Unmanned aerial vehicle (UAV) networks, which consist of UAVs equipped with mobile edge computing (MEC) servers, are becoming increasingly popular for providing on-demand computing services to IoT devices in areas lacking infrastructure. However, the limited resources of UAVs and the dynamic nature of the network environment present significant challenges for task offloading and resource allocation, underscoring the necessity of our research. This paper proposes a multicriteria optimization model for task offloading in UAV-MEC networks, taking into account the uncertainty in energy consumption. We formulate a chance-constrained optimization problem to minimize the weighted sum of latency and energy consumption while ensuring that the probability of exceeding energy constraints remains below a predefined threshold. We utilize a digital twin (DT) to estimate local computation latency and offloading delay to either the UAV-MEC server or a data center. We solve the formulated problem using branch and bound (BBA), simple relaxation (SR), integrality-gap minimization (IGM), and a gradient-based Renaldi heuristic under both intelligent and random placement. The simulation results indicate that our proposed approach is not just a theoretical concept but a practical solution that can be implemented. It outperforms existing methods in terms of number of connected users, overall utility, and computational complexity, demonstrating its real-world applicability.

Unmanned aerial vehicle (UAV) networks equipped with mobile edge computing (MEC) servers are increasingly deployed to deliver on-demand computing services in infrastructure-limited areas. However, the dynamic nature of UAV networks and their limited resources present significant challenges for task offloading and resource allocation. To address these challenges, we propose a framework that integrates the digital twin (DT) to optimize task offloading under uncertainty of energy consumption. The DT acts as a virtual replica of the UAV network, offering real-time predictions of local latency and offloading delays, enabling more accurate and adaptive decision making. We formulate a chance-constrained optimization problem to minimize task latency, ensuring that energy consumption exceeds a predefined threshold with only a limited probability. We propose a two-stage approach that combines an intelligent UAV placement algorithm with a modified iterative solver, referred to as the Renaldi algorithm, to solve the optimization problem efficiently and with low computational complexity. The simulation results show that the DT-assisted framework enhances user connectivity, improves resource utilization, and reduces computational complexity.

Stock price prediction is crucial for informed investment decisions, enabling investors to maximize returns and manage risks effectively in the dynamic and complex world of financial markets. It also aids in portfolio management and financial planning by providing insights into future market movements and asset valuations. This study delves into the intriguing realm of stock price prediction using two models, Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, leveraging the efficient market hypothesis framework. Analyzing historical market data for Apple, Google, and Tesla, ARIMA and LSTM models are independently developed to forecast closing stock values. The research compares the forecasting accuracy of each model through Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) assessment, aiming to provide insights into their distinct strengths. The findings offer nuanced perspectives on the predictive performance of ARIMA and LSTM models in stock price behavior.

This study delves into the crucial security considerations that consumers must weigh before deciding to invest in cryptocurrencies. Specifically focusing on Bitcoin, the largest and most valuable cryptocurrency, it examines the various security measures in place to safeguard against attacks and exploitation. While attacks on Bitcoin and attempts to exploit users are possible, the article highlights how dishonest activities, such as attempting to defraud other users, yield less profit compared to honest coin mining. Additionally, the article underscores that other cryptocurrencies often emulate Bitcoin’s security measures. However, it warns of the prevalence of fraudulent activities perpetrated by criminals and con artists, who employ social engineering tactics and schemes like Ponzi schemes to deceive investors. In a significant case study, the article exposes a cryptocurrency Ponzi scheme initiated by the Celsius Network company, which resulted in the loss of billions of dollars for unsuspecting users. Through this examination, the article aims to raise awareness about the potential risks associated with investing in cryptocurrencies and the importance of conducting thorough research and exercising caution in the volatile cryptocurrency market.

As a technology for 6G wireless communications, Intelligent Reflecting Surfaces (IRSs) are considered as a promising solution to boost the network capacity, spectrum and coverage in multiusers’ downlink communication systems. The users in blockage and cell edge areas can utilize this technology for data transfer purpose. In this paper, a machine learning-based policy optimization for downlink communication in distributed IRS aided multiple-input single-output (MISO) systems is proposed. Three categories of users are considered, namely, users who can utilize only the direct links, blockage area users who can utilize only the IRS links, and cell edge or poor link quality of users who can utilize both the direct and IRS links. The sum rate maximization problem is formulated to derive the optimal policy (i.e. communication link, IRS selection, power allocation and reflection coefficients) for those users, considering the IRS selection, link quality, power allocation and IRS reflection constraints. The proposed methods to achieve the optimal policy include reinforcement learning-based model with binary decision tree-based user categories, maximum posterior probability-based IRS selection, fractional programming method-based power and IRS coefficient allocation, and value function-based policy optimization. Through simulations, the sum data rate and energy efficiency performances of different categories of users are obtained and discussed.

Cloud-radio access network (C-RAN) is regarded as a promising solution to manage heterogeneity and scalability of future wireless networks. The centralized cooperative resource allocation and interference cancellation methods in C-RAN significantly reduce the interference levels to provide high data rates. However, the centralized solution will not be scalable due to the dense deployment of small cells with fractional frequency reuse by small cells, causing severe inter-tier and inter-cell interference turning the resource allocation and user association into a more challenging problem. In this paper, we propose an auction based distributed resource allocation method (ADRA) for a two-tier OFDM based C-RAN system. We investigate a joint user association, radio resource and power allocation problem for small cells underlying a macro C-RAN system. First, we establish a queueing model in C- RAN. We then formulate an optimization problem for joint user association and resource allocation with the aim to minimize mean response time. Resource allocation, interference and queueing stability constraints are considered in the optimization problem. To solve this problem, we propose a distributed method where small cell users and small cell base stations jointly participate using the concept of auction theory. The ADRA method is evaluated via simulations by considering the different ratio of bandwidth utilization.

In this paper, a user association and bandwidth allocation approach is proposed for heterogeneous networks (HetNets) using fuzzy logic controllers. Due to the heterogeneous nature of user demands, we categorize the incoming mobile users into low, medium, or high based on their data rate requirements. Similarly, the bandwidth utilization in a given small cell base station (SBS) is quantified and evaluated. Based on the per user demand and bandwidth availability, the controller decides whether a particular user should be associated with that SBS or offloaded to the macro base station (MBS). Moreover, the controller adjusts the fraction of bandwidth allocated to each user based on users data rate requirement and availability of resources towards maximizing the total data rate in the network. The proposed scheme, which is performed by SBSs in a distributed manner, is investigated and compared with two other approaches; namely, the best signal-to-interference-plus-noise ratio (SINR) which is considered as the baseline approach in the literature, and a greedy-based approach where priority in association is given to users demanding higher data rates. Our approach shows promising results regarding the improvement of data rate, bandwidth utilization, and blocking ratio, with an increased number of offloaded users.

The architecture of cloud radio access networks (C-RANs) is envisioned as an attractive paradigm of 5G that takes advantages of both centralized baseband and coordinated multi-point(CoMP) processing in radio access networks. In C-RAN the data rate provisioning can be significantly improved due to the fractional frequency reuse performed by small cells. The dense deployment of small cells, however, incurs severe inter-tier and inter-cell interference turning the user association into a more challenging problem. Moreover, multi-cell association problem occurs in CoMP and control/user planes (C/U planes) splitting based C-RAN system where users are associated with more than one cells to support joint-transmission and reception method. In this paper, we consider multi-cell user association approach taking into account the data rate and aggregated interference of mobile users. We propose the posterior probability based user association and power allocation (P2UPA) method that depends on prior knowledge of the channel state information (CSI). The objective of the proposed method is to maximize the sum data rate of small cell users while maintaining the constraints of aggregated interference, power consumption, and data rate among small cell users. Finally, the sum data rate and energy efficiency performance of P2UPA are evaluated through simulations.

Multimedia cloud data center is the core component of the multimedia cloud networks. In the cloud data center, software, service and storage visualizations are realized through the deployment of virtual machines (VMs). The reliability of multimedia service depends on the reliability of the data center as well as the reliability of the deployment and redundancy model of VMs. In this poster, we consider four deployment scenarios for VMs such as parallel, triple modular, triple modular/simplex, K-out-N redundancy model from the reliability point of view. The reliability performance using these redundancy models is presented and compared in this poster.

This study examines subcarrier and optimal power allocation in orthogonal frequency division multiple access based 5G device‐to‐device (D2D) networks. To improve spectrum efficiency, D2D users share same subcarriers with the legacy users using underlay approach. In this approach, it is challenging to design an efficient subcarrier and power allocation method for D2D networks which guarantees the quality of service requirements of legacy users. Therefore, the key constraint is to check the interference condition among D2D and legacy users while allocating the same resources to D2D users. In this study, the authors propose a throughput efficient subcarrier allocation (TESA) and geometric water‐filling based optimal power allocation (GWFOPA) method for multi‐class cellular D2D systems. First, the TESA method selects subcarriers and allocates power equally for D2D users according to their service classes while maintaining interference and data rate constraints. Then, the GWFOPA method is applied to optimise power in a computationally effective way. The objective of TESA and GWFOPA method is to maximise the data rate of each class while maintaining interference constraint and fairness among the D2D users. Finally, the authors present simulation results to evaluate performance of TESA and GWFOPA in terms of throughput, user data rate, and fairness.

In machine-type communication (MTC) over LTE cellular network, resource allocation problem becomes a challenging issue as MTC devices compete with LTE users for the same radio resources. Compared to the LTE users, MTC devices generate more uplink traffic requests and signalling which results in congestion arises in uplink transmission when a large number of devices send connection requests simultaneously. In this paper, we consider the resource allocation problem for MTC over LTE networks in which LTE users, MTC devices, and relay nodes co-exist. Firstly, we derive an analytical model which detects overload condition in the base station (eNB) and estimates available resources for MTC devices. We propose a relay-assisted radio resource allocation (R3A) scheme for MTC devices which utilize dynamic access class barring method in overload situations when the number of resource blocks are less than the MTC devices. In the case when the number of MTC devices is less than the available resources than we use relay nodes to maximize the throughput of MTC system. Numerical results demonstrate the significance of proposed R3A method. The results are evaluated in terms of access success probability, access drop percentage, and MTC channel capacity.

Cellular based M2M systems generate massive number of access requests which create congestion in the cellular network. The contention-based random access procedures are designed for cellular networks which cannot accommodate a large number of M2M traffic. In this paper, a contention-based slotted Aloha random access procedure for M2M network is first analyzed using different performance metrics. The impact of massive M2M traffic over cellular traffic is studied based on different arrival rate, random access opportunity and throughput. Then, an analytical model of selecting a base station (eNB) along with load balancing is developed. Finally, a dynamic access class barring method and RAN level congestion control mechanism by selecting the appropriate eNB is presented and evaluated with M2M traffic.