An Integrated System for Regional Environmental Monitoring and Management Based on Internet of Things

In order to solve the problems of single monitoring factor, weak comprehensive analysis ability, and poor real time performance in traditional environmental monitoring systems, a research method of residential environment pollution monitoring system based on cloud computing and Internet of Things is proposed. The method mainly includes two parts: an environmental monitoring terminal and an environmental pollution monitoring and management platform. Through the Wi-Fi module, the data is sent to the environmental pollution monitoring and management platform in real time. The environmental monitoring management platform is mainly composed of environmental pollution monitoring server, web server, and mobile terminal. The results are as follows. The data measured by the system is close to the data measured by the instrument, and the overall error is small. The measurement error of harmful gases is about 6%. PM 2.5 is about 6.5%. Noise is about 1%. The average time for sensor data update is 0.762 s. The average alarm response time is 2 s. The average data transfer time is 2 s. Practice has proved that the environmental pollution monitoring and alarm system operates stably and can realize real-time collection and transmission of data such as noise, PM 2.5, harmful gas concentration, illumination, GPS, and video images, providing a reliable guarantee for timely environmental pollution control.

Floods and water resource management are major challenges for human in present and the near future, and snowmelt floods which usually break out in arid or semi-arid regions often cause tremendous social and economic losses, and integrated information system (IIS) is valuable to scientific and public decision-making. This paper presents an integrated approach to snowmelt floods early-warning based on geoinformatics (i.e. remote sensing (RS), geographical information systems (GIS) and global positioning systems (GPS)), Internet of Things (IoT) and cloud services. It consists of main components such as infrastructure and devices in IoT, cloud information warehouse, management tools, applications and services, the results from a case study shows that the effectiveness of flood prediction and decision-making can be improved by using the IIS. The prototype system implemented in this paper is valuable to the acquisition, management and sharing of multi-source information in snowmelt flood early-warning even in other tasks of water resource management. The contribution of this work includes developing a prototype IIS for snowmelt flood early-warning in water resource management with the combination of IoT, Geoinformatics and Cloud Service, with the IIS, everyone could be a sensor of IoT and a contributor of the information warehouse, professional users and public are both servers and clients for information management and services. Furthermore, the IIS provides a preliminary framework of e-Science in resources management and environment science. This study highlights the crucial significance of a systematic approach toward IISs for effective resource and environment management.

In the face of escalating environmental challenges, smart solutions are essential for promoting sustainable living. This research paper examines the pivotal role of Internet of Things (IoT) applications in environmental monitoring, emphasizing how IoT technologies contribute to the creation of a more sustainable and resilient environment. By interlinking sensors, devices, and analytics platforms, IoT systems facilitate real-time monitoring and management of environmental parameters, offering a proactive approach to ecological conservation. The paper explores various IoT-based applications, from urban air quality sensors to remote forest fire detection, highlighting their capacity to gather precise data and trigger timely responses to environmental threats. The integration of IoT with cloud computing and AI is also discussed, showcasing advanced predictive capabilities that allow for preemptive actions in environmental protection. Case studies from around the globe provide insight into practical implementations and the tangible impact of IoT in environmental monitoring. The challenges associated with large-scale deployment, such as data security, energy consumption of IoT devices, and the digital divide, are critically analyzed. The paper concludes by emphasizing the transformative potential of IoT applications in driving sustainability, advocating for their widespread adoption as a means to enhance environmental stewardship and support the transition to greener living practices.

Medical care combined with information technology is a new trend and the completeness and consistency of information directly affect the efficiency and quality of healthcare services as well as the safety of diagnosis and treatment. The level of internet information technology is reflected in its realization degree of integration between information systems and processes. This concept is defined as Information Systems (IS) integration, the degree to which data and applications are shared and accessed over different communication networks. In order to fully realize IS integration, ensure security of the massive medical data as well as efficient, smooth and real-time sharing, and to realize scientific analysis and processing, all the technologies including internet technology, cloud computing, Internet of Things, Data Analytics, and Blockchain should be utilized. This paper reviews the previous studies in this field and puts forward the related concepts. With the analysis of the literature on IS Integration, we find that the development of IS Integration of healthcare management and medical services is worthy of advocacy and promotion although there are still different obstacles to be overcome. Opportunities and challenges coexist in the development of IS integration.

ROLAP (Relational OLAP) and MOLAP (Multidimensional OLAP) are two opposing techniques for building On-line Analytical Processing (OLAP) systems. MOLAP has good query performance while ROLAP is based on mature RDBMS technologies. Many data warehouses contain sparse but clustered multidimensional data which neither ROLAP or MOLAP handles effciently and scalably.We propose a dense-region-based OLAP (DROLAP) approach which surpasses both ROLAP and MOLAP in space effciency and query performance. DROLAP takes the bests of ROLAP and MOLAP and combines them to support fast queries and high storage utilization. The core of building a DROLAP system lies in the mining of dense regions in a data cube, for which we have developed an effcient index-based algorithm EDEM to handle. Extensive performance studies consistently show that the DROLAP approach is superior to both MOLAP and ROLAP in handling sparse but clustered multidimensional data. Moreover, our EDEM algorithm is effcient and effective in identifying dense regions.

In this chapter we ascertain the significance of OLAP (On-line Analytical Processing) and its various tools useful for Accounting. We start with a brief history of OLAP. Then we define OLAP. We move on to analyze various types of OLAP- the Relational OLAP (ROLAP), the Multi-dimensional OLAP (MOLAP), the Hybrid OLAP (HOLAP) and the Desktop OLAP (DOLAP). We discuss some applications of OLAP tools in various areas of accounting. Finally, through a series of comparisons, we evaluate the different types of OLAP and select the type that could suit your needs.

Remote Application Programming Interfaces (APIs) are technology enablers for major distributed system trends such as mobile and cloud computing and the Internet of Things. In such settings, message-based APIs dominate over procedural and object-oriented ones. It is hard to design such APIs so that they are easy and efficient to use for client developers. Maintaining their runtime qualities while preserving backward compatibility is equally challenging for API providers. For instance, finding a well suited granularity for services and their operations is a particularly important design concern in APIs that realize service-oriented software architectures. Due to the fallacies of distributed computing, the forces for message-based APIs and service interfaces differ from those for local APIs -- for instance, network latency and security concerns deserve special attention. Existing pattern languages have dealt with local APIs in object-oriented programming, with remote objects, with queue-based messaging and with service-oriented computing platforms. However, patterns or equivalent guidance for the structural design of request and response messages in message-based remote APIs is still missing. In this paper, we outline such a pattern language and introduce five basic interface representation patterns to promote platform-independent design advice for common remote API technologies such as RESTful HTTP and Web services (WSDL/SOAP). Known uses and examples of the patterns are drawn from public Web APIs, as well as application development and software integration projects the authors have been involved in.

With the development of society and the improvement of living standards, people pay more and more attention to their own living environment and life safety, and more people are gradually paying attention to the impact of the quality of the living environment on their health and work efficiency, and to meet this demand of people, it is necessary to effectively monitor and control the living environment. Based on this, this article applies cloud computing and Internet of things technologies that have developed rapidly in recent years, and proposes to design a residential environment intelligent monitoring system based on cloud computing and Internet of things, and use Internet of things and sensor technologies to achieve target connection and communication. It then uses distributed computing, a cloud computing technology, to implement integrated, standardized management of this system for truly intelligent monitoring. In this article, we first gathered a large amount of information through literature search methods, systematically introduced cloud computing and Internet of Things technologies, and introduced two applications in environmental intelligent monitoring. Next, we propose a design experiment of a residential environment intelligent monitoring system based on cloud computing and the Internet of Things, and propose an overall concept of system design, system hardware and software design requirements, and device selection and comfort evaluation. And living environment data integration is introduced in detail. We then tested the performance of the system in a specific application of the system in a real residential environment, using data parameters collected at different points in time as experimental data. Finally, it is concluded that the fuzzy close fusion algorithm used in the experiment can obtain data parameter values that are in good agreement with the real values, and the error range is controlled within 0–0.01; the indoor environment comfort is judged by 1 as the standard. $PMV>1$ indicates that the comfort level is excellent, and in the test experiment, each parameter value at 5 time points is all > 1, indicating that the indoor environment comfort level detected by the system is excellent.

Pervasive healthcare services have undergone a great evolution in recent years. The technological development of communication networks, including the Internet, sensor networks, and M2M (Machine-to-Machine) have given rise to new architectures, applications, and standards related to addressing almost all current e-health challenges. Among the standards, the importance of OpenEHR has been recognized, since it enables the separation of medical semantics from data representation of electronic health records. However, it does not meet the requirements related to interoperability of e-health devices in M2M networks, or in the Internet of Things (IoT) scenarios. Moreover, the lack of interoperability hampers the application of new data-processing techniques, such as data mining and online analytical processing, due to the heterogeneity of the data and the sources. This article proposes an Internet of Medical Things (IoMT) platform for pervasive healthcare that ensures interoperability, quality of the detection process, and scalability in an M2M-based architecture, and provides functionalities for the processing of high volumes of data, knowledge extraction, and common healthcare services. The platform uses the semantics described in OpenEHR for both data quality evaluation and standardization of healthcare data stored by the association of IoMT devices and observations defined in OpenEHR. Moreover, it enables the application of big data techniques and online analytic processing (OLAP) through Hadoop Map/Reduce and content-sharing through fast healthcare interoperability resource (FHIR) application programming interfaces (APIs).

The Internet of Things (IoT) has ushered in a new era of connectivity, enabling seamless communication and data exchange between interconnected devices and systems. At the heart of the IoT ecosystem lies the Wireless Sensor Networks (WSNs), which serve as the sensory nervous system, bridging the physical and digital worlds. This paper presents a comprehensive exploration of IoT-based Wireless Sensor Networks, focusing on their applications, communication protocols, and optimization techniques. The study begins with an overview of IoT and its significance in transforming various industries, such as agriculture, healthcare, and smart cities. WSNs' pivotal role in IoT is highlighted, emphasizing their ability to collect real-time data from the physical environment through sensor nodes and facilitate intelligent decision-making processes. The integration of WSNs into the IoT framework is discussed, emphasizing the importance of connectivity, data aggregation, and cloud computing in enabling seamless data flow and real-time insights. Communication models in IoT-based WSNs, including point-to-point, multi-hop, publish/subscribe, event-driven, and time-scheduled communication, are examined to understand their applications and advantages. The paper delves into various optimization techniques that address challenges faced by IoT-based WSNs. Energy efficiency and power management strategies are explored to extend the operational lifetime of battery-powered sensor nodes. Data compression and aggregation techniques are presented to minimize data transmission and storage requirements. Routing protocols are discussed to enhance network connectivity and scalability. Quality of Service (QoS) improvement strategies are examined to meet specific application requirements. Security and privacy enhancements are explored to safeguard sensitive data and ensure trust in the network. Furthermore, the study presents case studies and practical implementations of IoT-based WSNs in real-world scenarios. Applications in environmental monitoring, smart agriculture, healthcare, industrial automation, and smart cities demonstrate the practical impact and benefits of these networks in diverse domains. The paper concludes by highlighting future directions for IoT-based WSNs, including the need for interoperability and standardization, handling big data challenges, and integrating edge computing and fog computing for optimization. Additionally, the potential of blockchain technology to enhance security and trust in IoT-based WSNs is discussed.

With the rapid development of computer science and technology, cloud computing and Internet of Things technology have been widely used in the research of geological information technology. As the current big data era has more extensive data requirements, and the Internet of Things and cloud computing technologies are widely used in daily life and work. Especially in the fields of geological information surveying and mapping, management, query, storage, analysis, etc., applications are gradually increasing. This article mentions that the geoinformatization research algorithm based on the Internet of Things and cloud computing in the era of big data has improved the accuracy of geological information by 5% and 7.5%, respectively. The results show that the Internet of Things and cloud computing models play an important role in geological informatization.

The present survey examines the role of big data analytics in advancing remote sensing and geospatial analysis. The increasing volume and complexity of geospatial data are driving the adoption of machine learning (ML) and artificial intelligence (AI) techniques, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, to extract meaningful insights from large, diverse datasets. These AI methods enhance the accuracy and efficiency of spatial and temporal data analysis, benefiting applications in environmental monitoring, urban planning, and disaster management. Despite these advancements, challenges related to computational efficiency, data integration, and model transparency remain. This paper also discusses emerging trends and highlights the potential of hybrid approaches, cloud computing, and edge processing in overcoming these challenges. The integration of AI with geospatial data is poised to significantly improve our ability to monitor and manage Earth systems, supporting more informed and sustainable decision-making.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Towards the building of a dense-region-based OLAP system

Purpose: The general objective of this study was to explore the Internet of Things for environmental monitoring. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the Internet of Things for environmental monitoring. Preliminary empirical review revealed that IoT technologies have significantly enhanced environmental management practices by revolutionizing data collection and analysis across various ecosystems. By integrating IoT sensors with existing monitoring frameworks, real-time data on air and water quality, agriculture, wildlife habitats, and urban green spaces was efficiently gathered. This data facilitated proactive decision-making, early detection of environmental risks, and evidence-based policy formulation to address climate change, biodiversity conservation, and sustainable resource management challenges. Despite challenges like data security and interoperability, collaborative efforts among stakeholders paved the way for more effective environmental monitoring and sustainable development initiatives globally. Unique Contribution to Theory, Practice and Policy: The Complex Adaptive Systems Theory, Diffusion of Innovations Theory and Resource Dependence Theory may be used to anchor future studies on the Internet of Things technology. The study provided several recommendations that contributed significantly to theory, practice, and policy in environmental management. The study emphasized interdisciplinary approaches to enhance theoretical frameworks, advocating for advanced models and algorithms integrating IoT with environmental science and data analytics. In practice, it recommended widespread adoption of IoT-enabled sensor networks with enhanced capabilities for precise and reliable data collection. Policy-wise, the study called for regulatory frameworks supporting IoT integration, data standards, and international cooperation to address global environmental challenges collaboratively. Capacity building and continuous research and development were also highlighted to optimize IoT technologies for sustainable environmental monitoring and management globally.