Millimeter wave (mmWave) communication systems offer high data rates, but these systems are highly susceptible to environmental factors, particularly weather conditions such as rain, dust, and sand. This paper presents a novel approach to enhance the reliability of mmWave communication by implementing a Fuzzy Controller System (FCS) for dynamic channel switching. The proposed system integrates real-time measured weather data, such as rain rate, with the fuzzy logic controller to intelligently select the optimum frequency channel with the least attenuation under current atmospheric conditions. The fuzzy controller makes adaptive switching decisions by continuously analyzing environmental changes to maintain signal quality and system performance. Experimental results and simulations demonstrate that incorporating real measured data significantly improves the system’s ability to respond to weather variability, ensuring stable and efficient mmWave communication. This work provides a practical framework for implementing intelligent, weather-aware channel-switching mechanisms in next-generation wireless communication networks.

Vehicular networks rely on vehicle-to-vehicle (V2V) communication to enable real-time information sharing for collision avoidance, traffic efficiency and future autonomous driving systems. In V2V communication, vehicles constantly exchange messages; therefore, developing a power control method that saves energy and reduces interference is essential. Previous power control mechanisms typically rely on static configurations and neglect dynamic environmental factors such as weather conditions; some require feedback channels that may not be available in distributed V2V scenarios. As a result, those methods struggle to maintain reliable communication in rapidly changing conditions, particularly during adverse weather events. To address these limitations, this paper presents a novel weather-aware reinforcement learning-based power control algorithm to improve the robustness and efficiency of V2V communications. Unlike conventional open-loop power control schemes that rely solely on SINR-based heuristics, our algorithm incorporates weather-induced attenuation and vehicle density into the transmit power decision process to ensure reliable communication during link setup. Furthermore, we propose a reinforcement learning framework in which each vehicle autonomously learns optimal power control strategies over time by observing local SINR estimations, surrounding vehicle densities, and environmental variations. The learning agent dynamically adjusts the transmission power to maximize communication reliability and minimize interference between vehicles. We conducted simulations based on 3GPP TR37.885 scenarios, and the results show that our solution effectively addresses the impact of weather on power control in V2V networks and achieves better reliability and adaptability under challenging weather conditions.

Video prefetching techniques play a critical role in hiding the I/O latency of video delivery from cloud networks to access networks by optimizing edge networks. Content Delivery Networks rely on edge computing to improve Quality of Experience at the client-side, and improve resource utilization at edge and cloud networks simultaneously by utilizing prefetching optimization techniques. Recently, we have witnessed a trend in shifting intelligence to edge networks by building video prefetchers that utilize deep reinforcement learning to make online prefetching decisions. Unfortunately, these methods often lack generalization to different workloads when dealing with long sequences, rendering them incapable of adapting to distribution shifts and various changes in users' requests which represent a non i.i.d distribution. In this work, we tackle video prefetching at edge networks as a Partially Observable Markov Decision Process, and propose Techie, an intrinsically motivated policy-gradient reinforcement learning agent that differentiates intrinsic rewards from extrinsic rewards based on their availability to edge networks. Techie is adaptive to unseen user requests by prefetching aggressively to handle randomization in workloads, achieving 52.27% of prefetching accuracy and 34.34% of prefetching coverage. Our results show that Techie improves prefetching accuracy and coverage by at least 16.12% and 6.57%, respectively, compared to baseline approaches that utilize deep learning, deep reinforcement learning, or video popularity to build prefetching algorithms. Consequently, Techie minimizes end-to-end latency by at least 7.5% and reduces cache pollution with an improvement of at least 13.1% on unseen user requests compared to baselines.

Designed as a distributed data storage system, blockchain employs a consensus mechanism to validate the addition of new data blocks while saving the need for mediation by a centralized server. However, when the blockchain technology is applied in decision-making applications, the consensus mechanism requires the decision to be deterministic, which will ignore different recommendations made by multiple agents and may put the computing system at risk. In this paper, we propose and develop SMARPchain, a paradigm of reputational probabilistic blockchain which is based on smart markers. The distinguishing characteristic of our SMARPchain approach is that it enables multiway branching which allows the result produced by each agent to be kept on the blockchain as a branchchain. Moreover, SMARPchain computes a probabilistic score on each branchchain based on the associated agent's reputation to help the decision maker reach an informed decision. SMARPchain satisfies three desirable requirements of recognizability, compatibility, and authenticability, and evaluation results show that SMARPchain will enhance the security and robustness of multi-agent decision making systems with acceptable overhead.

The advent of millimeter wave (mm-Wave) technology in modern communication systems, including 5G networks, has brought about unprecedented data transmission speeds and bandwidths. However, environmental factors highly affect mm-Wave signals, particularly in regions susceptible to dust and sand storms. Dust storms, characterized by high concentrations of suspended particles, lead to significant signal attenuation and degradation during the absorbed and scattered incident wave. This attenuation poses challenges to the reliability and performance of mm-Wave communication systems. The previous research used Mie theory to compute the specific attenuation due to dusty storms because it provides a complete analytical solution to Maxwell’s equations compared to other analytical and numerical methods. However, the Mie scattering model lacks accuracy due to consideration of only the amplitude of the attenuation factor with respect to the dust and sand environment. This paper presents the development of predictive mathematical models designed to estimate mm-Wave signal degradation in dust and sand storm conditions. The models integrate key physical parameters such as dust particle size distribution, storm intensity, signal frequency, and atmospheric conditions.The predictive model demonstrates a significant accuracy in estimating signal attenuation by considering the phase shift in signal by introducing complex attenuation factor. We mathematically demonstrated that dust and sandstorms can cause mm-Wave signal attenuation but also cause a significant signal phase shift. This complex attenuation factor provides valuable insights for network engineers to design and optimize mm-Wave communi cation systems in dust-prone environments. Comparative analysis with existing models underscores the proposed models’ enhanced predictive capability and flexibility in adapting to diverse dust storm scenarios.The research outcomes contribute to the ongoing efforts to improve mm-Wave communication technologies’ resilience and robustness, ensuring reliable connectivity even in challenging environmental conditions.

Content Delivery Networks carry the majority of Internet traffic, and the increasing demand for video content as a major IP traffic across the Internet highlights the importance of caching and prefetching optimization algorithms. Prefetching aims to make data available in the cache before the requester places its request to reduce access time and improve the Quality of Experience on the user side. Prefetching is well investigated in operating systems, compiler instructions, in-memory cache, local storage systems, high-speed networks, and cloud systems. Traditional prefetching techniques are well adapted to a particular access pattern, but fail to adapt to sudden variations or randomization in workloads. This paper explores the use of reinforcement learning to tackle the changes in user access patterns and automatically adapt over time. To this end, we propose, DeePref, a Deep Reinforcement Learning agent for online video content prefetching in Content Delivery Networks. DeePref is a prefetcher implemented on edge networks and is agnostic to hardware design, operating systems, and applications. Our results show that DeePref DRQN, using a real-world dataset, achieves a 17% increase in prefetching accuracy and a 28% increase in prefetching coverage on average compared to baseline approaches that use video content popularity as a building block to statically or dynamically make prefetching decisions. We also study the possibility of transfer learning of statistical models from one edge network into another, where unseen user requests from unknown distribution are observed. In terms of transfer learning, the increase in prefetching accuracy and prefetching coverage are [$30%$, $10%$], respectively. Our source code will be available on Github.

Due to the enormous volume of data generated by connected vehicles (CVs), future vehicle-to-vehicle (V2V) commu-nications will require a high-throughput communication channel. While 5G millimeter-wave (mmWave) technology generally meets these requirements, it suffers from the risk of performance degradation under harsh weather conditions such as heavy rain, snow or sand storms. This decline is primarily because of the high frequency of mmWave, which results in significant propagation attenuation loss. In contrast, 4G LTE is less susceptible to these environmental challenges. In an effort to improve communication performance reliability under severe weather conditions, we propose a Deep Reinforcement Learning (DRL)-driven approach. Our proposed framework utilizes the Received Signal Strength Indicator (RSSI), short-term throughput, and weather parameters as input variables. It employs cumulative throughput as the reward metric in the reinforcement learning process. As the framework interacts with the environment, it learns to dynamically switch between 5G mmWave and 4G LTE channels to maintain a robust and reliable communication link between CVs. Our approach has been validated through simulations using the ns-3 network simulator, enhanced with a customized weather model for 5G mmWave and 4G LTE channels. The simulation results confirm that our DRL framework substantially improves the reliability and performance of V2V communication within minutes, even in harsh environmental conditions.

Given that databases often house sensitive and valuable data, ensuring data confidentiality and integrity of the databases is imperative. Encryption emerges as one of the predominant techniques employed in bolstering database security. Alongside encryption, shuffling also offers a viable approach to fortify the security of database. However, both encryption and shuffling requires huge amounts of system I/O requests which bring performance burden to the database server. In this paper, we propose a design to further enhance the security of shuffling algorithm and improve its efficiency by employing SmartSSD computational storage device from Samsung and AMD. We conduct experiments to evaluate the overhead of the improved effectiveness.

Artificial intelligence (AI) techniques, such as federated learning (FL), have been employed in Internet of Things (IoT) networks to harness the enormous amounts of data generated by ubiquitous IoT devices. However, using FL technique in IoT networks brings new security and privacy challenges like model inversion, data poisoning, and central aggregation failure. In this paper, we introduce an innovative framework that integrates blockchain technology and secure multi-party computation (MPC) with FL in IoT networks to address these challenges. Our proposed system enables decentralized and privacy-preserving FL across distributed IoT environments. Utilizing MPC protocols, our framework securely aggregates model updates from clients without revealing individual data, while blockchain technology ensures transparency and immutability of transactions. Security analysis shows that our framework can effectively address the three aforementioned security and privacy challenges and improve the reliability and robustness of FL processes in IoT networks. Our framework can be adapted and integrated with real-world AI-augmented IoT networks, such as smart cities and healthcare, to enhance their protection of security and privacy.

Column-oriented database systems have drawn a lot of attention in recent years because of their performance advantages in terms of data querying and computation on columns. Since databases often contain sensitive and valuable information, protecting the confidentiality, integrity, and availability of the database is of utmost importance. In addition to encryption, shuffling can also be used to secure the database storage files. In this paper, we present a novel approach called SCORD to prevent the leakage of confidential data stored in the database management system by applying the shuffling technique on column-oriented databases. SCORD can protect column-oriented databases in both offline storage mode and online running mode. SCORD also bundles semantically or statistically associated attributes, such that the data is shuffled in a way that appears deceptively authentic and indistinguishable to potential attacks. We implement a prototype of SCORD and evaluate it using a set of real-world data. The experiment results demonstrate the effectiveness of our approach and show that the processing overhead is acceptable.

Cloud, Edge, and Fog computing provide computational services to different end users. A federation among these computing paradigms is beneficial, as it enhances the capability, capacity, coverage, and services of cloud, edge, and fog. An authentication method is needed to realize such a federation among cloud, edge, and fog so that a user belonging to one of these computing paradigms can use the services offered by other computing paradigms in the federation without creating a new account. This paper proposes a standard-compliant universal federator that transparently provides third-party authentication among different protocols, used by cloud, edge, and fog, such as 3GPP EPS-AKA, OpenID Connect (OIDC), and 802.1x. The federator provides transparency by using a controller and modules that act as virtual counterparts of the authentication entities in EPS-AKA, OIDC, and 802.1x. These virtual counterparts play multiple roles, depending upon the involved protocols. We deployed a testbed, published our implementation on GitHub, and tested third-party authentication for 16 scenarios across EPS-AKA, OIDC, and 802.1x. The results show that our federator successfully provides third-party authentication while taking 4.07–51.8% of the total authentication time, which ranges between 1.193–3.825 s for 16 scenarios. Some scenarios involving 802.1x take considerably longer due to the bottleneck caused by the 802.1x switch. We also conducted a security analysis to show that our proposed federator fulfills multiple security requirements.

Advancements in blockchain technology and network technology are bringing in a new era in electronic voting systems. These systems are characterized by enhanced security, efficiency, and accessibility. In this paper, we compose a comparative analysis of blockchain-based electronic voting (e-voting) systems using blockchain technology, cryptographic techniques, counting methods, and security requirements. The core of the analysis involves a detailed examination of blockchain-based electronic voting systems, focusing on the variations in architecture, cryptographic techniques, vote counting methods, and security. We also introduce a novel blockchain-based e-voting system, which integrates advanced methodologies, including the Borda count and Condorcet method, into e-voting systems for improved accuracy and representation in vote tallying. The system’s design features a flexible and amendable blockchain structure, ensuring robustness and security. Practical implementation on a Raspberry Pi 3 Model B+ demonstrates the system’s feasibility and adaptability in diverse environments. Our study of the evolution of e-voting systems and the incorporation of blockchain technology contributes to the development of secure, transparent, and efficient solutions for modern democratic governance.

Recently, researchers have focused on the effect of changes in weather on the operation of wirelessly connected systems. Past researchers studied the impact of rain and snow on propagating electromagnetic waves. These researchers concluded that the millimeter waves are more sensitive to changing weather, and the vertical polarization is the best during rainy and snowy weather. Other studies concluded that the impact of dust and sand storms on the propagating 5G waves is not series when the visibility is greater than 10m. In this research, the normal and oblique incidence of the electrical and magnetic fields are considered during dusty/sandy medium with different forms of polarization to see which polarization is less affected by dust and sand storms. In the case of normal incidence, the proposed mathematical model of linear, circular, and elliptical polarization are used to see the behavior of different polarizations during dusty/sandy region. The transverse electric $(TE)$ and transverse magnetic field $(TM)$ are considered in the case of Oblique incidence uniform plane Wave. Maxwell’s equations and the Mie model are used to derive the mathematical model of the electric field and magnetic field in both incident forms. Finally, the numerical results are generated by using MATLAB to show the behavior of different polarization during severe weather.

We present a security management strategy of cyber-deception to protect the content-data cache sites of a Content Distribution Network. The design goal is to reduce the variance between client experienced delays in accessing content-data. This creates a homogeneous attack surface for an adversary who is unable to exploit latency differentials to learn the network topology which is a crucial prerequisite for carrying out attacks like LFAs (link flooding attacks). We show how this minimum variance paradigm results in a comprehensive scheme for cyber-deception management. The novelty of this approach is that it specifies not only the optimal network reconfigurations but also transition probabilities, unifying two common themes in security management: i) proactive obfuscation to increase the complexity of the attack surface and ii) reactive randomization of the target based on the attacker model. We illustrate this method of security management with several numerical examples.

With the development of 5G networks and advanced communication technologies, connected vehicles (CV) are becoming an increasingly important aspect of the future of transportation. The connected vehicles will usually generate a large amount of data that require fast and reliable communication channels with low latency. 5G millimeter-wave (mmWave) is crucial for the next generation of vehicle-to-vehicle (V2V) communications in CV scenarios. However, harsh weather conditions such as rain, snow, dust, and sand can significantly impact the performance of 5G mmWave channels for V2V communications. Maintaining seamless connections for connected vehicles during harsh weather conditions is a significant challenge that researchers must address. In this paper, we propose a two-stage strategy enabling connected vehicles to operate effectively under moderate and severe weather conditions. Our proposed approach involves a prediction step, which uses machine learning techniques to forecast weather patterns and determine the optimal communication strategy, followed by a switching step, which seamlessly chooses between frequency or channel switch based on the prediction. By incorporating these two steps, we aim to provide a robust and efficient communication system that can adapt to different weather conditions. The NS3 simulation results show that our switching strategy is effective and can benefit the field of connected vehicle technology.

The homogeneous strong designated verifier signature scheme cannot meet the requirements of heterogeneous cryptography communication. With the idea of Heterogeneous Signcryption and strong designated verifier signature, we present a securely and mutually heterogeneous strong designated verifier signature (SMHSDVS) scheme between Public Key Infrastructure and Identity-based Cryptography, which has correctness, non-transferability, unforgeability, strongness, source hiding and non-delegatability. In addition, we use the Computational Diffie-Hellman model in oracle to analyze unforgeability and use mechanized tool CryptoVerif to analyze non-transferability. Finally, we evaluate the performance of heterogeneous digital signature scheme by compared with related schemes, and the results show that the proposed scheme is secure and efficient.

With the explosive growth of amount of information exchanged over the Internet, we have witnessed fast propagation of mis/disinformation. Such trend of mis/disinformation must be detected early and curbed effectively in order to mitigate its potential harm to the nation and society. Our previous work successfully identified distinctive patterns of the propagation of true and fake news in the form of text over social media, with Twitter as a case study. In this work, our goal is to extend the target to include multimedia mis/disinformation and study the characteristics of their dissemination using machine learning based techniques. We also aim to investigate countermeasures that can be employed to slow down or prevent further propagation based on the identified characteristics.

The automated driving system is revolutionizing transportation, and more and more companies are investing in this technology. 5G wireless network will play a fundamental role in automated driving since 5G uses millimeter waves to provide high data transfer rates for Vehicle-to-Vehicle (V2V) communications. The performance of communications in 5GV2V networks is often affected by harsh weather conditions, such as rain, snow, sand, and dust, and the impact could be to a large extent. Therefore, it is crucial to have a precise forecasting model for the automated driving system to prepare for performance degradation in advance. In this paper, we use NS-3 to simulate the 5G mm-Wave V2V signal strength under different weather conditions. Weather conditions are represented as time series data, and we propose using various statistical or machine learning forecasting models to predict 5G mm-Wave signal strength. The forecasting models we evaluate in this paper include auto regressive integrated moving average (ARIMA), Meta Prophet, long short-term memory (LSTM), and gated recurrent unit (GRU). Our evaluation results show that LSTM is the best forecasting model for 5GV2V signal strength under harsh weather.

Different applications in our life use wireless connections to provide varying telecommunication services. These wireless channels are affected by different weather factors such as rain, snow, dust, and sand. In the past, more research was concentrated on the effect of weather factors on the millimeter plane waves especially during rainy days. The desert regions in the world are the sources of dust and sand. In this country, the south west is a source of dust and sand during windy days. Previous work investigated the effect of dust and sand on the 5G millimeter wave by calculating the path loss of the propagating length to estimate the received power at the destination point. In this research, the effect of dust and sand on the propagating electric field is investigated by determining the complex dielectric constant of dusty region to estimate the wave number and attenuation factor of the propagating plane wave. The Maxwell's equations are used to find the expression of the electric field in the form of linear, circular and elliptic polarization. Moreover, this work will investigate the behaviour of propagating waves through dusty/sandy region with different polarization. The numerical result displays the best polarization that is recommended to use during dusty/sandy weather. Also, the penetration of the millimeter wave in the dusty lossy medium is different with different values of humidity. In this research, MATLAB is used to simulate the effect of dust and sand on the propagating electromagnetic millimeter wave.

Recent research activities focused on improving Vehicle-to Vehicle Communication (V2V) based on the 5G Technology.V2V applications are important because they are expected toreduce the risk of accidents up to 80%, enhance traffic manage?ment, mitigate congestion, and optimize fuel consumption. Typ?ical autonomous vehicle applications require a high bandwidthtransmission channel, so the 5G communication channel is areliable solution to support this technology. The dedicated short?range communications (DSRC), characterized by a frequencybandwidth of 5.9 GHz, were used as vehicular connectivitywith bandwidth up to 200 mb/s and limited capacity, and itis here utilized for comparison to 5G. The 5G band can supportconnected autonomous vehicles with high data rates and largebandwidth. The 5G communication channel is considered forvehicular connectivity since it has a very high bandwidth in themillimeter waves spectrum range. The quality of 5G wirelesscommunication channels between connected vehicles is affectedby weather conditions such as rain, snow, fog, dust, and sand.In this paper, the effect of dust and sand on the propagationof millimeter waves is presented. The effect of dust and sandon the communication path loss of DSRC and 5G frequencyband is investigated in the case of Urban areas and the Highwaycondition. Results show that the attenuation caused by dustand sand depends on the particle size of sand, frequency ofpropagating wave, and concentration of dust. Finally, a newmodel of link margin is proposed to estimate the effect of dustand sand on DSRC (5.9 GHz) and 5G (28 GHz-73.5 GHz)communication path loss.