Internet Of Things Sensor Nodes Research Articles

Periodic Energy Optimization Using IOT and ML

The rapid expansion of Internet of Things (IoT) applications across various sectors generates an enormous volume of continuous time-series data. However, transmitting this massive amount of sensor data from energy constrained IoT nodes poses a significant challenge. The continuous transmission of such data consumes vast amounts of energy.In this work, we present a solution to this problem by predicting the periodic behavior of sensor data through a higher-level view of continuous transmission data from nodes in IoT at server side. Our system is composed of an IoT sensor network and a data processing unit. The local sensor network: temperature and humidity data is collected using 4 different nodes, as well, which afterward this info is transferred into a data processing unit built on the Raspberry Pi device. We use the machine learning model Autoregressive Integrated Moving Average (ARIMA) on the processing unit. This model is then applied individually to the data from each of the four nodes, predicting processed sensor values in the future accurately. In short, after getting highly accurate prediction, then we settle down proper energy saving pattern which reduces the data transmission requirements hence results in energy saving pattern.By utilizing the predictive capabilities of the ARIMA model, we minimize the need for constant transmission of raw sensor data. Instead, we transmit only essential updates or deviations from the predicted values. This approach substantially reduces energy consumption by eliminating the transmission of redundant information. In summary, our project aims to overcome the energy limitations of IoT sensor nodes by leveraging predictive modelling techniques, specifically the ARIMA model. By accurately predicting periodic patterns in sensor data, we can optimize energy usage by transmitting only the necessary information, while still ensuring effective monitoring of temperature and humidity in the IoT network.

IoT-based external attacks aware secure healthcare framework using blockchain and SB-RNN-NVS-FU techniques.

In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network nodes in IoT healthcare are vulnerable to an increased level of security threats. This paper introduces a secure Electronic Health Record (EHR) framework with a focus on IoT. Initially, the IoT sensor nodes are designated as registered patients and undergo initialization. Subsequently, a trust evaluation is conducted, and the clustering of trusted nodes is achieved through the application of Tasmanian Devil Optimization (STD-TDO) utilizing the Student's T-Distribution. Utilizing the Transposition Cipher-Squared random number generator-based-Elliptic Curve Cryptography (TCS-ECC), the clustered nodes encrypt four types of sensed patient data. The resulting encrypted data undergoes hashing and is subsequently added to the blockchain. This configuration functions as a network, actively monitored to detect any external attacks. To accomplish this, a feature reputation score is calculated for the network's features. This score is then input into the Swish Beta activated-Recurrent Neural Network (SB-RNN) model to classify potential attacks. The latest transactions on the blockchain are scrutinized using the Neutrosophic Vague Set Fuzzy (NVS-Fu) algorithm to identify any double-spending attacks on non-compromised nodes. Finally, genuine nodes are granted permission to decrypt medical records. In the experimental analysis, the performance of the proposed methods was compared to existing models. The results demonstrated that the suggested approach significantly increased the security level to 98%, reduced attack detection time to 1300 ms, and maximized accuracy to 98%. Furthermore, a comprehensive comparative analysis affirmed the reliability of the proposed model across all metrics. The proposed healthcare framework's efficiency is proved by the experimental evaluation.

Flood Prediction System Using IOT & Artificial Neural Network

Floods pose significant challenges as one of nature's most devastating disasters, making the development of accurate forecast model’s complex. This issue has led to severe consequences such as crop loss, population displacement, damage to infrastructure, and disruption of essential services. Advanced research on flood prediction models has played a crucial role in providing policy recommendations, mitigating risks, reducing human casualties, and minimizing property damage caused by floods. In this context, we propose an Internet of Things (IoT)-based flood prediction and forecasting model that prioritizes energy efficiency. Given the limited battery and memory capacity of IoT sensor nodes, we employ an energy-saving strategy within the fog layer, leveraging data diversity to minimize energy consumption. Additionally, cloud technology offers an effective storage solution. To accurately calibrate flood phases, we investigate climatic factors such as humidity, temperature, rainfall, as well as water body parameters including water flow and elevation. Neural networks are commonly used in constructing forecast systems, as they can replicate the complex calculations involved in flood physical processes, resulting in improved performance and cost-effectiveness. In our approach, the Artificial Neural Network (ANN) technique is employed for flood forecasting, and the effectiveness of different algorithms, such as Logistic Regression and Decision Tree, is assessed by comparing them to ANN. Accuracy values are computed using a classification report assessment, and graph parameters are carefully evaluated. Ultimately, our proposed system utilizes the ANN technique to train a predictive model by examining the dataset. This model generates real-time flood risk forecasts through a user-friendly graphical interface.

An energy efficient encryption technique for the Internet of Things sensor nodes

An energy efficient encryption technique for the Internet of Things sensor nodes

A MEASUREMENT APPROACH FOR INLINE INTRUSION DETECTION FOR HEARTBLEED-LIKE ATTACKS IN IOT FRAMEWORKS

Cyber security is one of the most crucial aspects of the Internet of Things (IoT). Among the possible threats, great interest is today paid toward the possible capturing of information caused by external attacks on both client and server sides. Whatever the IoT application, the involved nodes are exposed to cyber attacks mainly through the vulnerability of either the sensor nodes themselves (if they have the capabilities for networking operatively) or the IoT gateways, which are the devices able to create the link between the local nodes of the IoT network, and the wide area networks. Due to the low-cost constraints typical of many IoT applications, the IoT sensor nodes and IoT gateways are often developed on low performance processing units, in many cases customized for the specific application, and thus not easy to update against new cyber threats that are continuously identified. In the framework of cyber attacks aimed at capturing sensitive information, one of the most known was the heart bleed, which, has allowed attackers to remotely read protected memory from an estimated 24–55 percent of popular HTTPS sites. To overcome such a problem, which was due to a bug of the OpenSSL, a suitable patch was quickly released, thus allowing to avoid the problem in most of the cases. However, IoT devices may require more advanced mitigation techniques, because they are sometimes unable to be patched for several practical reasons. In this scenario, the paper proposes a novel measurement method for inline detecting intrusions due to heart bleed and heart bleed-like attacks. The proposed solution is based on an effective rule which does not require decoding the payload and that can be implemented on a low-performance general-purpose processing unit. Therefore, it can be straightforwardly implemented and included in either IoT sensor nodes or IoT gateways. The realized system has been tested and validated on a number of experiments carried out on a real network, showing performance comparable (in some cases better) with the heavier machine learningbased methods.

ALICA: A Multi-S-Box Lightweight Cryptographic Algorithm Based on Generalized Feistel Structure

With the development of science and technology, IoT devices have already become ubiquitous in the public eye. Through the perception layer, the collected data are displayed or transmitted to the server backend for analysis. Due to the increasing integration of IoT devices into people’s daily lives, privacy issues, such as data leaks, have received more attention. Most sensor nodes, such as temperature and pressure sensors in marine environments, have low computing power, storage capacity, and significant underlying heterogeneity, making it challenging to implement a standardized data security protection solution. Data security in the nodes is seriously challenged as a result. It is of great significance to design a lightweight block cipher for the Internet of Things (IoT) environment, which can ensure the security of node information. A new lightweight block cipher algorithm called ALICA is proposed in this paper, which is well-suited for the low computing power and heterogeneous device environment at the lower layers. A generalized Feistel structure and a linear structure with XOR and shift operations are used to achieve ease of software implementation. Two different S-boxes are used to create the nonlinear structure of the password, thereby enhancing the robust security of the cipher. In addition, the cipher adopts a design approach that prioritizes software efficiency while also considering hardware implementation efficiency. This makes it more suitable for low computing power, storage capacity, and resource-limited IoT sensor nodes at the lower layers.

T-Slot Antennas-Embedded ZigBee Wireless Sensor Network System for IoT-Enabled Monitoring and Control Systems

This research proposes a 2.4 GHz T-slot antennas-embedded ZigBee wireless sensor network system, consisting of an IoT gateway board and a sensor node board, for Internet of Things (IoT) applications. Simulations were first carried out to optimize the parameters for the T-shaped slot patch antenna. The prototypes of the IoT gateway and sensor node boards were subsequently fabricated and measurements undertaken. The measured impedance matching (|S11|), bandwidth, and gain of the proposed ZigBee sensor network system were -18 dB, 15.38%, and 1.722 dBi, respectively. Furthermore, the ZigBee IoT-based monitoring and control schemes based on the ZigBee wireless sensor network system were set up and experiments carried out in an enclosed area for the monitoring scheme and in an open area for the control scheme. The experimental results revealed that the proposed IoT-enabled 2.4 GHz ZigBee sensor network system with embedded T-slot patch antennas could efficiently be utilized in IoT-based monitoring and control systems. In essence, the novelty of this research lies in the integration of the IoT and ZigBee sensor network technologies to store data in a cloud server in a real-time fashion, as opposed to in the microcontroller memory which is common in conventional ZigBee systems. In addition, the data stored in the cloud server are retrievable and viewable via the Blynk application on smartphone, rendering the proposed 2.4 GHz T-slot antennas-embedded ZigBee wireless sensor network system operationally suitable for IoT-based monitoring and control systems.

EACH-COA: An Energy-Aware Cluster Head Selection for the Internet of Things Using the Coati Optimization Algorithm

In recent years, the Internet of Things (IoT) has transformed human life by improving quality of life and revolutionizing all business sectors. The sensor nodes in IoT are interconnected to ensure data transfer to the sink node over the network. Owing to limited battery power, the energy in the nodes is conserved with the help of the clustering technique in IoT. Cluster head (CH) selection is essential for extending network lifetime and throughput in clustering. In recent years, many existing optimization algorithms have been adapted to select the optimal CH to improve energy usage in network nodes. Hence, improper CH selection approaches require more extended convergence and drain sensor batteries quickly. To solve this problem, this paper proposed a coati optimization algorithm (EACH-COA) to improve network longevity and throughput by evaluating the fitness function over the residual energy (RER) and distance constraints. The proposed EACH-COA simulation was conducted in MATLAB 2019a. The potency of the EACH-COA approach was compared with those of the energy-efficient rabbit optimization algorithm (EECHS-ARO), improved sparrow optimization technique (EECHS-ISSADE), and hybrid sea lion algorithm (PDU-SLno). The proposed EACH-COA improved the network lifetime by 8–15% and throughput by 5–10%.

Near-zero-power infrared relay based on microfluidic switch and metamaterial absorber

Internet of Things sensor nodes, which integrate information acquisition, processing, exchange, and execution modules, have widely been used for unattended industrial production, environmental monitoring, and other fields. However, limited battery power constrains the lifespan of the sensor nodes. In this paper, we propose a near-zero-power infrared relay consists of microfluidic switches and a metamaterial absorber (MA). When target appears, the MA absorbs the infrared energy emitted from the target and uses it to turn on the microfluidic switch. When valid information is not present, the microfluidic switch is in “off” state with a high resistance of over 109 Ω. The infrared relay with a pair of microfluidic switches shows common mode suppression capability against environmental temperature variation. We further demonstrate a sensor node consists of the infrared relay and a MoS2 photodetector. In a standby mode, the sensor node shows near-zero power consumption. As target infrared signal occurs, the photodetector is wakened by the infrared relay and illustrates excellent optical sensing performance. The simplicity of this approach provides a route for significantly extending the lifespan of sensor nodes powered by batteries, especially the sensor nodes for detecting infrequent but critical events.

Low-Cost Manufacturing of Monolithic Resonant Piezoelectric Devices for Energy Harvesting Using 3D Printing.

The rapid increase of the Internet of Things (IoT) has led to significant growth in the development of low-power sensors. However; the biggest challenge in the expansion of the IoT is the energy dependency of the sensors. A promising solution that provides power autonomy to the IoT sensor nodes is energy harvesting (EH) from ambient sources and its conversion into electricity. Through 3D printing, it is possible to create monolithic harvesters. This reduces costs as it eliminates the need for subsequent assembly tools. Thanks to computer-aided design (CAD), the harvester can be specifically adapted to the environmental conditions of the application. In this work, a piezoelectric resonant energy harvester has been designed, fabricated, and electrically characterized. Physical characterization of the piezoelectric material and the final resonator was also performed. In addition, a study and optimization of the device was carried out using finite element modeling. In terms of electrical characterization, it was determined that the device can achieve a maximum output power of 1.46 mW when operated with an optimal load impedance of 4 MΩ and subjected to an acceleration of 1 G. Finally, a proof-of-concept device was designed and fabricated with the goal of measuring the current passing through a wire.

Optimizing the design of wide magneto-mechano-electric generators to maximize their power output and lifetime in self-powered environmental monitoring systems

Magneto-mechano-electric (MME) generators are increasingly being investigated as a sustainable power source for Internet of Things (IoT) systems owing to their ability to efficiently harvest electrical energy from stray magnetic field noise. However, to be practical, this technology must achieve a longer lifespan while maintaining high-power output and withstanding magnetic field fluctuations. To address this, extensive numerical simulations and experimental verifications have been performed to amplify the magneto-mechanic vibration by increasing the magnetic proof mass volume, generate high electrical output through a larger active piezoelectric volume, and enhance lifetime by optimizing the cantilever structure in a wide-structural MME architecture. MME generators with a wide and short (WS) trapezoidal structure (WS-MME) have demonstrated excellent performance, with a high output power of 7.4 mW (equivalent power density of 0.58 mW/cm3. Oe) and an excellent lifespan of over 1012 fatigue cycles under a magnetic noise field level of 3 Oe. Moreover, a self-powered environmental IoT sensor node has been demonstrated by continuously powering the multifunctional (temperature, humidity, light, sound noise, UV index, pressure, discomfort index, and heatstroke) sensor and wireless communication systems by utilizing the stray magnetic field noise around the electrical power cables of various home appliances. In conclusion, the combination of a longer lifespan and high-power generation achieved with WS-MME generators is a significant step towards the commercialization of MME generators for powering wireless sensor technology.

SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair

The modern innovation called the Internet of Things (IoT) empowers individuals to connect to anybody and anything at any point, wherever. The application of the IoT in smart cities concerning smart healthcare management can improve patient welfare, user acceptance, the standard of living, and accurate illness monitoring. Powered wheelchairs (PW) with sensors, computers, and other connected assistive technologies are called smart wheelchairs. Smart wheelchairs with sensing abilities are intended to offer universal connectivity using cloud and edge computing technology. Numerous outstanding people were impacted by paralyzing phenomena, including Stephen Hawking and Max Brito. The issue of legitimacy is one of the most important difficulties in e-health applications, because of how sensitive the technology is, and this needs to be appropriately handled. To safeguard the data transport, usage, and interchange between sensor nodes/smart wheelchairs and servers, e-health applications require an authentication method. As all conversations use wireless channels, e-health apps are exposed to various vulnerabilities. Additionally, the IoT has limited computational and power capacity limitations. To combat the various security risks, the present research offers a user authentication technique that is efficient and ensures anonymity. The suggested method creates a safe connection for the authorized entity and forbids unauthorized entities from accessing the Internet of Things sensor nodes. The suggested approach has lower communication and computation overheads than the traditional techniques, making it more effective. In addition, the security verification of the presented protocol is scrutinized through AVISPA. The proposed scheme, on average, requires only 12.4% more computation cost to execute. Compared to the existing approaches, the suggested protocol’s extra computational cost can be compensated for by its enhanced security, while the suggested method’s communication cost is 46.3% smaller.

Toward Sustainable Greenhouses Using Battery-Free LiFi-Enabled Internet of Things

As the world faces a changing climate, agriculture needs to develop more efficient and sustainable food production systems. Traditional farming methods consume considerable amounts of energy and are largely manually controlled, which leads to sub-optimal production. Green-houses, which enable year-round crop growth, can play an important role in efficient food production. Leveraging the need for artificial light in greenhouses when the natural sunlight available is not sufficient, we envision that recent progress in Internet of Things (IoT) technology, together with novel Light-Fidelity (LiFi)-based methods have the potential to significantly reduce energy and resources used in food production. In this article we describe our work towards sustainable and precision greenhouses by using LiFi-enabled IoT. Here we present a battery-free wireless network of IoT sensor nodes that exploit LiFi for both communication and power harvesting, while monitoring environmental conditions for optimal greenhouse operation and plant production. We highlight the research challenges and the way forward to integrate LiFi to monitor and control greenhouses, as well as a proof-of-concept LiFi-enabled IoT system for a real-world greenhouse.

Investigation of Self-Powered IoT Sensor Nodes for Harvesting Hybrid Indoor Ambient Light and Heat Energy.

Sensor nodes are critical components of the Internet of Things (IoT). Traditional IoT sensor nodes are typically powered by disposable batteries, making it difficult to meet the requirements for long lifetime, miniaturization, and zero maintenance. Hybrid energy systems that integrate energy harvesting, storage, and management are expected to provide a new power source for IoT sensor nodes. This research describes an integrated cube-shaped photovoltaic (PV) and thermal hybrid energy-harvesting system that can be utilized to power IoT sensor nodes with active RFID tags. The indoor light energy was harvested using 5-sided PV cells, which could generate 3 times more energy than most current studies using single-sided PV cells. In addition, two vertically stacked thermoelectrical generators (TEG) with a heat sink were utilized to harvest thermal energy. Compared to one TEG, the harvested power was improved by more than 219.48%. In addition, an energy management module with a semi-active configuration was designed to manage the energy stored by the Li-ion battery and supercapacitor (SC). Finally, the system was integrated into a 44 mm × 44 mm × 40 mm cube. The experimental results showed that the system was able to generate a power output of 192.48 µW using indoor ambient light and the heat from a computer adapter. Furthermore, the system was capable of providing stable and continuous power for an IoT sensor node used for monitoring indoor temperature over a prolonged period.

Decentralized power grid fault traceability system based on internet of things and blockchain technology

With the economic and social development of China, the scale of the power grid continues to expand. Rapid location and diagnosis of power failures have become significant for China to maintain its stable development of power system. In recent years, the Internet of Things (IoT) based on 5G technology has been applied to power grid more widely. Meanwhile, given the fact that the blockchain is traceable and tamper-resistant, the combination of the blockchain and IoT is considered to locate power failures quickly and assist professional maintenance personnel to deduce the cause of failures, minimizing economic loss. With the foundation of IoT sensor node data, this paper designs a decentralized electronic certificate scheme based on blockchain and Interplanetary File System (IPFS) to collect data of each node of the power system and store it in the blockchain. The model of data sharding, storage and certificate optimizes the utilization of storage space of the blockchain, reducing the time required for system access to nodes. Traceability of data stored on blockchain data is employed to quickly and accurately trace faults of the power system, providing strong technical support for the safe and stable operation of China’s power system.

A Measurement Approach for Inline Intrusion Detection of Heartbleed-Like Attacks in IoT Frameworks

Cyber security is one of the most crucial aspects of the Internet of Things (IoT). Among the possible threats, great interest is today paid toward the possible capturing of information caused by external attacks on both client and server sides. Whatever the IoT application, the involved nodes are exposed to cyberattacks mainly through the vulnerability of either the sensor nodes themselves (if they have the capabilities for networking operativity) or the IoT gateways, which are the devices able to create the link between the local nodes of the IoT network, and the wide area networks. Due to the low-cost constraints typical of many IoT applications, the IoT sensor nodes and IoT gateways are often developed on low-performance processing units, in many cases customized for the specific application, and thus not easy to update against new cyber threats that are continuously identified. In the framework of cyberattacks aimed at capturing sensitive information, one of the most known was the heartbleed, which, has allowed attackers to remotely read protected memory from an estimated 24%–55% of popular HTTPS sites. To overcome such a problem, which was due to a bug of the OpenSSL, a suitable patch was quickly released, thus allowing to avoid the problem in most of the cases. However, IoT devices may require more advanced mitigation techniques, because they are sometimes unable to be patched for several practical reasons. In this scenario, the article proposes a novel measurement method for inline detecting intrusions due to heartbleed and heartbleed-like attacks. The proposed solution is based on an effective rule which does not require decoding the payload and that can be implemented on a low-performance general-purpose processing unit. Therefore, it can be straightforwardly implemented and included in either IoT sensor nodes or IoT gateways. The realized system has been tested and validated on a number of experiments carried out on a real network, showing performance comparable (in some cases better) with the heavier machine learning-based methods.

Wirelessly powered large-area electronics for the Internet of Things

Powering the increasing number of sensor nodes used in the Internet of Things creates a technological challenge. The economic and sustainability issues of battery-powered devices mean that wirelessly powered operation—combined with environmentally friendly circuit technologies—will be needed. Large-area electronics—which can be based on organic semiconductors, amorphous metal oxide semiconductors, semiconducting carbon nanotubes and two-dimensional semiconductors—could provide a solution. Here we examine the potential of large-area electronics technology in the development of sustainable, wirelessly powered Internet of Things sensor nodes. We provide a system-level analysis of wirelessly powered sensor nodes, identifying the constraints faced by such devices and highlighting promising architectures and design approaches. We then explore the use of large-area electronics technology in wirelessly powered Internet of Things sensor nodes, with a focus on low-power transistor circuits for digital processing and signal amplification, as well as high-speed diodes and printed antennas for data communication and radiofrequency energy harvesting.

Fuzzy based energy proficient secure clustered routing (FEPSRC) for IOT-MWSN

IoT-Mobile Wireless Sensor Networks (IMWSNs) are being employed in a variety of simulators to visually demonstrate the exposure, energy usage situation, and expected life duration of Internet of Things (IoT) mobile sensors. The majority of academics have projected and expanded routing procedures in order to extend the network’s life cycle. In IMWSNs, clustering is the most important process for improving energy efficiency. In cluster approaches, each IoT sensor node provides the acquired data to the cluster-head of their own cluster. The cluster-head embraces the conscientiousness of gathering prepared information and directing it to the arranged network’s basestation. A fuzzy based energy proficient secure clustered routing (FEPSRC) is proposed in this research effort, which takes the residue energy, remoteness from the basestation, and compactness of IoT sensor nodes in its locality as input to the Fuzzy-Inference-System. For cluster-head selection, an eligibility ratio is calculated for each IoT sensor node. This protocol guarantees energy harmonizing by electing the preeminent IoT sensor node for the position of cluster-head, velocity of IoT sensor nodes are estimated and also provides best path for routing. The simulation consequence illustrates that projected fuzzy based energy proficient secure clustered routing condensed entire power expenditure, diminishes E-to-E delay, amplifies packet deliverance percentage and accomplishes maximal network life span.

A Subspace Approach to Sparse-Sampling-Based Multi-Attribute Data Aggregation in IoT

The emergence of the heterogeneous Internet of Things (IoT) has realized the demand for multi-attribute data collection in response to the increasing demand for information in diverse applications. Sparse sampling has been used to reduce network energy consumption in order to extend the life of energy-constrained networks. Real-time multi-attribute data aggregation under the sparse sensing framework has become a research focus. Therefore, we proposed a sparse-sampling-based IoT data aggregation approach to reduce network energy consumption and enable real-time multi-attribute reconstruction. For data collection, a sparse sampling data collection approach is proposed that can successfully collect and transmit the multi-attribute data to the sink even while certain IoT sensor nodes are in the sleep mode. For data reconstruction, a real-time multi-attribute data reconstruction method based on subspace is proposed. The proposed method arranges multi-attribute data in a tensor form in order to further utilize the correlation of multi-attribute data. Subspaces representing the spatial distributions of the multi-attribute data can be obtained from the previously reconstructed data. Incorporating total variation constraint, the proposed method reconstructs the current time slot multi-attribute data with high precision in real time. The experimental results demonstrate the effectiveness of the proposed method in real-time multi-attribute data reconstruction.

Analysis of the Role of Design-Driven Innovation in the Interaction Design of Image Indexing Software under the Background of the Internet of Things

More and more medical images are being produced quickly by modern imaging devices that are connected to the Internet of Things (IoT). For the retrieval of necessary images from a conventional big volume database, the continuous scanning of all image data in an IoT network appears to be ineffective and computationally expensive. Effective image retrieval from a big image database necessitates the use of an effective and scalable image indexing approach. Existing image indexing methods have significant drawbacks, including lower efficiency, constrained scalability, increased computing demands, and longer processing times. To index the medical images obtained by IoT sensors, we suggested a novel, effective Content-Based Cascaded Gabor Wavelet Algorithm (CBCGWA) in this study. Further in this study, the medical images from computed tomography (CT) are employed for medical indexing Totally, the dataset included 168 individually annotated square patches in a subset of 115 high-resolution CT (High-Resolution CT) slices used in this study. An adaptive median filter is used for preprocessing the CT medical images once they are acquired from IoT sensor nodes connected to medical imaging systems. The Gaussian Adaptive Attention Network is then used to cluster together images with comparable attributes (GAAN). The suggested method is used to index the images after image clustering. The cloud database is where the indexed images are eventually maintained. The comparison of the recommended indexing approach to the existing indexing strategies revealed that it is better in terms of processing time, power, and indexing efficiency.