This paper presents an energy-efficient and coverage-preserving communication protocol which distributes a uniform energy load to the sensors in a wireless microsensor network. This protocol, called Distance-based Segmentation (DBS), is a cluster-based protocol that divides the entire network into equal-area segments and applies different clustering policies to each segment to (1) reduce total energy dissipation and (2) balance the energy load among the sensors. Therefore, it prolongs the lifetime of the network and improves the sensing coverage. Moreover, the proposed routing protocol does not need any centralized support from a certain node which is at odds with aiming to establish a scalable communication protocol. Results from extensive simulations on two different network configurations show that by lowering the number of wasteful transmissions in the network, the DBS can achieve as much as a 20% reduction in total dissipated energy as compared with current cluster-based protocols. In addition, this protocol is able to distribute energy load more evenly among the sensors in the network. Hence, it yields up to a 66% increase in the useful network lifetime. According to the simulation results, the sensing coverage degradation of the DBS is considerably slower than that of the other cluster-based protocols. Copyright
Wearable sensing systems are becoming widely used for a variety of applications, including sports, entertainment, and military. These systems have recently enabled a variety of medical monitoring and diagnostic applications in Wireless Health. The need for multiple sensors and constant monitoring lead these systems to be power hungry and expensive, with short operating lifetimes. In this paper, we introduce a novel methodology that takes advantage of the influence of human behavior on signal properties and reduces those three metrics from the data size point of view. This, in turn, directly influences the wireless communication and local processing power consumption. We exploit intrinsic space and temporal correlations between sensor data while considering both user and system behavior. Our goal is to select a small subset of sensors to accurately capture and/or predict all possible signals of a fully instrumented wearable sensing system. Our approach leverages novel modeling, partitioning, and behavioral optimization, which consists of signal characterization, segmentation and time shifting, mutual signal prediction, and subset sensor selection. We demonstrate the effectiveness of the technique on an insole instrumented with 99 pressure sensors placed in each shoe, which cover the bottom of the entire foot, resulting in energy reduction of 56% to 96% for error rates of 5% to 17.5%.
The improvement in processor performance through continuous breakthroughs in transistor technology has resulted in the proliferation of lightweight embedded systems. Advances in wireless technology and embedded systems have enabled remote healthcare and telemedicine. Continuous and real-time monitoring can discretely analyze how a patient's lifestyle affects his/her physiological conditions and if additional symptoms occur under various stimuli. Diabetes is one of most difficult challenges facing the healthcare industry today. One of the primary afflictions of diabetic patients is peripheral neuropathy (loss of sensation in the foot). As a direct result of this condition, the likelihood of ulcer increases which in many cases leads to to amputation. We have developed a wireless electronic orthotics composed of lightweight embedded systems and non-invasive sensors which can be used by diabetic patients suffering from peripheral neuropathy. Our proposed system monitors feet motion and pressure distribution beneath the feet in real-time and classifies the state of the patient. The proposed system detects the conditions that could potentially cause a foot ulcer. This system enables a continuous feedback mechanism for instance in case of an undesired behavior or condition a preemptive message wirelessly to the patient and the patient's caregiver.
Several diseases and medical conditions require constant monitoring of physiological signals and vital signs on daily bases, such as diabetics, hypertension and etc. In order to make these patients capable of living their daily life it is necessary to provide a platform and infrastructure that allows the constant collection of physiological data even when the patient is not inside of the coverage area. The data must be rapidly "transported" to care givers or to the designated medical enterprise. The problem is particularly severe in case of emergencies (e.g. natural disasters or hostile attacks) when the communications infrastructure (e.g. cellular telephony, WiFi public access, etc) has failed or is totally congested. In this paper we present an evaluation of of the vehicular adhoc networks (VANET) as an alternate method of collecting patient pre-recorded physiological data and at the same time reconfiguring patient medical wearable body vests to select the data specifically requested by the physicians. Another important use of vehicular collection of medical data from body vests is prompted by the need to correlate pedestrian reaction to vehicular traffic hazards such as chemical and noise pollution and traffic congestion. The vehicles collect noise, chemical and traffic samples and can directly correlate with the "stress level" of volunteers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.