Internet of Things (IoT) is an emerging networking technology that connects both living and non-living objects globally. In an era where IoT is increasingly integrated into various industries, including healthcare, it plays a pivotal role in simplifying the process of monitoring and identifying diseases for patients and healthcare professionals. In IoT-based systems, safeguarding healthcare data is of the utmost importance, to prevent unauthorized access and intermediary assaults. The motivation for this research lies in addressing the growing security concerns within healthcare IoT. In this proposed paper, we combine the Multi-Step Deep Q Learning Network (MSDQN) with the Deep Learning Network (DLN) to enhance the privacy and security of healthcare data. The DLN is employed in the authentication process to identify authenticated IoT devices and prevent intermediate attacks between them. The MSDQN, on the other hand, is harnessed to detect and counteract malware attacks and Distributed Denial of Service (DDoS) attacks during data transmission between various locations. Our proposed method’s performance is assessed based on such parameters as energy consumption, throughput, lifetime, accuracy, and Mean Square Error (MSE). Further, we have compared the effectiveness of our approach with an existing method, specifically, Learning-based Deep Q Network (LDQN).

Previous works on 3D joint based feature representations of the human body as colour coded images (maps) were developed based on the joint positions, distances and angles or a combination of them for applications such as human action (sign language) recognition. These 3D joint maps have shown to singularly characterize both the spatial and temporal relationships between skeletal joints describing an action (sign). Consequently, the joint position and motion identification problem transformed into an image classification problem for 3D skeletal sign language (action) recognition. However, the previously proposed process of transforming 3D skeletal joints to colour coded maps has a negative proportionality component which resulted in a map with small pixel densities when the joint relationships are high. This drawback greatly impacts the learning of the classifiers to quantify the joint relationships within the colour coded maps. We hypothesized that a positive proportionality between joint motions and the corresponding maps would certainly improve classifiers performance. Hence, joint motion affinity maps(JMAM). These JMAMs use radial basis kernel on joint distances which assures a positive proportionality constant between joint motions and pixel densities of colour coded maps. To further improve the classification of 3D sign language, this work proposes congruent body part joints which results in motion directed JMAMs with maximally discriminating positive definite spatio temporal features. Finally, JMAMs are trained on the proposed multi-resolution convolutional neural network with spatial attention (MRCNNSA) architecture which produces an influencing result for the constructed 3D sign language data, KL3DISL. Consequently, online 3D datasets and standard deep learning models benchmark the proposed with respect to sign and action recognition. The results conclude that JMAMs with clustered joints characterize the subtle relationships which are otherwise difficult to be learned by a classifier.

This paper presents the functional analysis of a hybrid reconfigurable circularly polarized (CP) textile antenna in different on-body environments. The Tai-Chi sign inspires the current textile antenna design, where the primary radiator and ground elements are sickle-shaped structures. Hybrid reconfigurability was achieved by successfully operating two RF PIN-Diodes in the basal ground plane’s elliptical and rectangular structures. Thus, four switching states are attained by operating the textenna at 3.5, 4.5 and 5.4 GHz in the S1. 4.39 and 5.8 GHz in S2, 5 and 5.9 GHz in S3 and 2.4, 4.3, 5.8, and 9.4 GHz in S4 state. The reconfigurable textenna was devised to function on the human body. Its functional behavior in proximity to the human body was rigorously validated through testing involving bending at various vertical and horizontal angles. Additionally, a Specific Absorption Rate (SAR) analysis was conducted using a three-layered human phantom. The results revealed that the maximum SAR value is below 1.6 W Kg⁻¹ for 1 gram of tissue and 2 W kg⁻¹ for 10 grams of tissue. These findings establish the safe operational parameters for the proposed model. The developed antenna functionalities were also tested in other circumstances like body sweating environment and interaction with body fluids (Blood, Plasma, SBF and DMEM). In these body fluid conditions, a peak gain of 4.88 dBi at S1, 6.97 dBi at S2, 7.99 dBi at S3, and 4.13 dBi at S4, was observed while interacting with Plasma, and the measured efficiency in the above configuration is between 77%–79%. The textenna durability analysis was measured using an electrochemical workstation while interacting with different body fluids. All these results confessed the applicability of the developed antennas for on-body wireless communication applications in different on-body conditions.

Thermal imaging is a cutting-edge technology which has the capability to detect objects in any environmental conditions, such as smoke, fog, smog, etc. This technology finds its importance mainly during nighttime since it does not require light to detect the objects. Applications of this technology span into various sectors, most importantly in border security to detect any incoming hazards. Object detection and classification are generally difficult with thermal imaging. In this paper, a one-stage deep convolution network-based object detection and classification called retina net is introduced. Existing surveys are based on object detection using infrared information obtained from the objects. This research is focused on detecting and identifying objects from thermal images and surveillance data.

The fundamental principle of the paper is that the soil moisture sensor obtains the moisture content level of the soil sample. The water pump is automatically activated if the moisture content is insufficient, which causes water to flow into the soil. The water pump is immediately turned off when the moisture content is high enough. Smart home, smart city, smart transportation, and smart farming are just a few of the new intelligent ideas that internet of things (IoT) includes. The goal of this method is to increase productivity and decrease manual labour among farmers. In this paper, we present a system for monitoring and regulating water flow that employs a soil moisture sensor to keep track of soil moisture content as well as the land’s water level to keep track of and regulate the amount of water supplied to the plant. The device also includes an automated led lighting system.