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Abdallah Makhoul
Researcher at University of Burgundy
Publications - 147
Citations - 2214
Abdallah Makhoul is an academic researcher from University of Burgundy. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 24, co-authored 120 publications receiving 1596 citations. Previous affiliations of Abdallah Makhoul include University of Franche-Comté & Centre national de la recherche scientifique.
Papers
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An energy efficient IoT data compression approach for edge machine learning
TL;DR: This paper applies a fast error-bounded lossy compressor on the collected data prior to transmission, that is considered to be the greatest consumer of energy in an IoT device, and proposes an energy efficient approach for IoT data collection and analysis.
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Self-Adaptive Data Collection and Fusion for Health Monitoring Based on Body Sensor Networks
TL;DR: This paper proposes an adaptive data collection approach on the biosensor node level that uses an early warning score system to optimize data transmission and estimates in real time the sensing frequency and presents a data fusion model on the coordinator level using a decision matrix and fuzzy set theory.
Journal Article
A Two Tiers Data Aggregation Scheme for Periodic Sensor Networks
TL;DR: A new prefix filtering approach that avoids computing similarity values for all possible pairs of sets for data aggregation in sensor networks and defines a new filtering technique based on the quality of information.
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An Enhanced K-Means and ANOVA-Based Clustering Approach for Similarity Aggregation in Underwater Wireless Sensor Networks
TL;DR: A new clustering method to handle the spatial similarity between node readings is presented and validated via experiments on real sensor data and comparison with other existing clustering and data aggregation techniques.
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Energy-Efficient Sensor Data Collection Approach for Industrial Process Monitoring
Hassan Harb,Abdallah Makhoul +1 more
TL;DR: This paper proposes adaptive data collection mechanisms that allow each sensor node to adjust its sampling rate to the variation of its environment, while at the same time optimizing its energy consumption.