Efficient cluster-based routing algorithm for body sensor networks
International audience; Body Sensor Networks have gained a lot of research interest lately for the variety of applications they can serve. In such networks where nodes might hold critical information about people's lives, designing efficient routing schemes is very important to guarantee data delivery with the lowest delay and energy consumption. Even though some cluster-based routing schemes were proposed in the literature, none of them offer a complete solution that guarantees energy and delay efficient routing in BSN. In this paper, we propose a robust cluster- based algorithm that increases the routing efficiency through every step of the routing process: cluster formation, cluster head…
Efficient Hybrid Emergency Aware MAC Protocol for Wireless Body Sensor Networks
International audience; In Body Sensor Networks (BSNs), two types of events should be addressed: periodic and emergency events. Traffic rate is usually low during periodic observation, and becomes very high upon emergency. One of the main and challenging requirements of BSNs is to design Medium Access Control (MAC) protocols that guarantee immediate and reliable transmission of data in emergency situations, while maintaining high energy efficiency in non-emergency conditions. In this paper, we propose a new emergency aware hybrid DTDMA/DS-CDMA protocol that can accommodate BSN traffic variations by addressing emergency and periodic traffic requirements. It takes advantage of the high delay …
Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets
International audience; With the Internet's unprecedented growth and nations' reliance on computer networks, new cyber‐attacks are created every day as means for achieving financial gain, imposing political agendas, and developing cyberwarfare arsenals. Network security is thus acquiring increasing attention among researchers, practitioners, network architects, policy makers, and others. To defend organizations' networks from existing, foreseen, and future threats, intrusion detection systems (IDSs) are becoming a must. Existing surveys on anomaly‐based IDS (AIDS) focus on specific components such as detection mechanisms and lack many others. In contrast to existing surveys, this article co…
A critical review on the implementation of static data sampling techniques to detect network attacks
International audience; Given that the Internet traffic speed and volume are growing at a rapid pace, monitoring the network in a real-time manner has introduced several issues in terms of computing and storage capabilities. Fast processing of traffic data and early warnings on the detected attacks are required while maintaining a single pass over the traffic measurements. To palliate these problems, one can reduce the amount of traffic to be processed by using a sampling technique and detect the attacks based on the sampled traffic. Different parameters have an impact on the efficiency of this process, mainly, the applied sampling policy and sampling ratio. In this paper, we investigate th…
Collaborative body sensor networks: Taxonomy and open challenges
International audience; Single Body Sensor Networks (BSNs) have gained a lot of interest during the past few years. However, the need to monitor the activity of many individuals to assess the group status and take action accordingly has created a new research domain called Collaborative Body Sensor Network (CBSN). In such a new field, understanding CBSN's concept and challenges over the roots requires investigation to allow the development of suitable algorithms and protocols. Although there are many research studies in BSN, CBSN is still in its early phases and studies around it are very few. In this paper, we define and taxonomize CBSN, describe its architecture, and discuss its applicati…
Efficient anomaly detection on sampled data streams with contaminated phase I data
International audience; Control chart algorithms aim to monitor a process over time. This process consists of two phases. Phase I, also called the learning phase, estimates the normal process parameters, then in Phase II, anomalies are detected. However, the learning phase itself can contain contaminated data such as outliers. If left undetected, they can jeopardize the accuracy of the whole chart by affecting the computed parameters, which leads to faulty classifications and defective data analysis results. This problem becomes more severe when the analysis is done on a sample of the data rather than the whole data. To avoid such a situation, Phase I quality must be guaranteed. The purpose…
Toward fast and accurate emergency cases detection in BSNs
International audience; In body sensor networks (BSNs), medical sensors capture physiological data from the human body and send them to the coordinator who act as a gateway to health care. The main aim of BSNs is to save peoples' lives. Therefore, fast and correct detection of emergencies while maintaining low-energy consumption of sensors is essential requirement of BSNs. In this study, the authors propose a new adaptive data sampling approach, where the sampling ratio is adapted based on the sensed data variation. The idea is to use the modified version of the cumulative sum (CUSUM) algorithm (modified CUSUM) that they previously proposed for wireless sensor networks to monitor the data v…
SCCF Parameter and Similarity Measure Optimization and Evaluation
Neighborhood-based Collaborative Filtering (CF) is one of the most successful and widely used recommendation approaches; however, it suffers from major flaws especially under sparse environments. Traditional similarity measures used by neighborhood-based CF to find similar users or items are not suitable in sparse datasets. Sparse Subspace Clustering and common liking rate in CF (SCCF), a recently published research, proposed a tunable similarity measure oriented towards sparse datasets; however, its performance can be maximized and requires further analysis and investigation. In this paper, we propose and evaluate the performance of a new tuning mechanism, using the Mean Absolute Error (MA…
Investigating Low Level Protocols for Wireless Body Sensor Networks
The rapid development of medical sensors has increased the interest in Wireless Body Area Network (WBAN) applications where physiological data from the human body and its environment is gathered, monitored, and analyzed to take the proper measures. In WBANs, it is essential to design MAC protocols that ensure adequate Quality of Service (QoS) such as low delay and high scalability. This paper investigates Medium Access Control (MAC) protocols used in WBAN, and compares their performance in a high traffic environment. Such scenario can be induced in case of emergency for example, where physiological data collected from all sensors on human body should be sent simultaneously to take appropria…
A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks
International audience; In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink)…
An efficient data model for energy prediction using wireless sensors
International audience; Energy prediction is in high importance for smart homes and smart cities, since it helps reduce power consumption and provides better energy and cost savings. Many algorithms have been used for predicting energy consumption using data collected from Internet of Things (IoT) devices and wireless sensors. In this paper, we propose a system based on Multilayer Perceptron (MLP) to predict energy consumption of a building using collected information (e.g., light energy, day of the week, humidity, temperature, etc.) from a Wireless Sensor Network (WSN). We compare our system against four other classification algorithms, namely: Linear Regression (LR), Support Vector Machin…
A distributed real-time data prediction and adaptive sensing approach for wireless sensor networks
International audience; Many approaches have been proposed in the literature to reduce energy consumption in Wireless Sensor Networks (WSNs). Influenced by the fact that radio communication and sensing are considered to be the most energy consuming activities in such networks. Most of these approaches focused on either reducing the number of collected data using adaptive sampling techniques or on reducing the number of data transmitted over the network using prediction models. In this article, we propose a novel prediction-based data reduction method. furthermore, we combine it with an adaptive sampling rate technique, allowing us to significantly decrease energy consumption and extend the …
A new autonomous data transmission reduction method for wireless sensors networks
International audience; The inherent limitation in energy resources and computational power for sensor nodes in a Wireless Sensor Network, poses the challenge of extending the lifetime of these networks. Since radio communication is the dominant energy consuming activity, most presented approaches focused on reducing the number of data transmitted to the central workstation. This can be achieved by deploying both on the workstation and the sensor node a synchronized prediction model capable of forecasting future values. Thus, enabling the sensor node to transmit only the values that surpasses a predefined error threshold. This mechanism offers a decrease in the cost of transmission energy f…
Advances in the enumeration of foldable self-avoiding walks
<font color="#336633">Self-avoiding walks (SAWs) have been studied for a long time due to their intrinsic importance and the many application fields in which they operate. A new subset of SAWs, called foldable SAWs, has recently been discovered when investigating two different SAW manipulations embedded within existing protein structure prediction (PSP) software. Since then, several attempts have been made to find out more about these walks, including counting them. However, calculating the number of foldable SAWs appeared as a tough work, and current supercomputers fail to count foldable SAWs of length exceeding ≈ 30 steps. In this article, we present new progress in this enumeration, bo…
Writer identification for historical handwritten documents using a single feature extraction method
International audience; With the growth of artificial intelligence techniques the problem of writer identification from historical documents has gained increased interest. It consists on knowing the identity of writers of these documents. This paper introduces our baseline system for writer identification, tested on a large dataset of latin historical manuscripts used in the ICDAR 2019 competition. The proposed system yielded the best results using Scale Invariant Feature Transform (SIFT) as a single feature extraction method, without any preprocessing stage. The system was compared against four teams who participated in the competition with different feature extraction methods: SRS-LBP, SI…