The Internet of Things is a paradigm for connecting objects using a common set of network technologies. Implementing IoT by host-to-host communication models, such as IP, faces several challenges. It is because of the heterogeneous and constrained devices that connect temporarily to different networks, and different security domains. Also it requires communication capabilities, for both local network and on a global scale. This paper examines how NDN, a proposed architecture for future of the Internet, can respond to these challenges and provide a safer and more straightforward method for deploying IoT. The NDN content-centric communication model makes it possible to interact directly with the contents, and because of this, IoT networks can be more easily developed and configured. Also we show that using NDN would be more fruitful if it is combined with Fog Computing. So, we propose a four-layer model in order to achieve a better structure for IoT. In this model, NDN is used as the core of the network, and routing operations are performed based on contents' names. Also, Fog Computing is used as the mediator between low-level sensors of IoT, the core network, and at the highest level, the cloud which is a fundamental component in IoT networks. In order to evaluate this model in comparison with common models in IoT, we compare the NDN protocol with the MQTT protocol, one of the most commonly used IoT protocols, based on the amount of resource usage and delay.

In computer networks, introducing an intrusion detection system with high precision and accuracy is considered vital. In this article, a proposed model using a deep learning algorithm is presented and its results are analyzed. To evaluate the performance of this algorithm, NSL-KDD, CIC-IDS 2018, UNSW-NB15 and MQTT datasets have been used. The evaluation criteria include precision, accuracy, F1 score, and, readability. The new approach uses a hybrid algorithm that includes a convolutional neural network (CNN) to extract general features and long-short-term memory (LSTM) to extract periodic features that are in the form of a layer. are cross-connected, it is introduced to detect penetration. This algorithm showed the highest known accuracy of 99% on the NSL-KDD dataset. It has reached 97% in all criteria in UNSW-NB15, 96% in all criteria in CIC-IDS 2018, and also, in MQTT for three abstraction levels of features, i. e. packet-based flow features, unidirectional flow, and The two-way flow has reached above 97%, which shows the superiority of this algorithm.

Cardiovascular diseases present significant challenges to public health in developing countries. The high costs of traditional treatments and the limited availability of specialized medical equipment contribute to these challenges. Current diagnostic methods often rely on specific electrocardiogram (ECG) parameters, which may not capture the nuanced complexities necessary for accurate diagnosis. To address these issues, our study proposes an innovative solution: an accessible and cost-effective ECG monitoring system. This system not only captures electrical signals from the heart but also translates them into numerical values using advanced modulation techniques. A trained deep learning model then analyzes this data to accurately identify any potential complications or confirm a healthy cardiac state. Our approach also allows for remote diagnosis and treatment. By utilizing an MQTT server, ECG data can be efficiently transmitted to experts for evaluation and intervention when necessary. Our meticulously fine-tuned Artificial Neural Network (ANN) architecture has achieved an impressive accuracy of 95.64%, surpassing existing methodologies in this field. Designed with resource-strapped regions in mind, our system offers a lifeline to rural areas lacking access to medical professionals and advanced equipment. Its affordability ensures that even individuals with limited financial means can benefit from timely and accurate cardiac monitoring, potentially saving lives and reducing the burden of cardiovascular diseases in underprivileged communities.

We present a vertically stacked triple-layer rooftop module that integrates a SiO₂–TiO₂ radiative-cooling film, a TiO₂–ZnO photocatalytic coating, and a perovskite–silicon tandem photovoltaic device. Outdoor field experiments (n = 4 prototype and n = 4 control modules; rooftop tests in Chandragiri, Andhra Pradesh, India) showed a mean surface temperature reduction of 6.5 ± 0.8 °C and a 2.1% relative increase in PV power output under AM1.5G-equivalent conditions. Simultaneously, the photocatalytic layer achieved 72.4% removal efficiency for volatile air pollutants over a 6-hour test window. Real-time monitoring used an ESP32 microcontroller, K-type thermocouples, calibrated gas sensors, and MQTT-based telemetry to a Grafana dashboard. Statistical analysis confirmed significant differences (p < 0.01) in both cooling and pollutant removal compared with controls. The proposed architecture offers a reproducible and scalable pathway to multifunctional PV modules that enhance energy yield, reduce thermal stress, and actively contribute to urban air quality improvement addressing both environmental and energy challenges in a single integrated solution.