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.

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.

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.

Energy consumption forecasting is of paramount importance in achieving energy conservation goals. While numerous approaches have been developed to optimize building energy usage, predictive analytics stands out as a cornerstone tool for informed decision-making. Deep learning models have gained popularity for forecasting energy consumption in smart buildings. These models leverage a variety of techniques, including loss functions, activation functions, and optimizers, to enhance training effectiveness. However, the commonly used Mean Squared Error (MSE) as a loss function has a notable drawback as it treats overestimations and underestimations equally. In this study, we propose a novel Hybrid Loss Function (HLF) tailored to address this limitation. The HLF penalizes the model more for underestimating energy consumption during abnormal seasons while maintaining its ability to accurately predict actual consumption, particularly under normal operating conditions. Through extensive simulations, our findings demonstrate that our proposed approach outperforms existing methods in the literature, providing exceptionally accurate and robust forecasts of energy consumption.

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.

Wireless Sensor Networks (WSNs) represent a key component in smart building systems. An efficient WSN deployment involves selecting the most appropriate positions within the building to place sensors in order to maximize coverage and minimize the deployment cost. This paper proposes a novel approach called the Hybrid Binary Grey Wolf Optimiser (HBGWO) to automate the WSN deployment in indoor environments. The proposed approach integrates the Building Information Modeling (BIM) database to accurately model the physical layout and structural characteristics of the deployment area. Furthermore, a Steiner Tree-based heuristic has been developed to reduce the number of active sensors while preserving the network coverage. Experimental results demonstrate the efficiency and superiority of the HBGWO approach compared to existing methods in literature in terms of network coverage and deployment cost under the connectivity constraint.

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.

Finance, supply chain, healthcare, and energy have an increasing demand for secure transactions and data exchange. Permissioned blockchains fulfilled this need thanks to the consensus protocol that ensures that participants agree on a common value. One of the most widely used protocols in private blockchains is the Practical Byzantine Fault Tolerance (PBFT) which tolerates up to one-third Byzantine nodes, performs within partially synchronous systems and has a superior throughput compared to other protocols. It has, however, an important bandwidth consumption: 2N(N-1) messages are exchanged in a system composed of N nodes to validate only one block. It is possible to reduce the number of consensus participants by restricting the validation process to nodes that have demonstrated high levels of security, rapidity, and availability. In this paper, we propose the first database that traces the behavior of nodes within a system that performs PBFT consensus. It reflects their level of security, rapidity and availability throughout the consensus. We first investigate different Single-Task Learning techniques to classify the nodes within our dataset. Then, using Multi-Task learning techniques, the results are way more interesting with classification accuracies over 98%. Integrating nodes classification as a preliminary step to the PBFT protocol optimizes the consensus. In the best cases, it is able to reduce the latency by up to 94% and the communication traffic by up to 99%.

The wireless sensor deployment problem is one of the major issues in wireless sensor networks (WSNs). It involves designing the optimal network topology within the deployment area in order to maximize network coverage and lifetime and minimize cost and energy consumption under the connectivity constraint. The WSN deployment problem is a challenging NP-hard combinatorial optimization problem due to a number of factors, including the size and the type of the deployment area, the number of obstacles, and the number of objectives to optimize. Consequently, metaheuristics are assumed to be the most efficient methods to compute the deployment scheme in a reasonable amount of time. In this paper, several well-known metaheuristics have been tested on the problem of WSN deployment in indoor environments. The problem has been formulated as a constrained single objective optimization problem, and the performance of the selected algorithms has been evaluated through experimentation on a set of ten representative indoor architectural scenarios with varying dimensions and obstacles.

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.

Wireless sensor network connectivity is a crucial parameter since it keeps the network operative. Network connectivity may be lost due to a variety of factors, such as energy depletion and sensor node failure. Therefore, the network will be partitioned into a set of disjoint sets, resulting in a loss of data collected by isolated sets. In this paper, we address the problem of connectivity repair for stationary sensor networks (WSNs) in case of multiple disjoint partitions. We propose two heuristics based on Dijkstra algorithm and minimum Steiner tree respectively, to deploy the minimum number of additional nodes while preserving the initial topology. For the two heuristics, a procedure is executed in the first stage to merge disjoint sets having a shared zone in their neighboring deployment zones to reduce the complexity of the solution. The first heuristic is adapted to free-obstacle areas and areas with few obstacles. It connects the less distant segments using Dijkstra algorithm. The second heuristic is rather appropriate for areas with opaque obstacles. Simulation experiments validate the effectiveness of the proposed methods compared to existing approaches.

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.

As the world’s economy and urbanization develop rapidly, energy shortages and pollution are becoming major challenges. In general, buildings are responsible for approximately 40% of global energy consumption. To combat this challenge, the adoption of intelligent buildings is strongly recommended. Therefore, through the data collected by the smart buildings, effective solutions should be incorporated to limit their impact on the environment; Machine learning (ML) has proved great success in sensor-based Energy consumption. Trapping in 10-cal optima and slow convergence are the main difficulties of the Backpropagation (BP) learning algorithm. This has an impact on the neural network’s performance. To recover the drawback, this paper proposes a hybrid protocol FedLM-PSO that combines Particle Swarm optimization (PSO) and Levenberg Marquardt (LM) to train MLP models in a Federated Learning environment to find the near-optimal configurations for FL. In addition, FedLM-PSO evolves the way clients upload data to servers and reduces the amount of data sent, which enhances bandwidth consumption. According to the results, the FedLM-PSO is more accurate and requires fewer rounds of communication than FedAVG.

Nowadays, health care has become a constant concern of all countries around the world, especially after the emergence of the Coronavirus (COVID-19) and all its variants. Billions of dollars are being paid through the World Health Organization to improve health care. Scientific research laboratories play a pioneering role in this area as well. Due to the importance of information related to the patient and his medical history, it is necessary to exchange these information between various health centers in order to be better treated. Security in Health care information exchange (HIE) plays an important role because different healthcare facilities (HCFs) exchange sensitive data which can affect the patient’s privacy. Researchers propose many approaches in order to enhance security and maintain privacy. They also try to solve many drawbacks in this field like efficiency, accuracy, and scalability. Unfortunately, all the proposed techniques tackle some parameters and drop other ones. To decide which blockchain-based approach is more efficient for HIE systems, by evaluating each approach’s effectiveness based on its security, integrity, privacy, accuracy, scalability, efficiency, and latency qualities, we present a comparative analysis between a number of recent approaches in the HIE sector. Further, we use real patients’ data to measure each parameter, then we apply the Friedman test on the obtained results for each approach.

Nowadays, Healthcare Information Exchange (HIE) plays a vital role in healthcare systems; it allows healthcare providers to access and share patient medical data electronically and securely. Subsequently, HIE eliminates redundant or unnecessary testing, and improves public health reporting and monitoring. Security is a very important challenge in the HIE systems since data are exchanged between different healthcare facilities (HCF), thus, the data are subject to be modified or altered. Hence, detecting modified or risky transactions is becoming a fundamental operation in HIE systems. In this paper, we propose a secure framework that combines between deep learning and blockchain, called as DeepChain, for detecting risky transactions in HIE systems. On one hand, DeepChain uses two types of blockchain to enhance the data security: a blockchain to store ordinary patient data, and an off-chain to store the sensitive patient's data. On the other hand, DeepChain uses an advanced deep learning model called generative adversarial network (GAN) with two-folds: first, it enhances the training phase of the model by generating additional synthetic health data, then it uses a discriminator to accurately detect the risky transactions in the testing phases. We evaluated the performance of our framework based on real health data while the obtained results shows the efficiency of DeepChain in detecting risky transactions and enhancing HIE security.

As smart cities become more decentralized, the need for reliable and secure cyber-physical systems (CPS) that guarantee safe interactions and secure data storage without loss of privacy is continuously increasing. Blockchain is a rapidly emerging technology in this domain. It demonstrated effectiveness thanks to the cryptographic mechanisms it utilizes and to its immutability. Private blockchains are the most suited to applications that require privacy and confidentiality when data is very sensitive. In this case, the most commonly used consensus protocol is Practical Byzantine Fault Tolerance (PBFT). However, PBFT requires the participation of all nodes in the consensus process, which increases bandwidth consumption and consensus delay significantly. In this paper, we propose an adaptive PBFT protocol called APBFT that optimizes the number of nodes participating in the consensus based on their response time and credibility. Therefore, we reduce the amount of communication and the response delays. We maintain the asynchrony of the algorithm so that it remains resilient to DoS attacks. The simulation results show that our algorithm outperforms the original PBFT in terms of delays and message traffic.

In today's time, the security in IoT networks interests the scientific community. Indeed, IoT networks are confronted with numerous vulnerabilities, including denial of service, which represents a real threat. The greedy behavior attack is arguably one of the most dangerous and intelligent attacks. Its intelligence lies in the fact that the malicious node executes its attack internally by pretending to be a legitimate node and deliberately falsifying its CSMA-CA parameters. In this paper, we propose a new approach for greedy nodes detection based on an unsupervised machine learning method. In order to evaluate the effectiveness of the proposed method, and to prove the limits of this technique, several attack scenarios were carried out into cooja, and different simulation parameters were taken into account such as the number of packets sent, the energy consumption, and radio status. The detection efficiency of the proposed method is evaluated in two cases, best and worst case. In the first, the detection accuracy is equal to 88.5%, while in the worst it is equal to 86.42%.

A network of smart sensors, usually called the Internet of Things (IoT), has lately attracted the attention of academia, industry, and government researchers. However, the IoT has faced many challenges and issues which clearly show that the dilemma of today's Internet architecture requires great effort. That’s why the Future Internet of Things (FIoT) has been frequently discussed. We distinguish two characteristics of the FIoT which make it unique: the interconnection of billions of smart objects and the limited resources of these smart objects. Routing Quality of Service (QoS) is a critical issue in this type of network, due to the devices’ characteristics. In this paper, we propose, implement, and evaluate a new bio-inspired routing protocol designed for the FIoT environments called FIRP using the Simple Additive Weight (SAW) Multi-Criteria Decision Making (MCDM) method. The idea of this algorithm was inspired from the behavior of fireflies that use their luminosity to find mates and food sources. The results of the simulation and the statistical tests show the efficiency of our routing algorithm, in particular, it improves the energy consumption, the routing overhead, and it minimizes the end-to-end delay.

Healthcare requires the cooperation of many administrative units and medical specialties. The Internet of Things (IoT) is involved into healthcare field and plays an extremely important role by providing healthcare services. In the Internet of Healthcare Things (IoHT) several challenges appeared in terms of limited battery life, long processing time, large amounts of collected data, paquets overhead on sink,…etc. A lot of research studies have been done in order to improve the healthcare IoT based applications. However, these proposed systems have been focused on some specific purpose without ensuring an effective solution for all problems. This paper presents a comparative study for the recent advances in Internet of healthcare monitoring. In addition, an optimized intra WBSN communication (OIC) an is proposed in order to overcome the over-mentioned challenges. OIC is based on merging an energy efficient routing protocol with the data transmission process. This approach also reduces the data acquisition redundancy, and finds the optimum path for data transmission, according to the patient situation. Our approach is implemented and tested on real biosensor data while the obtained results prove the efficiency on extending the network lifetime (up to 84%), removing the data transmission redundancy (up to 60%), minimizing the overhead on sink (up to 83%), and optimizing the transmission time. Based on a comparative study with four state-of-the-art methods, the superiority of our approach is validated.

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).

The Internet of Things (IoT) is a new paradigm of networks that offers intelligent connections between different objects of daily basis needs. These connections are clearly characterized by various constraints, such as the high loss rate and the low throughput. To ensure these connections, it is necessary to use routing protocols adapted to these constraints. The routing protocol for low power and lossy networks (RPL) is one of the famous protocols used in this category of networks, thanks to its flexibility and adaptability. In this paper, the study attempts to present the deficiency of the RPL protocol against a new attack called divide and conquer-based attack. The idea is to introduce a malicious node periodically launching a process based on the rank value, in order to deteriorate the network performance. The attack effectiveness is investigated through a detailed simulation using the Cooja simulator in terms of the total number of victim nodes, the average network hops and the global energy consumption.

In today's time, we live in the big data era where every event and thing about us is monitored and registered for a later analysis. In addition, such amount of big data is collected with a large speed (velocity) and does not fit a fixed structure (unstructured type). One of the most contributors of big data in this era is wireless multimedia sensor network (WMSN). Typically, WMSN consists of a set of sensors that collect three types of data about a zone of interest: numerical, images and videos. Indeed, the big data collected in WMSN along with the density deployment of network, especially in large-scale zones, provide real challenges for the end users in terms of data storage and processing. In this paper, we propose an efficient and robust Hadoop-based framework for big data collection, processing and storage in WMSN. The proposed framework relies on Hadoop ecosystem tools and introduces two fault detection algorithms (moving average and exponential smoothing) in order to preprocess data before storage. Through real sensor data with various types, we show the effectiveness of our framework in terms of processing storage speed and regenerating of missing data.

Internet of things is the new era of networking and smart communication. Recent researches treat some issues and challenges of IoT. QoS routing protocols for IoT have been a rising research topic for years. In this paper we present a new approach called E-RPL; it is an enhancement of the Routing Protocol for Low power and lossy networks (RPL). Comparing to RPL, E-RPL decreases the number of control messages. The new protocol proposes also a new flexible multi-constrained objective function (OF) that can integrate several metrics including energy, delay and bandwidth to define the end-to-end path between the sink and a given node. The simulation results show a remarkable improvement of energy consumption, routing overhead, and end-to-end delay.

The works on Search Based Software Engineering (SBSE) have been a big increase in the last decade. An approach to software engineering in which search based optimisation algorithms are applied to address problems in software engineering. SBSE has been applied to problems throughout the software engineering lifecycle, from requirements and project planning to maintenance and re-engineering. This paper provides a modification and an implementation of SBSE on evolutionary multi-objective based approach for deployment of wireless sensor network (WSN) with the presence of fixed obstacle. In this work a multi-objective evolutionary algorithms based on elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed to address the deployment problem. Two functions namely ranking function and fitness function are used to select the best optimal solution from Pareto optimal fronts.

This paper proposes a new mechanism of path selection and admission control for IPTV in IEEE 802.16j simultaneously. The proposed mechanism takes into account some constraints of Quality of Services (QoS) which are required by real-time applications, such as available bandwidth, end-to-end delay, number of hops between MR-BS and SS as well as quality of radio signal. With all these four constraints, selecting the best path becomes a multi-objective optimization problem, since we have two criteria to maximize and two other criteria to minimize. It makes selection process of the optimal path more difficult to find a solution in polynomial time. To solve this multi-constrained problem, the proposed approach applied a cost function which simplifies the multi-objective problem into single objective. This function provides a deterministic solution which takes into account all the above constraints. To evaluate the proposed mechanism, we study the performance of the proposed approach through various simulation scenarios. The results show that the proposed mechanism outperforms other studies.

In this paper, a study about wireless sensor network (WSN) deployment strategy is demonstrated and made workable for the use of multi-objective approach. The development of sensor nodes by considering multiple objectives and existence of fixed obstacles is an important optimization problem. There are two objectives in this study, connectivity and coverage as two fundamental issues in wireless sensor networks deployment. In this work a multi-objective evolutionary algorithms based on elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed to address this problem. Two proposed functions, ranking function and fitness function, are used to determine the best optimal solution from Pareto optimal fronts. Further we presented simulation and analysis to verify and validate the deployment of wireless sensor network in area with the presence of permanent obstacles.

A mobile ad hoc network (MANET) is a wireless network with high of mobility, no fixed infrastructure and no central administration. These characteristics make MANET more vulnerable to attack. In ad hoc network, active attack i.e. DOS, and blackhole attack can easily occur. These attacks could decrease the performance of the routing protocol. We have proposed a new trust mechanism to secure the AODV routing protocol called Trust AODV. In this paper, we improve the performance of our proposed secure protocol by using an ant algorithm. Ant agent put a positive pheromone when the node is trusted. Path communication is chosen based on pheromone value. We evaluate and compare the performance of proposed protocol before and after using ant algorithm under DOS/DDOS attack. The simulation result shows the performance of proposed protocol increases while using ant algorithm in term of packet delivery ratio and throughput. However, in term of end-to-end delay there is no significant effect to the performance.

This paper is a study about deployment strategy for achieving coverage and connectivity as two fundamental issues in wireless sensor networks. To achieve the best deployment, a new approach based on elitist non-dominated sorting genetic algorithm (NSGA-II) is used. There are two objectives in this study, connectivity and coverage. We defined a fitness function to achieve the best nodes deployment. Further we performed simulation to verify and validate the deployment of wireless sensor network as an output from the proposed mechanism. Some performance parameters have been measured to investigate and analyze the proposed sensor-deployment. The simulation results show that the proposed algorithm can maintain the coverage and connectivity in a given sensing area with a relatively small number of sensor nodes.

A Mobile Adhoc Network (MANET) is characterized by high mobility, non-infrastructure network, and dynamic topology. This nature make MANET have some constraints such as energy constraints, limited bandwidth, and less memory. Energy consumption becomes an important issue in manet to cover the sustainability of the communication process. We have proposed optimize routing protocol based on AODV routing protocol for hybrid network. We used reverse mechanism to optimize the AODV routing protocol. In this paper, we will analyze the performance of our proposed protocol in term of energy consumption, packet delivery ratio, end to end delay and routing overhead. We evaluate our proposed protocol with NS-2, with some scenario using random waypoint and reference point group mobility model. In the Random Waypoint model (RWP), each node chooses a new destination randomly and then moves towards the destination at a constant speed. In RPGM, it will create some group of nodes. Each group has a logical center that defines the entire groups motion behavior, including location, speed, direction, and acceleration. The simulation result shows that our modified protocol is outperform in random waypoint rather than in rpgm. In term of energy consumption, our protocol achieved lower consumption for random waypoint mobility compare to the reference point group mobility model.

In this paper, we address the fundamental problem of forwarding table optimization in mobile ad hoc networks and we propose a new multicast tree aggregation mechanism based on the uniqueness property of prime numbers. Instead of creating, for any new session, a new entry in the routing table, our mechanism allocates a unique identity to each session in the multicast shared tree and ensures the delivery to concerned receivers. Evaluation performance results show the gain obtained when applying such a mechanism in multicast ad hoc networks by reducing significantly the entries number in the forwarding table although generating a low network overhead.

The IETF group is currently working on service differentiation in the Internet. However, in wireless environments such as ad hoc networks, where channel conditions are variable and bandwidth is scarce, the Internet differentiated services are suboptimal without lower layers' support. The IEEE 802.11 standard for Wireless LANs is the most widely used WLAN standard today. It has a mode of operation that can be used to provide service differentiation, but it has been shown to perform insufficiently. In this paper, we present a service differentiation scheme for support QoS in the wireless IEEE 802.11, which is based on a multiple queuing system to provide priority of user's flows. We simulate and analyze the performance of our algorithm and compare its performance with the original IEEE 802.11b protocol. Simulation results show that our approach outperforms the standard 802.11b in terms of throughput and packet loss.

Efficient delivery of real-time multicast applications requires quality of service (QoS) support from the underlying network. The differentiated services (DiffServ) approach is a scalable way to provide QoS for theses applications. However, integrating native IP multicasting with differentiated services IP network is a quite complex issue. First, the multicast state scalability problems arises since it is required that each router have to keep a forwarding state for each multicast tree passing through it. Therefore, the number of forwarding states grows with the number of multicast groups. Second, another main difficulty with IP multicasting is handling heterogeneous QoS requirements within the same multicast group. In this paper, we propose a new approach in order to solve multicast scalability problem and to provide heterogeneous QoS to multicast groups using MPLS labels aggregation and dynamic DSCP. The complexity of the algorithm used to forward packets using the proposed approach is reduced from O(M) to O(1) where M is the number of multicast sessions. Consequently the router's complexity (required memory and time processing) is optimized and thus the total packets processing delays are considerably reduced

This paper proposes a dynamic hierarchical architecture of k-local groups, where k represents the number of local groups composing the group communication system S. Each local group is defined as a finite set of processes. The proposed architecture ensures real-time causal ordering between alive local group members. This structure allows us to compensate for the local clock drift by synchronizing the local clocks, and ensures real-time causal delivering. We validate this model by using CPN tools (coloured Petri nets) to obtain a state space graph. The objective is to study the behaviour of this model, by verifying different properties such as boundedness and liveness. Simulation results show that the hierarchical architecture respects all studied properties. Therefore the real-time causal ordering is guaranteed.

With the evolution of network technologies, integrated services networks make possible real-time multimedia applications. However, the delay jitter and the absence of a physical global clock may disrupt the temporal relationships among media units composing theses multimedia applications. The /spl Delta/-causal ordering is designed to ensure real-time delivery of messages by respecting the causal order. This /spl Delta/ value represents the limited lifetime during which a message m can be used by a destination process. In this paper, we develop a new real-time causal ordering concept, in the context of multicast communication, where all participants can start to playback the same message. We validate this model by using CPN (coloured Petri nets) tools to obtain a state space graph. The objective is to study the behavior of this model, by verifying different properties such as, boundedness, and liveness. The simulation result shows that this model respects the real-time causal ordering, in the context of multicast communications.

With the increase use of the progress of distributed multimedia applications, the ability of networks to handle temporal relationships among media units is becoming more and more important. Yet delay jitter, and the absence of global clock may disrupt these temporal relationships. This paper proposes a hierarchical architecture of k-local groups, where k represents the number of local groups composing the distributed system G. Each local group is defined as a finite set of processes. The proposed architecture allows us to solve the local clocks drift by synchronizing the local clocks. The clocks synchronization is resolved by according to a time reference noted VMT (virtual master time). This hierarchical topology ensures real-time causal ordering, and guarantees a dynamic reconfiguration, in a failure detection, by insuring at least a partial real-time communication between alive local groups.

The evolution of communication technology makes possible distributed real-time multimedia applications. Unlike traditional data traffic, real-time multimedia traffic requires that temporal relationships among media units must be maintained, and needs that all sites impose a consistent causal order to receive the same media at a given time. Yet delay jitter, the absence of a global clock, and a crash failure may disrupt these temporal relationships. This paper proposes new hierarchical architecture of k-local groups, where k represents the number of local groups composing the group communication system. This architecture ensures real-time causal ordering and guarantees an automatic reconfiguration, in presence of a crash failure, by ensuring at least a partial real-time communication between active local groups, while keeping the amount of control information within a reasonable size.