The Internet of Things (IoT) is undergoing significant advancements, driven by the emergence of Backscatter Communication (BC) and Artificial Intelligence (AI). BC is an energy-saving and cost-effective communication method where passive Backscatter Devices (BD) communicate by modulating ambient Radio-Frequency (RF) carriers. AI has the potential to transform our way of communicating and interacting and represents a powerful tool for enabling the next generation of IoT devices and networks. By integrating AI with BC, we can create new opportunities for energy-efficient and low-cost communication and open the door to a range of innovative applications that were previously not possible. This paper brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with Reconfigurable Intelligent Surfaces (RIS), UAV, and vehicular networks. Finally, in the discussion section, we summarize the solutions, provide lessons learned and challenges, and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT.

Solar energy is directly converted into electrical energy by using photovoltaic (PV) panels. The efficiency of PV panel varies with its orientation and tilt angle with the horizontal plane. In this paper, we investigate the optimum tilt angle of solar panels installed at Sukkur IBA University. The optimum angle for tilted surfaces varying from 0° to 90° in steps of 1° was calculated for the values of which the daily total solar radiation was maximum for a specific period. It was found that the optimum tilt angle changed between 0° and 61.1° throughout the year in Sukkur IBA University, Sindh Pakistan ( N, E). For calculating irradiance, optimal fixed (15 and 29.5 degrees) and variable tilt angles are used for every month of year 2019. The irradiance calculated at 15 degrees tilt angle is compared with the fixed angle of 29.5 and variable angles. It was found that optimal tilt angle for the region of Sukkur located in northern Pakistan is to be 29.5 degrees.

Time plays a critical role in competitive influence maximization. Companies aim to promote their products before certain events, such as Christmas Eve or music concerts, to gain more benefit under competitions from other companies. Besides, these companies have a limited budget to spend on these product promotions. Therefore, in this paper, we examine a time-constrained competitive influence maximization where the parties wish to maximize their profits before the respective deadlines. Besides, the parties need to determine how to select the seed nodes and when to initiate information propagation in the network, such that the decision results in the optimal reward given the time and the budget constraint. To this end, we propose a novel reinforcement learning-based framework named seed-combination and seed-selection that is built on a nested Q-learning (NSQ) algorithm. This way, we can derive the optimal in both budget allocation and node selection that results in the maximum profit. In evaluating the proposed model, we consider the scenarios when the competitors’ strategy is known, unknown, and not available for training. The results show that the proposed NSQ algorithm could improve the rewards by up to 50% compared with the state-of-the-art algorithm, STORM-Q.

Influence Maximization problem, selection of a set of users in a social network to maximize the influence spread, has received ample research attention in the social network analysis domain due to its practical applications. Although the problem has been extensively studied, existing works have neglected the location’s popularity and importance along with influential users for product promotion at a particular region in Location-based Social Networks. Real-world marketing companies are more interested in finding suitable locations and influential users in a city to promote their product and attract as many users as possible. In this work, we study the joint selection of influential users and locations within a target region from two complementary perspectives; general and specific location type selection perspectives. The first is to find influential users and locations at a specified region irrespective of location type or category. The second perspective is to recommend locations matching location preference in addition to the target region for product promotion. To address general and specific location recommendations and influential users, we propose heuristic-based methods that effectively find influential users and locations for product promotion. Our experimental results show that it is not always an optimal choice to recommend locations with the highest popularity values, such as ratings, check-ins, and so, which may not be a true indicator of location popularity to be considered for marketing. Our results show that not only influential users are helpful for product promotion, but suitable influential locations can also assist in promoting products in the target region.

Rainfall prediction is critical in agriculture, water resource management, transportation, and disaster planning. As a critical component of the hydrological cycle, precisely forecasting daily rainfall helps to lessen the effects of floods, droughts, and other extreme weather events. However, due to the nonlinear and complicated interaction of meteorological factors, daily rainfall forecast remains a grim problem. The proposed work adopts a systematic deep learning-based framework, integrating Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models and Transformers to predict the rainfall of the next day. Besides deep models, a few machine learning classifiers, such as the Logistic Regression (LR), Support Vector Machines (SVM), and the K-Nearest Neighbor (KNN) will be applied to benchmark the performance.One of the main insights in this study is the systematic comparison of a wide variety of activation functions, such as Sigmoid, ReLU, Tanh, Swish, Leaky ReLU, and ELU to measure their effects on model accuracy, convergence, and generalization performance. An experiment was employed on real-life rainfall of the USA dataset, which proved the efficiency of using the BiLSTM model and ReLU and Leaky ReLU activation functions to achieve up to 99% accuracy, whereas Transformer model was also efficient as it obtain up to 98%, but it was not stable and showed a tendency towards occasional instability during the training process. On the contrary, traditional machine learning models displayed an intermediate predictive accuracy, with average accuracies of approximately 87%. Significantly, the results were validated through multi-seed experiments with confidence intervals and baseline benchmarks, demonstrating that the near-perfect scores were not incidental but consistently stable across different initializations. This study provides a new theoretical standpoint in terms of activation functions in deep sequence models as it studies beneficiaries and shortcomings through a mathematical point of view. The results highlight that activation function selection can have an important impact on the accuracy of forecasts and can be significant in building more accurate, scalable, and general learning subroutines in weather predictions.