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Wireless communication plays an increasingly important role in healthcare delivery. This further heightens the importance of wireless reliability, but quantifying wireless reliability is a complex and difficult challenge. Understanding the risks that accompany the many benefits of wireless communication should be a component of overall risk management. The emerging trend of using sensors and other device-to-device communications, as part of the emerging Internet of Things concept, is evident in healthcare delivery. The trend increases both the importance and complexity of this challenge. As with most system problems, finding a solution requires breaking down the problem into manageable steps. Understanding the operational reliability of a new wireless device and its supporting system requires developing solid, quantified answers to three questions: 1) How well can this new device and its system operate in a spectral environment where many other wireless devices are also operating? 2) What is the spectral environment in which this device and its system are expected to operate? Are the risks and reliability in its operating environment acceptable? 3) How might the new device and its system affect other devices and systems already in use? When operated under an insightful risk management process, wireless technology can be safely implemented, resulting in improved delivery of care.

Many researchers are devoting attention to the so-called "Internet of Things" (IoT), and wireless sensor networks (WSNs) are regarded as a critical technology for realizing the communication infrastructure of the future, including the IoT. Against this background, virtualization is a crucial technique for the integration of multiple WSNs. Designing virtualized WSNs for actual environments will require further detailed studies. Within the IoT environment, physical networks can undergo dynamic change, and so, many problems exist that could prevent applications from running without interruption when using the existing approaches. In this paper, we show an overall architecture that is suitable for constructing and running virtual wireless sensor network (VWSN) services within a VWSN topology. Our approach provides users with a reliable VWSN network by assigning redundant resources according to each user's demand and providing a recovery method to incorporate environmental changes. We tested this approach by simulation experiment, with the results showing that the VWSN network is reliable in many cases, although physical deployment of sensor nodes and the modular structure of the VWSN will be quite important to the stability of services within the VWSN topology.

In this work, the performance of ISM 2.4 GHz Wireless Sensor Networks (WSNs) deployed in judo training venues is analyzed. Judo is a very popular martial art, which is practiced by thousands of people not only at the competition level, but also as part of physical education programs at different school levels. There is a great variety of judo training venues, and each one has specific morphological aspects, making them unique scenarios in terms of radio propagation due to the presence of furniture, columns, equipment and the presence of human beings, which is a major issue as the person density within this kind of scenarios could be high. Another key aspect is the electromagnetic interference created by other wireless systems, such as WiFi or other WSNs, which make the radio planning a complex task in terms of coexistence. In order to analyze the impact of these features on the radio propagation and the performance of WSNs, an in-house developed 3D ray launching algorithm has been used. The obtained simulation results have been validated with a measurement campaign carried out in the sport facilities of the Public University of Navarre. The analysis is completed with the inclusion of an application designed to monitor biological constants of judokas, aimed to improve their training procedures. The application, that allows the simultaneous monitoring of multiple judokas (collective workouts) minimizing the efforts of the coach and medical supervisor, is based on commercial off-the-shelf products. The presented assessment of the presence of interfering wireless systems and the presence of human beings within judo training venues shows that an in-depth radio planning is required as these issues can have a great impact in the overall performance of a ISM 2.4 GHz WSN, affecting negatively the potential applications supported by wireless channel.

Wireless Sensor and Actuators Networks (WSANs) constitute one of the most challenging technologies with tremendous socio-economic impact for the next decade. Functionally and energy optimized hardware systems and development tools maybe is the most critical facet of this technology for the achievement of such prospects. Especially, in the area of agriculture, where the hostile operating environment comes to add to the general technological and technical issues, reliable and robust WSAN systems are mandatory. This paper focuses on the hardware design architectures of the WSANs for real-world agricultural applications. It presents the available alternatives in hardware design and identifies their difficulties and problems for real-life implementations. The paper introduces SensoTube, a new WSAN hardware architecture, which is proposed as a solution to the various existing design constraints of WSANs. The establishment of the proposed architecture is based, firstly on an abstraction approach in the functional requirements context, and secondly, on the standardization of the subsystems connectivity, in order to allow for an open, expandable, flexible, reconfigurable, energy optimized, reliable and robust hardware system. The SensoTube implementation reference model together with its encapsulation design and installation are analyzed and presented in details. Furthermore, as a proof of concept, certain use cases have been studied in order to demonstrate the benefits of migrating existing designs based on the available open-source hardware platforms to SensoTube architecture.

Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain-computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications.

Real-time personal health monitoring is gaining new ground with advances in wireless communications. Wireless body area networks (WBANs) provide a means for low-powered sensors, affixed either on the human body or in vivo, to communicate with each other and with external telecommunication networks. The healthcare benefits of WBANs include continuous monitoring of patient vitals, measuring postacute rehabilitation time, and improving quality of medical care provided in medical emergencies. This study sought to examine emerging trends in WBAN adoption in healthcare. To that end, a systematic literature survey was undertaken against the PubMed database. The search criteria focused on peer-reviewed articles that contained the keywords "wireless body area network" and "healthcare" or "wireless body area network" and "health care." A comprehensive review of these articles was performed to identify adoption dimensions, including underlying technology framework, healthcare subdomain, and applicable lessons-learned. This article benefits healthcare technology professionals by identifying gaps in implementation of current technology and highlighting opportunities for improving products and services.

Compressed sensing (CS) is an emerging sampling paradigm in data acquisition. Its integrated analog-to-information structure can perform simultaneous data sensing and compression with low-complexity hardware. To date, most of the existing CS implementations have a fixed architectural setup, which lacks flexibility and adaptivity for efficient dynamic data sensing. In this paper, we propose a dynamic knob (DK) design to effectively reconfigure the CS architecture by recognizing the biosignals. Specifically, the dynamic knob design is a template-based structure that comprises a supervised learning module and a look-up table module. We model the DK performance in a closed analytic form and optimize the design via a dynamic programming formulation. We present the design on a 130 nm process, with a 0.058 mm (2) fingerprint and a 187.88 nJ/event energy-consumption. Furthermore, we benchmark the design performance using a publicly available dataset. Given the energy constraint in wireless sensing, the adaptive CS architecture can consistently improve the signal reconstruction quality by more than 70%, compared with the traditional CS. The experimental results indicate that the ultra-low power dynamic knob can provide an effective adaptivity and improve the signal quality in compressed sensing towards biosignal dynamics.

Respiratory rate has been shown to be an important predictor of cardiac arrest, respiratory adverse events and intensive care unit admission and has been designated a vital sign. However it is often inadequately monitored in hospitals. We test the hypothesis that RespiraSense, a piezoelectric-based novel respiratory rate (RR) monitor which measures the differential motion of the chest and abdomen during respiratory effort, is not inferior to commonly used methods of respiratory rate measurement. Respiratory rate was compared between the developed RespiraSense device and both electrocardiogram and direct observation by nursing staff. Data was collected from 48 patients admitted to the post-anaesthesia care unit in a tertiary level hospital. The primary outcome measure was difference in average RR calculated over a 15 min interval between (1) RespiraSense and ECG and (2) RespiraSense and nurses' evaluation. The secondary outcome measure was the correlation between the respiratory rates measured using these three methods. The 95 % confidence interval for the difference in average RR between RespiraSense and ECG was calculated to be [-3.9, 3.1]. The 95 % confidence interval for the difference in average RR between RespiraSense and nurses' evaluation was [-5.5, 4.3]. We demonstrate a clinically relevant agreement between RR monitored by the RespiraSense device with both ECG-derived and manually observed RR in 48 post-surgical patients in a PACU environment.

There is broad consensus that remote health monitoring will benefit all stakeholders in the healthcare system and that it has the potential to save billions of dollars. Among the major concerns that are preventing the patients from widely adopting this technology are data privacy and security. Wireless Medical Sensor Networks (MSNs) are the building blocks for remote health monitoring systems. This paper helps to identify the most challenging security issues in the existing authentication protocols for remote patient monitoring and presents a lightweight public-key-based authentication protocol for MSNs. In MSNs, the nodes are classified into sensors that report measurements about the human body and actuators that receive commands from the medical staff and perform actions. Authenticating these commands is a critical security issue, as any alteration may lead to serious consequences. The proposed protocol is based on the Rabin authentication algorithm, which is modified in this paper to improve its signature signing process, making it suitable for delay-sensitive MSN applications. To prove the efficiency of the Rabin algorithm, we implemented the algorithm with different hardware settings using Tmote Sky motes and also programmed the algorithm on an FPGA to evaluate its design and performance. Furthermore, the proposed protocol is implemented and tested using the MIRACL (Multiprecision Integer and Rational Arithmetic C/C++) library. The results show that secure, direct, instant and authenticated commands can be delivered from the medical staff to the MSN nodes.

Wearable home-monitoring devices acquiring various biosignals such as the electrocardiogram, photoplethysmogram, electromyogram, respirational activity and movements have become popular in many fields of research, medical diagnostics and commercial applications. Especially ambulatory settings introduce still unsolved challenges to the development of sensor hardware and smart signal processing approaches. This work gives a detailed insight into a novel wireless body sensor network and addresses critical aspects such as signal quality, synchronicity among multiple devices as well as the system's overall capabilities and limitations in cardiovascular monitoring. An early sign of typical cardiovascular diseases is often shown by disturbed autonomic regulations such as orthostatic intolerance. In that context, blood pressure measurements play an important role to observe abnormalities like hypo- or hypertensions. Non-invasive and unobtrusive blood pressure monitoring still poses a significant challenge, promoting alternative approaches including pulse wave velocity considerations. In the scope of this work, the presented hardware is applied to demonstrate the continuous extraction of multi modal parameters like pulse arrival time within a preliminary clinical study. A Schellong test to diagnose orthostatic hypotension which is typically based on blood pressure cuff measurements has been conducted, serving as an application that might significantly benefit from novel multi-modal measurement principles. It is further shown that the system's synchronicity is as precise as 30 μs and that the integrated analog preprocessing circuits and additional accelerometer data provide significant advantages in ambulatory measurement environments.

With the development in science, methods of communication are also improved, replacing old ones with new advanced ways in an attempt to make data transfer more secure, safer for health, and time as well as cost efficient. One of such methods is Visible Light Communication, as the name implies data is transferred through a light equipment such as incandescent or florescent bulb having speed of 10 Kb/s or LEDs approaching speed of 500 Mb/s [1]. VLC uses visible light between 384 and 789 THz [2,3]. Though range is limitation of VLC, however data transfer up-to distance of 1 to 2 km although at lower transfer rate has been reached.The VLC system comprises of light source like LED and receiver equipment, however, with advancement, now LEDs are used for both sending and receiving data. LED remains on all the time, and there is no change in brightness level during the whole process, making it safe for eyes. Currently, VLC system is facing some serious technical challenges before it could be applied in daily life.

The micro-electronic neural bridge (MENB) aims to rebuild lost motor function of paralyzed humans by routing movement-related signals from the brain, around the damage part in the spinal cord, to the external effectors. This study focused on the prototype system design of the MENB, including the principle of the MENB, the neural signal detecting circuit and the functional electrical stimulation (FES) circuit design, and the spike detecting and sorting algorithm. In this study, we developed a novel improved amplitude threshold spike detecting method based on variable forward difference threshold for both training and bridging phase. The discrete wavelet transform (DWT), a new level feature coefficient selection method based on Lilliefors test, and the k-means clustering method based on Mahalanobis distance were used for spike sorting. A real-time online spike detecting and sorting algorithm based on DWT and Euclidean distance was also implemented for the bridging phase. Tested by the data sets available at Caltech, in the training phase, the average sensitivity, specificity, and clustering accuracies are 99.43%, 97.83%, and 95.45%, respectively. Validated by the three-fold cross-validation method, the average sensitivity, specificity, and classification accuracy are 99.43%, 97.70%, and 96.46%, respectively.

In this paper, we report a result of an experiment of a field trial of our network-based minimally invasive surgery simulator. In our previous paper, we proposed a network-based visuohaptic surgery training system for laparoscopic surgery. In addition, we proposed a volume-based haptic communication approach, which allows participants at remote sites on the network to simultaneously interact with the same target object in virtual environments presented by multi-level computer performance systems, by only exchanging a small set of manipulation parameters for the target object and additional packet for synchronization of status of binary tree and deformation of shared volume model. We implemented the approach into our network-based surgery simulator, and field trial of the simulator at three locations was performed.

Activity monitoring in daily life is gaining momentum as a health assessment tool, especially in older adults and at-risk populations. Several research-based and commercial systems have been proposed with varying performances in classification accuracy. Configurations with many sensors are generally accurate but cumbersome, whereas single sensors tend to have lower accuracies. To this end, we propose an instrumented shoes system capable of accurate activity classification and gait analysis that contains sensors located entirely at the level of the shoes. One challenge in daily activity monitoring is providing punctual and subject-tailored feedback to improve mobility. Therefore, the instrumented shoe system was equipped with a Bluetooth® module to transmit data to a smartphone and perform detailed activity profiling of the monitored subjects. The potential applications of such a system are numerous in mobility and fall risk-assessment as well as in fall prevention.

In the field of wearable biomedical sensors, the convergence revolution is more than a fanciful, utopian view of the way innovation should be done. Medical-grade wearable sensors rely on it. Their development requires technical know-how, computing expertise, clinical input, and collaboration-a true meeting of the minds to permit the conversion of wearables from neat gadgets into practical and proficient tools that will propel health care to new heights. Beyond the increasing miniaturization of hardware and the shift to wireless communication technology, flexible electronics and more powerful computing capabilities, including application specific integrated circuits (ASICs) and microelectromechanical systems (MEMS), have enabled new work on body sensor networks (BSNs) that monitor, analyze, and make sense of body signals for the prevention, diagnosis, and treatment of health disorders. Developments in processing, such as sensor-connected nodes combined with evolving algorithms and decreasing power requirements, have also contributed. In addition, new approaches to subjective measures (pain and emotion) have opened possibilities.

This paper describes a new approach to the risk assessment of exposure from wireless network devices, including an exposure setup and dosimetric assessment for in vivo studies. A novel desktop reverberation chamber has been developed for well-controlled exposure of mice for up to 24 h per day to address the biological impact of human exposure scenarios by wireless networks. The carrier frequency of 2.45 GHz corresponds to one of the major bands used in data communication networks and is modulated by various modulation schemes, including Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Radio Frequency Identification (RFID), and wireless local area network, etc. The system has been designed to enable exposures of whole-body averaged specific absorption rate (SAR) of up to 15 W/kg for six mice of an average weight of 25 g or of up to 320 V/m incident time-averaged fields under loaded conditions without distortion of the signal. The dosimetry for whole-body SAR and organ-averaged SAR of the exposed mice, with analysis of uncertainty and variation analysis, is assessed. The experimental dosimetry based on temperature measurement agrees well with the numerical dosimetry, with a very good SAR uniformity of 0.4 dB in the chamber. Furthermore, a thermal analysis and measurements were performed to provide better understanding of the temperature load and distribution in the mice during exposure., (© 2015 Wiley Periodicals, Inc.)

The development of telemonitoring via wireless body area networks (WBANs) is an evolving direction in personalized medicine and home-based mobile health. A WBAN consists of small, intelligent medical sensors which collect physiological parameters such as electrocardiogram, electroencephalography, and blood pressure. The recorded physiological signals are sent to a coordinator via wireless technologies, and are then transmitted to a healthcare monitoring center. One of the most widely used wireless technologies in WBANs is ZigBee because it is targeted at applications that require a low data rate and long battery life. However, ZigBee-based WBANs face severe interference problems in the presence of WiFi networks. This problem is caused by the fact that most ZigBee channels overlap with WiFi channels, severely affecting the ability of healthcare monitoring systems to guarantee reliable delivery of physiological signals. To solve this problem, we have developed an algorithm that controls the load in WiFi networks to guarantee the delay requirement for physiological signals, especially for emergency messages, in environments with coexistence of ZigBee-based WBAN and WiFi. Since WiFi applications generate traffic with different delay requirements, we focus only on WiFi traffic that does not have stringent timing requirements. In this paper, therefore, we propose an adaptive load control algorithm for ZigBee-based WBAN/WiFi coexistence environments, with the aim of guaranteeing that the delay experienced by ZigBee sensors does not exceed a maximally tolerable period of time. Simulation results show that our proposed algorithm guarantees the delay performance of ZigBee-based WBANs by mitigating the effects of WiFi interference in various scenarios.

Over the past decade, embedded systems and microelectromechanical systems have evolved in a radical way, redefining our standard of living and enhancing the quality of life. Health care, among various other fields, has benefited vastly from this technological development. The concept of using sensors for health care purposes originated in the late 1980s when sensors were developed to measure certain physiological parameters associated with the human body. In traditional sensor nodes, the signal sources are mostly different environmental phenomena (such as temperature, vibration, and luminosity) or man-made events (such as intrusion and mobile target tracking), whereas in case of the physiological sensors, the signal source is living human tissue. These sensor nodes, as their primary sensing element, have a diaphragm that converts pressure into displacement. This displacement, in turn, is subsequently transformed into an electrical signal. The concept of wireless physiological sensor nodes, however, gained popularity in the mid-2000s, with the sensed data from the nodes transmitted to the hub via a wireless medium. The network formed by this heterogeneous set of wireless body sensor nodes is termed a wireless body-area network (WBAN). Each WBAN is essentially a composition of multiple wireless body sensor nodes and a single hub. The hub is primarily responsible for acquisition of the raw sensed data from all the component sensor nodes and first-level aggregation of the data before transmitting the aggregated data for further analysis to a remote data acquisition center. Here, we outline the evolution of WBANs in the context of modern health care and its convergence with nanotechnology.

Addressing the correlation of hospital acquired infections and insufficient hand hygiene, we propose a supportive system to enhance the individual hygiene habits of health care workers. By applying gamification to incentivize health care professionals while maintaining a high standard of privacy and usability, the system focuses on technical simplicity by using concepts like bring your own device in a scaleable proof of concept implementation.

This article demonstrates an instrumented mouthguard capable of non-invasively monitoring salivary uric acid (SUA) levels. The enzyme (uricase)-modified screen printed electrode system has been integrated onto a mouthguard platform along with anatomically-miniaturized instrumentation electronics featuring a potentiostat, microcontroller, and a Bluetooth Low Energy (BLE) transceiver. Unlike RFID-based biosensing systems, which require large proximal power sources, the developed platform enables real-time wireless transmission of the sensed information to standard smartphones, laptops, and other consumer electronics for on-demand processing, diagnostics, or storage. The mouthguard biosensor system offers high sensitivity, selectivity, and stability towards uric acid detection in human saliva, covering the concentration ranges for both healthy people and hyperuricemia patients. The new wireless mouthguard biosensor system is able to monitor SUA level in real-time and continuous fashion, and can be readily expanded to an array of sensors for different analytes to enable an attractive wearable monitoring system for diverse health and fitness applications., (Copyright © 2015 Elsevier B.V. All rights reserved.)

With the advances in wearable computing and various wireless technologies, there is an increasing trend to outsource body signals from wireless body area network (WBAN) to outside world including cyber space, healthcare big data clouds, etc. Since the environmental and physiological data collected by multimodal sensors have different importance, the provisioning of quality of service (QoS) for the sensory data in WBAN is a critical issue. This paper proposes multiple level-based QoS design at WBAN media access control layer in terms of user level, data level and time level. In the proposed QoS provisioning scheme, different users have different priorities, various sensory data collected by different sensor nodes have different importance, while data priority for the same sensor node varies over time. The experimental results show that the proposed multi-level based QoS provisioning solution in WBAN yields better performance for meeting QoS requirements of personalized healthcare applications while achieving energy saving.

Advancements in computers and electronic technologies have led to the emergence of a new generation of efficient small intelligent systems. The products of such technologies might include Smartphones and wearable devices, which have attracted the attention of medical applications. These products are used less in critical medical applications because of their resource constraint and failure sensitivity. This is due to the fact that without safety considerations, small-integrated hardware will endanger patients' lives. Therefore, proposing some principals is required to construct wearable systems in healthcare so that the existing concerns are dealt with. Accordingly, this paper proposes an architecture for constructing wearable systems in critical medical applications. The proposed architecture is a three-tier one, supporting data flow from body sensors to cloud. The tiers of this architecture include wearable computers, mobile computing, and mobile cloud computing. One of the features of this architecture is its high possible fault tolerance due to the nature of its components. Moreover, the required protocols are presented to coordinate the components of this architecture. Finally, the reliability of this architecture is assessed by simulating the architecture and its components, and other aspects of the proposed architecture are discussed.

As players and soldiers preform strenuous exercises and do difficult and tiring duties, they are usually the common victims of muscular fatigue. Keeping this in mind, we propose FAtigue MEasurement (FAME) protocol for soccer players and soldiers using in-vivo sensors for Wireless Body Area Sensor Networks (WBASNs). In FAME, we introduce a composite parameter for fatigue measurement by setting a threshold level for each sensor. Whenever, any sensed data exceeds its threshold level, the players or soldiers are declared to be in a state of fatigue. Moreover, we use a vibration pad for the relaxation of fatigued muscles, and then utilize the vibrational energy by means of vibration detection circuit to recharge the in-vivo sensors. The induction circuit achieves about 68 % link efficiency. Simulation results show better performance of the proposed FAME protocol, in the chosen scenarios, as compared to an existing Wireless Soccer Team Monitoring (WSTM) protocol in terms of the selected metrics.

As cloud computing and wearable devices technologies mature, relevant services have grown more and more popular in recent years. The healthcare field is one of the popular services for this technology that adopts wearable devices to sense signals of negative physiological events, and to notify users. The development and implementation of long-term healthcare monitoring that can prevent or quickly respond to the occurrence of disease and accidents present an interesting challenge for computing power and energy limits. This study proposed an adaptive sensor data segments selection method for wearable health care services, and considered the sensing frequency of the various signals from human body, as well as the data transmission among the devices. The healthcare service regulates the sensing frequency of devices by considering the overall cloud computing environment and the sensing variations of wearable health care services. The experimental results show that the proposed service can effectively transmit the sensing data and prolong the overall lifetime of health care services.

Tree-based overlay multicast is an effective group communication method for media streaming applications. However, a group member's departure causes all of its descendants to be disconnected from the multicast tree for some time, which results in poor performance. The above problem is difficult to be addressed because overlay multicast tree is intrinsically instable. In this paper, we proposed a novel stability enhancing solution, VMCast, for tree-based overlay multicast. This solution uses two types of on-demand cloud virtual machines (VMs), i.e., multicast VMs (MVMs) and compensation VMs (CVMs). MVMs are used to disseminate the multicast data, whereas CVMs are used to offer streaming compensation. The used VMs in the same cloud datacenter constitute a VM cluster. Each VM cluster is responsible for a service domain (VMSD), and each group member belongs to a specific VMSD. The data source delivers the multicast data to MVMs through a reliable path, and MVMs further disseminate the data to group members along domain overlay multicast trees. The above approach structurally improves the stability of the overlay multicast tree. We further utilized CVM-based streaming compensation to enhance the stability of the data distribution in the VMSDs. VMCast can be used as an extension to existing tree-based overlay multicast solutions, to provide better services for media streaming applications. We applied VMCast to two application instances (i.e., HMTP and HCcast). The results show that it can obviously enhance the stability of the data distribution.

Wireless Body Area Networks (WBANs) can provide real-time and reliable health monitoring, attributing to the human-centered and sensor interoperability properties. WBANs have become a key component of the ubiquitous eHealth (electronic health) revolution that prospers on the basis of information and communication technologies. The prime consideration in WBAN is how to maximize the network lifetime with battery-powered sensor nodes in energy constraint. Novel solutions in Medium Access Control (MAC) protocols are imperative to satisfy the particular BAN scenario and the need of excellent energy efficiency in healthcare applications. In this paper, we propose a hybrid Lifetime Extended Directional Approach (LEDA) MAC protocol based on IEEE 802.15.6 to reduce energy consumption and prolong network lifetime. The LEDA MAC protocol takes full advantages of directional superiority in energy saving that employs multi-beam directional mode in Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) and single-beam directional mode in Time Division Multiple Access (TDMA) for alternative in data reservation and transmission according to the traffic varieties. Moreover, the impacts of some inherent problems of directional antennas such as deafness and hidden terminal problem can be decreased owing to that all nodes generate individual beam according to user priorities designated. Furthermore, LEDA MAC employs a Dynamic Polled Allocation Period (DPAP) for burst data transmissions to increase the network reliability and adaptability. Extensive analysis and simulation results show that the proposed LEDA MAC protocol achieves extended network lifetime with improved performance compared with IEEE 802.15.6.

Big data storage and processing are considered as one of the main applications for cloud computing systems. Furthermore, the development of the Internet of Things (IoT) paradigm has advanced the research on Machine to Machine (M2M) communications and enabled novel tele-monitoring architectures for E-Health applications. However, there is a need for converging current decentralized cloud systems, general software for processing big data and IoT systems. The purpose of this paper is to analyze existing components and methods of securely integrating big data processing with cloud M2M systems based on Remote Telemetry Units (RTUs) and to propose a converged E-Health architecture built on Exalead CloudView, a search based application. Finally, we discuss the main findings of the proposed implementation and future directions.

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

Objective: Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications., Approach: To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation., Main Results: We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm., Significance: We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

Wireless Sensor Network monitor and control the physical world via large number of small, low-priced sensor nodes. Existing method on Wireless Sensor Network (WSN) presented sensed data communication through continuous data collection resulting in higher delay and energy consumption. To conquer the routing issue and reduce energy drain rate, Bayes Node Energy and Polynomial Distribution (BNEPD) technique is introduced with energy aware routing in the wireless sensor network. The Bayes Node Energy Distribution initially distributes the sensor nodes that detect an object of similar event (i.e., temperature, pressure, flow) into specific regions with the application of Bayes rule. The object detection of similar events is accomplished based on the bayes probabilities and is sent to the sink node resulting in minimizing the energy consumption. Next, the Polynomial Regression Function is applied to the target object of similar events considered for different sensors are combined. They are based on the minimum and maximum value of object events and are transferred to the sink node. Finally, the Poly Distribute algorithm effectively distributes the sensor nodes. The energy efficient routing path for each sensor nodes are created by data aggregation at the sink based on polynomial regression function which reduces the energy drain rate with minimum communication overhead. Experimental performance is evaluated using Dodgers Loop Sensor Data Set from UCI repository. Simulation results show that the proposed distribution algorithm significantly reduce the node energy drain rate and ensure fairness among different users reducing the communication overhead.

Advances in wearable and implantable biosensors have enabled the applicability and usability of wireless body area networks (WBANs). A WBAN allows biosensors to collect and communicate human physiological data using wireless communication. The communication security of the collected data in WBAN is a major concern. Because of the dependability of cryptographic schemes for key management, these have become an important aspect of this security. However, the extremely constrained nature of biosensors has made designing key management schemes a challenging task. For this reason, many lightweight key management schemes have been proposed to overcome these constraints. In this article, we present a review of the state of the art of these solutions. We classify the WBAN schemes into three classes and evaluate them based on adequate metrics for key management in WBAN.

Specific to the selective forwarding attack on routing in cognitive radio network, this paper proposes a trust-based secure routing model. Through monitoring nodes' forwarding behaviors, trusts of nodes are constructed to identify malicious nodes. In consideration of that routing selection-based model must be closely collaborative with spectrum allocation, a route request piggybacking available spectrum opportunities is sent to non-malicious nodes. In the routing decision phase, nodes' trusts are used to construct available path trusts and delay measurement is combined for making routing decisions. At the same time, according to the trust classification, different responses are made specific to their service requests. By adopting stricter punishment on malicious behaviors from non-trusted nodes, the cooperation of nodes in routing can be stimulated. Simulation results and analysis indicate that this model has good performance in network throughput and end-to-end delay under the selective forwarding attack.

The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.

Neighbor discovery and the power of sensors play an important role in the formation of Wireless Sensor Networks (WSNs) and mobile networks. Many asynchronous protocols based on wake-up time scheduling have been proposed to enable neighbor discovery among neighboring nodes for the energy saving, especially in the difficulty of clock synchronization. However, existing researches are divided two parts with the neighbor-discovery methods, one is the quorum-based protocols and the other is co-primality based protocols. Their distinction is on the arrangements of time slots, the former uses the quorums in the matrix, the latter adopts the numerical analysis. In our study, we propose the weighted heuristic quorum system (WQS), which is based on the quorum algorithm to eliminate redundant paths of active slots. We demonstrate the specification of our system: fewer active slots are required, the referring rate is balanced, and remaining power is considered particularly when a device maintains rendezvous with discovered neighbors. The evaluation results showed that our proposed method can effectively reschedule the active slots and save the computing time of the network system.

This paper presents a new metric to evaluate electromagnetic exposure induced by wireless cellular networks. This metric takes into account the exposure induced by base station antennas as well as exposure induced by wireless devices to evaluate average global exposure of the population in a specific geographical area. The paper first explains the concept and gives the formulation of the Exposure Index (EI). Then, the EI computation is illustrated through simple phone call scenarios (indoor office, in train) and a complete macro urban data long-term evolution scenario showing how, based on simulations, radio-planning predictions, realistic population statistics, user traffic data, and specific absorption rate calculations can be combined to assess the index. Bioelectromagnetics. 36:451-463, 2015. © 2015 Wiley Periodicals, Inc., (© 2015 Wiley Periodicals, Inc.)

Spectrum scarcity is a major challenge in wireless communications systems requiring efficient usage and utilization. Cognitive radio network (CRN) is found as a promising technique to solve this problem of spectrum scarcity. It allows licensed and unlicensed users to share the same licensed spectrum band. Interference resulting from cognitive radios (CRs) has undesirable effects on quality of service (QoS) of both licensed and unlicensed systems where it causes degradation in received signal-to-noise ratio (SIR) of users. Power control is one of the most important techniques that can be used to mitigate interference and guarantee QoS in both systems. In this paper, we develop a new approach of a distributed power control for CRN based on utility and pricing. QoS of CR user is presented as a utility function via pricing and a distributed power control as a non-cooperative game in which users maximize their net utility (utility-price). We define the price as a real function of transmit power to increase pricing charge of the farthest CR users. We prove that the power control game proposed in this study has Nash Equilibrium as well as it is unique. The obtained results show that the proposed power control algorithm based on a new utility function has a significant reduction in transmit power consumption and high improvement in speed of convergence.

In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay.

This paper proposes a set of communication patterns to enable the construction of medical systems by composing devices and apps in Integrated Clinical Environments (ICE). These patterns abstract away the details of communication tasks, reduce engineering overhead, and ease compositional reasoning of the system. The proposed patterns have been successfully implemented on top of two distinct platforms (i.e., RTI Connext and Vert.x) to allow for experimentation.

Purpose: To develop a high performance, cost-effective digital optical console for scalable multichannel MRI., Methods: The console system was implemented with flexibility and efficiency based on a modular architecture with distributed pulse sequencers. High-speed serial links were optimally utilized to interconnect the system, providing fast digital communication with a multi-gigabit data rate. The conventional analog radio frequency (RF) chain was replaced with a digital RF manipulation. The acquisition electronics were designed in close proximity to RF coils and preamplifiers, using a digital optical link to transmit the MR signal., Results: A prototype of the console was constructed with a broad frequency range from direct current to 100 MHz. A temporal resolution of 1 μs was achieved for both the RF and gradient operations. The MR signal was digitized in the scanner room with an overall dynamic range between 16 and 24 bits and was transmitted to a master controller over a duplex optic fiber with a high data rate of 3.125 gigabits per second. High-quality phantom and human images were obtained using the prototype on both 0.36T and 1.5T clinical MRI scanners., Conclusion: A homemade digital optical MRI console with high-speed serial interconnection has been developed to better serve imaging research and clinical applications., (© 2014 Wiley Periodicals, Inc.)

Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.

A smart-card-based user authentication scheme for wireless sensor networks (hereafter referred to as a SCA-WSN scheme) is designed to ensure that only users who possess both a smart card and the corresponding password are allowed to gain access to sensor data and their transmissions. Despite many research efforts in recent years, it remains a challenging task to design an efficient SCA-WSN scheme that achieves user anonymity. The majority of published SCA-WSN schemes use only lightweight cryptographic techniques (rather than public-key cryptographic techniques) for the sake of efficiency, and have been demonstrated to suffer from the inability to provide user anonymity. Some schemes employ elliptic curve cryptography for better security but require sensors with strict resource constraints to perform computationally expensive scalar-point multiplications; despite the increased computational requirements, these schemes do not provide user anonymity. In this paper, we present a new SCA-WSN scheme that not only achieves user anonymity but also is efficient in terms of the computation loads for sensors. Our scheme employs elliptic curve cryptography but restricts its use only to anonymous user-to-gateway authentication, thereby allowing sensors to perform only lightweight cryptographic operations. Our scheme also enjoys provable security in a formal model extended from the widely accepted Bellare-Pointcheval-Rogaway (2000) model to capture the user anonymity property and various SCA-WSN specific attacks (e.g., stolen smart card attacks, node capture attacks, privileged insider attacks, and stolen verifier attacks).

Telemetry systems enable researchers to continuously monitor physiological signals in unrestrained, freely moving small rodents. Drawbacks of common systems are limited operation time, the need to house the animals separately, and the necessity of a stable communication link. Furthermore, the costs of the typically proprietary telemetry systems reduce the acceptance. The aim of this paper is to introduce a low-cost telemetry system based on common radio frequency identification technology optimized for battery-independent operational time, good reusability, and flexibility. The presented implant is equipped with sensors to measure electrocardiogram, arterial blood pressure, and body temperature. The biological signals are transmitted as digital data streams. The device is able of monitoring several freely moving animals housed in groups with a single reader station. The modular concept of the system significantly reduces the costs to monitor multiple physiological functions and refining procedures in preclinical research.

A non-invasive solution for monitoring of the activity and dehydration of organisms is proposed in the work. For this purpose, a wireless standalone chemical sensor platform using two separate measurement techniques has been developed. The first approach for activity monitoring is based on humidity measurement. Our solution uses new humidity sensor based on a nanostructured TiO2 surface for sweat rate monitoring. The second technique is based on monitoring of potassium concentration in urine. High level of potassium concentration denotes clear occurrence of dehydration. Furthermore, a Wireless Body Area Network (WBAN) was developed for this sensor platform to manage data transfer among devices and the internet. The WBAN coordinator controls the sensor devices and collects and stores the measured data. The collected data is particular to individuals and can be shared with physicians, emergency systems or athletes' coaches. Long-time monitoring of activity and potassium concentration in urine can help maintain the appropriate water intake of elderly people or athletes and to send warning signals in the case of near dehydration. The created sensor system was calibrated and tested in laboratory and real conditions as well. The measurement results are discussed.

An ultra wideband (UWB) technology is a potential candidate for implant body area networks (BANs), where wireless communications are established between inside and outside of a human body. The UWB can accomplish higher data rate than the other frequency band for the implant communication. However, due to its high frequencies, the UWB signals suffer from quite large attenuation in the implant communication link, which makes it difficult to achieve reliable communications. For achieving reliable communication, it is well known that a spatial diversity technique is efficient without any frequency extension. In our previous works, we developed a transmit polarization diversity antenna for the UWB implant communication. However, optimal UWB modulation scheme for transmit diversity were rarely discussed. In this paper, in order to investigate the optimal UWB modulation schemes for implant communication with transmit diversity, we compare the communication performances of UWB-impulse radio (UWB-IR) and multiband-orthogonal frequency division multiplexing (MB-OFDM). For this purpose, we first analyze the propagation characteristics in the implant UWB channel, which ranges from 3.4 GHz to 4.8 GHz, using a finite difference time domain (FDTD) numerical analysis technique. Then, we evaluate and discuss the communication performances of both modulation schemes for the transmit polarization diversity from the viewpoint of the BER and the required transmit power.

Telehealth systems and applications are extensively investigated nowadays to enhance the quality-of-care and, in particular, to detect emergency situations and to monitor the well-being of elderly people, allowing them to stay at home independently as long as possible. In this paper, an embedded telehealth system for continuous, automatic, and remote monitoring of real-time fall emergencies is presented and discussed. The system, consisting of a radar sensor and base station, represents a cost-effective and efficient healthcare solution. The implementation of the fall detection data processing technique, based on the least-square support vector machines, through a digital signal processor and the management of the communication between radar sensor and base station are detailed. Experimental tests, for a total of 65 mimicked fall incidents, recorded with 16 human subjects (14 men and two women) that have been monitored for 320 min, have been used to validate the proposed system under real circumstances. The subjects' weight is between 55 and 90 kg with heights between 1.65 and 1.82 m, while their age is between 25 and 39 years. The experimental results have shown a sensitivity to detect the fall events in real time of 100% without reporting false positives. The tests have been performed in an area where the radar's operation was not limited by practical situations, namely, signal power, coverage of the antennas, and presence of obstacles between the subject and the antennas.

Taking care for dementia persons with BPSD is burdening on caregivers. To reduce caregivers' burdens and improve dementia persons' quality of life, monitoring and communication intervention system has been proposed. A part of the system, wandering and falling down detection system has been developed. It is designed based on the requirement of the caregivers working in the care facility. Functional test was carried out and had positive impressions from the caregivers.

Implantable biomedical sensors with the ability to transmit wirelessly real-time physiological data to an external unit can enable better management of chronic diseases. The IEEE Standard 802.15.6-2012 specifies the implementation of implant communications within 402-405 MHz, which unfortunately allows low data transmission rates only. Ultra wideband (UWB) interfaces within 3.1-10.6 GHz offer a number of advantages at the expense of higher path losses. Efforts to characterize the implant UWB channel have been undertaken via computer simulations, but these may not capture completely the effects on the implant radio channel of multiple physiological functions. To overcome these limitations we provide insight into the frequency-domain behavior of the UWB implant channel within 3.1-8.5 GHz based on propagation measurements in a liquid phantom and a living swine. A thorough comparison of the relative received power in phantom-based and in vivo measurements for the in-body to on-body (IB2OB) and in-body to off-body (IB2OFF) channel scenarios are presented.

This paper investigates an enhanced WSN based monitoring system for elderly people in geriatric facilities. Apart from general physical monitoring functionalities, we explore the potential of using a wireless audio module to perform mental health monitoring. The telephone version of the mini mental status examination (T-MMSE) was adopted in the mental health monitoring and the RSSI (Received Signal Strength Indicator) localization algorithm was implemented in this system to track elderly people's the real-time location and send alerts based on their inactivity/activity levels, movement history and entry into restricted zones. Experimental test was performed to evaluate the performance of the proposed system. With results obtained and studied, this work can be extended to perform detection and rehabilitation function for elderly people with mental illness. This kind of monitoring system would be expected to make an important impact on many application scenarios for geriatric facilities.