Smart Environmental Monitoring Research Articles

3D position deployment and performance optimization of mmWave UAV-assisted HetIoT under jamming condition

Heterogeneous Internet of Things (HetIoT) has received widespread attention due to its provision of various convenient services in fields such as smart cities, intelligent transportation, environmental monitoring and security systems. Due to HetIoT inherently demands high data rates, bandwidth, and low latency, the application of millimeter-wave (mmWave) unmanned aerial vehicle (UAV) as emergency aerial base station (ABS) providing services to HetIoT users has become a low-cost and efficient means. However, due to the sensitivity of mmWave to obstacles and jamming, guaranteeing network performance has become a pressing issue. This paper considers a mmWave UAV-assisted HetIoT under jamming conditions, where auxiliary ABSs serve multiple ground users (GUs) who generate a large amount of sensor data. We establish a coverage maximization problem under the constraints of signal-to-interference ratio (SIR) threshold, maximum power of ABSs and maximum number of GUs that the base station can serve, and propose a novel ABS hovering deployment algorithm M-HiAPSO that combines the artificial potential field (APF) method and the improved particle swarm optimization (PSO) algorithm in a hierarchical manner. Specifically, the multi-element APF method is used to characterize the horizontal force between nodes, combined with the improved hierarchical adaptive PSO algorithm to adjust the horizontal position of the ABS to obtain the optimal UAV hovering position and power allocation strategy. Numerical results show that the coverage rate reached 96.2% when the number of iterations was 283, and it can reach up to 99.6%.

Theoretical study and analysis of advanced wireless sensor network techniques in Internet of Things (IoT)

Wireless sensor networks those based on the Internet of Things (IoT) have evolved fast in numerous areas in recent years. Devices and sensors may interact with each other without human involvement in the IoT network. An IoT-based system where people and things are connected by wired and wireless technology including Wireless Sensor Networks (WSNs), ZigBee, NFC, RFID, GPRS, LTE, and Bluetooth is now one of the most popular technical ideas. The potential of the Internet of Things (IoT) has received enormous attention from the corporate and scientific communities during the past 10 years. The IoT can provide a number of significant benefits to application spaces. Researchers may be able to show crucial facts about IoT-based smart environmental monitoring systems. IoT has faced a variety of challenges, including authentication, differentiating evidence, accessibility and security and protection. Sociotechnical trust systems have also been examined in the past in various studies (STTS). Existing smart settings are now dealing with serious IoT security, protection, and STTS challenges. Trust-the-board will be highlighted and characterized, as well as how it should be utilized in the Internet of Things. As a result, the potential benefits of data aggregation solutions that use less energy while increasing the organization's lifespan are enormous. IoT-WSN data aggregation methods and computations have been reimagined. Specifically, this article examines the writing with reference to the views of sensor systems administrators in order to preserve energy and gather data.

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works.

Efficient key distribution for secure and energy-optimized communication in wireless sensor network using bioinspired algorithms

Wireless Sensor Networks (WSNs) play a major part in numerous applications such as smart agriculture, healthcare, and environmental monitoring. Safeguarding protected communication in this network is dominant. Securing data transmission in WSNs needs a strong key distribution device to defend against malicious attacks as well as illegal access. Traditional techniques like pre-shared or centralized key management are often unreasonable owing to resource limitations, particularly in large-scale sensor systems. To overcome this challenge, a lightweight key distribution technique is employed for safeguarding the security and privacy of data transmission streamlining processes decreasing computational overhead as well as energy consumption. By optimizing and simplifying key distribution devices, we propose to improve the complete efficacy and trustworthiness of WSNs that aid safe communication while preserving valuable energy resources. Therefore, this article designs an Efficient Key Distribution for Secure and Energy-Optimized Communication using Bioinspired Algorithms (EKD-SOCBA) for WSN. The purpose of the EKD-SOCBA technique is to accomplish security and energy efficiency in WSNs. Initially, the EKD-SOCBA technique applies a golden jackal optimization (GJO) based clustering approach to cluster the nodes and select cluster heads (CHs). Also, a lightweight Dynamic Step-wise Tiny Encryption Algorithm (DS-TEA) is applied to secure data transmission in the network. Finally, a lightweight key management phase is employed to protect the encryption key and decrease energy utilization and overhead costs. To exhibit the enhanced act of the EKD-SOCBA model, a comprehensive set of imitations was involved. Extensive results stated enhanced presentation of EKD-SOCBA methodology over other models on WSN.

Raspberry-Pi Based Smart Environmental Monitoring and Controlling System

This study presents the design and implementation of an Internet of Things (IoT) based framework for continuous monitoring and control of air quality parameters. The system incorporates various sensors, including the DHT11 for temperature and humidity measurement, the MQ-135 for gas detection, an LM393 comparator sensor for rainfall detection, and an additional 12V fan controlled by a relay. These sensors seamlessly interface with a Raspberry Pi, serving as the central control unit. The collected data is transmitted to the ThingSpeak cloud platform for real-time visualization and analysis. The system's architecture allows for remote monitoring and control of air quality parameters, facilitating proactive decision-making for environmental management. Experimental results demonstrate the system's effectiveness in providing accurate and timely information about temperature, humidity, rainfall, and gas concentrations in the air, making it a valuable tool for environmental monitoring and control applications.

ILAPU-Q: An Improved Lightweight Authentication Protocol for IoT Based on U-quark Hash Function

Background: In the last decades, the development of Internet activities has been significantly accelerated, particularly with the emergence of the Internet of Things (IoT). Heterogeneous devices in the IoT can seamlessly and feasibly inter-connect with each other without human interaction. Due to this revolution, many applications have been adopted in the arena of smart healthcare, e-commerce, environmental and habitat monitoring, etc. In order to promote and facilitate people's standards of living around the world. However, these unbounded applications bring more challenges to the storage capabilities of devices, and their security and privacy preservation. Moreover, security issues suffer from weak authentication protocols. Methods: To address these issues, suitable and secure lightweight mutual authentication schemes based on Elliptic Curve Cryptography (ECC) are required for the approval of Identity Management (IDM) of devices in the IoT. In this paper, we will propose an improved mutual authentication scheme based on ECC, coupled with a relevant seminal work considered as a reference in the field. This scheme is combined with U-Quark, a lightweight hash function, to guarantee the security needed in the IoT environment. Results: We will compare our amended protocol with a seminal scheme as an established reference in terms of computation cost, storage cost, and executing CPU time to demonstrate that our version can ensure the most favorable performance during the authentication process. Conclusion: Finally, our proposed mutual authentication scheme has demonstrated its effectiveness in enhancing the security of IoT devices when compared to the seminal work in the same computational environment.

Artificial Intelligence: A New Paradigm for Distributed Sensor Networks on the Internet of Things: A Review

The confluence of Artificial Intelligence (AI) with the Internet of Things (IoT) has created new opportunities for distributed sensor networks in a variety of sectors. The potential of AI as a new paradigm for distributed sensor networks in the IoT is investigated in this review article, with an emphasis on the convergence of architecture, methodologies, platforms, sensors, devices, energy approaches, communication and networking, and applications. An analysis was conducted to examine the existing research in this field using a comprehensive literature review, revealing notable advancements, and indicating the need for further investigation. Furthermore, Moreover, suggestions and ideas are presented regarding potential innovation within the sector, with a particular emphasis on the imperative for further research and development. Our findings highlight AIoT's transformational ability in allowing more efficient and intelligent sensor networks, with implications for smart homes, healthcare, environmental monitoring, and industrial automation. The potential for harnessing the power of AI holds the key to unlocking novel opportunities and addressing the challenges inherent in dispersed sensor networks within the realm of the Internet of Things (IoT). This research advances our understanding of AIoT convergence and lays the groundwork for future breakthroughs in this interesting topic.

Overcoming cold start and sensor bias: A deep learning-based framework for IoT-enabled monitoring applications

Target localization is an essential aspect of smart environmental monitoring, which has gained prominence thanks to the Internet of Things (IoT) paradigm. Localization is the process of determining the location of an unknown target within an area of interest (AoI) based on data gathered by IoT sensors. Existing target localization works assume full knowledge about the sensors without considering the cold start problem, which may arise when new sensors join the network. This is exacerbated by the problem of bootstrapping in IoT sensors, where the system needs to identify and register unknown sensors. Additionally, the variability in sensors characteristics is inevitable due to the numerous factors that may alter the sensors behavior, raising the problem of sensor bias. Existing works dealing with the aforementioned problems are inadequate, since they involve a pre-processing step and assume the bias to be static over time, resulting in inaccurate localization systems. Therefore, this work proposes a robust and dynamic, holistic localization system that can find the location of an unknown emitting target regardless of the sensors characteristics. The proposed system comprises of two phases: 1) characterization and localization phase; where a deep learning model is utilized to characterize the sensors based on their locations and readings only, whereas Bayesian algorithm is employed to determine the unknown target location by leveraging the predicted sensors characteristics, and 2) correction phase, where an update metric is devised to refine the predicted sensors characteristics based on the estimated target location. The validation of the proposed approach was conducted using real-life and synthetic datasets and compared against an existing benchmark, where it shows an improvement of ∼79%, in terms of both localization and prediction errors. Thus, showcasing the effectiveness and potential advantages of the proposed approach in localizing unknown target(s).

IoT Resources and Their Practical Application, A Comprehensive Study

The Internet of Things (IoT) has become a paradigm shifter, connecting an enormous number of smart devices and facilitating seamless data exchange for a diverse array of applications. The availability and effective use of the IoT ecosystem's resources are key factors in determining how its practical applications will develop as they mature. The IoT resources and their practical application across several areas are thoroughly explored in this paper. The paper begins by classifying and describing the various sensor types, their applications in various fields, and IoT resources, highlighting their contributions to real-time data collection, processing, and transmission. It then goes on to demonstrate a wide range of real-world uses for these resources, such as smart cities, education, agriculture, business, healthcare, environment monitoring, transportation, and industrial automation. However, utilizing IoT resources effectively is not without difficulties. Critical difficulties such as resource allocation, scalability, security, interoperability, and privacy concerns are identified and discussed in the paper. Furthermore, the paper also highlights future directions and emerging trends in IoT resource management, including edge computing, cloud computing, human machine integration, and compatibility with other systems. These developments aim to increase the dependability of IoT applications in diverse settings and optimize resource allocation. This paper's conclusion highlights the crucial role that IoT resources play in advancing real-world applications across a variety of areas. Researchers, practitioners, policymakers, and other stakeholders may collaborate together to effectively leverage the full potential of IoT resources to build intelligent, effective ecosystems that meet the needs of contemporary society by solving difficulties and utilizing developing trends.

Knowledge Representation of Sensor Dataset with IoT Collaboration of Semantic Web and IoT: Storage of Temperature and Humidity Details

Introduction:: Today, Internet of Things applications offer new opportunities in all domains like home automation, transportation, medical diagnosis, agriculture, etc. According to McKinsey Global Institute research, IoT will cover a market share of over $11.1 trillion by 2025. Moreover, Semantic web technology approaches are used in IoT applications so that machines can understand and interpret sensor-collected data. Method:: Our proposed system uses a DHT11 sensor, NodeMCU for data collection, and ThingSpeak cloud for data analysis and visualization. It utilizes the Protégé tool to develop semantic data modelling using Ontology/RDF graphs and retrieval for future SPARQL queries. Result:: This approach ensures the optimal presentation of sensor data and the meaning of data and controls the information for the Home Automation System. By semantic layer, we improved integration, interoperability, discovery, and data analysis. Conclusion:: As far as applications are concerned, semantic technologies and IoT sensor data can be transformed into a more valuable and practical format, enabling intelligent applications and systems development across multiple fields, such as smart cities, industrial automation, healthcare, and environmental monitoring.

Support Vector Machine based Automatic Classification Method for IoT big Data Features

<p>As China’s information technology development shifts from a single high-speed growth stage to a multidimensional high-quality development stage, the Internet of Things (IoT) enters all aspects of life and becomes more and more popular. The demand for IoT big data information analysis and processing is increasing, and the important role of feature automatic classification methods becomes increasingly prominent. This research proposes SPO-SVM and WSPO-SVM models based on support vector machine for smart home environment monitoring data under the big data of Internet of Things, and then optimizes them with particle swarm optimization algorithm and adaptive method. Finally, the data set is selected for comparative experimental analysis of each optimization algorithm model. The experimental results show that the optimized WSPO-SVM model has less total misclassification and single class misclassification compared with other algorithms under Wine dataset. In cross-validation, both its classification accuracy performance outperformed other algorithms. Under 10 sets of smart home environment monitoring data sets, the WSPO-SVM algorithm model achieves 100% accuracy in 6 out of 10 test data sets, with an average accuracy of 97.67%, which is about 9% higher than the ordinary SVM algorithm model and about 15% higher than other feature classification algorithms. The experimental results prove that the WSPO-SVM algorithm can complete the feature classification work in the IoT big data environment, which meets the expectation.</p> <p> </p>

A Coupled-Optimization Based Master Node Selection and Path Finding on Mobile Ad Hoc Network for Smart Environment Monitoring

A Coupled-Optimization Based Master Node Selection and Path Finding on Mobile Ad Hoc Network for Smart Environment Monitoring

Reinforcement learning framework for UAV-based target localization applications

Smart environmental monitoring has gained prominence, where target localization is of utmost importance. Employing UAVs for localization tasks is appealing owing to their low-cost, lightweight, and high maneuverability. However, UAVs lack the autonomy of decision-making if met with uncertain situations. Therefore, reinforcement learning (RL) can introduce intelligence to UAVs, where they learn to act based on the presented situation. Existing works focus on UAV trajectory optimization, navigation, and target tracking. These methods are application-specific and cannot be adapted to localization tasks since they require prior knowledge of the target. Moreover, the current RL-based autonomous target localization systems are lacking since- 1) they must keep track of all visited locations and their corresponding readings, 2) they require retraining when encountering new environments, and 3) they are not scalable since the agent's movement is limited to slow speeds and for specific environments. Therefore, this work proposes a data-driven UAV target localization system based on Q-learning, which employs tabular methods to learn the optimal policy. Deep Q-network (DQN) is introduced to enhance the RL model and alleviate the curse of dimensionality. The proposed models enable smart decision-making, where the sensory information gathered by the UAV is exploited to produce the best action. Moreover, the UAV movement is modeled based on motion physics, where the actions correspond to linear velocities and heading angles. The proposed approach is compared with different benchmarks, where the results indicate that a more efficient, scalable, and adaptable localization is achieved, irrespective of the environment or source characteristics, without retraining.

A hybrid fast inference approach with distributed neural networks for edge computing enabled UAV swarm

Nowadays, unmanned aerial vehicle (UAV) swarm supported by mobile edge computing is attracting more and more attention, such as smart agriculture, smart transportation, smart security surveillance, and smart environmental monitoring. Nevertheless, small UAV devices cannot deploy advanced best-performing DNN models with insufficient computing and storage resources. Aerial computing centers serving as edges can collect tasks from UAVs, reduce UAV load, and provide high-accuracy inference. Limited radio resources in offloading and load imbalance in computing centers could lead to significant latency in task inference. Considering image processing in UAV applications, we propose a hybrid inference framework combining early exit and task offloading based on distributed neural network. The bottleneck-designed DNN provides a smaller intermediate data size than the original input. By deploying shallow neural networks at the drone terminals and advanced DNNs at the aerial computing servers, latency-constrained tasks can be exited after local inference, offloaded to the edge after local computation, or directly offloaded to the edge. Compared to binary or partial offloading, the early-exit-based framework provides fast inference as the workflow stops when a confident result is obtained. Combining task offloading into the inference process offers a more flexible option to release the UAV computing load. The proposed approach efficiently utilizes wireless transmission and balances the computation load to maximize the system inference accuracy within the latency constraints. Practical task inference simulations demonstrate that our approach obtains higher system accuracy under different radio resources, task generation rates, and terminal computing capacities conditions.

The Impact of a Collaborative IoT Framework for Smart Cities and Environmental Monitoring

this paper proposed an architecture that is SenSquare, it is capable to carry various data sources which is coming through an open IOT program and throng sensor campaign, as well as displaying an integrated access for the utilizers. The goals of smart cities are to progress the quality of citizen in life through leveraging details on process of the urban scales that removed by diverse data source gathered at city broad deployment. In this paper checking the whole surfaces of a compound system, traversing through problems of various quality, this paper contract with the diverse data ranking, the Mobile Crowd sensing (MCS) administration to data ecological, acting information and integration, the services of IOT installation and redeployments. This study explained the suggested resolutions in cooperation with a functions and current ways to sitting unlock defies. Eventually, it pretends the ability of the SenSquare by jointly the desktop client and mobile.

An Edge Device Framework in SEMAR IoT Application Server Platform

Nowadays, the Internet of Things (IoT) has become widely used at various places and for various applications. To facilitate this trend, we have developed the IoT application server platform called SEMAR (Smart Environmental Monitoring and Analytical in Real-Time), which offers standard features for collecting, displaying, and analyzing sensor data. An edge device is usually installed to connect sensors with the server, where the interface configuration, the data processing, the communication protocol, and the transmission interval need to be defined by the user. In this paper, we proposed an edge device framework for SEMAR to remotely optimize the edge device utilization with three phases. In the initialization phase, it automatically downloads the configuration file to the device through HTTP communications. In the service phase, it converts data from various sensors into the standard data format and sends it to the server periodically. In the update phase, it remotely updates the configuration through MQTT communications. For evaluations, we applied the proposal to the fingerprint-based indoor localization system (FILS15.4) and the data logging system. The results confirm the effectiveness in utilizing SEMAR to develop IoT application systems.

An event‐driven fusion framework with auto‐scaling of edge intelligence for resilient smart applications in developing countries

AbstractInternet of Things has been a popular technology in recent years for deploying a large‐scale, smart environment monitoring application across the country, using fog computing and the cloud. However, most locations of the developing countries suffer from power outages and limited network connectivity. Moreover, varied population in different locations, may lead to either frequent or rare changes in the state of the monitored environment. Due to these stochastic conditions, there may be substantial increase in service time of the application with unnecessary battery and resource consumption of the fog node. For efficient utilization of fog node resources in dynamic environment conditions, an event‐driven information fusion framework is proposed using docker containerization technology and MQTT (Message Queuing Telemetry Transport) as application layer protocol. The proposed framework provides resilience to the application in the presence of stochastic conditions and auto‐scales edge intelligence with minimum scaling operations. The performance of the framework is validated on a smart sanitation system use‐case application, proposed for autonomous monitoring of restrooms deployed in Indian rural and semi‐urban environment, and the experimental result show deviation of 2.8% in average response time of the application with average accuracy of 98.9%.

Artificial intelligence-assisted air quality monitoring for smart city management.

The environment has been significantly impacted by rapid urbanization, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosystems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete measurement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of machine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities. This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions. In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5concentration, LSTM performed the best overall high R2values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5,PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.

Low-Power IoT for Monitoring Unconnected Remote Areas.

This paper deals with IoT devices deployed in remote areas without terrestrial Internet connectivity. We consider connecting IoT devices on the ground to the Internet through an aerial system based on an Unmanned Aerial Vehicle (UAV) for smart agriculture and environmental monitoring. The UAV flying over the remote area receives data from distributed IoT devices. The transmissions between the ground sensors and the UAV are carried out via LoRa. We have proposed a synchronization protocol for the opportunistic communication of LoRa IoT devices with a gateway onboard the UAV to save node battery life. Class A LoRa nodes on the ground transmit only when the UAV is expected to pass close to them; otherwise, they stay in the sleeping state most of the time. This paper provides a detailed description of the formulation of the synchronization protocol. The UAV's flying dynamics have been considered for characterizing its speed and the time of visibility of each IoT sensor. Our model has allowed an analytical approach that can help to determine the best settings for LoRa transmissions. Finally, experiments have been carried out to assess the path loss attenuation, and a laboratory setup of the synchronization protocol has been implemented for the preliminary validation of our scheme.

A deep learning framework for target localization in error-prone environment

The use of Internet of Things (IoT) in environment monitoring has led to the development of Smart Environmental Monitoring (SEM) paradigm. Target or source localization, that determines the underlying cause of environmental occurrences, is an important aspect of SEM. To prevent an environmental event in becoming a potential disaster, swift and early localization of its source is crucial. Target localization is performed with the help of IoT sensors, which are typically deployed in hazardous environments and are not easily serviceable, making energy efficiency a requirement for SEM systems. Furthermore, IoT sensors may provide anomalous readings due to low battery, low sensor sensitivity, faults, or malicious attacks. This alters the decision-making process and leads to inaccurate localization of the source. Hence, detecting and dealing with anomalies, while utilizing a limited number of sensor nodes, are key factors in addressing reliability and energy efficiency issues. The current works are iterative in nature, employs redundant nodes, do not efficiently address anomalous readings, which leads to slow and imprecise localization, while increasing energy consumption. Moreover, they are application specific, making them less adaptable to other sensing tasks. Therefore, this work proposes a comprehensive and novel deep learning-based system that — (a) repopulates missing data from inactive sensors, (b) detects and rectifies anomalous data, and (c) instantly localizes a target. The efficacy of the proposed approach is validated in a radioactive environment and compared to existing benchmark. The results show that the proposed approach achieves precise, rapid, and efficient localization by achieving 5 times better accuracy, 50 times faster speeds, and 3 times lesser energy consumption.