasing levels of stress and burden. Therefore, an assistive solution that is able to adapt to the changing needs of the end-users is needed. This need was considered as a major requirement that emerged from our field work and deployment experience in Singapore. In this paper, we focus on the technical aspects of our deployment, where we were interested in solving the technical requirement of adaptability and extendibility of the framework that has emerged from our predeployment analysis and discussions with professional caregivers. We expose our approach for dynamic integration of assistive services with their related sensing technologies and interaction devices and provide the technical results of the deployment of this solution. We also provide guidelines for real-world deployment of assistive solutions.

In-network processing is touted as a key technology to eliminate data redundancy and minimize data transmission, which are crucial to saving energy in wireless sensor networks (WSNs). Specifically, operators participating in in-network processing are mapped to nodes in a sensor network. They receive data from downstream operators, process them and route the output to either the upstream operator or the sink node. The objective of operator tree placement is to minimize the total energy consumed in performing in-network processing. Two types of placement algorithms, centralized and distributed, have been proposed. A problem with the centralized algorithm is that it does not scale to large WSN's, because each sensor node is required to know the complete topology of the network. A problem with the distributed algorithm is their high message complexity. In this paper, we propose a heuristic algorithm to place a treestructured operator graph, and present a distributed implementation to optimize in-network processing cost and reduce the communication overhead. We prove a tight upper bound on the minimum in-network processing cost, and show that the heuristic algorithm has better performance than a canonical greedy algorithm. Simulation-based evaluations demonstrate the superior performance of our heuristic algorithm. We also give an improved distributed implementation of our algorithm that has a message overhead of O(M) per node, which is much less than the O(√NM log2M) and O(√NM) complexities for two previously proposed algorithms, Sync and MCFA, respectively. Here, N is the number of network nodes and M is the size of the operator tree.

In this paper, PAFusion, a power-aware framework for distributed data fusion applications in wireless sensor networks is proposed. It is designed for mapping distributed data fusion application into networks. We have evaluated the performances of PAFusion compared to DFuse and optimal solution through simulation. The simulation results show PAFusion has better performances than DFuse and PAFusion has much less overhead and causes only slightly more transmission cost than optimal solution.

Active Networks paradigm integrated with distributed data fusion has the potential to significantly reduce energy dissipation in WSNs, where energy conservation is the most challenging issue. This work aims to minimize energy cost when distributed data fusion is deployed for Active Networks computing paradigm. An approximate solution with much less computational complexity than optimal solution is proposed for large networks called P2lace, which includes two phases, task graph partition and task graph placement. An extensive experimental evaluation compares approximate solution with optimal solution. The results show that our approximate solution is scalable to different task graph characteristics and network size and only causes little more transmission cost than optimal solution.

In pervasive computing environments, understanding the context of an entity is essential for adapting the application behavior to changing situations. In our view, context is a high-level representation of a user or entity's state and can capture location, activities, social relationships, capabilities, etc. Inherently, however, these high-level context metrics are difficult to capture using uni-modal sensors only, and must therefore be inferred with the help of multi-modal sensors. However a key challenge in supporting context-aware pervasive computing environments, is how to determine in an energy-efficient manner multiple (potentially competing) high-level context metrics simultaneously using low-level sensor data streams about the environment and the entities present therein. In this paper, we first highlight the intricacies of determining multiple context metrics as compared to a single context, and then develop a novel framework and practical implementation for this problem. The proposed framework captures the tradeoff between the accuracy of estimating multiple context metrics and the overhead incurred in acquiring the necessary sensor data stream. In particular, we develop a multi-context search heuristic algorithm that computes the optimal set of sensors contributing to the multi-context determination as well as the associated parameters of the sensing tasks. Our goal is to satisfy the application requirements for a specified accuracy at a minimum cost. We compare the performance of our heuristic based framework with a brute-forced approach for multi-context determination. Experimental results with SunSPOT sensors demonstrate the potential impact of the proposed framework.

Incontinence is highly prevalent in Patients with Dementia (PWD) due to a decline in their physical and mental abilities. Those PWD may lie in soiled diaper for prolonged periods if timely diaper change is not in place. Current manual care practices may not be able to immediately detect soiled diaper, although costly and labor intensive scheduled checks are performed. Delays in diaper change can cause serious social and medical issues. So, timely and effective continence management is important to potentially avoid the undesirable consequences. By developing assistive system leveraging on sensors, wireless sensor network, ambient intelligence and reminders, it is feasible to detect soiled diaper and remind the carers for timely intervention. With around the clock sensing, distributed monitoring and context-aware intervention, timely diaper change is possible anywhere anytime without wasting unnecessary care-giving resources and without causing annoyances to the elderly.

Sleeping posture reveals important information for eldercare and patient care, especially for bed ridden patients. Traditionally, some works address the problem from either pressure sensor or video image. This paper presents a multimodal approach to sleeping posture classification. Features from pressure sensor map and video image have been proposed in order to characterize the posture patterns. The spatiotemporal registration of the two modalities has been considered in the design, and the joint feature extraction and data fusion is presented. Using multi-class SVM, experiment results demonstrate that the multimodal approach achieves better performance than the approaches using single modal sensing.

Due to the physical and mental impairments, frail elderly have to stay in the bed most of their time. So, serious health issues and physical injuries can occur to them if dangerous events and situations around the bed are not known in advance. This paper proposes continuous patient monitoring with situation awareness using multimodality sensors to recognize such dangerous events in and around the bed. With multimodal sensing intelligence, various contexts and situations of those bedridden elderly can be determined. By incorporating personalization and medical domain knowledge into detected events and activities, dangerous events can be recognized immediately. Then, timely intervention can be provided to the elderly with potential fall risks. So our solution supports situation aware patient monitoring and provides desirable personalized care at the institutions enabling to prevent potential health and well-being problems in and around the bed.

With the advance and prevalence of low-cost, low-power computing devices it is becoming increasingly clear that we are going to work and live in intelligent environments in the very near future. An intelligent environment is a physical space, e.g. an office, a hospital, a home or a car, wherein miniature computing devices perceive, monitor, and interact with the environments and human users within them. This insight and vision has provoked considerable research leading to a number of emerging research areas, such as mobile computing, ubiquitous/pervasive computing, sensor network and ambient intelligence. There has also been substantial progress on the conceptualization and design of innovative application scenarios, technological infrastructure and system prototypes. For example, a compelling real-world intelligent environment is "Smart Homes" -- an augmented home environment within which the daily activities of its inhabitants, usually the elderly or disabled, are monitored and analysed so that personalized context-aware assistances can be provided.

Most of the target tracking algorithms proposed for wireless sensor networks (WSNs) so far have been relying on sensors of single modality. To integrate multiple sensing modalities (e.g., by using the proximity sensors and ranging sensors together) to improve the tracking performance, the tracking algorithm shall be capable to deal with non-linear and non-Gaussian nature of the tracking problem. In this paper, we present a particle filter algorithm for multi-modality target tracking in WSNs. Simulation results show that the proposed algorithm can provide a good balance among sensor costs and tracking accuracy.

This paper presents a new approach using multimodal information fusion for automated recognition of complex agitation behavior among persons with dementia. In particular, we present a hierarchical information fusion framework to model complex agitation behaviors based on a clinical agitation scale widely used in hospitals. We present the detailed features extraction and selection to represent low level atomic agitation behaviors, classifiers for atomic agitation behavior recognition and inference combiners to detect and rate the onset of complex agitation behaviors in dementia patients using multimodal sensors such as pressure sensors, ultrasound sensors, video cameras, acoustic sensors, etc., in a hospital ward.

This paper presents the method for the detection and localization of moving targets in passive infrared (PIR) sensor networks in both indoor and outdoor settings. It reports our design and implementation of PIR sensor network, especially, we proposed a detection algorithm, which uses Adaptive Threshold with Constant False Alarm Rate; and developed a localization algorithm using direction search in the grid space of the network. The experimental results have shown that our PIR sensor network can detect and locate the moving targets with reasonable accuracy.

Privacy protection issue introduces numerous challenges in the multimedia processing domain. In this paper, we propose an anonymization framework for audio clinical data. The HMM based keyword recognition technique is used to locate the predefined sensitive keywords, which are identified by the users or patients in advance. These keywords will then be substituted by the synthesized nominal words of the similar nature and voice characteristics. The ultimate goal is to protect the privacy information as much as possible, while trying to preserve the speech properties, especially the disease-related symptoms, such as the loudness, the rhythm, the emotion, etc. A preliminary system is presented to demonstrate the usage of the process.

Agitated behavior is common in people with dementia, and one of the challenges faced by doctors and caregivers is the detailed and continuous monitoring of such people. With the use of modern sensor and networking technology, and system directed observation, objective outcomes may be expected and with removal of the manpower requirement. This paper presents a context aware patient data collection system (CAPDCS), which is done on the fly with multiple sensors of different modalities deployed in the hospital ward. These features extracted from each sensor modality are used in data fusion algorithms. We also present an approach for multi-modalities data fusion based on Bayesian network.

This paper presents the fusion considerations for a smart hospital application. In particular, we present the subtle design and implementation of a fusion architecture for monitoring and handling agitation behaviour for persons with dementia. In addition, we exploit Semantic Web standards to provide a reusable fusion middleware support for providing services that facilitate care-giving and clinical assessment of dementia patients in a context enlightened fashion.

Internet routing is based on a distributed system composed of many routers, grouped into management domains called autonomous systems (ASes). Routing information is exchanged between ASes using a border gateway protocol. When BGP is used to distribute a particular route, it can be also be used to distribute an MPLS label which is mapped to that route. In this paper the comparison of conventional BGP and MPLS based BGP has been done for different network topology with and without the application of route reflectors (RR).

The problem of resource management for many processor architectures can be viewed as the problem of simultaneously updating data structures that hold system state. An approach in which the possibility of using structures with weakened specifications is examined, is presented. Specifically, data structures that weaken the specification of a priority queue, permitting it to be updated simultaneously by multiple processes are introduced. Two structures, the concurrent heap and the software banyan are proposed, along with their associated algorithms for update. The algorithms are shown to possess attractive properties of simultaneous update and throughput. The results of simulation and actual implementations show that such data structures can improve the execution times of parallel algorithms quite significantly. These structures are proposed as possible basic building blocks for implementation of resource allocation in operating systems.>