Development Management Through The Use Of The Internet Of Things In Waste With An Emphasis On Smart Cities

With the development of the Internet of Things (IoT), waste management has emerged as a serious issue. Waste management is a day-to-day task in urban areas that requires a large workforce and affects the natural, budgetary, efficiency and social aspects. Lays; There are many methods for optimizing waste management such as using the nearest neighbor search, colony optimization, genetic algorithm and particle swarm optimization methods, however, the results are still very vague and cannot be found in real systems, such as universities Or cities, to be used, IoT-based citizens' waste management applications make urban waste management practices more sustainable, optimizing waste collection routes based on the amount of garbage filled by sensors is one of the most effective applications. Finally, waste recycling management requires deeper cooperation between the public and private sectors; in this article, while examining waste management with the IoT approach, the importance of this issue is examined and, of course, suggestions are made for metropolitan areas

The Eco-Smart Can V2.0

On a scorching summer day in 2015, a campus maintenance worker was observed emptying a trash bin. Upon closer observation, it was noted that the bin was not full; in fact, it was less than one third full. There were other bins that were full and needed to be emptied urgently. It was confusing and problematic to see that bins that needed more attention were not prioritized. After extended research, it was found that maintenance operates on daily routes to pick up trash at designated times, regardless of the level of trash in the bins. Therefore, to tackle this issue, the author decided to use the Internet of Things (IoT) to develop a prototype that will optimize trash collection and reduce costs of waste management and pollution; this device is named the Eco-Smart Can

Ciudad con Gestión Inteligente de Residuos: Introducción de Tecnologías de contenedores de desechos

El crecimiento de la población y la urbanización han provocado un aumento en la tasa de producción de residuos, cuya falta de gestión oportuna y adecuada tendrá efectos adversos sobre la vida humana y el medio ambiente. Dado que la mayoría de los costes de gestión de residuos se gastan en la recogida y el transporte de residuos, es necesario encontrar soluciones para controlar los enormes costes de este sector. Por otro lado, hoy en día, las tecnologías inteligentes se utilizan a ni el mundial como soluciones para enfrentar desafíos en diversos campos como la agricultura para mejorar la producción agroindustrial, el transporte y la gestión de residuos, lo que crea un concepto llamado ciudades inteligentes. Una de las categorías que ha cambiado el concepto de ciudades y las ha hecho tener respuestas más fáciles e inteligentes a diversos eventos y necesidades es el "Internet de las cosas", en el que se integran muchos casos e infraestructuras con nuevas tecnologías hardware y Software. La recolección de residuos no es una excepción a esta regla y se han hecho esfuerzos para hacerla más inteligente. En esta investigación, se han examinado algunas de las últimas innovaciones presentadas a nivel mundial para hacer que la basura sea más inteligent

SmartBin: An Approach to Smart Living Community Using IoT Techniques and Tools

Nowadays, individuals are getting steadily dynamic in achieving the possible ways to clean their environment. The concerned teams have initiated other developments to build tidiness. Previously, prior data on filling the trash container was required, which cautions and sends cautioning messages to the city workers for cleaning the trash receptacle on schedule and protecting the city. In this framework, numerous dustbins through urban areas from various regions are associated with utilizing IoT innovation. This program can be used conveniently to verify the status of the dust bin, the garbage in the dust containers, clean the dust bin on time, and maintain the atmosphere's safety and prevent contamination from overflows from the dust containers. So, people don't have to test everyone's work manually, so they'll get a warning if the container is full. A sensor over the garbage container would be placed to detect the full amount of waste, and when it exceeds the excessive volume, a warning will be transmitted to the company office. The proposed framework based on Arduino IDE, cloud computing concept and Load Sensor will help clean any city. Load Sensors are utilized to distinguish the dimension of trash gathered in the containers. The application also gets Latitude and Longitude estimations of the territory where the Garbage Bins are put

Advancements and Challenges in IoT Simulators: A Comprehensive Review

The Internet of Things (IoT) has emerged as an important concept, bridging the physical and digital worlds through interconnected devices. Although the idea of interconnected devices predates the term “Internet of Things”, which was coined in 1999 by Kevin Ashton, the vision of a seamlessly integrated world of devices has been accelerated by advancements in wireless technologies, cost-effective computing, and the ubiquity of mobile devices. This study aims to provide an in-depth review of existing and emerging IoT simulators focusing on their capabilities and real-world applications, and discuss the current challenges and future trends in the IoT simulation area. Despite substantial research in the IoT simulation domain, many studies have a narrow focus, leaving a gap in comprehensive reviews that consider broader IoT development metrics, such as device mobility, energy models, Software-Defined Networking (SDN), and scalability. Notably, there is a lack of literature examining IoT simulators’ capabilities in supporting renewable energy sources and their integration with Vehicular Ad-hoc Network (VANET) simulations. Our review seeks to address this gap, evaluating the ability of IoT simulators to simulate complex, large-scale IoT scenarios and meet specific developmental requirements, as well as examining the current challenges and future trends in the field of IoT simulation. Our systematic analysis has identified several significant gaps in the current literature. A primary concern is the lack of a generic simulator capable of effectively simulating various scenarios across different domains within the IoT environment. As a result, a comprehensive and versatile simulator is required to simulate the diverse scenarios occurring in IoT applications. Additionally, there is a notable gap in simulators that address specific security concerns, particularly battery depletion attacks, which are increasingly relevant in IoT systems. Furthermore, there is a need for further investigation and study regarding the integration of IoT simulators with traffic simulation for VANET environments. In addition, it is noteworthy that renewable energy sources are underrepresented in IoT simulations, despite an increasing global emphasis on environmental sustainability. As a result of these identified gaps, it is imperative to develop more advanced and adaptable IoT simulation tools that are designed to meet the multifaceted challenges and opportunities of the IoT domain

A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

Artificial intelligence (AI) has recently been used frequently, especially concerning the Internet of Things (IoT). However, IoT devices cannot work alone, assisted by Low Power Wide Area Network (LPWAN) for long-distance communication and Short-Range Network for a short distance. However, few reviews about AI can help LPWAN and Short-Range Network. Therefore, the author took the opportunity to do this review. This study aims to review LPWAN and Short-Range Networks AI papers in systematically enhancing IoT performance. Reviews are also used to systematically maximize LPWAN systems and Short-Range networks to enhance IoT quality and discuss results that can be applied to a specific scope. The author utilizes selected reporting items for systematic review and meta-analysis (PRISMA). The authors conducted a systematic review of all study results in support of the authors' objectives. Also, the authors identify development and related study opportunities. The author found 79 suitable papers in this systematic review, so a discussion of the presented papers was carried out. Several technologies are widely used, such as LPWAN in general, with several papers originating from China. Many reports from conferences last year and papers related to this matter were from 2020-2021. The study is expected to inspire experimental studies in finding relevant scientific papers and become another review

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming

Evolution towards Smart and Software-Defined Internet of Things

The Internet of Things (IoT) is a mesh network of interconnected objects with unique identifiers that can transmit data and communicate with one another without the need for human intervention. The IoT has brought the future closer to us. It has opened up new and vast domains for connecting not only people, but also all kinds of simple objects and phenomena all around us. With billions of heterogeneous devices connected to the Internet, the network architecture must evolve to accommodate the expected increase in data generation while also improving the security and efficiency of connectivity. Traditional IoT architectures are primitive and incapable of extending functionality and productivity to the IoT infrastructure’s desired levels. Software-Defined Networking (SDN) and virtualization are two promising technologies for cost-effectively handling the scale and versatility required for IoT. In this paper, we discussed traditional IoT networks and the need for SDN and Network Function Virtualization (NFV), followed by an analysis of SDN and NFV solutions for implementing IoT in various ways

Edge AI for Internet of Energy: Challenges and Perspectives

The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). This comprehensive review elucidates the promise and potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a meticulously curated research methodology, the article delves into the myriad of edge AI techniques specifically tailored for IoE. The myriad benefits, spanning from reduced latency and real-time analytics to the pivotal aspects of information security, scalability, and cost-efficiency, underscore the indispensability of edge AI in modern IoE frameworks. As the narrative progresses, readers are acquainted with pragmatic applications and techniques, highlighting on-device computation, secure private inference methods, and the avant-garde paradigms of AI training on the edge. A critical analysis follows, offering a deep dive into the present challenges including security concerns, computational hurdles, and standardization issues. However, as the horizon of technology ever expands, the review culminates in a forward-looking perspective, envisaging the future symbiosis of 5G networks, federated edge AI, deep reinforcement learning, and more, painting a vibrant panorama of what the future beholds. For anyone vested in the domains of IoE and AI, this review offers both a foundation and a visionary lens, bridging the present realities with future possibilities