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This paper employs the approach of the differential model to effectively improve the analysis of particle swarm optimization. This research uses a unified model to analyze four typical particle swarm optimization (PSO) algorithms. On this basis, the proposed approach further starts from the conversion between the differential equation model and the difference equation model and proposes a differential evolution PSO model. The simulation results of high-dimensional numerical optimization problems show that the algorithm's performance can be greatly improved by increasing the step size parameter and using different transformation methods. This analytical method improves the performance of the PSO algorithm, and it is a feasible idea. This paper uses simple analysis to find that many algorithms are improved by using the difference model. Through simple analysis, this paper finds that many AI-related algorithms have been improved by using differential models. The PSO algorithm can be regarded as the social behavior of biological groups such as birds foraging and fish swimming. Therefore, these behaviors described above are an ongoing process and are more suitable for using differential models to improve the analysis of PSO. The simulation results of the experiment show that the differential evolution PSO algorithm based on the Runge–Kutta method can effectively avoid premature results and improve the computational efficiency of the algorithm. This research analyzes the influence of the differential model on the performance of PSO under different differenced conditions. Finally, the analytical results of the differential equation model of this paper also provide a new analytical solution. [ABSTRACT FROM AUTHOR]

This study develops a novel remote healthcare system based on Wireless Sensors Network System (WSNs) and Radio Frequency Identification (RFID) technologies. Cloud equipment is used as sensing cloud architecture to create the system database, and Improved Particle Swarm Optimization (IPSO) is applied to build a personal physiological signal sensing system. The collected personal physiological signals are analyzed, and RFID technology is used to create an administrator identity and database. The integrated physiological instrument measures/monitors blood pressure, heart rate, blood oxygen content, body weight, BMI and cardiogram. This system can be applied to, say, employees, nursing-home residents and the elderly. Physiological changes are identified at any time via a self-health examination, promoting early diagnosis and treatment. The current ZigBee technology, which has many advantages, is used in medical institutions, industry, and agriculture, and for automated control and building monitoring. This study uses WSNs technology to transfer physiological data to the cloud for analysis, processing, and storage. The client-side and appropriate medical personnel are notified by e-mail and short messages via the Internet, such that they can provide timely diagnosis and deploy treatment. The IPSO scheme is used to increase the efficiency and accuracy when searching for at-risk groups, searching data, and defining and summing the weights of physiological data. If the first 10% of users with high weight values are a risky population that must be treated immediately, this system informs medical personnel immediately, potentially improving medical service quality and application of medical resources. [ABSTRACT FROM AUTHOR]

Purpose – This study aims to analyze the inertial weight factor value in the (PSO) algorithm and propose non-linear weights with decreasing strategy to implement the improved PSO (IPSO) algorithm. Using various types of sensors, combined with ZigBee wireless sensor networks and the TCP/IP network. The GPRS/SMS long-range wireless network will sense the measured data analysis and evaluation to create more effective monitoring and observation in a regional environment to achieve an Internet of Things with automated information exchange between persons and things. Design/methodology/approach – This study proposes a wireless sensor network system using ZigBee (PSoC-1605A) chip, sensor and circuit boards to constitute the IOT system. The IOT system consists of a main coordinator (PSoC-1605A), smart grid monitoring system, robotic arm detection warning system and temperature and humidity sensor network. The hardware components communicate with each other through wireless transmission. Each node collects data and sends messages to other objects in the network. Findings – This study employed IPSO to perform information fusion in a multi-sensor network. The paper shows that IPSO improved the measurement preciseness via weight factors estimated via experimental simulations. The experimental results show that the IPSO algorithm optimally integrates the weight factors, information source fusion reliability, information redundancy and hierarchical structure integration in uncertain fusion cases. The sensor data approximates the optimal way to extract useful information from each fusion data and successfully eliminates noise interference, producing excellent fusion results. Practical implications – Robotic arm to tilt detection warning system: Several geographic areas are susceptible to severe tectonic plate movement, often generating earthquakes. Earthquakes cause great harm to public infrastructure, and a great threat to high-tech, high-precision machinery and production lines. To minimize the extent of earthquake disasters and allow managers to deal with power failures, vibration monitoring system construction can enhance manufacturing process quality and stability. Smart grid monitoring system: The greenhouse effect, global energy shortage and rising cost of traditional energy are related energy efficiency topics that have attracted much attention. The aim of this paper is that real-time data rendering and analysis can be more effective in understanding electrical energy usage, resulting in a reduction in unnecessary consumption and waste. Temperature and humidity sensor network system: Environmental temperature and humidity monitoring and application of a wide range of precision industrial production lines, laboratories, antique works of art that have a higher standard of environmental temperature and humidity requirements. The environment has a considerable influence on biological lifeforms. The relative importance of environmental management and monitoring is acute. Originality/value – This paper improves the fixed inertial weight of the original particle swarm optimization (PSO) algorithm. An illustration in the paper indicates that IPSO applies the Internet of Things (IOT) system in monitoring a system via adjusted weight factors better than other existing PSO methods in computing a precise convergence rate for excellent fusion results. [ABSTRACT FROM AUTHOR]

Abstract: This work proposes an improved particle swarm optimization (PSO) method to increase the measurement precision of multi-sensors data fusion in the Internet of Things (IOT) system. Critical IOT technologies consist of a wireless sensor network, RFID, various sensors and an embedded system. For multi-sensor data fusion computing systems, data aggregation is a main concern and can be formulated as a multiple dimensional based on particle swarm optimization approaches. The proposed improved PSO method can locate the minimizing solution to the objective cost function in multiple dimensional assignment themes, which are considered in particle swarm initiation, cross rules and mutation rules. The optimum seclusion can be searched for efficiently with respect to reducing the search range through validated candidate measures. Experimental results demonstrate that the proposed improved PSO method for multi-sensor data fusion is highly feasible for IOT system applications. [Copyright &y& Elsevier]