0000000000242601
AUTHOR
Jacques Bou Abdo
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…
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…