Liang Lin, Miao Yu, Moayad Aloqaily, Zhenhong Zhou, Kun Wang, Linsey Pang, Prakhar Mehrotra, Qingsong Wen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Beamforming is a critical enabler of high-capacity, low-latency communication in 6G networks, leveraging massive Multiple-Input Multiple-Output (MIMO) technology to direct signals toward intended users while minimizing interference. Recent Deep Learning (DL) advancements have enhanced beamforming efficiency by enabling data-driven beam selection. However, adopting AI-based beamforming introduces security vulnerabilities, particularly against adversarial attacks that manipulate Channel State Information (CSI) to degrade communication performance. This study proposes a Large Language Model (LLM)-enhanced defense mechanism that detects and mitigates adversarial perturbations in CSI before the beamforming model processes them. The LLM functions as an intelligent anomaly detector, distinguishing between adversarially manipulated and legitimate inputs and refining CSI features to restore accurate beamforming decisions. Unlike adversarial training, this approach does not require retraining the beamforming model, offering a lightweight and scalable solution. Experimental results demonstrate that the LLM-based defense significantly reduces the impact of adversarial attacks, improving Mean Squared Error (MSE) and Achievable Rate metrics while maintaining a robustness ratio of 0.94. The proposed method enhances the security and reliability of AI-driven beamforming without modifying the underlying neural network, making it a practical and efficient defense for 6G communication systems.

As sixth-generation (6G) networks continue to evolve, the deployment of Machine Learning (ML) models in critical functions, such as millimeter-wave (mmWave) beam prediction, presents unique security challenges, particularly from adversarial attacks. Existing defenses against these attacks often have limitations, especially when gradient information is unavailable. This paper introduces the Lemur Optimizer for Adversarial Attacks (LO-Attack), a novel gradient-free approach inspired by swarm intelligence to enhance the robustness of 6G beam prediction models. By integrating LO-Attack into the adversarial training process, models become more resilient against both gradient-based and gradient-free attacks in dynamic, high-mobility environments. Experimental results from two 6G scenarios, encompassing indoor and outdoor settings, demonstrate that models trained with LOAttack achieve significant improvements in accuracy, resistance to adversarial perturbations, and computational efficiency. On average, adversarial training with LO-Attack enhances model robustness by 57.90%, outperforming traditional FGSM-based methods. These findings highlight LO-Attack's potential as an effective and scalable defense strategy for securing ML-driven applications in the complex 6G landscape.

Modern financial networks, particularly those supporting High-Frequency Trading (HFT) systems, are characterized by intense data flows and high packet rates, where even minor variations in latency can impact trading outcomes. The problem of queuing and latency management in such networks has become increasingly critical. Dynamic load balancing comes as a solution to this by splitting traffic and sending it on multiple paths. Usually, the problem observed is re-ordering. In response to this challenge, we present a novel Software-Defined Load Balancing Framework for splitting traffic at the packet level without causing packet reordering, which is especially suited for High-Frequency Trading Networks. We propose SOLT+ a traffic splitting algorithm that inspects bursts of packets specifically choosing packets to avoid re-ordering. We perform MATLAB simulations to show the accuracy and compare SOLT + with state-of-the-art algorithms.

Unmanned Aerial Vehicles (UAVs) will play a vital role in the operation, management and service provisioning process of Next-Generation Networks (NGNs). Given that the Sixth Generation (6G) network is an AI-native cooperative ecosystem that relies on the resource and intelligent capabilities of every layer of the network, especially edge devices, UAVs are thus critical and a key player in 6G. This paper presents a UAVsupported framework for 6G that relies on a multi-tiered approach to guarantee autonomous and optimal network coverage and bandwidth. The UAV network adapts Federated Learning (FL) to maintain self-organization and support for real-time edge processing. The multi-tiered UAV network approach uses realtime adjustments in swarm membership and task allocation to enhance the network energy consumption. Blockchain is integrated into each swarm to maintain the integrity of the trained models, and provides a decentralized device authentication and swarm coordination mechanism. System evaluations reveal that the proposed framework provides high levels of task completion ratio and learning accuracy.

Energy is a fundamental component of modern life, driving nearly all aspects of daily activities. As such, the inability to access energy when needed is a significant issue that requires innovative solutions. In this paper, we propose ED-DAO, a novel fully transparent and community-driven decentralized autonomous organization (DAO) designed to facilitate energy donations. We analyze the energy donation process by exploring various approaches and categorizing them based on both the source of donated energy and funding origins. We propose a novel Hybrid Energy Donation (HED) algorithm, which enables contributions from both external and internal donors. External donations are payments sourced from entities such as charities and organizations, where energy is sourced from the utility grid and prosumers. Internal donations, on the other hand, come from peer contributors with surplus energy. HED prioritizes donations in the following sequence: peer-sourced energy (P2D), utility-grid-sourced energy (UG2D), and direct energy donations by peers (P2PD). By merging these donation approaches, the HED algorithm increases the volume of donated energy, providing a more effective means to address energy poverty. Experiments were conducted on a dataset to evaluate the effectiveness of the proposed method. The results showed that HED increased the total donated energy by at least 0.43% (64 megawatts) compared to the other algorithms (UG2D, P2D, and P2PD).

Energy consumption forecasting in microgrids is critical to ensure efficiency, reliability, and sustainability. It can be beneficial for optimizing grids, reducing costs, demand-supply balancing, and enhancing sustainability. The forecast scope can range from hours or days to months and years, based on the goal of the entity doing the prediction. In the case of microgrids, the focus tends to be in the short to medium range, hours to days to achieve efficient energy distribution, cost minimization, and renewable energy utilization. In this dynamic and localized setup, the energy consumption forecast is an integral part of the process. Therefore, in this paper, we explore different ways we can generate reliable forecasts of energy consumption by a group of residential houses from the Pecan Street dataset. Leveraging on the advancement of LLMs and transformers, we propose an LLM-based model and we benchmark our findings against other models. Our results highlight the advantage and performance that can be achieved with our transformer based which demonstrates a superior predictive accuracy over other architectures.

In this paper, we introduce a novel framework designed specifically for federated reinforcement learning in IoT-driven networks, focusing on flow management in autonomous systems. Our framework optimizes flow table matching by monitoring IoT network traffic and ensuring efficient flow management across connected devices. By considering the specific flow requirements of IoT traffic, our framework enables an intelligent agent to make informed decisions regarding flow table entries, thereby improving the performance and management of autonomous systems. To gather essential data for decision-making, our framework utilizes an IoT-based SDN module that collects traffic statistics and relevant information from the network’s data plane. By leveraging SDN, our system enhances the learning and decision-making capabilities of IoT devices within autonomous systems. We introduce an optimization model called Software-Defined Network Assisted Federated Reinforcement Learning (SORE), based on the Markov decision process. SORE capitalizes on the advantages of SDN to boost the overall performance of IoT-based autonomous systems. By applying reinforcement learning techniques, our framework demonstrates significant improvements over existing models. Extensive simulations validate the effectiveness of our proposed system, showcasing superior performance and efficiency in IoT-enabled wireless networks within autonomous systems. The results underscore the potential of our approach in real-world IoT deployments.

Direct-to-satellite IoT (DtSIoT) enables scalable, global connectivity for diverse applications by leveraging LEO satellites and Non-Orthogonal Multiple Access to support massive device access with heterogeneous QoS needs. However, efficient resource allocation remains a key challenge. To address this, we propose a Deep AutoEncoder-based Model Predictive Controller (DAE-MPC) that learns system dynamics to optimize power allocation in real-time. Simulation results show that DAE-MPC improves transmission efficiency and reduces latency, offering a robust solution for intelligent resource management in DtSIoT networks1.

This paper presents a new framework that integrates Federated Learning (FL) with advanced privacy-preserving mechanisms to enhance the security of millimeter-wave (mmWave) beam prediction systems in 6G networks. By decentralizing model training, the framework safeguards sensitive user information while maintaining high model accuracy, effectively addressing privacy concerns inherent in centralized Machine learning (ML) methods. Adaptive noise augmentation and differential privacy principles are incorporated to mitigate vulnerabilities in FL systems, providing a robust defense against adversarial threats such as the Fast Gradient Sign Method (FGSM). Extensive experiments across diverse scenarios, including adversarial attacks, outdoor environments, and indoor settings, demonstrate a significant 17.45% average improvement in defense effectiveness, underscoring the framework’s ability to ensure data integrity, privacy, and performance reliability in dynamic 6G environments. By seamlessly integrating privacy protection with resilience against adversarial attacks, the proposed solution offers a comprehensive and scalable approach to secure mmWave communication systems. This work establishes a critical foundation for advancing secure 6G networks and sets a benchmark for future research in decentralized, privacy-aware machine learning systems.

This paper examines the security challenges while implementing Machine Learning (ML) algorithms to Sixth Generation (6G) networks, specifically in Millimeter-Wave (mmWave) beam prediction. While ML has significant benefits, the potential vulnerabilities of Artificial Intelligence (AI) models to adversarial attacks remain a concern. To mitigate these risks, a new Integrated Transformer and Feedforward Model (ITFM) has been introduced, which accomplishes real-time beamforming vector prediction and adversarial defense. This approach enhances the reliability and security of 6G applications. The proposed model demonstrated a 37.86% average increase in defense effectiveness against adversarial threats in indoor and outdoor scenarios.

As autonomous vehicles, smart infrastructure, and connected devices become integral components of our daily lives, the need to protect communication and establish trust among entities is fundamental. This problem necessitates the development of robust authentication mechanisms tailored for autonomous systems. In this paper, we address the critical challenge of designing authentication methods to verify the legitimacy of messages and participants in autonomous systems. The proposed methods aim to validate the origin of messages and the identity of participants, ensuring that only authorized entities interact with the system. Achieving this requires cryptographic techniques, digital signatures, and secure key management. To that end, we developed an algorithmic framework that includes message digest generation, digital signature creation, and recipient-side verification. Additionally, participant authentication and message integrity checks are incorporated to fortify the authentication process. The algorithm leverages public key cryptography to verify digital signatures and ensure the message's integrity. Second, we develop a simulation by harnessing Federated Learning (FL) which provides a dynamic and self-improving authentication mechanism that aligns with the high-reliability demands of modern autonomous applications on MATLAB. We elaborate on how addressing this problem is essential to bolster the security of autonomous systems, safeguard against cyber threats, and instill trust in the reliability and authenticity of communication within these systems. The proposed framework has been tested and validated on MATLAB.

The rapid evolution of 6G networks has brought novel security and privacy challenges in managing user data, particularly with the extensive use of millimeter wave communications (mmWave) technology. Ensuring robust security and privacy preservation in this dynamic network environment is paramount. This paper explores a multifaceted approach to address these challenges, focusing on Federated Learning (FL) for mmWave beam prediction and utilizing outlier detection in the aggregation process to mitigate adversarial attacks like the Fast Gradient Sign Method (FGSM). The potential of FL to enhance beam prediction accuracy while safeguarding user data privacy is investigated by training models on edge devices and aggregating model updates rather than raw data. Furthermore, integrating outlier detection in the aggregation process identifies and filters malicious model up-dates that may compromise the FL system's integrity. Simulations assessing the robustness of the proposed system against various threats reveal substantial enhancement in data security, with an average 15.30% increase in defense effectiveness.

Carbon footprint reduction can be achieved through various methods, including the adoption of renewable energy sources. The installation of such sources, like photovoltaic panels, while environmentally beneficial, is cost-prohibitive for many. Those lacking photovoltaic solutions typically resort to purchasing energy from utility grids that often rely on fossil fuels. Moreover, when users produce their own energy, they may generate excess that goes unused, leading to inefficiencies. To address these challenges, this paper proposes innovative blockchain-enabled energy-sharing algorithms that allow consumers -without financial means- to access energy through the use of their own energy storage units. We explore two sharing models: a centralized method and a peer- to- peer (P2P) one. Our analysis reveals that the P2P model is more effective, enhancing the sharing process significantly compared to the centralized method. We also demonstrate that, when contrasted with traditional battery-supported trading algorithm, the P2P sharing algorithm substantially reduces wasted energy and energy purchases from the grid by 73.6%, and 12.3% respectively. The proposed system utilizes smart contracts to decentralize its structure, address the single point of failure concern, improve overall system transparency, and facilitate peer-to-peer payments.

In this paper, we present a peer-to-peer (P2P) energy trading system designed for equitable energy exchanges within a microgrid. Our objective is to enhance the fairness of energy distribution among all microgrid members. To achieve this, we introduce a novel objective function that enables us to simultaneously maximize overall welfare and minimize disparities among microgrid participants, thus promoting equitable transactions. The core of our system is an agent proficiently trained via deep reinforcement learning, ensuring efficient and equitable energy distribution across the network. We simulated the energy consumption of 8 houses, and their energy production to assess the feasibility and efficiency of the proposed system. The utilization of the proposed approach has yielded promising results. The intelligent agent was able to execute transactions and improve the fairness of energy trading within the microgrid. The overall average welfare of the microgrid over a day increased by 99.8%, while the disparities among the members were reduced.

The emergence of the Internet of Things (IoT) has revolutionized people’s daily lives, providing superior quality services in cognitive cities, healthcare, and smart buildings. However, smart buildings use heterogeneous networks. The massive number of interconnected IoT devices increases the possibility of IoT attacks, emphasizing the necessity of secure and privacy-preserving solutions. Federated learning (FL) has recently emerged as a promising machine learning (ML) paradigm for IoT networks to address these concerns. In FL, multiple devices collaborate to learn a global model without sharing their raw data. However, FL still faces privacy and security concerns due to the transmission of sensitive data (i.e., model parameters) over insecure communication channels. These concerns can be addressed using fully homomorphic encryption (FHE), a powerful cryptographic technique that enables computations on encrypted data without requiring them to be decrypted first. In this study, we propose a secure FL approach in IoT-enabled smart cities that combines FHE and FL to provide secure data and maintain privacy in distributed environments. We present four different FL-based FHE approaches in which data are encrypted and transmitted over a secure medium. The proposed approaches achieved high accuracy, recall, precision, and F-scores, in addition to providing strong privacy and security safeguards. Furthermore, the proposed approaches effectively reduced communication overhead and latency compared to the baseline approach. These approaches yielded improvements ranging from 80.15% to 89.98% in minimizing communication overhead. Additionally, one of the approaches achieved a remarkable latency reduction of 70.38%. The implementation of these security models is nontrivial, and the code is publicly available athttps://github.com/Artifitialleap-MBZUAI/Secure-Federated-Learning-with-Fully-Homomorphic-Encryption-for-IoT-Communications.

Real-time video stream monitoring is gaining huge attention lately with an effort to fully automate this process. On the other hand, reporting can be a tedious task, requiring manual inspection of several hours of daily clippings. Errors are likely to occur because of the repetitive nature of the task causing mental strain on operators. There is a need for an automated system that is capable of real-time video stream monitoring in social systems and reporting them. In this article, we provide a tool aiming to automate the process of anomaly detection and reporting. We combine anomaly detection and video captioning models to create a pipeline for anomaly reporting in descriptive form. A new set of labels by creating descriptive captions for the videos collected from the UCF-Crime (University of Central Florida-Crime) dataset has been formulated. The anomaly detection model is trained on the UCF-Crime, and the captioning model is trained with the newly created labeled set UCF-Crime video description (UCFC-VD). The tool will be used for performing the combined task of anomaly detection and captioning. Automated anomaly captioning would be useful in the efficient reporting of video surveillance data in different social scenarios. Several testing and evaluation techniques were performed. Source code and dataset:https://github.com/Adit31/Captionomaly-Deep-Learning-Toolbox-for-Anomaly-Captioning.

The integration of blockchain and Web 3.0 technologies offers significant advancements in identity management and trust within decentralized Metaverse environments. Despite the rapid development of numerous Metaverse platforms, these environments often operate in isolation, lacking interoperability and cohesive security measures. This article proposes a novel architecture leveraging self-sovereign identity (SSI) principles and blockchain technology to achieve secure and interoperable interactions across different Metaverse platforms. Our framework encompasses a digital wallet application, an interactive virtual environment, and a secure blockchain-based backend infrastructure. We detail the system architecture, operational specifics, and demonstrate significant improvements in scalability, efficiency, and security through comprehensive performance evaluations. Additionally, we address the challenges of real-world implementation and propose viable solutions. By enhancing user control over personal data and enabling secure cross-platform interactions, our approach aims to foster a more secure, interoperable, and user-centric decentralized Metaverse.

With the increasing popularity of Cyber-physical Systems (CPS), there is a growing need for efficient and reliable methods for detecting and responding to threats. Federated Learning (FL) is a distributed Machine Learning (ML) technique that can be used to train models on data from multiple devices (i.e., edge devices) while keeping the data local. FL has the potential to improve the security and privacy of data while also reducing the training time and cost. Particularly, CNN-based FL has been shown to be effective for various tasks such as image classification and object detection. However, selecting suitable hyperparameters for constructing local ML models in FL is a significant challenge for practical inference and training on edge devices. In this paper, we focus on the optimization of CNN-based federated learning for the task of cyber-physical detection and we propose employing a novel metaheuristic optimization algorithm called Honey Badger Algorithm (HBA) for tuning the hyperparameters in local ML models (FL-HBA). To show the effectiveness of FL-HBA, we make an evaluation using an intelligent healthcare case study where we consider Sleep Apnea (SA) and use the PhysioNet apnea ECG dataset to diagnose SA. Our results show that the FL-HBA is superior to a Convolutional Neural Network (CNN) baseline, traditional ML techniques, and centralized learning models. Furthermore, we demonstrate that the proposed method for assigning the near-optimal hyperparameter values for centralized learning models improves accuracy by 2%.

The rapid development of 6G millimeter-wave (mmWave) networks has introduced new challenges for network security. Adversarial attacks on beamforming algorithms in these networks can lead to severe communication performance degradation. This paper proposes an optimization framework for Deep Learning (DL) hyperparameters that enhances adversarial security in 6G mmWave networks through beam prediction. We develop a robust DL model that can adapt to various adversarial attacks and maintain high prediction accuracy. The proposed framework optimizes hyperparameters using hybrid Particle Swarm Optimization (PSO) with Multi-Verse Optimizer (MVO) for improved security. The framework is evaluated through extensive simulations, demonstrating its effectiveness in improving network security and robustness against adversarial attacks. Under normal conditions, the optimized model achieves the lowest mean squared error (MSE) of 9.4410E – 05 for beamforming codeword predictions. Subjected to Fast Gradient Sign Method (FGSM) adversarial attacks, the optimized model maintains the lowest MSE of 2.2910E – 03, indicating greater resilience against adversarial perturbations. With adversarial training, the optimized model achieves the lowest MSE of 2.7110E – 03, demonstrating the most robust defense against adversarial attacks. In contrast, the non-optimized model suffers significant performance degradation under adversarial and defended conditions. The source code is available at [1].

Motivated by the need to provide a precisely determined delay between source and sink nodes in time-sensitive networks, we propose an architecture that provisions near-zero queuing delay in new Quality-of-Service frameworks, e.g., those of$\mathbf{5G}$solutions. To this end, various studies have shown how load balancing can reduce delay. Most of these studies consider$N$parallel processing queues with exponential service rates and Poisson arrivals with mean rate$\lambda$. These queues are handled by a single controller that assigns a new task to the shortest queue. The so-called power-of-d-servers or power-of-d-choices approach was proven to provide necessary delay improvements. In this strategy, the controller allocates the request to the least-loaded server among$d(N), 1\leq d(N)\leq N$randomly selected servers. However, none of these studies have considered realistic scenarios of fractional resource assignment to flow requests. To address this key shortcoming, we make the following contributions: (1) We design a software-defined network (SDN) controller framework called SOLT that considers the keys aspects of available resources in a time-sensitive network (TSN) setting, (2) We prove theoretically, how these bounds can be achieved and show the necessary conditions for achieving asymptotically zero delays in such networks, and (3) Through simulations, we demonstrate the improvements achieved by SOLT in comparison with state-of-the-art algorithms.

The Sixth Generation (6G) network is a cooperative network that relies on the capabilities of edge and end-devices. Unmanned Aerial Vehicles (UAV) will play a significant role in this cooperative environment, by enabling aerial connectivity, high-speed data transmission, and network densification for both ground and aerial users. Such a cooperative non-conventional network infrastructure, especially with a one that relies on UAV swarms, cannot adopt conventional centralized intrusion detection and prevention systems. This paper presents a new framework that integrates the Zero-Trust Architecture (ZTA) into 6G networks to secure UAV communication. Contrary to the conventional ZTA, the proposed framework adapts a trust mechanism suitable for decentralized networks that maintains the security, privacy and authenticity of both UAV devices and their metaverse counterparts. A Federated Learning (FL) approach is adopted on UAV devices to support accurate and on-time decision making. Learnt models and trust scores are added onto a blockchain to support the ZTA. Experimental results reveal that the proposed architecture can maintain high levels of intrusion prevention and authenticity for UAVs.

Guerraoui et al. [PODC, 2019] implement a cryptocurrency (in a permissioned setting) using an asset transfer object. Their main result implies that consensus is not necessary for implementing a cryptocurrency, since computationally speaking, an asset transfer object is equivalent to an atomic read/write register. In this work, we take a step further to understand fundamental limitations of cryptocurrency under the CAP framework. Particularly, we show that no cryptocurrency that tolerates Byzantine adversary works in a partitioned network. We point out future directions that can circumvent the impossibility.

Neural networks have demonstrated remarkable success in learning and solving complex tasks in a variety of fields including cognitive cities. Nevertheless, the rise of those networks in modern computing has been accompanied by concerns regarding their vulnerability to adversarial attacks. In this work, we propose a novel gradient-free, gray box, incremental attack that targets the training process of neural networks. The proposed attack, which implicitly poisons the intermediate data structures that retain the training instances between training epochs acquires its high-risk property from attacking data structures that are typically unobserved by professionals. Hence, the attack goes unnoticed despite the damage it can cause. Moreover, the attack can be executed without the attackers' knowledge of the neural network structure or training data, making it more dangerous. The proposed attack was tested under a sensitive application of secure cognitive cities, namely, biometric authentication. The conducted experiments showed that the proposed attack is effective and stealthy. Finally, the attack effectiveness property was concluded from the fact that it was able to flip the sign of the loss gradient in the conducted experiments to become positive, which is noisy and unstable training. Moreover, the attack was able to decrease the inference probability in the poisoned networks compared to their unpoisoned counterparts by 15.37%, 14.68%, and 24.88% for the Densenet, VGG, and Xception, respectively. Finally, the attack retained its stealthiness despite its high effectiveness. This was demonstrated by the fact that the attack did not cause a notable increase in the training time, in addition, the Fscore values only dropped by an average of 1.2%, 1.9%, and 1.5% for the poisoned Densenet, VGG, and Xception, respectively.

The development of the Internet of Things (IoT) has dramatically expanded our daily lives, playing a pivotal role in the enablement of smart cities, healthcare, and buildings. Emerging technologies, such as IoT, seek to improve the quality of service in cognitive cities. Although IoT applications are helpful in smart building applications, they present a real risk as the large number of interconnected devices in those buildings, using heterogeneous networks, increases the number of potential IoT attacks. IoT applications can collect and transfer sensitive data. Therefore, it is necessary to develop new methods to detect hacked IoT devices. This paper proposes a Feature Selection (FS) model based on Harris Hawks Optimization (HHO) and Random Weight Network (RWN) to detect IoT botnet attacks launched from compromised IoT devices. Distributed Machine Learning (DML) aims to train models locally on edge devices without sharing data to a central server. Therefore, we apply the proposed approach using centralized and distributed ML models. Both learning models are evaluated under two benchmark datasets for IoT botnet attacks and compared with other well-known classification techniques using different evaluation indicators. The experimental results show an improvement in terms of accuracy, precision, recall, and F-measure in most cases. The proposed method achieves an average F-measure up to 99.9%. The results show that the DML model achieves competitive performance against centralized ML while maintaining the data locally.

The increasing number of applications and devices in the Sixth-generation (6G) networks and the diversity of mobile data, architectures, and technologies make security and privacy a critical concern. Advanced metaheuristics algorithms (MHAs) have recently become a viable solution for optimizing security and privacy in wireless networks, combining game theory and convex optimization, and several other advanced models. As a subfield of Artificial Intelligence (AI), MHAs are inspired by concepts from Evolutionary Algorithms (EAs), Trajectory-based Algorithms (TAs), and Swarm Intelligence (SI). Recent implementations of MHAs in the 6G networks have effectively solved complex security and privacy problems. This study examines MHAs’ utilization in addressing security and privacy challenges in 6G networks. The paper provides a comprehensive overview of MHAs and their use in solving security and privacy problems in 6G. The current limitations of the literature are also identified, and avenues for further research are suggested. The reader will have a clear image of the needed technologies and tools for securing 6G networks using MHAs.

Recently, the benefits of moving computation to the edge were researched and proven. The paradigm of Mobile Edge Computing (MEC) provides a clear view of how offloading core network burden leads to the shortening of data access latencies, which is primal for supporting services at the edge. Increasing the density of edge data centers for supporting such requests is uneconomical. However, it is important to have a clear resource allocation strategy in such a way that user scheduling is considered. Therefore, in this paper, we study and prove how resource scheduling at the edge can substantially affect network performance. To this end, we focus on the stochastic nature of the wireless links and reduce this problem to a stochastic game. We design a software edge controller called Edge-Boss that resides at one base station and controls the wireless links available at each base station. In addition, the model ensures that Edge-Boss achieves an equilibrium that enables the Base stations (BS) to provide a higher performance (as this equilibrium acts as an incentive for the BS). Using simulations we show that Edge-Boss achieves latency reductions and a high network throughput.

The Internet of Things (IoT) has revolutionized the manufacturing process in the industry. It has created a new ecosystem allowing a diversified set of devices to be controlled remotely with minimal human intervention. Today, with the advances in intelligence, processing, storage, communication, and networking capabilities of IoT devices, we are one step closer to realizing the vision of Industry 4.0. Cyber-physical systems (CPS) are now significantly more intelligent and automated with the aid of advances in Machine Learning (ML). Intelligent IoT (IIoT), Digital Twins (DT) and the advances in mobile networks are now paving the path towards decentralized self-managed CPS in the industry. DT permits mobile networks to provide adaptive and dynamic configurations for cooperative CPS. Moreover, trustworthy cooperation may be realized with blockchain. In this article, we present a blockchain-assisted hierarchical federated learning (FL)-enabled platform (HFL) for Industry 4.0. The solution integrates DT into CPS to accurately capture the characteristics of industrial IoT devices and assist in the HFL process. A two-stage FL algorithm is used that groups Internet-enabled factory machinery and their DTs into groups in accordance with their organizational structure. A global model is created for the groups from the averaged local models and the DT model in the first stage. During the second stage, federated aggregation is used to create a global model from the first-stage models. Blockchain is used to cross-verify and validate newly added blocks with the support of validator nodes. Numerical analysis is performed to compare between the presented DT-enabled and blockchain-assisted HFL solution and benchmark solutions in terms of network overhead, block optimization, and accuracy.

The advancement of wireless connectivity in smart cities will enhance connections between their various key elements. Federated intelligent health monitoring systems inside autonomous vehicles will achieve smart cities’ goal of improving the quality of life. This paper proposes a novel cooperative health emergency response system within Cooperative Intelligent Transportation Environment, namely, C-HealthIER. C-HealthIER is a cooperative health intelligent emergency response system that aims to reduce the time of receiving the first emergency treatment for passengers with abnormal health conditions. C-HealthIER continuously monitors passengers’ health and conducts cooperative behavior in response to health emergencies by vehicle-to-vehicle and vehicle-to-infrastructure information sharing to find the nearest treatment provider. A conducted simulation that integrates three different tools (Veins, SUMO, and OMNET++) to simulate the proposed system showed that C-HealthIER reduces the total time to receive the emergency treatment by at least 92.5% and the time to receive the first emergency treatment by at least 73.2% compared to the time taken by AutoPilot mode in self-driving cars. C-HealthIER also reduces the travel distance to the first emergency treatment place by 40.9% and thus reduces the travel time by 43.8% compared to receiving the treatment at the same hospital in the AutoPilot mode.

Industrial Internet of Things (IIoT) applications have diverse network session requirements. Certain critical applications, such as emergency alert relays, as well as industrial floor evacuation and surveillance systems, require fresh updates that can maintain the most recently delivered packets. This requires high reconfigurability to an extent where the system can measure the impact of an event and adapt the network accordingly. Recent research has demonstrated that network failures can undermine the sustainability of Industry 4.0 or Industrial IoT in general. In this paper, we design an intelligent Federated learning based Time-Sensitive Networking (Fed-TSN) controller framework to optimize the failure recovery. In industrial IoT scenarios, such as emergency evacuations on factory floors due to natural disasters, there can be multiple link failures with no disjoint paths which require a sustainable recovery solution. Accordingly, we consider multiple simultaneous link failures, both for networks with and without disjoint paths. The typically probabilistic network failures on a factory floor call for designing a mechanism that can search for routes with minimum joint failure probability (JFP). We formulate the JFP minimization problem as a non-linear integer program. We design a Software Defined Networking (SDN) controller that runs an application to produce near-optimal solutions for providing enhanced sustainability in a wide range of Industry 4.0 scenarios. We employ this non-linear integer program solution as input to our intelligent Fed-TSN fault recovery strategy that predicts the migration location based on the changes in the TSN gate schedule. We conduct simulations to quantify the improvements achieved with Fed-TSN compared to state-of-the-art approaches.

Federated Learning (FL) is a type of Machine Learning (ML) technique in which only learned models are stored on a server to sustain data security. The approach does not gather server-side data but rather directly shares only the models from scattered clients. Due to the fact that clients of FL frequently have restricted connection bandwidth, it is necessary to optimize the communication between servers and clients. FL clients frequently interact through Wi-Fi and must operate in uncertain network situations. Nevertheless, the enormous number of weights transmitted and received by existing FL aggregation techniques dramatically degrade the accuracy in unstable network situations. We propose a federated GWO (FedGWO) algorithm to reduce data communications. The proposed approach improves the performance under unstable network conditions by transferring score principles rather than all client models' weights. We achieve a 13.55% average improvement in the global model's accuracy while decreasing the data capacity required for network communication. Moreover, we show that FedGWO achieves a 5% reduction in accuracy loss compared to FedAvg and Federated Particle Swarm Optimization (FedPSO) methods when tested on unstable networks.

Recently, IoT devices have become affordable to support various types of applications which have encouraged their usability in data collection, behavior tracking, and pattern analysis to gain knowledge to achieve certain goals. The Internet of Behavior (IoB) allows organizations and individuals to achieve all of this simultaneously. Various technologies and approaches can be used to support IoB systems to operate efficiently, such as 6G networks and decentralized systems structure that support IoT-based systems to distribute operations across devices and influence each device individually. Therefore, this paper proposes a decentralized IoB framework for achieving energy sustainability by tracking, analyzing, and influencing IoT devices’ behavior. The collected results from an extensive decentralized IoB electrical power consumption experiment show that the decentralized system achieved higher accuracy compared to the centralized system, thus sending 3.5% fewer alerts and saving 3.4% more power for 3 sub-meters over a period of 500 hours.

Asynchronous Byzantine reliable broadcast receives renewed attention recently, as it is fundamental to many fault-tolerant critical applications. This paper focuses on the Byzantine Reliable Broadcast protocol, which was first proposed by Bracha in 1987. Several recent protocols have improved the round and bit complexity of these algorithms. Motivated by practical network constraints in modern applications, this paper revisits the problem and reduces both complexity in communication and local computation. State-of-the-arts protocols are evaluated using the developed framework that simulates realistic bandwidth constraints. The evaluation demonstrates that our protocols, which use cryptographic hash functions and erasure coding in a novel way, have superior performance in critical applications such as asset transfer and smart home.

Network slicing is a key enabler of multi-tenancy in 5G-and-beyond networks that satisfies the distinct requirements of different use-cases. As the density of tenants increases over time, admission requests may be put in waiting queues leading to impatient tenant behaviors. Due to such behaviors, tenants may frequently leave-and-join the slice admission queues in search for an alternate mobile network provider (MNO). This can be a severe problem when slices are leased and released on a short-term basis. In this paper, we argue that the instant behavior of a slice may deviate considerably from the predicted average behavior known to the tenant through a slice controller and thus gives rise to impatient tenant behaviors. To address this problem, we propose Mutes, a multi-tenant switching algorithm, that aids tenants in finding the best MNO. For a fixed number of tenants, we show that Mutes attains a Nash Equilibrium. We also show that Mutes stabilizes the system under strict admission conditions in scenarios where tenants are allowed to randomly move between MNOs. Through simulations, we justify that the proposed Mutes algorithm significantly improves the resource assignment performance and converges faster than state-of-the-art policies.

The problem of Emergency Department (ED) over-crowding is a worldwide public health issue that has several side effects, such as overworked medical staff, increased infections, and high mortality rates among patients. The process of conducting initial medical assessment and sorting for ED patients without the need for direct contact between medical staff and patients is called “remote triage”. In this work, we tackle the automation of this process. Three fully automated computer vision-based architectures for IoMT are proposed, namely home-based, portable and smart triage road units. The proposed methods utilize state-of-the-art deep learning architectures to automate the remote triage process. The utilized deep architectures are lightweight, thus, mobile-friendly, and capable of assigning triage scores to a broad spectrum of medical conditions. We furthermore formulate patients' ED wait time mathematically. We setup all architectures to consider EDs at a regional level in order to facilitate making a convenient ED choice for patients. Moreover, a novel ED selection criteria that considers the ED distance and the expected wait time is proposed in order to minimize the wait time and commuting distance for patients. Our experiments show an improvement in the quality and duration of patients' wait time throughout the triage process. Additionally, our experiments show that the proposed methods achieve accurate triage results with an average macro F-score of 97.8% with the capability of providing triage to 98 patients/second compared to the non-automated current approach followed in EDs which takes 15 minutes/patient in the best case.

Intelligent Transportation Systems (ITS), mainly Autonomous Vehicles (AV's), are susceptible to security and safety problems that risk the users' lives. Sophisticated threats can damage the security of AV's communications and computational capabilities, slowing down their integration into our daily lives. Cyber-attacks are getting more complex, posing greater hurdles in identifying intrusions effectively. Failing to prevent the intrusions could tarnish the security services' reliability, including data confidentiality, authenticity, and reliability. IDS is an overall prediction paradigm for detecting malicious network traffic in the ITS. This article studies the role of machine or deep learning in Software Defined-Intrusion Detection System (SD-IDS) in ITS; discusses the mathematical analysis of existing deep learning models and evaluates their performances on the basis of the various metrics (i.e., accuracy, precision, recall, f-measure) to observe which model gives the best results for the existing state of art. The results show that improved Recurrent Neural Networks (RNN) is best suited for the detection of SD-IDS attacks in the data plane and control plane.

Today’s Internet mainly relies on TCP protocol to ensure reliable communications between two endpoints. Unfortunately, this widely-used protocol may incur significant delay when transmitting lost packets. Indeed, with TCP, the source of the data detects lost packets using a timeout or duplicated acknowledgements before retransmitting them. As a result, the delay needed for a packet to reach the destination may become significant. This delay is estimated to be at least three times the end-to-end delay when the packet is lost once and could be even worse when the same packet is lost several times. As a matter of fact, this high delay cannot be tolerated by critical applications.To address this problem, in this paper, we focus on minimizing TCP retransmission delays and we introduce a novel network function called Transport Assistant that could be deployed within the network in order to cache, detect and retransmit lost packets. Thanks to this function, there is no need to wait for the source to detect and retransmit lost packets as the TA ensures packet retransmission from the network itself and thereby minimize retransmission delays. Through extensive experiments, we show that the TA allows to outperform the standard TCP by minimizing the average packet transmission time, the flow completion time, the packet loss and the number of retransmitted packets from the source.

Due to recent privacy trends and the increase in data breaches in various industries, it has become imperative to adopt new technologies that support data privacy, maintain accuracy, and ensure sustainability at the same time. The healthcare industry is one of the most vulnerable sectors to cyberattacks and data breaches as health data are highly sensitive and distributed in nature. The use of IoT devices with machine learning models to monitor the health status has made the challenge more acute, as it increases the distribution of health data and adds a decentralized structure to healthcare systems. A new privacy-preserving technology, namely, federated learning (FL), is promising for such a challenge as implementing solutions that integrate FL with deep learning, for healthcare applications that rely on IoT, provides several benefits by mainly preserving data privacy, building robust and high accuracy models, and dealing with the decentralized structure, thus achieving sustainability. This article proposes a deep FL (DFL) framework for healthcare data monitoring and analysis using IoT devices. Moreover, it proposes an FL algorithm that addresses the local training data acquisition process. Furthermore, it presents an experiment to detect skin diseases using the proposed framework. The extensive results collected show that the DFL models can preserve data privacy without sharing it, maintain the decentralized structure of the system made by IoT devices, improve thearea under the curve(AUC) of the model to reach 97%, and reduce the operational costs (OC) for service providers.

With the rapid growth of edge-assisted solutions in Internet of Things (IoT) networks, connected healthcare progressively relies on such solutions. This refers to systems in which all the healthcare stakeholders are connected to each other. These systems employ novel technologies, such as IoT, edge computing, and artificial intelligence (AI) to convert conventional health systems to more effective, appropriate, and customized intelligent systems. However, such systems encounter many restrictions and require new policies. By moving the computation and processing closer to the data sources and end-users, fog becomes edge computing which can reduce latency, bandwidth usage, and energy consumption. To the best of our knowledge, there is no systematic and methodological research in this scope that investigates the existing studies considering various vital and relevant factors. Thus, this survey aims to examine the state-of-the-art research in this area. We have reviewed a significant number of papers in this area and divided them into two main taxonomies, patient-centric and process-centric techniques. Furthermore, essential factors, such as available data sets and parameters like accuracy, mobility, and data rates are described and examined. Our aim is to bridge the gap between edge computing and connected healthcare solutions by discussing the challenges and highlighting future trends.

Abstract Growing intelligent cities is witnessing an increasing amount of local energy generation through renewable energy resources. Energy trade among the local energy generators (aka prosumers) and consumers can reduce the energy consumption cost and also reduce the dependency on conventional energy resources, not to mention the environmental, economic, and societal benefits. However, these local energy sources might not be enough to fulfill energy consumption demands. A hybrid approach, where consumers can buy energy from both prosumers (that generate energy) and also from prosumer of other locations, is essential. A centralized system can be used to manage this energy trading that faces several security issues and increase centralized development cost. In this paper, a hybrid energy trading system coupled with a smart contract named SynergyGrids has been proposed as a solution, that reduces the average cost of energy and load over the utility grids. To the best of our knowledge, this work is the first attempt to create a hybrid energy trading platform over the smart contract for energy demand prediction. An hourly energy data set has been utilized for testing and validation purposes. The trading system shows 17.8% decrease in energy cost for consumers and 76.4% decrease in load over utility grids when compared with its counterparts.

Computational social systems (CSSs) focus on topics such as modeling, simulation, analysis, and understanding of social systems from the quantitative and/or computational perspective. “Systems” can be man–man, man–machine, and machine–machine organizations and adversarial situations as well as social media structures and their dynamics[1],[2]. With the advance of the Internet of Things and communication technologies, various kinds of data from diverse areas can be acquired nowadays. As a result, CSSs are becoming ever more complex. Data-driven CSSs aim to conduct pre-competitive research on architectures and design, modeling, and analysis techniques for cyber-physical systems, with emphasis on making full use of big data and artificial intelligence. These applications include transportation systems, automation, security, smart buildings, smart cities, medical systems, energy generation and distribution, water distribution, agriculture, military systems, process control, asset management, and robotics[3],[4],[5]. However, due to the progressive transformation from host-centric networking to information-centric networking, CSSs pose fundamental challenges in multiple aspects, such as heterogeneous data generation, efficient data sensing and collection, real-time data processing, and greater request arrival rates. Thus, there is a great need for a powerful way that can deal with emerging issues in data-driven CSSs more efficiently and effectively in the age of big data.

Industry 5.0 is emerging as a result of the advancement in networking and communication technologies, artificial intelligence, distributed computing, and beyond 5G. Among the important enabling technologies, federated learning, industrial edge computing, and Byzantine-tolerant machine learning (ML) are key accelerators in Industry 5.0. We propose a framework to integrate these key components. Recent works have designed various Byzantine-tolerant ML algorithms for a datacenter or a cluster. However, these algorithms are difficult to be applied to industrial edge computing paradigms. In this article, a novel Byzantine-tolerant federated learning algorithm, CRACAU, is designed for the popular three-level edge computing architecture. In this algorithm, edge devices jointly learn an ML model using the data collected at each device, and their private data are never shared with others. Under standard assumptions, we formally prove that CRACAU converges to the optimal point, i.e., CRACAU finds the optimal parameters of the ML model. We also implement CRACAU in the MXNet framework and evaluate it on the popular benchmark MNIST and CIFAR-10 image classification datasets. Experimental results show that CRACAU achieves satisfying accuracy.

The aerial capabilities and flexibility in movement of Unmanned Aerial Vehicles (UAVs) has enabled them to adaptively provide both traditional and more contemporary services. In this article, we introduce a solution that integrates the capabilities of both UAVs and Unmanned Ground Vehicles (UGVs) to provide both intelligent connectivity and services to both aerial and ground connected devices. A cooperative solution is adopted that considers nodes’ power and movement constraints. The UAV and UGV cooperative process ensures continuous power availability to UAVs to support seamless and continuous service availability to end-devices. A Federated Learning (FL) approach is adopted at the edge to ensure accurate and up-to-date service provisioning in accordance with the surrounding environment and network constraints. Moreover, Blockchain technology is used to decentralize the provisioning and control aspects, and ensure authenticity and integrity. Extensive simulations are conducted to test the soundness and applicability of the proposed solution. Results show significant improvement in terms of connectivity, service availability, and UAV energy enhancements when compared to traditional mobile and vehicular communication techniques.

The shift towards Industry 4.0 has seen significant steps forward with the advancements in processing, communication, and storage capabilities of Internet of Things (IoT) devices. Cyber-physical systems (CPS) have become more intelligent and withhold advanced processing, storage, and communication capabilities. Rejuvenated network and service management architectures must incorporate the capabilities of intelligent CPS. With that said, this article introduces a cooperative blockchain (BC)-assisted resource and capability sharing approach to fulfill CPS tasks. The solution uses Federated Learning (FL)-enabled Intelligent IoT (IIoT) devices to support Next-Generation Networks (NGNs). A clustering multi-stage blockchain and FL algorithm is used to create local and global models for CPS tasks. Local models are created for each cluster during the first stage. At the second stage, Federated Averaging is used by fog devices to create fog models. A global deep model is then created on the cloud using Federated Aggregation. Blockchain is used to record and validate the added models and ensure that records are not altered under cyber-attacks. Simulation results have shown that the proposed solution outperforms conventional FL and blockchain approaches in terms of accuracy and delay tolerance.

Advanced services leveraged for future smart cities have played a significant role in the advancement of 5G networks towards the 6G vision. Interactive immersive applications are an example of those enabled services. Such applications allow for the interaction between multiple users in a 3D environment created by virtual presentations of real objects and participants using various technologies such as Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Digital Twin (DT) and holography. These applications require advanced computing models which allow for the processing of massive gathered amounts of data. Motions, gestures and object modification should be captured, added to the virtual environment, and shared with all the participants. Relying only on the cloud to process this data can cause significant delays. Therefore, a hybrid cloud/edge architecturewith an intelligent resource orchestration mechanism, that is able to allocate the available capacities efficiently is necessary. In this paper, a blockchain and federated learning-enabled predicted edge-resource allocation (FLP-RA) algorithm is introduced to manage the allocation of computing resources in B5G networks. It allows for smart edge nodes to train their local data and share it with other nodes to create a global estimation of future network loads. As such, nodes are able to make accurate decisions to distribute the available resources to provide the lowest computing delay.

Advancements in communication technologies such as Beyond 5G have made it possible for on-demand services in vehicular networks. Novel advancements in the transportation area are needed to meet these feature requirements. Most of the communications in vehicular networks are based on names of content and thus caching inside the network makes Named Data Networking (NDN) a suitable option for content dissemination for connected vehicles. To achieve NDN maximum benefits, a new effective data forwarding scheme is needed as the size of the cache is limited to store all of the requested content, and cache replacement scheme to replace the data which is not required. In this paper, the challenge of in-network caching is addressed in terms of efficient cache utilization by considering the cache hit ratio and the challenge of vehicles' mobility. The volume of data cached during communication is called cache utilization. Many in-network caching strategies are proposed in the literature, but very few of the proposed schemes consider the efficient utilization of cache resources while considering the mobility of vehicles. Default cache replacement technique is Least Recently Used (LRU) whereas Leave Copy Everywhere (LCE), Probabilistic Caching (PC), Always Cache (AC), Leave Copy Down (LCD), Reactive Caching (RC), and Edge Caching (EC) are used as data forwarding schemes. In this paper, existing in-network caching strategies with important role in cache utilization such as data forwarding schemes, and cache replacement schemes, are implemented, evaluated and a new caching scheme is proposed which will address the issues faced by current schemes.

Managing cache content at the edge is one of the many use cases of 5G-and-beyond networks. However, increasing the density of Edge Data Centers (EDCs) to service requests is a crucial problem. To overcome this problem, recent research has advanced mobile device architecture paradigms and the content caching in a Mobile Device Cloud (MDC). These two service locations (EDCs and MDC) are registered with the Mobile Network Operator (MNO), enabling the MNO to control the content placement for profit maximization. As the user demands for content items are directly related to the QoS perceived by the user, it is important to understand the future popularity of the content items and to place them appropriately. Additionally, privacy issues have increased over time because of sensitive user information being divulged at the MDC. To preserve privacy, a branch of machine learning called Federated Learning (FL) can train machine learning models leaving the data in the end user devices. The paper contributions are as follows: (1) We introduce an FL algorithm called FedCo, that trains a deep-neural network (DNN) to predict the user demand of a specific content, so as to manage the content files placement at EDC and MDC sites. (2) We then conduct a theoretical evaluation of user demand behavior via prospect theory to justify revenue maximization for an MNO. (3) We show numerically via a multimedia content delivery use-case how the proposed model compares favorably with two state-of-the-art designs.

Recent trends in the healthcare industry, such as the use of wearable IoT for continuous health monitoring, are setting new requirements for healthcare systems that boost data analysis. These systems should support decentralization and maintain the privacy and ownership of users' data due to the sensitivity of healthcare data. Therefore, the use of federated learning techniques is recommended for systems that need such requirements. This paper proposes a Deep Federated Learning framework for decentralized healthcare systems that maintain user privacy in a distributed architecture. It also proposes an algorithm for an automated training data acquiring process. Furthermore, it presents an experiment for using deep federated learning in detecting skin diseases and using Transfer Learning to address the problem of limited availability of healthcare data in building deep learning models. The evaluated results show how the federated learning increased the Area Under the Curve of the centralized learning model up to 0.97, as it also shows good model performance during federated rounds in terms of accuracy, precision, recall, and F1-score. Moreover, although the FL system has affected the quality of service to the user in terms of model conversion time, the Federated Learning system meets the requirements of building models in a decentralized manner with no sharing of users' private data.

Recent technological advances have reshaped many aspects of our lives, especially modern transportation systems. For instance, AI and B5G Networks have raised the level of automation as autonomous vehicles (AV) become decision-independent and self-aware. However, in-vehicle health monitoring is still an open issue. Therefore, a cooperative healthcare emergency response framework has been proposed that employs in-vehicle intelligent health monitoring and local networks for AV to minimize the time to receive emergency treatment for passengers with abnormal health conditions. The extensive simulation results show that the framework minimizes the First Emergency Treatment Time by at least 75%, and eliminates hospital waiting time, the Total Time for Emergency Treatment is minimized by at least 93%. Finally, it reduces Travel Time by nearly 50%. All results compared to the autopilot approach.

Since the emergence of digital and smart healthcare, the world has hastened to apply various technologies in this field to promote better health operation and patients’ well being, increase life expectancy, and reduce healthcare costs. One promising technology and game changer in this domain is digital twin (DT). DT is expected to change the concept of digital healthcare and take this field to another level that has never been seen before. DT is a virtual replica of a physical asset that reflects the current status through real-time transformed data. This article proposes and implements an intelligent context-aware healthcare system using the DT framework. This framework is a beneficial contribution to digital healthcare and to improve healthcare operations. Accordingly, an electrocardiogram (ECG) heart rhythms classifier model was built using machine learning to diagnose heart disease and detect heart problems. The implemented models successfully predicted a particular heart condition with high accuracy in different algorithms. The collected results have shown that integrating DT with the healthcare field would improve healthcare processes by bringing patients and healthcare professionals together in an intelligent, comprehensive, and scalable health ecosystem. Also, implementing an ECG classifier that detects heart conditions gives the inspiration for applying ML and artificial intelligence with different human body metrics for continuous monitoring and abnormalities detection. Finally, neural-network-based algorithms deal better with ECG data than traditional ML algorithms.

The next revolution of the smart industry relies on the emergence of the Industrial Internet of Things (IoT) and 5G/6G technology. The properties of such sophisticated communication technologies will change our perspective of information and communication by enabling seamless connectivity and bring closer entities, data, and “things.” Terahertz-based 6G networks promise the best speed and reliability, but they will face new man-in-the-middle attacks. In such critical and high-sensitive environments, the security of data and privacy of information still a big challenge. Without privacy-preserving considerations, the configuration state may be attacked or modified, thus causing security problems and damage to data. In this article, motivated by the need to secure 6G IoT networks, an ant colony optimization (ACO) approach is presented by adopting multiple objectives as well as using transaction deletion to secure confidential and sensitive information. Each ant in the population is represented as a set of possible deletion transactions for hiding sensitive information. We utilize the use of a prelarge concept to assist in the reduction of multiple database scans in the evaluation progress. We then also adopt external solutions to maintain discovered Pareto solutions, thus improving effectiveness to find optimized solutions. Experiments are conducted comparing our methodology to state-of-the-art bioinspired particle swarm optimization (PSO) as well as genetic algorithm (GA). Our strong results clearly show that the designed approach achieves fewer side effects while maintaining low computational cost overall (Chen et al., 2020).

Industrial investments into distributed energy resource technologies are increasing and playing a pivotal role in the global transactive energy, as part of a wider drive to provide a clean and stable source of energy. The management of prosumers, which consume and as well as generate energy, with heterogeneous energy sources is critical for sustainable and efficient energy trading procedures. This article proposes a blockchain-assisted adaptive model, namely SynergyChain, for improving the scalability and decentralization of the prosumer grouping mechanism in the context of peer-to-peer energy trading. Smart contracts are used for storing the transaction information and for the creation of the prosumer groups. SynergyChain integrates a reinforcement learning module to further improve the overall system performance and profitability by creating a self-adaptive grouping technique. The proposed SynergyChain is developed using Python and Solidity and has been tested using Ethereum test nets. The comprehensive analysis using the hourly energy consumption dataset shows a 39.7% improvement in the performance and scalability of the system as compared to the centralized systems. The evaluation results confirm that SynergyChain can reduce the request completion time along with an 18.3% improvement in the overall profitability of the system as compared to its counterparts.

The industrial Internet of Things (IIoT) supports recent developments in data management and information services, as well as services for smart factories. Nowadays, many mature IIoT cloud platforms are available to serve smart factories. However, due to the semicredibility nature of the IIoT cloud platforms, how to achieve secure storage, access control, information update and deletion for smart factory data, as well as the tracking and revocation of malicious users has become an urgent problem. To solve these problems, in this article, a blockchain-enhanced security access control scheme that supports traceability and revocability has been proposed in IIoT for smart factories. The blockchain first performs unified identity authentication, and stores all public keys, user attribute sets, and revocation list. The system administrator then generates system parameters and issues private keys to users. The domain administrator is responsible for formulating domain security and privacy-protection policies, and performing encryption operations. If the attributes meet the access policies and the user's ID is not in the revocation list, they can obtain the intermediate decryption parameters from the edge/cloud servers. Malicious users can be tracked and revoked during all stages if needed, which ensures the system security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption and can resist multiple attacks. The evaluation has shown that the size of the public/private keys is smaller compared to other schemes, and the overhead time is less for public key generation, data encryption, and data decryption stages.

The Fifth Generation (5G) communication technology has paved the way for intelligent and diversified Internet of Connected Vehicles (IoCV) services that meet stringent Quality of Service (QoS) requirements. Both Artificial Intelligence (AI) and Blockchain are playing and will continue to play an imperative role in providing secure and decentralized resource sharing to solve complex and time-sensitive problems at the edge. The integration of both those techniques will enhance the performance of smart vehicular services, especially in beyond 5G (B5G) networks. Ensuring secure transactions in complex autonomous network architectures is an immense challenge. This article addresses computational, storage, connectivity and intelligence concerns using a collaborative approach to engage multiple Internet of Things (IoT) nodes such as connected- vehicles, drones and mobile devices for the provisioning of QoS-optimal complex service compositions in autonomous mobile networks. Continuous and fast compositions emerge using decentralized decisions and interactions with diversified neighboring nodes with the aid of reinforcement learning. Blockchain is used to ensure that nodes interact with each other verifiably and record transactions without the need for trusted intermediaries. We assess whether having an AI-enabled blockchain collaborative composition solution improves service availability and delivery of smart city vehicular services.

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this article, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN, and DT) using a standard security dataset (NSL-KDD).

The rise of fast communication media both at the core and at the edge has resulted in unprecedented numbers of sophisticated and intelligent wireless IoT devices. Tactile Internet has enabled the interaction between humans and machines within their environment to achieve revolutionized solutions both on the move and in real-time. Many applications such as intelligent autonomous self-driving, smart agriculture and industrial solutions, and self-learning multimedia content filtering and sharing have become attainable through cooperative, distributed, and decentralized systems, namely, volunteer computing. This article introduces a blockchain-enabled resource sharing and service composition solution through volunteer computing. Device resource, computing, and intelligence capabilities are advertised in the environment to be made discoverable and available for sharing with the aid of blockchain technology. Incentives in the form of on-demand service availability are given to resource and service providers to ensure fair and balanced cooperative resource usage. Blockchains are formed whenever a service request is initiated with the aid of fog and mobile edge computing (MEC) devices to ensure secure communication and service delivery for the participants. Using both volunteer computing techniques and tactile internet architectures, we devise a fast and reliable service provisioning framework that relies on a reinforcement learning technique. Simulation results show that the proposed solution can achieve high reward distribution, increased number of blockchain formations, reduced delays, and balanced resource usage among participants, under the premise of high IoT device availability.

Green Internet of things (GIoT) generally refers to a new generation of Internet of things design concept. It can save energy and reduce emissions, reduce environmental pollution, waste of resources, and harm to human body and environment, in which green smart device (GSD) is a basic unit of GIoT for saving energy. With the access of a large number of heterogeneous bottom-layer GSDs in GIoT, user access and control of GSDs have become more and more complicated. Since there is no unified GSD management system, users need to operate different GIoT applications and access different GIoT cloud platforms when accessing and controlling these heterogeneous GSDs. This fragmented GSD management model not only increases the complexity of user access and control for heterogeneous GSDs, but also reduces the scalability of GSDs applications. To address this issue, this article presents a blockchain-empowered general GSD access control framework, which provides users with a unified GSD management platform. First, based on the World Wide Web Consortium (W3C) decentralized identifiers (DIDs) standard, users and GSD are issued visual identity ( VID ). Then, we extended the GSD-DIDs protocol to authenticate devices and users. Finally, based on the characteristics of decentralization and non-tampering of blockchain, a unified access control system for GSD was designed, including the registration, granting, and revoking of access rights. We implement and test on the Raspberry Pi device and the FISCO-BCOS alliance chain. The experimental results prove that the framework provides a unified and feasible way for users to achieve decentralized, lightweight, and fine-grained access control of GSDs. The solution reduces the complexity of accessing and controlling GSDs, enhances the scalability of GSD applications, as well as guarantees the credibility and immutability of permission data and identity data during access.

Network Function Virtualization is known for its ability to reduce deployment costs and improve the flexibility and scalability of network functions. Due to processing capacity limitation, the infrastructure provider needs to instantiate one or more instances of a particular network function when the amount of traffic increases. Most of network functions are stateful, which means that they keep a state that may be frequently read or updated (e.g., statistics like number of packets or bytes per flow). As a result, the instances of the same virtual network function should constantly share the same state to prevent incorrect operation. In this context, a major challenge is how to efficiently ensure the consistency among instances while minimizing communication cost for synchronizing their state and ensuring the synchronization delay does not exceed a certain bound set by the operator.In this paper, we propose a technique to identify the optimal communication pattern between the instances of the same network function in order to minimize their synchronization cost. Moreover, we propose to use a special network function named Synchronization Function to ensure consistency among a set of instances and to minimize the synchronization cost. We first mathematically model the problem of finding the optimal synchronization pattern and the optimal placement and number of synchronization functions as an integer linear program that minimizes the synchronization cost and ensures a bounded synchronization delay. Last, we put forward three algorithms to cope with large-scale scenarios of the problem. Extensive simulations show that the proposed algorithms efficiently find near-optimal solutions with minimal computation time.

Evaluating Blockchain performance is not an easy task. It is difficult to compare different systems, since the evaluation is often incomprehensible and conducted in different environments with distinct workloads. Only a handful of prior tools were proposed, e.g., BLOCKBENCH and HFBench. Unfortunately, these tools have several limitations. We first identify these limitations. Second, motivated by our observations, we then present a benchmarking tool, Boston Blockchain Benchmarking (BBB). BBB is configurable, extensible, and easy-touse. In particular, BBB can be used to test Blockchain from a networking perspective, a feature that we have not observed in prior tools. Similar to BLOCKBENCH, we focus on the private Blockchain. Concretely, we integrate our tool with Mininet, and provide a simple mechanism to test how network properties (e.g., latency, bandwidth, package loss rate) affect the performance of the chosen Blockchain. We present our preliminary result of evaluating Ethereum. We stress that the architecture of BBB is general, and could be extended to other Blockchain systems. BBB is extremely lightweight and can be used on your laptop to test a small network. Such a feature allows quick evaluation of the Blockchain and speeds up innovation and development.

Connected healthcare is the process of integrating healthcare smart applications into smart devices. These systems can enable better patient-hospital experience, efficient time usage, reduced errors, safety and security, and ultimately improved treatments. These smart devices which form an IoT network are extremely dynamic because of the user movement. In an environment where there is a constant change in the network topology and its traffic profile, it is a challenging task to provide reliable network connectivity and to maintain the IoT network. Therefore, ensuring healthcare traffic is resilient towards change in the traffic profile is of paramount importance. This paper leverages graph theory concepts to understand the behaviour of the healthcare IoT network. The paper highlights the importance of the PN (PN) and traffic splitting (stratification). A PN is a node in the network which has enough computing resources to share with other network devices. By optimizing the selection of PN, the drastic improvement in the network performance could be achieved. Moreover, we show that splitting the traffic along with optimized PN selection minimizes the chances of healthcare traffic drop during the period of high network usage.

Diversified Internet of Things services are becoming more complex and strictly user-defined. Traditional cloud solutions proved to be both costly in terms of resources and time efficiency. To overcome such a burden, researchers developed fog solutions for faster service responsiveness. Fog-to-Fog communication and cooperation was then introduced to compose services on-the-go for user-specific requests with the aid of mobile edge devices. This paper introduces a blockchain-based decentralized service composition solution for complex multimedia service delivery to cloud subscribers. The proposed work dynamically creates user-defined services without requiring any intermediary service or network provider entities to authenticate and deliver composite services. The composition process uses a reinforcement learning technique to construct secure and reliable composition paths. Participants are rewarded by cloud and fog entities for solving complex composition processes. Simulation results conducted on the system show that by adapting the proposed technique, fog and cloud entities require less resources and reduced power usage with increased service delivery success rates to cloud subscribers.

Top-k considers as a technique to retrieve, from a hypothetically big data set, only the k (k ≥ 1) best (most relevant/important) candidates. Top-k query processing is a decisive necessity in various collaborative environments that comprise big data such as the Internet of Things (IoT) networks. Particularly, efficient top-k processing in large-scale distributed systems has shown a positively noticeable effect on their performance. This paper considers the distributed approximate top-k processing algorithms dedicated to the IoT-based networks and improve the accuracy of algorithms introduced previously. We then propose a safety-based fault-tolerance notation and contribute to improving a known algorithm in terms of accuracy. Our algorithms have been evaluated using simulation and real-world data and show superiority over conventional methods.

Software Defined Networking (SDN) has eased the management and control of networks through separation of the control and data planes. Software defined data centers (SDDC) automate the management of end systems which are physical machines and virtual machines. In data centers, although there is a vast work on minimizing power consumption of physical machines and virtual machine migration performance, energy efficiency of the network components is given little attention. In this paper, a software-based energy efficiency framework that jointly minimizes the power consumption of end systems and network components in SDDC is proposed. Moreover, a novel physical server utility interval based metric, namely Ratio for Energy Saving of Physical Machines (RESPM) which measures how energy efficient the physical servers with respect to virtual machines residing within is proposed. To jointly maximize network energy efficiency and RESPM values, an Integer Programming (IP) formulation has been introduced. Experiments conducted on real-world virtual migration traces show that the proposed framework jointly reduces the power consumption of end systems and network components. The system has shown an improvement of 9% in RESPM, 35% energy saving in Ratio of Energy Saving in SDN (RESDN), and more than 50% in links saving.

With improvements in computation and storage resources, data access through the network becomes the bottleneck for several cloud applications. Even with high-speed networks, the high latency of the cloud access makes it unfeasible or un-favourable for latency-sensitive applications such as autonomous driving, smart factories, and video streaming. Edge computing provides a solution by utilizing the network edge resources that are closer to the end users. Nevertheless, it is a non-trivial task to design a large-scale edge-capable system that is stable, fault-tolerant, and efficient [1]. In this paper, present the design of EdgeKV: a novel general-purpose distributed key-value store for the network edge. We demonstrate the features of EdgeKV for achieving high efficiency and scalability while providing flexibility, ease of use, and data privacy. We evaluated our prototype on the Grid'5000 framework with multiple realistic Yahoo! Cloud Serving Benchmark (YCSB) workloads. Our initial results show that EdgeKV achieves 72% higher throughput and 47% lower latency on average than centralized cloud storage, for read-dominated workloads.

In this paper, we propose a new concept of vehicular networks, namely a Federated Vehicular Networks (FVN), which can be viewed as a stationary vehicular cloud. We first identify the motivation - namely the limits of traditional vehicular clouds due to their instability and rapidly changing topology - and then present the design and applications of an FVN. Finally, we model and describe the unique routing problem in FVN, and present and evaluate different routing algorithms.

In recent years, the amounts of network traffic targeted towards cloud data centers have fluctuated based on user requests. This traffic is bursty and requires a high degree of attention. Due to the variable nature of this traffic, some requests need to be re-allocated on-the-fly. Such circumstances result in performance degradations due to resource management. As appropriate solutions can be proposed only based on understanding the workload and the environment, Reinforcement Learning (RL) is a strategy that is predominantly used. Further, it has been shown that the Poisson arrival rates do not capture real-world burstiness. Thus, we mainly have a two-fold problem to address: (i) the traffic requires a new modelling approach that can characterize the burstiness, and (ii) balancing the load that can maximize the reward to the provider in such circumstances. In this paper, we propose a novel, yet simple traffic modelling that enables burst detection based on an index policy. We show that the throughput constraints play a crucial role in scheduling and our proposed RL technique produces reliable results in such a scenario. Our RL algorithm decides what instance of the request traffic needs to be processed so that the cloud provider can maximize its profit and the decisions made in hindsight are non-biased. We compare the proposed policy with two state-of-the-art approaches and draw key inferences as to why an index policy performs better in scenarios that demand RL. We observe over five times shorter average wait times while bursty workload crosses a saturation limit of 150% compared to conventional policies.

Connected and Autonomous Electric Vehicles (CAEVs) are becoming a feature of our roads in the imminent future. This disruptive technology is likely to enhance the way we get around the city in many ways by collecting accurate data in regards to the surrounding environment and events in a timely-manner. As such data that is time-sensitive where human life may be at risk requires reliable and on-time data delivery. In crowded dense environments, several issues can reduce the network performance due to the high density of objects such as skyscrapers and vehicles as well as the large number of exchanged data between connected vehicles and other objects on the road. Involving a swarm of autonomous Unmanned Aerial Vehicles (UAVs), namely drones, would enhance network connectivity, reduce CAEVs communication delay, facilitate CAEVs tasks distribution, and elevate provisioning services. In this paper, a routing scheme in an autonomous UAV-connected vehicles network is proposed that provides reduced communication delay. The proposed solution has been evaluated using simulations. The collected results show the feasibility and the advantage of the UAV-assisted connected vehicle network in meeting delay and energy consumption requirements.

Energy trading systems have revolutionized by taking advantage of energy users who produce surplusenergy. In the cyberphysical energy sharing systems, the participation of such consumers who can also sell their residuum energy for profit, namely prosumers, is critical for the sustainable and efficient energy sharing procedure and requires improved prosumer management. The idea of grouping the prosumers for better profits is a promising approach for prosumer management which is currently carried out in centralized manner; that face trust, security and scalability issues. Hence, a strong tool that can protect the prosumer privacy; log the changes for audit purposes and eventually improve the performance of the system is necessary. This paper proposes a blockchain-assisted approach using smart contracts for improved scalability and decentralization of the prosumer grouping mechanism in the context of P2P energy trading. The results show around 38.7% improvement in the performance and scalability of the system.

Fog-to-fog communication has been introduced to deliver services to clients with minimal reliance on the cloud through resource and capability sharing of cooperative fogs. Current solutions assume full cooperation among the fogs to deliver simple and composite services. Realistically, each fog might belong to a different network operator or service provider and thus will not participate in any form of collaboration unless self-monetary profit is incurred. In this paper, we introduce a fog collaboration approach for simple and complex multimedia service delivery to cloud subscribers while achieving shared profit gains for the cooperating fogs. The proposed work dynamically creates short-term service-level agreements (SLAs) offered to cloud subscribers for service delivery while maximizing user satisfaction and fog profit gains. The solution provides a learning mechanism that relies on online and offline simulation results to build guaranteed workflows for new service requests. The configuration parameters of the short-term SLAs are obtained using a modified tabu-based search mechanism that uses previous solutions when selecting new optimal choices. Performance evaluation results demonstrate significant gains in terms of service delivery success rate, service quality, reduced power consumption for fog and cloud datacenters, and increased fog profits.

This paper considers device-to-device (D2D) network with Simultaneous Wireless Information and Power Transfer (SWIPT) enabled devices to ensure self-sustained communication in situations like disasters. Such direct link networks can ensure connectivity with devices having drained back-up, when trapped in collapsed infrastructure, through mutual sharing of energy on RF link. To guarantee successful execution of SWIPT session for an isolated device in wake of disasters, it is pertinent to select a reliable peer with ultimate aim to maximize link Energy Efficiency (EE). In practice, Energy Harvesting (EH) is not achievable after Information Decoding (ID); however, it has been made possible through splitting the signal in the time domain. Selection of D2D peer for self-sustained communication with an objective to maximize EE through optimum time based splitting of signal has not been extensively studied. In this paper to manifest the aforesaid goal, we worked out a joint problem of peer association and time switching ratio allocation with an objective to maximize the EE for a device contained under collapsed infrastructure. We propose an Energy efficient Peer Selection and Time switching Ratio Allocation (EPS-TRA) algorithm to solve the proposed mixed integer problem. Numerical results validate our proposed approach in acquiring better EE when compared with Uniform Allocation Scheme of time slots for EH & ID. Furthermore, results explain how EE of the link varies with the choice of constrained variables i.e., data rate and harvested energy.

Non-performing assets (NPAs) or bad loans limit the banks from lending to new borrowers. As a result, this would slow down the credit utilization that ends up lowering the deposit interest rates and raising the lending rates, thus, discouraging authentic borrowers. This paper presents a Hierarchical Timed Colored Petri Net (HTCPN) based model that simulates an intelligent agents system which automates credit monitoring and follow-ups by relationship managers (RMs) in bank branches. Efficient follow-ups help the RMs track the borrower's repayment and assess the current values of their counter-assets regularly. In case any loan is kept unattended by the responsible RM, an automated reporting escalation is initiated according to an escalation matrix when due checking time is reached. The HTCPN helped model the expected behaviour of the intelligent agents system used for loan monitoring and also allowed to model a scoring system that is used to monitor the performance of the RMs in handling the loans under their authority and the punctuality of their actions. The performance evaluation through cpn tools simulation resulted in performance ranking and scoring of the RMs, and helped collect statistics on the overall performance of the bank that pointed out to the stages and locations where the flaws are more severe thus providing decision support utilities.

Broadcast is one of the fundamental primitives to enable large-scale networks such as sensor networks and IoT. There is a rich study on achieving reliable broadcast under various kind of failures. In this paper, we use the notion of trust to improve the performance of reliable broadcast. We focus on Certified Propagation Algorithm (CPA), one of the simple algorithms that does not rely on a cryptographic infrastructure and has a proven guarantee on resilience (number of node failures tolerated). Specifically, the paper has two main contributions: (i) A new algorithm Trust-CPA which integrates CPA with trusted nodes has been proposed and shown to increase the resilience from the original CPA, and (ii) A natural optimization problem related to Trust-CPA (i.e., finding the location to place trusted nodes to reduce the broadcast latency) has been proposed as well. We first show that it is NP-hard to find an exact answer and even NP-hard to find a good approximation. A greedy heuristic algorithm has been used and its efficacy has been examined using simulation. We show that our algorithm performs relatively well in geometric random graphs, an appropriate model for large- scale wireless sensor networks.

Mobile Edge Computing (MEC) and Network Slicing techniques have a potential to augment 5G-IoT network services. Telecommunication operators use a diverse set of radio access technologies to provide services for users. Mobility management is one such service that needs attention for new 5G deployments. The QoS requirements in 5G networks are user specific. Network slicing along with MEC has been promoted as a key enabler for such on-demand service schemes. This paper focuses on radio resource access across heterogeneous networks for mobile roaming users. A unified service architecture is proposed enabling seamless handover between a 5G (New Generation Core) service and a 4G (Evolved Packet Core) service via the network slicing paradigm. An identifier-locator (I-L) concept that allows active source-IP sessions is used to handle the seamless hand-over. Signaling costs, service disruptions and other resource reservation requirements are considered in the evaluation to assure that profit for mobile edge operators is achieved. Simulation experiments are considered to provide performance comparisons against the state-of-the-art Distributed Mobility Management Protocol (DMM).

The great revitalization of information and communication technologies has facilitated broad connectivity to the Internet. However, this convenience in terms of connectivity comes with costly caveats, including internet fraud, information damage or theft, and cybersecurity issues. Most individuals rely on anti-virus software for protection. This anti-virus software has long been a foe to malware authors, but there are brief moments when new techniques slip through the cracks, and even the most sophisticated engines sometimes fail. A new tool, namely Metasploits new evasion modules, claims to exploit that. In this study, we compare and evaluate legacy evasion techniques with the novel tactics presented by Metasploits lead researcher Wei Chen. We consider the benefits and pitfalls of each technique and evaluate the new modules successes (or failures!).

Wireless backhaul of outdoor small cells is a cost-effective solution in a dense heterogeneous network as it reduces the need to provide a wired connection for each small cell access point to the core network. On the other hand, non-orthogonal multiple access (NOMA) has emerged as a promising technology to improve the spectral efficiency of a network. This paper investigates the impact of applying NOMA at the backhaul of small cells to enhance the spectral efficiency of the system. However, this requires a careful pairing of desired small cells to increase the system performance of NOMA. In this regard, a joint pairing and resource (bandwidth and power) allocation scheme for the backhaul of small cells is studied based on the load of the small cells. Furthermore, our performance evaluation shows that the proposed scheme outperforms the existing user pairing approaches in terms of achieving high spectral efficiency.

The recent emerging fields in data processing and manipulation has facilitated the need for synthetic data generation. This is also valid for mobility encounter dataset generation. Synthetic data generation might be useful to run research-based simulations and also create mobility encounter models. Our approach in this paper is to generate a larger dataset by using a given dataset which includes the clusters of people. Based on the cluster information, we created a framework. Using this framework, we can generate a similar dataset that is statistically similar to the input dataset. We have compared the statistical results of our approach with the real dataset and an encounter mobility model generation technique in the literature. The results showed that the created datasets have similar statistical structure with the given dataset.

Since years, road collisions are a public issue which needs to be dealt. In this regard, the researchers have worked on developing autonomous vehicles. In this way, the number of collisions have been reduced to a great extent. But their is a need to develop the autonomous simulator which helps to pretest the autonomous vehicles in different road scenarios before launching them in the market. Hence, in this research work, we propose an autonomous simulator which helps to pre-train the autonomous vehicles in the real world. The effectiveness of the simulator has been proved through performing extensive experiments.

Integrating Cognitive Radio (CR) technology in Internet-of-Things (IoT) devices allows efficient large-scale deployment of IoT systems. Recently, research efforts are shifted toward adopting CR in IoT as a response for the spectrum scarcity problem. Unfortunately, CR Networks (CRNs) share the same security weaknesses with traditional wireless networks. CR communication is also vulnerable to jamming attacks which can significantly affect network performance, consume network resources and results in delays, that make it less suitable for IoT time-critical systems. Routing in CR-based IoT networks, in general, considered as a challenging issue. Under the jamming attack, routing becomes even more challenging. In this paper, we introduce a new jamming-aware routing and channel assignment protocol that deals with proactive jamming attacks in CR-based IoT networks without requiring extra resources. The proposed protocol attempts at improving the overall packet delivery ratio in the network while considering the primary user's activities, multi-channel fading and jamming behavior. The proposed protocol consists of three phases: route discovery, channel assignment, and path selection. The channel assignment problem along each path is formulated as an optimization problem with the objective of maximizing the end-to-end probability of success. This problem is shown to be an uni-modular problem, which can be solved in polynomial-time using linear programming techniques. Compared to reference protocols, simulation results reveal that the proposed protocol significantly improves network performance in terms of packet delivery ratio.

Mobile cellular users traveling in city buses are experiencing poor quality of signals due to the interference and the large number of mobile devices. To enhance the Quality-of-Service (QoS), deployment of small cell networks in city buses is a promising solution. The deployment of small cells in vehicular environment makes the resource allocation more challenging because of the dynamic interference relationships experienced by them. Therefore, resource allocation in vehicular environment within moving small cells (MSCs) needs to be handled carefully. In this study, we investigate the problem of resource allocation in city bus transit system with multiple routes. Then, we propose a Percentage Threshold Interference Graph (PTIG) based allocation of resources to MSCs in a network. City buses of multiple routes travel with variable speed and may share some of the same road segments which make it difficult to extract the exact interference patterns between them. Therefore, Long Short Term Memory (LSTM) neural networks are used to predict the city buses locations. The predicted locations of city buses are then used to generate PTIG by finding the dynamic interference relationship between MSCs. Graph coloring algorithm is used to allocate the resources to PTIG. Numerical results are presented to show the comparison of resource allocation using PTIG and Time Interval based Interference Graph (TIIG) in terms of resource block utilization and time complexity.

Brain computer interface translates electroencephalogram (EEG) signals into control commands so that paralyzed people can control assistive devices. This human thought translation is a very challenging process as EEG signals contain noise. For noise removal, a bandpass filter or a filter bank is used. However, these techniques also remove useful information from the signal. Furthermore, after feature extraction, there are such features which do not play any significant role in effective classification. Thus, soft computing-based EEG classification followed by extraction and then selection of optimal features can produce better results. In this paper, subband common spatial patterns using sequential backward floating selection is being proposed in order to classify motor-imagery-based EEG signals. The signal is decomposed into subband using a filter bank having overlapped frequency cutoffs. Linear discriminant analysis followed by common spatial pattern is applied to the output of each filter for features extraction. Then, sequential backward floating selection is applied for selection of optimal features to train radial basis function neural networks. Two different datasets have been used for evaluation of results, i.e., Open BCI dataset and EEG signals acquired by Emotiv Epoc. The proposed system shows an overall accuracy of 93.05% and 85.00% for both datasets, respectively. The results show that the proposed optimal feature selection and neural network-based classification approach with overlapped frequency bands is an effective method for EEG classification as compared to previous techniques.

The significant advancements achieved in wireless communications over the past few years has facilitated successful deployment of LTE-A, and heralded great efforts in 5G development. However, with the increase in the number and variety of connected devices, wireless video transmission in real-time is challenging for the aforementioned network paradigm. Many studies have shown that the centric focus of communications should be the content type, rather than the communication itself, which means real-time multimedia communications are considered crucial for future Internet architectures. This requires high capacity channels and techniques to mitigate inherent wireless channel errors. Thus, we propose an application-layer and middleware-based solutions that increase network reliability and flexibility and provide Quality of Service (QoS) control based on Scalable Video Coding (SVC). Due to the real-time and QoS support of the Data Distribution Service (DDS) middleware, it can be used to implement the three types of SVC scalability: Viz. Temporal, Spatial, Quality (SNR). The open source Scalable Video-streaming Evaluation Framework (SVEF) tool has been used to assess the video transmission performance with performance metrics, Viz. Peak Signal to Noise Ratio (PSNR), Mean Opinion Score (MOS), and frame delay. The results showed a graceful degradation of video quality when using the DDS-based SVC, particularly when the number of receivers is increased. The acquired results show excellent improvements which can be applied to different Future Internet architectures.

As vehicles get smarter, with supplementary onboard gear providing advanced applications and provisioning services related to traffic management, the requirement for simple and effective access to information has grown extensively. The new applications manage more complex operations and, unlike other mobile devices, mobile vehicle devices provide location based services, real-time functionality, provisioning services and storage, all without the shortcomings of traditional mobile devices. Vehicular cloud computing can perform a broad set of on-demand services and applications, which make this method highly applicable to urban settings. Provisioning services often encounter unexpected interruptions that increase provisioning latency and service usage duration, ultimately leading to higher charges for the driver. This paper advances our previously proposed distributed model to handle service management in vehicular clouds, by using the concept of Vehicular Trusted Third Party (VTTP) with different type of provisioning services. This model has the capability to switch between TTPs, which allows drivers to exploit the benefits of different existing services, and connect to the TTP that best meets their specific requirements. Two new service latency modes are proposed and evaluated: Service Latency Sensitive Mode (SLSM) and Neutral mode. The proposed model has been implemented and evaluated using simulations of real-time light and heavy duty services, and various simulation scenarios show that using a VTTP can significantly help drivers reduce their service latency (~30%) and costs (~26%).

The Smart City vision is to improve quality of life and efficiency of urban operations and services while meeting economic, social, and environmental needs of its dwellers. Realizing this vision requires cities to make significant investments in all kinds of smart objects. Recently, the concept of smart vehicle has also emerged as a viable solution for various pressing problems such as traffic management, drivers' comfort, road safety and on-demand provisioning services. With the availability of onboard vehicular services, these vehicles will be a constructive key enabler of smart cities. Smart vehicles are capable of sharing and storing digital content, sensing and monitoring its surroundings, and mobilizing on-demand services. However, the provisioning of these services is challenging due to different ownerships, costs, demand levels, and rewards. In this paper, we present the concept of Smart Vehicle as a Service (SVaaS) to provide continuous vehicular services in smart cities. The solution relies on a location prediction mechanism to determine a vehicle's future location. Once a vehicle's predicted location is determined, a Quality of Experience (QoE) based service selection mechanism is used to select services that are needed before the vehicle's arrival. We provide simulation results to show that our approach can adequately establish vehicular services in a timely and efficient manner. It also shows that the number of utilized services have been doubled when prediction and service discovery is applied.

As an emergent technology the IoT promises to harness the computational and data resources distributed across different remote clouds. Fog computing extends cloud computing by bringing the network and cloud resources closer to the network edge. As the number of resources contributing to the cloud/fog system grows, so the problems associated with efficient and effective resource selection and allocation. In this paper, we introduce a fog-to-fog (F2F) data caching and selection method, which allows IoT devices to retrieve data in a faster and more efficient way. The proposed solution is based on a data caching and selection strategy using a multi-agent cooperation framework. Caching is achieved by decomposing cloud data into a set of files and then placed into fog storage sites. The selection process is based on a run-time file location prediction technique, which collects and maintains a repository of fog data in the form of log files. When data needs to be retrieved, prediction is made with the aid of these logs and previous successful search queries resulting in realistic run-time location estimates as well as best fog selection. Simulation results showcase the reduced data retrieval latency that enable tactile Internet in 5G. Additionally, results show increased successful file hit ratio leading to a reduced number of repeated downloads.

Vehicular cloud computing can perform a broad set of on-demand applications and services, which makes it highly suitable for urban settings. Despite a wide range of benefits to various services and applications by vehicular clouds, there are several issues and challenges that need to be carefully addressed in the context of provisioning services. This paper proposes a cooperative distributed game model to handle service management in vehicular clouds. Under this model, service providers play a cooperative game to maximize their total utility taking into consideration their recourse availability, current load, and total payoff. The proposed game has been implemented and evaluated using simulations with scenarios of light and heavy weight services. The game demonstrates that a cooperative technique leads players to handle higher number of services when compared to a non-cooperative setting. Furthermore, we also show that the proposed game mimics the behaviour of an optimization-based baseline solution. Through various simulation scenarios, we show that the proposed scheme introduces more than 85% similarity to the optimal solution when a few number of players participate, and its similarity to the optimal solution is improved to 99% when the number of the players increases by only 50%.

In current practice, the fire-fighters communication system is verbal, using a simplex radio frequency system (walkie-talkie). This system has a flat communication structure, which prevents any private communication among groups of firefighters. In addition, only one fire-fighter is allowed to talk at a time and no other functionalities (e.g. video communications or conferencing) are supported. This paper proposes a new multimedia conferencing system for fire-fighters, which overcomes the current system limitations. The new system is based on Mobile Ad-Hoc Networks, an infrastructure-less and self-organized wireless networks that are suitable for emergency situations. The proposed system has a cluster-based architecture in order to offer more scalability. A proof-of-concept prototype has been implemented and performance have been evaluated and analysed.