Federated learning (FL) has gained attention for enabling efficient distributed learning while maintaining data privacy. However, the data privacy constraint reduces the transparency in the agents’ model update making the learning process vulnerable to Byzantine attacks. In this paper, a mathematical proof is provided to show that when the traditional model-combining scheme is used, the model will eventually diverge to non-useful solutions in the presence of Byzantine agents independently from their number or their contributions. A low complexity norm-control based aggregation approach is also proposed and shown to converge to the optimal and sub-optimal solutions in the absence or presence of Byzantine nodes, respectively. Monte-Carlo simulations are also conducted to verify and validate the mathematical derivations and the efficiency of the proposed approach in protecting the FL model.

Several people suffer from sudden falls, which puts them seriously at high risk if they do not get help right away. However, if people own a wearable device, they can utilize it to get them help from a nearest hospital whenever they feel tired or suffer from a sudden fall. Elderly people also tend to live alone. After a fall, it is rare for an elderly person to be able to get up or ask for assistance. Therefore, to enable patients to request assistance even in cases where they are unable to get up after a fall, an advanced fall detection system is required, with appropriate hardware and software components. Wireless sensors and Global Positioning System (GPS) gadgets are among the technologies that can be utilized in this regard. While sensors collect information about movement, a GPS device uses information from satellites to pinpoint an object’s exact location inside a given space. The goal of this paper is to describe a developed and implemented fall detection wearable wristband with lightweight security for health monitoring. In the event of an emergency, the smartphone application will communicate the user’s specific location to the nearest hospital and their emergency contacts. The proposed wristband achieved our goal by developing a system that can communicate with the mobile application. Furthermore, the model testing produced $92 \%$ overall accuracy.

This paper introduces a database of multi-script (Arabic and English) for indoor scene text detection, taken from different angle-of-view. This database can be used in a variety of real-world applications, such as image search, robot navigation, and assisting the visually impaired. The database contains 944 images taken with smartphones in an indoor environment at Qatar University. These images were taken from at least three angles, making the database even more challenging. To evaluate the database, an OCR method based on multiple language detection is considered. The results show that multi-language detection should be given more attention in practice. The database is publicly available11https:/www.dropbox.com/s/7s7f936y4etzsu7/QU_door_dataset%20%282%29.zip?dl=0.

Asthma is a common lung illness that causes breathing difficulties. It affects people of all ages and generally begins in childhood; however, it can also appear in adults for the first time. There is presently no cure, but there are basic treatments that can help keep the symptoms under control. This paper aims to monitor asthma attacks for pediatrics under twelve years according to the following parameters: oxygen blood level, respiratory rate, and pulse rate. The resulting measures would be compared to pre-determined health standards. The respiratory rate is calculated and deduced by analyzing the result of air pressure sensor. The pulse rate and oxygen level are obtained from an oximeter. These measurements were compared with the normal health status to decide if the child has an asthma attack or not. The result is given within two minutes. The values of the three parameters and the final status of asthma are displayed on the screen. The main aim of the paper is to propose a monitoring device that can be used in a hospital or at home where parents can constantly check the status of their children in a short period of time without having to go to the hospital. The success of this device is getting accuracy higher than 85%.

More than three years into the coronavirus disease 2019 (COVID-19) pandemic, it can be noted that the measures put in place for societies to manage the spread of this disease could have been better. For example, contact tracing mobile applications used to curb the spread of COVID-19 need additional enhancements to allow health care professionals to better understand the proliferation of the disease and to lessen the burden on hospitals and medical centers. In this paper, we present an intelligent solution to remotely self-monitor COVID-19 symptoms to help rapidly identify and detect suspected positives. The proposed intelligent solution is based on using a near-field communications (NFC) wristband that collects body temperature heart rate and SpO2 levels. It is connected to a dedicated mobile application to intelligently draw conclusions from the data (COVID-19 symptoms) it collects. Moreover, the application is trained to analyze cough sounds and detect the probability of infection. Results show more than 90% of detection accuracy. The proposed system can be adapted to future pandemics based on respiratory symptoms.

The gigantic increase in population, social isolation, and mobility constraint lifestyle since the COVID-19 era has resulted in challenges like remote availability of critical bio-instrumentation like magnetocardiography (MCG) and electrocardiography (ECG). This availability can only be made possible through portable hand-held bio-instrumentation systems. Unmanned aerial vehicles (UAVs) can be used to deliver these systems to remote areas. In cardiological bioinstrumentation, MCG and ECG are two major innovations-based field effect techno-scientific approaches. The MCG systems face several major challenges that have hampered their applications and utility for cardio patients and their inspection labs. In this work, the main challenges were addressed by using a novel ML-based probabilistic interpolation algorithm over a dual ECG probing system-on-chip (SoC) with IoT capabilities to generate the identical MCG signal from two ECG signals with a segmented translation of PR, QRS, ST, and QT characteristic patches at real-time. The implementation findings provided a rich resource for approximating wave-shaping filters, frequencies, mean, and variance whilst addressing redundancy.

Recently, developing automated video surveillance systems (VSSs) has become crucial to ensure the security and safety of the population, especially during events involving large crowds, such as sporting events. While artificial intelligence (AI) smooths the path of computers to think like humans, machine learning (ML) and deep learning (DL) pave the way more, even by adding training and learning components. DL algorithms require data labeling and high-performance computers to effectively analyze and understand surveillance data recorded from fixed or mobile cameras installed in indoor or outdoor environments. However, they might not perform as expected, take much time in training, or not have enough input data to generalize well. To that end, deep transfer learning (DTL) and deep domain adaptation (DDA) have recently been proposed as promising solutions to alleviate these issues. Typically, they can (i) ease the training process, (ii) improve the generalizability of ML and DL models, and (iii) overcome data scarcity problems by transferring knowledge from one domain to another or from one task to another. Although the increasing number of articles proposed to develop DTL- and DDA-based VSSs, a thorough review that summarizes and criticizes the state-of-the-art is still missing. To that end, this paper introduces, to the best of the authors’ knowledge, the first overview of existing DTL- and DDA-based video surveillance to (i) shed light on their benefits, (ii) discuss their challenges, and (iii) highlight their future perspectives.

Decision tree (DT) models are widely used in medical applications where the size of the data sets is usually small or medium. Moreover, DT ensemble models are preferred over single DT models because of their higher accuracy in spite of the need for more overhead due to using multiple trees. Several schemes have been proposed for privacy-preserving cloud-based medical diagnosis using ensemble models. However, these schemes suffer from several limitations. First, they suffer from high computation/communication overheads due to using inefficient public-key cryptosystems. Second, none of them can simultaneously protect the intellectual property of the model and preserve the privacy of the patients’ data and diagnosis results. Finally, they do not provide inherent access control for the outsourced model and micropayment, in which only the registered patients can use the model and pay for the service. In this article, we develop a lightweight and privacy-preserving cloud-based medical diagnosis scheme using ensemble models with high accuracy and acceptable overhead. Using our scheme, the model owner can control the patients who can use the model. Also, for each classification operation, patients must make a micro-payment to pay for the diagnosis service. Our analysis indicates that our scheme can protect the model’s intellectual property and diagnose diseases without leaking any sensitive information about the patients’ medical data and the diagnosis results. Our experimental results demonstrate that our scheme requires less communication/computation overhead compared to the existing schemes.

Federated learning (FL) enables an effective and private distributed learning process. However, it is vulnerable against several types of attacks, such as Byzantine behaviors. The first purpose of this work is to demonstrate mathematically that traditional arithmetic-averaging model-combining approach will ultimately diverge to an unstable solution in the presence of Byzantine agents. This article also proposes a low-complexity, decentralized Byzantine resilient training mechanism. The proposed technique identifies and isolates hostile nodes rather than just mitigating their impact on the global model. In addition, the suggested approach may be used alone or in conjunction with other protection techniques to provide an additional layer of security in the event of misdetection. The suggested solution is decentralized, allowing all participating nodes to jointly identify harmful individuals using a novel cross-check mechanism. To prevent biased assessments, the identification procedure is done blindly and is incorporated into the regular training process. A smart activation mechanism based on flag activation is also proposed to reduce the network overhead. Finally, general mathematical proofs combined with extensive experimental results applied in a healthcare electrocardiogram (ECG) monitoring scenario show that the proposed techniques are very efficient at accurately predicting heart problems.

People counting is one of the computer vision tasks that can be useful for crowd management. In addition, estimating the crowdedness of a surveilled scene for crowd behavior analysis is one of the prominent challenges in video surveillance systems. With the introduction of deep learning, this operation has become doable with a convincing performance. However, this task still represents a challenge for these methods. In this regard, we propose a combination of deep reinforcement learning (DRL) networks and deep learning architecture for crowd counting. DRL network used the Context-Aware Attention (CAA) module for segmenting the crowd region, Then, on the segmented results, the crowd density estimation is performed using an encoder-decoder. The proposed method is evaluated and compared with and without the segmentation parts on the existing datasets including UCF_QNRF, UCF_CC_50, ShangaiTech_(A, B), while the obtained results in terms of MAE metric achieved 84,8, 179.2, 44.6, and 8.2 respectively.

With the emergence of three new paradigms, namely the Internet of Things (IoT), cloud/edge computing and mobile social networks; Mobile Crowd Sensing (MCS) has emerged as a potential approach for data collecting in numerous applications, such as traffic management, infotainment, disaster management or public safety. MCS mechanisms are receiving a lot of attention, both from research and development areas, showing their impact and benefit. But their optimization is still under development, mainly due to the large number of involved parameters. A major field within MCS relates to crowd management for emergency situations, where the management and optimization mechanisms become crucial to local authorities. To tackle this problem, in this work, we propose an MCS hybrid worker selection scheme that operated various modes depending on the delay-cost requirements. Our scheme exploits the user behavior to achieve an optimal bi-objective for any delay-cost requirement. We use simulations to evaluate the performance of our proposal, and we show the optimal and different sub-optimal solutions that can match the delay-cost requirements.

According to the Global Organization for Stress, 80 percent of people are stressed at work and according to the American Institute of Stress, stress causes 48 percent of people to have difficulty sleeping. Relaxation reduces stress, depression, and anxiety. Electroencephalography (EEG) is used by scientists to analyze the brainwave signals that explore the emotions and the cognitive processes of the brain. In recent years, Virtual reality (VR) technology has drawn lots of attention. Thus, the use of VR as a technique of relaxation is being investigated for assisting students and workers in achieving the relaxation to help them focus on their studies and work. This work aims to design an experimental setup for acquiring the EEG signals to analyze the characteristic frequency bands of the brainwave related to relaxation, when the subject is immersed in a VR environment. Different metrics, calculated from captured brain wave signals, are analyzed, compared, and discussed in this paper.

Federated Learning (FL) has attracted a lot of attention in numerous applications due to recent data privacy regulations and increased awareness about data handling issues, combined with the ever-increasing big-data sizes. This paper proposes a server-less, robust FL training mechanism that allows any set of participating data-owners to train a neural network (NN) model collaboratively without the assistance of any central node and while being resilient to Byzantine attacks. The proposed approach makes use of a dual-way update mechanism to allow each node to take a model forwarding decision towards a global collaborative decision of isolating any malicious updates. The efficiency of the proposed approach in detecting cardiac irregularities is verified using simulation results conducted based on the Physikalisch-Technische Bundesanstalt Database electro-cardiogram (PTBDB ECG) dataset.

Federated Learning (FL) is a machine learning approach that enables private and decentralized model training. Although FL has been shown to be very useful in several applications, its privacy constraints cause a lack of model update transparency which makes it vulnerable to several types of attacks. In particular, based on detailed convergence analyses, we show in this paper that when the traditional model-combining scheme is used, even a single Byzantine node that keeps sending random reports will cause the whole FL model to diverge to non-useful solutions. A low complexity model combining approach is also proposed to stabilize the FL system and make it converge to a suboptimal solution just by controlling the model norm. The Physikalisch-Technische Bundesanstalt extra-large electrocardiogram (PTB-XL ECG) dataset is used to validate the findings of this paper and show the efficiency of the proposed approach in identifying heart anomalies.

The Internet of Things (IoT) is used to interconnect a massive number of heterogeneous resource-constrained smart devices. This makes such networks exposed to various types of malicious attacks. In particular, jamming attacks are among the most common harmful attacks to IoT networks. Therefore, an anti-jamming power allocation (PA) strategy is first proposed in this article for health monitoring IoT networks by exploiting the game theory to minimize the worst case jamming effect under multichannel fading. This strategy uses an iterative algorithm based on gradient descent to identify the Nash Equilibrium (NE) of the game. An artificial neural network (ANN) model is also proposed to accelerate the convergence of the algorithm making it more suitable for IoT networks. Furthermore, novel data population (DP), extension, and balancing techniques are proposed to enhance the efficiency of the proposed strategy in combating jamming attacks even for network configurations that were never used in the training phase. In addition, time and spatial diversities are exploited using a heterogeneous iterative algorithm to enhance the security of the network.

A secure energy efficient approach is proposed to connect Internet of Things (IoT) sensors that operate with limited power resources. This is done by optimizing simultaneously the energy efficiency, the communication rate and the network security while limiting the potential data leakage and tracking the finite battery status evolution. The proposed model uses spatial diversity in addition to artificial jamming introduced by an intermediate device to forward the data from the sensors to the destination and to secure the communication links without draining the rechargeable batteries. The energy harvested by the source is also maximized without affecting the security level of the network. The outage secrecy capacity is derived to evaluate the security level. Furthermore, the system power stability is analyzed using Markov chains and statistical approaches to validate the efficiency of the proposed technique in maintaining the system in a self-sufficient mode and making it operate without the assistance of external power resources.

A jamming combating power allocation strategy is proposed to secure the data communication in IoT networks. The proposed strategy aims to minimize the worst case jamming effect on the intended transmission under multi channel fading and total power constraints by modelling the problem as a Colonel Blotto game Nash Equibrium (NE). Both Logistic Regression as well as a specifically designed algorithm are used to iteratively and rapidly obtain the equilibrium strategy. The conducted theoretical derivations and Monte Carlo simulations confirm that the proposed approach can secure the IoT network with a limited amount of power and with a number of iterations that is much reduced compared to state-of-the-art techniques.

In spite of the wide spread of practical crypto- systems and ciphers nowadays, they still lack a unique metric to measure the secrecy level they provide. Their strength is measured in an ad-hoc way by exposing them to different kinds of attacks. In addition, their ability to hold secure against these attacks is evaluated in time and computations. In this paper, we introduce an approach for calculating the equivocation of the secret key used in these ciphers. In addition, we prove that it can be used as an indicator for the work required to break the cipher. This will help in unifying the metrics used in evaluating the strength of the ciphers and in comparing them with the classical information theoretic secreacy measures.

Telemedicine is a technique that allows patients to have health-related consultations without the need to be physically present in the hospital through phone and video calling technologies. In recent years, researchers have made many contributions to reform and facilitate better telemedicine services through the use of body area networks or wireless body area networks. This paper presents a testbed where we implement a lightweight physical layer security scheme, using gray code, on an IoT-based health monitoring system to secure transmitted patient readings while preserving its clinical features. We address several existing adversarial scenarios, where an adversary can eavesdrop on the packets and infer their content using some of the existing packet inspection techniques. We prove that the introduced physical layer security scheme effectively protects the patient transmitted data, even if read by an adversary.

The open nature of the wireless communication medium renders it vulnerable to jamming attacks by malicious users. To detect their presence and to avoid such attacks, several techniques are present in the literature. Most of these techniques aim to reduce the effect of the jamming signals by increasing the transmission power or by using complex coordination schemes. However, the implementation of such power consuming techniques might be challenging or not feasible in limited resources Internet-of-Things (IoT) devices. Therefore, a defending strategy against jamming attacks in health monitoring IoT networks is proposed in this article. This strategy operates in orthogonal frequency-division multiplexing channels and takes into consideration the effect of slow fading channels in the strategy design. Specifically, the jamming combating problem is formulated as a Colonel Blotto game where the equilibrium defines the minimization of the worst case jamming effect on the IoT sensors communications. Then, the optimal power allocation strategy for all the potential jammer power ranges is derived by investigating the Nash equilibrium of the game. This proposed strategy is shown to be efficient in combating jamming attacks while minimizing the IoT sensors power consumption.

Radio frequency identification (RFID) systems are gaining more attention in new frontiers of applications due to their simplicity, low size factor and low cost of deployment. One of the emerging frontiers of applications of RFID is the healthcare system. In this paper, we propose a novel physical layer security technique (PLS) to secure radio frequency identification (RFID) tags used in healthcare systems. To achieve secure communications for RFID systems, directional modulation (DM) scheme is exploited at the reader side. The proposed system maintains the simple circuity and processing nature of passive tags. The results obtained demonstrate that DM techniques can achieve physical layer security for RFID systems.

Electrocardiogram (ECG) is an electrical activity of the heart, which can be recorded by placing electrodes near heart or on the limbs. ECG is a vital body signal, which reflects the heart health condition. This paper presents a new wearable ECG system, which can be used for long-term rhythm monitoring with the potential of increased sensitivity to detect intermittent or subclinical arrhythmia. This study presents the design and development of a wearable pervasive healthcare monitoring system by ECG measurement systems and internet of things (IoT) platform. In this design, non-intrusive healthcare system was designed based on wireless body area network (WBAN) for wide area coverage with minimum battery power to support wireless transmission. Data were transmitted via Wi-Fi to the personalized mobile system. These were integrated into a comfortable, easy to wear, and ergonomically designed armband ECG sensor system, which can acquire an ECG signal from the upper arm of the user over a period of 72 hours.

The Internet of things (IoT) is one of the most exposed networks to attackers due to its widespread and its heterogeneity. In such networks, jamming attacks are widely used by malicious users to compromise the private and secure communications. Many techniques are proposed in the literature to secure the network from malicious jamming attacks. However, most of these techniques require either the implementation of complex coordination schemes or the use of high transmission power and are therefore challenging to implement in limited resources IoT networks. In this paper, a low complexity anti-jamming defending strategy using smart power allocation under limited power constraints is proposed for health monitoring IoT networks. This strategy is designed by formulating the worst case jamming effect minimization problem as a Colonel Blotto game while considering the slow channel fading effect. By analyzing the Nash Equilibrium (NE) of the game, making use of efficient and fast equilibrium approximation techniques, designing a fast numerical solving approach, training an artificial neural network (ANN) to enhance the accuracy of the estimation, an anti-jamming power allocating strategy is proposed and is shown to be effective in reducing the power consumption and in combating jamming attacks with less resources. A data population scheme is also proposed to make the proposed ANN exploit as much possible the available data to provide accurate NE estimation.

Recently, there is a revolution in internet of things (IoT) technologies. Research advancements in this field proved to be very useful to automate daily tasks, it quickly reached the medical field resulting in the creation of a new research term called Internet of Medical Things (IoMT). Medical IoT devices have many applications that adds accessibility and reach to the medical field. Such applications vary from remote patient monitoring, remote surgery, and many more health-related tasks. Medical IoT applications require precise readings of biometrics and real time haptic feedback to work as intended without putting any risk on the human life. With all of these IoT medical applications, securing the information becomes a priority. Any un-intentional modification in a biometric reading can prove to be fatal in most scenarios. In this paper, we try to survey the current state-of-the-art encryption techniques that provide different solutions with varying levels of security.

This paper addresses the problem of the detection of the presence of a pre-classified (i.e., pulse shape is known) signals with IQ imbalance considering free-space path-loss. We study the effect of IQ distortion on the probability of detection and false alarm and find the degradation in the probability of detection compared with the case of ideal IQ branches. We propose closed form expressions for the probability of detection and false alarm for signals suffering from IQ imbalances.

In this paper, we propose a novel PHY layer security technique in radio frequency identification (RFID) backscatter communications system. In order to protect the RFID tag information confidentiality from the eavesdroppers attacks, the proposed technique deploys beam steering (BS) using a one dimensional (1-D) antenna array in the tag side in addition to noise injection from the reader side. The performance analysis and simulation results show that the new technique outperforms the already-existing noise injection security technique and overcomes its design limitations.

Channel-aware encryption is investigated as a physical layer security technique in internet of things (IoT) scenarios. Clustering algorithms for grouping sensor nodes into cooperative clusters are proposed, with the purpose of decreasing energy consumption and reducing the transmission time of sensor data. Bit flipping is implemented with the clustering method in order to "encrypt" the transmitted data based on channel state information. The simulation results validate the performance of the proposed approach in terms of reducing energy consumption, reducing transmission time, and of confusing the eavesdropper from guessing the correct transmissions of sensor nodes.

Physical layer security has attracted research attention as a means to achieve secure communication without the need for complicated upper layer encryption techniques. The secure degrees of freedom (SDoF) of various networks in the absence of instantaneous eavesdropper channel state information is still unknown. In this work, we study the SDoF of a multiple access network composed of two transmitters and a single receiver in the presence of an eavesdropper. All parties are equipped with multiple antennas and are subject to Gaussian noise in addition to fading channel conditions. A realistic, worst case scenario, where the channel state information (CSI) for the channels between the trusted parties is known to everyone, while the trusted parties can only estimate the channel statistics (environment based) of the eavesdropper is considered. The asymptotic secure network sum capacity (aka sum SDoF) is provided utilizing a novel proposed comprehensive upperbound along with a novel achievable scheme based on exploiting jamming.

The evolution of Internet of Things (IoT) as a new application in wireless networks mandates the utilization of wireless cooperative relaying to overcome the energy limitations of IoT devices. Multi-hop relaying is a communication scheme, where packets are forwarded from source to destination through intermediate relay nodes. All these relay nodes are assumed to have buffers for temporarily storing their received packets. During each time-slot, one node can be selected among all nodes to transmit and forward a single packet to the consequent relay node towards the final destination. Based on the nature of the data and its sensitivity to the delay, different schemes can be used to control the movement of packets in the multi-hop networks. This paper presents a framework for the delay analysis of buffered multi-hop networks based on a recently proposed packet-forwarding scheme that uses the best hop for transmission. Based on the channel, the best hop, having the highest signal-to-noise ratio (SNR), is selected. This hop selection procedure produces selection diversity, which minimizes the error and outage probability. The network delay is studied analytically, based on a finite-state Markov chain model. Also, we derive analytical closed form expressions for the average queue length for each relay buffer in the network and the end-to-end network delay. Finally, we compare the delay and outage of the best hop scheme with the conventional multi-hop transmission scheme. The results show how the number of intermediate relays and the buffer size of each one can affect the network delay.

Epileptic seizure detection could be detected through investigating the electroencephalography (EEG), which is deemed to be very important for IoT wearable sensor-based health systems. EEG-based classification is crucial for a wide-range of applications to analyze real-time vital signs using features concerning predefined set of data classes. The aim of this paper is to conduct a comparative study for several classification techniques and demonstrate the effect of uncertainty in the EEG data on the classification accuracy. We define a model for decomposing the EEG using various transformation such as discrete cosine transform, discrete wavelet transform into several sub-bands. After feature extraction, a comparative study to assess the classification algorithms' performance is conducted. In addition, we evaluate their overall accuracy and complexity as performance measures. For this purpose, we use the support vector machine (SVM) and the Artificial Neural Network (ANN). These are chosen as classifier models to study the performance of the obtained features. The discussion will include the evaluation of the classifiers' performance using the EEG-based epileptic seizure data in two categories, noiseless and noisy. In addition, there are some statistical features extracted to characterize the complete EEG data feeding to these two classifiers. A publically available EEG dataset is employed for both normal and epileptic seizure for automatic epileptic seizure detection as a benchmark.

In an mHealth remote patient monitoring scenario, usually control units/data aggregators receive data from the body area network (BAN) sensors then send it to the network or “cloud”. The control unit would have to transmit the measurement data to the home access point (AP) using WiFi for example, or directly to a cellular base station (BS), e.g., using the long-term evolution (LTE) technology, or both (e.g., using multi- homing to transmit over multiple radio access technologies (Multi-RATs). Fast encryption or physical layer security techniques are needed to secure the data. In fact, during normal conditions, monitoring data can be transmitted using best effort transmission. However, when real-time processing detects an emergency situation, the current monitoring data should be transmitted real-time to the appropriate medical personnel in emergency response teams. In this paper, a fast and secure approach for transmitting monitoring data over multi-RATs is proposed. The presented approach consists of benefiting of the presence of multi-RATs in order to exchange the secrecy information more efficiently while optimizing the transmission time.

In the context of mobile Health (mHealth) applications, data are prone to several sources of contamination which would lead to false interpretation and misleading classification results. In this paper, a robust deep learning approach with low rank model is proposed to classify mHealth vital signs. Further-more, we propose using the Schatten-p norm instead of the classic nuclear norm since it has shown better recovery performance for several applications. We conduct a comprehensive study where we compare our method to the state-of-art methods and evaluate its performance with respect to the key system parameters. Our findings show indeed that combining deep network with dictionary learning model is effective for vital signs classification even in presence of 50% corruption with 8% improvement over the closest performance.

Wireless sensor networks (WSNs) are one of the emerging technologies in the 21stcentury. In the structural health monitoring (SHM). WSNs are used as one of the vitally capable technologies in the SHM. The accelerometer module in the existing sensor nodes enables several novel applications. In this paper, a prototype for monitoring and detecting the damage for the real bridge using these sensor nodes is built. The prototype consists of sensor nodes, shaking table including its amplifier, and real bridge. The sensors are placed on a scaled down concrete bridge model that is mounted on a shaking table. The results are demonstrated in terms of acceleration on different nodes at a particular excitation frequency in the case of normal, single-side damage, and double-side damage.

Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems. Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes. In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI. The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands. In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers. Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets. The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders.

Worldwide Interoperability for Microwave Access (WiMAX) and Wireless LAN (WLAN) has emerged as a promising solution for last mile access technology to provide high speed internet access in the residential as well as small and medium sized enterprise sectors. Application Response Time is the key performance measure in WiMAX and WLAN Network Quality of Service (QoS). The WiMAX network does not provide sufficient QoS with respect to Application Response Time. Wavelength Division Multiplexing (WDM) has emerged as the promising technology to meet the ever-increasing demand for bandwidth In this paper, we developed a Distributed Client-Server Model to improve QoS with respect to Application Response Time in the Fixed WiMAX and WLAN Network in order to enhance the services that are provided to the end users. The new distributed Client-Server model was simulated in OPNET modeler 16.0 with multiple Base Stations (BSs), Subscribers Stations (SSs) and some Server BSs selected by the Nearest Neighborhood Algorithms using Orthogonal Frequency Division Multiplexing (OFDM) techniques and compared with the existing Centralized model using Frequency Division Multiplexing (FDM) techniques. The simulation results obtained for the application response time of the proposed Client-Server model show an improvement in network performance.

Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different physiological brain states. In this paper, we propose an efficient framework for evaluating the power-accuracy trade-off for EEG-based compressive sensing and classification techniques in the context of epileptic seizure detection in wireless tele-monitoring. The framework incorporates compressive sensing-based energy-efficient compression, and noisy wireless communication channel to study the effect on the application accuracy. Discrete cosine transform (DCT) and compressive sensing are used for EEG signals acquisition and compression. To obtain low-complexity energy-efficient, the best data accuracy with higher compression ratio is sought. A reconstructed algorithm derived from DCT of daubechie's wavelet 6 is used to decompose the EEG signal at different levels. DCT is combined with the best basis function neural networks for EEG signals classification. Extensive experimental work is conducted, utilizing four classification models. The obtained results show an improvement in classification accuracies and an optimal classification rate of about 95% is achieved when using NN classifier at 85% of CR in the case of no SNR value. The satisfying results demonstrate the effect of efficient compression on maximizing the sensor lifetime without affecting the application's accuracy.