IoT - Toxic Gas Detection System in Sewage

This research presents a cutting-edge approach that combines chemical engineering principles with artificial intelligence (AI) to enhance battery maintenance in Internet of Things (IoT) applications. The study develops predictive maintenance models capable of accurately forecasting battery degradation, thereby optimizing usage patterns and extending battery life. By integrating electrochemical data with AI-driven predictive analytics, the project enables real-time monitoring of battery health, achieving over 90% accuracy in anticipating maintenance needs, which reduces unplanned downtime and lowers operational costs. Employing machine learning frameworks, data analysis tools, and chemical engineering simulation software, the focus is on lithium-ion and solid-state batteries, commonly used in IoT networks across sectors such as smart cities, agriculture, and healthcare. Key results include a 20−30% increase in battery lifespan and a 15% improvement in energy efficiency, significantly reducing electronic waste and environmental footprint. This interdisciplinary approach provides a foundation for sustainable battery technology in IoT technology, paving the way for future advancements in AI-augmented predictive maintenance and chemical engineering models for next-generation applications. The integration of AI with chemical engineering principles allows for the development of models that adapt to the unique electrochemical behavior of different battery types, such as lithium-ion and solid-state batteries. By analyzing lifecycle data and device usage patterns, predictive models can account for variables that influence battery performance over time, including temperature fluctuations and charging cycles. This project utilizes neural networks and reinforcement learning algorithms to train models capable of learning from historical data, enhancing their predictive capabilities. A significant aspect of this research involves the preprocessing of battery lifecycle data to ensure high data quality, which is critical for accurate model training and validation.

Internet of Things (IoT) applications are generally developed in a vertical manner, i.e., each IoT application is developed for a certain scenario which generally does not share data with IoT applications for recommendation services. This leads to an inefficient exploitation of other IoT service applications. In fact, such recommendation services can be achieved with the help of Social IoT (SIoT) by using data generated by various IoT applications. SIoT builds a profile of objects based on IoT applications data that can be exchanged with SIoT network for accessible to other IoT applications. In this manner, the SIoT network provides recommendation services for reusability of IoT applications' data among various IoT applications, as well as adapting IoT services according to users' needs which improve user experience. Additionally, the profiles built by a SIoT network can also help a single IoT application by looking for similar conditions that have been addressed in the past for the same IoT application. We propose a concept for exploiting the SIoT for recommendation services among various IoT applications with the help of a sample application scenario that highlights how the SIoT can help by providing recommendations. We also provide some implementation challenges for this concept.

The advancement of Industry 4.0 has spurred the integration of Internet of Things (IoT) technologies into predictive maintenance systems, particularly in industrial machinery management. Traditional maintenance models—reactive and time-based—have proven insufficient in addressing operational uncertainties and unexpected equipment failures. This study aims to explore how IoT applications in predictive maintenance can enhance the operational reliability of industrial machines. Utilizing a qualitative literature review method, the research synthesizes findings from 10 scholarly articles published between 2023 and 2025, focusing on various IoT applications such as digital twins, intelligent networks, and energy-optimized communication protocols. The data collection involved systematic searches through major academic databases using targeted keywords, and data analysis was conducted using thematic analysis to extract key themes such as architecture, machine learning integration, and infrastructure readiness. The findings reveal that IoT significantly contributes to predictive maintenance through real-time data collection, anomaly detection using AI models, and automated decision-making processes. The use of digital twins, fog computing, and sensor-integrated systems has demonstrated measurable improvements in equipment uptime, fault detection accuracy, and cost efficiency. The integration of IoT with Enterprise Asset Management (EAM) platforms has further enabled organizations to transform their maintenance strategies into data-driven, proactive systems. In conclusion, the study confirms that IoT is not merely a supporting technology but a foundational enabler of operational resilience in modern industries. This research provides a framework for sustainable implementation and highlights critical success factors such as sensor quality, model precision, and infrastructure scalability.

The correct implementation and behavior of Internet of Things (IoT) applications are seldom investigated in the literature. This paper shows how the simulation mechanism can be integrated well into an IoT application development platform for correct implementation and behavior investigation. We use an IoT application development platform called IoTtalk as an example to describe how the simulation mechanism called SimTalk can be built into this IoT platform. We first elaborate on how to implement the simulator for an input IoT device (a sensor). Then we describe how an output IoT device (an actuator) can be simulated by an animated simulator. We use a smart farm application to show how the simulated sensors are used for correct implementation. We use applications including interactive art (skeleton art and water dance) and the pendulum physics experiment as examples to illustrate how IoT application behavior investigation can be achieved in SimTalk. As the main outcome of this paper, the SimTalk simulation codes can be directly reused for real IoT applications. Furthermore, SimTalk is integrated well with an IoT application verification tool in order to formally verify the IoT application configuration. Such features have not been found in any IoT simulators in the world.

As a major agricultural province in China, Shandong Province has always played an important role. At the same time, it is also closely related to the strong support of science and technology. As a new technology, the Internet of things (IoT) has made positive contributions. Agricultural IoT is a mode that uses information technology, wireless communication technology and radio frequency automatic identification technology to share the identified agricultural information to the network. The application of agricultural IoT is a major change in agricultural development, which provides convenient conditions for the development of agriculture, provides a favorable environment for the agricultural products, promotes the transformation of agricultural technology, realizes the informatization of agricultural production, improves the efficiency of rural land, integrates agricultural resources and provides a foundation for food safety. In this paper, the application of IoT in the field of agriculture in Shandong Province is described from two practical cases. One is about how to use wireless sensor network technology to transmit data in the big data platform based on the Bohai granary project. The other describes how to use IoT for food quality and safety traceability. It takes pigs as the research object, runs through all links of the whole industrial chain of pig breeding, breeding, slaughtering, processing, logistics and sales, collects the data of the whole industrial chain with the help of IoT, and realizes the information tracking and traceability of the whole industrial chain. At the end of the paper, it discusses the countermeasures.

The internet of things (IoT) is focused in offering new technologies which is changing the business environment by showing the potential in different sectors like retail, transportation, healthcare etc. Through IoT, the managers can gather and process data and information in an effective manner, also they can use them to automate in order to enhance productivity and efficiency. With the usage of such technologies, human resource managers can access information for possible candidates which can be accessed through IoT. The overall behaviour of potential candidate tends to help them in shortlisting and choosing the desirable ones. The application of IoT has emerged in enhancing the recruitment of candidates in search of candidates based on large volume of information, assist in screening the candidates, selecting the most suitable ones. The application of IoT technology can modify the HR roles, enable in changing the company perspective in enhancing recruitment process, source the candidates effectively and select them so as to meet the organisational requirements. The implementation of IoT driven technologies support in removing the biases in screening the candidates and aids in candidate selection with the support of other technologies like artificial intelligence, predictive analytics etc. Furthermore, IoT is also involved in better inventory management as it supports the organisation in converting the data which are sourced through RFID (Radio Frequency Identification) into more insights related to inventory data, location of the products, tracking the movement of goods until it reaches the hands of the customers. With the application of IoT inventory managers possess better control in managing the inventory through automated ordering request and thereby reduce manual time and cost for the business. This paper is focused in presenting a conceptual understanding on the application of environmental IoT in recruitment process and also the overall impact created by the technology in managing the inventory efficiently.KeywordsInternet of thingsRecruitment processInventory managementPredictive analytics

Under the background of prosperity of Internet of things (IoT), in order to accelerate the training of talented persons of China for IoT, we propose to design and develop a new course for Chinese students: Essential Technologies, Applications, and Commercial Patterns of Internet of Things. Aiming at three-dimensional target, which has dimensionalities of attitude and thoughts, knowledge and skills, process and methods, the contents of the course include the following parts: system and application architectures, key technologies of IoT, applications of IoT, commercial patterns of IoT, IoT industry and social significance etc. The course highlights the training of abilities of innovation, creativity, comprehensive analysis and practical operations. The construction of teaching resources, such as textbooks, lecture notes, teaching equipments, practice bases, are also emphasized in the process of development of this course.

The term Internet of Things (IoT) refers to a technological revolution that allows nearly all environmental devices to connect to the internet and share data to create services and applications that improve our standard of living. As IoT systems generate huge amounts of data, new IoT applications and facilities arise. Machine learning has made significant progress in several fields and is now being used to solve a variety of IoT network problems. Machine learning has recently become more popular as a means of improving IoT applications. Users can find deep analytics and develop efficient intelligent IoT applications using machine learning for IoT. This chapter focuses on offering an overview of machine learning techniques in the IoT platform. The chapter begins with an overview of the IoT, including its history, architecture, and its applications. Then, many machine learning algorithms, various types, and their applications, with examples, are discussed.

SummaryIn recent years, applying Internet of Things (IoT) applications has significantly increased to facilitate and improve quality of human life activities in various fields such as healthcare, education, industry, economics, etc. The energy aware cloud‐edge computing paradigm has developed as a hybrid computing solution to provide IoT applications using available cloud service providers and fog nodes for the smart devices and mobile applications. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) metrics which can deploy on the cloud‐edge providers with different resource capabilities, finding an efficient placement solution as one of challenging topics to be measured for IoT applications. The service placement issue arranges IoT applications on the cloud‐edge providers with various capabilities of atomic services though sufficient different QoS factors to support service level agreement (SLA) contracts. This paper presents a technical analysis on the cloud‐edge service placement approaches in IoT systems. The key point of this technical analysis is to identify substantial studies in the service placement approaches which need additional consideration to progress more efficient and effective placement strategies in IoT environments. In addition, a side‐by‐side taxonomy is proposed to categorize the relevant studies on cloud‐edge service placement approaches and algorithms. A statistical and technical analysis of reviewed existing approaches is provided, and evaluation factors and attributes are discussed. Finally, open issues and forthcoming challenges of service placement approaches are presented.

With the advancement of computing technology, we are witnessing the dawn of a new era of the Internet of Things (IoT) paradigm in which objects equipped with sensors, actuators and processing capabilities communicate with each other to serve a given goal. The IoT’s intrinsic nature, which uses heterogeneous devices, resources and different communication protocols, complicates IoT applications’ design, development, and validation. Reducing the complexity of building IoT applications is one of the current challenges in this area.To address this challenge, we focus on a model-driven approach to support IoT systems’ management and the development of IoT applications. In particular, we propose the FloWare approach and its toolchain, which combine Software Product Line and Flow-Based Programming paradigms to manage the complexity in the various stages of the IoT application development process. An automatic transformation procedure generates the final IoT application, an executable Node-RED flow, starting from a configuration of the designed Feature Models.KeywordsIoTModel-drivenFeature modelFlow-based programmingNode-RED

An empirical investigation into the enhancement of decision-making capabilities in corporate sustainability leadership through Internet of Things (IoT) integration

The concept of Internet-of-Things (IoT) emerged around two decades ago but has gained momentum during the last few years. The modern technology has infused this innovative paradigm on several scales but still lacks shadowing many aspects of our daily life. Several businesses have attempted its interjection in their processes, however, some of the features of IoT have been avoided by most of them due to its cryptic nature and requiring technical skills prior to its replete use. The development of IoT is well ahead of its need to be and linked to an inability of adopting its fast-paced changes along with the lack of strong business operational models. This paper contributes to the developing of a smart home where electrical appliances would react according to the environment while users would be able to control them through smart gadgets. The conventional setup of arranging and organising the electrical appliances is compared to the newly-developed, several patterns have been observed through which carbon footprints and electricity consumption could be reduced. The collected data has been analysed and it proves that IoT does not only helps save energy consumption but also to save the environment. This paper also highlights the challenges faced by consumers who are affected by the penetration of IoT into their lives along with benefits of infusing IoT applications. The overall theme of this paper is, therefore, to make the consumer understand the benefits of IoT applications into their lives using Arduino setup to control electrical home appliances as a benchmark.

Purpose – The purpose of this paper is to study lightweight security scheme for Internet of Things (IoT) applications using Constrained Application Protocol (CoAP). Resource-constrained characteristics of IoT systems have ushered in compelling requirements for lightweight application protocol and security suites. CoAP has already been established as the candidate protocol for IoT systems. However, low overhead security scheme for CoAP is still an open problem. Existing security solutions like Datagram Transport Layer Security (DTLS) is not suitable, particularly due to its expensive handshaking, public key infrastructure (PKI)-based authentication and lengthy ciphersuite agreement process. Design/methodology/approach – This paper proposes a lightweight security scheme in CoAP using Advanced Encryption Standard (AES) 128 symmetric key algorithm. The paper presents an object security (payload embedded)-based robust authentication mechanism with integrated key management. The paper introduces few unique modifications to CoAP header to optimize security operation and minimize communication cost. Findings – It is resilient to number of security attacks like replay attack, meet-in-the-middle attack and secure under chosen plaintext attack. This scheme is generic in nature, applicable for gamut of IoT applications. The paper proves efficacy of our proposed scheme for vehicle tracking application in emulated laboratory setup. Specifically, it compares with DTLS-enabled CoAP to establish the lightweight feature of our proposed solution. Research limitations/implications – This paper mainly focuses on implementing in-vehicle tracking systems as an IoT application and used CoAP as the application protocol. Practical implications – Such a lightweight security scheme would provide immense benefit in IoT systems so that resource constraint-sensing devices and nodes can be made secure. This would impact IoT eco systems to a large extent. Originality/value – Such kind of security suite that provides both robustness and lightweight feature is hitherto not known to the authors, particularly in CoAP for IoT applications.

Connected devices are known as Internet of Things (IoT) gadgets because they are able to communicate with other IoT gadgets and systems over the Internet or other communication networks. An IoT security system protects Internet devices and networks they link to against attacks and breaches by guarding, detecting, monitoring, and fixing vulnerabilities from various devices that might pose security concerns to your company. The discipline of IoT security is what makes your IoT systems secure. Security solutions for IoT guard against attacks and breaches, detect and monitor risks, and assist in patching any holes that may have been introduced. The availability, integrity, and confidentiality of your IoT solution are all ensured by IoT security. Mobile devices and IoT are making it possible for educational institutions to increase campus security, monitor critical resources, and provide students with easier access to information. Teachers may also employ this technology to build “smart lesson plans,” as opposed to the standard boring plans of the past. Using IoT to secure the education sector from cyberattacks is one of the recommended methods in this paper. A comparison study of existing research to suggested research on the basis of security, dependability, and performance characteristics is being carried out.KeywordsIoTOnline education systemSecurityCompression

Cost-effective, low-power and wireless connectivity are among the key considerations for interconnecting the end-nodes or end-devices in the Internet of Things (IoT) applications. While wireless technologies such as WiFi, Bluetooth, Zigbee have been popular choices for last-mile connectivity, they however significantly restrict the distance between the communicating entities. While the use of additional network gear like repeaters or higher signal transmission power would increase the range of these wireless communication interfaces, the consequent increase in the cost has been a critical reason for not readily employing them in WAN based IoT applications such as Smart Cities. The emergence of the LoRa (Long Range) standard and the Low Power Wide Area Network (LPWAN) technology, in recent times, have however widened the scope and also broadened the deployment scenarios of the IoT end-nodes including the viability of the battery-powered and non-stationary ones. Yet another significance of these new long-distance-low-power interconnect standards is the empowerment for broader and independent Machine to Infrastructure (M2I) and Machine to Machine (M2M) communication for the end-nodes. This paper discusses details of the work that was carried out to explore the suitability of LoRa and LoRaWAN to address the short as well as the medium-range connectivity requirements of the IoT applications while also ensuring the flexibility and low-cost benefits with the help of results obtained from the representative experimental setups. The overall study and assessment were performed in steps, each with specific objectives. The first step was aimed at evaluating the performance of LoRa for communication between stationary as well as moving nodes. The next step was directed towards the validation of node-to-gateway and node-to-node communication, and also, with simultaneous use of different channels within LoRaWANs that were configured in star and mesh topologies. The subsequent step was devoted to assessing the viability of LoRa in broader IoT applications such as Smart Cities and Smart Irrigation, with the help of an illustrative setup comprising of nodes with sensors and actuators and, a gateway which was connected to an open-source cloud infrastructure to epitomize aspects of storage and analysis of data of a comprehensive IoT deployment.