Analysis of using interpulse intervals to generate 128-bit biometric random binary sequences for securing wireless body sensor networks

Wireless body sensor network (WBSN), a key building block for m-Health, demands extremely stringent resource constraints and thus lightweight security methods are preferred. To minimize resource consumption, utilizing information already available to a WBSN, particularly common to different sensor nodes of a WBSN, for security purposes becomes an attractive solution. In this paper, we tested the randomness and distinctiveness of the 128-bit biometric binary sequences (BSs) generated from interpulse intervals (IPIs) of 20 healthy subjects as well as 30 patients suffered from myocardial infarction and 34 subjects with other cardiovascular diseases. The encoding time of a biometric BS on a WBSN node is on average 23?ms and memory occupation is 204 bytes for any given IPI sequence. The results from five U.S. National Institute of Standards and Technology statistical tests suggest that random biometric BSs can be generated from both healthy subjects and cardiovascular patients and can potentially be used as authentication identifiers for securing WBSNs. Ultimately, it is preferred that these biometric BSs can be used as encryption keys such that key distribution over the WBSN can be avoided.     

A Three Layered Peer-to-Peer Energy Efficient Protocol for Reliable and Secure Data Transmission in EAACK MANETs

The wireless body sensor network (WBSN) an extensive of WSN is in charge for the detection of patient’s health concerned data. This monitored health data are essential to be routed to the sink (base station) in an effective way by approaching the routing technique. Routing of tremendous sensed data to the base station minimizes the life time of the network due to heavy traffic occurrence. The major concern of this work is to increase the lifespan of the network which is considered as a serious problem in the wireless network functionalities. In order to recover this issue, we propose an optimal trust aware cluster based routing technique in WBSN. The human body enforced for the detection of health status is assembled with sensor nodes. In this paper, three novel schemes namely, improved evolutionary particle swarm optimization (IEPSO), fuzzy based trust inference model, and self-adaptive greedy buffer allocation and scheduling algorithm (SGBAS) are proposed for the secured transmission of data. The sensor nodes are gathered to form a cluster and from the cluster, it is necessary to select the cluster head (CH) for the effective transmission of data to nearby nodes without accumulation. The CH is chosen by considering IEPSO algorithm. For securable routing, we exhibit fuzzy based trust inference model to select the trusted path. Finally, to reduce traffic occurrence in the network, we introduce SGBAS algorithm. Experimental results demonstrate that our proposed method attains better results when compared with conventional clustering protocols and in terms of some distinctive QoS determinant parameters.     

无线躯体传感器网络中低能耗的时间同步算法

时间同步是无线躯体传感器网络(WBSN)的一项支撑技术.文章针对WBSN能源有限的问题,提出了一种改进的时间同步算法.该算法结合基准节点单向广播机制和成对双向消息传递机制,在保证一定同步精度的前提下,减少消息传递次数,降低通信开销,达到了低能耗的要求.最后进行仿真验证了算法性能.     

Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes

Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for "good" reconstruction quality.     

A Supervised Learning Based Decision Support System for Multi-Sensor Healthcare Data from Wireless Body Sensor Networks

Wireless body sensor network (WBSN) is also known as wearable sensors with transmission capabilities, computation, storage and sensing. In this paper, a supervised learning based decision support system for multi sensor (MS) healthcare data from wireless body sensor networks (WBSN) is proposed. Here, data fusion ensemble scheme is developed along with medical data which is obtained from body sensor networks. Ensemble classifier is taken the fusion data as an input for heart disease prediction. Feature selection is done by the squirrel search algorithm which is used to remove the irrelevant features. From the sensor activity data, we utilized the modified deep belief network (M-DBN) for the prediction of heart diseases. This work is implemented by Python platform and the performance is carried out of both proposed and existing methods. Our proposed M-DBN technique is compared with various existing techniques such as Deep Belief Network, Artificial Neural Network and Conventional Neural Network. The performance of accuracy, recall, precision, F1 score, false positive rate, false negative and true negative are taken for both proposed and existing methods. Our proposed performance values for accuracy (95%), precision (98%), and recall (90%), F1 score (93%), false positive (72%), false negative (98%) and true negative (98%).

     

Autonomous allocation of wireless body-sensor networks to frequency channels: modeling with repeated “balls-in-bins” experiments

Wireless Networks - In this paper we model a situation where several wireless body sensor networks (WBSN) compete for occupation of a number of frequency channels. Each channel can host at most one...     

Communication-efficient implementation of join in sensor networks

A sensor network is a multi-hop wireless network of sensor nodes cooperatively solving a sensing task. Each sensor node generates data items that are readings obtained from one or more sensors on the node. This makes a sensor network similar to a distributed database system. While this view is somewhat traditional, efficient execution of database (SQL) queries in sensor network remains a challenge, due to the unique characteristics of such networks such as limited memory and battery energy on individual nodes, multi-hop communication, unreliable infrastructure, and dynamic topology. Since the nodes are battery powered, the sensor network relies on energy-efficiency (and hence, communication efficiency) for a longer lifetime of the network.In this article, we have addressed the problem of communication-efficient implementation of the SQL “join” operator in sensor networks. In particular, we design an optimal algorithm for implementation of a join operation in dense sensor networks that provably incurs minimum communication cost under some reasonable assumptions. Based on the optimal algorithm, we design a suboptimal heuristic that empirically delivers a near-optimal join implementation strategy and runs much faster than the optimal algorithm. Through extensive simulations on randomly generated sensor networks, we show that our techniques achieve significant energy savings compared to other simple approaches.     

基于使用者反馈的用户体验质量管理构架

Recently, Quality of Experience (QoE) has been introduced as a subjective measure of a user’s experience of communication services. QoE was expected to take the place of traditional Quality of Ser-vice (QoS) measure in that QoE may express a direct and accurate user experience. In this paper, we propose a QoE management scheme which is based on a user’s simple feedback. We explain the proposed QoE management steps and the dominant reason extraction algorithm to determine the quality-falling instance. We also present a QoE prediction method that will provide an optimal quality management scheme in communication services. Experiments on mul-timedia streaming service prove the efficiency of the dominant factor extraction algorithm, and the experiment using the QoE prediction method present a very high accuracy. The QoE management scheme proposed in this paper can be generally adapted to any communication services, to increase the efficiency and effectiveness of quality man-agement systems.     

Cache-aware optimization of BAN applications

Body-area sensor network or BAN-based health monitoring is increasingly becoming a popular alternative to traditional wired bio-monitoring techniques. However, most biomonitoring applications need continuous processing of large volumes of data, as a result of which both power consumption and computation bandwidth turn out to be serious constraints for sensor network platforms. This has resulted in a lot of recent interest in design methods, modeling and software analysis techniques specifically targeted towards BANs and applications running on them. In this paper we show that appropriate optimization of the application running on the communication gateway of a wireless BAN and accurate modeling of the microarchitectural details of the gateway processor can lead to significantly better resource usage and power savings. In particular, we propose a method for deriving the optimal order in which the different sensors feeding the gateway processor should be sampled, to maximize cache reuse. In addition, we also investigate the effects on cache reuse of different memory layouts of the code processing the different sensor data. The joint optimization of code layout and the order in which the different sensors should be sampled—in order to maximize code cache reuse—turns out to be a difficult combinatorial optimization problem. But our experiments show that optimizing the sampling order of the sensors has a much larger influence on cache reuse, compared to the effects that different code layouts have. Based on this, we also propose a heuristic that obtains near-optimal solutions in jointly optimizing both code layout as well the sensor sampling order. Our case study using a faint fall detection application—from the geriatric care domain—which is fed by a number of smart sensors to detect physiological and physical gait signals of a patient show very attractive power consumption in the underlying processor. Alternatively, our method can be used to improve the sampling frequency of the sensors, leading to higher reliability and better response time of the application.     

AUTHENTICATED SYMMETRIC-KEY ESTABLISHMENT FOR MEDICAL BODY SENSOR NETWORKS

This study concerns security issues of the emerging Wireless Body Sensor Network (WBSN) formed by biomedical sensors worn on or implanted in the human body for mobile healthcare appli-cations. A novel authenticated symmetric-key establishment scheme is proposed for WBSN,which fully exploits the physiological features obtained by network entities via the body channel available in WBSN but not other wireless networks. The self-defined Intrinsic Shared Secret (ISS) is used to replace the pre-deployment of secrets among network entities,which thus eliminates centralized services or au-thorities essential in existing protocols,and resolves the key transport problem in the pure symmet-ric-key cryptosystem for WBSN as well. The security properties of the proposed scheme are demon-strated in terms of its attack complexity and the types of attacks it can resist. Besides,the scheme can be implemented under a light-weight way in WBSN systems. Due to the importance of the ISS concept,the analysis on using false acceptance/false rejection method to evaluate the performance of ISS for its usage in the scheme is also demonstrated.     

基于事件优先级的地理哈希表分发算法

无线传感器网络综合了传感器技术,分布式信息处理技术和无线通信技术,能够协作地实时监测、感知和采集各种信息并对其进行处理,进而传给目标用户,能量和效率是传感器网络最重要的考虑因素。GHT-DCS是一种能量高效且在查询时延和存取能效上取得较好平衡的一种数据分发方式。然而,该算法主要集中在降低能量消耗,很少综合考虑查询时延,网络负载均衡,能量消耗这三个方面。针对多优先级的事件查询,在改进GHT-DCS的条件下,该文提出了基于事件优先级的地理哈希表的分发算法GP-GHT,通过划分扇形区域存储,节省存储能耗;通过定义优先级,使得高优先级的事件离查询节点更近,能更快地被搜索到。该算法降低了能源消耗,缩短查询时延,可满足不同优先级事件时延的需求。     

Beamforming, diversity, and interference rejection for multiuser communication over fading channels with a receive antenna array

We consider M-ary communication with K users over a space diversity channel, consisting of a single transmit antenna for each user and multiple receive antennas. We examine two different flat fading models, namely, phase coherent wavefront fading and noncoherent element-to-element fading. In the case of wavefront fading, the fade is constant across the face of the receive antenna and we can associate an angle of arrival to the signal. We present a variation of the MUSIC algorithm for estimating this parameter and use it to form a spatial beam. In the case of noncoherent element-to-element fading, the fading path to each sensor is different (although possibly correlated) and no angle of arrival can be exploited for conventional beamforming. For each channel model, we develop several detection strategies which assume various amounts of prior information about the fading. We then consider blind extensions of these detectors based on subspace tracking, which do not require a prior model for the interfering users' signals.     

Minimum latency joint scheduling and routing in wireless sensor networks

Wireless sensor networks are expected to be used in a wide range of applications from environment monitoring to event detection. The key challenge is to provide energy efficient communication; however, latency remains an important concern for many applications that require fast response. In this paper, we address the important problem of minimizing average communication latency for the active flows while providing energy-efficiency in wireless sensor networks. As the flows in some wireless sensor network can be long-lived and predictable, it is possible to design schedules for sensor nodes so that nodes can wake up only when it is necessary and asleep during other times. Clearly, the routing layer decision is closely coupled to the wakeup/sleep schedule of the sensor nodes. We formulate a joint scheduling and routing problem with the objective of finding the schedules and routes for current active flows with minimum average latency. By constructing a novel delay graph, the problem can be solved optimally by employing the M node-disjoint paths algorithm under FDMA channel model. We further present extensions of the algorithm to handle dynamic traffic changes and topology changes in wireless sensor networks.     

PAC vs. MAC for Decentralized Detection Using Noncoherent Modulation

In decentralized detection, local sensor observations have to be communicated to a fusion center through the wireless medium, inherently a multiple-access channel (MAC). As communication is bandwidth- and energy-constrained, it has been suggested to use the properties of the MAC to combine the sensor observations directly on the channel. Although this leads to an array-processing gain if the sensors' transmissions combine coherently on the channel, it has been shown that this is not the case when they combine noncoherently. We review known results for the coherent case and then analyze the noncoherent case based on a simple on/off scheme combined with optimal sensor “censoring.” Since the optimal forwarding function is not available, we also bound the performance using an equivalent communication problem and a centralized estimator to verify trends. We find that for noncoherent modulation, there is no processing gain using the MAC for decentralized detection, but compared to parallel-access channels (PACs) the MAC avoids the noncoherent combining loss. Still the performance of the MAC approach is only of diversity one, as the output of the MAC is approximately a zero-mean complex Gaussian random variable for a large number of sensor. The MAC performance can be increased by using multiple independent channels, each used as a MAC by all sensors, which we term diversity-MAC. This approach always outperforms the PAC scheme on Rayleigh fading channels, where the output is exactly Gaussian, but has inferior performance across random phase channels when few sensors are used, as the PAC does not create “artificial” fading.      

Bandwidth management in distributed sequential detection

The problem of distributed sequential detection in the presence of communication constraints is considered. The observations available at each sensor are first compressed to multibit sensor decisions and sent to the fusion center. At the fusion center, a sequential data fusion scheme is implemented in order to reach a global decision. An algorithm is developed for optimal bandwidth distribution among sensors under a fixed bandwidth constraint. Under symmetry and conditional independence assumptions, the algorithm can be simplified substantially: the cooperative quantizer design algorithm reduces to independent quantizer design. The case when communication bandwidth is the only constraint is also considered.     

Energy Minimization for Real-Time Data Gathering in Wireless Sensor Networks

This paper studies the challenging problem of energy minimization for data gathering over a multiple-sources single-sink communication substrate in wireless sensor networks by exploring the energy-latency tradeoffs using rate adaptation techniques. We consider a real-time scenario for mission-critical applications, where the data gathering must be performed within a specified latency constraint. We first propose an offline numerical optimization algorithm with performance analysis for a special case with a complete binary data gathering tree. Then, by discretizing the transmission time, we present a simple, distributed on-line protocol that relies only on the local information available at each sensor node. Extensive simulations were conducted for both long and short-range communication scenarios using two different source placement models. We used the baseline of transmitting all packets at the highest speed and shutting down the radios afterwards. Our simulation results show that compared with this baseline, up to 90% energy savings can be achieved by our techniques (both off-line and on-line), under different settings of several key system parameters     

面向体域网智能客户端的数据传输方案实现

在构建小型无线体域网总体架构的基础上,通过分析两种体域网低功耗无线通信技术,设计了一种面向体域网智能客户端的数据传输方案,重点设计了几种数据传输方法,最终在一个智慧医疗系统中实现了该数据传输方案,在运行测试后符合设计要求,并具有一定的应用性。     

基于红外图像和逆向投影算法的室内人体跌倒检测方法

研究表明跌倒是我国老年人伤害的主要原因,缩短跌倒到救治的时间能降低跌倒造成的伤害。为此,室内老年人跌倒检测需求逐年增加。红外传感器具有受光照影响小,保护隐私等优点,越来越广泛地应用于室内人体跌倒检测中。然而,由于红外图像存在分辨率低、信噪比差等缺陷,导致传统方法的检测精度较低。针对这个问题,本文提出一种基于逆向投影算法的室内人体跌倒检测方法。首先,通过人体温度计算出人体与传感器之间的距离;其次,结合图像信息,逆推出人体在真实世界的高度;最后,对获取的人体真实高度数据进行平滑处理,并根据其变化情况进行跌倒检测。实验结果表明,本文所提方法的检测准确率达到98.57%,优于传统非逆向投影方法,其性能完全可以应用于实际检测中。     

高效的攻击检测与数据融合算法

给出了一种高效的无线多媒体传感器网络攻击检测和数据融合算法EIDSART。该算法从节点的多元属性方面对节点行为特征进行界定,通过选择合适的邻居节点集合,可以运用于任意规模的多媒体传感器网络;另外,在经过精确检测攻击行为的情况下,对传感数据进行了融合,降低了网络通信开销。仿真结果表明,EIDSART在攻击检测精度和误报率等方面具有优势,并能得到精确的数据融合结果。     

Design and Analysis of Circular Microstrip Patch Antenna for White Space TV Band Application

The wide range of wireless sensor network applications has made it an interesting subject for many studies. One area of research is the controlled node placement in which the location of nodes is not random but predetermined. Controlled node placement can be very effective when either the price of the sensor nodes is high or the sensor coverage is of a specific type and it is necessary to provide special characteristics such as coverage, lifetime, reliability, delay, efficiency or other performance aspects of a wireless sensor network by using the minimum number of nodes. Since node placement algorithms are NP-Hard problems, and characteristics of a network are often in conflict with each other, the use of multi-objective evolutionary optimization algorithms in controlled node placement can be helpful. Previous research on node placement has assumed a uniform pattern of events, but this study shows if the pattern of events in the environment under investigation is geographically dependent, the results may lose their effectiveness drastically. In this study, a controlled node placement algorithm is proposed that aims to increase network lifetime and improve sensor coverage and radio communication, assuming that the event pattern is not uniform and has a geographical dependency. The proposed placement algorithm can be used for the initial placement or, for repairing a segmented network over time. In this study, multi-objective evolutionary optimization algorithms based on decomposition (MOEA/D) have been used, and the performance results have been compared with other node placement methods through simulation under different conditions.