The phrase "COVID-19" has become the most popular and highly searched term on Google since its emergence in November 2019. The world must combat this pandemic. With advancements in mobile technology and sensors, Developing Internet of Things (IoT)-based healthcare solutions is now feasible. Proactive as well as preventative, these new systems, are in contrast to the traditional reactive healthcare systems. In this study, a paradigm based on the Internet of Things is proposed for detecting COVID-19 suspects. The framework presented in this research focuses on predicting COVID-19 in its early stages. To determine whether the virus is present, it uses health measurements collected from sensors and other Internet of Things devices. By gathering symptom data related to COVID-19, the behavior of the virus can be better understood. Four machine learning models are introduced in this paper: CatBoost, Decision Tree, Random Forest, and Logistic Regression. These models are used to create a Diagnostic Learning Classifier (DLC) model. The top three performing models, Decision Tree, Logistic Regression, and CatBoost, are combined using a weighted average or majority voting approach, depending on the nature of the problem. The DLC model is compared to base models such as Random Forest, Decision Tree, Logistic Regression, and CatBoost, using multiple evaluation criteria. The goal of this ensemble model is to increase forecast accuracy by utilizing the variety and advantages of the chosen models. It delivers great diagnostic accuracy when used as a machine learning model on the gathered data. Notably, the DLC classifier achieves an accuracy of 91%, followed by Random Forest (86%), CatBoost (85%), Logistic Regression (84%), and Decision Tree (82%). These results demonstrate the effectiveness of the voting classifier in efficiently managing COVID-19 by accurately diagnosing patients and notifying health authorities of suspected cases.

This study introduces a novel integration of a discretized Grey Wolf Optimizer (DGWO) with Q-Learning (QL), marking a significant step in optimization techniques to solve Job Shop Scheduling Problem (JSSP). This innovative approach not only tailors the GWO for discrete optimization challenges but also utilizes QL for dynamic parameter tuning, a first in the field of JSSP. The synergy between these two methodologies advances solution quality and computational efficiency to new heights. Rigorous benchmarking against leading algorithms demonstrates the DGWO-QL’s superior performance, achieving the best-known solutions in 32 out of 34 cases. This breakthrough underscores the method’s robustness and its promising applicability to real-world scheduling complexities.

The Internet of things (IoT) is a network of technologies that support a wide variety of healthcare workflow applications to facilitate users' obtaining real-time healthcare services. Many patients and doctors' hospitals use different healthcare services to monitor their healthcare and save their records on the servers. Healthcare sensors are widely linked to the outside world for different disease classifications and questions. These applications are extraordinarily dynamic and use mobile devices to roam several locales. However, healthcare apps confront two significant challenges: data privacy and the cost of application execution services. This work presents the mobility-aware security dynamic service composition (MSDSC) algorithmic framework for workflow healthcare based on serverless, serverless, and restricted Boltzmann machine mechanisms. The study suggests the stochastic deep neural network trains probabilistic models at each phase of the process, including service composition, task sequencing, security, and scheduling. The experimental setup and findings revealed that the developed system-based methods outperform traditional methods by 25% in terms of safety and 35% in application cost.

Abstract The delay in transportation of necessary items is due to traffic congestion throughout the world. This is a serious phenomenon which results in waste of time and fuel. The detection of road conditions and dissemination of traffic information efficiently and effectively is a big challenge to authorities. Recently, the technologies of vehicular ad hoc networks (VANETs) have been utilized and become an important part of the intelligent transportation system (ITS). For this existing problem, vehicle‐to‐vehicle (V2V) communication provides a means for cooperation and route management in transport networks. This paper proposed a novel congestion detection system based on the combination of k‐means clustering and analytical hierarchy process. In the simulation of urban mobility (SUMO) simulator, a transport network is created and parameters of vehicles facing congestion are taken to extract the key parameter by using the k‐means clustering technique and mathematical mean algorithm. This parameter is utilized in analytical hierarchy process to detect the highest priorities parameter and based on that the congestion is detected in particular lane. The result can be a better technique for congestion detection as it requires low installation cost and can be incorporate in vehicles for congestion avoidance which will alternatively improve the traffic flow.

Sensor-aware distributed workflow applications are becoming increasingly popular underwater. The apps are marine operations that generate data and process it based on its characteristics. Mobile-fog-cloud paradigms, as well as computing such as sensor nodes, have emerged. As previously stated, the nodes can be combined into a single system to achieve several goals. Many factors are considered, including network contents, workload fluctuation, variable execution durations, deadlines, and bandwidth. As a result, scheduling mobile workflow systems with multiple parameters might be challenging. The study suggests a novel content-efficient decision-aware task scheduling (CATSA) method for defining and adapting to complicated environmental changes. The CATSA consists of several components that work together to perform various benchmarks in the system, including a decision planner, sequencing, and scheduling. As evidenced by test findings during evaluation, the suggested architecture outperforms current studies regarding workflow execution quality of services and improved the makespan 30% and deadline meeting 40% in the study.

In recent years, there has been a growing interest in wireless sensor networks because of their potential usage in a wide variety of applications such as remote environmental monitoring and target tracking. Target tracking is a typical and substantial application of wireless sensor networks. Generally, target tracking aims basically at estimating the location of the target while it is moving within an area of interest and consequently reporting it to the base station on time. However, achieving high accuracy of tracking together with energy efficiency in target tracking algorithms is extremely challenging. This paper presents an efficient target tracking approach by combining dynamic clustering technique with the predictive tracking technique using the Long Short Term Memory (LSTM) networks which is the extension of Recurrent Neural Networks (RNNs), in this work two models from the LSTM was used, where the first model used for predict the three nodes that will track the object, and the second model was used to classify the direction of movement of the object and then calculate the new position of the object. Through the results obtained, The accuracy was up to 98%, and the error rate was 0.66892, and since the location of the object is random, as well as the distribution of sensors is random, every time the program is executed, the accuracy rate is very high, and this proves that the approach that was proposed, was very successful and the use of two models of LSTM increased the work efficiency.

Study on positioning indoors has been one of the most well-studied topics in recent years. Localization approaches using RSS are attestable for collecting position information since they can reuse the current wireless infrastructure for localization. This paper introduces an indoor location device with 2.4 GHz RFID Reader signals based on RFID technology in a wide-area network for indoor localization. These techniques are compared when using loT devices in terms of position accuracy and power consumption. Modality values have been used, and trilateration has been performed by the obtained signal strength indicator (RSSI). The data collection can be accessed online. In the selection of wireless technology for an indoor localization system following application criteria, the experimental results can be used as an indicator.

Wireless sensor network has emerged recently as an effective way of monitoring inhospitable or remote physical environments. One of the main challenges in the networks lies in the constrained energy and computational resources available to sensor nodes. The deployment of the sensor nodes is the initial step to establishing a sensor network. In this paper the objective of the method of the location estimation is to estimate locations of the sensor nodes with respect of the set of the anchor nodes that their locations' information is known. This network consists of sensors that are battery-powered, and that gather the data and route it to the central Base Station(BS). Cluster-based routing efficiently utilizes with a limited energy of the sensor devices by selecting the Cluster Head (CH) at every round depending in the number, energy, and prior of the nodes. Also, Sensor Mode Setup Phase(SMSF) help increase the wireless sensor networks lifetime, and also see that the lifetime increases when the sensor nodes number is increased.