We present FedADHD, a distributed Federated Learning (FL) framework designed to enhance the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) while preserving patient privacy. Leveraging the HYPERAKTIV dataset, which contains health activity and neuropsychological data from adults diagnosed with ADHD, we developed a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The model analyzes key diagnostic indicators such as the ADHD Confidence Index, omission/commission T-scores, and reaction times extracted from the Conners CPT-II. FedADHD enables collaborative training across multiple mental healthcare institutions, treated as autonomous agents in a federated network, without requiring raw data sharing. This privacy-preserving architecture addresses critical ethical and legal concerns in mental health research. Evaluated using Accuracy, Precision, Recall, F1-score, and Matthews Correlation Coefficient (MCC), FedADHD outperforms traditional centralized Machine Learning (ML) models, showcasing its robustness and generalization capabilities. This work demonstrates how distributed ML and secure collaboration can significantly advance mental health diagnostics in real-world, multi-institutional settings.

The Internet of Things (IoT) has revolutionized the way devices communicate and interact with each other, but it has also created new challenges in terms of security. In this context, intrusion detection has become a crucial mechanism to ensure the safety of IoT systems. To address this issue, a comprehensive comparative study of advanced techniques and types of IoT intrusion detection systems (IDS) has been conducted. The study delves into various architectures, classifications, and evaluation methodologies of IoT IDS. This paper provides a valuable resource for researchers and practitioners interested in IoT security and intrusion detection.

Mobile behavioural biometrics, leveraging touchscreen and background sensor data, offer a promising approach to Continuous Authentication (CA). However, the performance of these systems can vary significantly under different attack scenarios. This study evaluates the effectiveness of the regularisation technique in improving authentication accuracy within Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) architecture. Using the BehavePassDB dataset, we test four regularisation techniques (Ridge, Lasso, Bayesian, and ElsticNet) on accelerometer sensor data across various tasks, including Keystroke, Readtext, Gallery, and Tap. Results demonstrate that integrating regularisation techniques with LSTM-based models consistently outperforms the BBCA system, particularly in random and skilled attack scenarios, with Area Under the Curve (AUC) improvements of up to 15%. These findings underscore the potential of combining advanced neural networks with regularisation techniques to enhance mobile biometric systems.

The Internet of Things (IoT) enables smart cities, efficient logistics, and intelligent infrastructure. Low-Power Wide Area Networks (LPWANs), such as LoRaWAN, offer long-range connectivity with low energy consumption; however, designing and testing these networks often requires programming skills, which can limit accessibility. This paper presents NC-LoRaSim, a No Code LoRaWAN simulation platform based on NS-3, suitable for users with varying backgrounds. It offers three operation modes: Manual, for step-by-step scenario configuration; Automatic, for running simulations under multiple predefined parameters; and Advanced, which allows users, like researchers with basic C++ knowledge, to develop and evaluate new protocols using pre-built functions. A user-friendly interface and real-time terminal simplify parameter selection and result visualization. By combining NS-3’s modeling capabilities with an intuitive interface, NC-LoRaSim enables students, engineers, and researchers to explore, analyze, and optimize LoRaWAN networks without extensive programming, bridging usability and technical depth.

As decentralized and self-organizing wireless networks, Vehicular Ad-hoc Networks (VANETs) enable direct communication among vehicles and infrastructure, forming a key component of Cooperative Intelligent Transportation Systems (C-ITS). However, their open and dynamic nature makes them vulnerable to severe security threats, particularly internal attacks carried out by legitimate but malicious nodes. A notable internal threat is position falsification, where a malicious vehicle transmits false location data in Basic Safety Messages (BSMs), misleading nearby vehicles and potentially causing traffic disruptions and accidents. To combat these threats, Misbehavior Detection Systems (MDSs) have been developed to identify abnormal vehicle behavior through contextual and data-driven analysis. This study evaluates several machine learning algorithms on the VeReMi dataset, addressing both binary and multi-class classification attacks. The experiments achieved high performance metrics, including F1 scores of up to 99% in multi-class scenarios through effective code optimization and hyperparameter tuning. Methods like Bagging with RF, and Gradient Boosting showed excellent accuracy, while K-Nearest Neighbors (KNN) excelled with low execution times. Overall, RF proved to be one of the most effective classifiers, offering a scalable approach to detect position falsification and VANET attacks.

In smart-city scenarios, the high density of buildings and nodes poses significant challenges to the Quality of Service (QoS) in LoRaWAN communications, resulting in frequent data loss and degraded network performance. This paper introduces the LoRa Loss Node Relay Mechanism (LLNRM), a novel solution designed to enhance the reliability of LoRaWAN networks in urban environments. LLNRM addresses signal attenuation and data transmission failures caused by dense infrastructure by categorizing nodes based on their data loss levels and leveraging geographically closest neighbors to relay data from nodes with total loss to gateways. Through extensive NS3 simulations, LLNRM demonstrates a significant improvement in Packet Delivery Ratio (PDR) in high-density, multi-gateway deployments, outperforming the standard Adaptive Data Rate (ADR) mechanism without requiring modifications to the Spreading Factor (SF). Our results show an enhancement of over$\mathbf{3 0 \%}$compared to a geographical-based solution and nearly 40 % compared to ADR. These findings highlight LLNRM's potential to significantly boost network performance in smart-city applications.

Federated Learning (FL) is emerging as a crucial approach to enhance data privacy and security, particularly in smart buildings and Internet of Things (IoT) ecosystems. By distributing learning across multiple clients, FL minimizes the need for centralized data transfers. This decentralized approach allows clients to collaboratively improve machine learning models without sharing raw data, and only their model updates are sent to a central server for aggregation. However, the problem with the existing aggregation approaches is randomizing and fixing the choice of participating clients during the FL process without evaluating the quality and potential anomalies in individual client model updates during training rounds, which can impact the aggregation and the global model performance. Therefore, we introduce a novel dynamic client selection approach called FedCSA, which selects clients using a scoring mechanism that prioritizes model quality and anomaly detection. Clients with scores above a threshold are chosen, and any client not selected for several consecutive cycles is flagged as malicious and removed. This ensures bad or malfunctioning clients are secluded and not selected during the remaining training rounds. Simulation results using smart building datasets demonstrate superior global per-formance compared to other client selection methods, including loss, SMAPE, RMSE, and MAE, across varying client numbers. This shows the scalability and consistency of our method for large-scale FL tasks with IoT time-series data.

The Controller Area Network (CAN) protocol is the main communication backbone in modern vehicles, enabling data exchange between Electronic Control Units (ECUs). However, its lack of built-in security mechanisms exposes Connected Vehicles (CV) to a growing range of cyber threats. To address this, we propose a distributed intrusion detection framework that integrates Model-Agnostic Meta-Learning (MAML) with Federated Learning (FL), offering adaptability and privacy preservation. The system leverages an LSTM-based model to learn temporal patterns in CAN message IDs, enabling the detection of known and previously unseen attacks. MAML enhances the model's generalization by facilitating rapid adaptation to new threat types using limited data, while FL enables collaborative training across multiple CVs without sharing raw data. Our distributed approach ensures continuous learning and robustness in real-world automotive environments. Experimental results demonstrate the framework's effectiveness in detecting complex intrusions while maintaining data privacy across participating vehicles.

Mobile behavioural biometrics, leveraging touchscreen and background sensor data, have emerged as a promising solution for Continuous Authentication (CA) on mobile devices, enabling secure user authentication. In this paper, we introduce an enhanced CA framework named AI-MBBCA, that integrates a Genetic Algorithm (GA) for optimal training hyper-parameter selection and an Isolation Forest (IF) as a secondary layer for impostor attack detection. A hybrid Long Short-Term Memory (LSTM) network, trained using a triplet loss function and augmented with a regularisation method, effectively captures spatial and temporal patterns in user behaviour. Experimental evaluations on the BehavePassDB dataset demonstrate that AI-MBBCA significantly improves authentication accuracy and reduces error rates across multiple tasks, with notable improvements in the Area Under the Curve (AUC) compared to two other approaches from the literature. Integrating AI-Driven optimisation, including GA and IF-based anomaly detection, paves the way for more resilient and adaptive Behavioural Biometrics Continuous Authentication (BBCA) systems, addressing the challenges posed by sophisticated forgery scenarios in dynamic mobile environments.

Federated Learning (FL) is a powerful Machine Learning (ML) technique that allows multiple clients to collaborate on training models while keeping their data private. Unlike traditional centralized methods, FL ensures that data are kept separate, which helps to protect privacy. However, an important area that needs more research while using the FL system is detecting harmful models within the Internet of Things (IoT) context. For example, poisoning attacks, where compromised clients introduce harmful data, can degrade the model’s overall performance or lead to incorrect predictions. This paper comprehensively reviews of recent attacks in FL within IoT networks, along with defense mechanisms and common FL frameworks. It begins by highlighting the significance of FL in IoT networks, exploring its applications, benefits, and inherent security challenges. It then explores specific attacks targeting FL in IoT networks. The defensive strategies are evaluated, including their performance metrics, datasets used, and related work, providing a comparative analysis of these techniques. Common FL frameworks and their criteria are reviewed. Our goal is to offer a detailed understanding and solutions to enhance the strength and resilience of FL systems in IoT networks.

Federated Learning (FL) is a technique used in Internet of Things (IoT) networks to enhance data privacy through decentralised Machine Learning (ML). However FL faces challenges due to the Non-Independent and Identically Distributed (Non-IID) data that is stored on various devices. Each device typically has a unique Non-IID subset of data from its local environment. This Non-IID distribution can be manipulated by poisoning attacks, where malicious modifications disrupt the global model. To addresses these complex in both IID and Non-IID data environments, we introduce AM2DN-FL. This adaptive approach identifies and removes malicious models in FL system, using a dual-sided defense strategy that leverages server and client components to combat Label-Flipping (LF) and backdoor attacks. AM2DN-FL employs an refined Local Outlier Factor (LOF) algorithm with an adaptive threshold based on Genetic Algorithms (GA) to fine-tuning the optimal threshold selection. Our simulation outcomes, utilizing the MNIST and CIFAR10 datasets for IID and Non-IID scenarios, demonstrate that our innovative approach outperforms other previously examined approaches in the literature across various performance metrics, such as Accuracy Rate (ACC), Attack Success Rate (ASR), Recall, Precision, and CPU run-time.

The Long Range Wide Area Network (LoRaWAN) is the leading open protocol reference for Internet of Things operator networks worldwide. Strengthened by the dynamics of its anchoring in a large and very active non-profit community, it offers technological flexibility that can allow it to adapt to the perpetual challenges of the contextual complexity of the IoT environment. The success of the LoRa network is due to the various contributions of improvements to LoRaWAN’s native ADR data rate self-adaptation mechanism. In this paper, after reviewing some improvement proposals, we study how the number of upstream messages used to assess the decision to change node parameters impacts the network performance. We found that minimizing this hidden parameter, called history range, increases the success rate of received packets in the case of a heavy traffic network. Typically, by considering an urban network consisting of a thousand nodes served by five LoRa gateways with a history range varying from 4 to 20, our results show an improvement in the packet delivery ratio metric with a history range of 4. Also, the interference is reduced by up to 42 % in the best case.

Federated Learning (FL) is a crucial technology in decentralized Machine Learning (ML), prominently used within Internet of Things (IoT) networks to enhance data privacy. However, it is threatened by poisoning attacks, where harmful data alterations can significantly disrupt learning processes. This paper introduces a novel solution, Entropy-based Robust Defense Federated Learning (ERDFL), to counteract these disruptions. Our approach leverages entropy information for enhanced detection of malicious models and also innovatively adjusts detection thresholds in real-time, thereby effectively identifying and excluding potentially malicious clients within the FL process. Our simulation results, using the Mnist, Fashion-Mnist, and IMDB datasets, demonstrate that our novel approach surpasses other previously studied approaches in the literature across multiple performance metrics, including Accuracy Rate (ACC), Attack Success Rate(ASR), Loss Rate (LR) and CPU aggregation run-time.

Federated Learning (FL) is rapidly growing in popularity as a decentralized approach and is being adopted in smart building systems and energy forecasting without accessing sensitive data. Specifically, clients train their models using their own data. After that, only their model parameters are sent to the central server, which aggregates them by averaging the weights and then sends back the newly formed model to each client. However, challenges arise when dealing with heterogeneous multivariate time-series data with different distributions. This leads to higher-performing clients contributing to the global update more than the others, and slower convergence where the global model takes more time to generalize across the clients. In this paper, we propose an enhanced aggregation approach, where the server sorts clients’ models based on their local training losses before swapping them all consecutively according to the best and worst-performing ones. Our proposed approach is applied to a smart building dataset and compared with two other FL approaches from the literature. Our simulation results demonstrate improved forecasting precision for each client and faster convergence. Moreover, we optimized the global model’s evaluation error scores and overall loss, reduced the communication rounds required for convergence, and ensured less bias and more fairness between clients during each training cycle.

Federated learning (FL) enables collaborative machine learning among multiple devices without sharing private data. However, FL systems are vulnerable to poisoning attacks where malicious participants send malicious model updates to compromise the global model's accuracy. To enhance malicious model detection, we propose an EMDG-FL approach that optimizes the threshold used to identify attacks through a Genetic Algorithm (GA). The threshold indicates the degree of divergence between benign and malicious model updates. A tightly tuned threshold improves detection efficiency by reducing false positives and negatives. Our approach also includes a comparison study evaluating EMDG-FL against other defenses from literature across metrics like Accuracy Rate (ACC), Attack Success Rate (ASR) and Loss Rate (LR). Simulation results using two datasets demonstrate that EMDG-FL outperforms prior works in detecting poisoning attacks in FL. The optimized threshold calculation enables more precise and efficient identification of malicious models.

Today, detecting outliers plays a crucial role in modern transportation systems, improving traffic management and road safety. This paper introduces a new outlier detection-based model for recognizing event patterns in Vehicular Ad hoc NETworks (VANETs). Our solution utilizes advanced techniques, combining outlier detection and multiclassification capabilities to enhance the resilience and reliability of transportation systems in dynamic and complex traffic scenarios. The approach involves three stages: data preprocessing and feature extraction, outlier detection, and multiclassification. In the first stage, an image-based dataset is transformed into a feature-based dataset after essential preprocessing operations, such as feature extraction by analyzing local patterns in the image pixels using the Local Binary Patterns (LBP) method. In the second stage, a hybrid classification model based on a neural network is proposed to identify outlier events in real-time vehicle data, followed by the employment of machine learning models to classify them as normal or abnormal. When an abnormal traffic situation is detected, the final stage utilizes multiclassification deep neural networks, specifically ResNet and Inception, to categorize events into predefined classes. Through extensive simulations using real VANET data, we have demonstrated the relevance and accuracy of our model in recognizing event patterns in traffic systems compared to other existing techniques.

Today's scientific and corporate communities are highly interested in Wireless Sensor Networks (WSNs) and the Internet of Things (IoT). This kind of network consists of sensors with low resources that gather information for various real-life applications (healthcare, industrial, security, etc.), with streaming data requiring online processing. However, since outliers may occur in sensors collected data, it is necessary to identify and classify them into errors and events using online outlier detection and classification techniques suitable for the WSNs real-life applications. In this paper, we propose a centralised method for online outlier detection and classification in WSN. Our approach can differentiate between errors caused by malfunctioning sensors and errors caused by events. We also consider the spatial-temporal connection between sensor data vectors and nearby sensor nodes. Our approach, titled O2DCA, for Online Outlier Detection and Classification Approach, combines the benefits of the Fixed Width Clustering (FWC) and the Inter-Cluster Distance (ICD) algorithms for clustering outlier detection, respectively. For classification, we use the Inverse Distance Weighting (IDW) method, which allows us to classify outliers into errors that will be discarded and relevant events for which a necessary decision must be taken. We show through simulation using both synthetic and real-world datasets that our novel online approach is suitable for working with real-life applications where the Detection Rate (DR) performance metric stays stable and better than the offline approach.

The rapid economic growth along with the population concentration in urban areas have led to caused urban traffic problem. It is one of the most serious problems in big cities that people have to deal in daily life. In recent years, researchers show wide interest in overcoming traffic issues where new models and frameworks have been rapidly developed for efficient traffic management in suitable vehicular environments; the emergence of Vehicular Ad Hoc Networks (VANETs). Nevertheless, traffic flow prediction is a major challenge in VANETs that has taken much attention. Subsequently, performing accurate and real-time traffic flow prediction plays an important role in reducing traffic congestion, saving traveler time, improving traffic safety, detect accidents rapidly, and reduce infrastructure damage. In this paper, we propose an efficient traffic prediction model called prophet traffic flow predictor (Flophet) for vehicular ad hoc networks. In this model, two major enhancement on the traditional neural prophet model were done. First, we propose an efficient algorithm for predicting the traffic flow trend and, then, we implement a new future regressor component called network mobility. Through simulations in real VANET data, we show the relevance of Flophet compared to other existing models.

In today's time, Wireless Sensor Networks (WSNs) and Internet of things (IoTs) have attracted a lot of interest from scientific and businesses communities. They are made up of limited-resource sensors that collect data for various applications (medical, manufacturing, militarily, etc.). However, data collected by sensors are susceptible to have outliers, which need to be detected and classified into errors and events using outlier detection and classification methods. In this paper, we propose a centralized outlier detection and classification approach for WSN. Our solution can distinguish between errors due to a faulty sensor and those due to an event. We also consider the spatial-temporal correlation between sensors' data values and neighbouring sensor nodes. Our approach, titled O2DNC for OPTICS-Based Outlier Detection with Newton Classification, combines the benefits of the OPTICS algorithm with a new method for outlier detection based on computing the variance and the average of the reachability distances. Furthermore, O2DNC uses a new approach based on the Newton interpolation and the K-Nearest Neighbours (KNN) algorithms to classify the outliers. For evaluation, we conduct a comparison study between our approach and two works from the literature and thus for the multivariate data case. Simulation results with both synthetic and real-life datasets show that the O2DNC outperforms the studied techniques in terms of several metrics like Detection Rate (DR), False Alarm Rate (FAR) and Receiver Operating Characteristic (ROC) curve.

Big data is becoming a research focus in Intelligent Transportation Systems (ITS), which can be seen in many projects worldwide. Intelligent transportation systems will produce a large amount of data. The produced big data will profoundly impact the design and application of the ITS, which makes them safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper aims to provide a comparative study of some recent works in Big Data analytics for Vehicular Ad Hoc Networks. The study includes reviewing some frameworks that conduct big data analytics in ITS while discussing the data source and collection methods, data analytics methods, platforms and big data analytics application categories. The comparison and implementation of five recent works, with a focus on the data collection and application layers, are followed to pick up the best approach based on the Friedman test results.

Wireless Sensor Network (WSN) is one of the main components of the Internet of things (IoT) for gathering information and monitoring the environment in a variety of applications (medical, agricultural, manufacturing, militarily, etc.). However, data collected and transferred from sensors to the base station are susceptible to have outliers. These outliers can occur due to sensor nodes itself or to the harsh environment where they are deployed. Thus, it is necessary for the WSN to be able to detect the outliers and take actions in order to ensure network quality of service (in terms of reliability, latency, etc.) and to avoid further degradation of the application efficiency. In this paper, we propose a distributed outlier detection and classification algorithm for WSN. Our approach is capable of distinguish between an error due to a faulty sensor and an error due to an interesting event. We take into consideration the spatial-temporal correlation between sensors' data values and between neighbouring sensor nodes. Simulations with both synthetic and real datasets showed that our proposed approach outperforms other techniques by obtaining high Detection Rate (DR) and low False Alarm Rate (FAR).

Nowadays, involved novel communication technologies for littoral environment monitoring becomes a necessity. In fact, ensuring reliable monitoring tasks in marine condition require to face up many challenges. The most important one is relative to the harshest environment which impacts considerably the communication capability of the monitoring components as buoys or Unmanned Surface Vehicles (USVs). Among the proposed solutions in the literature, the use of Low Power Wide Area Networks (LPWANs) technologies, part of the Internet of Things (IoT) trend, attracts a great scientific interest. However, any solution based only on the LPWANs communication will not fulfill the expected monitoring quality due to many issues such as the sensing coverage constraint and the LPWANs gateway limitation. In this paper, we provide an analysis of these issues and how they impact environmental monitoring tasks. Therefore, we present our solution based on the cooperative communication between LPWANs and Low-Rate Wireless Personal Area Networks (LR-WPANs) to achieve efficient monitoring.

Multimedia transfer in ad-hoc wireless networks is a challenging issue and attracts many researchers. In this paper, we propose a cross layer scheme to handle video transfer. Our staring point is to provide a multipath forwarding protocol able to transmit efficiently video streams and regular data from many sources to a unique base station (BS). This protocol will have a close cooperation with the application layer in order to consider with care different video frames and to assign high priority to the most important frames and lower priority for the least important ones. In the meantime, many paths could be selected, our aim is to guarantee independence between paths in terms of interference in order to be able to use more than one path in the same time. Experimental analysis has been undertaken, they show improvements of some performance indicators such as packet data rate, delay, loss packet rate and user experience feedback.

Recently, the topology control strategies attract more and more the research community as being a crucial issue to be handled for the Wireless sensors networks (WSNs). In this paper, we propose a centralized power control mechanism based on the link gain information to reduce network energy consumption. Our mechanism works through two steps, first we construct a skeleton that ensure network connectivity while reducing considerably the number of links in the network. Second, based on the obtained skeleton, we reduce the power transmission for each node while keeping a connectivity with a node of the skeleton with a link gain greater than a predefined threshold. The simulation results conducted over TOSSIM, show that our proposal can save the network energy up to 10% while achieving better PDR value, which goes near to 12% compared to non-optimized network.

The recent enhancement of sensor devices, such as the Micro-Electro Mechanical Devices (MEMs) used for information collection and dissemination, has led to the emergence of the Internet of Things (IoT). This new paradigm overlaps with many research area such as the Wireless Sensor Networks (WSNs) where sensor nodes are deployed over an area to perform local computations based on information gathered from the surrounding. Virtual Backbone is a mechanism that aims at constructing a path with multi-hop from cluster-heads (CHs) to a Base Station (BS) via gateway nodes. This mechanism is efficient since it allows to enhance the reliability and prolong the network lifetime. In this paper, we propose a new routing protocol, denoted Multi-Hop Energy Aware Neighbor Oriented Clustering (MH-EANOC), which uses a virtual backbone to improve the network lifetime and reduce the number of lost packets. The aim of our proposition is to find the best connection between the CHs to ensure a fast and efficient backbone construction while minimizing the energy consumption. MH-EANOC uses the number of Advertisement (ADV) messages, the residual energy and the distance to the BS in the choice of the backbone's gateways. Our simulation results show that MH-EANOC outperforms EEUC and MH-LEACH in terms of packet delivery ratio and network lifetime.

Abstract Over the last few years, wireless sensor networks have become a great field of interest for the scientific community. This novel kind of network provides an array of applications for different aspects of human life. To give a satisfying performance to the final user, the wireless sensor networks must ensure the quality of service. The use of multipath technique was widely applied in the literature. Nevertheless, there might be a problem if the interference issues are not taken into account by the multipath routing design. In this paper, we propose a novel multipath routing protocol calledCarrier Sense Aware Multipath Geographic Routing protocol(CSA‐MGR). This protocol creates multiple paths while avoiding any shared carrier sense range by using a distributed and dynamic process. In addition, the CSA‐MGR employs a new metrics named the Number of Common Neighbors to guarantee a faster and an efficient path construction. Simulations conducted over the NS‐2 simulator show promising results in terms of delay, Packet Delivery Ratio and routing overhead. The performance gain of CSA‐MGR in terms of delay is up to 275% compared with the Two‐Phase geographical Greedy Forwarding and up to 565% compared with the ad hoc on‐demand multipath distance vector. For the Packet Delivery Ratio, the performance gain of CSA‐MGR is up to 16% compared with the Two‐Phase geographic Greedy Forwarding and up to 28% compared with the ad hoc on‐demand multipath distance vector. Copyright © 2015 John Wiley & Sons, Ltd.

Nowadays, adopting an optimized irrigation system has become a necessity due to the lack of the world water resource.Moreover, many researchers have treated this issue to improve the irrigation system by coupling the novel technologies from the information and communication field with the agricultural practices.The Wireless Sensor and Actuators Networks (WSANs) present a great example of this fusion.In this paper, we present a model architecture for a drip irrigation system using the WSANs.Our model includes the soil moisture, temperature and pressure sensors to monitor the irrigation operations.Specifically, we take into account the case where a system malfunction occurs, as when the pipes burst or the emitters block.Also, we differentiate two main traffic levels for the information transmitted by the WSAN, and we use an adequate priority-based routing protocol to achieve high QoS performance.Simulations conducted over the NS-2 simulator show promising results in terms of delay and Packet Delivery Ratio (PDR), mainly for the priority traffic.

Nowadays, a recent trend has emerged in the Wireless Multimedia Sensor Networks (WMSNs) field. This trend consists of ensuring the best Quality of Experience (QoE) while transferring the multimedia content, as well as keeping an acceptable Quality of Service (QoS) concerning the scalar data transmission. However, ensuring this request needs more flexible and robust protocols at different communication layers. Especially, the network layer must provide an efficient routing protocol with awareness of the interference phenomenon and the carrier sense range effect. In this paper we provide analysis of the carrier sense range effect on multimedia communication in the WMSNs. Therefore, we present a realistic solution at the routing layer to enhance the QoS/QoE while avoiding any type of multipath coupling effect. Our solution differs from the existing approaches that handle the problem at the MAC layer. Simulations conducted over the NS-2 simulator show promising results in terms of delay, Packet Delivery Ratio (PDR) and QoE.

In the recent years, Wireless Sensor Networks (WSNs) have become a great field of interest for scientific community. This kind of network provides a panoply of applications in different areas of human life. However WSNs must ensure the quality of service (QoS) to give the requested performance for the final user. Among different issues presented in the literature to provide high QoS, multipath routing is commonly used. But such a solution could be not enough efficient if the multipath routing design does not consider the phenomena of interference. Indeed the constructed paths can have an interference zone, mainly a shared carrier sense range. In this paper we show by analytical and experimental results, that using multipath routing can never overshoot the performance of single path when the interferences are not taken into account. Also, we show how the carrier sense range can influence the network performance.

Wireless multimedia sensor networks (WMSNs) is one of the hotest topic nowadays which attracts more and more researchers as being an interdisciplinary research interest. Its cost decreases continously due to advances in micro-electromechanical systems, and the proliferation and progression of wireless communications. However, the transmission of multimedia information must satisfy QoS criteria which increases energy consumption. This issue should be taken into consideration in the protocol design for WMSNs. In this paper1, we propose an implementation and an evaluation of the TPGF routing protocol (Two Phase geographical Greedy Forwarding) over the network simulator NS2. The TPGF module over NS-2 is available for the research community. In this evaluation, we compare the TPGF performances with two other protocols: the well known AODV protocol and the EA-TPGF protocol (our previous work on an extension of the TPGF protocol which takes into account the remaining energy of nodes during the process of path identification). The performance metrics measured to evaluate the QoS of each protocol are: delay, PDR, remaining energy of each node at the end of the communication and the standard deviation of remaining energy. Simulations show promising results in terms of network life extension when TPGF is used.

The field of wireless multimedia sensor networks (WMSN) is attracting more and more research community as an interdisciplinary field of interest. This type of network is low-cost, multifunctional due to advances in micro-electromechanical systems, and the proliferation and progression of wireless communications. However transmit multimedia information captured must satisfy the criteria of QoS , which increases energy consumption, fact that should be taken into consideration in the design of any routing protocols for WMSNs. In this paper we present routing protocol which we call an Energy Aware TPGF (EA-TPGF), that improves the Two Phase geographical Greedy Forwarding (TPGF). The basic idea is to take into account the residual energy of the nodes in the identification of pathways. Simulations and statistics produced by the simulator NeTtopo showing a significant contribution with regard to the extension of the life of the network.