2690-6791ISSN (Online)
2769-786XISSN (Print)
In an increasingly fast-paced world of 6G-IoT networks, optimal beamforming techniques will be effective in improving strength, latency, and quality of service delivery in the networks. This work presents a new paradigm in beamforming optimization, especially in tackling dynamic environments and high computational costs in existing approaches. The problems of long training times with traditional methods, along with threats in security make them out rightly less applicable for real time applications. The data is collected from 6G IoT networks then, Laplacian Eigenmaps is used for feature extraction and modelling in time and applied for dimensionality reduction, ConvMarkov is used for model development RC4 encryption secures data exchange, while blockchain supports data logging and promotes transparency. This is a combination of deep learning techniques and advanced encryption methods, which will lead to a wide boost in beamforming efficiency, flexibility, and security. This study achieved the beamforming optimization achieved 97% accuracy with significant gain improvements, as indicated by an ROC curve (AUC = 0.9970) and precision-recall curve. The training loss stabilized below 0.01, while the validation loss fluctuated above 0.1, suggesting minor overfitting. The main achievements converge on proving improvements in optimization under real time conditions in a network, besides integrity and privacy of data. These become great merits into a strong solution for future 6G.
Read MoreDoi: https://doi.org/10.54216/JISIoT.180201
Vol. 18 Issue. 2 PP. 01-19, (2026)
Blockchain technology has recently emerged as a fundamental pillar of decentralized and secure systems. However, many Proof-of-Work (POW) algorithms suffer from some challenges, including their inefficiency in discovering the value of Nonces due to their reliance on random attempts, which consume significant resources, energy, and time, making them difficult to use in lightweight blockchain environments, especially in resource-limited environments such as mobile devices and others. The main goal of this paper is to introduce a smart system that replaces random guessing with a more intelligent and predictive approach using deep learning models like CNN2D, GRU, LSTM, and hybrid models. The intelligent optimization algorithm (GWO) is also used, which has been enhanced with random Lévy jumps, in addition to improved clustering using a genetic algorithm. The results, after applying the system to health data across three difficulty levels (4, 6, and 8), showed that the intelligent neural model was the most stable and accurate, achieving the lowest error values ​​and the highest generalization ability, with a maximum error value of (0.0136) at the highest difficulty level (8). The hybrid GA–KMeans algorithm demonstrated high efficiency in improving clustering accuracy. It achieved the highest similarity index value (0.9980) and the lowest Davis-Bolden index value (0.0000), which plays a significant role in guiding searches efficiently and effectively. The CNN2D model also achieved ideal numerical results, but it is prone to overlearning, while the GRU neural model provided an efficient balance between stability and accuracy. Other hybrid models, such as GRU+CNN, have shown excellent performance, but with varying results. The proposed system proves to be an efficient and intelligent alternative to the low-cost random approach for Nonce discovery in lightweight blockchain environments.
Read MoreDoi: https://doi.org/10.54216/JISIoT.180202
Vol. 18 Issue. 2 PP. 20-35, (2026)
Area coverage and exploration of unknown environments by swarm robots autonomously is one of the challenges in the robotics domain. This paper proposes a new strategy for area coverage in two parts; firstly, enhancing a Pelican Optimization Algorithm (POA) using swarm robots to explore an unknown area. Secondly, merges many algorithms with the proposed POA, such as Timed Elastic Band (TEB) as a local planner for obstacle avoidance, SLAM (Simultaneous Localization and Mapping), and a training model which is called You Only Look Once version 8 nano (YOLOv8n) for person detection. The proposed POA algorithm successfully monitored a large area and achieved a high exploration ratio with minimal time. In this work, the new strategy is applied to a robot warehouse environment, utilizing a swarm of robots to explore the area and find targets, which are employees suffocated by the effects of chemical pollution. The simulation and real-world tests for a new strategy were done in the Robot Operating System (ROS) using the TurtleBot3 robot. The total time-consuming for exploration and detection time is less by POA, while the coverage ratio is the largest when compared with the original RRT exploration algorithm for empty, small, and large environments, respectively.
Read MoreDoi: https://doi.org/10.54216/JISIoT.180203
Vol. 18 Issue. 2 PP. 36-59, (2026)
Reducing energy losses and increasing power grid efficiency need accurate prediction of power consumption accurate prediction of future energy consumption requires the use of time series data. To overcome the shortcomings of conventional techniques for forecasting energy consumption in India for the period from 2 January, 2019 to 23 May, 2020, we used an attention mechanism, which is still relatively new and not well known. In this paper, we propose a new approach for predicting energy consumption by combining local feature extraction with convolutional neural networks (CNNs), long short-term memory (LSTM) to capture long-term temporal dependencies, and attention mechanisms to deal with the issue of information loss brought on by extremely lengthy input time series data. After high-dimensional features are extracted from the input data using a one-dimensional CNN layer, temporal correlations within historical sequences are captured using an LSTM layer. In order to optimize the weighting of the LSTM outputs, strengthen the impact of important information, and enhance the prediction model as a whole, an attention mechanism is finally implemented. This integration improves the model's ability to represent complex spatio-temporal patterns. The mean absolute error (MAE) and root mean square error (RMSE) are used to assess the performance of the proposed model. The results demonstrate that the CNN-LSTM-Attention model outperforms conventional hybrid CNN-LSTM and LSTM models, demonstrating superior performance across a range of prediction scenarios. By supporting more reliable grid management, proactive intervention methods, and predictive maintenance, these developments contribute to reducing load imbalances and energy waste in India. The Future developments could see the proposed model extended to other time series prediction domains.
Read MoreDoi: https://doi.org/10.54216/JISIoT.180204
Vol. 18 Issue. 2 PP. 60-71, (2026)
The rapid development of intelligent computing has led to Internet of Things (IoT) applications and embedded devices suffering from severe constraints on energy, processing, and memory. This calls for fast and lightweight algorithms that maintain performance accuracy without draining resources or affecting response time. This paper presents a new hybrid metaheuristic algorithm that combines the advantages of four optimization algorithms to achieve efficient results and reduce computational complexity without compromising output quality. Experiments demonstrate significant improvements in performance and execution time compared to traditional algorithms, in addition to the algorithm's ability to scale and handle diverse workloads. The lowest improvement of the proposed algorithm compared to other algorithms was approximately 25.7%. This algorithm opens up prospects for effective applications in smart systems in urban and industrial areas.
Read MoreDoi: https://doi.org/10.54216/JISIoT.180205
Vol. 18 Issue. 2 PP. 72-84, (2026)
Wireless Sensor Networks (WSNs) support intelligent data acquisition systems across environmental monitoring, industrial automation, and smart cities. As a fundamental enabler of the Internet of Things (IoT), WSNs rely heavily on battery-powered sensor nodes for sustained operation in dynamic and often remote environments. However, predicting battery lifetime in WSNs remains a critical challenge due to the complex interplay between environmental conditions and operational behaviors. Conventional energy models often fail to consider the simultaneous influence of temperature, humidity, and data traffic intensity on battery depletion rates. This study proposes a battery lifetime prediction model based on a Markov framework integrated with an exponential energy consumption function to address this issue. The model incorporates three primary variables—ambient temperature, relative humidity, and data movement to simulate energy usage dynamically. The framework calculates transition probabilities and energy load based on environmental states, enabling accurate forecasting. Additionally, the model evaluates the impact of different battery chemistries (Ni-MH, LiPo, Li-ion, and Alkaline) on lifespan performance across varying environmental scenarios. Simulation results reveal that temperature and humidity significantly influence energy depletion, while data transmission intensity plays a supporting role in high-traffic cases. LiPo and Li-ion batteries demonstrate superior performance and stability, especially under extreme environmental conditions. This study contributes a novel multi-variable model that bridges physical sensing environments with predictive battery analytics. The findings provide a foundation for strategic energy planning and adaptive deployment of WSNs in sustainability-critical applications.
Read MoreDoi: https://doi.org/10.54216/JISIoT.180206
Vol. 18 Issue. 2 PP. 85-98, (2026)
Nowadays, with the proliferation of mobile devices and the internet around the world that are available for everyone, and due to the low prices versus their high capabilities, images are considered one of the most common ways of transmitting information between users, advancement of image processing and editing tools, simplified the process of editing and changing photographs such as in magazines, newspapers, scientific journals, and on social media or on the Internet. As a result, the propagation of manipulated photographs that misrepresent the truth is prevalent, whether deliberate or inadvertent. We propose a method that uses deep learning based convolutional neural network in order to detect instances of the copy-move forgeries in images which can help to ensure data authenticity in digital forensic investigations. In this case, our method is intended to improve digital evidence integrity by detecting complicated changes quickly and precisely. This work can supports cybersecurity applications like anti-fraud systems, fake news detection, and social media forensics. The findings of the experiment demonstrate that the suggested approach is capable of detecting forgery against multiple copies and post-processing activities. The dataset's images used for both training and testing are MICC-F2000, composed of 2,000 images, 700 tamper and 1,300 originals. The findings indicate a testing accuracy of 98.00% and a training accuracy of 99.17%.
Read MoreDoi: https://doi.org/10.54216/JISIoT.180207
Vol. 18 Issue. 2 PP. 99-110, (2026)