With recent advances in smart phones and wearable sensors, Body Sensor Networks (BSNs) have been proposed for use in continuous, remote electrocardiogram (ECG) monitoring. In such systems, sampling the ECG at clinically recommended rates (250 Hz) and wireless transmission of the collected data incurs high energy consumption at the energy-constrained body sensor. The large volume of collected data also makes data storage at the sensor infeasible. Thus, there is a need for reducing the energy consumption and data size at the sensor, while maintaining the ECG quality required for diagnosis. In this paper, we propose GeM-REM, a resource-efficient ECG monitoring method for BSNs. GeM-REM uses a generative ECG model at the base station and its lightweight version at the sensor. The sensor transmits data only when the sensed ECG deviates from model-based values, thus saving transmission energy. Further, the model parameters are continually updated based on the sensed ECG. The proposed approach enables storage of ECG data in terms of model parameters rather than data samples, which reduces the required storage space. Implementation on a sensor platform and evaluation using real ECG data from MIT-BIH dataset shows transmission energy and data storage reduction ratios of 42.1:1 and 37.3:1 respectively, which are better than state of the art ECG data compression schemes.
Recently, several wireless body sensor-based systems have been proposed for continuous, long-term physiological monitoring. A major challenge in such systems is that a large amount of data is collected, and transmission of this data incurs significant energy consumption at the sensor. In this work, we demonstrate a data reporting method that significantly reduces energy consumption while maintaining a high diagnostic accuracy of the reported physiological signal. This is achieved by using a generative model of the physiological signal of interest at the sensor, and suppressing data transmission when sensed data matches the model. In this demonstration, we implement the proposed technique for electrocardiogram (ECG) signal and illustrate its performance in terms of energy savings and accuracy of reported data.
Multiple-path source routing protocols allow a data source node to distribute the total traffic among available paths. In this paper, we consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. We formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics. We show that in multisource networks, this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). We demonstrate the network's ability to estimate the impact of jamming and incorporate these estimates into the traffic allocation problem. Finally, we simulate the achievable throughput using our proposed traffic allocation method in several scenarios.
We present a framework for throughput optimization for multipath unicast routing in wireless networks in the presence of probabilistic jamming. The framework introduces a statistical characterization into the maximum network flow problem to compensate for the reduction in network flow due to the loss of jammed packets. We map the problem of throughput optimization under probabilistic jamming to that of optimal investment portfolio selection, treating the network throughput as the return on financial investments and using a common portfolio selection framework from financial statistics. Based on the portfolio selection framework, we present approaches to maximize expected throughput and to minimize throughput variance. We include both a detailed example and a simulation study to illustrate the application of the throughput optimization framework.
Wearable photoplethysmogram (PPG) sensors are extensively used for remote monitoring of blood oxygen level and flow rate in numerous pervasive healthcare applications with diverse quality of service requirements. These sensors operate under severe resource constraints and communicate over an adverse wireless channel with human body-induced path loss and mobility-caused fading. In this paper, we take a generative model-based data collection approach towards achieving energy-efficient and reliable PPG monitoring. We develop two models that can generate synthetic PPG signals given a set of input parameters. These generative models are then used to design and implement a resource-efficient, reliable data reporting method for wireless PPG sensors. We investigate the performance of our method under realistic wireless channel error models and provide methods to improve accuracy at a marginal energy cost. We implement the proposed technique using existing sensor platforms and evaluate its performance on two datasets: the MIMIC database and data collected using commercial wearable sensors. Results for wearable sensor-based data show bandwidth and communication energy savings of 300:1, while maintaining a diagnostic accuracy above 94%.
Body Sensor Networks (BSNs) consist of sensor nodes deployed on the human body for health monitoring. Each sensor node is implemented by interfacing a physiological sensor with a sensor platform consisting of components such as microcontroller, radio and memory. Diverse needs of BSN applications require customized platform development for optimizing performance. In this paper, we propose a two-phase framework to evaluate the performance of sensor platforms to match a BSN's computation, communication and sensing requirements: 1) Design Space Determination, wherein we investigate salient features of BSN platforms and quantify them as design coordinates through evaluation metrics such as SPSW (Samples Processed per Second per Watt) and EPC (Expected Power Consumption). To measure these metrics for a platform under typical BSN application workloads, we propose BSN-Bench, a benchmarking suite composed of basic tasks that occur in diverse BSN applications. BSNBench enables an accurate profiling of platforms based on the design coordinates; 2) Design Space Exploration, wherein we explore the design space to find the most suitable platform for a given application. We demonstrate the usage of our framework through a case study, where we consider two practical BSN applications and choose suitable platforms for them.
Multiple-path source routing protocols allow a data source node to distribute the total traffic among available paths. In this paper, we consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. We formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics. We show that in multisource networks, this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). We demonstrate the network's ability to estimate the impact of jamming and incorporate these estimates into the traffic allocation problem. Finally, we simulate the achievable throughput using our proposed traffic allocation method in several scenarios.
With a rapidly aging population, the healthcare community will soon face severe medical personnel shortage and rising costs. Pervasive Health Monitoring Systems (PHMS) can help alleviate this situation. PHMS provides continuous real-time monitoring of a person’s health using a (usually wireless) network of medical and ambient sensors/devices on the host (patients), called Body Area Networks (BANs). The sensitive nature of health information collected by PHMS mandates that patient’s privacy be protected by securing the medical data from any unauthorized access. The authors’ approach for addressing these issues focuses on a key observation that PHMS are cyber-physical systems (CPS). Cyber-physical systems are networked, computational platforms, deeply embedded in specific physical processes for monitoring and actuation purposes. In this work, they therefore present a novel perspective on securing PHMS, called Cyber Physical Security (CYPSec) solutions. CYPSec solutions are environmentally-coupled security solutions, which operate by combining traditional security primitives along with environmental features. Its use results in not only secure operation of a system but also the emergence of additional “allied” properties which enhance its overall capabilities. The principal focus of this chapter is the development of a new security approach for PHMS called CYPsec that leverages their cyber-physical nature. The authors illustrate the design issues and principals of CYPSec through two specific examples of this generic approach: (a) Physiological Signal based key Agreement (PSKA) is designed to enable automated key agreement between sensors in the BAN based on physiological signals from the body; and (b) Criticality Aware Access Control (CAAC) which has the ability to provide controlled opening of the system for emergency management. Further, they also discuss aspects such as altered threat-model, increased complexity, non-determinism, and mixed critical systems, that must be addressed to make CYPSec a reality.
The Network Simulator-3 (ns-3) is rapidly developing into a flexible and easy-to-use tool suitable for wireless network simulation. Since energy consumption is a key issue for wireless devices, wireless network researchers often need to investigate the energy consumption at a battery powered node or
The Network Simulator-3 (ns-3) is rapidly developing into a flexible and easy-to-use tool suitable for wireless network simulation. Since energy consumption is a key issue for wireless devices, wireless network researchers often need to investigate the energy consumption at a battery powered node or in the overall network, while running network simulations. This requires the underlying simulator to support energy consumption and energy source modeling. Currently however, ns-3 does not provide any support for modeling energy consumption or energy sources. In this paper, we introduce an integrated energy framework for ns-3, with models for energy source as well as energy consumption. We present the design and implementation of the overall framework and the specific models therein. Further, we show how the proposed framework can be used in ns-3 to simulate energy-aware protocols in a wireless network.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'%3e%3cg id='e'%3e%3cg id='c' fill='none' stroke='%23fff' stroke-width='2'%3e%3cpath id='b' d='M0 1h26M1 10V5h8v4h8V5h-5M4 9h2m20 0h-5V5h8m0-5v9h8V0m-4 0v9' transform='scale(1.4)'/%3e%3cpath id='a' d='M0 7h9m1 0h9' transform='scale(2.8)'/%3e%3cuse xlink:href='%23a' y='120'/%3e%3cuse xlink:href='%23b' y='145.2'/%3e%3c/g%3e%3cg id='d'%3e%3cuse xlink:href='%23c' x='56'/%3e%3cuse xlink:href='%23c' x='112'/%3e%3cuse xlink:href='%23c' x='168'/%3e%3c/g%3e%3c/g%3e%3cuse xlink:href='%23d' x='168'/%3e%3cuse xlink:href='%23e' x='392'/%3e%3c/g%3e%3cg fill='%23da0000' transform='matrix(45 0 0 45 315 180)'%3e%3cg id='f'%3e%3cpath d='M-.548.836A.912.912 0 0 0 .329-.722 1 1 0 0 1-.548.836'/%3e%3cpath d='M.618.661A.764.764 0 0 0 .422-.74 1 1 0 0 1 .618.661M0 1l-.05-1L0-.787a.31.31 0 0 0 .118.099V-.1l-.04.993zM-.02-.85 0-.831a.144.144 0 0 0 .252-.137A.136.136 0 0 1 0-.925'/%3e%3c/g%3e%3cuse xlink:href='%23f' transform='scale(-1 1)'/%3e%3c/g%3e%3c/svg%3e)