Body Sensor Networks

Divergent modulation schemes have been proposed for the Internet of Things (IoT). Low Power Wide Area Networks (LPWAN) technologies are gaining unprecedented acceptance in IoT application of sensor networks. Chirp Spread Spectrum (CSS) is a prominent modulation technique proposed for LPWAN. Chirps can traverse long distance and are resilient to noise and Doppler effects. Noise resilience along with transmission range and low power requirement makes CSS a preferred modulation scheme for sensor networks. LoRaWAN TM , with its physical (PHY) layer using CSS, has emerged as the widely accepted LPWAN solution. By using CSS modulation with orthogonal spreading factors (SF), LoRa offers wide coverage to LPWAN applications while supporting a high volume of devices. However, scalability performance of CSS has not been inadequately modeled. As with the suitability of the modulation scheme, there are concerns on how chirps interact with the surrounding as the number of deployments bursts out into higher volumes. We evaluate CSS at ISM band 868 MHz for spreading factor 7 to 12 at bandwidth 125 kHz for performance and scalability. Simultaneous transmissions were simulated with repeated iterations and conclusions are arrived on collisions rate, packet error rate, and bit error. Suitability of using CSS for sensor networks for future deployments is commended.

Handshaking is an important component of social interaction between people in many cultures. Thus, for further applications in human/humanoid-robot interaction it is important to understand and measure the characteristics of a handshake during interaction between humans. In this paper, a new wearable sensor network to measure a handshake is described. It consists of a set of several sensors (accelerometers, gyroscopes and force sensors) attached to the glove, and of a microcontroller for signal acquisition and conditioning. The paper focuses on the applicability and qualitative analysis of the proposed architecture of sensors through several experiments of handshaking between two human subjects. The results show that the proposed system allows reproducible experiments to quantify handshake characteristics such as duration and strength of the grip, vigor and rhythmicity of a handshake.

The development of systems capable of recognizing and categorising emotions is of interest to researchers in various scientific areas including artificial intelligence. The traditional notion that emotions and rationality are two separate realms has gradually been challenged. The work of neurologists has shown the strong relationship between emotional episodes and the way humans think and act. Furthermore, emotions not only regulate human decisions but could also contribute to a more satisfactory response to the environment, i.e., faster and more precise actions. In this paper an analysis of physiological signals employed in real-time emotion detection is presented in the context of Intelligent Inhabited Environments (IIE). Two studies were performed to investigate whether physical exertion has a significant effect on bodily signals stemming from emotional episodes with subjects having various degrees of affect intensity: 1) a statistical analysis using the Wilcoxon Test, and 2) a cluster analysis using the Davies- Bouldin Index. Preliminary results demonstrated that the heart rate and skin resistance consistently showed similar changes regardless of the physical stimuli while blood volume pressure did not show a significant change. It was also found that neither physical stress nor affect intensity played a role in the separation of neutral and non-neutral emotional states.

A progressive improvement in gait following knee arthroplasty surgery can be observed during walking and transitional activities such as sitting/standing. Accurate assessment of such changes traditionally requires the use of a gait lab, which is often impractical, expensive, and labour intensive. Quantifying gait impairment following knee arthroplasty by employing wearable sensors allows for continuous monitoring of recovery. This study employed a recognised protocol of activities both pre-operatively, and at regular intervals up to twenty-four weeks post-total knee arthroplasty. The results suggest that a wearable miniaturised ear-worn sensor is potentially useful in monitoring post-operative recovery, and in identifying patients who fail to improve as expected, thus facilitating early clinical review and intervention.

Advances in ultimate technologies should be begun to the growth of the Internet of Things (IOT). During the new health care situation, the usage of IOT technologies makes the assistance of doctors and patients since they are utilized in various medical sections (such as real-time monitoring, patient data administration, and healthcare supervision). The body sensor network (BSN) technology is one of the central technologies of IOT developments in the healthcare system, wherever a patient can be observed using a group of small powered and lightweight wireless sensor nodes. Yet, the evolution of this new technology in healthcare applications externally thinking security makes patient privacy unprotected. highlight some essential protection provisions in BSN based modern healthcare system. Consequently, begin a secure IOT based healthcare system using BSN, which can efficiently fulfill those conditions.

Non invasive and continuous measurement of blood pressure can enable an effective 24/7 monitoring of hypertensive patients to timely prevent cardiovascular problems and precisely regulate anti-hypertension cures. Unfortunately, to date, blood pressure can be only measured through either cuff-based or invasive instruments that cannot be continuously used due to their characteristics. This paper proposes an overview of techniques and approaches based on the photoplethysmographic (PPG) signal for non invasive and continuous measurement of blood pressure. In particular, the PPG signal can be easily acquired from an optical sensor applied on the epidermis and used, alone or integrated with the ECG signal, to estimate the blood pressure. On the basis of such methods new instruments and sensor-based systems can be developed and integrated with computer-based health care systems that aim at supporting continuous and remote monitoring of assisted livings.

Modbus is the de facto standard communication protocol for the industrial world. It was initially designed to be used in serial communications (Modbus RTU/ASCII). However, not long ago, it was adapted to TCP due to the increasing popularity of the TCP/IP stack. Since it was originally designed for controlled serial lines, Modbus does not have any security features. In this paper, we wrote several benchmarks to evaluate the performance of networking devices that run Modbus TCP. Parameters reported by our benchmarks include: (1) response time for Modbus requests, (2) maximum number of requests successfully handled by Modbus devices in a specific amount of time, and (3) monitoring of Modbus devices when suffering a Distributed Denial of Service attack. Due to the growing adoption of IoT technologies, we also selected two widely known and inexpensive development boards (ESP8266 and Raspberry Pi 3 B+/OpenPLC) to realize a performance evaluation of Modbus TCP.

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.

The avalablllty and wide range of apphcatlon of low cost sensors have encouraged a demand for improved sensor performance Integrated sensors are being developed to meet the designer's need for simpler systems Smart sensors are becoming mtegral parts of systems performmg functions that previously could not be performed or were not economically viable Capacitive pressure transducers with Integrated clrcults, Sensors and Actuators, 4 (1983) 403 -411 A Hanneborg, T E Hansen, P A Ohlckers, E Carlson, B Dahl and 0 Howlech, A new integrated capacitive pressure sensor with frequency modulated output, 1985 Int Conf on Solid State Sensors and Actuators Drgest, p 186 H L Chau and K D Wise, Scaling hmrts m batch fabricated &con pressure sensors, 1985 Int Conf on Solld State Sensors and Actuators Digest, p 174 I H Chow and K D Wise, A linear thermoplle infrared detector array with on-chip multlplexmg, 1985 Int Conf on Soled State Sensors and Actuators Dtgest, p 135 K E Petersen, A Shartel and N F Raley, Micromechanical accelerometer Integrated with MVS detection circuitry, IEEE Truns Electron Devrces, ED-29 (1982) 23 -27 R T Howe and R S Muller, Resonant mlcrobrldge vapor sensor, IEEE Electron Devtce Lett , (April) (1986) 499 -506 P W Cheung, Recent Developments m Integrated chemical sensors, Proc 1981 Int Electron Devwes Meet, Washmgton, DC, 1982, pp 110 -113 A Ikegaml, and M Kaneyasu, Olfactory detection usmg mtegrated sensor, Proc 3rd Int Conf SolldState Sensors and Actuators (Transducers 85), Phdadelphq PA, June 11 -14, 1985, pp 136 -139 J W Knuttl, H V Allen and J D Memdl, Integrated circuit implantable telemetry systems,

Many theorists would agree that, had it not been for reinforcement learning, the development of Boolean logic might never have occurred. It might seem unexpected but entirely conflicts with the need to provide the lookaside buffer to leading analysts. After years of compelling research into multi-processors, we confirm the exploration of IPv7. In order to overcome this obstacle, we use ubiquitous epistemologies to verify that the seminal distributed algorithm for the visualization of SCSI disks by Maruyama [1] runs in Θ(n) time.

The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend
for the development of daily routine applications. However, in the case of heterogeneous BSNs
integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must
be solved, especially when talking about the detection of human state factors that impair the driving
of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive
review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk,
driving under emotional state disorders and distracted driving) that may cause traffic accidents is
presented; (2) A middleware architecture is proposed. This architecture can communicate with the
car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities.
This architecture seeks on the one hand to improve the car driving experience of the driver and on
the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of
concept, an Android real-time attention low level detection application that runs in a next-generation
smartphone is developed. The application features mechanisms that allow one to measure the degree
of attention of a driver on the base of her/his EEG signals, establish wireless communication links via
various standard wireless means, GPRS, Bluetooth andWiFi and issue alarms of critical low driver
attention levels.

Building management systems (BMS) in smart buildings are supposed to support the optimization of energy and resources consumption, while ensuring basic users' comfort. A common and effective optimizing strategy is to detect, with high accuracy, room occupancies, events, and activities that occur within a building, to accordingly control the energy usage. Several approaches have been implemented to achieve this goal, combining many technologies (e.g., sensor networks, machine learning techniques) as well as new data sources (e.g., sensed data, social networks) allowing to better detect occupant activities. In this context, the purpose of this study is twofold: (i) identify existing solutions related to capturing occupant activities and events to better manage energy usage and provide occupants' comfort, and (ii) pin down the lessons to learn from existing approaches and technologies in order to design better solutions in this regard. We do not pretend to give an exhaustive revision, but throughout this review, we aim at showing that several data can significantly enrich the typology and content of information managed to detect occupant activities and highlight new possibilities in terms of activities diagnosis and analysis to generate more opportunities in optimizing the energy consumption and providing comfort in smart building. K EYWORDS Nowadays, advancements in low-cost sensor technology, wireless networks, electronic devices , as well as new powerful data processing methods have fostered the emergence of intelligent Building Management Systems (BMS) [1]. These latter are describing to-day's buildings as complex systems, embedding several subsystems (heating, ventilation, airconditioning systems, lighting systems, etc.) and actors/occupants with different behaviours and needs, aiming to optimize energy and resources usage, while ensuring basic users comfort [2]. To do so, several data sources need to be analyzed, such as: (i) Data related to buildings (physical features, purpose, etc.); (ii) Data related to building equipment (lighting, temperature, heating, etc.); (iii) Data concerned to activities and occupancy (events, number of people, etc.); and (iv) Data from occupants (interests, preferences, etc.). Data related to buildings and equipment have been exploited in most of the existing BMS solutions. However, they have some common limitations related to do not consider individual needs of occupants (which are heterogeneous) and their various activities which cannot be treated as a whole. A better strategy is to detect the activities occurring within a building in order to fine-tune energy and resources usage. This strategy is proven to be very effective if events, activities, and building occupancies can be detected accurately [3]. In order to achieve this goal, several approaches have been implemented by combining various technologies (e.g., sensor networks, machine learning techniques) as well as by enriching the BMS data with new data sources (e.g., Internet of Things, social networks).

Smartphone technology has become more popular and innovative over the last few years, and technology companies are now introducing wearable devices into the market. By emerging and converging with technologies such as Cloud, Internet of Things (IoT) and Virtualization, requirements to personal sensor devices are immense and essential to support existing networks, e.g. mobile health (mHealth) as well as IoT users. Traditional physiological and biological medical sensors in mHealth provide health data either periodically or on-demand. Both of these situations can cause rapid battery consumption, consume significant bandwidth, and raise privacy issues, because these sensors do not consider or understand sensor status when converged together. The aim of this research is to provide a novel approach and solution to managing and controlling personal sensors that can be used in various areas such as the health, military, aged care, IoT and sport. This paper presents an inference system to transfer health data collected by personal sensors efficiently and effectively to other networks in a secure and effective manner without burdening workload on sensor devices.

—In this paper, we present the design of a wearable photoplethysmography (PPG) system, R-band for acquiring the PPG signals. PPG signals are influenced by the respiration or breathing process and hence can be used for estimation of respiration rate. R-Band detects the PPG signal that is routed to a Bluetooth low energy device such as a nearby-placed smartphone via microprocessor. Further, we developed an algorithm based on Extreme Learning Machine (ELM) regression for the estimation of respiration rate. We proposed spectral kurtosis features that are fused with the state-of-the-art respiratory-induced amplitude, intensity and frequency variations-based features for the estimation of respiration rate (in units of breaths per minute). In contrast to the neural network (NN), ELM does not require tuning of hidden layer parameter and thus drastically reduces the computational cost as compared to NN trained by the standard backpropagation algorithm. We evaluated the proposed algorithm on Capnobase data available in the public domain.

The lifestyle management area has become increasingly important during the past years due to an individual and societal need for increasing self-awareness. Technologies are the core of this area as they can support humans in reflecting over what happened through capturing, interpreting, correlating and visualizing various aspects of their lives. For this, we created an interactive contextaware experience platform that can bring together lifestyle-related information. Given that close involvement of end users in such systems, information presentation plays a central role. We present here our story-inspired framework that we used to transform sensed information into abstract multimedia objects. This framework allows for dynamic and adaptive creation of concise information correlations, for personalisation and sharing. This paper presents the design framework and realization of the storyinspired paradigm and the user experiments performed in order to explore and advance various aspects of the framework.

International Journal of Mobile ad hoc and sensor networks(IJMSN) is a open access journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the Computer Networks & Communications.

We have developed a wearable, battery-free tag that monitors heart sounds. The tag powers up by harvesting ambient RF energy, and contains a low-power integrated circuit, an antenna and up to four microphones. The chip, which consumes only 1.0µW of power, generates digital events when the outputs of any of the microphones exceeds a programmable threshold voltage, combines such events together by using a programmable logic array, and transmits them to a base station by using backscatter modulation. The chip can also be programmed to trade-off microphone sensitivity for power consumption. In this paper, we demonstrate that the tag, when attached to the chest, can reliably measure heart rate at distances up to 7m from an FCC-compliant RF power source. We also suggest how delays between signals measured by microphones at the wrist and neck can be used to provide information about relative blood-pressure variations.

This text investigates unique challenges and opportunities that arise when one applies the general principles and practices of information security to the particular domain of advanced neuroprosthetics. Issues discussed include the distinction between a neuroprosthetic device and its host-device system; the need for a device to provide free access to outside parties during medical emergencies while rigorously restricting access to outside parties at other times; challenges relating to implanted devices' limited power supply and processing and storage capacities, physical inaccessibility, and reliance on wireless communication; new kinds of biometrics that can be utilized by neuroprostheses; complications and opportunities arising from the use of nontraditional computing structures and platforms such as biomolecular computing and nanorobotic swarms; and psychological, social, and ethical concerns relating to the agency, autonomy, and personal identity of human beings possessing advanced neuroprostheses. Having considered such issues, we discuss why traditional InfoSec concepts that are often applied to general-purpose computing and information systems are inadequate to address the realities of advanced neuroprosthetic host-device systems and why the creation of new specialized conceptual models of information security for advanced neuroprosthetics is urgently required.

Cloud computing provides great benefits for applications hosted on the Web that also have special
computational and storage requirements. This paper proposes an extensible and flexible architecture for
integrating Wireless Sensor Networks with the Cloud. We have used REST based Web services as an
interoperable application layer that can be directly integrated into other application domains for remote
monitoring such as e-health care services, smart homes, or even vehicular area networks (VAN). For proof
of concept, we have implemented a REST based Web services on an IP based low power WSN test bed,
which enables data access from anywhere. The alert feature has also been implemented to notify users via
email or tweets for monitoring data when they exceed values and events of interest.

— Continuous monitoring of internal bleeding in tissue is essential for assessment and treatment of medical conditions. In this study we present a new body sensor for diagnosis of internal tissue bleeding based on contact multi-frequency electromagnetic inductive measurements of the tissue. This contributes to the existing family of body network sensors. This sensor can be used in emergency medicine in the field, in a hospital, or during daily life for patients at risk of bleeding. Unlike other sensors that detect bleeding, this sensor is portable, low cost, non-invasive and does not require galvanic coupling between the electrode and the tissue under measurement. Our study includes description of the new body sensor, criteria for data processing, basic diagnostic algorithms and calibration techniques. We show feasibility of this new sensor with a sensor prototype and diagnosis of an experimental simulation of internal bleeding and hypoperfusion conditions in the brain.

In this work we present A-Wristocracy, a novel framework for recognizing very fine-grained and complex inhome activities of human users (particularly elderly people) with wrist-worn device sensing. Our designed A-Wristocracy system improves upon the state-of-the-art works on in-home activity recognition using wearables. These works are mostly able to detect coarse-grained ADLs (Activities of Daily Living) but not large number of fine-grained and complex IADLs (Instrumental Activities of Daily Living). These are also not able to distinguish similar activities but with different context (such as sit on floor vs. sit on bed vs. sit on sofa). Our solution helps accurate detection of in-home ADLs/ IADLs and contextual activities, which are all critically important for remote elderly care in tracking their physical and cognitive capabilities. A-Wristocracy makes it feasible to classify large number of fine-grained and complex activities, through Deep Learning based data analytics and exploiting multi-modal sensing on wrist-worn device. It exploits minimal functionality from very light additional infrastructure (through only few Bluetooth beacons), for coarse level location context. A-Wristocracy preserves direct user privacy by excluding camera/ video imaging on wearable or infrastructure. The classification procedure consists of practical feature set extraction from multi-modal wearable sensor suites, followed by Deep Learning based supervised fine-level classification algorithm. We have collected exhaustive home-based ADLs and IADLs data from multiple users. Our designed classifier is validated to be able to recognize very fine-grained complex 22 daily activities (much larger number than 6-12 activities detected by state-ofthe-art works using wearable and no camera/ video) with high average test accuracies of 90% or more for two users in two different home environments.

Wearable computing devices can greatly enhance the quality of life, helping interaction with smart environment, activity recognition and healthcare applications. Smart garments offer the opportunity to integrate sensors and electronics in unobtrusive wearable systems. The paper presents a case study of an embedded hand gesture recognition system, which uses EMG electrodes embeddable in smart clothes. We analyze the main challenges of a real-time system for pattern recognition and the results of the proposed experiment demonstrate the feasibility of a real-time system for pattern recognition, which can be integrated in smart clothes.

The efficiency of sensor networks strongly depends on the routing protocol used. In this paper, we analyze three different types of routing protocols: LEACH, PEGASIS, and VGA. Sensor networks are simulated using Sensoria simulator. Several simulations are conducted to analyze the performance of these protocols including the power consumption and overall network performance. The simulation results, using same limited sensing range value, show that PEGASIS outperforms all other protocols while LEACH has better performance than VGA. Furthermore, the paper investigates the power consumption for all protocols. On the average, VGA has the worst power consumption when the sensing range is limited, while VGA is the best when the sensing range is increased.

Problems on healthcare delivery have become the current concern in most studies in the academe and industries. These studies have been conducted by using evolving technical solutions. These solutions are driven by technological advancements both in medical sensors and low-power networked systems. Wireless Sensor Networks (WSNs) prompted the emergence of most healthcare systems in recent years. This work proposes a system architecture for a remote healthcare monitoring system using pulse sensor and temperature sensor to measure the physiological parameters including Heart Pulse (HP) and temperature of patients. The presented system is evaluated using several persons with different ages, gender, and situations. The conducted technique is achieved respecting to the cost of service, signal quality, and quality of service. Finally, the current system is effective given that all reported results are standard values.

Body Sensor Networks (BSNs) form a promising technology to supply healthcare to an ageing population. A large number of sensor devices, radios and MAC protocols are being developed. However, because of the small scale of a BSN, node mobility will arise frequently. This work presents the first algorithm to support such Mobile BSNs, while remaining energy efficient.

Divergent modulation schemes have been proposed for the Internet of Things (IoT). Low Power Wide Area Networks (LPWAN) technologies are gaining unprecedented acceptance in IoT application of sensor networks. Chirp Spread Spectrum (CSS) is a prominent modulation technique proposed for LPWAN. Chirps can traverse long distance and are resilient to noise and Doppler effects. Noise resilience along with transmission range and low power requirement makes CSS a preferred modulation scheme for sensor networks. LoRaWAN TM , with its physical (PHY) layer using CSS, has emerged as the widely accepted LPWAN solution. By using CSS modulation with orthogonal spreading factors (SF), LoRa offers wide coverage to LPWAN applications while supporting a high volume of devices. However, scalability performance of CSS has not been inadequately modeled. As with the suitability of the modulation scheme, there are concerns on how chirps interact with the surrounding as the number of deployments bursts out into higher volumes. We evaluate CSS at ISM band 868 MHz for spreading factor 7 to 12 at bandwidth 125 kHz for performance and scalability. Simultaneous transmissions were simulated with repeated iterations and conclusions are arrived on collisions rate, packet error rate, and bit error. Suitability of using CSS for sensor networks for future deployments is commended.

El incremento en el uso de dispositivos móviles ha generado un
crecimiento de puntos de acceso, públicos y privados, esto es aprovechado por
los sistemas mHealth, los cuales utilizan la infraestructura de los servicios
móviles para brindar servicios de salud, realizando aplicaciones como el
monitoreo de señales biológicas. Una de estas aplicaciones puede ser la
medición de la señal de electroencefalografía (EEG), la cual consiste en
adquirir la señal eléctrica de la corteza cerebral de forma no invasiva.
Este artículo propone un sistema capaz de obtener múltiples registros por medio
de módulos con 19 sensores de señal EEG y un envío de datos usando el
protocolo Wi-Fi hacia un servidor por medio de socket TCP.
El sistema desarrollado permite la realización de hasta 12 estudios en forma
paralela y de manera remota, como puede ser el hogar del paciente, una clínica
rural, etc., con una eficiencia del 99.21% de datos trasmitidos correctamente.

• Distributed algorithms of mobile computing • OS and middleware support for mobile computing and networking • Routing, and communication primitives in ad-hoc and sensor networks • Synchronization and scheduling issues in mobile and ad hoc networks • Resource management in mobile, wireless and ad-hoc networks • Data management on mobile and wireless computing • Integration of wired and wireless networks • Broadband access networks • Energy saving protocols for ad hoc and sensor networks • Complexity analysis of algorithms for mobile environments • Information access in wireless networks • Algorithms and modeling for tracking and locating mobile users • Satellite communications • Cryptography, security and privacy of mobile & wireless networks

The efficiency of sensor networks strongly depends on the routing protocol used. In this paper, we analyze three different types of routing protocols: LEACH, PEGASIS, and VGA. Sensor networks are simulated using Sensoria simulator. Several simulations are conducted to analyze the performance of these protocols including the power consumption and overall network performance. The simulation results, using same limited sensing range value, show that PEGASIS outperforms all other protocols while LEACH has better performance than VGA. Furthermore, the paper investigates the power consumption for all protocols. On the average, VGA has the worst power consumption when the sensing range is limited, while VGA is the best when the sensing range is increased.

Critically ill new born babies admitted at the Neonatal Intensive Care Unit (NICU) are extremely tiny and vulnerable to external disturbance. Smart Jacket proposed in this paper is the vision of a wearable unobtrusive continuous monitoring system realized by body sensor networks (BSN) and wireless communication. The smart jacket aims for providing reliable health monitoring as well as a comfortable

In this paper, we present an open-source platform for wireless body sensor networks called DexterNet. The system is motivated by shifting research paradigms to support real-time, persistent human monitoring in both indoor and outdoor environments. The platform utilizes a three-layer architecture to control heterogeneous body sensors. The first layer, called the body sensor layer (BSL), deals with design of different wireless body sensors and their instrumentation on the body. We detail two custom-built body sensors: one measuring body motions and the other measuring the ECG and respiratory patterns. At the second layer, called the personal network layer (PNL), the wireless body sensors on a single subject communicate with a mobile base station, which supports Linux OS and the IEEE 802.15.4 protocol. The BSL and PNL functions are abstracted and implemented as an opensource software library, called Signal Processing In Node Environment (SPINE). A DexterNet network is scalable, and can be reconfigured on-the-fly via SPINE. At the third layer, called the global network layer (GNL), multiple PNLs communicate with a remote Internet server to permanently log the sensor data and support higher-level applications. We demonstrate the versatility of the DexterNet platform via three applications: avatar visualization, human activity recognition, and integration of DexterNet with global positioning sensors and air pollution sensors for asthma studies.

Critically ill new born babies admitted at the Neonatal Intensive Care Unit (NICU) are extremely tiny and vulnerable to external disturbance. Smart Jacket proposed in this paper is the vision of a wearable unobtrusive continuous monitoring system realized by body sensor networks (BSN) and wireless communication. The smart jacket aims for providing reliable health monitoring as well as a comfortable clinical environment for neonatal care and parent-child interaction. We present the first version of the neonatal jacket that enables ECG measurement by textile electrodes. We also explore a new solution for skin-contact challenges that textile electrodes pose. The jacket is expandable with new wearable technologies and has aesthetics that appeal to parents and medical staff. An iterative design process in close contact with the users and experts lead to a balanced integration of technology, user focus and aesthetics. We demonstrate the prototype and the experimental results obtained in clinical setting.

The shortcoming of conventional bus architecture has been overcome by adopting Network on Chip (NoC) in multi core System on Chip (SoC). Many works have been anticipated for NoC, based on network topology, switching methodology and routing techniques. This paper proposes a Modified TACIT algorithm for secure routing in NoC architectures. The key generation is based on four types of HASH function (4H-key) that find hard hitting to the intruder. The algorithm is realized under various block sizes as the 8-bit, 64-bit and 128-bit. The device utilization of the proposed algorithm is determined and the results show that, the maximum frequency achieved by the proposed algorithm (for 8-bit block size) is 640.968 MHz with the total memory usage of 78045 Kb.

and more specifically in wireless networks and mobile computing have driven new directions in the development of e-Health sector. New emerging concepts like mobile health (m-Health) and Personal Health Systems (PHS) are expected to revolutionize the way healthcare services are delivered. In the paper a description on the specifics of the PHS's building blocks is presented. An example multi-tiered architectural model for realization of PHS is included and a description of characteristics and functions of the tiers is suggested.

Wearable devices have become popular and innovative and are converging with technologies such as big data, Cloud and Internet of Things (IoT). Traditional physiological sensors in fitness tracking and mHealth provide health data periodically or are captured manually when required. In future, physicians as well as IoT devices will benefit from this data to provide their services. These situations can cause rapid battery consumption, consume significant bandwidth, and raise privacy issues. There have been many attempts to extend battery life and improve communication methodologies; however, they have not been able to solve the resource constraints arising from physical hardware limits, such as the size of sensors. As an alternative, this paper presents a novel approach and solution to controlling body sensors to reduce both unnecessary data transmission and battery consumption. This can be done by implementing an inference system on sensors using sensed data to transfer it efficiently to other networks without burdening the workload from IoT onto sensor devices. In this paper, we experimented with reducing the bandwidth requirements for heart-rate sensors. Our results show savings in resource usage of between 66% and 99%. Such savings have the potential of making always-on mHealth devices a practical reality.

The assessment of Ground Reaction Forces (GRF) is important for gait analysis for sports, pathological gaits and rehabilitation. To capture GRF, force plates and foot pressure insoles are commonly used. Due to cost and portability issues, such systems are mostly limited to lab-based studies. Longterm, continuous and pervasive measurement of GRF is not feasible. This paper presents a novel concept of using an earworn sensor for pervasive gait analysis. By emulating the human vestibular system, the bio-inspired design sensor effectively captures the shock wave generated by the GRF. A hierarchical Bayesian network is developed to estimate the plantar force distribution from the ear sensor signals. The accuracy of the ear sensor for detecting GRF is demonstrated by comparing the results with a high-accuracy commercial foot pressure insole system.

This paper presents the fabrication, characterization and the performance of a wearable, robust, flexible and disposable barcode system based on novel ionic liquid polymer gels (ionogels) for monitoring in real time mode the pH of the sweat generated during an exercise period. Up to now sweat analysis has been carried out using awkward methods of collecting sweat followed by laboratory analysis. The approach presented here can provide immediate feedback regarding sweat composition. The great advantage of sweat analysis is the fact that it is a completely non-invasive means of analyzing the wearer's physiological state and ensuring their health and wellbeing.

Parkinson's disease (PD) is a degenerative neurological disorder, associated with movement impairments. Recent studies have shown that auxiliary cueing in the form of video, audio, or haptic feedback can improve the gait performance in PD patients. We have developed a new platform to support gait rehabilitation in PD patients based on a wearable system able to produce real-time feedback to the user in a comfortable and effective way. Using a set of custom wearable inertial sensors, with advanced on-board processing capabilities, our application employs a smartphone to analyze in real time the patients gait and to return an appropriate real time audio bio-feedback (ABF) message to the user to correct and improve gait performance. The main advantages of the system are mobility and unobtrusiveness: it can be comfortably worn and carried by the patient with no range restrictions, giving the possibility to monitor and rehabilitate the patient in real-life scenarios, both indoors and outdoors.

Home Control and Automation systems are often modular and offer the flexibility and dependability to make life easier. Wearable sensor systems for health monitoring are an emerging trend and are expected to enable proactive personal health management. Using home-based technology and personal devices the aim is to motivate and support healthier lifestyle; this is a challenge which has been addressed in the framework of FARSEEING and CuPiD EU projects. Contrary to visions that consider home automation and personal health systems as a mean to replace or to simplify the subject control and actions, in the FARSEEING and CuPiD approach smartphones, wearable devices, and home based technology are used to stimulate the user by making life mentally and physically more challenging but without losing comfort.

Body Sensor Network (BSN) is a suitable combination of wearable tiny devices attached to patient's body. Their purpose is to monitor patient's physiological data (or BSN data) values. Sensors continuously monitor and collect patient's data and send it to a remote server through a network. This server can be called Database Server (DBS). DBS collect and stores the received patient's medical data which can be later used for any medical emergency by the Healthcare provider. Further, patient's data may be used to educate medical students, to provide data for medical research and analysis. Since the patient's physiological data are highly sensitive and BSN is very susceptible to attacks, therefore, it must be ensured that patient identity should not be exposed and altered as well as patient's data should not fall into hands of unauthorized users. Hence, maintaining privacy of patient's data over the network is an important aspect. So communication between BSN and DBS has to be secure. A strong security mechanism should be applied to maintain patient's privacy and confidentiality. This paper proposes information security of physiological data which flow through network which is highly susceptible to attack and unauthorized access. Paper proposing physiological value based Encryption and Mutual Authentication (PVEMA) mechanism to enable mutual authentication and data encryption for a patient's physiological data. The work used practical approach of Elliptic Curve Cryptography (ECC), Message Authentication Code and Symmetric Encryption Scheme for maintaining the confidentiality, authenticity and integrity of patient's data through previous stored physiological value.

Body sensor networks face different challenges than those faced in traditional wireless sensor networks. Challenges faced include fewer, more accurate sensor nodes and an increased requirement of context awareness, however, body sensor networks are relieved from high scalability. Programming sensor networks, in general, can be a daunting task due to the limited computation, memory and energy resources available. Operating systems for wireless sensor networks have been proposed which focus on the challenges they face. We believe that an operating system and toolset which focuses primarily on the challenges and properties of body sensor networks could help ease the burden of programming body sensor network applications. In this paper, we present an operating system which is focused

With the availability of low cost, short range sensor technology along with advances in wireless networking, sensor networks has become a hot topic of discussion. The International Journal of Advanced Smart Sensor Network Systems is an open access peer-reviewed journal which focuses on applied research and applications of sensor networks. While sensor networks provide ample opportunities to provide various services, its effective deployment in large scale is still challenging due to various factors. This journal provides a forum that impacts the development of high performance computing solutions to problems arising due to the complexities of sensor network systems. It also acts as a path to exchange novel ideas about impacts of sensor networks research.

The use of wireless sensor networks (WSNs) is growing up in the last few years. Therefore, new challenges arise every day and one of the emerging challenges in WSNs is the nodes mobility support. This feature increases the application areas of these technologies but also raises new challenges to solve. This paper proposes a new handover mechanism, called Hand4MAC (Handover mechanism for MAC layer supporting continuous communication in mobile wireless sensor networks), to deal with body sensors mobility in scenarios where patients are hospitalized. This approach tries to provide continuous monitoring and communication with these sensor nodes when they move across different access points wireless coverage range. The proposed method for medium access control (MAC) layer considers that nodes remain within the same network. The evaluation study of the proposed algorithm was performed by simulation and evaluated in comparison with the well-known RSSI-based handover algorithm. It was concluded that Hand4MAC performs better and reveals promising results for real deployment.

Smartphone technology has become more popular and innovative over the last few years, and has led to the prevalence of wearable devices embedded with body sensors for fitness tracking and various smartphone features. Internet of Things (IoT), which can interact with wearables and personal sensor devices (PSDs), is emerging with technologies such as mobile health (mHealth), the cloud, big data and smart environments like smart homes. It may also provide enhanced services utilising health data obtained from physiological sensors. When these sensors are converged with IoT devices, the volume of transactions and traffic are expected to increase immensely due to the increased demand of health data from the IoT network. These additional demands will affect the existing mHealth services. Health service providers may also demand more data to enhance their services such as real-time monitoring and actuation of sensors alongside the existing monitoring of traffic. Both of these situations can cause rapid battery consumption and consume significant bandwidth. Some PSDs are implanted on or inside the body, and may require invasive surgical operations to replace batteries, such as for a heart pacemaker. It is therefore crucial to save and conserve power consumption in order to reduce the frequency of such procedures as well as health data transmission when needed. There has not yet been any research into managing and controlling data processing and transmission to reduce transactions by applying intelligence onto body sensors. This paper provides a novel approach and solution to reduce data transactions in sensors and allow for the transfer of critical data without failure to medical practitioners over IoT traffic. This can be done via an inference system to transfer health data collected by body sensors efficiently and effectively to mHealth and IoT networks. The results from the experiments to reduce bandwidth and battery resources with heart rate sensors show a possible savings in resource usage of between 66% and 99.5%. Battery power can be saved by 3.14 Watts in the experiments if the transmission of a single 1KB data point is reduced, and by 7.47 Watts if the transmission of 628 data points totalling the size of 120KB is reduced. The accuracy of data inference between the originally sensed data and the data transmitted after inference can be maintained by up to 99% or more. Such savings have the potential of making always-on mHealth devices a practical reality. This research contributes a low-overhead approach to mHealth sensors by inferring the processing and transferring of data.

Abstract—Even though mental health is an important part of our well-being we believe that, so far, it has been overlooked in favour of physical health by most of the existing self-monitoring solutions. Our goal is to understand how we can better utilise technology to provide support for people with mental health problems and to gain a deeper insight to the challenges of designing solutions such as how user might effectively interact with such systems. This paper describes our system, the design challenges we have encountered, ...

In this research work we have proposed a machine to machine model of authentication. This model is applicable where multiple electronic devices are working continuously 24 hours for monitoring and periodically need to have authentication proving their identity and to remain the member of the network . The proposed work is implemented on health care devices. These devices form the “internet of medical devices “or simply body sensor networks connected with internet backbone. The proposed security measures include implementation of Zero proof Key based authentication scheme to protect the network
from getting compromised. The efficacy of the proposed algorithm shows that the algorithm proposed is better in terms of Hardening measures.

Healthcare costs as well as the vast array of diseases are drastically on the rise. Individuals
are thus looking to devices into increase their standard of life as well as decrease the amount
of time that they spend in Hospitals and other medical establishment. This has led to the large
growth in the adoption of Body Sensory Network (BSN) devices into the healthcare industry,
this has posed numerous issues surrounding the devices and the data in which they collect in
the industry.
The research presented thus investigates, through an in depth analysis of literature, the main
security and privacy issues surrounding BSN devices in the healthcare industry. The paper
identifies the main security and privacy issues surrounding BSN devices in healthcare and
presents a comprehensive framework for the protection of patient’s personal data collected by
the BSN devices as it is transmitted to the healthcare practitioner.

We present an ultra-portable and low-cost body sensor network (BSN), which enables wireless recording of human motor movement kinematics and neurological signals in unconstrained, daily-life environments. This is crucial as activities of daily living (ADL) and thus metrics of everyday movement enable us to diagnose motor and neurological disorders in the patients context, and not artificial laboratory settings. Moreover, ADL kinematics inform us how to control neuroprosthetics and brain-machine interfaces in a natural manner. Our system uses a network of battery-powered embedded micro-controllers, to capture data from motion sensors placed all over the human body and wireless connectivity to stream process data in real time at 100 Hz. Our prototype compares well against two gold-standard measures, a ground-truth motion tracking system and high-end motion capture suit as reference. At 2.5% of the cost, performance in capturing natural joint kinematics are accurate R2 = 0.89 and precise RMSE = 1.19°. The system's low-cost (approximately $100 per unit), wireless capability, low weight and millimetre-scale size allow subjects to be unconstrained in their actions while having the sensors attached to everyday clothing. These features establish our system's usefulness in clinical studies, risk-group monitoring, neuroscience and neuroprosthetics.

Finally, my eternal gratitude to my parents Fernando Rodrigues, Ascenção Neto, my sister Cristina Rodrigues and also to my girlfriend Carla Neves as well as to my closest family for all the constant encouragement, patience and support and to all my friends not mentioned here.