8.1 Current Challenges in IoT-Based Weather Monitoring
The central thrust of the literature review revolves around the pervasive use of IoT-based systems, tasked with monitoring a panoply of environmental parameters spanning meteorological conditions, flood occurrences, soil moisture levels, seismic activities, and air quality. The comprehensive evaluation embodied in this review encapsulates a discerning assessment of the myriad sensors and platforms that feature prominently in these systems, meticulously scrutinizing their efficacy with respect to data collection and subsequent analysis (Sankar et al., 2023). A salient focus pertains to gathering weather-related data, which is achieved through the interrogation of diverse sensors, including but not limited to temperature and humidity sensors, barometric pressure sensors, and rain sensors. This trove of data is thoughtfully processed and securely stored via the ThingSpeak IoT platform.
The study goes on to vividly elucidate the manner in which weather-related information is intuitively conveyed through a visually intuitive interface hosted on a website. A noteworthy case in point is presented in the form of an IoT-based flood monitoring system, ingeniously deploying an Ultrasonic HC-SR04 sensor (Rani et al., 2020). By virtue of its design, this system seamlessly transmits critical data to the ThingSpeak IoT Cloud Platform, facilitating astute analysis and visual representation. A cardinal emphasis is placed upon the early detection and timely alerting mechanisms integral to flood monitoring. Another intriguing facet explored within the literature resides in the domain of IoT-based soil moisture tracking systems, underpinned by the integration of a NODEMCU ESP8266 and a dedicated soil moisture sensor. This technological amalgamation empowers the system to continually transmit data to the ThingSpeak IoT platform, thereby affording real-time monitoring of soil moisture levels (Mohammed et al., 2023). The study thoughtfully delineates the intricacies of the soil moisture monitoring circuit, underscoring its robust design.
Moreover, the paper delves into the realm of IoT-based earthquake monitoring, artfully harnessing a Wi-Fi module and a specialized vibration sensor engineered for seismic activity detection (Chen et al., 2023). The earthquake-checking circuit is shown in Fig. 18. This technological ensemble proficiently dispatches data to the ThingSpeak platform, thereby facilitating its cogent visual representation. The study further augments its contributions by furnishing an illustrative blueprint of the earthquake monitoring circuit.
The study's culmination pivots towards deploying an MQ135 sensor, specifically tailored to the task of air pollution detection. The system artfully segregates and categorizes distinct vapor entities as it endeavors to discern the presence of air pollution (Roberts, 2018). Of paramount importance is the study's diligent emphasis on the formidable challenge posed by the monitoring of air pollution across diverse geographic locales, underscored by the pivotal role played by IoT-based technologies in mitigating this vexing conundrum (Alam et al., 2018). The research meticulously unveils the intricate design underpinning the air pollution monitoring system. Collectively, these experiments resoundingly underscore the proficiency of IoT-based systems in the tracking and surveillance of environmental variables, concurrently shedding light on the manifold platforms and sensors strategically marshaled for the triumvirate of data collection, data processing, and data visualization. In the broader context of environmental monitoring systems, the studies fervently extol the virtues of early detection mechanisms, real-time monitoring capabilities, and proactive alerting protocols (Marsden, 2019).
For the envisioned system to metamorphose into a holistic and comprehensive stratagem, due consideration must be accorded to the seamless integration of myriad environmental facets into the monitoring apparatus, transcending the traditional focus on isolated variables. This ambitious integration paradigm might potentially encompass the fusion of meteorological data with insights into soil moisture content or the amalgamation of air pollution metrics with contemporaneous weather conditions, thereby facilitating a panoramic comprehension of environmental dynamics (Karthikeyan & Mishra, 2021). The concomitant tribulations associated with scalability and implementation of these systems ought to be subjected to rigorous scrutiny. It is incumbent upon future research endeavors to diligently investigate the multifaceted challenges underpinning the scaling of these systems to encompass more expansive geographic domains, coupled with a pragmatic evaluation of practical implementation facets. This holistic assessment mandate should encompass an exploration of the communication protocols, power management strategies, and data processing modalities indispensable for the effective operation of such systems, particularly in the context of large-scale deployment (Hussain et al., 2018). Notably, extant studies frequently confine their purview to small-scale deployments, thereby accentuating the imperative of embracing a broader horizon.
8.2 Future Prospects and Emerging Technologies
The author has proposed a system that leverages the Internet of Things (IoT) and big data technologies to develop a portable automatic weather monitoring and forecasting system. This innovative system acquires data related to five crucial meteorological factors and employs a temperature prediction model to generate more accurate regional weather forecasts (Wang et al., 2022). It comprises two main components: a weather data monitoring device and a cloud server database. The monitoring device collects sensor data and transmits it to the cloud server via 4G connectivity (Petrakis et al., 2018). The overarching objective of this system is to enhance meteorological service capabilities and contribute to the development of weather information infrastructure. Notably, the research paper underscores the system's accuracy, highlighting that the forecasted weather conditions typically fall within a 5% margin of error compared to the actual weather conditions. The system's design aligns with the prevailing trend of mobility and intelligence in weather monitoring and forecasting, reflecting the growing use of IoT and big data technologies in this industry (Darvazeh et al., 2020). The paper introduces an innovative approach to weather monitoring and forecasting, harnessing the potential of IoT and big data technologies to elevate the precision and efficiency of meteorological services. Incorporating big data involves extensive data analysis to construct a temperature prediction model capable of forecasting temperature variations over 24 hours. This model relies on the characteristics of five key dimensions: temperature, pressure, humidity, wind speed, and time information (Wang et al., 2021). The study effectively amalgamates various technological components, including IoT, extensive data analysis, statistical forecasting methodologies, cloud computing, and embedded software development, to conceptualize and design this portable automatic weather monitoring and forecasting system. This interdisciplinary approach underscores the importance of integrating diverse technologies to enhance the accuracy and effectiveness of meteorological services in the contemporary landscape.
The existing body of research in the domains of multimode distribution monitoring, extensive cloud-based data analysis in water quality monitoring systems, and the development of intelligent observation and forecast services, smart weather services, and global weather services remains limited and relatively underexplored (Sagan et al., 2020). Future research endeavours could aim to bridge these gaps and further explore the potential of emerging technologies such as the Internet of Things (IoT), big data analytics, and others to enhance weather monitoring and forecasting, water quality monitoring, and the operation and maintenance of electricity distribution systems. One notable contribution in this direction is an IoT-enabled weather monitoring system proposal. The research focuses on creating an IoT-enabled weather monitoring system utilizing Arduino and sensors for measuring meteorological variables like temperature, humidity, and rainfall (Shashidhara et al., 2022). The overarching objective of this project is to establish a smart community by implementing the system in a cluster of smart homes. This system effectively leverages Arduino and LoRa technology to wirelessly collect and transmit data to a cloud-based platform for subsequent analysis and visualization. The accompanying research paper delves into the challenges encountered during the development phase and outlines potential avenues for future expansion of the project.
The paper comprehensively expounds upon the system's design, implementation, and findings and provides detailed representations, including the system's block diagram (Fig. 19), process flow, and outcomes. The weather monitoring systems employ diverse methodologies, featuring portable IoT-based devices equipped with GPS connectivity and an array of sensors. These systems effectively utilize Bluetooth sensor modules to facilitate the transmission of data from the transmitter to the receiver (Qadir et al., 2018). Embedded technologies and IoT principles automate weather tracking and gather real-time meteorological data. Sensors are tasked with measuring variables such as temperature, humidity, and rainfall, with IoT integration facilitating the extraction, storage, and upload of relevant data to cloud-based repositories. The study also showcases using ThingSpeak, a customizable software, to present the collected data to external audiences via a dedicated mobile application.
This paper endeavors to address the challenges encountered in rural areas of India, specifically pertaining to the detection of lightning risks, by proffering an advanced warning and alert system as show in Fig. 20. It contends that the current lightning alarm systems, which hinge on radar and satellite technologies, are costly and fail to extend their reach into remote regions (Nerella & Ahmed, 2023). To surmount these limitations, the proposed solution harnesses the amalgamation of the Internet of Things (IoT) and artificial intelligence (AI) technologies. The paper lucidly elucidates the drawbacks inherent in existing lightning alert systems, underscoring the need for a more cost-effective and easily accessible solution tailored to rural areas. It introduces an innovative approach predicated on real-time weather and lightning data garnered from weather stations, which is subsequently subjected to a machine-learning algorithm capable of forecasting lightning incidents (Fang et al., 2023). Moreover, the system encompasses a multiplicity of communication channels, encompassing announcement systems, voice calls, WhatsApp messages, and mobile applications, meticulously orchestrated to ensure the effective dissemination of alerts to rural locales. Notably, the system's functionality extends to areas bereft of internet access, rendering it available even in remote and disconnected environs.
The proposed system comprises several integral components: client systems, gateways, databases, cloud platforms, and weather-based stations. These constituent elements play pivotal roles in gathering and analysing sensor data, forecasting lightning events, and disseminating alerts to remote regions (Khan et al., 2020). The incorporation of machine learning algorithms enhances the system's capacity to scrutinize sensor data meticulously and yield precise predictions. Additionally, the paper contextualizes its research within the broader landscape of AI and IoT-based lightning detection and forecasting systems. It underscores the potential of such systems to ameliorate the impact of lightning-related disasters in rural India. Furthermore, the paper sheds light on the lacunae that persist in the present research regarding lightning detection and prediction systems harnessing AI and IoT technology (Doshi & Varghese, 2022). Notably, extant studies predominantly center on urban locales or regions endowed with superior infrastructure, inadvertently overlooking the unique challenges faced by rural areas. Future research endeavors could pivot towards the tailored development of lightning detection and alert systems explicitly designed for rural settings (Ndlela, 2021). This approach should consider nuanced factors such as low literacy rates, restricted technology access, and a lack of awareness regarding lightning risks.
While the system under discussion leverages real-time weather and lightning data gleaned from weather stations, prospective research could delve into the incorporation of supplementary data sources, including satellite imagery, radar data, and ground-based sensors. This multi-pronged data integration strategy holds the promise of bolstering accuracy and reliability (Heggli, 2023). A crucial facet of forthcoming research should entail a rigorous evaluation and validation of the system's efficacy within authentic, real-world scenarios. This would necessitate conducting field tests and subjecting the system's performance to a comparative analysis vis-à-vis existing lightning alert system. Such an evaluation would encompass an assessment of the system's reliability, timeliness, and capacity to reach rural areas effectively. Addressing these discernible gaps would facilitate the development of more robust and accessible lightning detection and alert systems tailored for rural areas, ultimately contributing to mitigating the impact of lightning-related disasters and preserving lives (Elsom, 2018).
Amidst these possibilities, the imperatives of localization and customization beckon, challenging researchers to contemplate strategies for tailoring weather monitoring systems to cater to distinct regions and locales' unique exigencies and exigencies. In this evocative tapestry, the core focus of this research materializes as the development and implementation of an economical IoT-based weather monitoring system (Mohapatra & Subudhi, 2022). The bedrock of this system resides in open-source technology and IoT devices, aptly configured to monitor and archive vital meteorological data encompassing parameters such as temperature, humidity, air pressure, and dust particles. This endeavor embarks upon a structured research methodology, commencing with the judicious selection of IoT devices, exemplified by the NodeMCU ESP 8266 and Raspberry Pi Zero W (shown in Fig. 21), coupled with the appropriate sensors calibrated to monitor pivotal weather variables. The research's progression culminates in the establishment of a virtual private server (VPS), its environs enriched with the requisite software infrastructure to engender an IoT server. The data transmission and logging bear the imprimatur of the MQTT protocol, facilitating the seamless relay of information to a remote server, where it is adeptly cataloged in a dedicated database. In the crucible of practical implementation, the data thus collated embarks upon a transformative odyssey, traversing to a distant server to find its lodestar in a reservoir of data. The system's seamless functionality finds validation in real-time implementation and data monitoring, bolstered by a dashboard replete with real-time visualization capabilities (Reinertsen & Clifford, 2018). This visualization owes its existence to the Thingboard program, distinguished for its data analysis and visualization proficiency. Indeed, the Thingsboard API lends its transformative touch, enabling the extraction of telemetry history and facilitating an even more nuanced inquiry with specialized software tools. This study casts a compelling spotlight on the tangible viability and consummate effectiveness of the IoT-based weather monitoring system that has been meticulously conceived and implemented. Through the prism of practical deployment, data processing, and visualization, the study underscores the system's prowess and acumen, offering a persuasive testament to its potential and utility (Campello de Souza, 2023).
The current findings set the stage for several prospective directions in future research on IoT-based weather monitoring systems. The quest for a more comprehensive understanding of weather conditions beckons further exploration, particularly through the incorporation of additional meteorological characteristics. Parameters such as wind speed, rainfall, and solar radiation represent fertile domains for future inquiry. Additionally, the imperative of optimizing power consumption emerges as a pivotal avenue for research, with a particular focus on extending the battery life of IoT devices, especially in rural or off-grid areas (Pradhan & Priyanka, 2020). The potential for precision in weather predictions holds immense promise, and future research should endeavor to harness cutting-edge data analysis techniques, prominently including machine learning algorithms. These sophisticated methods bear the potential to yield timelier and more accurate weather forecasts (Ramsey et al., 2021). Research endeavours should grapple with scalability challenges for the expansive implementation of weather monitoring systems across broader geographic areas, including cities and regions. These encompass critical facets such as data transmission, network infrastructure, and data administration, necessitating innovative solutions. The confluence of meteorological data with other IoT applications, such as smart cities, smart agriculture, or disaster management, opens vistas for building interconnected IoT ecosystems (Ud Din et al.). This convergence augments resource management and decision-making capabilities across diverse industries. By addressing these gaps, future research can significantly augment the capabilities and applications of IoT-based weather monitoring systems. This can bolster weather forecasting accuracy and support overarching environmental monitoring and planning initiatives.
The paper underscores the potential of the devised weather monitoring system in propelling advancements in consumer technologies and enhancing existing ones (Gaikwad et al., 2023). By proffering an affordable means of monitoring and analyzing meteorological data through the utilization of IoT devices and open-source technology, the system strides toward democratizing access to high-accuracy meteorological measurements. The paper's chief objective resides in the creation of an IoT-based automatic weather station, characterized by its capacity to yield precise measurements of meteorological variables. These variables encompass quintessential parameters such as temperature, humidity, barometric pressure, and rainfall. Evidently, the suggested system prioritizes affordability, ease of use, and reliability as its defining attributes. To this end, a comprehensive literature review is enmeshed within the paper, offering a lens to discern the comparative landscape of similar weather monitoring systems, encompassing their capabilities and outcomes (Wartmann & Purves, 2018).
The bedrock of the paper's primary methodology lies in the creation of an automated weather station harnessed through IoT technologies. Through this empirical exploration, the researchers harness sensors such as DHT11, BMP180, and FC-37 to record measurements of meteorological variables. The circuit diagram of the system is shown in Fig. 22. The data thus accumulated finds its repository on a web page, facilitating uncomplicated accessibility (Nguyen et al., 2019). An integral facet of this system materializes in the amalgamation of a Node MCU ESP8266 microcontroller and a Wi-Fi module, the dynamic duo responsible for its operational framework. The resultant system extends an array of user-friendly features, foremost among them the facile access to meteorological data via a dedicated web page. Furthermore, the system exhibits an admirable precision in measuring weather parameters under ordinary atmospheric conditions. By juxtaposing the performance and attributes of the proposed system against incumbent weather monitoring systems, the researchers proffer an incisive comparative analysis. In tandem with the literature review, the study employs a comparative analysis as a complementary research method. The underpinning rationale for this approach resides in gaining a nuanced understanding of current weather monitoring systems and IoT-based weather stations. The comparative analysis endeavors to dissect these extant systems' features, methodologies, and performance metrics, elucidating their strengths and weaknesses. This analytical endeavor serves a dual purpose—first, it unveils the lacunae in current systems, highlighting the imperative for the proposed Internet of Things-based weather station. Second, it accentuates the distinct advantages and contributions of the researchers' system. Importantly, the comparative analysis spotlights systems characterized by suboptimal graphical user interfaces (GUIs) and limited data visualization capabilities, underscoring the pressing need for enhanced GUIs and data visualization techniques. Moreover, the literature review casts a spotlight on the paramount importance of curtailing power consumption in IoT-based weather monitoring systems (Popli et al., 2018). It espouses the urgency of delving into energy-efficient methodologies and novel power management strategies. Beyond these imperatives, future prospects beckon in the form of incorporating additional sensors, envisaging the detection of light intensity and air quality. Furthermore, the infusion of machine learning algorithms into weather monitoring systems augurs a realm ripe for further investigation, promising augmented precision and prognostication capabilities. This constitutes a compelling trajectory for future research endeavors.
The authors successfully achieved their objectives through the implementation of the proposed IoT-based weather monitoring system. Under standard atmospheric conditions, each sensor dutifully fulfilled its designated role, leading to an accurate measurement of meteorological parameters. The results obtained were notably precise and reliable. The report, however, forwards recommendations for future system enhancements, including integrating sensors capable of detecting light intensity and air quality. One pivotal avenue for forthcoming research pertains to the incorporation of air quality monitoring into these systems. It underscores the pressing need to extend the purview of meteorological monitoring setups, as many existing configurations predominantly focus on weather-related factors, inadvertently sidelining the crucial dimension of air quality (Carralero, 2019). Additionally, addressing the constraint of subpar graphical user interfaces (GUIs) observed in certain extant systems emerges as a priority. Developing intuitive and engaging GUIs is pivotal in ameliorating user experience and enhancing comprehension of the presented data. The research highlights the significance of augmenting the longevity and maintenance efficiency of IoT-based weather stations through judicious power optimization (Maraveas et al., 2022). Prospective investigations can delve into the realm of energy-efficient practices and the development of efficacious power management systems. The incorporation of machine learning algorithms, drawing insights from historical meteorological data, presents another propitious avenue. Such an endeavor holds promise in terms of elevating accuracy and forecasting capabilities. Additionally, future research can be directed towards formulating effective strategies for managing the large-scale deployment of weather monitoring equipment, with scalability and network management representing critical areas of concern. In the context of innovative urban projects, the paper underscores the potential dividends of exploring the integration of weather stations with other smart city infrastructures, thus contributing to more sustainable and effective urban design.
8.3 Scalability and Sustainability
The principal thematic tenets underpinning this study revolve around the meticulous design and implementation of a remote monitoring system tailored to preserve endangered trees, leveraging the robust communication framework afforded by LoRa technology (Xu et al., 2022). This innovative solution is meticulously architected to enhance the efficacy of maintenance managers and grapple with the extant challenges inherent to monitoring and managing rare arboreal specimens. It is worth mentioning that the study is underpinned by funding derived from both the National College Students' Innovation and Entrepreneurship Training Programme and the Zhejiang Province Natural Science Foundation. The operational framework of this system is predicated upon the judicious utilization of an array of sensors, meticulously calibrated to capture a diverse array of variables. The hardware design ambitiously incorporates sensor modules as the vanguard of data acquisition. Subsequently, the indispensable LoRa communication paradigm is judiciously harnessed to facilitate the seamless transmission of the accrued data to a centralized server, which plays a pivotal role in data storage, real-time accessibility provisioning, and ongoing monitoring. This sophisticated orchestration allows users to monitor the condition of rare arboreal specimens remotely. Augmenting this multifaceted system is a cloud-based data processing, analysis, and presentation platform underpinning the entire information ecosystem (Guo et al., 2020). On the software front, the system is underpinned by a suite of meticulously crafted algorithms encompassing data collection, transmission, and processing, thereby ensuring efficient and accurate data transmission from the sensors to the central server. Notably, the methods employed in conceiving and implementing this system have been subjected to comprehensive evaluation, corroborating their adeptness in collecting and transmitting data within the defined operational milieu.
Figure 23 is the block diagram delineating the architecture of the remote monitoring system devised for the preservation of rare trees encompasses a constellation of pivotal components working in harmonious synergy to execute the data collection and transmission functions. The system's inception is marked by the deployment of diverse sensor modules, each endowed with a unique measurement capability, encompassing functions such as temperature and humidity measurement via the DHT11 sensor, attitude determination through the MPU6050 sensor, infrared detection facilitated by the HC-SR501 sensor, and sound detection executed by the MAX4468 sensor. Subsequently, the system elegantly incorporates the ATK-LORA-01-V3.0 LoRa communication module, a wireless serial module distinguished by its compact dimensions and energy-efficient operation (Zhao et al., 2022). This module is fortified with the sx1278 spread spectrum chip, characterized by its operation within the 410MHz–441MHz frequency range, replete with 32 channels. At the system's core resides the STM32F103CBT6 microcontroller, functioning as the principal controller and wielding indispensable authority in orchestrating the seamless handling of data. The data collection node, which is a pivotal nexus, assumes the responsibility of assimilating data emanating from the sensor modules and dispatching it to the LoRa gateway (Akbulut et al., 2023). Functioning as a communication bastion, the LoRa gateway adeptly receives data streams from the data collection nodes and serves as the conduit for their transmission to the cloud platform. This gateway forges the vital nexus between the cloud platform and the data nodes. The cloud platform assumes the role of the epicenter for data-centric operations, encompassing processing, storage, analysis, and visualization. It unfailingly receives data contributions from both LoRa and NB-IoT data nodes. To foster effective monitoring of the rare arboreal denizens, the platform deftly undertakes the onerous task of data decoding, validation, and archival, concomitantly executing analysis and data mining tasks of paramount significance. The report ardently underscores the paramount importance of safeguarding the well-being of rare tree species, casting the spotlight on the transformative potential of NB-IoT and LoRa technology in addressing the multifaceted challenges associated with monitoring and management. Furthermore, the precision and efficacy underpinning the system's design have been impeccably corroborated by rigorous data analysis across several empirical studies. Consequently, this study bequeaths a comprehensive framework replete with insights and methodologies, poised to serve as a compass guiding forthcoming remote monitoring endeavors aimed at safeguarding invaluable arboreal treasures. The comprehensive overview (see in Table 6) delves into the intricate details of the most recent system and devices architecture, providing a deep insight into their performance, reliability, scalability, and sustainability aspects.
Table 6
Summary of the recent system and devices and scalability and Sustainability.
Author | Title | Method | References |
Sankar et al. | IoT based Weather, Soil, Earthquake, Air pollution Monitoring System | ThingSpeak IoT Cloud Platform, IoT-based system utilizing a NODEMCU ESP8266. | (Sankar et al., 2023) |
Wang et al. | Design of Automatic Weather Monitoring and Forecasting System based on Internet of Things and Big Data | Internet of Things (IoT), big data, statistical forecasting methodologies, cloud computing, and embedded software development. | (Wang et al., 2022) |
Nerella et al. | Advance Warning and Alert System for Detecting Lightning Risk to Reduce Human Disaster Using AIoT PlatforM - A Proposed Model to Support Rural India | Internet of Things (IoT) and artificial intelligence (AI) technology. | (Nerella & Ahmed, 2023) |
Mohapatra et al. | Development of a Cost-Effective IoT-Based Weather Monitoring System | IoT devices, such as NodeMCU ESP 8266 and Raspberry Pi Zero W. | (Mohapatra & Subudhi, 2022) |
Gaikwad et al. | IoT-based Automatic Weather Station | IoT-based weather station, Node MCU ESP8266 microcontroller and a Wi-Fi module. | (Gaikwad et al., 2023) |
Chen et al. | Design and Implementation of Remote Monitoring System for Rare Trees Based on LoRa Communication | LoRa communication, and sensor modules. | (Xu et al., 2022) |