The rapid development of the internet of electric vehicles (IoEV) and the advancement of electric vehicle (EV) charging technology are transforming energy management for both residential and commercial users. Vehicle-to-building (V2B) energy trading is emerging as a groundbreaking approach that incorporates the exchange of energy between EVs and buildings. Despite the fact that V2B energy trading is able to reduce energy costs, main-grid complexity, and greenhouse gas emissions, it faces challenges when it comes to cooperative decision-making, resource efficient computation, and user transaction security. To address these challenges, this study aims to enhance energy exchange efficiency and dynamic energy interactions with enhanced security in urban environments. With these objectives, this paper proposes a novel energy trading method that integrates transfer learning (TL) and blockchain technology. TL makes it possible to adapt the knowledge gathered from vehicle-to-vehicle (V2V) systems to V2B settings, which reduces the computational resources required and boosts the overall efficiency. Blockchain technology, on the other hand, provides a secure framework for transaction verification while granting users enhanced control over their data privacy. We demonstrate the effectiveness of our proposed technique using detailed simulations conducted with real-world data. Our simulation results indicate that the proposed approach improves the convergence speed by around 50% compared to training from scratch, while buildings achieve up to 38% higher profits relative to scenarios without our proposed strategy. We also simulate the system model using the Ethereum blockchain platform to determine its real-world feasibility. These experiments demonstrate that the system has the potential to facilitate efficient energy trading to ensure user security and economically beneficial transactions.

In the realm of private permissioned blockchain platforms, increasing throughput is a pivotal objective. This paper focuses on the optimization of throughput in Hyperledger Fabric, a private permissioned leading blockchain framework tailored for enterprise applications. The paper proposes a novel approach to enhancing the platform’s performance by reevaluating the endorsement policy. By implementing a Less-Than-Half endorsement policy, the paper aims to streamline transaction validation processes and bridge the gap between Fabric’s throughput and the demands of large-scale industrial applications. The proposed method objects to boost transaction throughput without compromising security or reliability. The paper provides an overview of Hyperledger Fabric architecture, discusses the pre-verification mechanism, and presents the proposed method for optimizing throughput. The performance of the system is analyzed using Hyperledger Caliper and Prometheus. Simulation results show increase in the throughput of the Less-Than-Half of the endorsement policy as compared to the majority and it also demonstrates the significant reduction in the latency of the Less-Than-Half endorsement policy.

There is a very serious problem plaguing people right now, and that is the shortage of blood banks around the world. This is because blood is at the heart of a major healthcare challenge, blood plays a vital role as the body’s energy source. To address this issue, this paper describes a post-donation blood quality testing system, as well as a cross-matching testing system for blood type suitability for patients, that utilizes the advanced security features of Amazon Web Services (AWS), rigorously stores all user data, including personal information and blood type, and adds a commitment to protecting the integrity and confidentiality of blood donor data. AWS’s scalability ensures that the system can adapt to the growing demand for blood supply while maintaining performance and security, enhancing trust and transparency between donors, recipients, and healthcare providers. Blood is at the heart of a major healthcare challenge. Blood plays a vital role as the body’s energy source. Hence, we propose a web-based application system that is integrated in real-time with hospital databases in various regions, especially in remote areas, enabling rapid identification of blood needs and effective distribution, greatly reducing emergency response time. Also, the system will effectively help people living in remote areas. This system not only solves today’s blood supply management challenges but also sets a new standard for global healthcare equity and access in the future, ensuring that life-saving blood is available everywhere and to everyone who needs it.

WhisperLink is an innovative anonymous messaging service that aims to enhance privacy in digital communication. Hosted on the secure and robust infrastructure of the Google Cloud Platform, WhisperLink enables users to create secure, temporary chat rooms that self-destruct after 24 hours. By not requiring logins, WhisperLink ensures confidentiality, enabling users to communicate without worrying about the risks associated with the exposure of personal data. WhisperLink places a strong emphasis on confidentiality and anonymity by employing end-toend encryption, which ensures that messages can only be read by the intended recipients. It also has additional authentication features such as security questions, allowing only authorized users to access chat rooms. The platform deletes messages after $\mathbf{2 4}$ hours and does not store any residual data, therefore keeping conversations private and transient. The platform can be used innovatively in various user scenarios, making it ideal for social, professional, and private interactions. WhisperLink provides a flexible and secure platform for immediate, private communication, offering a user-friendly and efficient messaging experience. With these features, WhisperLink stands out as a leading solution in secure, anonymous digital communication.

Geographic clustering of businesses holds significant importance in understanding local economic dynamics, identifying areas of commercial activity, and assisting in spatial analysis for economic development. Artificial intelligence (AI) driven analysis is employed in this paper to investigate patterns of geographic clustering, particularly focusing on legal businesses within a given area. Data extraction techniques help preprocess business directories and classification codes to aggregate business addresses and visualize their spatial distribution. Clustering algorithms are used in conjunction with Geographic Information System (GIS) tools for data visualization and precise mapping, with respect to economic indicators. Expected outcomes include generating geographical distribution maps, comparing clustering algorithm results, and insight into urban business clustering patterns. This research considers potential external factors influencing business agglomeration and data currency. Recommendations focus on integrating AI-driven analysis with GIS tools and future research domains. Overall, this paper highlights the intersection of AI and geospatial analysis, providing stakeholders with valuable insights into the spatial distribution of economic activities within a target area.

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.

Ultra-dense unmanned aerial vehicle (UAV) plays an important role in the field of communications due to its flexibility and low-cost feature. Ultra-dense unnamed aerial vehicle base station (UAV-BS) can improve communication quality by providing temporary and cost-effective wireless communication services for hotspots. In this paper, a multiple UAV-BSs assisted downlink network is investigated to maximize the system throughput. It is still a challenging problem to jointly optimize the power allocation and the 3D deployment of multiple UAV-BSs. Therefore, in this paper, for effective interference management, the power allocation problem is first formulated as a non-cooperative game with a pricing mechanism to imitate the interactions among users served by UAV-BSs. Then, based on the combination of deep reinforcement learning (DRL) and the game theory, the power allocation and the 3D deployment of UAV-BSs are transformed into a Markov decision problem. Finally, a novel price-based proximal policy optimization (3PO) algorithm is proposed to explore the optimal policy to maximize the system throughput. Simulation results reveal that the proposed 3PO algorithm can significantly improve system throughput and energy efficiency compared to other baselines by jointly optimizing power allocation and 3D deployment for UAV-BSs.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent strategy where a UAV equipped with an MEC server is deployed to serve terminal devices. This paper considers a multi-UAV assisted network in which multiple UAVs and a terrestrial base station (BS) are deployed to provide MEC services to mobile users. The objective is to minimize an energy and latency-based cost function by jointly optimizing task offloading and MEC server selection decision, transmission power, UAV trajectory, and CPU frequency allocation. An alternating iterative approach based on the block descent method is proposed to solve this problem. In the first layer, task offloading and server selection decision subproblem is solved using a game theoretic approach. The second layer handles offloading and downloading transmission power allocations by utilizing a simplistic geometric waterfilling (GWF) technique, and the UAV trajectory by successive convex approximation (SCA). Whereas, the third layer solves the computation resource subproblem by performing CPU frequency allocation using a gradient descent method. The proposed method uses a segment-by-segment approach, which divides the entire UAV flight trajectory into shorter timeframe segments to reduce the computation time. Simulation results are presented to show that the proposed approach outperforms various benchmark schemes.

In recent years, Internet of Things (IoT) has become a radical evolution due to the exponentially growing demand for various promising applications toward the sixth-generation (6G) networks. As an energy-efficient and spectrum-efficient solution, device-to-device (D2D) communication has emerged as an enabling technology for 6G-based IoT networks. Recently, reconfigurable intelligent surface (RIS) has drawn much attention due to its capability to control the wireless environments so as to enhance the spectrum and energy efficiencies for the beyond fifth generation (B5G) wireless networks. Therefore, in this paper, a RIS-assisted D2D underlay cellular network is investigated to maximize the overall network’s spectrum efficiency (SE) by jointly optimizing the resource reuse indicators, the transmit power, the RIS’s passive beamforming and the BS’s receive beamforming. Instead of using traditional optimization techniques to solve the formulated mixed integer problem, in this paper, a reinforcement learning (RL) based solution is utilized. The formulated optimization problem is modeled by the Markov Decision Process (MDP) in the RL environment. In order to learn the optimal policy under high-dimensional continuous-valued state and action spaces, an actor-critic algorithm based on the deep deterministic policy gradient (DDPG) scheme (AC-DDPG) is proposed. Simulation results reveal that the proposed AC-DDPG scheme achieves significant SE enhancements as compared to the state-of-the-art existing optimization schemes.

In the rapidly evolving landscape of electric vehicles (EVs), blockchain technology emerges as a transformative force, empowering the industry with enhanced security, transparency, and efficiency in transactions and energy management. However, the successful integration of blockchain technology in the context of EV is still in its infancy. Thus, this work aims to provide a comprehensive study of this young field from a broader perspective. We first discuss the integration process of blockchain technology in the EV domain. Then we explore the potential applications of blockchain technology in enhancing the efficiency, reliability, and sustainability of EV infrastructure and discuss some of the promising research in each category. Finally, we provide a typical application scenario and its specific embodiment in the EV infrastructure and strive to shed light on all-inclusive future research directions, which may facilitate the integration of blockchain technology in the EV ecosystem from theory to practice.

Device‐to‐device (D2D) communication can effectively use cognitive radio network approach to coexist with cellular users. For such a cognitive D2D system, two approaches (underlay and overlay) are considered to manage the spectrum sharing among the cellular (primary) users and the D2D (secondary) users. Energy efficiency (EE) is crucial in both these cases due to limited battery capacity and quality of service requirements of the D2D users. This study effectively models the power allocation problem of such a cognitive D2D system by maximising the EE of the D2D users subject to a minimum rate requirement for both the D2D users and the cellular users. This leads to a non‐linear fractional optimisation problem which is more complicated and computationally intractable. Alternatively, geometric water‐filling approach have been utilised for power allocation to solve this optimisation problem which results in an ‘ exact ’ and ‘ low complexity ’ solution. Simulation results reveal the benefits of the proposed algorithm.

Cognitive radio (CR) is a promising wireless paradigm that provides efficient spectral usage. Orthogonal frequency division multiplexing (OFDM) is a potential technology providing many advanced functionalities in terms of power and rate control for cognitive radio networks (CRNs). Power allocation for CRNs is a crucial task for better interference management. In this paper, a subcarrier assignment scheme and a novel power allocation algorithm using geometric water filling is presented for OFDM based CRNs. This algorithm is optimized such a way to maximize the sum rate of secondary users by allocating power more efficiently, while constraining the 1) total transmit power, 2) individual subchannel transmit power as well as 3) individual subcarrier peak power of secondary users, for a given interference level to the primary users. Numerical results show that this algorithm provides better utilization of power resources thus maximizes the sum rate than the existing algorithms.