International audience

International audience

International audience

International audience

International audience

Early detection of breast cancer is crucial for improving survival rates and patient outcomes. However, accurately identifying the early signs of breast cancer remains a significant challenge for healthcare professionals, often due to the subtlety of early-stage indicators and the complexity of interpreting medical data. In this paper, we propose a comprehensive AI-driven approach to assist healthcare professionals in the early detection of breast cancer. Comparing both Machine Learning (ML) and Deep Learning (DL) techniques, we conduct an extensive evaluation of several models, using the Wisconsin Breast Cancer dataset. Our analysis reveals that ensemble learning models, particularly Random Forest (RF) and Adaptive Boosting (AB), demonstrate superior performance, with both models achieving an accuracy of$\mathbf{9 8. 2 \%}$and an Area Under the Curve of$\mathbf{0. 9 9}$. These results underscore the potential of these models to accurately predict breast cancer, thereby helping in early detection.

The Internet of Medical Things (IoMT) leverages interconnected devices to enhance healthcare operations through data collection, monitoring, and automation. However, IoMT systems are vulnerable to cyberattacks, necessitating robust detection mechanisms. This paper presents a comprehensive comparison of traditional Machine Learning (ML) and Deep Learning (DL) models for detecting attacks in IoMT environments. We address class imbalance using the Synthetic Minority Over-sampling Technique and evaluate models based on various classification performance metrics and computational complexity. Our findings highlight that XGBoost (XGB) achieves the highest performance metrics, including accuracy (0.957), F1-score (0.955), and AUC (0.992), while maintaining competitive prediction times. In comparison, DL models, despite their high accuracy, exhibit significantly higher computational demands. This study demonstrates the effectiveness of XGB for real-time attack detection in IoMT systems, offering practical insights for selecting models that balance performance with computational efficiency. This research contributes to the development of more resilient IoMT systems by enhancing cyberattack detection strategies.

Anomaly detection in the Internet of Medical Things (IoMT) is important for ensuring the timely identification of potential health issues. To address this challenge, this paper presents a novel approach combining clustering and unsupervised learning with online adaptation. Patient data collected from IoMT sensors, including vital signs and health alerts, is first preprocessed to ensure quality and reliability. We then employ clustering algorithms to identify clusters containing normal data, which are then used to train unsupervised anomaly detection models. The models evaluated include Isolation Forest, One-Class Support Vector Machine (OCSVM), Local Outlier Factor (LOF), and Elliptic Envelope (EE). To maintain the model's effectiveness over time, an online learning mechanism updates the model with new data every 5000 samples. Our experimental results demonstrate that the LOF model provides the best performance with high precision, recall, F1-score, and Area Under the Curve (AUC) while maintaining computational efficiency with varying amounts of training data. For instance, with 30,000 samples, the LOF model achieved an accuracy of 0.867, and an AUC score of 0.964. Despite similar performance metrics, Spectral Clustering was found to be impractical due to its excessive training time. These results highlight the effectiveness and practicality of our approach for real-time anomaly detection in IoMT.

This paper presents a comprehensive approach for detecting respiratory diseases using IoMTs and Machine Learning (ML) algorithms, leveraging audio recordings from multiple sensor locations on the body. By capturing, extracting, and analyzing diverse audio features, such as MFCC, STFT, and Mel-spectrogram, we aim to detect and classify six respi-ratory conditions: Bronchiectasis, Bronchiolitis, COPD, Healthy, Pneumonia, and URTI. We used a publicly annotated dataset to conduct the experiment and analyze the performance of our proposed approach. This dataset underwent preprocessing, which included feature extraction, removal of rare diseases, data flattening, and encoding for model training. Our findings demonstrate that Deep Learning (DL), such as the Convolutional Neural Network (CNN) model achieved the highest accuracy of 92.4 % and an AU C of 97 %, highlighting its potential in audio-based diagnostics. Our experimental results prove that DL, particularly CNN, outperforms traditional ML techniques in detection accuracy, which makes them a good choice in developing non-invasive, efficient, and cost-effective solutions for respiratory disease detection.

With the growing importance of privacy in data-driven applications, ensuring the security and confidentiality of personal information has become a significant challenge. Federated Learning (FL) offers a promising solution by enabling collaborative model training across multiple clients while keeping individual data localized and private. In FL, outputs computed by various devices are aggregated at a central server, which uses iterative algorithms to develop a globally shared model. However, the presence of malicious participants can result in the intentional manipulation of training data or the model, compromising the system's accuracy and reliability. In this study, we propose a novel FL technique based on Split Learning (SL) to enhance robustness against data poisoning attacks. Our approach aims to develop a robust FL system based on SL, integrate it with existing aggregation methods, and compare its performance with traditional FL approaches.

Heart disease is a major health concern, and this study investigates how machine learning, particularly Random Forest (RF), can be used to identify it early and assess risk. We tested various algorithms using a large dataset (BRFSS 2022) and found that RF performed best, achieving an accuracy of 93% and an AUC of 98%. To make the model run faster, we identified the four most important features and focused on those. This reduced accuracy slightly (86% for RF and Extra Trees) but with AUCs of 0.92 and 0.91 respectively. Importantly, training and testing times improved significantly. This trade-off between accuracy and speed makes these models more suitable for real-world use, potentially aiding healthcare professionals in the early detection and prevention of heart disease.

Our study challenges the conventional understanding that deep learning models consistently outperform traditional machine learning approaches in classification tasks. By evaluating the performance of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Random Forest (RF) models on QUIC traffic classification, we demonstrate that RF achieves superior accuracy, precision, recall, F1-score, and computational efficiency compared to CNN and LSTM. This finding underscores the importance of considering both performance and computational efficiency when selecting an appropriate model. Additionally, we emphasize the practical applicability of RF, especially in resource-constrained environments, where its efficiency makes it a compelling alternative to deep learning methods. These insights offer valuable guidance for enhancing network security, optimizing resource utilization, and deploying effective traffic classification systems in real-world scenarios.

Early detection of anomalies in network traffic data is critical for robust cybersecurity. This study investigates the effectiveness of various Machine Learning and Deep Learning models for identifying anomalous patterns in NetFlow v9 traffic. We address data preprocessing challenges and explore feature engineering techniques to optimize anomaly detection system performance. Our study evaluates the performance of several models based on key metrics like accuracy, Area Under the Curve (AUC), and computational efficiency. The results highlight the strengths and limitations of each model, emphasizing the importance of balancing performance with real-world deployment feasibility. Random Forest emerged as the most effective model, achieving an accuracy of 93.8% and an AUC of 0.99. Additionally, it demonstrated superior training and testing times, requiring only 0.19 seconds for training and 0.23 microseconds per prediction. Conversely, the Recurrent Neural Network model exhibited limitations in training efficiency and overall performance. Through a nuanced analysis of model performance and computational considerations, this study contributes to advancing anomaly detection techniques for network security applications.

In this paper, we propose an approach for anomaly detection in Internet of Medical Things based on the soft voting between the most accurate machine learning algorithms. We compare 12 machine learning and 5 deep learning algorithms for anomaly detection to identify the top 3. We apply these algorithms over public annotated dataset with network and physiological parameters. The soft voting predicts the anomaly based on the predicted probability by each individual model from the selected 3. We also compare the performance of the 17 algorithms before and after dimensionality reduction using two different techniques, and we found that soft voting using CatBoost, XGBoost and LightGBM outperforms hard voting and other algorithms, achieving a detection accuracy of 97.45 % and a false alarm rate of 2%.

Cyberbullying is a growing problem in today's digital society, with serious consequences for the mental health, professional life, and general well-being of victims. To combat this phenomenon, it is essential to effectively detect cases of cyberbullying online. In this article, we propose a cyberbullying detection model based on machine learning. We collect a dataset containing messages classified as cyberbullying, with multiclass labels. We then use machine learning techniques to extract relevant features from these messages and identify cases of cyberbullying. The results of our experiments show that our detection model achieves high accuracy, making it effective for detecting cyberbullying online. This research thus contributes to the fight against cyberbullying by proposing an innovative method for detecting this phenomenon online.

The purpose of this paper is to develop an intrusion detection system for IoT systems using deep learning and the genetic algorithm to optimize (or, more precisely, compress) the model. To meet the security requirements of IoT systems, we aim to design a powerful IDS system capable of detecting novel attacks, with a lightweight architecture and reduced classification time. We proposed two neural network architecture-based systems: the multi-layer perceptron and the auto-encoder model. The NSL-KDD dataset is utilized for the evaluation of these two systems.

Recent advancements in the realm of Graph Neural Networks (GNN) have introduced interesting opportunities for classifying and detecting anomalies in medical data. This paper explores the effectiveness of GNNs by utilizing various graphical representations, such as tree and bipartite graphs, to capture and exploit the intricate relationships inherent in medical data. Our investigation demonstrates that through this graph conversion process, GNNs acquire the capability to train and classify on a limited range of configurations, thereby improving both efficiency and classification performance. Furthermore, by employing complete graphs and multi-parametric temporal graphs, we can identify anomalies in health values by considering correlation factors. This sequential approach, where GNNs are repeatedly applied to graphs free from erroneous values, facilitates more precise classification, leading to exceptional accuracy rates. These findings highlight the crucial role of graphs in medical data analysis and demonstrate the effectiveness and robustness of GNNs in this specific domain.

This paper presents a comparative analysis of various machine learning models applied to a diabetes dataset. The study evaluates a range of ML algorithms, employing exploratory data analysis and feature engineering techniques. The primary objective is to forecast the onset of diabetes mellitus in a high-risk population of Pima Indians. Each model is trained, evaluated using performance metrics such as accuracy and F1-score, and assessed using ROC curves and AUC values. The results highlight the performance and suitability of each model for the prediction task. Notably, an Support Vector Machines (SVM) model achieved an accuracy of 77.27%. This study contributes valuable insights for selecting appropriate models in similar scenarios.

Bluetooth Low Energy (BLE) is extensively employed in various fields such as medication, home automation, transportation, and agriculture, enabling the integration of smart applications into resource-constrained devices that can operate for prolonged periods, often powered by a coin cell battery. While many BLE devices rely on the Just Works pairing option to establish connections with peer devices, the responsibility for security implementation falls on application developers and device manufacturers due to its lightweight nature. Unfortunately, the market's reluctance to invest in security measures has resulted in a proliferation of vulnerable smart devices. This paper aims to address the vulnerability of the Just Works pairing method in BLE. Through a case study involving a smart lightbulb, we intercept BLE exchanges between two entities and propose an algorithm to enhance the security of the Just Works pairing mechanism.

The deployment of the Internet of Medical Things (IoMTs) for remote monitoring has grown exponentially as an alternative for in-hospital diagnosis. The COVID-19 lockdown and the shortage of human and medical apparatus pushed the detection and diagnostics toward the Wireless Body Area Network to prevent the spread of infection. In this paper, we present a novel approach to detect cardiovascular diseases using the IoMTs. The proposed approach is based on the use of autoencoders to derive a compressed representation of the input record, followed by a Random Forest to classify the latent data. The objective is to provide a lightweight and accurate model compatible with the constrained resources of sensors. Our experiment results on a public annotated dataset show that our approach can enhance the performance where the obtained area under the curve is 89%. We also compare the performance of widely used classification algorithms to prove the efficiency of our proposed model.

The objective of this paper is to propose a new system to assist drivers with hearing disability, deaf or unfocused persons by recognizing and transforming audible signals, such as emergency vehicle sirens or honks into alerts displayed in the dashboard. Such conversion from audio to alert messages attracts the attention of unfocused drivers to hear the honking of other cars, and enhances the safety and the quality of life for deaf or hard-of-hearing drivers. We develop an IoT based system to identify, denoise and translate any significant voice signal around the driver's car into alert messages. The signal acquired by sensors is processed to identify the source and display the associated message through the use of several machine learning models with majority voting. Our experiments results show that the proposed solution is able to achieve a 95% accuracy when trained and validated against a real dataset of 600 files.

Cyberbullying is a prevalent social issue that can cause significant harm to individuals, particularly young people. Traditional approaches to detecting cyberbullying rely on text-based analysis of online messages, but the use of multiple modes of communication in cyberbullying presents challenges for detection. In this study, we compare the effectiveness of audio-only, visual-only, and text-only approaches for cyberbullying detection. We collected different datasets for the study and used machine learning algorithms, speech emotion recognition, text conversion, middle finger, and landmarks techniques to analyze the data. Our results show that the text-only approach achieved the highest accuracy of 95%. Our study demonstrates that text-based features remain the most informative for cyberbullying detection, while audio and visual features alone are less effective. These findings suggest that the development of more comprehensive cyberbullying detection methods should focus on improving text-based analysis while considering the potential benefits of incorporating audio and visual features.

The exponential development and widespread emergence of the Internet of Medical Things (IoMT) have led to a growing need for effective anomaly detection techniques to ensure the reliability and security of healthcare systems. This article provides a review of existing machine learning and deep learning algorithms for anomaly detection in IoMT, followed by the presentation of a novel approach combining ARIMA for predicting health parameter values and a decision tree for anomaly detection. This hybrid approach aims to improve the accuracy and efficiency of anomaly detection in IoMT by leveraging both time series models and the discriminative features of decision trees. The preliminary results of this approach are presented and discussed, highlighting its potential to enhance early detection of anomalies in IoMT and contribute to safer and more reliable healthcare.

This paper proposes a dual autoencoder approach for anomaly detection in industrial network systems. Our system is based on two separate autoencoders: one trained on raw sensor data and the other on derived features, both aimed at capturing normal system behavior. Anomalies are identified by comparing reconstruction errors from both autoencoders and employing classification algorithms such as ANN. The proposed approach achieves a high accuracy on benchmark datasets, surpassing traditional anomaly detection methods.

Bluetooth Low Energy (BLE) is widely applied in various gadgets and Internet of Things (IoT) packages through-out diverse fields which include healthcare, home automation, transportation, and agriculture. It evolves classic Bluetooth generation, permitting integration into aid-constrained gadgets that can operate for months, or even years, on an unmarried coin cell battery. To establish connections with peer gadgets, the bulk of BLE devices in the marketplace hire the “Just Works” pairing approach. This lightweight mode locations the onus of protection implementation on application builders and device producers. Unfortunately, marketplace pressures frequently sideline safety issues, leading to a plethora of inclined clever devices. In this paper, we observe the vulnerabilities related to the “Just Works” pairing in BLE. We behavior a case examination related to a smart mild bulb to intercept BLE communications between entities. Subsequently, we advocate an algorithm aimed at improving the safety of the “Just Works” pairing method.

In this article, we present a method for detecting cyberbullying that goes beyond understanding the text's content and also considers the underlying intent. Our approach involves analyzing both the acoustic and linguistic features of audio to gain insights into the emotional intent conveyed in the text. Using the librosa library, we extract acoustic features from audio files to identify whether the emotions expressed are aggressive or non-aggressive. We then combine this information with the results of linguistic analysis to develop a comprehensive understanding of the text's content. By incorporating the emotional aspect, our method enables more precise and nuanced identification of cyberbullying instances.

Cyberbullying is a growing concern, with serious consequences especially for children. In this paper, we propose a machine learning approach to detect cyberbullying messages accurately and distinguish them from regular ones. We used a public dataset of social media messages to fit a binary classification of either cyberbullying or non-bullying messages. We compared 15 classifiers using two methods: Term Frequency-Inverse Document Frequency (TF-IDF) for traditional algorithms and word embedding for deep learning algorithms. The voting classifier, a combination of the best algorithms from the first method, achieved the highest accuracy of 96.5% during tests. This approach can be used in social media or chat applications to detect and prevent cyberbullying.

In this paper, we propose a secure framework for healthcare monitoring using the Internet of Medical Things (IoMT). In spite of their deployment, these devices still vulnerable to several cyber-attacks, ranging from unauthorized access to private medical data to data modification and injection. These attacks can compromise the privacy of the monitored patient, reduce the reliability of the monitoring system and may harm the life of monitored patient. In this paper, we propose a new framework to detect attacks and secure the communications in IoMT. To prevent eavesdropping and modification attacks, we propose the Ephemeral Elliptic Curves Diffie-Hellman (EECDH) to derive a session key used to provide confidentiality and authenticity. To detect injected measurements, flooding triggered by compromised devices and medical changes in physiological data, we applied the sequential change point detection algorithm Pruned Exact Linear Time (PELT) followed by the boxplot. Our experimental results show that our approach is able to increase the reliability and the accuracy of remote monitoring system, while reducing the false alarms triggered by injected measurements.

To improve the communications between the deaf and the hearers using hand-held devices, we propose a lightweight approach to quickly identify the word in American Sign Language (ASL). We acquire inertial data and muscular activity during hands movements. Then we aggregate the received data to reduce the required processing complexity and memory usage in a portable device. Afterward, we feed extracted features from aggregated data into the Support Vector Machine (SVM) to identify the associated word. Our experimental results showed that our data aggregation approach was able to enhance the recognition accuracy of the associated word when comparing the performance of SVM and Decision Tree (DT) classifiers with and without data aggregation. We conduct a performance analysis and we showed that our proposed approach is faster and able to achieve better recognition accuracy (92%) when compared with existing work.

The Internet of Medical Things are susceptible to Man-in-the-Middle (MitM) attack, which can identify healthcare emergency of monitored patients and replay normal physiological data to prevent the system from raising an alarm. In this article, we propose a framework to prevent a MitM from disrupting the operations and prohibiting the raise of alarms by the remote healthcare monitoring system. To reduce energy consumption for normal data transmission, and preserve the privacy of health data, our framework transmits a smaller size signature derived from acquired data with message authentication code, where the key is derived from received signal strength indication. Our experimental results for emergency detection show that our approach can achieve a high detection accuracy with a low false alarm rate of 3%.

The use of Wireless Body Area Networks (WBANs) in healthcare for pervasive monitoring enhances the lives of patients and allows them to fulfill their daily life activities while being monitored. Various non-invasive sensors are placed on the skin to monitor several physiological attributes, and the measured data are transmitted wirelessly to a centralized processing unit to detect changes in the health of the monitored patient. However, the transferred data are vulnerable to various sources of interference, sensor faults, measurement faults, injection and alteration by malicious attackers, etc. In this article, we propose a change point detection model based on a Markov chain for centralized anomaly detection in WBANs. The model is derived from the Root Mean Square Error (RMSE) between the forecasted and measured values for whole attributes. The RMSE transforms the monitored attributes into a univariate times series which is divided into overlapping sliding window. The joint probability of the sequence of RMSE values in each sliding window is calculated to decide whether a change has occurred or not. When an effective change is detected over k consecutive windows, the number of deviated attributes is used to distinguish faulty measurements from a health emergency. We apply our proposed approach on real physiological data from the Physionet database and compare it with existing approaches. Our experimental results prove the effectiveness of our proposed approach, as it achieves high detection accuracy with a low false alarm rate (5.2%).

In this paper, we propose a novel model for real-time detection of nocturnal epileptic seizures from physiological and inertial data, which are collected by a wireless wristband with integrated muscular activity sensors, 3D accelerometer and 3D gyroscope. The wristband transmits the data to a portable unit for processing and seizures detection. It must be able to distinguish normal nocturnal movements from seizures, and to raise an alarm upon detection of seizures for patient relatives and for predefined contacts. Our real-time detection model starts by reducing the dimensionality of collected data through the use of root mean square to derive one signal from 3D accelerometer and one signal from 3D gyroscope. With the derived 3 signals (accelerometer, gyroscope and electromyogram), we apply the vector triple product to derive one signal used as input for anomaly detection mechanism. The robust version of z-score is applied on the resulting product signal to detect deviations associated with seizures before raising an alarm for patient relative for assistance to prevent further injuries when the patient loses control with the excessive discharge of neurons.

Accurate and fast recognition of sign language would greatly improve communications between the deaf and the hearers using hand-held devices. We used Myo armband as our wireless data measurement device, which is wearable technology equipped with on-board 3D Accelerometer, 3D Gyroscope and 8 channel Electromyogram acquisition system. The main objective of this paper is to provide a lightweight approach for American Sign Language recognition by reducing the dimensionality of inputs using a novel method. Data from each sensors are aggregated into one dimension to reduce the required time for data processing, as well as the amount of required memory for storage. Afterward, we extract features from aggregated data and we proceed to classification using Support Vector Machine (SVM). We compare the performance of SVM with and without our aggregation approach. Our experimental results prove that our proposed approach improves the speed of model derivation (four times faster than existing methods) and reduces the size of input data with the same accuracy.

The accurate individuals' activities description is an important assignments in human computer interface. Many applications can be developed based on correct and fast activity recognition, such as healthcare monitoring and fall detection applications. Human Activity Recognition (HAR) is an active research field, where some key factors still challenging and need to be enhanced for faster and accurate recognition. This paper presents a novel approach aims to improve the activity recognition time by aggregating the raw data from inertial sensors into one time series used as input, then various features are extracted from the resulted signal by dimensionality reduction procedure, Those features are used as input data for the classification algorithm without affecting the information associated with activity in the raw data. The results of the novel method showed an improvement in recognition time while retaining the same level of accuracy.

Collisions in underwater acoustic networks can not be tolerated due to the fundamental differences between underwater acoustic propagation and terrestrial radio propagation. Thus, conceiving medium access protocols that avoid collision to the most possible extent is of paramount importance. In this paper, a multi-channel MAC protocol, MC-UWMAC, especially designed for underwater acoustic sensor networks, is proposed and evaluated. MC-UWMAC is an energy efficient MAC protocol that aims at achieving a collision free communication. MC-UWMAC operates on a single slotted control channel to avoid the missing receiver problem and multiple data channels to improve the network throughput. To guarantee to the most possible extent a collision free communication, MC-UWMAC uses two key newly designed procedures: i) a grid based slot assignment procedure on the common slotted control channel that approaches the 2-hop conflict free slot assignment and ii) a quorum based data channel allocation procedure. More precisely, according to MC-UWMAC, a sender uses its own dedicated slot on the common control channel for handshaking with an intended neighbor receiver. However, data transmission takes place in a unique data channel especially reserved for this pair of neighbor nodes. In fact, MC-UWMAC reserves for each pair of neighbor nodes a unique data channel that aims at being 2-hop conflict free. As such, the probability of collision is highly reduced and even completely mitigated in some scenarios. In addition, by using multiple channels, MC-UWMAC allows multiple data communications along with handshaking on the common control channel to take place at the same time and hence the network throughput as well as energy efficiency are improved. Simulation results show that MC-UWMAC can greatly improve the network performance especially in terms of energy consumption, throughput, and end-to-end delay.

This paper presents a lightweight approach for the early detection of nocturnal epileptic seizures through analysis of inertial data and muscle contractions. Our approach uses an overlapping sliding window to derive the variance of data acquired by the MPU 9,250 motion tracking device and single channel surface ElectroMyoGram (sEMG). The Exponentially Weighted Moving Average (EWMA) is used to forecast the current value of the data variance. When the Kullback-Leibler divergence between the forecasted and measured variances deviates from past values, a signal is transmitted to the base station to set the current counter in an alarm window. If the filling ratio of the alarm window is greater than a predefined threshold, an alarm is triggered by the base station. The proposed approach is intended to improve the performance of existing detection systems based on data analysis from Accelerometer. The MPU 9,250 is 9-axis motion tracking and used to detect motor seizures, and it contains a 3-axis Accelerometer, Gyroscope, and Magnetometer. The sEMG is used to detect silent seizures without jerky movements. Our experimental results on a real dataset from an epileptic patient show that our proposed approach is able to increase detection accuracy and reduce the low false alarm rate. Comparison with a Probability Density Function (PDF) further demonstrates the detection efficiency of our approach.

The collected data by biomedical sensors must be analyzed for automatic detection of physiological changes. The early identification of an event in collected data is required to trigger an alarm upon detection of patient health degradation. Such alarms inform healthcare professionals and allow them to quickly react by taking appropriate actions. However, events result from physiological change or faulty measurements, and lead to false alarms and unnecessary medical intervention. In this paper, we propose a framework for automatic detection of events from collected data by biomedical sensors. The proposed approach is based on the Kalman filter to forecast the current measurement and to derive the baseline of the time series. The power divergence is used to measure the distance between the forecasted and measured values. When a change occurs, this metric significantly deviates from past values. To distinguish emergency events from faulty measurements, we exploit the spatial correlation between the monitored attributes. We conduct experiments on real physiological data set and our results show that our proposed framework achieves a good detection accuracy with a low false alarm rate. Its simplicity and processing speed make our proposed framework efficient and effective for real-world deployment.

The performance of wireless body area networks (WBANs) may be degraded due to co-channel interference, i.e., when sensors of different coexisting WBANs transmit at the same time-slots using the same channel. In this paper, we exploit the 16 channels available in the 2.4 GHz unlicensed international band of ZIGBEE, and propose a distributed scheme that opts to avoid interference through channel to time-slot hopping based on Latin rectangles, DAIL. In DAIL, each WBAN's coordinator picks a Latin rectangle whose rows are ZIGBEE channels and colunms are time-slots of its superframe. Subsequently, it assigns a unique symbol to each sensor; this latter forms a transmission pattern according to distinct positions of its symbol in the rectangle, such that collisions among different transnnssions of coexisting WBANs are minimized. We further present an analytical model that derives bounds on the collision probability of each sensor's transmission in the network. In addition, the efficiency of DAIL in interference mitigation has been validated by simulations.

The goal of this paper is to provide a lightweight approach for the early detection of epileptic seizures using data from inertial measurement unit and muscular activity. The detection procedure runs in a portable data collection device and raises an alarm for family member or other persons in the vicinity for fast assistance and eventually for saving the life of the monitored patient. The instantaneous power of sliding window is derived for inertial measurements from 3D Accelerometer (ACM), 3D Gyroscope (Gyro) and for the muscular activity from the ElectroMyoGram (EMG). The residual between forecasted and measured power is used as input for the detection algorithm based on Shewhart control chart. When the error between forecasted and derived power exceeds statistical chart limits [lower, upper] for several consecutive slots, an alarm is raised. The proposed approach is intended to improve the performance of existing detection systems by increasing the detection accuracy and reducing the false alarms through correlation analysis of collected data from 3D ACM, 3D Gyro and EMG. Our experimental results on real data set collected in Necker hospital from epileptic patients show that our proposed approach is robust against nocturnal movements and achieves a high level of detection accuracy with low false alarm rate.

The main goal of this paper is to propose an effective approach for the early detection of nocturnal seizures from multi-signals gathered by an Armband. An inertial measurement unit and muscular activities acquisition sensors are integrated in the armband, and used to collect acceleration, angular velocity and electromyogram from the arm of the monitored patient. These signals are transmitted from the arm-band to a portable unit (e.g., SmartPhone) for preprocessing, detection and identification of seizures. Our approach starts by deriving the root mean square for acceleration and gyroscope, followed by the normalization of whole signals in the same range, and aggregation into one signal. The chart's control with its upper and lower limits are derived in the training phase (when the patient at rest) and used to detect abnormal seizures and to raise an alarm for patient relative for assistance to prevent further injuries when he loses consciousness.

We formulate and study a broadcast problem arising in multi-channel duty cycling wireless body area networks (WBANs), where the sink needs to broadcast the control message to all sensor nodes. The objective is to design robust multichannel wake-up schedule with minimum worst-case broadcast delay while guaranteeing the full broadcast diversity regardless of clock drifts and asymmetric duty cycles. To that end, we first derive the lower-bound of worst-case broadcast delay with full diversity of any broadcast protocol and then design a multichannel broadcast protocol (MCB) that satisfies the performance requirement for the latency and diversity. Finally, the simulation results demonstrate the capability of MCB of ensuring successful broadcast delivery on every channel within the theoretical worst-case broadcast delay, even under asymmetric duty cycles and any amount of clock drifts.

Recent advances in microelectronics have enabled the realization of Wireless Body Area Networks (WBANs). However, the massive growth in wireless devices and the push for interconnecting these devices to form an Internet of Things (IoT) can be challenging for WBANs; hence robust communication is necessary through careful medium access arbitration. In this paper, we propose a new protocol to enable WBAN operation within an IoT. Basically, we leverage the emerging Bluetooth Low Energy technology (BLE) and promote the integration of a BLE transceiver and a Cognitive Radio module (CR) within the WBAN coordinator. Accordingly, a BLE informs WBANs through announcements about the frequency channels that are being used in their vicinity. To mitigate interference, the superframe's active period is extended to involve not only a Time Division Multiple Access (TDMA) frame, but also a Flexible Channel Selection (FCS) and a Flexible Backup TDMA (FBTDMA) frames. The WBAN sensors that experience interference on the default channel within the TDMA frame will eventually switch to another Interference Mitigation Channel (IMC). With the help of CR, an IMC is selected for a WBAN and each interfering sensor will be allocated a time-slot within the (FBTDMA) frame to retransmit using such IMC.

Body-to-Body Networks (BBNs) have recently gained momentum as a revolutionary technology for the monitoring of people behavior with real-time updates of medical records and interactive assistance in emergency situations, like the spread of pandemic diseases. This paper investigates the epidemic control issue in mass gathering areas (i.e., the airports) from a practical point of view by using BBNs and by adopting some key features from existing epidemiology models. We first introduce a BBN-based epidemic control framework. Second, we define an Epidemic-aware Routing Metric and then propose a Location-Aided Routing protocol tailored to BBNs, called BB-LAR, along with an epidemic control mechanism, in order to exchange epidemic data and help the authority control unit in detecting and quarantining the infected subjects. Finally, we evaluate the performance of BB-LAR with respect to existing routing schemes in terms of packet delivery ratio, end-to-end delay, and energy consumption.

UnderWater Acoustic Sensor Networks (UW-ASNs) require protocols that make judicious use of the limited energy budget of the underwater sensor nodes. In this paper, we tackle the problem of energy holes in UW-ASNs. We show that we can balance the energy consumption through the network provided that sensors can use multiple transmission ranges when they send or forward the periodically generated data. In particular, we suppose that sensors can adjust their communication ranges up to three possible levels and we determine the set of possible next hops with the associated load weights that lead to a fair energy consumption among all underwater sensors. Hence energy holes can be avoided and consequently the network lifetime is highly increased.

Fundamental differences between underwater acoustic propagation and terrestrial radio propagation impose the design of new networking protocols. In this paper, a multichannel MAC protocol, MC-UWMAC, especially designed for underwater acoustic sensor networks, is proposed and evaluated. MC-UWMAC is a low power MAC protocol operating on multichannel using a single slotted control channel and multiple data channels. To guarantee a collision free communication, MC-UWMAC uses a virtual grid based slot assignment linked with a quorum based data channel allocation. Specifically, control channel slots are dedicated for handshaking. Data transmission takes place in a unique data channel especially reserved for each communicating pair. Simulation results show that MC-UWMAC can greatly improve the network performance especially in terms of energy consumption, packet delivery ratio and end-to-end delay.

We formulate and study a broadcast problem arising in multichannel duty-cycling wireless body area networks (WBANs) which the sink needs to broadcast control information to all sensor nodes on or implanted in the human body. Despite its fundamental importance for the network configuration and secure key management, the multichannel broadcast problem is largely unaddressed in duty-cycling WBANs. In this paper, we devise novel 2-D scheduling specifying the rule of channel hopping and wake-up time slot selection, which achieves the order-minimal worst-case broadcast delay while guaranteeing the full broadcast diversity regardless of clock drifts and asymmetric duty cycles and channel perceptions. Specifically, we first employ the Chinese remainder theorem to design an effective multichannel broadcast (MCB) algorithm and further propose improved MCB that enhances the granularity of MCB in matching actual duty cycles and number of channels, reducing the theoretically worst-case broadcast delay of MCB by up to 75%. We demonstrate the performance of the proposed algorithms through theoretical analysis and extensive simulations.

When sensors of different coexisting wireless body area networks (WBANs) transmit at the same time using the same channel, a co-channel interference is experienced and hence the performance of the involved WBANs may be degraded. In this paper, we exploit the 16 channels available in the 2.4 GHz international band of ZIGBEE, and propose a distributed scheme that avoids interference through predictable channel hopping based on Latin rectangles, namely, CHIM. In the proposed CHIM scheme, each WBAN's coordinator picks a Latin rectangle whose rows are ZIGBEE channels and columns are sensor IDs. Based on the Latin rectangle of the individual WBAN, each sensor is allocated a backup time-slot and a channel to use if it experiences interference such that collisions among different transmissions of coexisting WBANs are minimized. We further present a mathematical analysis that derives the collision probability of each sensor's transmission in the network. In addition, the efficiency of CHIM in terms of transmission delay and energy consumption minimization are validated by simulations.

A Wireless Body Area Network (WBAN) provides health care services. The performance and utility of WBANs can be degraded due to interference. In this paper, our contribution for co-channel interference mitigation among coexisting WBANs is threefold. First, we propose a distributed orthogonal code allocation scheme, namely, OCAIM, where, each WBAN generates sensor interference lists (SILs), and then all sensors belonging to these lists are allocated orthogonal codes. Secondly, we propose a distributed time reference correlation scheme, namely, DTRC, that is used as a building block of OCAIM. DTRC enables each WBAN to generate a virtual time-based pattern to relate the different superframes. Accordingly, DTRC provides each WBAN with the knowledge about, 1) which superframes and, 2) which time-slots of those superframes interfere with the time-slots within its superframe. Thirdly, we further analyze the success and collision probabilities of frames transmissions when the number of coexisting WBANs grows. The simulation results demonstrate that OCAIM outperforms other competing schemes in terms of interference mitigation and power savings.

Duty-cycle has recently attracted significant research attention due to its paramount importance on energy conservation in Wireless Body Area Networks (WBANs). However, the additional delay resulted from applying duty-cycle is overlooked in most, if not all, of existing work, despite the fundamental importance of the delay in healthcare applications. In order to bridge this gap, we devote this paper to analyzing the delay of IEEE 802.15.6 CSMA/CA mechanism under duty-cycle. Technically, we first explicitly formulate the expressions of the random delay that a sensor node spends on transmitting packets under asynchronous duty- cycling protocol of IEEE 802.15.6 CSMA/CA. Moreover, we mathematically derive the probabilistic characteristics in terms of the expectation and variance of the delay. Furthermore, we conduct elaborate simulations to demonstrate the correctness of the theoretical analysis.

In this paper, we propose a distributed multi-hop interference avoidance algorithm, namely, IAA to avoid co-channel interference inside a wireless body area network (WBAN). Our proposal adopts carrier sense multiple access with collision avoidance (CSMA/CA) between sources and relays and a flexible time division multiple access (FTDMA) between relays and coordinator. The proposed scheme enables low interfering nodes to transmit their messages using base channel. Depending on suitable situations, high interfering nodes double their contention windows (CW) and probably use switched orthogonal channel. Simulation results show that proposed scheme has far better minimum SINR (12dB improvement) and longer energy lifetime than other schemes (power control and opportunistic relaying). Additionally, we validate our proposal in a theoretical analysis and also propose a probabilistic approach to prove the outage probability can be effectively reduced to the minimal.

The electromyogram signals generated by muscles are used in numerous fields such as augmented reality, biomedical, kinematics, gaming, 3D animations and Human-Machine Interfaces. The latter is specifically used to help persons with reduced mobility or amputee with specific constraints to remote control machines. In this paper, we present a novel EMG-based system that aims to control multimedia player in simple, efficient and flexible manner. The implementation of our proposed approach was realized in order to achieve experiments and to conduct performance analysis. Our approach uses pattern recognition and contraction duration to derive four predefined actions. Our experimental results show the capacity of our system to achieve good detection accuracy of user EMG-based commands and to translate these commands into actions in media player system.

The goal of this paper is to propose a new approach for controlling smart home devices using physiological and movements signals (electromyogram, accelerometer and gyroscope). Our proposed approach exploits the ElectroMyoGram (EMG) signal to detect user muscles contraction and to trigger the associated actions on the controlled device. The triggered action is based on the position of the hand or its movement pattern. The Support Vector Machine (SVM) is used to classify the gyroscope data and to detect hand movements. The position of the hand is determined using the K-Nearest Neighbors (KNN) algorithm. Our proposed approach can be used to command any electronic device or connected objects and it is intended to work with data from wearable arm band containing triaxial accelerometer, gyroscope and able to measure EMG signal. Our experimental results are very encouraging where we achieve fast processing and reliable evaluation of hand movement with very low rate of actions miss interpretation.

Wireless Body Area Networks (WBANs) which offer a variety of promising applications in the areas of medical and consumer electronics have been paid lots of attention. However, very limited work has been done on the network reliability combined with energy conservation in spite of their fundamental importance. In order to bridge this gap, we devote this paper to developing a reliable and energy-efficient leader election (REELE) algorithm for WBANs. To this end, technically, we first partition a WBAN into regions and build the reliability and energy consumption models. By the reliability analysis, we then propose a novel communication strategy for nodes and further derive the total energy consumption of a region. With the reliability and residual energy of a node and total energy consumption considered jointly, REELE algorithm can considerably enhance reliability and conserve energy. Extensive simulation results demonstrate the effectiveness and the efficiency of REELE in terms of longer network lifetime, better energy characteristics as well as higher reliability.

This paper focuses on the resource allocation problem in the context of Cloud Computing. More specifically, this work considers the problem of optimizing the mapping cost of Infrastructure as Cloud Service (IaaS) onto a Networked Edge Data-Centers (DCs) with respect to Quality of Service (QoS) requirements. This work proposes to dynamically partition the networked DCs resources over IaaS requests belonging to different QoS classes. In literature, a number of works have proposed IaaS mapping approaches; however their focus was mainly on the cloud hosting requirements and do not take into account the dynamics of IaaS QoS requirements. Consequently, they may not offer QoS guarantees for accepted IaaS requests which may result in a higher customer dissatisfaction ratio. The originality of our work is in the forethought and the investigation of these issues. To do so, a column generation based-formulation is proposed coupled with the Branch and Bound technique in order to solve it efficiently. Doing so, this allows the Cloud Provider to: (i) minimize IaaS mapping cost, and (ii) calculate the optimal and dynamic partitioning of DCs resources to uphold QoS guarantees for IaaS requests.

This paper considers the problem of intra-interference in a Wireless Body Area Network (WBAN). The problem arises mainly because each bio-sensor collects different parameters with different data rate and alternation. Another source of interference is related to normal patient movement. Proposals in the literature usually assume that the interference can be handled using time multiplexing or by listening before transmission to avoid collision. However, these adaptive approaches, given the high-occupancy channels, bring with them major problems of collision and extra energy consumption. One solution could be a power control mechanism. Nevertheless, techniques of that kind are challenging in that they require periodic information on the condition of the wireless channels, conditions that are difficult to estimate. To address these concerns, a tree-based WBAN topology using a set of relay nodes with stable communication called a "virtual backbone" is proposed. As bio-sensors report data mainly in uplink traffic, it is assumed that they share a small number of wireless channels using the TDMA technique. Relay nodes, on the other hand, share the most number of channels in order to improve the fluidity of data across the WBAN. To mitigate co-channel interference among relays, two techniques from the literature called the adaptive data rate and the adaptive duty cycle are used. Simulation experiments showed that the proposed architecture, when combined with intra-interference mitigation techniques, improves energy efficiency and increases data rate.

In Wireless Body Area Networks (WBANs), the energy consumption determines the lifetime of the entire network. As a result, how to conserve the energy to prolong the network lifetime becomes a key problem in WBANs. In this paper, to address the energy conservation problem in WBANs, we develop an Energy-Efficient Leader Election mechanism, called EELE. In EELE, each node competes for the leader following the distributed leader election algorithm in which a utility function is constructed with the consideration of the residual energy and the location of the node. Moreover, a distance-aware hybrid communication mode is proposed such that a node can choose either direct communication or cooperative communication to alleviate the burden of the leader or the far node. Extensive simulation results demonstrate the effectiveness and the efficiency of EELE mechanism in terms of longer network lifetime, better energy characteristics and higher throughput.

The sleep apnea is a sleep disorder characterized by cessation of respiratory flow (apnea) or a reduction in the flow (hypopnea). This disorder is often invalidating and may in some cases lead to death. During the night, symptoms can include nocturnal choking, heavy snoring, sweating, restless sleep, impotence, and witnessed apnea. As the sleep centers for apnea detection are usually overloaded and inaccessible, an automatic apnea detection algorithm for portable devices is required for in-home detection. In this paper, we propose a lightweight approach for pervasive detection of sleep apnea using Wireless Sensor Networks. The experimental results show that our proposed approach achieves good detection accuracy with low delay and low false alarm rate.

The aim of this paper is to provide a lightweight approach for early detection of nocturnal epileptic seizures using data from wireless 3-D accelerometer sensors. We use the exponentially weighted moving average algorithm to forecast the current value of the accelerometer measurement, and when the difference between measured and forecasted values is greater than the dynamic threshold on any axis, a notification is transmitted to the base station, which maintains a sliding window of received notifications. When the filling ratio is greater than a predefined threshold, an alarm is triggered by the base station. The proposed approach is intended to improve the performance of existing mobile health detection systems based on the analysis of electroencephalogram (EEG). To reduce their false alarm rate, we seek to correlate detection results from 3-D accelerometer with other physiological parameters through a majority voting. Our experimental results on real dataset collected from the epileptic patient show that our proposed approach is robust against temporal fluctuations and achieves a high level of detection accuracy, which in turn proves the effectiveness of this approach in enhancing the reliability of existing detection approaches based on EEG signal analysis.

In this paper, we propose an Anomaly Detection (AD) approach for medical Wireless Sensor Networks (WSNs). This approach is able to detect abnormal changes and to cope with unreliable or maliciously injected measurements in the network, without prior knowledge of anomalous events or normal data pattern. The main objective is to reduce the false alarms triggered by abnormal measurements. In our proposed framework, each sensor applies the Exponentially Weighted Moving Average (EWMA) for one-step forecasting. To reduce the energy consumed by periodic data transmission to the Local Processing Unit (LPU), the sensor transmits only when the data point (measured, expected) falls outside the dynamically updated ellipsoidal region enclosing the normal data. The LPU exploits correlation and uses chi-square distance for spatial analysis before raising a medical alarm. We evaluate our approach on real medical data set. Experimental results through computer simulation demonstrate that our proposed approach can achieve a good detection accuracy with low false alarm rate (less than 4%).

Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information about its activities. In this paper, we propose a new approach for the early detection of epileptic seizure in EEG. The proposed approach is based on Discrete Wavelet Transform (DWT) and Ant Colony (AC) Classifier. We started by applying DWT to decompose the EEG signal into its sub-bands to extract the energy ratio from wavelet coefficients. Beside we extract some statistical features from the original signal, and we use the extracted features as the input for the AC algorithm to derive classification rules, which are used to detect epileptic seizures in the EEG of the monitored patient. Our experimental results on real dataset show that our proposed approach achieves a high level of detection accuracy.

Wireless body area network (WBAN) witness an upward interest in several domain. Mainly, the medical domain takes advantages from the health service facility, the high flexibility and the mobility. However, interferences from coexisting wireless networks may lose critical informations and greatly affect on network reliability. In this paper, we are conducting to improve coexistence between WBAN based IEEE 802.15.4 protocol and WIFI. We adopt multi-hop routing to ensure reliability, connectivity and battery life. Then we investigate different effects of transmit power, transmission frequency and packet size on WBAN performances under heavy and real interferences circumstances. We propose a well suited model and simple adaptive algorithm which adjust dynamically its parameters with received performances indicators.

In this paper, we propose a lightweight approach for online detection of faulty measurements by analyzing the data collected from medical wireless body area networks. The proposed framework performs sequential data analysis using a smart phone as a base station, and takes into account the constrained resources of the smart phone, such as processing power and storage capacity. The main objective is to raise alarms only when patients enter in an emergency situation, and to discard false alarms triggered by faulty measurements or ill-behaved sensors. The proposed approach is based on the Haar wavelet decomposition, nonseasonal Holt-Winters forecasting, and the Hampel filter for spatial analysis, and on for temporal analysis. Our objective is to reduce false alarms resulting from unreliable measurements and to reduce unnecessary healthcare intervention. We apply our proposed approach on real physiological dataset. Our experimental results prove the effectiveness of our approach in achieving good detection accuracy with a low false alarm rate. The simplicity and the processing speed of our proposed framework make it useful and efficient for real time diagnosis.

In this paper, we propose a reliable topology design and provisioning approach for Wireless Body Area Networks (named RTDP-WBAN) that takes into account the mobility of the patient while guaranteeing a reliable data delivery required to support healthcare applications' needs. To do so, we first propose a 3D coordinate system able to calculate the coordinates of relay-sensor nodes in different body postures and movements. This system uses a 3D-model of a standard human body and a specific set of node positions with stable communication links, forming a virtual backbone. Next, we investigate the optimal relay nodes positioning jointly with the reliable and cost-effective data routing for different body postures and movements. Therefore, we use an Integer Linear Programming (ILP) model, that is able to find the optimal number and locations of relay nodes and calculate the optimal data routing from sensors and relays towards the sink, minimizing both the network setup cost and the energy consumption. We solve the model in dynamic WBAN (Stand, Sit and Walk) scenarios, and compare its performance to other relaying approaches. Experiment results showed that our realistic and dynamic WBAN design approach significantly improves results obtained in the literature, in terms of reliability, energy-consumption and number of relays deployed on the body.

Cardiovascular diseases are the leading cause of death in the world, and Myocardial Infarction (MI) is the most serious one among those diseases. Patient monitoring for an early detection of MI is important to alert medical assistance and increase the vital prognostic of patients. With the development of wearable sensor devices having wireless transmission capabilities, there is a need to develop real-time applications that are able to accurately detect MI non-invasively. In this paper, we propose a new approach for early detection of MI using wireless body area networks. The proposed approach analyzes the patient electrocardiogram (ECG) in real time and extracts from each ECG cycle the ST elevation which is a significant indicator of an upcoming MI. We use the sequential change point detection algorithm CUmulative SUM (CUSUM) to early detect any deviation in ST elevation time series, and to raise an alarm for healthcare professionals. The experimental results on the ECG of real patients show that our proposed approach can detect MI with low delay and high accuracy.

This work further investigates paradigm of radiation awareness in WBAN network environments. We incorporate the effect of topology as well as the time domain and environment aspects. Even, if the impact of radiation to human health remains largely unexplored and controversial. In this paper, we propose a multi objectives flow model for WBSNs which allows describing a new optimal deployment model for WBAN sensor devices dynamic topology and the relevant possible trade-offs between coverage, connectivity, network life time and radiation to human health. We propose oblivious deployment heuristics that are radiation aware. Simulation results show that the algorithm balances the energy consumption of nodes effectively and maximize the network lifetime. It will meet the enhanced WBANs requirements, including better delivery ratio, less reliable routing overhead. Our proposed radiation aware deployment heuristics succeed to keep radiation levels low, while not increasing latency.

The aim of this paper is to propose a new approach for the detection and isolation of faulty measurements in medical wireless sensors networks. The proposed approach is based on the combination of statistical model and machine learning algorithm. We begin by collecting physiological data and then we cluster the data collected during the first few minutes using the Gaussian mixture decomposition. We use the resulted labeled data as the input for the Ant Colony algorithm to derive classification rules, which are used to detect abnormal values. Finally, we exploit the spatial correlation between monitored attributes to differentiate between faulty sensor readings and emergency situations. Our experimental results on real patient dataset show that our proposed approach achieves a high level of detection accuracy, which in turn proves the effectiveness of this approach in enhancing the reliability of medical wireless sensors networks.

Wireless Sensor Networks are vulnerable to a plethora of different fault types and external attacks after their deployment. We focus on sensor networks used in healthcare applications for vital sign collection from remotely monitored patients. These types of personal area networks must be robust and resilient to sensor failures as their capabilities encompass highly critical systems. Our objective is to propose an anomaly detection algorithm for medical wireless sensor networks. Our proposed approach firstly classifies instances of sensed patient attributes as normal and abnormal. Once we detect an abnormal instance, we use regression prediction to discern between a faulty sensor reading and a patient entering into a critical state. Our experimental results on real patient datasets show that our proposed approach is able to quickly detect patient anomalies and sensor faults with high detection accuracy while maintaining a low false alarm ratio.

In this paper, we focus on online detection and isolation of erroneous values reported by medical wireless sensors. We propose a lightweight approach for online anomaly detection in collected data, able to raise alarms only when patients enter in emergency situation and to discard faulty measurements. The proposed approach is based on Haar wavelet decomposition and Hampel filter for spatial analysis, and on boxplot for temporal analysis. Our objective is to reduce false alarms resulted from unreliable measurements. We apply our proposed approach on real physiological data set. Our experimental results prove the effectiveness of our approach to achieve good detection accuracy with low false alarm rate.

In recent years, Body-to-Body Networks (BBNs) have gained momentum as a means to monitor people behavior and simplify their interaction with the surrounding environment; thus representing a key element of the Internet of Things (IoT) networking paradigm. Within BBNs, several transmission technologies sharing the same unlicensed band (namely the ISM band) coexist, increasing dramatically the level of interference, which in turn negatively affects the network performance. In this paper, we consider an IoT system composed of several BBNs and we analyze the Cross Technology Interference (CTI) problem caused by the utilization of different transmission technologies that share the same radio spectrum. We formulate an optimization model considering both the Mutual and Cross Technology Interference in order to mitigate the overall level of interference within the IoT system, taking explicitly into account the node mobility. We further develop two heuristic approaches to solve efficiently the interference mitigation problem in large scale network scenarios. Numerical results show that the proposed heuristics represent two efficient and practical alternatives to the optimal solution for solving the CTI mitigation problem in large scale IoT scenarios.

In managing multimedia services, it is important to understand how network performance affects user experience. The model presented in this paper aims to estimate user perception of video quality based on defect events, which are automatically classified by machine learning techniques. The underlying principle of our model is that human experience is event-based and there is a strong correlation between defective events and user MOS. Through experiments, we show that our model can detect different types of defect events with good accuracy even under small data set, and we find that indeed different defect event types affect user experience with different sensitivity.

The increasing use of wireless networks and the constant miniaturization of electrical devices has empowered the development of Wireless Body Sensor Networks (WBSNs). The wireless nature of the network and the wide variety of sensors offer numerous new, practical and innovative applications to improve health care and the Quality of Life. WBSNs like any other sensor networks suffer limited energy resources and hence preserving the energy of the nodes is of great importance. Unlike typical sensor networks WBSNs have few and dissimilar sensors. In addition, an extremely low transmit power per node is needed to minimize interference to cope with health concerns and to avoid tissue heating which means that the existing solution for preserving energy in wireless sensor networks might not be efficient in WBSNs. Most of the attention has been given to the energy routing where energy awareness is an essential consideration. In this paper, we propose a Min-Max multi-commodity flow model for WBSNs which allows to prevent sensor node saturation, by imposing an equilibrium use of sensors during the routing process taking into account the specific characteristics of the wireless environment on the human body. The Min-Max objective is transformed to a Min objective by adding a set of constraints to the model. Based on the energy consumption for sending and receiving data and the available residual energy of nodes, the max-min based mathematical programming model is designed to find optimal routing. Simulation results show that the algorithm balances the energy consumption of nodes effectively and maximize the network lifetime.

WBSNs (wireless body sensor network) like any other sensor networks suffers limited energy and are the highly distributed network, in which its nodes are the organizer itself and each of them has the flexibility of collecting and transmitting patient biomedical information to a sink. When knowledge sent to sink from a path that doesn't have a definite basis, the routing is a crucial challenge in Wireless Body Area Sensor Networks, additionally reliability and routing delay are the considerable factors in these type of networks. Most of the attention should be given to the energy routing where energy awareness is an essential consideration in WBSNs and the frequent topology change increases the dynamics of network topology, and complicates the process of relay selection in cooperative communications. In this paper, we propose a Min-Max multi-commodity flow model for WBSNs which allows to prevent sensor node saturation and take best action against reliability and the path loss, by imposing an equilibrium use of sensors during the routing process. Simulation results show that the algorithm balances the energy consumption of nodes effectively and maximize the network lifetime. It will meet the enhanced WBSNs requirements, including better delivery ratio, less reliable routing overhead.

Wireless Body Area Networks (WBANs) represent one of the most promising approaches for improving the quality of life, allowing remote patient monitoring and other healthcare applications. In such networks, traffic routing plays an important role together with the positioning of relay nodes, which collect the information from biosensors and send it towards the sinks. This work investigates the optimal design of wireless body area networks by studying the joint data routing and relay positioning problem in a WBAN, in order to increase the network lifetime. To this end, we propose an integer linear programming model which optimizes the number and location of relays to be deployed and the data routing towards the sinks, minimizing both the network installation cost and the energy consumed by wireless sensors and relays. We solve the proposed model in realistic WBAN scenarios, and discuss the effect of different parameters on the characteristics of the planned networks. Numerical results demonstrate that our model can design energy-efficient and cost-effective wireless body area networks in a very short computing time, thus representing an interesting framework for the WBAN planning problem.

In this paper we propose a new framework able to ensure security, multimedia quality and energy efficiency in Multimedia Wireless Sensor Networks (MWSNs). The energy is an important and limited resource, which has a direct impact on the lifetime of nodes in MWSNs. In addition, the characteristics of MWSNs like the limited bandwidth and non-deterministic channel access have a significant impact on the QoS of multimedia traffic. We propose the Efficient Dynamic Selective Encryption Framework (EDES) in order to reduce the energy consumption and increase the QoS while ensuring a secure multimedia traffic. EDES proposes three security levels (high, medium and low) and the selection of each level depends on the energy and QoS parameters. Moreover, the cross-layer approach is selected for EDES to take into account the different parameters at physical, MAC and upper layers. The capacity metric is proposed to evaluate the possibility to increase or decrease the security level. The simulation results illustrate the importance of the security level adaptation according to the QoS and the energy parameters. EDES increases the lifetime duration of nodes by almost 40% compared to static encryption.

Anomaly detection in high speed networks is well known to be a challenging problem. It requires generally the analysis of a huge amount of data with high accuracy and low complexity. In this paper, we propose an anomaly detection mechanism against flooding attacks in high speed networks. The proposed mechanism is based on Jensen-Shannon divergence metric over sketch data structure. This sketch is used to reduce the required memory, while monitoring the traffic, by maintaining them into a predefined fixed size of hash tables. This sketch is also used to develop a probabilistic model. The Jensen-Shannon divergence is used for detecting deviations between previously established and current distributions of network traffic. We have implemented our approach and evaluated it using real Internet traffic traces, obtained from MAWI trans-Pacific wide transit link between USA and Japan. Our results show that the proposed approach is scalable and efficient in detecting anomalies without maintaining per-flow state information.

Scalable video applications have been gaining popularity during last years. Providing high quality video over wireless networks towards heterogeneous receivers is a challenging task due to erratic and time-varying nature of a wireless channel. Compressed video bit-streams, such as Scalable Video Coding (SVC), are very vulnerable to channel disturbances when transmitted over error-prone wireless channels. Transmission errors not only corrupt the current frame, but also propagate to next frames along motions prediction path. In this paper we propose a novel scheme for transmission of SVC-based streams over Wireless Mesh Network (WMN) with Unequal Error Protection (UEP) based on Appropriate Path Selection (APS) and Network Coding (NC). The scheme tries to select appropriate paths based on Grey Relational Analysis (GRA) for different SVC layers. It chooses the best path for most important layer, base layer, and less stable paths for enhancement layers. Some nearby nodes along the transmission path are selected for recoding their received packets and store them in buffers for some period of time. The nearby nodes coded packets can be transmit to receivers for recovering the lost packets.

Internet services are vulnerable to flooding attacks that lead to denial of service. This paper proposes a new framework to detect anomalies and to provide early alerts for flooding attacks in backbone networks. Thus allow to quickly react in order to prevent the flooding attacks from strangling the victim server and its access network. The proposed detection scheme is based on the application of Least Mean Square (LMS) filter and Pearson Chi-square divergence on randomly aggregated flows in Sketch data structure. Instead of analyzing one time series for overall traffic, random aggregation of flows is used to investigate a fixed number of time series for grained analysis. Least mean square filter is used to predict the next value of the time series based on previous values, and Pearson Chi- square divergence is used to measure the deviations between the current and estimated probability distributions. We evaluate our approach using publicly available real IP traces (MAWI) collected from the WIDE backbone network, on trans-Pacific transit link between Japan and USA. Our experimental results show that the proposed approach outperforms existing techniques in terms of detection accuracy and false alarm rate. It is able to detect low intensity attacks covered by the large number of traffic in high speed network.

In the last decade, both mobile and multimedia communications have experienced unequaled rapid growth and commercial success. However, transmitting multimedia flows over wireless Ad hoc network remains an extremely challenging issue due to the limited battery lifetime of the wireless nodes. The focus, of this paper, is to design a new efficient protocol optimizing the energy consumption when transmitting video streams. We propose to exploit the SVC coding to adapt dynamically the received video quality to the instantaneous wireless nodes' characteristics. This is achieved through determining the number of the transmitted/received enhancements layers of an SVC video based on the wireless node context. The proposed solution also considers the routing aspects to guarantee to destinations the requested QoS. In order to evaluate the performance of our scheme, we have carried out several sets of simulation experiments. Our results indicate that our proposal outperforms the conventional approach by increasing the overall network lifetime while maintaining a high perceived video quality.

The emerging and promising Vehicular Ad hoc Networks (VANET) are receiving a lot of attention from both industry and academia due to the wide variety of services such as safe navigation support, entertainment and many more. Providing robust and reliable dissemination of data over VANET is the key issue. Due to varying network topology packet loss and delay are very common in such type of networks which severely influence the perceived video quality at receiving end. We need reliable transmission protocols for text data dissemination while the video data can tolerate a certain amount of packet loss. Therefore, we are more interested in robust Scalable Video Coding (SVC) based streaming over VANET for multi-purpose including safe navigation support, because SVC is a very good solution to alleviate the effects of the error-prone channels. In this paper, we propose a robust scheme for SVC-based streaming over an urban VANET with path diversity and network coding. The scheme calculates the quality of all candidate paths based on Grey Relational Analysis (GRA) and then assigns paths to different layers according to their importance. We also select some nearby nodes along the transmission path for recoding their received packets and store them in buffers for unit period of time. These network coded packets can be retransmit to receivers for recovering lost packets.

The explosive increasing amount of multimedia content to be offered in the Internet and the heterogeneity of the underlying networking technologies demand the provision of new QoS-enabled mechanisms and architecture to efficiently control, manage and monitor the networks. This paper first briefly introduces a complex system for multimedia content generation, transport and consumption, over heterogeneous network environment. This is developed in ENTHRONE I and II projects, where a central concept and subsystem is the ENTHRONE integrated management supervisor (EIMS). Then, the focus of the paper is to present a novel service oriented monitoring framework as an essential part of the EIMS. The proposed QoS monitoring system aims at providing service performance verification with respect to the QoS guarantees specified in contractual agreements between providers and users. This is achieved by not only monitoring services inside wired and wireless access/core networks but also at the customer side. The presented system also provides monitoring information to service providers for providing quantified QoS-based services and service assurance and to network providers for managing network resources.

This paper introduces a buffer occupancy-based admission control mechanism aimed to counter link congestion while fairly sharing the bandwidth in converged IP and broadcasting networks. The proposed connection admission control (CAC) scheme favors fairness and downlink bandwidth sharing between different served satellite terminals (sub-networks) in presence of congestion events. Our CAC is intended to overcome major obstacles such as: instantaneous link capacity (available bandwidth) assessing, high computational complexity in flows characterization, and use of traffic descriptors with new flows. This makes the scheme more suitable for DVB-S2 environments where the link capacity may vary as consequence of physical layer adaptive coding/modulation during noisy periods. Simulations results show that the proposed system can avoid uplink and downlink congestion and share fairly the bandwidth between satellite terminals. Further, our CAC minimize the probability of congestion events while still achieving high resources utilization.

Multimedia streaming over ad hoc networks forms a continuous progress domain due to users' growing needs and wireless devices incessant evolution. Our objective, in this study, is to increase individual wireless nodes lifetime by a saving energy model based on video transmission/reception which allows increasing the ad hoc network lifetime. When arriving at a predefined threshold, we reduce the number of multimedia transmitted/received packets by exploiting the MPEG4 enhancement layers. In our proposal we optimize the tradeoff between the energy consumption and the perceived video quality. Simulation results show that we outperform the conventional approach by increasing the overall network lifetime.

Diffserv over MPLS networks is a widely accepted approach to considerably improve networks' ability to support delay-sensitive applications such as voice over IP. In such networks, over-provisioning and careful admission control are still needed, although insufficient to ensure guaranteed QoS performance. Increased end-to-end loss rates and delays experienced by a service are mostly due to one or few congested switches along the label switched path "LSP" while the other routers are in relaxed conditions. In this paper, we tackle this issue by extending the traditional local management of congestions into a cooperative process involving all switches along the service path at network operator's scale. Going from AQM limitation, we propose a network self-managing framework that dynamically re-adjusts switches' parameters throughout the LSP, at the point where congestion would most likely occur. In this way, the prospective congestion impact is absorbed through balancing the routers aggressiveness without reconsidering other traffic engineering strategies (e.g., re-routing decision). While considering QoS guarantees along a given service path, network loss ratio is reduced. This obviously allows network operators to further exploit theirs underlying resources by accepting more QoS-enabled services.

The actual trend in the IT (information technology) market is to create a new environment that will enable end-users to access a variety of services (TV/Radio programs, Internet, Multimedia applications, Games ...), wherever they are, whenever they want and whatever terminal they use. No existing solution exists that allows the common user to have access, instantly and in real time, to his desired range of services and applications no matter when and where he may geographically be. With the emergence of Beyond-3G/4G systems and environments, network access possibilities will be much more present throughout the world. Based on this assumption, we present an approach that would permit to provide users anywhere around the globe with their desired services, even where the service is not accessible. Through the use of an ambient middleware, with MPEG-21 and metadata-based functionalities, the requested service will also be efficiently adapted for the benefit of the user.

Audio and video streaming over the Internet is becoming very attractive and popular. Thus, there is a need to provide efficient delivery of such applications to the growing number of demanding users. In addition, new types of devices, transportable and mobile are emerging rapidly. Now, common users are expecting to access their favorite Internet services anywhere, and through any equipment (laptop, PDA, ...). This paper combines those two requests by proposing an efficient adaptive solution to mobile users willing to receive audio-video streaming services. Our solution is focused on MPEG-4 coded streams, the use of audio-visual objects (AVOs) and the partitioning into different sets of layers. According to the changes of networks experienced by the mobile user, a classification followed by an adaptation process is performed to the video stream. AVOs are added or dropped according to the newly accessed network constraints and following a TCP-friendly rate control algorithm

In this paper, we describe an ongoing work focusing on a level of adaptation for multimedia services instantiated by a mobile user. The focus is based on video streaming over Internet, for which neither quality of service nor guarantee of resources in terms of bandwidth, transfer delay, delay variation and packet losses are offered. According to the movement of the user and the constraints of its new location, we propose a way of achieving better video service quality based on an adaptive layer. The proposed approach lies on the same principle as the TCP-friendly rate control.

With the steadily growing synergy between existing heterogeneous networks, the wireless LAN appears as the de-facto wireless access network in the end-to-end multimedia services distribution chain. Unlike in the traditional wired multi-hop networks (Internet) where congestions increase persistently both delays and losses, wireless packet losses are often location- and time-varying. Particularly, WLAN communication is characterized by high bit error rates that translates into tight loss dependency. The loss process may rapidly shift between different loss correlations levels, resulting in poor forward error correction (FEC) recovery capabilities. In this paper, we address this issue by providing a combined loss model to accurately characterize the wireless loss distribution features. We use control theory guided parameter tuning in order to urge the convergence of the loss models towards seizing the instantaneous loss distribution trends. Finally, we derive a new loss-specific QoS metrics for new FEC block allocation scheme.

Many works have recently addressed the IEEE 802.11 QoS issues by proposing different monitoring-based contention window (CW) differentiation techniques. In network saturation, however, it is difficult to guarantee firm services differentiation while achieving high network exploitation. Particularly, most existing QoS-capable MAC protocols rely on a backoff interval sampled from a dynamic range [0 CW/sub i/]. In order to ensure more deterministic service differentiation, we propose a new MAC protocol featuring a sliding contention window (SCW) for each network flow. The different flows are now able to select backoff intervals from different (separated) CW ranges. The SCW dynamically adjusts to changing network conditions, but remains within a per-class predefined range, in order to maintain a separation between different service classes. Simulation results show that compared to the EDCA scheme of 802.11e, SCW consistently excels, in terms of network utilization, strict service separation, and service-level fairness.

There is an increasing demand for supporting real-time audiovisual services over next-generation wired and wireless networks. Various link/network characteristics make the deployment of such demanding services more challenging than traditional data applications like e-mail and the Web. These audiovisual applications are bandwidth adaptive but have stringent delay, jitter, and packet loss requirements. Consequently, one of the major requirements for the successful and wide deployment of such services is the efficient transmission of sensitive content (audio, video, image) over a broad range of bandwidth-constrained access networks. These media will be typically compressed according to the emerging ISO/IEC MPEG-4 standard to achieve high bandwidth efficiency and content-based interactivity. MPEG-4 provides an integrated object-oriented representation and coding of natural and synthetic audiovisual content for its manipulation and transport over a broad range of communication infrastructures. In This work, we leverage the characteristics of MPEG-4 and Internet protocol (IP) differentiated service frameworks, to propose an innovative cross-layer content delivery architecture that is capable of receiving information from the network and adaptively tune transport parameters, bit rates, and QoS mechanisms according to the underlying network conditions. This service-aware IP transport architecture is composed of: 1) an automatic content-level audiovisual object classification model; 2) a reliable application level framing protocol with fine-grained TCP-Friendly rate control and adaptive unequal error protection; and 3) a service-level QoS matching/packet tagging algorithm for seamless IP differentiated service delivery. The obtained results demonstrate, that breaking the OSI protocol layer isolation paradigm and injecting content-level semantic and service-level requirements within the transport and traffic control protocols, lead to intelligent and efficient support of multimedia services over complex network architectures.

Recently, many active queue management (AQM) algorithms have been proposed to address performance degradations of end-to-end congestion control. However, these AQM algorithms present weaknesses for stabilizing delays in heavily loaded networks. In this paper, we describe a novel adaptive fuzzy control algorithm to improve best effort TCP/IP networks performance. Compared to traditional AQM algorithms (RED, PID and others), our proposal avoids buffer overflows/underflows, and minimizes packet dropping. We propose an on-line adaptation mechanism that captures fluctuating network conditions, while classical AQM algorithms require static tuning. The algorithm stability is mathematically proven. Simulation results show that for the same link utilization, our fast adaptive fuzzy controller provides better performance than RED and PID.

Robust video streaming over wireless local area networks (IEEE 802.11) faces many challenges, including bandwidth channel variations, data errors/losses, and terminal capacity heterogeneity. This is worsen by the TCP/IP architecture that does not offer any quality of service QoS guarantees to demanding applications such as video streaming. In order to improve error resilience and user-perceived video quality, a novel error control protocol, called "unequally interleaved forward error correction" (UI-FEC), is proposed. UI-FEC is particularly efficient for adaptive MPEG-4 video multicast over wireless LAN. The proposed protocol is composed of (1) a coordinated unequal and interleaved MPEG-4 data protection mechanism, that gracefully degrades video quality at receivers while minimizing the overall link bandwidth consumption; (2) an adaptive MPEG-4 video fragmentation and encapsulation protocol for a higher wireless link utilization.

This article describes a fair and robust video streaming framework over IP networks. It is based on an MPEG-4 audio-visual object (AVOs) classification, TCP-friendly transport and out-of-band unequal forward error protection. According to network congestion feedback, video source servers dynamically adjust their bit rates by adding and dropping MPEG-4 AVO to conform to the TCP-friendly rate control (TFRC) algorithm and by taking into consideration media semantic relevancy. Thus, an accurate MPEG-4 access unit (AU) partitioning and packetization can be performed to cope with decoding error propagation and network bandwidth fluctuation. Finally, AVOs requiring similar network QoS level are automatically classified, packetized and mapped to one of the available EP DiffServ PHB (per hop behaviors). Simulation results show a significant improvement regarding to user-perceived video quality, packet loss recovery and bandwidth share fairness.

In this article, we investigate efficient transmission of object-based MPEG-4 video over IP networks with QoS management capabilities. MPEG-4 audio visual objects (AVOs) are classified based on application-level QoS criteria and AVOs semantic descriptors according to MPEG-7 framework MPEG-4 AVOs requiring same QoS performance form the network are automatically classified and multiplexed within one of the IP DiffServ PHB (per hop behaviours). Object data-packets within the same class are then transmitted over the selected transport layer with the corresponding bearer capability and priority level. We propose to extend the MPEG-4 system architecture with a new "media QoS classification layer". This layer provides automatic and accurate mapping between MPEG-4 application-level QoS metrics and underlying transport network with QoS mechanisms such as IP DiffServ. The "media QoS classification layer" makes use of a neural network classification model that is transparent to application and network layers. Implementation and performance evaluation of the proposal are also described.

This article discusses technical issues related to delivery and control of IP multimedia services, such as videoconferencing, involving heterogeneous end terminals. In particular, it describes the design and implementation of an experimental system for interworking between IETF SIP (session initiation protocol) and ISO MPEG-4 DMIF (delivery multimedia integration framework) session and call control signaling protocols. This IP videoconferencing interworking system is composed of two core units for supporting delivery of audio-video streams from a DMIF domain to a SIP domain (i.e. DMIF2SIP unit) and from a SIP domain to a DMIF domain (i.e. SIP2DMIF unit). These units perform various translation functions for transparent establishment and control of multimedia sessions across an IP networking environment, including, session protocol conversion, service gateway conversion and address translation.

In this article an implementation of a JAVA-based MPEG-4 video on demand service over IP with DMIF signaling is proposed. The focus is on performance evaluation on transmitting video streams over IP Differentiated Services with Linux-based IP DiffServ gateways and real MPEG video sequences. Our contribution is to demonstrate that IP DiffServ's Assured Forwarding PHB is a serious candidate for supporting real-time video communications in association with intelligent packet marking and scheduling mechanisms.

This article describes a cooperative framework for the transport and the QoS control of delay-tolerant video streaming applications using MPEG-2 encoding and broadband ATM networks. The proposed framework integrates three components: (1) a dynamic video frame-level priority assignment mechanism based on MPEG data structure and feedback from the network (DexPAS); (2) an audio-visual AAL-5 SSCS with forward error correction capabilities (AV-SSCS); and (3) an intelligent packet video discard mechanism called FEC-PSD, that adaptively and selectively adjusts cell drop levels to switch buffer occupancy, video cell payload type and forward error correction capability of the destination. The proposed QoS control and video delivery framework is evaluated using simulation.

Traditional IP networks offer users best-effort service. In this model, all user packets compete equally for network resources. Much attention is being given to developing IP QoS (quality of service), which allows network operators to offer differing levels of treatment to user packets. In this article, we investigate QoS interaction provisioning between MPEG-4 video applications and IP DiffServ networks. QoS interaction is performed through intelligent encapsulation and marking of MPEG-4 elementary streams (video, audio, signaling, scenes and object descriptors) on IP Diffserv's AF/EF per hop behavior. Our contribution to this issue is twofold: first, we analyze the different proposals for MPEG-4 encapsulation protocols over RTP/IP; and second, we propose and evaluate a video-oriented marking mechanism for IP Diffserv edge routers. A performance evaluation is carried through simulation using ns2.

Transmission of MPEG2-encoded video is one of the most demanding applications in terms of network resources and QoS requirement. It needs high bandwidth with stringent transmission delays. It can not tolerate large variations on delays and it requires low error and loss data rates. Therefore, in order to design efficient integrated video communication systems over ATM networks, we propose in this paper to analyze the effects of errors and delays on both video signal and network performance.

This paper addresses the transport of real-time multimedia traffic generated by MPEG-2 applications over ATM networks using an enhanced UBR best effort service (UBR+). Based on the factors affecting the picture quality during transmission, we propose an efficient and cost-effective ATM best effort delivery service. The proposed service integrates three components: a dynamic frame level priority assignation mechanism based on the MPEG data structure and feedback from the network (DexPAS), a novel audiovisual AAL5 SSCS with FEC, and an intelligent packet video discard scheme named SA-PSD, which adaptively and selectively adjusts the cell drop level to switch buffer occupancy, video cell payload type and forward error correction ability of the destination. The overall best effort video delivery framework is evaluated using ATM network simulation and MPEG-2 video traces. The ultimate aim of this framework is twofold. First, minimizing loss for critical video data with bounded end-to-end delay for arriving cells. Second, reducing the bad throughput crossing the network during congestion. Compared to previous approaches, performance evaluation shows a good protection of predictive coded and bidirectional predictive coded frames at the video slice layer.

This paper addresses the transport of real-time multimedia traffic generated by MPEG-2 applications over ATM best effort services (ABR, UBR+). To cope with network congestion and the unreliability of the ATM adaptation layer type 5 (AAL5), we propose a new service-specific convergence sublayer for audiovisual applications. The proposed AV-SSCS includes adaptive forward data error correction (FEC) based on Reed-Solomon and parity codes. With respect to network load and video packet loss ratio measured at the destination, the source can dynamically increase or reduce the FEC efficiency (i.e., the amount of data redundancy) to provide a higher protection to the video packet or a better use of the shared bandwidth respectively.

Transmission of compressed video over ATM networks requires efficient data priority partition techniques. In association with intelligent cell discard schemes, these techniques aim to minimize the loss probability of critical information in the situation of congestion. We propose a new video-oriented priority data partition mechanism named the extended priority assignation scheme (ExPAS). This mechanism better uses the cell header and allows the definition of up to three service classes per connection. To evaluate its performance, we have also designed an adaptive cell dropping scheme which takes benefits of the new features. In comparison with previous priority partition techniques based on the cell loss probability (CLP) mechanism, ExPAS with the adaptive selective cell discard (A-SDC) scheme shows better results in minimizing cell losses of intra-coded frames.

We propose and evaluate a new MPEG based video delivery framework for use with ATM best effort services (e.g. available bit rate and unspecified bit rate). The presented framework relies on three components: a video oriented cell discarding scheme, which adaptively and selectively adjusts drop level to switch buffer occupancy and video cell payload types; a dynamic frame level priority data partition mechanism based on MPEG data structure and feedback from the network; and an enhanced ATM Adaptation Layer type 5 associated with a new slice based MPEG2 encapsulation strategy. This best effort video delivery framework is evaluated using simulation and real MPEG video data.

While the two solitudes of our title, computerized control and telecommunications, are both concerned with computerized solutions to real world problems, we suggest that they are really addressing different needs and are therefore naturally preoccupied by slightly different concerns. We present a taxonomy of key elements intended to make such differences explicit in order to promote more mutual understanding and more collaborative work where concerns are shared. In particular, we suggest that one key difference lies in the nature of the timing requirements associated with reactivity, and whether they are considered as extensions to logical correctness or performance.