Internet Of Things Based Sensor Networks Research Articles

Mobility Assisted Adaptive Clustering Hierarchy for IoT Based Sensor Networks in 5G and Beyond

One of the massive machine type communication (mMTC) applications for monitoring and sensing in 5G cellular network is the Internet of Things (IoT) based wireless sensor network (WSN). Non uniform battery usage by the nodes in these networks often results in creating energy holes or voids in the network making the network disconnected. An effective solution is to deploy multiple mobile nodes throughout the network, however finding optimal path for these mobile nodes is reported to be an NP hard problem. This paper proposes MAACH (Mobility Assisted Adaptive Clustering Hierarchy), an efficient mobility assisted clustering and routing framework for IoT based sensor network in 5G and beyond. Also, an elaborate method to calculate the exact path optimally for multiple mobile nodes is presented to alleviate non uniform energy dissipation of the sensing nodes. Simulation results show that our algorithm effectively finds the optimal trajectory of multiple mobile nodes in a distribute manner and also improves network stability period by 60-70% and the network lifetime by 70-90% across multiple network deployments.

Modeling indoor PM2.5 using Adaptive Dynamic Fuzzy Inference System Tree (ADFIST) on Internet of Things-based sensor network data

Indoor air pollution is crucial for the building environment as it has a direct impact on human health, prosperity, comfort, and productivity levels. Repeated exposure to toxic materials and harmful chemicals leaves a considerable impact on occupant well-being. The degraded air quality in the indoor environment is closely connected to the rising cases of respiratory health issues, cardiovascular disease, lung cancer, and a wide spectrum of symptoms. Therefore, it is required to design a reliable system to assess indoor air quality (IAQ) in residential and commercial spaces to improve the health outcomes and productivity levels of the building occupants. The main contribution of this paper is to provide a rigorous design and development approach for the IAQ monitoring system using the Internet of Things-based sensor network. Furthermore, a forecasting model was designed using Adaptive Dynamic Fuzzy Inference System Tree to predict PM2.5 conditions in the target building environment. The performance of the proposed system is tested over multiple datasets obtained from different rural and urban locations; moreover, the findings are further validated against benchmark datasets. The updates about real-time IAQ conditions in the target building environment were made accessible to occupants via an online web portal, and the entire system was named “Vayuveda” – an approach for enhanced air quality in the living spaces. The authors tested multiple methods at every step of the development process and compared performance at each stage to come out with the best model for real-time IAQ assessment.

Intelligent ubiquitous computing model for energy optimization of cloud IOTs in sensor networks

PurposeLimitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model.Design/methodology/approachIn this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model.FindingsThe newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism.Research limitations/implicationsIn this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies.Practical implicationsThe developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks.Social implicationsThe social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission.Originality/valueIn this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.

5G Integrated Spectrum Selection and Spectrum Access using AI-based Frame work for IoT based Sensor Networks

The convulsive advancement of multiple-input multiple-output devices and ultra-dense networks has been extensively considered as the key facilitators that ease the evolution and formation of 5G systems. The explosive growth of wireless devices necessitates the deployment of the Internet of Things (IoT), which is the potential of interconnecting diversified things using wireless communications. To enable wireless accesses of IoT devices, Artificial Intelligence (AI) plays a significant role in 5G network. While existing end-to-end learning and adaptive model require continuous monitoring and dynamic changes cannot achieve global optimization due to wireless signal classifiers and a higher amount of interference. In this work, an integrated spectrum selection and spectrum access using a greedy and AI-based framework to allow the forthcoming and subsequent demands on 5G and beyond is presented. Fractional Knapsack Greedy-based strategy is introduced, and Langrange Hyperplane-based approach is utilized to realize the AI-based strategies for spectrum selection and spectrum allocation for IoT-enabled sensor networks. This framework is called as Fractional Knapsack and Langrange Hyperplane Spectrum Access (FK-LHSA). First Fractional Knapsack Multi-band spectrum selection (FKMSS) model is designed along with an energy consumption model to optimize channel or spectrum throughput. Next, a Lagrange Hyperplane (LH) spectrum access model is designed to minimize spectrum access delay and improve spectrum access accuracy. The simulation results show that the proposed FKM model and LH model can effectively reduce the spectrum access delay along with the improvement of throughput and spectrum access accuracy.

A Secure Internet of Things based Smart Monitoring System

Internet of Things (IoT) is a connection of interconnected any things deployed in different applications. The IoT is emerging as a technique for real-time monitoring the environment like Earthquake early detection. Snow level monitoring, forest fire detection, Gas level monitoring in a Smart Kitchen. IoT based sensor network need secure communication, processing, analytics and transient storage for better monitoring of smart environments. In this article, the authors proposed a secure framework for data collection and further processing from the sensors network to the core of the network using Fog-Cloud based architecture. The paper implemented the proposed framework taking different sensors deploy in an IoT based smart kitchen environment. For experimental purpose, Raspberry Pi used as a fog node and local cloud server to monitor the environment in real-time, SQLite is used as buffer storage. The experimental setup and result show that the proposed framework is secure to monitoring in a sensor network.

Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT

Wireless Sensor Networks (WSNs) are used in the design of Internet of Things (IoT) for sensing the environment, collecting the data and to send them to the base station and the locations used for analysis. In WSNs for IoT, intelligent routing is an important phenomena that is necessary to enhance the Quality of Service (QoS) in the network. Moreover, the energy required for communication in the IoT based sensor networks is an important challenge to avoid immense packet loss or packet drop, fast energy depletion and unfairness across the network leading to reduction in node performance and increase in delay with respect to packet delivery. Hence, there is an extreme need to check energy usage by the nodes in order to enhance the overall network performance through the application of intelligent machine learning techniques for making effective routing decisions. Many approaches are already available in the literature on energy efficient routing for WSNs. However, they must be enhanced to suite the WSN in IoT environment. Therefore, a new Neuro-Fuzzy Rule Based Cluster Formation and Routing Protocol for performing efficient routing in IoT based WSNs. From the experiments conducted in this research work using the proposed model, it is proved that the proposed routing algorithm provided better network performance in terms of the metrics namely energy utilization, packet delivery ratio, delay and network lifetime.