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Adaptive Sampling Algorithms with Local Emergency Detection for Energy Saving in Wireless Body Sensor Networks
Abstract
Nowadays, Wireless Body Sensor Networks (WBSN) are emerging as a low cost solution for healthcare application to find new solutions, regarding patient monitoring
which is becoming the elusive requirement. Quicker emergency
detection is the main purpose to create a quicker reaction
and treatment if required, such as an abnormal variation of
the respiration rate, which satisfies the goal of extending life
expectancy. This process can help all the chronic patients who
are most of the time living alone or in nursing homes. However,
the limited lifetime bio-medical sensors bring on the energy
consumption challenge as one of the leading challenges in WBSN.
Moreover, detecting locally an emergency is also one of the main
challenges in WBSN. In this paper, we propose an adaptive
sampling approach, based on fisher test theory, that estimates
and adapts the sensing frequency based on previous readings and
the patient criticality. The main goal is to optimize the energy
consumption. Furthermore, we show how emergency alerts can
be supported locally on each node of the network. To validate
the effectiveness of our approach we conducted several series of
simulations and built a simple energy saving comparison.
... Furthermore, the sampling rate of each sensor is estimated through statistical methods that rely on the diversity of measured data. These methods estimate the sampling rate for each sensor separately without considering the patient's overall health status and fusing the vital signs [6,[9][10][11][12][15][16][17][18]. ...
... In [16], a few changes are made to the local emergency detection method of the Ref. [6] to improve the transmission of redundant data and prevent sending redundant critical data. ...
... This can be done using a fuzzy system. To better understand the proposed method's process, the FASR-LED method's flowchart is shown in Fig. 2. Similar to the previous methods, each period equals 100 s [6,9,10,12,13,[15][16][17]. Data are measured at the SR sampling rate for each vital sign in each period. ...
Wireless body area networks (WBANs) are a low-cost solution for healthcare applications. However, in these networks, a huge amount of transmission is required to record the patient's status, leading to reduce energy rapidly. To solve this problem, adjusting the sampling rate based on the patient's condition is among efficient methods to save energy. However, most sampling rate methods have two drawbacks: 1-They do not estimate adaptive sampling rate for multiple sensors based on the patient’s condition simultaneously. 2-They are not high-level data fusion methods. To solve these problems, a fuzzy data fusion method called fuzzy adaptive sampling rate with local emergency detection (FASR-LED) is proposed in this paper. FASR-LED has two stages: (1) At the node level, the local emergency detection system deletes redundant data that considers patient conditions. (2) At the coordinator level, a fuzzy adaptive sampling rate is proposed as an inference engine to reduce energy consumption. The coordinator level by a fuzzy inference engine gives vital signs of the sensors as inputs and estimates the sampling rate as output. The main advantages of FASR-LED are: (1) Data fusion is done by a fuzzy high-level method to estimate the sampling rate. Therefore, data validity is increased by a fuzzy approach. (2) Medical diagnoses based on analyzing the vital signs are made faster due to the same sampling rate of all sensors. Since WBAN's sensors with different sampling rates need more complex synchronization methods, FASR-LED does not require synchronizing the sensors. (3) FASR-LED reduces the energy consumption of WBAN by creating high-level data. Simulations are performed on MIMIC I and MIMIC II datasets to evaluate the proposed method. Results show that FASR-LED, compared to other methods, overcomes others in terms of energy consumption by 50% and data reduction by 30%.
... But the problem with their method is that they compress and send all the data, which leads to sending unnecessary data and impose high energy consumption. The authors also provide adaptive sampling methods [6][7] [13][14][15]. In these methods, the sensors send the data at a specified sampling rate instead of sending all the captured data. ...
... AHSWN-8271JLM.indd6 12/7/2021 10:07:31 AM ...
Wireless Body Area Networks (WBANs) are one of the essential remedies for patient and elderly care. Due to the ageing population in the coming years and the occurrence of unknown and epidemic diseases in the future, the need for a mechanism such as WBANs to manage the limitation of hospital resources is felt more than ever. However, WBANs seriously face energy limitations, and the battery nodes in these networks have limited energy. Unlike other electronic equipment, replacing or charging the batteries of WBANs is not easily possible. Therefore, it is necessary to reduce the energy consumption in these networks and extend their lifetime. Although several methods have been proposed to reduce energy consumption, the critical point that has not been considered in previous methods is the similarity of the data. In this regard, in this paper first, a comprehensive study is done on the real data of MIMIC dataset to inspect data similarity in transferred data of WBANs. Then, an efficient method called Shorted Repeating Code Modulation (SRCM) based on data similarity is proposed. SRCM has four different steps of sampling, quantization, encoding and compression and uses the similarity of data to reduce energy consumption. This is done by reducing the amount of data sent during the sampling step. Data similarity is recognized by cross-correlation process to prevent transferring similar data. Simulations using MATLAB show that the use of SRCM has minimized the energy consumption of the network by 85.7%.Simulations using MATLAB show that the use of SRCM has minimised the energy consumption of the network by 85.7%.
... They manually designed sampling algorithms that adjust the sampling rate according to the variation in the environment. Salim et al. [22] propose an algorithm that uses analysis of variance with Fisher test to adapt the sensing frequency according to the environmental variation. ...
In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This reward function enables IoT sensor devices to learn to spend available energy on measurements at otherwise unpredictable moments, while conserving energy at times when measurements would provide little new information. This is a highly general approach, which allows for a wide range of use cases without significant human design effort or hyper-parameter tuning. We illustrate the approach in a scenario of workplace noise monitoring, where results show that the learned behavior outperforms a uniform sampling strategy and comes close to a near-optimal oracle solution.
... The greater the value of r 0 is , the more the vital sign is considered critical and the lower its value is, the less the vital sign is considered critical. Having the Fisher Test result F and the risk level r 0 , a Quadratic Bezier curve is used as a Behavior Function (BV) to assign the appropriate sampling rate for the following period [27]. On the other hand, our proposed algorithm reduces the transmission by reducing the amount of measurements sent to the coordinator. ...
This paper proposes a generalized multi-sensor fusion approach and a health risk assessment and decision-making (Health-RAD) algorithm for continuous and remote patient monitoring purposes using a Wireless Body Sensor Network (WBSN). Health-RAD determines the patient's health condition severity level routinely and each time a critical issue is detected based on vital signs scores. Hence, a continuous health assessment and a monitoring of the improvement or the deterioration of the state of the patient is ensured. The severity level is represented by a risk variable whose values range between 0 and 1. The higher the risk value, the more critical the patient's health condition is and the more it requires medical attention. Moreover, we calculate the score of a vital sign using its past and current value, thus assessing its status based on its evolution during a period of time and not only on sudden deviations. We propose a generalized multi-sensor data fusion approach regardless of the number of monitored vital signs. The latter is employed by Health-RAD to find the severity level of the patient's health condition based on his/her vital signs scores. It is based on a fuzzy inference system (FIS) and early warning score systems (EWS). This approach is tested with a previously proposed energy-efficient data collection approach, thus forming a complete framework. The proposed approach is evaluated on real healthcare datasets and the results are compared with another approach from the literature in terms of data reduction, energy consumption, risk assessment of vital signs, the patient's health risk level determination and accuracy. The results show that both approaches have coherently assessed the health condition of different Intensive Care Unit (ICU) patients. Yet, our proposed approach overcomes the other approach in terms of energy consumption (around 86% less energy consumption) and data reduction (around 70% for sensing and more than 90% for transmission). Additionally, contrary to our proposed framework, the approach taken from the literature requires an offline model building and depends on available patient datasets.
... Nguyen et al. [34] propose an information-driven adaptive sampling strategy in mobile robotic wireless sensor networks, which aims to minimize the uncertainty at all of the unobserved locations of interest. Salim et al. [35] propose an adaptive sampling approach for wireless body sensor networks to estimate and adapt the sensing frequency based on previous readings and patient criticality. The adaptive sampling approach aims to handle emergency detection with energy saving. ...
Air pollution is one of the major problems of the modern world. The popularization and powerful functions of smartphone applications enable people to participate in urban sensing to better know about the air problems surrounding them. Data sampling is one of the most important problems that affect the sensing performance. In this paper, we propose an Adaptive Sampling Scheme for Urban Air Quality (AS-air) through participatory sensing. Firstly, we propose to find the pattern rules of air quality according to the historical data contributed by participants based on Apriori algorithm. Based on it, we predict the on-line air quality and use it to accelerate the learning process to choose and adapt the sampling parameter based on Q-learning. The evaluation results show that AS-air provides an energy-efficient sampling strategy, which is adaptive toward the varied outside air environment with good sampling efficiency.
... The coordinator usually is the smartphone, PDA or any other portable device which is carried by the patient. Many challenges exist in such a network such as: the heterogeneity of the collected data (heart rate, respiration rate, blood pressure etc.) and its huge amount, the energy consumption due to periodic transmission as well as privacy and security issues [1], [5], [7]. In this paper we address the energy consumption and data reduction issues which are directly related to one another. ...
Wireless Body Sensor Networks (WBSNs) are a low-cost solution allowing remote patient monitoring and continuous health assessment, thus reducing healthcare expenditure. In such networks, sensor nodes periodically collect vital signs and send them to the coordinator for fusion. However, sensor nodes have limited energy and processing resources and transmission is the most power-hungry task. In this paper, we target data reduction and energy consumption. We propose to locally adapt, in real-time, the sampling rate of a sensor node according to the variations in the vital sign being monitored and its risk. We propose to dynamically evaluate, in real-time, the risk of any vital sign given the information about the severity level of the patient's health condition and the severity level of the vital sign itself. We have tested our proposed approach on real health datasets in order to evaluate it. The results show that the percentage of detected critical events and the mean-square error (MSE) are both acceptable. In addition, the percentage of data reduction is around 50% implying a reduction of the energy consumption. Adjusting the risk of a vital sign, over time, ensures the adaptation of the sampling rate according to the overall health condition of the patient as well as the severity level of the collected measurements.
... Therefore, Modified LED * adapts the sampling rate of each biosensor in accordance to the dynamic evolution of each vital sign. This is done using the Fisher Test and the ANOVA Model as well as the Quadratic Bezier curve as a Behavior Function (BV) while taking into account the patient's risk level in terms of the importance of a vital sign on the patient's health condition [17,7]. In addition, our proposed algorithm further reduces the transmission by reducing the number of sent measurements to the coordinator. ...
Wireless Body Sensor Networks (WBSNs) are a low-cost solution for healthcare applications allowing continuous and remote monitoring. However, many challenges are addressed in WBSNs such as limited energy resources, early detection of emergencies and fusion of large amount of heterogeneous data in order to take decisions. In this paper, we propose a multisensor data fusion approach enabling one to determine the patient risk level based on vital signs scores. Consequently, a corresponding decision is taken routinely and
each time an emergency is detected. This approach is based on early warning score systems, a fuzzy inference system and a technique determining the score of a vital sign given its past and current value. We evaluate our approach on real healthcare datasets.
In a variety of Internet of Things (IoT) applications, there is a growing need to constantly transmit large amounts of real-time data from IoT sensors to enable precise environmental monitoring. However, this constant transmission of data can result in high energy consumption of IoT sensors, which presents a significant challenge for IoT systems. Therefore, this paper presents a new approach for controlling the transmission period of IoT sensors called Imputation Error Cluster-based Transmission Period Control (IEC-TPC) framework, with the goal of reducing energy consumption while maintaining accurate data collection. In order to effectively balance energy consumption and data collection accuracy, the proposed approach uses an Imputation Error Centroid (IEC) model that forms clusters based on imputation error vectors and approximates the centroid of each cluster using a logistic function. Additionally, a new Imputation Error Cluster Prediction (IECP) model is designed using Long Short Term Memory (LSTM)-based encoding and Convolutional Neural Network (CNN) models to predict the label of each cluster in advance. By combining the IEC model with a numerically modeled energy consumption function, a min-max optimization problem is properly formulated to minimize the worst-case performance of transmission period control. To find the optimal solution, a Uniqueness Condition function is properly defined, and an Optimal Objective Value Searching Algorithm (O2VSA) leveraging a bisection search is proposed. Performance evaluation shows that the proposed method achieves similar data collection accuracy in CO2 and humidity datasets while reducing energy consumption by 12.92% and 35.83% than other baseline models, respectively. Moreover, the proposed algorithm outperforms the state-of-the-art method in the temperature dataset, with 13% lower energy consumption value. The proposed method also achieves a significant reduction in energy consumption of 95.21% compared to the uniform transmission period model in this dataset, while maintaining a low data reconstruction error of only 0.42%. Overall, the proposed method offers a promising approach for efficient and accurate transmission period control in IoT applications.
With the recent explosive growth of the Internet of Things (IoT), edge computing is emerging as a modern computing paradigm that coexists with the cloud to process massive amounts of data particularly distributed at the edge network. Meanwhile, edge computing directly permits the use of artificial intelligent (AI) models at the edge. Currently in numerous IoT applications, a tremendous amount of data is being measured and transmitted from IoT sensors to monitor surrounding information in real-time. Considering that continuous transmission of sensed data substantially requires high energy consumption of IoT sensors, this paper proposes a PPO-based autonomous transmission period control (PPO-ATPC) system in IoT edge computing that can automatically and adaptively control the transmission period of each IoT sensor. Here, the design of state, action, and reward is shaped in an unprecedented way and furthermore, a proximal policy optimization (PPO), which is one of the most effective model-free deep reinforcement learning (DRL) algorithms, is fully leveraged to achieve an optimal or nearly-optimal policy that can significantly shorten the data volume while maintaining high data quality. The merits of the proposed PPO-ATPC are extensively validated through quantitative comparison with other alternative approaches using three different sensor data collected from real-time environmental monitoring, and in-depth insights into the effectiveness of PPO-ATPC are further provided from diverse perspectives. The performance results show that total data volume for each dataset could be reduced by 73.849%, 89.931%, and 81.310%, with the average RMSE of 0.322, 13.896, and 0.048.
This paper proposes a novel deep learning-based robust Internet of Things (IoT) sensor data transmission period control (DL-RDTPC) framework in an IoT edge computing system. In general, as the data transmission period of IoT sensors increases, the energy consumption of IoT sensors is reduced, and contrarily, the amount of un-transmitted data (i.e., missing values) becomes continuously accumulated. Therefore, the IoT server is in charge of accurately imputing these missing data for reliable data analysis. By addressing this issue, we newly design the imputation accuracy prediction (IAP) module, which captures the complicated relationships between the imputation accuracy and the data transmission period, in order to estimate the imputation accuracy, precisely. For constructing the IAP, three sub-modules, which include a stacked bidirectional long-short term memory (Bi-LSTM) model, a multi-head convolutional neural network (CNN), and a neural network-based period information encoding network (PIEN) are leveraged. To balance the trade-off between the imputation accuracy and energy consumption regarding the data transmission period, the multi-objective optimization problem is formulated for minimizing the maximum value of both i) the energy consumption of IoT sensors obtained from the analytical model and ii) the imputation accuracy predicted from IAP module. The optimal solution is consequently obtained by utilizing the bisection search algorithm. Extensive performance evaluations validate the effectiveness of the proposed RDTPC algorithm in terms of both the average energy consumption (maximum 68% reduction) and missing data imputation accuracy (maximum 64% RMSE reduction) over other benchmarks. Finally, this paper provides a practical implementation of the proposed RDTPC framework via the HTTP protocol under the IEEE 802.11-based WLAN network, as well as inter-working with the commercial cloud server.
Since one of the main reasons for improvement network lifetime is communications reduction in collecting vital signs and transmit them to the coordinator. In this paper, it is tried to reduce communications through adaption the sampling rate through individual's discovered pattern, activity prediction and watchdog biosensor. The first, the daily behavior pattern of the individual is identified, then the individual's activities are predicted; if the predicted activity exists in the individual's behavioral pattern, all the sensors are activated to read information with maximum sampling rate. Otherwise, the sensors read information with the minimum sampling rate and the watchdog biosensor is activated to sense and send the vital signs with the maximum sampling rate. The simulation results show that the proposed method improves network traffic by 80% and decreases the energy consumption of the network by four times.
In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This reward function enables IoT sensor devices to learn to spend available energy on measurements at otherwise unpredictable moments, while conserving energy at times when measurements would provide little new information. This is a highly general approach, which allows for a wide range of use cases without significant human design effort or hyperparameter tuning. We illustrate the approach in a scenario of workplace noise monitoring, where results show that the learned behavior outperforms a uniform sampling strategy and comes close to a near-optimal oracle solution.
Growing population, sedentary lifestyle and spreading epidemics in today’s world have led to a need for ubiquitous healthcare systems. Wireless body area network (WBAN) is one such concept which serves as a health monitoring technology. In a WBAN sensors are attached to various parts of the human body to monitor the health or in general the bodily functions such as heart rate and blood pressure of a person. The readings obtained from the patient are transmitted to a medical professional so that the patient will be constantly and remotely monitored. This gives location flexibility for the patient instead of being in a hospital or being bound at home. But one of the downsides in adopting WBAN is the security and privacy issues. Medical records are sensitive information and hence for a patient to trust the system, data needs to be sent securely. Moreover, every detail captured by the sensors need to be reliably transmitted to the medical authorities concerned. Another issue is the limited battery power of the sensors. A sensor should not be taxed to do too many computations as that will drastically drain the battery. In this work, we propose a power efficient methodology for secure transmission of patient data to the medical authorities. To improve the reliability of the system we propose a modified adhoc on-demand distance vector (AODV) protocol called RelAODV (Reliable AODV). Simulations have shown that the proposed methodology is energy efficient and improves the overall QoS of the system.
With the increasing use of wireless networks and miniaturization of electronic Devices has allowed the realization of Wireless Body Area Networks (WBANs). It is one of the latest technologies in health care diagnosis and management. WBAN consists of various intelligent bio sensors attached on or implanted in the body like under the skin. These sensors offer promising applications in areas such as real time health monitoring, interactive gaming and consumer electronics. WBAN does not compel the patient to stay in the hospital thereby giving much physical mobility. This paper presents an overview on the various aspects of WBAN. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of ICCTSD 2011
Swallowable body sensor networks (BSNs) are composed of sensors which are swallowed by patients and send the collected data to the outside coordinator. These sensors are energy constraint and the batteries are difficult to be replaced. The medium access control (MAC) protocol plays an important role in energy management. This paper investigates an energy efficient MAC protocol design for swallowable BSNs. Multi-hop communication is analyzed and proved more energy efficient than single-hop communication within the human body when the circuitry power is low. Based on this result, a centrally controlled time slotting schedule is proposed. The major workload is shifted from the sensors to the coordinator. The coordinator collects the path-loss map and calculates the schedules, including routing, slot assignment and transmission power. Sensor nodes follow the schedules to send data in a multi-hop way. The proposed protocol is compared with the IEEE 802.15.6 protocol in terms of energy consumption. The results show that it is more energy efficient than IEEE 802.15.6 for swallowable BSN scenarios.
Wireless body area networks (WBANs) are emerging as important networks, applicable in various
fields. This paper surveys the WBANs that are designed for applications in healthcare. We present a
comprehensive survey consisting of stand-alone sections focusing on important aspects of WBANs. We
examine the following: monitoring and sensing, power efficient protocols, system architectures, routing
and security. We conclude by discussing some open research issues, their potential solutions and future
trends.
Energy conservation techniques for wireless sensor networks generally assume that data acquisition and processing have energy consumption that is significantly lower than that of communication. Unfortunately, this assumption does not hold in a number of practical applications, where sensors may consume even more energy than the radio. In this context, effective energy management should include policies for an efficient utilization of the sensors, which become one of the main components that affect the network lifetime. In this paper, we propose an adaptive sampling algorithm that estimates online the optimal sampling frequencies for sensors. This approach, which requires the design of adaptive measurement systems, minimizes the energy consumption of the sensors and, incidentally, that of the radio while maintaining a very high accuracy of collected data. As a case study, we considered a sensor for snow-monitoring applications. Simulation experiments have shown that the suggested adaptive algorithm can reduce the number of acquired samples up to 79% with respect to a traditional fixed-rate approach. We have also found that it can perform similar to a fixed-rate scheme where the sampling frequency is known in advance.
We present a mobile platform for body sensor networking based on a smartphone for lightweight signal processing of sensor mote data. The platform allows for local processing of data at both the sensor mote and smartphone levels, reducing the overhead of data transmission to remote services. We discuss how the smartphone platform not only provides the ability for wearable signal processing, but it allows for opportunistic sensing strategies, in which many of the onboard sensors and capabilities of modern smartphones may be collected and fused with body sensor data to provide environmental and social context. We propose that this can help refine data reduction at the local level. We describe three examples related to health and wellness, to which our system has been applied.
Due to its potential applications and the density of the deployed sensors, distributed wireless sensor networks are one of the highly anticipated key contributors of the big data in the future. Consequently, massive data collected by the sensors beside the limited battery power are the main limitations imposed by such networks. In this paper, we consider a periodic sensor networks (PSNs) where sensors transmit their data to the sink on a periodic basis. We propose an efficient adaptive model of data collection dedicated to PSN, in order to increase the network lifetime and to reduce the huge amount of the collected data. The main idea behind this approach is to allow each sensor node to adapt its sampling rate to the physical changing dynamics. In this way, the oversampling can be minimized and the power efficiency of the overall network system can be further improved. The proposed method is based on the dependence of measurements variance while taking into account the residual energy that varies over time. We study three well known statistical tests based on One-Way Anova model. Then, we propose a multiple levels activity model that uses behavior functions modeled by modified Bezier curves to define application classes and allow for sampling adaptive rate. Experiments on real sensors data show that our approach can be effectively used to minimize the amount of data retrieved by the network and conserve energy of the sensors, without loss of fidelity/accuracy.
Patient monitoring is becoming a requirement for offering a better healthcare to an increasing number of chronic patients whether they are living alone, or in nursing homes. Thus, it is necessary to constantly monitor their vital signs to effectively control their health condition and to provide urgent treatment while an emergency such as an abnormal variation of heart rate occurs. In recent years, wireless body sensor networks have emerged as a low cost solution for healthcare applications. However, processing the huge amount of raw data captured by bio medical sensors is a major challenge for this type of networks, along with energy, as in almost all the cases the only source of energy is battery with limited lifetime. In this paper we address how emergency alerts can be supported locally on each node of the network. Our approach proposes some criticality models which allow to reduce considerably the amount of sent data through the network, thus improving power efficiency by sending only critical measures when needed. Simulation results are presented to validate the performance of the proposed approach.
Lowdata delivery efficiency and high energy consumption are inherent problems in sensor networks. Finding accurate data is difficult and leads to expensive sensor readings. Adaptive data collection and aggregation techniques provide opportunities for reducing the energy consumption of continuous sensor data collection. In this paper, we propose an adaptive data collection and aggregation scheme for periodic sensor networks. Our approach is two-fold: firstly, we present an efficient adaptive sampling approach based on the dependence of conditional variance on measurements varies over time. Then, over-sampling can be minimised and power efficiency can be improved. Furthermore, we propose a multiple levels activity model that uses behaviour functions modelled by modified Bezier curves to define application classes. Secondly, a new data aggregation method is applied on the set of data collected from all sensor nodes. We investigate the problem of finding all pairs of nodes generating similar datasets. Our proposed models are validated via simulation on real sensor data.
The expected lifetime of any wireless sensor network is a critical issue since sensor nodes are powered by small batteries. The propagation of redundant highly correlated data is costly in terms of system performance, and results in energy depletion, network overloading, and congestion. Data aggregation is regarded as an effective technique to reduce energy consumption and prevent congestion. This paper objective is to identify near duplicate nodes that generate similar sets of collected data in periodic applications. We propose a new prefix filtering approach that avoids computing similarity values for all possible pairs of sets. We define a new filtering technique based on the quality of information. To the best of our knowledge, the proposed algorithm is a pioneer in using "sets similarity functions" for data aggregation in sensor networks. To evaluate the performance of the proposed method, experiments on real and synthetic sensor data have been conducted. The analysis and the results show the effectiveness of our method dedicated to sensor networks.
Networking with various healthcare devices on human body is one of the targeted applications of the IEEE 802.15.6 standard. Among those, human body communication is considered to have better performance in terms of power consumption and security for implanted and swallowable devices. Different from general applications of BAN, healthcare device network with implanted/swallowable devices requires a better understanding of the necessary time delay for the emergency messages transmission. In this article, we analyzed the delay of emergency message transmission based on IEEE802.15.6 at HBC. Both theoretical analysis and simulation were employed to calculate the average and shortest transmission delay, and also the distribution of the transmission delay at multi-user multi-UPs for slotted Aloha access protocol.
A wireless body area network offers cost-effective solutions for healthcare infrastructure. An adaptive transmission algorithm is designed to handle channel efficiency, which adjusts packet size according to the difference in feature-point values that indicate biomedical signal characteristics. Furthermore, we propose a priority-adjustment method that enhances quality of service while guaranteeing signal integrity. A large number of simulations were carried out for performance evaluation. We use electrocardiogram and electromyogram signals as reference biomedical signals for performance verification. From the simulation results, we find that the average packet latency of proposed scheme is enhanced by 30% compared to conventional method. The simulation results also demonstrate that the proposed algorithm achieves significant performance improvement in terms of drop rates of high-priority packets around 0.3%-0.9 %.
Data collection from unreachable terrain and then transmit the information to the sink is a fundamental task in periodic sensor networks. Energy is a major constraint for this network as the only source of energy is a battery with limited lifetime. Therefore, in order to keep the networks operating for long time, adaptive sampling approach to periodic data collection constitutes a fundamental mechanism for energy optimization. The key idea behind this approach is to allow each sensor node to adapt its sampling rates to the physical changing dynamics. In this way, over-sampling can be minimised and power efficiency of the overall network system can be further improved. In this paper, we present an efficient adaptive sampling approach based on the dependence of conditional variance on measurements varies over time. Then, we propose a multiple levels activity model that uses behavior functions modeled by modified Bezier curves to define application classes and allow for sampling adaptive rate. The proposed method was successfully tested in a real sensor data set.
Wireless body sensor networks are expected to extend human-centered applications in large-scale sensing and detecting environments. Energy savings has become one of the most important features of the sensor nodes to prolong their lifetime in such networks. To provide reasonable energy consumption and to improve the network lifetime of wireless body sensor network systems, new and efficient energy-saving schemes must be developed. An energy-saving routing architecture with a uniform clustering algorithm is proposed in this paper to reduce the energy consumption in wireless body sensor networks. We adopted centralized and cluster-based techniques to create a cluster-tree routing structure for the sensor nodes. The main goal of this scheme is to reduce the data transmission distances of the sensor nodes by using the uniform cluster structure concepts. To make an ideal cluster distribution, the distances between the sensor nodes are calculated, and the residual energy of each sensor node is accounted for when selecting the appropriate cluster head nodes. On the basis of the uniform cluster location, the data transmission distances between the sensor nodes can be reduced by employing an adaptive multi-hop approach. The energy consumption is reduced, and the lifetime is extended for the sensor nodes by balancing the network load among the clusters. Simulation results show that the proposed scheme outperforms the previously known schemes in terms of the energy consumption and the network lifetime for the wireless body sensor networks.
The recent research in Body Area Networks (BANs) is focused on making its communication more reliable, energy efficient, secure, and to better utilize system resources. In this paper we propose a novel BAN architecture for indoor hospital environments, and a new mechanism of peer discovery with routing table construction that helps to reduce network traffic load, energy consumption, and improves BAN reliability. The three scenarios with fixed and variable number of packets sent by source nodes are considered for better analysis. Static nodes are considered in first and second scenarios whereas mobile nodes are used in third scenario. We have performed extensive simulations in the OMNeT++ based Castalia-3.2 simulation environment to show that our proposed protocol has better performance in terms of reduced BAN traffic load, increased successful transmission rate, reduced number of packets forwarded by intermediate nodes, no packets dropped due to buffer overflow, and overall lower energy consumption when compared with a similar protocols.
Patient monitoring is becoming a requirement for offering a better healthcare to an increasing number of patients in nursing homes and hospitals. During the monitoring, vital signs of patients could fluctuate significantly and/or match certain undesirable patterns and therefore “alerts” or emergency messages must be delivered to healthcare professionals. In this paper, we address how patient monitoring, specifically emergency messages, can be supported over wireless ad hoc network formed among patients' devices. The framework describes a wireless patient monitoring system that includes patient monitoring devices, routing protocols and information presentation for vitals signs and parameters. This involves a series of decisions in obtaining and processing vital signs, routing over networks, and delivering to healthcare professionals, who must make suitable medical decisions on patients' healthcare needs. Additionally, several design enhancements to improve the quality and coverage of wireless patient monitoring are presented. The performance results for the proposed ad hoc network based architecture show that reliable message delivery and low monitoring delays can be achieved by using multicast or broadcast-based routing schemes. The proposed monitoring architecture is shown to be scalable and the cognitive load on healthcare professionals is found to be dependent on routing protocols and reliability requirement of emergency messages. The proposed work can be extended to provide personalized healthcare services to people in nursing homes, assisted living, home, and while being mobile.
Energy is a major constraint in wireless sensor networks. Data Aggregation constitutes a fundamental mech- anism for energy optimization. The idea is to minimize redun- dancy from the raw data captured by the sensors, minimizing the number of transmissions to the sink and thus saving energy. Since the data is often captured on a periodic basis, and sensor nodes detect common phenomena, a periodic based protocol that manages collected data sets can help to preserve the scarce energy. This paper proposes a new filtering technique for identifying duplicate sets of periodically captured data. We suggest a data aggregation model based on set joins similarity functions that conserves data integration while eliminating inherited redundancy. We show through the result that our approach offers significant data reduction by eliminating in- network redundancy and sending only necessary information to the sink.
Advances in wireless communication tech- nologies, such as wearable and implantable biosensors, along with recent developments in the embedded com- puting area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of inno- vative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sen- sor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications proto- cols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications.
The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.
Sensor network for patient monitoring
- Dipali Phadat
- Ashish Bhole
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