Data fusion of multi-sensor for IOT precise measurement based on improved PSO algorithms

AbstractThis 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

Simulation of undular bores evolution with damping

Propagation of undular bores with damping is considered in the framework of perturbed extended Korteweg-de Vries (peKdV) equation. Two types of damping terms for the peKdV equation, namely linear and Chezy frictional terms, which describe the turbulent boundary layers in the fluid flow are considered. Solving the peKdV equation numerically using the method of lines shows that under the influence of damping, the lead-ing solitary wave of the undular bores will split from the nonlinear wavetrain, propagates and behaves like an isolated solitary wave. The amplitude of the leading wave will remain the same for some times before it starts to decay again at a larger time. In general the amplitude of the leading wave and the mean level across the undular bore decreases due to the effect of damping

Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems

Smart buildings are increasingly using Internet of Things (IoT)-based wireless sensing systems to reduce their energy consumption and environmental impact. As a result of their compact size and ability to sense, measure, and compute all electrical properties, Internet of Things devices have become increasingly important in our society. A major contribution of this study is the development of a comprehensive IoT-based framework for smart city energy management, incorporating multiple components of IoT architecture and framework. An IoT framework for intelligent energy management applications that employ intelligent analysis is an essential system component that collects and stores information. Additionally, it serves as a platform for the development of applications by other companies. Furthermore, we have studied intelligent energy management solutions based on intelligent mechanisms. The depletion of energy resources and the increase in energy demand have led to an increase in energy consumption and building maintenance. The data collected is used to monitor, control, and enhance the efficiency of the system

Intelligent Devices for IoT Applications

Internet of Things (IoT) devices refer to a vast network of physical devices that are connected to the internet and can communicate with each other through sensors and software. These devices range from simple household appliances, like smart thermostats and security cameras, to more complex industrial equipment, such as sensors used in manufacturing and logistics. Specially, IoT enabled wireless gas sensing systems which can withstand harsh environments without compromising the performance are getting popular day by day, which necessitates adequate developments in this field. By being the essential components of a wireless gas sensing system, both the sensor and the elements for communication should be agile and resilient when it comes to tackle unfavorable scenario. Moreover, gas sensors are prone to drift, which can lead to inaccurate readings and decreased reliability over time. Again, recent advancements in antenna design, such as fractal antennas and metamaterial structures, have shown promises in improving the bandwidth and gain parameters of the antennas built on top of high temperature tackling substrates. This piece of research targets three fundamental sections: demonstration of recent advances in data driven techniques for gas sensing system optimization, designing of antennas for different applications, and device design as well as fabrication. The Dimatix DMP-2831 inkjet printer has been optimized to operate with six different inks and two different substrates including PET and 3 mol yttria-stabilized zirconia (3YSZ) based ceramic substrate. Later, the feature oriented gas sensor data analysis to investigate correlations among stability, selectivity and long term drift is illustrated, which should significant relations among those parameters that can be considered while designing different intelligent data driven models to compensate drift. Moreover, a subspace transfer based approach is proposed to classify drifted gas sensor response to detect particular gas with higher accuracy. The model achieved an average accuracy greater than 87% while using only 40% of the total dataset to be trained. In the field of antenna technology, a co-planar waveguide (CPW) fed super wideband antenna is proposed which can cover C, X, Ku, K, Ka, Q, V, and W bands according to the simulated performance with high gain and radiation efficiency. Again, a high temperature tolerant antenna based on 3YSZ substrate is proposed which achieved good alignment between the simulated and fabricated device performance

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime

Strength, water absorption and thermal comfort of mortar bricks containing crushed ceramic waste

This present study investigated the crushed ceramic waste utilisation as sand replacement in solid mortar bricks. The percentage of crushed ceramic waste used were 0% (CW0), 10% (CW10), 20% (CW20) and 30% (CW30) from the total weight of sand. The dimension prescribed of mortar bricks are 215 mm x 102.5 mm x 65 mm as followed accordance to MS 2281:2010 and BS EN 771-1:2011+A1:2015. Four (4) tests were conducted on mortar bricks namely crushing strength, water absorption, compressive strength of masonry units and thermal comfort. The incorporation of ceramic waste in all designated mortar bricks showed the increment of crushing strength between 23% and 46% at 28 days of curing and decrement water absorption between 34% and 44% was recorded corresponding to control mortar bricks. The prism test of masonry units consists of mortar bricks containing ceramic waste indicated the high increment of compressive strength at about 200% as compared to mortar brick without ceramic waste. The thermal comfort test of ceramic mortar bricks were also showed the good insulation with low interior temperature. Therefore, the ceramic waste can be utilised as a material replacement to fine aggregate in mortar brick productions due to significant outcomes performed

Computational intelligent sensor-rank consolidation approach for Industrial Internet of Things (IIoT).

Continues field monitoring and searching sensor data remains an imminent element emphasizes the influence of the Internet of Things (IoT). Most of the existing systems are concede spatial coordinates or semantic keywords to retrieve the entail data, which are not comprehensive constraints because of sensor cohesion, unique localization haphazardness. To address this issue, we propose deep-learning-inspired sensor-rank consolidation (DLi-SRC) system that enables 3-set of algorithms. First, sensor cohesion algorithm based on Lyapunov approach to accelerate sensor stability. Second, sensor unique localization algorithm based on rank-inferior measurement index to avoid redundancy data and data loss. Third, a heuristic directive algorithm to improve entail data search efficiency, which returns appropriate ranked sensor results as per searching specifications. We examined thorough simulations to describe the DLi-SRC effectiveness. The outcomes reveal that our approach has significant performance gain, such as search efficiency, service quality, sensor existence rate enhancement by 91%, and sensor energy gain by 49% than benchmark standard approaches

Minimizing the Localization Error in Wireless Sensor Networks Using Multi-Objective Optimization Techniques

When it comes to remote sensing applications, wireless sensor networks (WSN) are crucial. Because of their small size, low cost, and ability to communicate with one another, sensors are finding more and more applications in a wide range of wireless technologies. The sensor network is the result of the fusion of microelectronic and electromechanical technologies. Through the localization procedure, the precise location of every network node can be determined. When trying to pinpoint the precise location of a node, a mobility anchor can be used in a helpful method known as mobility-assisted localization. In addition to improving route optimization for location-aware mobile nodes, the mobile anchor can do the same for stationary ones. This system proposes a multi-objective approach to minimizing the distance between the source and target nodes by employing the Dijkstra algorithm while avoiding obstacles. Both the Improved Grasshopper Optimization Algorithm (IGOA) and the Butterfly Optimization Algorithm (BOA) have been incorporated into multi-objective models for obstacle avoidance and route planning. Accuracy in localization is enhanced by the proposed system. Further, it decreases both localization errors and computation time when compared to the existing systems

Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model

In smart cities, air pollution is a critical issue that affects individual health and harms the environment. The air pollution prediction can supply important information to all relevant parties to take appropriate initiatives. Air quality prediction is a hot area of research. The existing research encounters several challenges that is, poor accuracy and incorrect real‐time updates. This research presents a hybrid model based on long‐short term memory (LSTM), recurrent neural network (RNN), and Curiosity‐based Motivation method. The proposed model extracts a feature set from the training dataset using an RNN layer and achieves sequencing learning by applying an LSTM layer. Also, to deal with the overfitting issues in LSTM, the proposed model utilises a dropout strategy. In the proposed model, input and recurrent connections can be dropped from activation and weight updates using the dropout regularisation approach, and it utilises a Curiosity‐based Motivation model to construct a novel motivational model, which helps in the reconstruction of long short‐term memory recurrent neural network. To minimise the prediction error, particle swarm optimisation is implemented to optimise the LSTM neural network's weights. The authors utilise an online Air Pollution Monitoring dataset from Salt Lake City, USA with five air quality indicators for comparison, that is, SO2, CO, O3, and NO2, to predict air quality. The proposed model is compared with existing Gradient Boosted Tree Regression, Existing LSTM, and Support Vector Machine based Regression Model. Experimental analysis shows that the proposed method has 0.0184 (Root Mean Square Error (RMSE)), 0.0082 (Mean Absolute Error), 2002*109 (Mean Absolute Percentage Error), and 0.122 (R2‐Score). The experimental findings demonstrate that the proposed LSTM model had RMSE performance in the prescribed dataset and statistically significant superior outcomes compared to existing methods