Dr Djamel Djenouri's Outputs (96)

A Secure Intelligent System for Internet of Vehicles: Case study on traffic forecasting (2023)
Journal Article

Significant efforts have been made for vehicle-to-vehicle communications that now enable the Internet of Vehicles (IoV). However, current IoV solutions are unable to capture traffic data both accurately and securely. Another drawback of current IoV m... Read More about A Secure Intelligent System for Internet of Vehicles: Case study on traffic forecasting.

Deep learning for estimating sleeping sensor’s values in sustainable IoT applications (2022)
Presentation / Conference Contribution

The aim of this work is to develop a deep learning model that uses spatial correlation to enable turning turn off a subset of sensors while predicting their readings. This considerably saves the energy that would be consumed by those sensors both for... Read More about Deep learning for estimating sleeping sensor’s values in sustainable IoT applications.

Vehicle detection using improved region convolution neural network for accident prevention in smart roads (2022)
Journal Article

This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. T... Read More about Vehicle detection using improved region convolution neural network for accident prevention in smart roads.

Hybrid RESNET and regional convolution neural network for accident estimation (2022)
Journal Article

Road safety is tackled and an intelligent deep learning framework is proposed in this work, which includes outlier detection, vehicle detection, and accident estimation. The road state is first collected, while an intelligent filter, based on SIFT ex... Read More about Hybrid RESNET and regional convolution neural network for accident estimation.

LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST (2022)
Presentation / Conference Contribution

The present paper considers emerging Internet of Things (IoT) applications and proposes a Long Short Term Memory (LSTM) based neural network for predicting the end of the broadcasting period under slotted CSMA (Carrier Sense Multiple Access) based MA... Read More about LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST.

Intelligent deep fusion network for anomaly identification in maritime transportation systems (2022)
Journal Article

This paper introduces a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with decomposition is explored to find abnormal behavior in maritime dat... Read More about Intelligent deep fusion network for anomaly identification in maritime transportation systems.

Emergent deep learning for anomaly detection in internet of everything (2021)
Journal Article

This research presents a new generic deep learning framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect outliers in IoE environment... Read More about Emergent deep learning for anomaly detection in internet of everything.

On predicting sensor readings with sequence modeling and reinforcement learning for energy-efficient IoT applications (2021)
Journal Article

Prediction of sensor readings in event-based Internet-of-Things (IoT) applications is considered. A new approach is proposed, which allows turning off sensors in periods when their readings can be predicted, thus preserving energy that would be consu... Read More about On predicting sensor readings with sequence modeling and reinforcement learning for energy-efficient IoT applications.

Towards energy efficient clustering in wireless sensor networks: A comprehensive review (2021)
Journal Article

Clustering is one of the fundamental approaches used to optimize energy consumption in wireless sensor networks. Clustering protocols proposed in the literature can be classified according to different criteria related to their features such as the c... Read More about Towards energy efficient clustering in wireless sensor networks: A comprehensive review.

Towards optimized one-step clustering approach in wireless sensor networks (2021)
Journal Article

This paper introduces a nonlinear integer programming model for the clustering problem in wireless sensor networks, with a threefold contribution. First, all factors that may influence the energy consumption of clustering protocols, such as cluster-h... Read More about Towards optimized one-step clustering approach in wireless sensor networks.

Trajectory outlier detection: New problems and solutions for smart cities (2021)
Journal Article

This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for th... Read More about Trajectory outlier detection: New problems and solutions for smart cities.

Deep learning vs. traditional solutions for group trajectory outliers (2020)
Journal Article

This article introduces a new model to identify a group of trajectory outliers from a large trajectory database and proposes several algorithms. These can be split into three categories: 1) algorithms based on data mining and knowledge discovery, whi... Read More about Deep learning vs. traditional solutions for group trajectory outliers.

When the decomposition meets the constraint satisfaction problem (2020)
Journal Article

This paper explores the joint use of decomposition methods and parallel computing for solving constraint satisfaction problems and introduces a framework called Parallel Decomposition for Constraint Satisfaction Problems (PD-CSP). The main idea is th... Read More about When the decomposition meets the constraint satisfaction problem.

A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories (2020)
Journal Article

This paper addresses the taxi fraud problem and introduces a new solution to identify trajectory outliers. The approach as presented allows to identify both individual and group outliers and is based on a two phase-based algorithm. The first phase de... Read More about A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories.

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection (2020)
Journal Article

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories... Read More about Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection.

A recurrent neural network for urban long-term traffic flow forecasting (2020)
Journal Article

This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, na... Read More about A recurrent neural network for urban long-term traffic flow forecasting.

DFIOT: Data Fusion for Internet of Things (2020)
Journal Article

In Internet of Things (IoT) ubiquitous environments, a high volume of heterogeneous data is produced from different devices in a quick span of time. In all IoT applications, the quality of information plays an important role in decision making. Data... Read More about DFIOT: Data Fusion for Internet of Things.

A novel parallel framework for metaheuristic-based frequent itemset mining (2019)
Presentation / Conference Contribution

Frequent Itemset Mining (FIM) is an important but very time-consuming data mining task. As a result, traditional FIM algorithms are often not scalable to large databases. To address this issue, several metaheuristics have been developed in recent yea... Read More about A novel parallel framework for metaheuristic-based frequent itemset mining.

Wireless energy efficient occupancy-monitoring system for smart buildings (2019)
Journal Article

© 2019 Elsevier B.V. Rationalizing energy consumption in smart buildings is considered in this paper, and a wireless monitoring system based on Passive Infrared sensors (PIRs) is proposed. The proposed system is pervasive and can be integrated in exi... Read More about Wireless energy efficient occupancy-monitoring system for smart buildings.

Machine learning for smart building applications: Review and taxonomy (2019)
Journal Article

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first clas... Read More about Machine learning for smart building applications: Review and taxonomy.