Rajib Rana

Nonuniform Compressive Sensing for Heterogeneous Wireless Sensor Networks

In this paper, we consider the problem of using wireless sensor networks (WSNs) to measure the temporal-spatial profile of some physical phenomena. We base our work on two observations. First, most physical phenomena are compressible in some transform domain basis. Second, most WSNs have some form of heterogeneity. Given these two observations, we propose a nonuniform compressive sensing method to improve the performance of WSNs by exploiting both compressibility and heterogeneity. We apply our proposed method to real WSN data sets. We find that our method can provide a more accurate temporal-spatial profile for a given energy budget compared with other sampling methods.

Real-time classification via sparse representation in acoustic sensor networks

Acoustic Sensor Networks (ASNs) have a wide range of applications in natural and urban environment monitoring, as well as indoor activity monitoring. In-network classification is critically important in ASNs because wireless transmission costs several orders of magnitude more energy than computation. The main challenges of in-network classification in ASNs include effective feature selection, intensive computation requirement and high noise levels. To address these challenges, we propose a sparse representation based feature-less, low computational cost, and noise resilient framework for in-network classification in ASNs. The key component of Sparse Approximation based Classification (SAC), ℓ1 minimization, is a convex optimization problem, and is known to be computationally expensive. Furthermore, SAC algorithms assumes that the test samples are a linear combination of a few training samples in the training sets. For acoustic applications, this results in a very large training dictionary, making the computation infeasible to be performed on resource constrained ASN platforms. Therefore, we propose several techniques to reduce the size of the problem, so as to fit SAC for in-network classification in ASNs. Our extensive evaluation using two real-life datasets (consisting of calls from 14 frog species and 20 cricket species respectively) shows that the proposed SAC framework outperforms conventional approaches such as Support Vector Machines (SVMs) and k-Nearest Neighbor (kNN) in terms of classification accuracy and robustness. Moreover, our SAC approach can deal with multi-label classification which is common in ASNs. Finally, we explore the system design spaces and demonstrate the real-time feasibility of the proposed framework by the implementation and evaluation of an acoustic classification application on an embedded ASN testbed.

Non-uniform compressive sensing in wireless sensor networks: Feasibility and application

In this paper, we consider the problem of using wireless sensor networks (WSNs) to measure the temporal-spatial profile of some physical phenomena. We base our work on two observations. Firstly, most physical phenomena are compressible in some transform domain basis. Secondly, most WSNs have some form of heterogeneity. Given these two observations, we propose a non-uniform compressive sensing method to improve the performance of WSNs by exploiting both compressibility and heterogeneity. We apply our proposed method to a real WSN data set. We find that our method can provide a more accurate temporal-spatial profile for a given energy budget compared with other sampling methods.

SimpleTrack: Adaptive Trajectory Compression With Deterministic Projection Matrix for Mobile Sensor Networks

Some mobile sensor network applications require the sensor nodes to transfer their trajectories to a data sink. This paper proposes an adaptive trajectory (lossy) compression algorithm based on compressive sensing. The algorithm has two innovative elements. First, we propose a method to compute a deterministic projection matrix from a learnt dictionary. Second, we propose a method for the mobile nodes to adaptively predict the number of projections needed based on the speed of the mobile nodes. Extensive evaluation of the proposed algorithm using six data sets shows that our proposed algorithm can achieve submeter accuracy. In addition, our method of computing projection matrices outperforms two existing methods. Finally, comparison of our algorithm against a state-of-the-art trajectory compression algorithm shows that our algorithm can reduce the error by 10-60 cm for the same compression ratio.

Determination of Activities of Daily Living of independent living older people using environmentally placed sensors

The rapid increase in the ageing population of most developed countries is presenting significant challenges to policymakers of public healthcare. To address this problem, we propose a Smarter Safer Home solution that enables ageing Australians to live independently longer in their own homes. The primary aim of our approach is to enhance the Quality of Life (QoL) of aged citizens and the Family Quality of Life (FQoL) for the adult children supporting their aged parents. To achieve this, we use environmentally placed sensors for non-intrusive monitoring of human behaviour. The various sensors will detect and gather activity and ambience data which will be fused through specific decision support algorithms to extract Activities of Daily Living (ADLs). Subsequently, these estimated ADLs would be correlated with reported and recorded health events to predicate health decline or critical health situations from the changes in ADLs.

Distributed sparse approximation for frog sound classification

Sparse approximation has now become a buzzword for classification in numerous research domains. We propose a distributed sparse approximation method based on ℓ1 minimization for frog sound classification, which is tailored to the resource constrained wireless sensor networks. Our pilot study demonstrates that ℓ1 minimization can run on wireless sensor nodes producing satisfactory classification accuracy.

Opportunistic and Context-Aware Affect Sensing on Smartphones

The authors identify recent advances, key solutions for implementing opportunistic sensing on smart phones. They explore robustness issues and the challenges of mental health patients as participants.

Guiding Ebola patients to suitable health facilities: an SMS-based approach

Access to appropriate health services is a fundamental problem in developing countries, where patients do not have access to information and to the nearest health service facility. We propose building a recommendation system based on simple SMS text messaging to help Ebola patients readily find the closest health service with available and appropriate resources. The system will map people’s reported symptoms to likely Ebola case definitions and suitable health service locations. In addition to providing a valuable individual service to people with curable diseases, the proposed system will also predict population-level disease spread risk for infectious diseases using crowd-sourced symptoms from the population. Health workers will be able to better plan and anticipate responses to the current Ebola outbreak in West Africa. Patients will have improved access to appropriate health care. This system could also be applied in other resource poor or rich settings.

Mobile Health in the Developing World: Review of Literature and Lessons From a Case Study

The mHealth trend, which uses mobile devices and associated technology for health interventions, offers unprecedented opportunity to transform the health services available to people across the globe. In particular, the mHealth transformation can be most disruptive in the developing countries, which is often characterized by a dysfunctional public health system. Despite this opportunity, the growth of mHealth in developing countries is rather slow and no existing studies have conducted an in-depth search to identify the reasons. We present a comprehensive report about the factors hindering the growth of mHealth in developing countries. Most importantly, we outline future strategies for making mHealth even more effective. We are also the first to conduct a case study on the public health system of Pakistan showing that mHealth can offer tremendous opportunities for a developing country with a severe scarcity of health infrastructure and resources. The findings of this paper will guide the development of policies and strategies for the sustainable adoption of mHealth not only in Pakistan but also for any developing country in general.

Gait velocity estimation using time-interleaved between consecutive passive IR sensor activations

Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. It is often assessed clinically, but the assessments occur infrequently and do not allow optimal detection of key health changes when they occur. In this paper, we show that the time gap between activations of a pair of passive infrared motion sensors in the consecutively visited room-pair carry rich latent information about a persons gait velocity. We name this time gap transition time and modeling the relationship between transition time and gait velocity, and using a support vector regression approach, we show that gait velocity can be estimated with an average error of <;2.5 cm/s. Our method is simple and cost effective and has advantages over competing approaches such as: obtaining 20-100 times more gait velocity measurements per day. It also provides a pervasive in-home method for context-aware gait velocity sensing that allows for monitoring of gait trajectories in space and time.