Federated Learning (FL) is susceptible to adversarial attacks, such as Label Flipping (LF) and Backdoor, where malicious clients manipulate the updates of their local model to reduce the global model’s performance. Traditional FL relies on a centralised aggregator, which must be trusted, creating a single point of failure. This centralization not only increases computational cost but also introduces scalability challenges. To address these issues, we propose a Blockchain (BC) based FL framework that decentralises the aggregation process and incorporates an enhanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method to identify and remove malicious updates without ignoring minor groups. Our approach eliminates the need for a centralised aggregator by leveraging BC’s smart contracts to aggregate the global model. Simultaneously, Enhanced DBSCAN identifies malicious updates in the parameter space, effectively mitigating adversarial influence while preserving privacy. We evaluate both of our framework and the traditional FL under LF red and Backdoor attacks, experimental results demonstrate that our approach outperforms the traditional FL according to multiple metrics, including accuracy, loss, precision, recall, and F1-score. These findings emphasise the effectiveness of our BC-based decentralised aggregation combined with enhanced DBSCAN technique in improving the robustness and security of FL systems.

This paper introduces AMBA, an adaptive backoff algorithm that strengthens security at the Medium Access Control (MAC) layer for IoT and vehicular networks. AMBA effectively counters jamming and denial-of-service (DoS) attacks, delivering impressive results: 15 Mbps throughput, a 60% reduction in latency compared to JRMP, and a 67% improvement in packet loss resilience. The Security Threat Resilience Metric (STRM) shows a 20% boost in resilience in hostile environments. Leveraging real-time traffic analysis and physical layer feedback, AMBA detects and mitigates malicious activity while maintaining optimal network performance. Its lightweight design is perfect for resource-limited environments, offering a scalable, efficient solution for securing next-generation wireless networks.

The rapid expansion of the Internet of Things(IoT) has underscored the critical need for efficient and autonomous communication systems to sustain the massive, interconnected network of smart devices. Central to this challenge is optimizing communication protocols to ensure energy efficiency, reliability, and self-configurability across diverse IoT applications. This paper reviews the essence of leveraging artificial intelligence, specifically deep reinforcement learning, distributed AI services, swarm intelligence, metaheuristic optimization, and cross-layer approaches, for autonomous optimization and configuration in IoT and IoE (Internet of Everything) environments within the context of emerging 6 G technologies. It also implies a focus on comparing various methodologies and approaches to achieve efficient communication systems for IoT. The key criteria used in this comparison study are energy efficiency, transmission power, protocols, scalability, and Quality of service (QoS). By synthesizing these findings, our study highlights the strengths, limitations, and potential synergies between different approaches, offering insights into the future direction of IoT communication optimization.

The Internet of Things (IoT) continues to grow at remarkable speed, connecting different devices and facilitating communication and data exchange. Ambient Backscatter Communication (AmBC) has emerged as a low-power, low-cost alternative suitable for the connectivity of IoT. However, this technology still requires development to be adopted on a large scale. In this paper, we present a brief overview of AmBC, and highlight how it is appealing for IoT. We then go over the major challenges that AmBC faces with a concise explanation for each, as well as outline the directions future research should take to enhance the performance AmBC and push its integration with IoT forward. We also include a survey of some contributions in this domain.

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.

ABSTRACT Vehicular ad hoc networks (VANETs) are very changeable networks due to the highly dynamic movement of their nodes, resulting in frequent link disconnection and variable node density. One of the most challenging issues in VANETs is to propose a suitable routing scheme that is adapted to the characteristics of such a dynamic topology. Position‐based routing schemes that are effective in handling dynamic changes in the topology of VANETs are proposed. This article proposes an efficient routing protocol based on a greedy forwarding approach called ERGF. The proposed protocol is a position‐based routing protocol that uses fresh vehicular traffic information in the routing process. ERGF consists of two main algorithms: the vehicle traffic freshness dissemination algorithm and the greedy forwarding algorithm. The both algorithms work together to provide fresh, up‐to‐date information about vehicle traffic, enabling the proposed routing protocol to effectively withstand dynamic changes in VANET network topology. The proposed protocol has been developed over OMNET++ simulator, evaluated and compared with some other protocols. The simulation results showed that the proposed protocol provided better performance in terms of packet delivery rate and end‐to‐end delay than the EGyTAR and PBRP protocols. The packet delivery ratio of our proposal is approximately 75% and 6% higher than EGyTAR and PBRP, respectively, and the end‐to‐end delay of our protocol is reduced by 37% and 7%, respectively, compared with EGyTAR and PBRP for most scenarios.

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.

We propose a novel approach to energy-efficient radio frequency (RF) communication based on uncertainty-aware semantic filtering and multi-task learning. The framework utilizes a Bayesian Neural Network (BNN) with Monte Carlo Dropout to estimate predictive uncertainty and filter semantically redundant RF frames. This adaptive mechanism enables efficient data reduction based on confidence levels, optimizing bandwidth without sacrificing performance. The retained high-confidence frames are used for multi-class modulation classification and signal-to-noise ratio (SNR) prediction, supporting intelligent transmission under varying channel conditions while minimizing unnecessary processing. Experimental results show that entropy-based filtering achieves bandwidth savings of up to 40%, maintaining 90.80% classification accuracy, and reducing energy consumption by 10% compared to the baseline. Uniform Manifold Approximation and Projection (UMAP) visualizations confirm improved latent space separability after filtering. Energy consumption is tracked using CodeCarbon, showing a significant reduction in energy per transmitted frame. The proposed framework is lightweight, ideal for real-time inference in resource-constrained environments, and offers applications in edge artificial intelligence (AI), low-power Internet of Things (IoT), and vehicular networks (V2X), paving the way for energy-efficient communication in future 6G systems.

The Internet of Things (IoT) and its industrial counterpart, the Industrial Internet of Things (IIoT), have transformed sectors such as home automation, healthcare, and manufacturing by enhancing data management through advanced networking. However, the rapid growth of IIoT has introduced significant cybersecurity challenges, necessitating a comprehensive approach to securing data across the TCP/IP model. This paper presents a novel cybersecurity investment strategy formulated as a bi-objective optimization problem, validated through genetic and iterative algorithms. The strategy effectively balances security and cost, achieving nearly 50% efficiency in solution effectiveness. By utilizing these optimization techniques, the approach provides a practical and cost-effective solution to improve IIoT security within budget constraints, offering valuable insights for cybersecurity professionals seeking robust and economically viable solutions.

Intelligent Transportation Systems (ITS) dwell on Vehicular Ad-hoc NETworks (VANETs) for message dissemination to achieve the goal of traffic safety and efficiency. VANETs achieve communication among vehicles and roadside units via wireless communication. Hence, security and privacy are significant concerns to be addressed for an effective application of secure VANETs in any ITS. Researchers have addressed these issues with trust management-based schemes or cryptography-based schemes. While these schemes can secure VANETs, they have various limitations presenting hindrances in their deployment. In this context, we have proposed a Blockchain-assisted Trust Computation with a Conditional Privacy-preserving Authentication (BTC2PA) scheme for connected vehicles. The BTC2PA scheme uses a blockchain (Ethereum) assisted PKI infrastructure with digital signatures to achieve authentication for secure communication. Furthermore, it integrates a trust score computation scheme based on a reward and punishment mechanism to provide resistance against internal attacks. The feasibility and validity of the proposed BTC2PA scheme have been studied by implementation work in Rinkeby (Ethereum test network) and extensive simulations using NS-3. The results obtained show that the proposed BTC2PA scheme meets the security and privacy requirements while significantly improving the performance metrics such as communication, storage, computation cost, and end-to-end delay when compared to existing schemes.

Recently, Session Initiation Protocol (SIP) involved as a core communication protocol in several technologies such as IMS, VoLTE and integrated with other applications. SIP known to be highly efficient protocol for providing high multimedia communication (voice and video) in industry and research society. In production, SIP service providers support millions of users and manage thousands of concurrent call-requests efficiently via a wide scale of cluster SIP servers. However, asynchronous processes-load among cluster nodes and weak resource utilization considered to be a serious issue in SIP technology, especially with high call-requests distribution. This paper proposes a dispatcher mechanism called SIP- Cluster Load Synchronization (SIP-CLS) which distributes SIP call-requests in cluster based on processes load utilization for efficient resource allocation. The mechanism takes advantage of NoSQL cache system to share and update cluster nodes load status. Evaluation have been conducted by torturing SIP server with high traffic in two different scenarios. As a results, the proposed mechanism performs efficiently compared to other load balancing mechanism by providing high resource utilization in term of bandwidth and CPU.

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.

Massive MIMO (Multiple Input, Multiple Output) is considered a promising technology for the next generation of mobile communications: 5G. MIMO transmission mechanisms improve transmission robustness, SU-MIMO (Single-User) and MU-MIMO (Multi-User) throughput, and interference reduction. This paper includes an analysis and optimization of network performance regarding the limits and behaviors of MIMO wireless links. The Gaussian linear random matrix is a mathematical model that allows the evaluation and analysis of these performances. The obtained results allow for interpreting and analyzing the theoretical basics of massive MIMO, exploring the main behavior of the capacity ladder that can be reached from the massive MIMO systems in the Open Digital Space (ODS). In other words, the dependence between transmission antennas (NT) and reception antennas (NR) in terms of interferences is very close to the ergodicity of Shannon. An ergodic process is a stochastic process for which the statistics can be approximated by the study of a single and sufficient production.

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) 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.

The increase in popularity of vehicles encourages the development of smart cities. With this advancement, vehicular ad-hoc networks, or VANETs, are now frequently utilized for inter-vehicular communication to gather data regarding traffic congestion, vehicle location, speed, and road conditions. Such a public network is open to various security risks. Overall, protecting personal information on VANET is a vital responsibility. The integration of fog computing and VANETs has gained significant importance in recent years, driven by advancements in cloud computing, Internet of Things (IoT) technologies, and intelligent transportation systems. However, ensuring secure communication in fog-based VANETs remains a major challenge. To overcome this challenge, we introduce a novel authenticated key agreement protocol that achieves mutual authentication, generates a secure session key for secret communication, and provides privacy protection without the use of bilinear pairing. We rigorously prove the security of our proposed protocol, which is designed specifically for fog-based VANETs, and has been shown to meet their stringent security requirements. Moreover, we performed formal and informal analysis that shows our proposed protocol is highly efficient,our protocol’s computational and communication overhead are lower than those of other relevant protocols by 45.570% and 29.432%, respectively. Finally we use NS-3 simulation to prove that our proposed algorithm is a practical and scalable solution for secure communication in fog-based VANETs.

Federated Learning (FL) enabled the reliability and robustness of 5G communication networks for wireless edge computing to provide collaborative Deep Learning (DL) of complex models while protecting privacy for healthcare systems. Wireless end devices are more susceptible to corruption due to the vulnerability offered by open network settings, however, this creates security issues and lessens the effectiveness of DL-based security models for healthcare systems. Furthermore, disaster reliability in communication networks has garnered unprecedented attention from governments and companies, particularly during the current COVID-19 pandemic scenario. In this work, a novel reliable personalized Federated Learning-based Customized Inequality-Aware Federated Learning (CusIAFL) technique is proposed for securing color images while communicating with a wireless network. The proposed technique adjusts each data sampling to the local target during optimization using knowledge of client-label availability. The work that is being presented uses a hybrid technique to maintain consistency in the time-series data. and a novel Pix2Pix Generative Adversarial Network (GAN) technique is used to generate realistic images. This novel work is tested on different non-medical and medical images. The experimental results have been evaluated using performance metrics, namely accuracy, entropy, PSNR, HD95, SSIM, and MSE. Furthermore, the accuracy varies from 89 to 93 percent with different datasets outperforming well with existing SOTA techniques. The outcomes demonstrate that the proposed CusIAFL-based scheme is more effective than the State-Of-The-Art (SOTA) models.

No abstract available.

Mobile communication networks could make a significant difference in rescuing affected people in post-disaster scenarios. However, the existing communication infrastructures tend to be out of service in such scenarios. To solve this issue, Unmanned Aerial Vehicles (UAVs) could be launched as flying base stations to provide the required coverage to Rescue Members (RMs) and allow them to communicate and transmit crucial information through the established links. Meanwhile, with the unpredictable movements of RMs, three serious issues are affecting the deployment of UAVs: (i) the control of their mobility, (ii) their limited energy capacity, and (iii) their restricted communication ranges. Aiming to address these issues, we propose deploying an intelligent connected group of energy-efficient UAVs assisting RMs and providing them communication coverage in the long run. These requirements are satisfied using a deep reinforcement learning strategy to learn the environment dynamics and make good trajectory decisions. Simulation experiments have demonstrated the potential of our framework compared to baseline methods to provide temporary communication networks for emergency response teams during disaster relief missions.

Session Initiation Protocol (SIP) has a promise future in real time communication, it is known to be highly efficient protocol found to provide desired services in multimedia communication (voice and video). In production, SIP service providers have to provide services for millions of clients and able to handle thousands of concurrent calls efficiently. That can be achieved through a wide scale of cluster SIP servers with high control level. However, poor management of resource utilization considered to be a serious issue in SIP infrastructure, especially for high call traffic distribution. This paper proposed a mechanism called SIP-Cluster Memory Utilization (SIP-CMU) which distributes SIP calls in cluster based on memory utilization to ensure better resource allocation. The mechanism makes use of NoSQL cache system to update cluster nodes memory status. Evaluation have been conducted by applying high load traffic in variant scenarios. In sum, results have shown that the proposed mechanism performs efficiently against other load-balancing mechanism by insuring a wised resource utilization such as bandwidth and CPU.

Nowadays, almost all applications use Internet of Things (IoT) to modernize their process of data communication. The area of smart cities is considered as an industry whose the exploitation of IoT is in constant increasing. Due to the specific requirements of the smart cities' applications, especially in terms of QoS (Quality of Services), IoT communications face multiple constraints. The limited resources, whether for devices or for communication links, is considered one important constraint. The frequent changes in the states and situations of devices and communication links make this constraint more complex. Considering this obstacle when designing communication algorithms for the IoT is an active research axis that is conducted in the IoT context. Given the unpredictable and uncertain nature of the situations that can be occurred in the network, it is important that these algorithms be adaptive and intelligent. The objective of the presented work in this paper is the proposition of a new communication solution, named IAAC-IoT (Intelligent and Adaptive Algorithms for IoT Communications), gathering adaptive and intelligent algorithms to improve the QoS and energy consumption within IoT. During the performance evaluation of the IAAC-IoT, we obtained satisfactory performance results in terms of QoS and energy efficiency.

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.

Cloud storage system provides on-demand and pay-per-use storage models with low computing costs. However, this storage system suffers from various security risks. Blockchain technology is an advanced technique that stores data in a distributed manner, and once the data are stored, it cannot be altered. Therefore, a blockchain-based distributed architecture has been proposed in this paper for the cloud storage system. The proposed scheme utilizes the functionality of the smart contract to provide security features to the stored cloud data. The proposed scheme encodes the data before uploading it to the cloud server to achieve confidentiality. Furthermore, the proposed system includes an enhanced optimization technique and a challenge-response-based integrity checking mechanism, which provides a more secure cloud environment. The proposed technique also minimizes the system bandwidth cost using an enhanced fruit fly optimization algorithm that optimizes the node failure repair process. The experimental results and performance evaluation show that the proposed scheme is secured and reliable for the cloud computing environment.

The evolution and rapid adoption of the Internet of Things (IoT) led to a rise in the number of attacks that target IoT environments. IoT environments are vulnerable to several attacks because many devices lack memory, processing power, and battery. Most of these vulnerabilities are relatively easy to mitigate when best practices are followed. However, even when best practices are followed, an attack to obtain a device credential and use it to generate false data is difficult to detect. Such an attack is called a replication attack and its impact can be catastrophic in crucial IoT scenarios such as smart transportation. In this sense, this paper proposes a solution to detect these attacks by analyzing abnormal network traffic through machine learning.

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%.

Wireless Sensor Networks (WSNs) are widely spread to monitor all type of parameters with the aim of improving our every-day lives. Typical WSN are networks on which only one wireless technology is used, so applicability and restrictions are determined by it. In this paper, a WSN of WSNs with the configuration of multiple wireless technologies is proposed. A network discovery system is defined to explain how to build the topology of a network with different standards. In the same way, a routing protocol is defined to communicate devices with different wireless technologies. As part of the network, LoRa technology is intended to cover long distances so it can be used to interconnect WSNs located on the edge to the Core of the network. Low-cost Heltec LoRa WiFi v2 devices are employed to determine the performance of lowcost solutions for one hop. This study is done for both 433 MHz and 868 MHz devices, which are the available frequencies in Europe. The results show that the best coverage results are obtained for SF 7 in all cases. Furthermore, the use of these low-cost devices is not advised for distances above 1 Km.

COVID which is one of the deadliest Pandemic of this era stuck the entire world which emerged from Wuhan, China in 2019. The pandemic had an extensive impact on unemployment and even deaths. This Pandemic was so new that a lot of medical doctors were involved in research towards diagnosing the chest X-ray images for COVID symptoms. Along with COVID, there have been other complications found like Pneumonia which resulted in the second wave of COVID leading to deaths. There has been good research done by Deep learning researchers in predicting the COVID and also COVID with Pneumonia classification based on Chest X-rays. But the challenge in earlier work is the limited data set which ultimately resulted in higher accuracy. The reason being smaller data set had very fewer number features for training which ultimately resulted in higher accuracy during prediction. So, towards obviating the above-mentioned challenge, we here have collected a fairly larger data set for better prediction. In addition, authors have proposed Convolution Neural Network - Long Short-Term Memory (CNN-LSTM) model by allowing ResNEt-101 as pretrained model for CNN along with other pretrained deep learning models like ResNEt-101, Inception V3, DenseNET-169, and Inception-ResNET V2. In addition to the prediction of chest X-ray images into different classes as COVID, COVID with Pneumonia, Viral Pneumonia, and Healthy, GradCAM has been used for giving a visual explanation for deep learning model resulting in higher accuracy which are ResNET-101, DenseNET-169 and CNN-LSTM. The GradCAM shows the Model built can predict the image perfectly. These would be stored in Cloud for access by doctors for medication.

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.

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.

Internet of Things (IoT) is considered nowadays as the most important and indispensable support to ensure all types of communication, over almost all sectors of activities. The specificity of each area, as well as its own requirements in terms of Quality of Service (QoS), make this communication difficult to ensure and thus, face multiple challenges. One of these challenges is related to the needed autonomy for IoT, not only in terms of available resources (energy and bandwidth for example) but also in terms of self-configuring and self-organizing within the network. It is in this context that we propose a new cross layers approach for better self-configuring and self-organizing of devices and communications within IoT environments. The proposed approach is named 2SAEC-IoT (self-organizing and self-configuring algorithms for efficient communications within IoT) that leads to guarantee an efficient data communication for IoT applications. 2SAEC-IoT is a cross layers solution since it considers important communication parameters related to three levels which are MAC, network, and transport. The proposed approach allows the continuity of services for IoT applications, especially for those with very sensitive data (e-health for example), by tolerating possible communications failures or devices breakdown. The evaluation of the proposed approach shows a clear improvement in terms of QoS, and energy efficiency compared to those obtained by three other IoT networks using different communication algorithms.

In a jiffy after the outbreak of the 2019 novel coron-avirus, also called COVID-19 or SARS-CoV-2, the World Health Organization (WHO) considered it as a pandemic that threatens the demise of humanity. This quick decision was in conjunction with a real situation of biological inability to find a vaccine that can eliminate the virus or at least limit its spread. For that reason, technological intervention and cooperation are needed more than ever to face this pandemic. In this same context, we propose a novel system that deploys Vehicle-to-everything (V2X) technology and Blockchain in collaboration to face such a pandemic. Our proposal is centered on a triple-stage processing i) zone identification and classification based on vehicles' thermal cameras detection, ii) Blockchain-based information storage for enhanced patient medical information privacy, and iii) drones-based zone neutralization processes. Simulation results show that, thanks to the use of Blockchain technology, the network-related performance remain almost unchanged and hence, all Intelligent Transportation System (ITS) including inter-vehicles and inter-drones functionalities are not affected. In addition the additional overhead is very acceptable and does not exceed the 5 Kb in the worst case.

During the last decade Wireless Sensor Networks and IoT have taken an overwhelming place in engineering and environmental applications. Thus, with growing interest in large size and complex networks, some critical challenges have to be taken care of such as energy consumption and node localization, especially when dealing with real-time applications. Such factors affect strongly the system performance in sensitive fields like e-health or military applications. The primary purpose is to determine the location of the sensor node that triggers the event. Energy consumption of each node is a serious problem for WSN when it comes to extend the life time of the whole network. In this paper, we propose a multicriteria optimization scheme based on a bio-inspired algorithm called Harris Hawks Optimization Algorithm (HHOA). It is shown that the proposed paradigm is capable of increasing the localization rate, as well as minimizing the energy consumption of the nodes. HHOA populations are able to share information in a multi-agent fashion to compute the trigger's position. Developing this algorithm on a huge WSN with millions of nodes reveals a particularly high performance. To assess this, several experiments in different scenarios are carried out in a decentralized environment of WSN. Finally, a comparative study is carried out also to some recent bio-inspired algorithms.

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).

Participatory crowdsensing is a collaborative framework of sensing devices coordinated by computing and networking components. In this regard, this article proposes the Collaborative Participatory Crowd Sensing Using Reputation and Reliability with Expectation Maximization (CPCSRREM) for IoT Networks. CPCSRREM comprises of Task Allocators (TA) (interacting IoT servers) that meet the service provider’s demands and participant expectations. TAs apply the Maximum Likelihood (ML) and Expectation Maximization (EM), evaluate the node reputation and reliability, form the collaborative and non-collaborative participant groups and select the Lead Participant Nodes (LPN) with maximum reputation and reliability in the sensing region. CPCSRREM applies the max-min approach, restricts the sensing cost, increases the task incentives and enables the participant nodes to complete the on-demand tasks allocated by TAs. Simulation results indicate that the proposed model improves the collaborative participant tasks and incentive sharing in IoT as compared with non-collaborative methods.

The evolution of Internet of Things (IoT) led to the construction of many IoT middleware, a software that plays a key role since it supports the communication among devices, users, and applications. Although various solutions and studies were proposed, they rarely address crucial privacy and security considerations, especially regarding the message queuing telemetry transport (MQTT) protocol. Moreover, in the majority of the solutions, integrating new devices is a time-consuming task performed manually that cannot be accomplished in a scenario with thousands, maybe millions of devices. In this sense, this article proposes a new IoT middleware, called In.IoT, a scalable, secure, and innovative middleware solution that addresses the middleware concerns identified in this article. In.IoT architectural recommendations and requirements are detailed and can be replicated by new and available solutions. It supports MQTT, CoAP, and HTTP as application-layer protocols. Its performance is evaluated in comparison with the most promising solutions available in the literature and the results obtained by the proposed solution are extremely promising. In.IoT is evaluated, demonstrated, validated, and it is ready and available for use.

Security and knowledge systems effectively identify the node behavior based on device identity, location, social attributes, and networking parameters. In this article, we propose a novel approach of location, context, and social objectives using knowledge-based rules and conflict resolution for security (LOCSKS) in the Internet of Things. The proposed system applies the Bayesian decision theory and analyzes the node behavior based on prior and posterior knowledge of the location, context, and social objectives in the Internet of Things. LOCSKS exclusive and economical keys consider the context to service type mapping and risk levels to ensure the location privacy and trust in the system. The knowledge-based and inference rules identity the conformity and conflicting nodes. The conflict resolution approach blocks the invalid nodes, suspends the malicious nodes, and delays the suspicious nodes. Simulations indicate that the proposed LOCSKS scheme effectively identifies the node behavior and conflicting conditions, reduces the key violations, and enhances location privacy as compared to the existing schemes.

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.

It is evident that the Internet of Things (IoT) is a technology that is not only already being implemented but it is also going to be part of our everyday life in many aspects regarding our homes, workplace and the cities we live in. As the concept of IoT evolves, new terminologies are being developed to specify certain characteristics of the IoT network. The Internet of Underground Things (IoUT) is an example of these new terms, considering, in this case, the soil medium. IoUT is then separated from IoT in the fact that the requirements of underground communications and the underground nodes are different from those in regular IoT networks and, therefore, IoUT should be studied separately. However, as IoUT is a new concept, there are not many studies available. Therefore, in this paper, an overview of the IoUT concept and the most important conclusions that have been reached on this topic is presented. Furthermore, transmission experiments between underground and above-ground low-cost WiFi nodes deploying them at different depths, heights, and distances to determine the suitability of the currently available low-cost nodes for this new IoT concept have been performed. The results show that the optimal height is 1.5 meters and the optimal depth is 20 cm. Furthermore, the connection was able to reach distances up to 10 m. Lastly, an underground transmission decision-making algorithm for soil monitoring nodes in precision agriculture has been provided.

In mobile and sensor wireless networks, liaising between wireless nodes is performed using routing protocols. Each protocol has its advantages and drawbacks. OLSR is one of the most widely used protocols, due its proactive scheme. It is based on the principle of Multi Point Relay (MPR) which essentially consists in building a “relevant” set of links and direct neighbors by ignoring redundancies in order to have optimal paths. This relevance is demonstrated by criteria that allow the entire neighborhood of a node to be reached by two hops (all neighbors of neighbors). However, these criteria do not take into consideration other important factors that have a strong influence on the protocol’s behavior such as the residual energy, which may lead to degrading the overall network lifetime. In this paper, we present a new method to overcome this problem, allowing mobile nodes within ad hoc and sensor wireless networks to determine an efficient set of their routers using OLSR Protocol, based on the energy constraint of their neighborhood. Due to the random mobility of nodes in such networks, an efficient calculation algorithm of routers for each node seems an obligation, in order to avoid link breakage and assure a high level of the network availability. Our new algorithm consists on introducing a dynamic weighting ratio between the reachability and the residual energy of the one hop neighbors of each node for selecting its routers. One of a statistical method to discover the neighborhood is the dispersion calculation of the residual energy that allows a node to find out how spreads are energies of its neighborhood. This calculation is introduced in the router selection algorithm. Thus, this method allows a load balancing in the network and avoids the rapid decrease of nodes batteries that have been selected as routers for a long time. Simulations results show that our new approach significantly increases the lifetime of the mobile ad hoc networks by decreasing the dead nodes number.

In the recent literature, sensor localization has been considered as the most technical challenge of internet of thing and Wireless Sensor Networks. The main objective is to predict an accurate localization of sensor nodes. Recent approaches dealing with localization rely on meta-heuristics algorithms; hence, the localization procedure becomes an optimization issue in a multidimensional space. Bat algorithms are well suited for this kind of problems. It is also known in several recent papers that the Bat algorithm is well suited in space exploitation, but not as much in exploration. Throughout the paper, we propose an enhanced hybrid approach, namely “Enhanced Bat Algorithm with Doppler Effect or “EBADE”. Bat parameters are tuned using a modified frequency equation. EBADE computes thus iterative the position of the nodes through optimizing node Euclidean distances. Experimental results come up with remarkable improvement in terms of localization error and CPU run-time.

Routing data in Vehicular Ad hoc Networks is still a challenging topic. The unpredictable mobility of nodes renders routing of data packets over optimal paths not always possible. Therefore, there is a need to enhance the routing service. Bus Rapid Transit systems, consisting of buses characterized by a regular mobility pattern, can be a good candidate for building a backbone to tackle the problem of uncontrolled mobility of nodes and to select appropriate routing paths for data delivery. For this purpose, we propose a new routing scheme called Bus-based Routing Technique (BRT) which exploits the periodic and predictable movement of buses to learn the required time (the temporal distance) for each data transmission to Road-Side-Units (RSUs) through a dedicated bus-based backbone. Indeed, BRT comprises two phases: (i) Learning process which should be carried out, basically, one time to allow buses to build routing tables entries and expect the delay for routing data packets over buses, (ii) Data delivery process which exploits the pre-learned temporal distances to route data packets through the bus backbone towards an RSU (backbone mode). BRT uses other types of vehicles to boost the routing of data packets and also provides a maintenance procedure to deal with unexpected situations like a missing nexthop bus, which allows BRT to continue routing data packets. Simulation results show that BRT provides good performance results in terms of delivery ratio and end-to-end delay.

Vehicular Ad-hoc NETworks (VANETs) have an extremely dynamic nature, which leads to unstable connectivity between nodes. Link failures may occur at any time and cause packets loss. Several routing protocols tackle this issue by supplying mechanisms to detect route failures. The aim is to seek an alternative route before the current one becomes unavailable. In this paper, we propose an enhancement of the AODV routing protocol. We substitute AODV's route breakage detection system with a link breakage forecasting indictor (LBFI). LBFI is based on information derived from the OFDM (Orthogonal frequencydivision multiplexing) packet decoding process that are combined with Dempster-Shafer belief theory. Once an upcoming link failure is detected, the route maintenance process starts. We evaluate this new routing protocol called LBFI-AODV, by comparing it to AODV using the NS3 network simulator. Results obtained reveal that LBFI-AODV's efficiency is significantly improved providing both higher packet delivery ratio (PDR) and lower end-to-end delay (E2ED).

Anonymizing communication becomes a substantial issue to enhance security and facing attacks. Huge researches are made in this field aiming to ensure a secure and anonymous communication. In vehicular ad hoc networks (VANets), this concept is used in different application areas like military domains, in which hiding destinations identities is necessary to avoid consequences attacks. In this paper, we propose a model of security to ensure anonymity in vehicular ad-hoc network. We inspire this model from the Invisible Internet project (I2P) in which we continue our previous work by adapting some of I2P mechanisms and algorithms in VANets. We adapt the I2P protocol to respond to several requirements of VANets. The proposed model is based on tunnels and encryption algorithms that use digital signatures and authentication mechanisms. We aim to make the proposed protocol more secure by ensuring anonymity, integrity, non- repudiation and confidentiality. We prove the effectiveness and the security of our proposed model by analysing different cases of anonymity and showing performance results. We have launched our simulations using NS3 platform.

Content delivery supported by Software defined network (SDN) controllers achieves high data extraction rate, granularity levels, content distribution and availability in the network. Proactive and dynamic content placement schemes at distributed servers facilitate content fetching ability that reduces the server replication cost and access delays in the network. In this regard, this article proposes a Proactive Replication scheme for Resilient Content Delivery (PRRCD) in SDN. The proposed model consists of the content origin server, proactive content replication servers, content providers, content flow and management controllers, internet service providers, gateway nodes and OpenFlow switches. The novelty approach of proposed model classifies the content objects based on client priority, event-driven contents and access rates, and resolve the sub-problems of content flow maximization. PCRS functions in controller-initiated (proactive push of content objects) and client-initiated modes (reactive pull of content objects). Integer linear programming with branch and bound approach eliminates the constraints of replication rate, cost and access delays, and defines the optimal bounded replication response. Simulation results indicate that the proposed model improves the content delivery rate and minimizes the replication cost and access delay in SDN as compared to the non-SDN based methods.

Cloud computing technology hosts application for users to accesses computing as services. Its application has been widely used and increases to become a part of enterprises' computing infrastructures. However, Cloud computing latency and request deadline fulfillment issues are among the major problems. Therefore, there is a need for a solution to synchronize a request states in Cloud computing. In this paper, the resource allocation and scheduling problem for services in the cloud are addressed and a solution model is presented. Our approach is based on MAX-Plus algebra, to provide a deterministic and exact solution to the minimization of services queries response times. Our proposal is tested on various problems sizes to evaluate its performance and scalability.

The Internet of Things (IoT) concept of connecting everything to the Internet is ambitious and disruptive. Its market is promising and data centric, which is stored and processed in a software known as IoT middleware. However, a plethora of middleware solutions is available and most of the comparisons presented in the literature are qualitative, which is insufficient when choosing a solution for a real scenario. Moreover, the few quantitative comparisons that are available in the literature can only determine the best solution in separated given categories. This paper complements the conclusions of a quantitative comparison study that is available in the literature through a comparison of five middleware solutions across five different scenarios. Such conclusions were possible through PROMETHEE, a multicriteria decision-making method (MCDM). This study also confirms that best solution depends on which criteria are prioritized in a given scenario. The outcome was analyzed in detail and it was concluded that MCDMs are useful when choosing the best middleware platform to deploy in a given IoT solution. Orion (a Fiware project), InatelPlat, and Sitewhere are the platforms that performed better in the study.

Due to the recorded success by Internet of Things (IoT) technology, more and more domains use it as a communications and exchange network such as e- health, smart cities, vehicles, etc. IoT do not stop integrating an important number of components and objects that are characterized by their complexity and heterogeneity. Such constraints make the existing routings protocols unsuitable for IoT communications. To accomplish all the expected tasks and satisfy the user services, it is important to guarantee a quality of communication that answers to the requirements of the various applications in terms of data and processing (availability, integrity, efficiency, etc.). The objective of our work in this paper is to propose a smart and efficient routing algorithm to improve the IoT communications performance. The proposed method is called SERA (Smart and Efficient Routing Algorithm) which is based on self-organization of the communications between devices according to their new parameters. The evaluation of SERA has shown that SERA improves considerably the IoT communications performance in terms of some QoS and energy efficiency parameters.

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.

Wireless sensor network (WSN) represents a very important research that targets a very large number of possible applications in healthcare, smart cities, environmental monitoring, military, industrial automation and recently in smart grids. It consists of three main components: a large number of nodes, gateways and software. WSN workload needs an unlimited lifetime energy and it doesn't depend on a limit energy usage while sensor nodes are supplied by batteries or supercapacitors with limited energy. Particular algorithms must be employed so that energy consumption is reduced. In this paper, clustering protocols are investigated and a new approach is proposed to increase the lifetime of the wireless sensor network. Simulation results show that its performance is better in terms of prolonging the lifetime of the network and increasing the number of data packets received by the Base Station (BS).

The objective of the work carried out in this paper is to propose a novel load balancing algorithm that adapts the functioning of transport layer to the characteristics of the IoT (Internet of Things) communications when applied to e-health applications. The proposed algorithm is called LBA-Ie (Load Balancing Algorithm for IoT communications within e-health environment) and it is based on the integration of IoT communication parameters in the flow control process supported by TCP (Transmission Control Protocol). LBA-Ie is self-organized and adaptive algorithm by taking into account the changes occurred within the network, the links situations and the objects parameters. LBA-Ie is evaluated in terms of QoS (Quality of Service) and energy efficiency. The simulation results are compared to those obtained by three other solutions. LBA-Ie improves the QoS of IoT communications by increasing the data reliability which improves then the e-health applications. LBA-Ie allows also economizing the consumed energy by the objects and increase then their average lifetime.

The cooperation of heterogeneous unmanned systems, for instance, between aerial engines and terrestrial engines, relies on reliable communication. Data delivery is ensured by routing protocols, but traditional routing approaches, MANET and DTN, are not efficient in such networks. In this paper, we propose the S-ROGUE routing protocol combining the paradigms MANET and DTN and switching between them according to the network connectivity. On the one hand, the S-ROGUE MANET algorithm relies on a proactive approach and a novel metric to anticipate link disruptions and detect unidirectional links. On the other hand, the S-ROGUE DTN algorithm uses on a reinforcement learning technique to select the best routing action. It implements also a replication control and packet prioritization to improve routing performances. We lead a performance evaluation of S-ROGUE with similar routing protocols in realistic simulated environments and conclude that S-ROGUE has the best routing performance regardless the scenarios.

Considered as a primordial component of Cooperative Intelligent Transportation Systems (C-ITS), Vehicular Ad Hoc Network (VANET) plays a weighty role to facilitate different on-road applications, most of which primary rely on multi hop communications. To ensure reliability and security of such communication, it is very pertinent to guarantee that the most proper and trustworthy vehicles are selected as relays. This paper takes up this challenge by introducing a weighted probabilistic and trust-aware strategy called WeiSTARS, to ensure high delivery ratios within reduced delays. Simulation results conducted using NS2 tool depicted that our technique enhances the delivery ratio by more than 8% with around 30% less delay compared to both GytAR and GPSR routing protocols even in the presence of high ratios of dishonest vehicles.

The wireless mobile networks (WMNs) are nowadays faced to several challenges such as the guarantee of quality of service (QoS) in the presence of multiple constraints specific to the wireless communication environments. The limited energy of the different nodes is one of these constraints which constitute the main source of the connectivity breaks within the network. Such breaks when repeated may lead to the appearance of uncovered areas which degrade the performance of some applications for which network coverage is very important such as e-health. In order to overcome to this problem, we present in this paper a new solution called MCoS (Maximization of the network Coverage by Self-organization) for better performance communications in WMNs when applied to e-health. MCoS aims to allow an intelligent control of the network topology and a self-organization between nodes in order to cover as possible the operating area of the e-health applications (such as home or hospital). MCoS maximizes the network coverage and then improves, in addition to the coverage quality, important QoS parameters such as the end to end delay, the energy consumption and the nodes lifetime.

Clustering is the key for energy constrained wireless sensor networks (WSNs). Energy optimization and communication reliability are the most important consideration in designing efficient clustered WSN. In lossy environment, channel coding is mandatory to ensure reliable and efficient communication. This reliability is compromised by additional energy of coding and decoding in cluster heads . In this paper, we investigated the trade-off between reliability and energy efficiency and proposed adaptive FEC/FWD and FEC/ARQ coding frameworks for clustered WSNs. The proposed schemes consider channel condition and inter-node distance to decide the adequate channel coding usage. Simulation results show that both the proposed frameworks are energy efficient compared to ARQ schemes and FEC schemes, and suitable to prolong the clustered network lifespan as well as improve the reliability.

The formula E = Pt still hold true as we forge ahead in our unending quest to extend power lifetime in e-Health wireless sensors. Our research work proposes predictive MAC manipulation and intelligent node clustering with associated algorithms to further lower the energy consumption of e-Health devices equipped with wireless sensors. Predictive manipulation of MAC involves powering on and off the wireless radio at predetermined intervals for maximum energy utilization, coupled with the ability of each node to predictively send messages only to predetermined nodes. Our approach equally utilizes a central gateway server to periodically optimize, and reprogram nodes' predictive behavior. This approach is similar to an orchestra conductor, predictively directing which instrument or tenor should be played and for how long. Our predictive MAC manipulation equally reduces channel contention rates amongst related nodes, since the channel is available only to the sending node within each cluster.

In VANET (Vehicular Ad Hoc NETwork), the act of transferring important information is a main concern in every routing protocol conception. Hence, selecting next packet relay is crucial decision for the network performances. The highly dynamic topology of VANET which is result of the frequent changes in vehicles' positions, their speeds and the unpredictable drivers' directions at intersections mostly lead to wrong packet forwarding decisions. To overcome the above mentioned countermeasure, we propose in this paper a GPSR-based routing protocol incorporating weighted link quality estimation, a probabilistic reception approximation and moving direction. Based on vehicles' new coordinates and link status, our proposal called P-GPSR aims at selecting reasonable relay node to smoothly disseminate messages between vehicles. This addition allows our proposal to significantly enhance the overall GPSR performances, more specifically, the packet delivery ratio and the end-to-end delay. Simulation experiments performed using NS2 simulator have shown that our proposal outperforms both GPSR and GPSR-L in terms of throughput and packet delivery ration without negatively affecting the end-to-end delay.

Due to their lack of assessment mechanisms of link quality in VANET environments, routing protocols do not deal efficiently with highly volatile links. One way to fill this gap would be to anticipate links breakages with new route computation. Currently available link quality indicators are not sufficiently responsive to consider forecasting. In this paper we present a novel predictive link quality indicator that is based on the OFDM decoding steps into the PHY layer. The events generated by these steps are threated by a data fusion algorithms. The resulting link quality indicator presents interesting forecasting characteristics and is suitable for a cross-layer usage in routing protocols.

Our contribution in this paper is to propose a new solution called EQ-AODV (Energy and QoS supported AODV) for better performance in WMSNs (Wireless Multimedia Sensor Networks). EQ-AODV aims to improve AODV protocol and make it effective for multimedia data in WMSNs. This improvement is based on the adaptation of the routing process of AODV according to two parameters. The first one is the energy of sensors belonging to the routing roads and the second parameter is the nature of the packets received by these sensors. The considered data are text data, audio and video streaming data. After the evaluation of the performance and based on the obtained results, EQ-AODV showed a better performance compared to AODV. EQ-AODV improves important QoS parameters namely the network load and the end to end delay. Important improvement in terms of the life time of sensors and the consumed energy is recorded too.

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.

In Wireless Sensor Networks (WSNs), the nodes mobility model determines the network functionality which strongly influences the network lifetime, coverage and the energy consumption as well. So, the mobility strategy choice is very important in several critical application areas. The present paper proposes a mobility model that respects the network coverage while efficiently managing the spent energy. This model uses a controlled placement of sensors (deterministic node deployment method) based on scanning grid in order to achieve the target sensing field coverage and the nodes connectivity.

Abstract In the current paper, we propose a novel mobile‐based architecture that enforces security and privacy protection in the context of mParticipation. This architecture enables the citizens to cast their opinions anonymously and without limitation in time and space. It also removes the burden of preceding registration before any voting process. As the new electronic identity (eID) Cards are replacing the conventional identity cards in most European countries, we also discussed, in this paper, how the new German eID cards can be utilized for mobile participation. The use of the eID cards offers better flexibility and stronger security. A prototype implementation of this architecture is also described in this document, and the related performance is discussed. Copyright © 2016 John Wiley & Sons, Ltd.

The objective of our work is to propose a new approach called ES-WSN (Energy Efficiency by Switching between roles in WSNs) for the efficient use of energy in wireless sensor networks (WSN). Our solution ES-WSN extends the lifetime of the nodes in the network and guarantees a fair evolution of their level of energy. This is based on the exchanging of roles between relays and sensors nodes based on their positioning and deployment in the WSN. This process is provided according to two parameters from the communication environment. The first one is the distance between the sensors and relays nodes. The second one is the remaining energy of nodes in the network. After evaluation, ES-WSN has achieved better performance compared to two other solutions proposed in the literature.

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.

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Mobile Ad-hoc Networks (MANET) and more specifically their vehicular variant (VANET) have to deal with fast changing channel conditions, specifically in urban areas. Routing protocols that have to build end to end paths over such volatile links typically react to link failure. In this paper we present a novel PHY layer based link state indicator which aims to predict such failure. The proposed link state indicator is related to the IEEE 802.11 standard and relies on the OFDM decoding process. PhySimWifi is a detailed and accurate implementation of the OFDM-based IEEE 802.11 standard that provide access to all the steps of a packet reception and incorporates realistic channel models. When using it in the ns-3 simulator, the received packets decoding errors / success gives us the material to compute our predictive estimator. This new link state indicator is entirely based on the PHY level. The efficiency of the proposed indicator is validated by reference to PHY and NET packet reception ratio of the monitored link. The efficiency of the proposed new indicator is validated by comparing it with an Signal to Noise Ratio based predictive link state estimator.

The energy efficient in medical ad hoc sensors network (MASN) is one of the most important areas of researches to ensure better services for applications using such an environment. The challenge is how to use of energy of the sensors nodes in a fair way to avoid a break of connectivity in the network as long as possible. Our work focus on how to optimize the energy consumption and to increase the performance of the user applications in medical context. Our proposed approach is called M-EE (Medical Energy Efficient) which is based on the routing protocols with adding a new algorithm for energy fairness. M-EE takes into account the medical communication environment to manage efficiency the energy of the sensor nodes. With this mechanism, sensor nodes which are using image and video medical data are allowed more energy than other sensors nodes. The simulation results showed that our proposed M-EE approach allow significant energy consumption of the network, a reduction of the data loss and an increase in average working time of the sensors.

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.

Several research effort in recent years has shown its significant interest in the development of security measure in the routing of Mobile Ad-hoc Network (MANET). Ad-hoc On-demand Distance Vector (AODV) routing protocol has been one of the basic protocol that is modified to cope with security demands. This paper present an analytical model to evaluate the performance of AODV variant that is developed to be secure with malicious node detection and trust mechanism. Since the performance of MANET is tightly depends on the underlying topology of the nodes, route establishment may fail due to node's movement or malicious activity. The analytical model aims to provide the basic performance insight in the form of the route establishment probability of the MANET with and without malicious nodes. The model is important for MANET designer to optimize the connectivity for all nodes.

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.

Health monitoring of patients is a common task in healthcare houses from nursing homes to hospitals. Medical staff moves close to patients and collects their monitoring body parameters. To help the overall state control of monitored patients, it could be performed in autonomous, real-time, and remotely way. The application of healthcare wireless sensor networks to these scenarios could perform this job. Through a network it is possible to reach each one of the patients' nodes anytime anywhere as long as a network terminal is accessible. Considering this scenario, the paper proposes a solution to deal with mobile nodes in healthcare wireless sensor networks. The proposed solution is based on a handover procedure that guarantees continuous accessibility to the mobile nodes while moving through different access points' coverage range within the same network (intra-mobility). The most recent mobility approaches although supporting handover solutions, they are not compatible with continuous access to the nodes of a WSN. This proposal uses a distributed method to evaluate and initiate the handover process. Therefore, the procedure could be performed either by the nodes or by the access points. To validate this solution it is presented a laboratory prototype and two evaluation tools.

Wireless Sensor Network (WSN) are usually used in hostile environment to collect informations. The sensors use to get there energy form batteries. It is a crucial point for live time of the network. Energy has to be saved for this type of networks. In WSN, routing protocol usually take care on this parameter. In this paper, we propose to use an image representation of the energy dissemination in the network to make a location-aided routing protocol.

New communication technologies must be provided in order to improve the energy distribution. As far as we know, there is not any network protocol designed exclusively for this purpose. In this paper, we present the design and simulation of a group-based protocol to improve the energy distribution in smart grids. We will show the group-based architecture and the protocol operation. Finally we will show some measurements taken from a test bench in order to test its performance.

Nowadays, television is the most popular media for informing and entertaining in the world. The generalization of IPTV brings advantages in terms of IP service convergence, but has also some drawbacks. One of the most them is the channel zapping delay. In this paper we show how it is possible, when IPTV is multicasted, to improve the zapping delay and therefore the quality experienced by viewers, by using an additional stream with specific packet ordering rules.

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 objective of pervasive computing is to provide anytime and everywhere, computing and communication services in particular, for mobiles that interact through ad hoc connections. This paper presents centralized and distributed service based clustering approaches wherein ad hoc or composite services are represented by clusters of nodes that establish relationships based on affinities. The relationships are adaptive according to the limitation of the mobile nodes battery power and to the dynamic network topology changes.

The design of CDMA networks (such as HSDPA, UMTS) and in particular the implementation of base stations is a crucial point for operators. For economical reasons, the number of base stations has to be well balanced to answer to traffic and services needs. In UMTS networks, each base station can serve a limited number of mobile nodes. This number is related to the maximum power that a base station can handle (see [1]). We will describe this problem for a homogeneous UMTS network. Since the base stations of a real network are not regularly distributed, we will consider a 'disturbance' of the base stations positions. In this new configuration, the mobiles stay in set positions and the base stations are distributed according to a random location. In this context, we show that the network total transmitting power is an increasing function of the perturbation amplitude. We establish that the difference with the total transmitting power of a homogeneous network is very low.

In the context of mobile ad hoc networks (MANETs) routing, we propose a clustering algorithm called Connectivity, Energy and Mobility driven Clustering Algorithm (CEMCA). The aim of CEMCA consists in appropriately choosing the cluster head to reduce routing overhead. In order to reduce traffic and energy consumption, the control messages are sent only when needed, according to the speed of the node. Each node has a quality that indicates its suitability as a cluster head. This quality takes into account the node connectivity, battery energy and mobility. These parameters are very important for the stability of the cluster. Simulation experiments are carried out to validate our algorithm in terms of stability of the clusters and their members and the quality of the connectivity. The results are compared to a previous approach called Weight Clustering Algorithm (WCA) and they show that CEMCA is performing better.

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.

A growing number of applications create overlays on top of the Internet. Several unsolved issues at the network layer can explain this trend to implement network services such as multicast, mobility and security at the application layer. However overlays require some form of internal addressing, routing and naming. Therefore their topologies are usually kept simple but this limits their flexibility and scalability. Our aim is to design an efficient and robust addressing, routing and naming framework for complex overlays. Our only assumption is that they are constrained by the Internet topology. Applications using our framework will be relieved from managing their own overlay topologies. This paper presents our framework in detail as well as some performance results concerning its routing efficiency, its reliability to network dynamics and its naming scalability.