Fakhraddin Alwajih, Abdellah El Mekki, Samar Mohamed Magdy, AbdelRahim A. Elmadany, Omer Nacar, El Moatez Billah Nagoudi, Reem Abdel-Salam, Hanin Atwany, Youssef Nafea, Abdulfattah Mohammed Yahya, Rahaf Alhamouri, Hamzah A. Alsayadi, Hiba Zayed, Sara Shatnawi, Serry Sibaee, Yasir Ech-chammakhy, Walid Al-Dhabyani, Marwa Mohamed Ali, Imen Jarraya, Ahmed Oumar El-Shangiti, Aisha Alraeesi, Mohammed Anwar AL-Ghrawi, Abdulrahman S. Al-Batati, Elgizouli Mohamed, Noha Taha Elgindi, Muhammed Saeed, Houdaifa Atou, Issam Ait Yahia, Abdelhak Bouayad, Mohammed Machrouh, Amal Makouar, Dania Alkawi, Mukhtar Mohamed, Safaa Taher Abdelfadil, Amine Ziad Ounnoughene, Anfel Rouabhia, Rwaa Assi, Ahmed Sorkatti, Mohamedou Cheikh Tourad, Anis Koubaa, Ismail Berrada, Mustafa Jarrar, Shady Shehata, Muhammad Abdul-Mageed. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Unmanned Aerial Vehicles (UAVs) are reshaping Intelligent Transportation Systems (ITS) through real-time data collection, dynamic traffic monitoring, and emergency response coordination. The integration of UAVs with Software-Defined Networking (SDN) offers centralized control, flexible resource management, and enhanced scalability. This survey explores SDN-enabled UAV-ITS frameworks, reviews real-world application scenarios, addresses key challenges, and outlines future research directions to advance intelligent, secure, and efficient transportation systems in the smart city.

Faced with multiple societal challenges, the healthcare sector has been compelled to leverage recent and emerging technologies to adapt. Blockchain is one of the leading technologies, offering transparency, process automation, immutability of traces and the ability to scale up in terms of both the volume of processes and the number of players interacting. The goal of the paper is to show the potential of blockchain technology - alone or merged with other technologies - to help the healthcare system evolve and provide scalable, efficient and secure solutions to four healthcare applications: electronic health record (EHR) storage, health data sharing, remote patient monitoring, and pharmaceutical supply chains. After identifying the functional and security requirements of healthcare systems, the paper conducts an in-depth review of the literature. The survey assesses the effectiveness of blockchain-based solutions in meeting functional, privacy and security needs. It is completed by an analysis of the synergies that can be expected between blockchain and emerging technologies, e.g. artificial intelligence, federated learning, the Internet of Things (IoT), and Large Language Models (LLM), to the benefit of security or privacy in healthcare.

Abstract This article presents an innovative concept that harnesses the capabilities of large language models (LLMs) to revolutionize human‐robot interaction. This work aims to connect large language models with the Robot Operating System (ROS), the primary development framework for robotics applications. We develop a package for ROS that seamlessly integrates ChatGPT with ROS2‐based robotic systems. The core idea is to leverage prompt engineering with LLMs, utilizing unique properties such as ability eliciting, chain‐of‐thought, and instruction tuning. The concept employs ontology development to convert unstructured natural language commands into structured robotic instructions specific to the application context through prompt engineering. We capitalize on LLMs' zero‐shots and few‐shots learning capabilities by eliciting structured robotic commands from unstructured human language inputs. To demonstrate the feasibility of this concept, we implemented a proof‐of‐concept that integrates ChatGPT with ROS2, showcasing the transformation of human language instructions into spatial navigation commands for a ROS2‐enabled robot. Besides, we quantitatively evaluated this transformation over three use cases (ground robot, unmanned aerial vehicle, and Robotic arm) and five LLMs (LLaMA‐7b, LLaMA2‐7b, LLaMA2‐70b, GPT‐3.5, and GPT‐4) on a set of 3000 natural language commands. Our system serves as a new stride towards Artificial General Intelligence (AGI) and paves the way for the robotics and natural language processing communities to collaborate in creating novel, intuitive human‐robot interactions. The open‐source implementation of our system on ROS 2 is available on GitHub.

Alzheimer's disease is a severe brain disorder that causes harm in various brain areas and leads to memory damage. The limited availability of labeled medical data poses a significant challenge for accurate Alzheimer's disease detection. There is a critical need for effective methods to improve the accuracy of Alzheimer's disease detection, considering the scarcity of labeled data, the complexity of the disease, and the constraints related to data privacy. To address this challenge, our study leverages the power of Big Data in the form of pre-trained Convolutional Neural Networks (CNNs) within the framework of Few-Shot Learning (FSL) and ensemble learning. We propose an ensemble approach based on a Prototypical Network (ProtoNet), a powerful method in FSL, integrating various pre-trained CNNs as encoders. This integration enhances the richness of features extracted from medical images. Our approach also includes a combination of class-aware loss and entropy loss to ensure a more precise classification of Alzheimer's disease progression levels. The effectiveness of our method was evaluated using two datasets, the Kaggle Alzheimer dataset, and the ADNI dataset, achieving an accuracy of 99.72% and 99.86%, respectively. The comparison of our results with relevant state-of-the-art studies demonstrated that our approach achieved superior accuracy and highlighted its validity and potential for real-world applications in early Alzheimer's disease detection.

Optimal routing in Vehicular Ad-hoc Networks (VANETs) presents significant challenges due to the dynamic nature of vehicular movements. Cluster-based routing schemes have emerged as promising strategies to manage the scalability and efficiency of routing in VANETs. However, they often face limitations in coverage due to their inherent reliance on distributive route discovery methods. This paper introduces the Software-Defined networking-enabled cluster-based Evolving Graph Routing Scheme (SE-CEGRS), designed to address these coverage challenges by integrating the flexibility and centralized control of Software-Defined Networking (SDN) with the established Cluster-based VANET-oriented Evolving Graph (CVoEG) framework. Unlike traditional CVoEG, which employs a distributive approach with Evolving Graph Dijkstra (EG-Dijkstra) for both intra- and inter-cluster communications, SE-CEGRS maintains this strategy for intra-cluster scenarios but shifts to a centralized model for inter-cluster communications. Our evaluation demonstrates that SE-CEGRS significantly outperforms conventional CVoEG in inter-cluster communication scenarios by offering reduced discovery times, lower communication overhead, and shorter average path lengths. The findings underscore SECEGRS’s potential to enhance the efficiency and scalability of routing in VANETs, pointing toward a new direction for future research and development in vehicular communication technologies.

Deep Learning (DL) has demonstrated remarkable success in various Remote Sensing Image (RSI) analysis applications. However, due to disparities in data distributions, DL models find it challenging to generalize meaningfully, especially when training and testing datasets are collected at different locations with varying resolutions, by different sensors, or due to climatic conditions. DL techniques applied to RSI have shown interest in domain adaptation as a suitable solution for addressing discordance among domains. In this study, we focus specifically on two DL approaches for Domain Adaptation (DA) in RSI: Self-Supervised Learning (SSL) and Graph Neural Networks (GNNs). First, we elucidate the motivation for utilizing DA techniques to address challenges in the field of RSI, along with their applications in conjunction with GNNs and SSL. Then, we present related surveys on domain adaptation and provide background information. This paper suggests a classification system for DL approaches and draws attention to challenges and research directions for DA in RSI. This study aims to deliver scholars in the remote sensing field with current references on DA using SSL and GNNs.

The integration of Industrial Internet of Things (IIoT) technology into the industrial sector has produced numerous significant advantages. However, the notable concern remains the absence of robust security and privacy measures in these interconnected critical environments. To secure IIoT networks, several researchers and experts employ intrusion detection systems (IDS) for detecting cyberattacks. The current systems exhibit efficient performance when handling a few categories of attack classes, even in the presence of slight imbalances. However, these models face challenges when confronted with vast categories of attack classes and highly imbalanced data. To tackle these issues, this study introduces an attention-based hybrid deep learning (AB-HDL) model designed to monitor network traffic and predict cyberattacks within the network. The proposed model comprises an attention mechanism and a hybrid deep learning model that integrates convolutional neural networks (CNN) and an autoencoder (AE). The effectiveness of the proposed AB-HDL is assessed using publicly accessible datasets: Edge-IIoTset and X-IIoTID. To ascertain the efficacy of AB-HDL, a comparative analysis is conducted with various other machine learning (ML) and deep learning (DL) algorithms. The outcome analysis indicates that the proposed AB-HDL surpasses the performance of the other algorithms and exhibits optimal efficiency in detecting cyber attacks within IIoT networks.

In 2022, OpenAI's unveiling of generative AI Large Language Models (LLMs)- ChatGPT, heralded a significant leap forward in human-machine interaction through cutting-edge AI technologies. With its surging popularity, scholars across various fields have begun to delve into the myriad applications of ChatGPT. While existing literature reviews on LLMs like ChatGPT are available, there is a notable absence of systematic literature reviews (SLRs) and bibliometric analyses assessing the research's multidisciplinary and geographical breadth. This study aims to bridge this gap by synthesising and evaluating how ChatGPT has been integrated into diverse research areas, focussing on its scope and the geographical distribution of studies. Through a systematic review of scholarly articles, we chart the global utilisation of ChatGPT across various scientific domains, exploring its contribution to advancing research paradigms and its adoption trends among different disciplines. Our findings reveal a widespread endorsement of ChatGPT across multiple fields, with significant implementations in healthcare (38.6%), computer science/IT (18.6%), and education/research (17.3%). Moreover, our demographic analysis underscores ChatGPT's global reach and accessibility, indicating participation from 80 unique countries in ChatGPT-related research, with the most frequent countries keyword occurrence, USA (719), China (181), and India (157) leading in contributions. Additionally, our study highlights the leading roles of institutions such as King Saud University, the All India Institute of Medical Sciences, and Taipei Medical University in pioneering ChatGPT research in our dataset. This research not only sheds light on the vast opportunities and challenges posed by ChatGPT in scholarly pursuits but also acts as a pivotal resource for future inquiries. It emphasises that the generative AI (LLM) role is revolutionising every field. The insights provided in this paper are particularly valuable for academics, researchers, and practitioners across various disciplines, as well as policymakers looking to grasp the extensive reach and impact of generative AI technologies like ChatGPT in the global research community.

In the dynamic field of the Industrial Internet of Things (IIoT), the networks are increasingly vulnerable to a diverse range of cyberattacks. This vulnerability necessitates the development of advanced intrusion detection systems (IDSs). Addressing this need, our research contributes to the existing cybersecurity literature by introducing an optimized Intrusion Detection System based on Deep Transfer Learning (DTL), specifically tailored for heterogeneous IIoT networks. Our framework employs a tri-layer architectural approach that synergistically integrates Convolutional Neural Networks (CNNs), Genetic Algorithms (GA), and bootstrap aggregation ensemble techniques. The methodology is executed in three critical stages: First, we convert a state-of-the-art cybersecurity dataset, Edge_IIoTset, into image data, thereby facilitating CNN-based analytics. Second, GA is utilized to fine-tune the hyperparameters of each base learning model, enhancing the model’s adaptability and performance. Finally, the outputs of the top-performing models are amalgamated using ensemble techniques, bolstering the robustness of the IDS. Through rigorous evaluation protocols, our framework demonstrated exceptional performance, reliably achieving a 100% attack detection accuracy rate. This result establishes our framework as highly effective against 14 distinct types of cyberattacks. The findings bear significant implications for the ongoing development of secure, efficient, and adaptive IDS solutions in the complex landscape of IIoT networks.

Ambient computing is gaining popularity as a major technological advancement for the future. The modern era has witnessed a surge in the advancement in healthcare systems, with viable radio frequency solutions proposed for remote and unobtrusive human activity recognition (HAR). Specifically, this study investigates the use of Wi-Fi channel state information (CSI) as a novel method of ambient sensing that can be employed as a contactless means of recognizing human activity in indoor environments. These methods avoid additional costly hardware required for vision-based systems, which are privacy-intrusive, by (re)using Wi-Fi CSI for various safety and security applications. During an experiment utilizing universal software-defined radio (USRP) to collect CSI samples, it was observed that a subject engaged in six distinct activities, which included no activity, standing, sitting, and leaning forward, across different areas of the room. Additionally, more CSI samples were collected when the subject walked in two different directions. This study presents a Wi-Fi CSI-based HAR system that assesses and contrasts deep learning approaches, namely convolutional neural network (CNN), long short-term memory (LSTM), and hybrid (LSTM+CNN), employed for accurate activity recognition. The experimental results indicate that LSTM surpasses current models and achieves an average accuracy of 95.3% in multi-activity classification when compared to CNN and hybrid techniques. In the future, research needs to study the significance of resilience in diverse and dynamic environments to identify the activity of multiple users.

Medical imaging is an essential tool for diagnosing various healthcare diseases and conditions. However, analyzing medical images is a complex and time-consuming task that requires expertise and experience. This article aims to design a decision support system to assist healthcare providers and patients in making decisions about diagnosing, treating, and managing health conditions. The proposed architecture contains three stages: 1) data collection and labeling, 2) model training, and 3) diagnosis report generation. The key idea is to train a deep learning model on a medical image dataset to extract four types of information: the type of image scan, the body part, the test image, and the results. This information is then fed into ChatGPT to generate automatic diagnostic reports. The proposed system has the potential to enhance decision-making, reduce costs, and improve the capabilities of healthcare providers. The efficacy of the proposed system is analyzed by conducting extensive experiments on a large medical image dataset. The experimental outcomes exhibited promising performance for automatic diagnosis through medical images.

The Red Palm Weevil (RPW) is a highly destructive insect causing economic losses and impacting palm tree farming worldwide. This paper proposes an innovative approach for sustainable palm tree farming by utilizing advanced technologies for early detection and management of RPW. Our approach combines computer vision, deep learning (DL), the Internet of Things (IoT), and geospatial data to effectively detect and classify RPW-infested palm trees. The main phases include; (1) DL Classification using sound data from IoT devices, (2) palm tree detection using YOLOv8 on UAV images, and (3) RPW mapping using geospatial data. Our custom DL model achieves 100% precision and recall in detecting and localizing infested palm trees. The integration of geospatial data enables the creation of a comprehensive RPW distribution map for Efficient monitoring and targeted management strategies. This technology-driven approach benefits agricultural authorities, farmers, and researchers in managing RPW infestations, safeguarding palm tree plantations’ productivity.

The increasing trend of the Industrial Internet of Things (IIoT) within industrial environments magnifies the risk of security breaches and vulnerabilities. Maintaining confidentiality is a pivotal requirement for effectively establishing the IIoT environment. To promptly detect malicious endeavors, integrating an intrusion detection system (IDS) becomes imperative for continuously monitoring IIoT activities. The sophisticated automated IDSs are built upon the foundation of machine learning (ML) and deep learning (DL). However, these algorithms encounter challenges related to heavily imbalanced training data and the need for accurate predictions in a short timeframe. This paper introduces an attention-based deep neural network (ABDNN) designed to tackle these challenges for intrusion detection within the IIoT environment. The attention mechanism plays a pivotal role in determining the significance of each attribute in the input data. Subsequently, the deep neural network (DNN) comes into play, leveraging the previously determined attribute importance to predict network behaviors. This process yields the advantage of predicting network behaviors more efficiently in less time. The performance of the proposed ABDNN model was evaluated using the X-IIoTID dataset. To validate its effectiveness, a comparison was made between the performance of the proposed model and that of state-of-the-art approaches. This comparative analysis serves to validate the superior performance of the proposed ABDNN model.

The Internet of Things (IoT) is a global network that connects a large number of smart devices. MQTT is a de facto standard, lightweight, and reliable protocol for machine-to-machine communication, widely adopted in IoT networks. Various smart devices within these networks are employed to handle sensitive information. However, the scale and openness of IoT networks make them highly vulnerable to security breaches and attacks, such as eavesdropping, weak authentication, and malicious payloads. Hence, there is a need for advanced machine learning (ML) and deep learning (DL)-based intrusion detection systems (IDS). Existing ML-based IoT-IDSs face several limitations in effectively detecting malicious activities, mainly due to imbalanced training data. To address this, this study introduces a transformer neural network-based intrusion detection system (TNN-IDS) specifically designed for MQTT-enabled IoT networks. The proposed approach aims to enhance the detection of malicious activities within these networks. The TNN-IDS leverages the parallel processing capability of the Transformer Neural Network, which accelerates the learning process and results in improved detection of malicious attacks. To evaluate the performance of the proposed system, it was compared with various IDSs based on ML and DL approaches. The experimental results demonstrate that the proposed TNN-IDS outperforms other systems in terms of detecting malicious activity. The TNN-IDS achieved optimum accuracies reaching 99.9% in detecting malicious activities.

Recently, Convolutional Neural Networks (CNNs) have made a great performance for remote sensing image classification. Plant recognition using CNNs is one of the active deep learning research topics due to its added-value in different related fields, especially environmental conservation and natural areas preservation. Automatic recognition of plants in protected areas helps in the surveillance process of these zones and ensures the sustainability of their ecosystems. In this work, we propose an Enhanced Randomly Initialized Convolutional Neural Network (ERI-CNN) for the recognition of columnar cactus, which is an endemic plant that exists in the Tehuacán-Cuicatlán Valley in southeastern Mexico. We used a public dataset created by a group of researchers that consists of more than 20000 remote sensing images. The experimental results confirm the effectiveness of the proposed model compared to other models reported in the literature like InceptionV3 and the modified LeNet-5 CNN. Our ERI-CNN provides 98% of accuracy, 97% of precision, 97% of recall, 97.5% as f1-score, and 0.056 loss.

With the emergence of cloud robotics, computation offloading presents a new trend in cloud computing that has been applied to robots; to provide them with resources for performing computationally intensive tasks. In most scientific research, the main objectives behind computation offloading are reducing energy consumption and minimizing the execution time of robotics applications. However, these two metrics are conflicting, and optimizing them simultaneously is challenging. Reducing energy consumption may lead to a rise in the completion time, and vice-versa. In this paper, we consider the problem of optimization of energy consumption and completion time in a cloud robotic system. We formulated the offloading decision as a multi-objective optimization problem. We further adapted the Non-dominated Sorting Genetic Algorithm (NSGA-II) to find a set of Paretooptimal solutions. Through simulations, we demonstrated that our offloading solution can save 80% of the robot’s energy consumption; and reduce 70% of the application completion time. We proved also the adaptability of the model against bandwidth changes.

This paper deals with the medium between two reconfigurable sensor nodes characterized by radio interfaces that support multiple channels for exchanging real-time messages under energy constraints. These constraints are violated if the consumed energy in transmission is higher than the remaining quantity of energy. A reconfiguration, i.e., any addition or removal of tasks in devices and consequently of messages on the medium, can cause the violation of real-time or energy constraints at run time. To achieve a feasible scheduling in time (i.e., message deadlines will be respected) and energy (i.e., there is available energy) on the medium, we propose new dynamic solutions: Balance, Dilute, and a Combination of them to manage any addition or removal of messages. The proposed approach utilizes the energy harvesting techniques and the PowerControl algorithm to reduce the nonharvested consumed energy. The proposed strategies achieve significant improvement over existing methods and provide the highest percentage of adding messages, with a lower average in response time and energy consumption. They reach a percentage of success in adding the highest priority messages while meeting deadlines up to 85%.

Traffic congestion, collision, and long delays on traffic signals are prominent issues in today's transportation system. Several solutions were proposed in the literature; however, most of them suffer from the broadcast storm and network disconnection in the dense and sparse topology, respectively. In this paper, we propose a new solution to the problems above, namely SmartFlow for traffic monitoring and delay avoidance on traffic signal using vehicular ad-hoc networks (VANETs). The main contribution is to monitor the traffic congestion as well as to assess the driver in speed selection, aiming to avoid long waiting delays on traffic light signals. The proposed SmartFlow protocol improves the state-of-the-art by considering vehicle-to-infrastructure (V2I) communication that does not only help in solving the above problems, but also preventing long delays on traffic light signals. The SmartFlow protocol estimates the traffic states and inter-traffic signal distance from frequent beacon messages. Simulation results demonstrate that SmartFlow significantly outperforms than the previous solutions in terms of traffic monitoring, speed recommendation for inter-vehicle safety, and delay avoidance on the traffic signal. Moreover, from the result, empirical equations are derived that can be used for the prediction of speed recommendations to the drivers.

Low power and lossy networks (LLNs) require a routing protocol under real-time and energy constraints, congestion aware and packet priority. Thus, Routing Protocol for Low power and lossy network (RPL) is recommended by Internet Engineering Task force (IETF) for LLN applications. In RPL, nodes select their optimal paths towards their preferred parents after meeting routing metrics that are injected in the objective function (OF). However, RPL did not impose any routing metric and left it open for implementation. In this paper, we propose a new RPL objective function which is based on the quality of service (QoS) and congestion-aware. In the case paths fail, we define new RPL control messages for enriching the network by adding more routing nodes. Extensive simulations show that QCOF achieves significant improvement in comparison with the existing objective functions, and appropriately satisfies real-time applications under QoS and network congestion.

Cooperative vehicle platooning applications increasingly demand realistic simulation tools to ease their validation, and to bridge the gap between development and real-word deployment. However, their complexity and cost, often hinders its validation in the real-world. In this paper we propose a realistic simulation framework for vehicular platoons that integrates Gazebo with OMNeT++ over Robot Operating System (ROS) to support the simulation of realistic scenarios of autonomous vehicular platoons and their cooperative control.

The MAVLink is a lightweight communication protocol between Unmanned Aerial Vehicles (UAVs) and ground control stations (GCSs). It defines a set of bi-directional messages exchanged between a UAV (aka drone) and a ground station. The messages carry out information about the UAV's states and control commands sent from the ground station. However, the MAVLink protocol is not secure and has several vulnerabilities to different attacks that result in critical threats and safety concerns. Very few studies provided solutions to this problem. In this paper, we discuss the security vulnerabilities of the MAVLink protocol and propose MAVSec, a security-integrated mechanism for MAVLink that leverages the use of encryption algorithms to ensure the protection of exchanged MAVLink messages between UAVs and GCSs. To validate MAVSec, we implemented it in Ardupilot and evaluated the performance of different encryption algorithms (i.e. AES-CBC, AES-CTR, RC4 and ChaCha20) in terms of memory usage and CPU consumption. The experimental results show that ChaCha20 has a better performance and is more efficient than other encryption algorithms. Integrating ChaCha20 into MAVLink can guarantee its messages confidentiality, without affecting its performance, while occupying less memory and CPU consumption, thus, preserving memory and saving the battery for the resource-constrained drone.

Cloud robotics is incessantly gaining ground, especially with the rapid expansion of wireless networks and Internet resources. In particular, computation offloading is emerging as a new trend, enabling robots with more powerful computation resources. It helps them to overcome the hardware and software limitations by leveraging parallel computing capabilities and the availability of large amounts of resources in the cloud. However, the performance gain of computation offloading in cloud robotics is still an ongoing research problem because of the conflicting factors that affect the performance. In this paper, we investigate this issue and we design a distributed cloud robotic architecture for computation offloading based on Kafka middleware as messaging broker. We experimentally validated our solution and tested its performance using image processing algorithms. Experimental results show a significant reduction in robot CPU load, as expected, with an increase in robot communication delays.

Several IoT applications are tightly dependent on the locations of the devices. However, localization algorithms can be easily compromised by injecting false locations. In this paper, we propose a Blockchain-based secure localization algorithm for the Internet of Things (IoT). The algorithm uses a public ledger (Blockchain) that contains nodes position and the list of their neighbor nodes. This ledger is shared among the IoT devices. Once an IoT device is localized its new position and the list of neighbor nodes are added to the Blockchain. This shared localization data will be used later by other IoT devices for their localization process. To avoid the attack where a malicious node sends a fake position, the correctness of the claimed position are verified before adding it to the Blockchain. Moreover, data exchanged between nodes (IoT devices) are signed to guarantee their authenticity and integrity. The integration of these security mechanisms into the localization process permits to exclude false data and therefore reduces the localization error. The simulation results show that adding the proposed security mechanism improves the localization accuracy of the algorithm when running in the presence of malicious nodes.

Deterministic Synchronous Multichannel Extension (DSME) is a prominent MAC behavior first introduced in IEEE 802.15.4e that supports deterministic guarantees using its multisuperframe structure. DSME also facilitates techniques like multi-channel and CAP reduction that help to increase the number of available guaranteed timeslots in a network. However, no tuning of these functionalities in dynamic scenarios is supported in the standard. In this paper, we present an effective multisuperframe tuning technique that also helps to utilize CAP reduction in an effective manner improving flexibility and scalability, while guaranteeing bounded delay.

Wireless Sensor Networks have been enabling an ever increasing span of applications and usages in the industrial, domestic and commercial domains. Recent advancements in information and communication technologies have been fueling the increasing pervasiveness and ubiquity of this infrastructures, making them an obvious candidate to support the future Internet of Things. Among the prospective applications, however, there are those which present strict requirements in terms of timeliness and reliability, specially in the industrial domain. To address these, the IEEE 802.15.4 standard functionalities were recently enhanced by the IEEE 802.15.4e amendment. Ideas which are prominent in the industrial communication field such as frequency hopping, dedicated and shared timeslots and multichannel communication have been implemented in 802.15.4e. In this line, proposed MAC behaviors such as the Deterministic and Synchronous Multi-channel Extension (DSME) and Time Synchronous Channel Hopping (TSCH), are gaining a lot of attention. Nevertheless, to efficiently address the network demands in terms of latency, resources, and reliability, it is mandatory to carry out a thorough network planning. To achieve this, modeling the fundamental performance limits of such networks is of paramount importance to understand their behavior under the worst-case conditions and to make the appropriate design choices. Network Calculus is an established tool which can accurately compute the worst case bounds of a network. In this paper we provide an insight towards DSME and TSCH by modeling, using Network Calculus formalism, the delay bounds of these MAC behaviors. As a continuation of this work the end-to-end delay bounds will be derived for the rest of the MAC behaviors of IEEE 802.15.4e. Scheduling algorithms will be developed, analyzed and validated as a future work.

Low power and lossy networks (LLNs) require efficient routing protocols that should meet the requirements of the critical applications, such as real-time, reliability and high availability. RPL has been recently proposed by the ROLL working group as a tree routing protocol specifically designed for LLNs. It relies on objective functions to construct routes that optimize or constrain a routing metric on the paths. However, the working group did not specify the set of metrics and/or constraints to be used to specify the preferred path, and left it open to implementations. In this paper, we design OF-FL, a novel objective function that combines a set of metrics in order to provide a configurable routing decision based on the fuzzy parameters. OF-FL has the advantage to consider the application requirements in order to select the best paths to the destination. Our evaluation with a large-scale testbed in ContikiOS reveals that OF-FL can achieve remarkable performance of the RPL-based LLNs in comparison with the existing objective functions, and appropriately satisfy the quality of service contract of the different applications.

Hand-off (or hand-over), the process where mobile nodes select the best access point available to transfer data, has been well studied in wireless networks. The performance of a hand-off process depends on the specific characteristics of the wireless links. In the case of low-power wireless networks, hand-off decisions must be carefully taken by considering the unique properties of inexpensive low-power radios. This paper addresses the design, implementation and evaluation of smart-HOP, a hand-off mechanism tailored for low-power wireless networks. This work has three main contributions. First, it formulates the hard hand-off process for low-power networks (such as typical wireless sensor networks - WSNs) with a probabilistic model, to investigate the impact of the most relevant channel parameters through an analytical approach. Second, it confirms the probabilistic model through simulation and further elaborates on the impact of several hand-off parameters. Third, it fine-tunes the most relevant hand-off parameters via an extended set of experiments, in a realistic experimental scenario. The evaluation shows that smart-HOP performs well in the transitional region while achieving more than 98 percent relative delivery ratio and hand-off delays in the order of a few tens of a milliseconds.

Path planning is a critical combinatorial problem essential for the navigation of a mobile robot. Several research initiatives, aiming at providing optimized solutions to this problem, have emerged. Ant Colony Optimization (ACO) and Genetic Algorithms (GA) are the two most widely used heuristics that have shown their effectiveness in solving such a problem. This paper presents, smartPATH, a new hybrid ACO-GA algorithm to solve the global robot path planning problem. The algorithm consists of a combination of an improved ACO algorithm (IACO) for efficient and fast path selection, and a modified crossover operator for avoiding falling into a local minimum. Our system model incorporates a Wireless Sensor Network (WSN) infrastructure to support the robot navigation, where sensor nodes are used as signposts that help locating the mobile robot, and guide it towards the target location. We found out smartPATH outperforms classical ACO (CACO) and GA algorithms (as defined in the literature without modification) for solving the path planning problem both and Bellman-Ford shortest path method. We demonstrate also that smartPATH reduces the execution time up to 64.9% in comparison with Bellman-Ford exact method and improves the solution quality up to 48.3% in comparison with CACO.

Radio link quality estimation in Wireless Sensor Networks (WSNs) has a fundamental impact on the network performance and also affects the design of higher-layer protocols. Therefore, for about a decade, it has been attracting a vast array of research works. Reported works on link quality estimation are typically based on different assumptions, consider different scenarios, and provide radically different (and sometimes contradictory) results. This article provides a comprehensive survey on related literature, covering the characteristics of low-power links, the fundamental concepts of link quality estimation in WSNs, a taxonomy of existing link quality estimators, and their performance analysis. To the best of our knowledge, this is the first survey tackling in detail link quality estimation in WSNs. We believe our efforts will serve as a reference to orient researchers and system designers in this area.

Monitoring of Wireless Sensor Networks (WSNs) is a fundamental task to track the network behavior and measure its performance in real-world deployments. In this paper, we present Z-Monitor, a monitoring and a protocol analyzer solution to control and debug IEEE 802.15.4-compliant Low Power Wireless Personal Area Networks (LoWPANs). Z-Monitor does not only support the analysis of well-known ZigBee, 6L0WPAN and RPL protocols, but is also designed to be modular and easily extensible for adding new protocols. The motivation behind the design and implementation of Z- Monitor is the fact that commercially-available products for monitoring and testing IEEE 802.15.4-compliant LoWPANs are mainly too expensive, and typically require special sniffing hardware. Z-Monitor represents a free and extensible solution, does not require special sniffing hardware, and provides comparable services to proprietary and commercial products. We also share our experimental study results that demonstrate the effectiveness of Z-Monitor in meeting its objectives.

Link quality estimation is a fundamental building block for the design of several different mechanisms and protocols in wireless sensor networks. The accuracy of link quality estimation greatly impacts the efficiency of these protocols. Therefore, a thorough experimental evaluation of link quality estimators (LQEs) is mandatory. This motivated us to build a benchmarking testbed-RadiaLE, that automates LQEs evaluation by analyzing their statistical properties. Our testbed includes (i.) hardware components that represent the WSN under test and (ii.) a software tool for setting up and controlling the experiments and also for analyzing the collected data, allowing for LQEs evaluation. To demonstrate the usefulness of RadiaLE, we carried out a comparative performance study of a set of well-known LQEs.

User authentication in classical networks is deeply addressed, but few results are related to Wireless Sensor Networks (WSNs). In addition, the proposed schemes do not provide mutual authentication or session-key agreement between the server and the user. Therefore, we present in this paper a lightweight user authentication scheme adapted to WSNs that provides mutual authentication and session-key agreement. The proposed scheme allows a user equipped with mobile device (typically PDA) to authenticate himself before gaining access to the WSN. The scheme is executed at two sides; the client side which controls the user's mobile device and the server side represented by the coordinator of the WSN. A security analysis of the scheme is presented and it proves its resilience against classical types of attacks. The scheme is also implemented on real platform of sensor nodes. This implementation proves that our scheme is lightweight and rapid as it requires approximately only 1s to be fully executed. In addition, we have made a comparison between our scheme and the existing ones based on their security properties, and shown that our proposed scheme outperforms the existing ones in terms of confidentiality, integrity, mutual authentication and session key generation with a lightweight computation overhead.

The IEEE 802.15.4 is the most widespread used protocol for Wireless Sensor Networks (WSNs) and it is being used as a baseline for several higher layer protocols such as ZigBee, 6LoWPAN or Wireless HART. Its MAC (Medium Access Control) supports both contention-free (CFP, based on the reservation of guaranteed time-slots GTS) and contention based (CAP, ruled by CSMA/CA) access, when operating in beacon-enabled mode. Thus, it enables the differentiation between real-time and best-effort traffic. However, some WSN applications and higher layer protocols may strongly benefit from the possibility of supporting more traffic classes. this happens, for instance, for dense WSNs used in time-sensitive industrial applications. In this context, we propose to differentiate traffic classes within the CAP, enabling lower transmission delays and higher success probability to time critical messages, such as for event detection, GTS reservation and network management. Building upon a previously proposed methodology (TRADIF), in this paper we outline its implementation and experimental validation over a real-time operating system. Importantly, TRADIF is fully backward compatible with the IEEE 802.15.4 standard, enabling to create different traffic classes just by tuning some MAC parameters.

Modeling the fundamental performance limits of Wireless Sensor Networks (WSNs) is of paramount importance to understand their behavior under the worst-case conditions and to make the appropriate design choices. This is particular relevant for time-sensitive WSN applications, where the timing behavior of the network protocols (message transmission must respect deadlines) impacts on the correct operation of these applications. In that direction this article contributes with a methodology based on Network Calculus, which enables quick and efficient worst-case dimensioning of static or even dynamically changing cluster-tree WSNs where the data sink can either be static or mobile. We propose closed-form recurrent expressions for computing the worst-case end-to-end delays, buffering and bandwidth requirements across any source-destination path in a cluster-tree WSN. We show how to apply our methodology to the case of IEEE 802.15.4/ZigBee cluster-tree WSNs. Finally, we demonstrate the validity and analyze the accuracy of our methodology through a comprehensive experimental study using commercially available technology, namely TelosB motes running TinyOS.

In 2004, Das et al. proposed a ldquoDynamic ID-based Remote User Authentication Scheme using Smart Cardsrdquo. This scheme have the advantage that users can choose and change their password freely and the server does not maintain any verifier table, which avoid the risk of stolen/modifying this table. However, in 2005, Liao et al. demonstrated that Das et al.'s scheme suffers from guessing attacks, unilateral authentication and revealing of user password and propose improvements to prevent these shortcomings. However, in this paper, we demonstrate that Liao et al.'s scheme is not secure and it is vulnerable to stolen/lost smart card attack, impersonation (forgery) attack and password revealing attack. In fact, we prove that the scheme is equivalent to no password scheme. Then, we propose possible improvements to Liao et al.'s scheme. We demonstrate through comparison between the three schemes that the proposed one is more secure while maintaining the same computational overhead as Das et al.'s scheme.

Link quality estimation (LQE) in wireless sensor networks (WSNs) is a fundamental building block for an efficient and cross-layer design of higher layer network protocols. Several link quality estimators have been reported in the literature; however, none has been thoroughly evaluated. There is thus a need for a comparative study of these estimators as well as the assessment of their impact on higher layer protocols. In this paper, we perform an extensive comparative simulation study of some well-known link quality estimators using TOSSIM. We first analyze the statistical properties of the link quality estimators independently of higher-layer protocols, then we investigate their impact on the Collection Tree Routing Protocol (CTP). This work is a fundamental step to understand the statistical behavior of LQE techniques, helping system designers choose the most appropriate for their network protocol architectures.

Wireless sensor networks (WSNs) emerge as underlying infrastructures for new classes of large-scale networked embedded systems. However, WSNs system designers must fulfill the quality-of-service (QoS) requirements imposed by the applications (and users). Very harsh and dynamic physical environments and extremely limited energy/computing/memory/communication node resources are major obstacles for satisfying QoS metrics such as reliability, timeliness, and system lifetime. The limited communication range of WSN nodes, link asymmetry, and the characteristics of the physical environment lead to a major source of QoS degradation in WSNs-the ldquohidden node problem.rdquo In wireless contention-based medium access control (MAC) protocols, when two nodes that are not visible to each other transmit to a third node that is visible to the former, there will be a collision-called hidden-node or blind collision. This problem greatly impacts network throughput, energy-efficiency and message transfer delays, and the problem dramatically increases with the number of nodes. This paper proposes H-NAMe, a very simple yet extremely efficient hidden-node avoidance mechanism for WSNs. H-NAMe relies on a grouping strategy that splits each cluster of a WSN into disjoint groups of non-hidden nodes that scales to multiple clusters via a cluster grouping strategy that guarantees no interference between overlapping clusters. Importantly, H-NAMe is instantiated in IEEE 802.15.4/ZigBee, which currently are the most widespread communication technologies for WSNs, with only minor add-ons and ensuring backward compatibility with their protocols standards. H-NAMe was implemented and exhaustively tested using an experimental test-bed based on ldquooff-the-shelfrdquo technology, showing that it increases network throughput and transmission success probability up to twice the values obtained without H-NAMe. H-NAMe effectiveness was also demonstrated in a target tracking application with mobile robots over a WSN deployment.

Modelling the fundamental performance limits of wireless sensor networks (WSNs) is of paramount importance to understand the behaviour of WSN under worst case conditions and to make the appropriate design choices. In that direction, this paper contributes with a methodology for modelling cluster tree WSNs with a mobile sink. We propose closed form recurrent expressions for computing the worst case end to end delays, buffering and bandwidth requirements across any source-destination path in the cluster tree assuming error free channel. We show how to apply our theoretical results to the specific case of IEEE 802.15.4/ZigBee WSNs. Finally, we demonstrate the validity and analyze the accuracy of our methodology through a comprehensive experimental study, therefore validating the theoretical results through experimentation.

While the IEEE 802.15.4/Zigbee protocol stack is being considered as a promising technology for low-cost low-power Wireless Sensor Networks (WSNs), several issues in their specifications are still open. One of those ambiguous issues is how to build a synchronized cluster-tree network, which is quite suitable for ensuring QoS support in WSNs. In fact, the current IEEE 802.15.4/Zigbee specifications restrict the synchronization in the beacon-enabled mode (by the generation of periodic beacon frames) to star-based networks, while they support multi-hop networking using the peer-to-peer mesh topology, but with no synchronization. Even though both specifications mention the possible use of cluster-tree topologies, which combine multi-hop and synchronization features, the description on how to effectively construct such a network topology is missing. This paper tackles this problem, unveiling the ambiguities regarding the use of the cluster-tree topology and proposing a synchronization mechanism based on Time Division Beacon Scheduling to construct cluster-tree WSNs. We also propose a methodology for an efficient duty-cycle management in each router (cluster-head) of a cluster-tree WSN that ensures the fairest use of bandwidth resources. The feasibility of the proposal is clearly demonstrated through an experimental test bed based on our own implementation of the IEEE 802.15.4/Zigbee protocols.

Wireless sensor networks (WSN) have been attracting growing interests for developing a new generation of large-scale embedded computing systems, with a great potential for a wide range of applications such as surveillance, environmental monitoring, emergency medical response or building automation. However, the communication paradigms in wireless sensor networks differ from the ones associated to traditional wireless networks, triggering the need for new communication protocols and architectures. The ART-WiSe (architecture for real-time communications in wireless sensor networks) framework aims at the design of a scalable multiple-tiered WSN architecture for supporting large-scale embedded computing applications with critical requirements. An overlay wireless local area network (Tier-2) serves as a backbone for a WSN (Tier 1), relying on existing standard communication protocols and commercial-off-the-shell (COTS) technologies -IEEE 802.15.4/ZigBee for Tier 1 and IEEE 802.11 for Tier-2. This paper outlines ongoing work on the design of the architectural requirements and features for a QoS-aware gateway between both networks.

The IEEE 802.15.4 protocol has the ability to support time-sensitive Wireless Sensor Network (WSN) applications due to the Guaranteed Time Slot (GTS) Medium Access Control mechanism. Recently, several analytical and simulation models of the IEEE 802.15.4 protocol have been proposed. Nevertheless, currently available simulation models for this protocol are both inaccurate and incomplete, and in particular they do not support the GTS mechanism. In this paper, we propose an accurate OPNET simulation model, with focus on the implementation of the GTS mechanism. The motivation that has driven this work is the validation of the Network Calculus based analytical model of the GTS mechanism that has been previously proposed and to compare the performance evaluation of the protocol as given by the two alternative approaches. Therefore, in this paper we contribute an accurate OPNET model for the IEEE 802.15.4 protocol. Additionally, and probably more importantly, based on the simulation model we propose a novel methodology to tune the protocol parameters such that a better performance of the protocol can be guaranteed, both concerning maximizing the throughput of the allocated GTS as well as concerning minimizing frame delay. Keywords - IEEE 802.15.4; GTS; OPNET Modeler; simulation model; analytical model

The IEEE 802.15.4/ZigBee protocols are gaining increasing interests in both research and industrial communities as candidate technologies for Wireless Sensor Network (WSN) applications. In this paper, we present an open-source implementation of the IEEE 802.15.4/ZigBee protocol stack under the TinyOS operating system for the MICAz and TelosB motes. This work has been driven by the need for an open- source implementation of the IEEE 802.15.4/ZigBee protocols, filling a gap between some newly released complex C implementations and black-box implementations from different manufacturers. In addition, we share our experience on the challenging problems that we have faced during the implementation of the protocol stack. We strongly believe that this open-source implementation will potentiate research works on the IEEE 802.15.4/ZigBee protocols, allowing their demonstration and validation through experimentation.

The IEEE 802.15.4 medium access control (MAC) protocol is an enabling technology for time sensitive wireless sensor networks thanks to its guaranteed-time slot (GTS) mechanism in the beacon-enabled mode. However, the protocol only supports explicit GTS allocation, i.e. a node allocates a number of time slots in each superframe for exclusive use. The limitation of this explicit GTS allocation is that GTS resources may quickly disappear, since a maximum of seven GTSs can be allocated in each superframe, preventing other nodes to benefit from guaranteed service. Moreover, the GTSs may be only partially used, resulting in wasted bandwidth. To overcome these limitations, this paper proposes i-GAME, an implicit GTS allocation mechanism in beacon-enabled IEEE 802.15.4 networks. The allocation is based on implicit GTS allocation requests, taking into account the traffic specifications and the delay requirements of the flows. The i-GAME approach enables the use of a GTS by multiple nodes, while all their (delay, bandwidth) requirements are still satisfied. For that purpose, we propose an admission control algorithm that enables to decide whether to accept a new GTS allocation request or not, based not only on the remaining time slots, but also on the traffic specifications of the flows, their delay requirements and the available bandwidth resources. We show that our proposal improves the bandwidth utilization compared to the explicit allocation used in the IEEE 802.15.4 protocol standard. We also present some practical considerations for the implementation of i-GAME, ensuring backward compatibility with the IEEE 801.5.4 standard with only minor add-ons

The IEEE 802.15.4 protocol proposes a flexible communication solution for low-rate wireless personal area networks including sensor networks. It presents the advantage to fit different requirements of potential applications by adequately setting its parameters. When enabling its beacon mode, the protocol makes possible real-time guarantees by using its guaranteed time slot (GTS) mechanism. This paper analyses the performance of the GTS allocation mechanism in IEEE 802.15.4. The analysis gives a full understanding of the behavior of the GTS mechanism with regards to delay and throughput metrics. First, we propose two accurate models of service curves for a GTS allocation as a function of the IEEE 802.15.4 parameters. We then evaluate the delay bounds guaranteed by an allocation of a GTS using network calculus formalism. Finally, based on the analytic results, we analyse the impact of the IEEE 802.15.4 parameters on the throughput and delay bound guaranteed by a GTS allocation. The results of this work pave the way for an efficient dimensioning of an IEEE 802.15.4 cluster

Time-sensitive wireless sensor network (WSN) applications require finite delay bounds in critical situations. This paper provides a methodology for the modeling and the worst-case dimensioning of cluster-tree WSNs. We provide a fine model of the worst-case cluster-tree topology characterized by its depth, the maximum number of child routers and the maximum number of child nodes for each parent router. Using Network Calculus, we derive "plug-and-play " expressions for the end-to-end delay bounds, buffering and bandwidth requirements as a function of the WSN cluster-tree characteristics and traffic specifications. The cluster-tree topology has been adopted by many cluster-based solutions for WSNs. We demonstrate how to apply our general results for dimensioning IEEE 802.15.4/Zigbee cluster-tree WSNs. We believe that this paper shows the fundamental performance limits of cluster-tree wireless sensor networks by the provision of a simple and effective methodology for the design of such WSNs

We propose a trade-off between hard and soft real-time guarantees to maintain an acceptable QoS guarantee in overload condition and maximize efficiently the utilization of network resources. The key of our solution is that many real-time applications are loss-tolerant, but the loss profile must be well defined since successive packet losses are not suitable. We use the concept of (m,k)-firm timing constraints to define a novel guaranteed loss-tolerant QoS. Therefore, we extend the basic WFQ algorithm to take into account the firm timing constraints to provide lower delay guarantees without violating bandwidth fairness or misusing network resources. The proposal is called (m,k)-WFQ. Using network calculus formalism, analytic study gives the deterministic delay bound provided by the (m,k)-WFQ algorithm for upper bounded arrival curve traffic. Theoretical results and simulations show a noticeable improvement on delay guarantee made by (m,k)-WFQ compared to standard WFQ algorithm without much degrading bandwidth fairness.