Body Sensor Networks

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A Study on Security in Wireless Body Sensor Networks

A Study on Security in Wireless Body Sensor Networks

ABSTRACT:Body sensor networks is a wireless network of wearable computing devices.BSN is used in various applications. In medical field, BSN plays a major role. Sensors are both wearable and implantable in our body. With the help of sensors, we can monitor the patient’s health. In this paper it comprises of applications of WBAN, requirements of security, security attacks and security mechanisms.

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Mid Range Wireless Power Transfer and Its Application to Body Sensor Networks

Mid Range Wireless Power Transfer and Its Application to Body Sensor Networks

Wireless Body Sensor Networks (wBSNs) have emerged in recent years as a key enabling technology to address a number of significant and persistent challenges in health- care and medical research, including continuous, non- invasive, and inexpensive monitoring of physiological variables. Typically, a wBSN is composed of a number of sensor nodes dedicated to different forms of measure- ments, such as the electrocardiogram (ECG), electro- myogram (EMG), body temperature, glucose, and blood pressure. Each sensor node, which can be either inside (as an implant) or outside (as a wearable device) the human body, is usually composed of an analog readout front-end, a microprocessor, a radio transmitter/receiver, and a power supply. These sensor nodes, wire-connected to a battery, transmit data continuously through a wireless connection to a central node, typically a PDA or a smart cellphone. The central node collects, visualizes and ana- lyzes data and/or wirelessly relays the data or partially processed results to a remote terminal for more advanced off-line processing or evaluation by healthcare profess- sionals. While longer battery capacity, lower power con- sumption, smaller size of the battery and other circuit components, and higher manufacturing volumes have made wBSN data collection more continuous, non-inva- sive, and inexpensive, the progress towards wireless
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A Systematic Key Management Mechanism for Practical Body Sensor Networks

A Systematic Key Management Mechanism for Practical Body Sensor Networks

In the last decade, Wireless Body Sensor Networks (BSNs) draw considerable attentions as a viable solution to human physiological status monitoring [1]. Compared with general Wireless Sensor Networks (WSNs), human physiological data generated by BSNs have more rigorous security and privacy preserving requirements [2]. For instance, the broadcasting nature of wireless communication leads to the vulnerability of BSNs: attackers can breach personal privacy of BSN users by eavesdropping the communication. In addition, false data may be injected to incur detrimental physiological status judgement and may lead to a fatal consequence. Therefore, a practical BSN system must be carefully secured.
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Distance Based Method for Outlier Detection of Body Sensor Networks

Distance Based Method for Outlier Detection of Body Sensor Networks

We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE) to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided as an outlier. Further, we formalize a sliding window based method to improve the outlier detection performance. Finally, to estimate the KDE by training sensor readings with errors, we introduce a Hidden Markov Model (HMM) based method to estimate the most probable ground truth values which have the maximum probability to produce the training data. Simulation results show that the proposed method possesses a good detection accuracy with a low false alarm rate.
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An Energy Efficient ASIC for Wireless Body Sensor Networks in Medical Applications

An Energy Efficient ASIC for Wireless Body Sensor Networks in Medical Applications

Diabetes is a metabolic disorder in which the person has high blood glucose levels..It is estimated that over 382 million peo ple throughout the world have diabetes. Therefore, it is very essential to determine the blood sugar level well before it causes adverse effects. The ASIC designed can be used to monitor blood glucose levels and provide necessary subsequent treatments. Recently, researchers are spending great efforts on the wireless body sensor networks (WBSNs) for med ical applications, such as vital sign monitoring, the diagnose assistant and the drug delivery. In these applications, the master-slave protocol is commonly adopted to lower the system comple xity and power consumption as well. A typical WBSN is usually composed of a portable device which serves as the master node for central control and a number of min iaturized sensor nodes placed around, on, or inside the huma n bodies that act as the slave nodes. Compared to the master node, the slave nodes have more stringent constrain ts in terms of power consumption and size limitation.
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Reference Range Correlation (Rrcr) Ranging and Performance Bounds for on-Body UWB-Based Body Sensor Networks

Reference Range Correlation (Rrcr) Ranging and Performance Bounds for on-Body UWB-Based Body Sensor Networks

Abstract—In this paper, we propose a reference range correlation- based (RRcR) ranging technique suitable for low-power on-body wireless body sensor networks (WBSNs) via ultra wideband (UWB) radios. The proposed technique is based on the presence of reference nodes, and is assumed to have line-of-sight (LOS) links. We show that the performance of the proposed technique outperforms matched- filtering-based time-of-arrival (MF-TOA) estimators with no a priori and with perfect channel knowledge. We further show that increasing the number of reference node up to twenty provides significant enhancement in the performance traded for higher overall power consumption. Then, we study the effect of timing-misalignment on the Ziv-Zakai lower bound (ZZLB), and provide numerical results. The presented results are based on simulations in the IEEE 802.15.6a on- body-to-on-body channel (CM3) in the UWB band as well as actual measurements.
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On the Efficiency of Cluster-based Approaches for Motion Detection using Body Sensor Networks

On the Efficiency of Cluster-based Approaches for Motion Detection using Body Sensor Networks

Numerous studies have proposed the use of body sensor networks (BSN) for healthcare applications [3, 4, 6]. Earlier works also pointed out that QoS [5] and energy conservation [2, 16] are key research issues for the BSN, since the former could affect life-or-death matters while the latter decides the lifetime of the network especially for those sensors embedded in a patient’s body. In addition, BSN can be also applied to sports applications using inertial sensors to monitor the trainee’s posture during actions such as walking/running [7], golf swings [8], and hand swings [9]. However, most of these prior studies either can only identify basic postures (sitting, standing, walking, and running) or can only detect single motions. While using some techniques such as machine learning [9] to detect more complex motion is possible, this might increase the latency or computation cost of the BSN.
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Enhanced DNA Cryptography for Wireless Body Sensor Networks

Enhanced DNA Cryptography for Wireless Body Sensor Networks

In the past years, the Wireless Sensor Networks (WSNs) have very great interest specially the Wireless Body Sensor Networks (WBSNs) that uses to allocate the vital information from the body. The main motivation is the enormous development in the field of tiny and intelligent electronics that known as micro-electronical systems "MEMS". Today, we can see medical body sensors which can worn on or implanted in the body. WBSNs today take an attractive attention of the researches working in the cryptography area [1][2].
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Energy-Saving MAC Scheme with Dynamic Transmission Thresholds for Body Sensor Networks

Energy-Saving MAC Scheme with Dynamic Transmission Thresholds for Body Sensor Networks

In this research, energy-saving MAC scheme with dynamic transmission thresholds for body sensor networks is proposed. Dynamic transmission thresholds can limit the transmitted data. The more valuable fraction of captured data is transmitted. Negligible data are subject to omit. In node data processing by dynamic transmission thresholds evaluates the data before the execution. Simulations results show that ours schemes is effective in all nodes. It reduces the data transmission as caregiver’s request. Our method outranks three other schemes in transmission reduction. Burst communication method is only effective for low traffic nodes. It saves up to 19% transmission energy in these nodes. This method loses the efficiency as traffic generation increases and header to data ratio reduces. Sampling rate reduction [9] and sampling resolution reduction [10] may cause a valuable data loss before they have an evaluation chance. Our novel protocol omits data only after analog to digital conversion and comparison to thresholds. It saves up to 30% energy on low traffic nodes and up to 90% energy on high traffic nodes.
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Design and Analysis of an Energy Saving Distributed MAC Mechanism for Wireless Body Sensor Networks

Design and Analysis of an Energy Saving Distributed MAC Mechanism for Wireless Body Sensor Networks

In this paper, we have been evaluating IEEE 802.15.4 MAC limitations under new challenging healthcare requirements for wireless body sensor networks (BSNs). Further, a new energy-efficiency theoretical analysis for an enhanced dis- tributed queuing medium access control (DQ-MAC) proto- col has been introduced, as a potential candidate for future BSNs. For that purpose, an energy-saving DQ-MAC super- frame optimization has been presented taking energy-aware radio activation policies into account. This allows body sen- sors a power management regulation to minimize the energy consumption per information bit. The analytical study has been compared with a BSN state-of-the-art MAC protocol (BSN-MAC) and validated by simulation results, which have shown that the proposed mechanism outperforms IEEE 802.15.4 MAC and BSN-MAC energy-efficiency for all traffic loads in a generalized BSN scenario. This favorable energy- efficient behavior is especially achieved thanks to the inher- ent protocol performance at eliminating collisions in data transmissions, while minimizing the control overhead and hence the overall energy consumption per information bit.
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Autonomous Allocation of Wireless Body Sensor Networks to FrequencyChannels: Modeling with Repeated "Balls In Bins" Experiments

Autonomous Allocation of Wireless Body Sensor Networks to FrequencyChannels: Modeling with Repeated "Balls In Bins" Experiments

Wireless body sensor networks (WBSNs) have received lots of interest recently, be- cause they enable a range of applications in health and well-being [21], [6], [2], [34]. A number of technologies are considered for WBSNs, including the IEEE 802.15.4 [20] and IEEE 802.15.6 [19] standards. On the physical layer the IEEE 802.15.4 standard specifies three different frequency bands that can be used. Two of these are sub-divided into a number of frequency channels, and a WBSN at any time operates on only one of these channels. For example, the popular 2.4 GHz ISM range is sub- divided into 16 channels of 5 MHz width each. Similarly, the IEEE 802.15.6 standard supports a number of different frequency ranges in its narrowband physical layer, and all these frequency ranges are sub-divided into several channels (79 in the 2.4 GHz range). In both standards it is foreseen that a WBSN does not routinely hop over the channels but rather picks a channel and stays there.
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Qos aware error recovery in wireless body sensor networks using adaptive network coding

Qos aware error recovery in wireless body sensor networks using adaptive network coding

Wireless body sensor networks (WBSNs) or body area networks (BANs) constitute an emerging and promising technology that will change people’s healthcare experiences radically [1,2]. Unlike traditional healthcare systems, WBSNs will release patients from long hospital stays, thus reducing medical labor and infrastructural costs. In general, WBSNs will be cost effective, and they can continuously monitor physiological signals of patients, which will be very helpful, especially for the aging population [3]. Moreover, the use of WBSNs may enable ubiquitous healthcare and could lead to proactive and even remote diagnosis of diseases in an early stage. These systems provide uninterrupted health monitoring services, allowing patients to perform everyday activities, which lead to the enhancement of the quality of life [4]. However, wearable health monitoring technology is still young, and some challenging issues, such as quality of service (QoS), security and privacy, as well as social issues need to be resolved before this technology can be used widely. QoS is one of the main concerns for this technology. Generally, in network systems, such as WBSNs, QoS is viewed from two perspectives: the network and user/applications [5]. In the user/application perspective, QoS refers to an assurance of a set of requirements/services that are expected of the system by the users or applications. From the network perspective, QoS refers to a set of service qualities that the network offers to a user or application in terms of network QoS parameters, such as delay, reliability, energy efficiency, etc., during data delivery. As medical wearable systems deal with real-time and life-critical applications, they require a strict guarantee of QoS in both perspectives. To support QoS in WBSNs, the QoS requirements of these systems in user and network perspectives should be identified and addressed accordingly [6–10].
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Towards Implantable Body Sensor Networks - Performance of MICS Band Radio Communication in Animal Tissue

Towards Implantable Body Sensor Networks - Performance of MICS Band Radio Communication in Animal Tissue

Reliable wireless communication inside the human body is crucial for the design of implantable body sensor networks (IBSN). The tissues in human body are heterogeneous and have different conductivity and permittivity, which make the modeling of the wireless channel challenging. The design of upper layers of the network stack requires the physical layer characteristics including the channel model. Currently, there is no unique channel model available for implant com- munication inside body. Various measurement campaigns of channel characteristics are underway. The channel model characteristics depends on the hardware components used such as antenna and matching circuit as well as the oper- ating frequency, which are not taken into account by the existing channel models for implant communication. More- over, hardware losses and different tissue characteristics have not been taken into account in the link budget of the exist- ing channel models. The approach used in this paper pays special attention to the losses introduced by hardware com- ponents of the implant itself and the physical medium. This paper presents characteristics of radio channel using ani- mal tissue. A comparison is made between these measured characteristics and the existing channel characteristics pro- vided by the IEEE 802.15.6 standard. The empirical mea- surements are used to validate the simulations of the IEEE 802.15.6 model.
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An Adaptive Congestion Alleviating Protocol for Healthcare Applications in Wireless Body Sensor Networks: Learning Automata Approach

An Adaptive Congestion Alleviating Protocol for Healthcare Applications in Wireless Body Sensor Networks: Learning Automata Approach

Wireless Body Sensor Networks (WBSNs) involve a convergence of biosensors, wireless communication and networks technologies. WBSN enables real-time healthcare services to users. Wireless sensors can be used to monitor patients’ physical conditions and transfer real time vital signs to the emergency center or individual doctors. Wireless networks are subject to more packet loss and congestion. To alleviate congestion, the source transmission rate and node arrival rate should be controlled. In this paper, we propose Learning based Congestion Control Protocol (LCCP) for wireless body sensor networks. LCCP joins active queue management and rate adjustment mechanism to alleviate congestion. The proposed system is able to discriminate different physiological signals and assign them different priorities. Thus, it would be possible to provide better quality of service for transmitting highly important vital signs. The simulation results confirm that the proposed protocol improves system throughput and reduces delay and packet dropping. We also evaluate the performance of the AQM mechanism with no rate adjustment mechanism to show the advantage of using both AQM and rate adjustment mechanism together.
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Characterization of Communication Mechanisms for Implantable Body Sensor Networks

Characterization of Communication Mechanisms for Implantable Body Sensor Networks

It was around August 2012 when I started the Master’s education in Embedded systems at the University of Twente. I am always fascinated about wireless sensor networks, which is a part of embedded systems and is also widely applied in different industries for different scenarios. In this regard, I was fortunate to get introduced to Prof. Paul Havinga, by Ir. Andrea Sanchez Ramirez, during one of my courses called ”Energy efficient embedded systems”. Based on my work in EEES course, I was given an opportunity to work as a student assistant in an EU-FP7 project ”WiBRATE”, which involves application of WSN in industrial vibration monitoring. I continued working in the project for my intern-ship and also was given an opportunity to extend the work as a part-time job during my thesis. It was during this tenure as a student assistant I was supervised and guided by Dr. Niravana Meratnia. With highly constructive comments and progressive meetings together with Dr. Paul and Dr. Nirvana, I successfully proposed my idea of research about in-body sensor networks and continued with this thesis work. Although the assistantship work was not related to my thesis, I was given an opportunity to explore my own research interests. With their support, I was able to publish two international articles during my master study and of course a trip to Singapore for presentation. I was able to successfully complete this thesis overcoming all the difficulties. Even though this master thesis is just an exploration and characterization of existing wireless communication mechanisms, I hope with further research I can materialize the closed loop architecture for medical devices. I thank my professors for giving me another opportunity to continue with my research towards a PhD degree. I must thank Dr. Niels Moseley for his continuous and valuable technical support and Dr. Berend Jan van der Zwaag, for his constructive feedback at my writing skills. I thank Ir. Kyle zhang who developed the medical implants which were also used for the final hardware char- acterization in this thesis. I thank Ir. Saeid Yazdani for all his support in debugging codes at times. Apart from being thanked, I must acknowledge them for being excellent colleagues for the last one year. I also thank all the people of PS for making me feel comfortable at the office. I thank my friends, Alex, Frank, Yoppy, Gebremedhin, Anantha, Hasib, Anand, Nolie, Ramesh, Morshed, for being with me at difficult times. Apologies, for not having a whole list of names. I thank all of my friends who supported me either directly or indirectly during my master studies. Without all their support, staying far away from home and focusing on studies would not have been possible
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A Review : Interference Mitigation In  Mobile Body Sensor Networks

A Review : Interference Mitigation In Mobile Body Sensor Networks

Ehsan Tabatabaei Yazdi, Andreas Willig and Krzyszt of Pawlikowski [2] This paper is related to orphan time in IEEE 802.15.4 Wireless sensor networks. The energy consumption is related to the duration time spent by end devices in orphan state in beacon- enabled IEEE 802.15.4 network. The latency skillful for performing a coordinator discovery process and a successful association is linked to such elements as beacon channel interference, massage signaling interval length, etc. for mitigating the total energy consumption of the end devices in Wireless Body Sensor Networks (WBSNs), different coordinator discovery schemes are introduce in this study. The main attention of this paper is to progress the overall success rate and energy consumption of end devices. However, the performance evaluation results reveal that the proposed passive coordinator discovery schemes have insignificant statistical difference in the overall success rate and energy consumption of the WBSN.
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On PHY and MAC Performance in Body Sensor Networks

On PHY and MAC Performance in Body Sensor Networks

The requirements of RF communication for on-body and in-body sensor networks are di ff erent due to their cor- responding channel characteristics. In an on-body sensor network, signals often propagate across the body surface. This propagation may be a combination of surface waves, creeping waves, di ff racted waves, scattered waves, and free space propagation depending on the antenna position [15]. In an in-body sensor network, the signals propagate inside the human body where the electrical properties of a body affect the signal propagation. All existing formulas to design free-air communication are used for on-body communica- tion systems. However, it is very difficult to calculate the performance of in-body communication systems [16]. To compound the design challenges, the location of the implant is also variable. During surgery the implant is placed in the best position to perform its primary function, with a little consideration for the wireless performance.
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An Efficient Drowsiness Detection System For Pilot Using Wearable  Body Sensor Networks

An Efficient Drowsiness Detection System For Pilot Using Wearable Body Sensor Networks

Driver drowsiness detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads. Negative emotional responses are a growing problem among drivers, particularly in countries with heavy traffic, and may lead to serious accidents on the road. The focus of this study was to develop and verify an emotional response-monitoring paradigm for drivers, derived from Respiration signals, photoplethysmography signals, and eye blink signal. The relevant sensors were connected to a microcontroller unit equipped with a ZIGBEE-enabled low energy module, which allows the transmission of those sensor readings to a vehicle. When drowsiness is detected in driver then the driving mode is automatically going to automatic mode for driving using image processing and sent information to the transport department officer using GSM.
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An Enhanced Protocol H-Tooth for Opportunistic Computing Followed in Body Sensor Networks

An Enhanced Protocol H-Tooth for Opportunistic Computing Followed in Body Sensor Networks

different ranges, in order to save power – supported by detailed quantitative measurements. The system and policies do not require any changes to the mobile applications themselves, and changes required to existing infrastructure are minimal. [10] Devices in disruption tolerant networks (DTNs) must be able to communicate robustly in the face of short and infrequent connection opportunities. Unfortunately, one of the most inexpensive, energy-efficient and widely deployed peer-to-peer capable radios, Bluetooth, is not well-suited for use in a DTN. Bluetooth’s half-duplex process of neighbor discovery can take tens of seconds to complete between two mutually undiscovered radios. This delay can be larger than the time that mobile nodes can be expected to remain in range, resulting in a missed opportunity and lower overall performance in a DTN.
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Implementation of Continues Body Monitoring System with Wireless Body Sensor Networks(WBSN) using IoT Application

Implementation of Continues Body Monitoring System with Wireless Body Sensor Networks(WBSN) using IoT Application

Pulse Sensor is a well-designed plug-and-play heart-rate sensor for Arduino. It can be used by students, artists, athletes, makers, and game & mobile developers who want to easily incorporate live heart rate data into their projects. The sensor clips onto a fingertip or earlobe and plugs right into Arduino with some jumper cables. It also includes an open-source monitoring app that graphs your pulse in real time.

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