Internet Of Things Components Research Articles

DESIGN OF IOT BASED TRICYCLE

The rapid advancement in Internet of Things (IoT) technology has opened up numerous opportunities for innovation across various industries. In the transportation sector, IoT has the potential to revolutionize conventional vehicles into smart, connected modes of transportation. This project focuses on the design of an IoT-based electric tricycle, aimed at providing a sustainable and efficient mode of personal transportation.The electric tricycle integrates IoT technology to enhance its functionality and user experience. Key features include real-time monitoring of vehicle parameters such as speed, battery status, and location, enabling remote tracking and management through a dedicated mobile application. Additionally, the tricycle incorporates smart safety features such as collision detection and automatic emergency braking, enhancing rider safety on the road.The design phase involves the selection of appropriate components and the integration of IoT modules, sensors, and actuators into the tricycle framework. The fabrication process encompasses building the chassis, integrating the electric propulsion system, and assembling the IoT components. Emphasis is placed on ensuring durability, reliability, and energy efficiency throughout the construction process.Overall, the project aims to contribute to the advancement of sustainable transportation solutions by leveraging IoT technology to create an innovative, eco-friendly electric tricycle. Through this endeavor, we envision promoting the adoption of electric vehicles and facilitating a more connected and efficient urban mobility ecosystemTop of Form Keywords: IoT-based electric tricycle, Sustainable transportation, Connected vehicles, Electric vehicle innovation, Real-time monitoring, Safety features, Fabrication process, Integration of IoT technology, User experience, Smart mobility solution

Monitoring and Mitigating Climate-Induced Natural Disasters with Cloud IoT

Innovative solutions are necessary for efficient monitoring and mitigation techniques as climate change increases the frequency and severity of natural catastrophes. In this study, we investigate how Cloud Internet of Things (IoT) might help mitigate climate-related calamities. Through the use of cloud computing and sensor networks, Cloud IoT facilitates the capture, processing, and distribution of data in near-real time. This strategy promotes catastrophe planning, response, and recovery by offering early warnings, predictive insights, and simplified communication. The article emphasises the cumulative influence of Cloud IoT components such as sensor networks, data analytics, decision support systems, and remote control in the context of disaster management. Cloud IoT is useful in real-world scenarios, such as the tracking of floods in Bangladesh and the identification of wildfires in California. These cases show how the technology may prevent injuries and preserve property through early warnings and well-coordinated responses. Despite its potential, it faces obstacles including ensuring the safety of data and dealing with issues related to the necessary infrastructure. In conclusion, including Cloud IoT in disaster management provides a cost-effective, scalable, and efficient solution, greatly contributing to constructing resilient communities and creating a sustainable future in the face of climate-induced natural catastrophes.

An energy efficient fusing data gathering protocol in wireless sensor networks

The Internet of Things (IoT) is rapidly developing as a promising technology for today’s digital world. Wireless sensor networks (WSN) are a crucial component of IoT, and one of the major problems faced is energy constraints. The clustering protocol is an effective energy saving solution. In this paper, we propose an energy efficient fusing data gathering protocol via distributed second level clustering and deep learning-based data fusion (EEFDG) for WSN. EEFDG divides the WSN into several second-level clusters by a novel distributed cluster algorithm. The transmission distance and the number of neighbors are jointly considered to select an energy-efficient parent node in each second-level cluster. A new cluster head weight function is proposed for intra-cluster parent node rotation. To minimize redundant data transmission, a multivariate convolutional neural network and long short-term memory network (MVCLNet) are designed for data fusion. MVCLNet is deployed hierarchically into the second-level clustered structure to extract features of the multi-sensor data. The EEFDG is an effective solution to the energy limitation problem of WSN. The proposed second-level clustering and data fusion algorithm based on MVCLNet typically decreases communication distances and the amount of data, thereby minimizing communication overhead and energy consumption. Simulation results show that EEFDG significantly prolongs the network lifetime in terms of the first node dead, compared to existing protocols including MR-LEACH, BPDA, CNNDA, FDEAM, DCNN, and LPLL-LEACH, achieving improvements of 181.3%, 42.2%, 48.2%, 19.6%, 62.0%, and 30.2%, respectively. The EEFDG enables highly accurate data fusion and has superior performance in terms of remaining energy, network lifetime, and data collection.

Green IoT Based on Tropical Weather: The Impact of Energy Harvesting in Wireless Sensor Network

Wireless Sensor Networks (WSNs) are a key component of Green IoT, as they play a critical role in many applications. However, a major challenge faced by WSNs is their limited energy capacity, which can impede their effectiveness. To address this issue, energy harvesting techniques are used to harness ambient energy and power the nodes, eliminating the need for frequent battery replacements or recharging. This study proposes a solar energy harvesting technique to prolong the lifespan of each wireless sensor node in a network. The aim of the research is to assess the impact of energy harvesting on wireless sensor networks with a focus on solar ambient light energy, as it is a critical component of achieving a Green Internet of Things (IoT). The study is adapted to the tropical weather of Malaysia and its forecasted conditions. Through the use of NetSim simulations, the energy efficiency and reliability of the network are evaluated using solar energy harvesting. The recharge rate for energy harvesting in a wireless sensor network is calculated to be 2.34W per day using a 55x70mm solar photovoltaic panel based on Malaysia weather forecasts and sun hours. This proposed method is successfully executed using NetSim, which utilizes real device parameters for a WSN in a simulated agriculture environment. The results show an overall improvement of 22.4 percent in performance parameters in all areas, including energy consumption, network layer, routing performance, and IEEE 802.15.4 metrics, representing a significant improvement. With the implementation of solar energy harvesting, traditional energy of WSNs can benefit from developing smart agricultural monitoring systems. This efficient method and calculation for solar energy harvesting can help to sustain the infinite lifetime of WSN by optimizing the operational time to 18% of the duty cycle. Results demonstrate that the proposed method can effectively increase the energy efficiency and reliability of the network, making it a promising solution for WSNs operating in tropical regions. This research contributes to the development of sustainable and energy-efficient WSNs, promoting the Green IoT concept.

Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection

A Wireless Sensor Network (WSN) aided by the Internet of Things (IoT) is a collaborative system of WSN systems and IoT networks are work to exchange, gather, and handle data. The primary objective of this collaboration is to enhance data analysis and automation to facilitate improved decision-making. Securing IoT with the assistance of WSN necessitates the implementation of protective measures to confirm the safety and reliability of the interconnected WSN and IoT components. This research significantly advances the current state of the art in IoT and WSN security by synergistically harnessing the potential of machine learning and the Firefly Algorithm. The contributions of this work are twofold: firstly, the proposed FA-ML technique exhibits an exceptional capability to enhance intrusion detection accuracy within the WSN-IoT landscape. Secondly, the amalgamation of the Firefly Algorithm and machine learning introduces a novel dimension to the domain of security-oriented optimization techniques. The implications of this research resonate across various sectors, ranging from critical infrastructure protection to industrial automation and beyond, where safeguarding the integrity of interconnected systems are of paramount importance. The amalgamation of cutting-edge machine learning and bio-inspired algorithms marks a pivotal step forward in crafting robust and intelligent security measures for the evolving landscape of IoT-driven technologies. For intrusion detection in the WSN-IoT, the FA-ML method employs a support vector machine (SVM) machine model for classification with parameter tuning accomplished using a Grey Wolf Optimizer (GWO) algorithm. The experimental evaluation is simulated using NSL-KDD Dataset, revealing the remarkable enhancement of the FA-ML technique, achieving a maximum accuracy of 99.34%. In comparison, the KNN-PSO and XGBoost models achieved lower accuracies of 96.42% and 95.36%, respectively. The findings validate the potential of the FA-ML technique as an active security solution for WSN-IoT systems, harnessing the power of machine learning and the Firefly Algorithm to bolster intrusion detection capabilities.

Acceptance of IoT by Students in Universities of Sindh, Pakistan: A Proposed Framework

The Internet of Things (IoT) encompasses a convergence of physical and virtual technologies, connecting devices through networks to enhance the overall quality of life. These technologies offer diverse interactions, featuring unique user interfaces and functionalities. Beyond its technological prowess, IoT presents contemporary methodologies and tools, catering to both modern and traditional learning approaches. While IoT components have received considerable attention, there exists a notable gap in exploring factors influencing students' receptiveness to these services. This research centers on the Unified Theory of Acceptance and Use of Technology (UTAUT 2), a widely acknowledged framework for assessing user willingness to adopt new technologies. The primary objective of our pilot study is to establish a conceptual framework based on UTAUT 2 and assess its reliability and validity. This framework sets the stage for future investigations into IoT acceptance among higher education students in Pakistan. To achieve this, an online pilot research questionnaire was distributed via Google Forms to students across various institutions, including the University of Karachi, University of Sindh Jamshoro, University of Sufism and Modern Sciences Bhitshah, Government College University Hyderabad, and SALU Khairpur. Fifty participants completed the questionnaire, and the ensuing data attests to the questionnaire's high reliability (ranging from .795 to .961), affirming its suitability for broader surveys.

Enterprise Architecture Design and Implementation for IoT Integration in Manufacturing Electrical Panels

Internet of Things technology has transformed manufacturing efficiency and optimization. Electrical panel manufacturing benefits from Internet of Things for better functionality, predictive maintenance, and smoother operations. This study examines the design and implementation of an Enterprise Architecture strategy for seamless Internet of Things integration in electrical panel manufacturing. This research aims to explain Enterprise Architecture and use it as a framework for Internet of Things integration in electrical panel manufacturing. This study examines the complex relationships between Internet of Things components, their connectivity, and a broad Enterprise Architecture framework needed to organize their functionality. This integration uses Enterprise Architecture principles to optimize resource use, reduce downtime, and improve manufacturing efficiency. This effort involves analyzing existing infrastructure, identifying Internet of Things deployment points, and creating an Enterprise Architecture plan that meets business goals. This research emphasizes the need for close IT-operations collaboration to achieve a unified vision and smooth Internet of Things integration. This research addresses Internet of Things implementation challenges in manufacturing, including security, data interoperability, and scalability. Strong governance and adaptable architecture are stressed to address these challenges within an Enterprise Architecture framework. This research aims to help electrical panel manufacturers harness the transformative power of the Internet of Things. Strategic Enterprise Architecture helps businesses navigate complexity, leverage Internet of Things, and create a more agile, connected, and optimized manufacturing landscape.

Evaluating dynamics, security, and performance metrics for smart manufacturing

The role of the Internet of Things (IoT) in Smart Manufacturing, aimed to illuminate its transformative impact on operational efficiency, responsiveness, and environmental sustainability. The aim of the investigation was to explore IoT's pivotal significance in reshaping manufacturing processes towards heightened efficiency, responsiveness, and environmental consciousness. The study presents results from performance metric assessments, visualizations, and data simulations. It contains information about way IoT data is shown in manufacturing environments, the clear relationship between temperature and pressure, the distribution of security risks and related safety measures, and the dynamic behaviour of important IoT components. The importance of IoT in real-time environmental management, process optimization, and security enhancement within paradigms of smart manufacturing are the key points of observation. The comparative analysis of Conventional Analytical Models and Composite Models highlights the choice between stability and adaptability, providing crucial insights for modeling approaches tailored to distinct manufacturing requirements. IoT's transformative potential within Smart Manufacturing, emphasizing data integrity, security, sensor dynamics, analytics, and sustainability.

An Optimized Hierarchal Cluster Formation Approach for Management of Smart Cities

A smart city is a metropolis technology that employs information technology with several internet of things (IoT) devices to enhance the quality of services for citizens, such as the traffic system, energy consumption, and waste collection. In fact, the quality of service (QoS) of these daily routine services are based on an assistive observation system. Wireless sensor networks (WSNs), as the key component of IoT, are used here to gather data into surveillance subsystems for supporting the decision making. To enhance the collected data management of the surveillance subsystems, many clustering techniques are introduced. The low-energy adaptive clustering hierarchy protocol (LEACH) is a key clustering technique of WSN. However, this protocol has deterring limitations, especially in the cluster formation step, which negatively impacts the residual power of many nodes. In fact, a limited number of efforts that try to optimize the clustering formation step represent the main motivation of this work. Considering this problem, the current research proposes an optimized approach to enhance the cluster formation phase of LEACH. The proposed approach depends on the suitability of the residual energy in the nodes to cover the communication energy, with CHs (cluster heads) as a key factor when allocating the node clusters in the first competition. The remaining power and the density of CHs are employed to weigh the accepted CHs and adjust the optimized size of the clusters in the secondary competition. The third competition helps each cluster to select the optimal members from the candidate members according to the impact of each. The advantages and efficiency of the ICSI (intelligent cluster selection approach for IoT) are observed via the ratio of surviving nodes increasing by 21%, residual energy increasing in 32% of the nodes, and a 34% higher network lifetime.

A comprehensive survey on IoT attacks: Taxonomy, detection mechanisms and challenges

The Internet of Things (IoT) has set the way for the continuing digitalization of society in various manners during the past decade. The IoT is a vast network of intelligent devices exchanging data online. The security component of IoT is crucial given its rapid expansion as a new technology paradigm since it may entail safety-critical procedures and the online storage of sensitive data. Unfortunately, security is the primary challenge when adopting Internet of Things (IoT) technologies. As a result, manufacturers’ and academics’ top priority now is improving the security of IoT devices. A substantial body of literature on the subject encompasses several issues and potential remedies. However, most existing research fails to offer a comprehensive perspective on attacks inside the IoT. Hence, this survey aims to establish a structure to guide researchers by categorizing attacks in the taxonomy according to various factors such as attack domains, attack threat type, attack executions, software surfaces, IoT protocols, attacks based on device property, attacks based on adversary location and attacks based on information damage level. This is followed by a comprehensive analysis of the countermeasures offered in academic literature. In this discourse, the countermeasures proposed for the most significant security attacks in the IoT are investigated. Following this, a comprehensive classification system for the various domains of security research in the IoT and Industrial Internet of Things (IIoT) is developed, accompanied by their respective remedies. In conclusion, the study has revealed several open research areas pertinent to the subject matter.

GOVERNANCE OF IOT DEVICES USING NODE-RED ORCHESTRATOR AND WEB-BASED DASHBOARD

High electricity bills are often caused by a lack of monitoring the use of electronic equipment in the household. One common problem is forgetting to turn off appliances that are not in use, which causes electricity to continue flowing and increases electricity bills. Currently, although there are several technologies that allow the control of appliances, their features are still limited, especially in lamps. This research focuses on developing Internet of Things (IoT) technology in the field of energy and control. One of the implementations is using a Raspberry Pi microcontroller to monitor the use of electrical energy in households. In order to make the use of IoT components more efficient and easier, a NODE-RED-based system is used which serves as a link between end users and IoT equipment. For monitoring IoT components, a web dashboard using the Laravel framework was also built. This allows users to view the components used and the activities that occur on their IoT equipment. High electricity bills are often caused by a lack of monitoring the use of electronic equipment in the household. One common problem is forgetting to turn off appliances that are not in use, which causes electricity to continue flowing and increases electricity bills. Currently, although there are several technologies that allow the control of appliances, their features are still limited, especially in lamps. This research focuses on developing Internet of Things (IoT) technology in the field of energy and control. One of the implementations is using a Raspberry Pi microcontroller to monitor the use of electrical energy in households. In order to make the use of IoT components more efficient and easier, a NODE-RED-based system is used which serves as a link between end users and IoT equipment. For monitoring IoT components, a web dashboard using the Laravel framework was also built. This allows users to view the components used and the activities that occur on their IoT equipment.

Joint IoT/ML Platforms for Smart Societies and Environments: A Review on Multimodal Information-Based Learning for Safety and Security

The application of the Internet of Things (IoT) is highly expected to have comprehensive economic, business, and societal implications for our smart lives; indeed, IoT technologies play an essential role in creating a variety of smart applications that improve the nature and well-being of life in the real world. Consequently, the interconnected nature of IoT systems and the variety of components of their implementation have given rise to new security concerns. Cyber-attacks and threats in the IoT ecosystem significantly impact the development of new intelligent applications. Moreover, the IoT ecosystem suffers from inheriting vulnerabilities that make its devices inoperable to benefit from instigating security techniques such as authentication, access control, encryption, and network security. Recently, great advances have been achieved in the field of Machine Intelligence (MI), Deep Learning (DL), and Machine Learning (ML), which have been applied to many important applications. ML and DL are regarded as efficient data exploration techniques for discovering “normal” and “abnormal” IoT component and device behavior inside the IoT ecosystem. Therefore, ML/DL approaches are required to convert the security of IoT systems from providing safe Device-to-Device (D2D) communication to providing security-based intelligence systems. The proposed work examines ML/DL technologies that may be utilized to provide superior security solutions for IoT devices. The potential security risks associated with the IoT are discussed, including pre-existing and newly emerging threats. Furthermore, the benefits and challenges of DL and ML techniques are examined to enhance IoT security.

IoT-based intelligent waste management system

Recently, the population density in cities has increased at a higher pace, so waste generation is on the rise in most societies due to population growth. Given this concern, it would be highly important to manage waste generation. Intelligent city planning is necessary to improve the quality of city life and make cities more livable. This paper presents an intelligent waste management system (IWMS) in smart cities based on Internet of Things components like sensors, detectors, and actuators. IWMS contains three main phases. The first phase of the system is to adapt the low energy adaptive clustering hierarchy approach as an optimization process to better balance the energy consumption of smart waste bins (SBs), thus leading to extending the life of the smart waste network. The second phase is handling the missing values which are retrieved from SBs using an improved version of the k-nearest neighbor algorithm based on artificial hummingbird optimization (AHA), while the third phase presents an optimal energy-efficient route process for the routing of waste trucks that improves fuel efficiency and reduces the time to get an appropriate SB. According to the experimental results, the proposed system has achieved energy savings of 34% for the smart waste bin network. Moreover, compared to other systems, it has a lower mean error rate when generating missing values, and the results related to convergence and running time validate its superiority compared with other metaheuristic algorithms.

Aerial Edge Computing: A Survey

In the beyond 5G/6G era, Aerial Edge Computing (AEC) is expected to be used as significant components in Internet of Things. AEC brings flexible deployment with Line-of-Sight communication links for task offloading and computing services. In this paper, we present a comprehensive survey of the AEC technology. Firstly, we introduce a three-layer architecture of AEC, which includes the satellite, UAVs and ground terminals. Secondly, we illustrate challenges in AEC, and summarize recent studies in terms of AEC performance metrics that include energy efficiency, latency and operation cost to address challenges in AEC. Further, we introduce the advanced technologies applied in AEC management, e.g., Artificial Intelligence (AI) and the distributed optimization. Finally, the applications and the open issues with AEC are summarized and classified in the study.

Internet of Things – one of the Digital Steps that Companies Should Consider towards Circular Economy and Sustainability An Exploratory Case Study Defined by the Sustainable Retrospective of one of the Top Global Retail Groups

Abstract Are digitalisation and Internet of Things (IoT) enablers for the circular economy? This question is based on relatively new concepts, but with a strong foundation applied in the relationship between environment, economy, and society, which, through their roles, determines a sustainable approach towards a future which companies and consumers should share. In order to highlight the attributes of the circular economy in a direct relation with the influence of digitalisation and the IoT component, the theories of various experts prove our ability to perceive sustainability from a circular and digital perspective. Whether we talk about the interconnection between the process of monitoring the life cycle of a product and the adoption of artificial intelligence during the creation process, the circular economy is the solution that comes to support the business models which also use promotion through green marketing. The adoption of the principles of circularity overlapping technological practices supports the idea of creation of a world built on social responsibility, generating long-term social, environmental and governance benefits. Nowadays, society increasingly feels the need to adopt sustainable behaviour, building pressure on the way companies operate, produce, and sell. The case study performed on a global group that has already started to adopt circular business models through digital and especially IoT technologies is proof that this solution could be implemented quite easily by other companies and the race has already started.

Interactive System Modelling for the Internet of Things

The rapid growth in the number of Internet of Things (IoT) systems and their increasing use in safety-critical domains has led to the need for an evolution of development methods. Model-Driven Development (MDD) approaches have been used in software engineering to reduce development time and minimise errors in implemented systems. This paper introduces a novel Model-Driven Development (MDD) approach for Internet of Things (IoT) systems which considers interactivity from the perspective of both users and IoT components. A real-world example is presented to explain the motivation for the work and demonstrate the benefits and use of lightweight interactive system models adapted for the design and development of IoT systems.

An IoT-Enabled Intelligent and Secure Manufacturing Model Using Blockchain in Hybrid Cloud Communication System

The smart manufacturing system can become a linked network with the help of the Internet of Things (IoT). Devices connected to the IoT are susceptible to various attacks and assaults. An effective protection plan is needed to ensure that the billions of IoT nodes are protected from these hazards. The security mechanisms on IoT devices are ineffective due to resource limitations. As a result, the academic community has recently paid attention to the cloud-, fog-, and edge-based IoT systems. A robust cloud provider is in the cloud or fog to perform computationally demanding activities, including safety, data analysis, decision-making process, and monitoring. Hash identities and upgraded Rivest–Shamir–Adleman (RSA) have been used to secure the IoT device’s data. A four-prime integer of 512 bits makes up the proposed security algorithm. A hash signature is used to provide device authentication. An effective clustering method for sensing devices based on the node level, separation from the clusters, remaining energy, and fitness has been presented for long network life. The suggested swarm-based method determines the sensor nodes’ fitness. A deep neural network- (DNN-) based resource scheduling algorithm (DNN-RSM) is meant to reduce the delay and communications overhead for IoT components in the hybrid cloud system. For optimum resource allocation, all queries originating from the cluster head are categorised using DNN based on their storage, processing, and bandwidth needs. The suggested structure delivers better outcomes, particularly regarding energy use, delay, and safety level. The results of the simulation provide credence to the concept that the proposed strategy is superior to the current system. The suggested scheme includes stringent security, decreased energy usage, decreased latency, and efficient resource utilization.

Internet of things (IoT) for smart agriculture: Assembling and assessment of a low-cost IoT system for polytunnels.

Internet of things (IoT) applications in smart agricultural systems vary from monitoring climate conditions, automating irrigation systems, greenhouse automation, crop monitoring and management, and crop prediction, up to end-to-end autonomous farm management systems. One of the main challenges to the advancement of IoT systems for the agricultural domain is the lack of training data under operational environmental conditions. Most of the current designs are based on simulations and artificially generated data. Therefore, the essential first step is studying and understanding the finely tuned and highly sensitive mechanism plants have developed to sense, respond, and adapt to changes in their environment, and their behavior under field and controlled systems. Therefore, this study was designed to achieve two specific objectives; to develop low-cost IoT components from basic building blocks, and to study the performance of the developed systems, and generate real-time experimental data, with and without plants. Low-cost IoT devices developed locally were used to convert existing basic polytunnels to semi-controlled and monitoring-only polytunnels. Their performances were analyzed and compared with each other based on several matrices while maintaining the planted tomato variety and agronomic practices similar. The developed system performed as expected suggesting the possibility of commercial applications and research purposes.

A blockchain-based SLA monitoring and compliance assessment for IoT ecosystems

A Service Level Agreement (SLA) establishes the trustworthiness of service providers and consumers in several domains; including the Internet of Things (IoT). Given the proliferation of Blockchain technology, we find it compelling to reconsider the assumption of trust and centralised governance typically practised in SLA management including monitoring, compliance assessment, and penalty enforcement. Therefore, we argue that, such critical tasks should be operated by blockchain-based smart contracts in a non-repudiable manner beyond the influence of any SLA party. This paper envisions an IoT scenario wherein a firefighting station outsources end-to-end IoT operations to a specialised service provider. The contractual relationship between them is governed by an SLA which stipulates a set of quality requirements and violation consequences. The main contribution of this paper lies in designing, deploying and empirically experimenting a novel blockchain-based SLA monitoring and compliance assessment framework in the context of IoT. This is done by utilising Hyperledger Fabric (HLF), an enterprise-grade blockchain technology. Our work highlights a set of considerations and best practice at two sides, the IoT application monitoring-side and the blockchain-side. Moreover, it experimentally validates the reliability of the proposed monitoring approach, which collects relevant metrics from each IoT component and examines them against the quality requirements stated in the SLA. Finally, we propose a novel design for smart contracts at the blockchain-side, analyse and benchmark the performance, and demonstrate that the new design proves to successfully handle Multiversion Concurrency Control (MVCC) conflicts typically encountered in blockchain applications, while maintaining sound throughput and latency.

An Intelligent Waste Management Application Using IoT and a Genetic Algorithm–Fuzzy Inference System

The Internet of Things (IoT) is being used to create new applications for smart cities. Waste management is one issue that requires various IoT components for assistance, such as RFIDs and sensors. An efficient and innovative waste collection system is required to minimize investment, operational, and expenditure costs. In this paper, the novel idea is to develop an intelligent waste management model for smart cities using a hybrid genetic algorithm (GA)–fuzzy inference engine. The system can read, collect, and process information intelligently using a fuzzy inference engine that decides dynamically how to manage a waste collection. The aim of this model is to enhance its correctness and robustness, primarily, in addition to reducing errors that arise due to working conditions. GA is used for optimization to determine the best combination of rules for the fuzzy inference system (FIS). A Mamdani model is used to estimate waste management. The proposed model uses sensors to collect vital information, and FIS is trained using fuzzy logic to determine the probability that the smart bin is nearly full. The primary issue with the traditional genetic algorithm is that during the execution of the algorithm, there is a possibility of essential gene loss. The essential gene loss refers to information relevant to location, details regarding waste filling parameters, etc., which may lead to efficiency or accuracy loss. This problem is overcome by integrating fuzzy logic with a genetic algorithm to identify crucial genes by preserving the FIS interpretability. Our system uses cost-effective, small-size sensors and ensures this solution is reproducible. The Proteus simulator is used for experiments, and satisfactory results are obtained. Overall accuracy, precision, and recall of 95.44%, 96.68%, and 93.96% are obtained in the proposed model. Classification of recyclable items is also performed, and accuracy is determined for every item, resulting in the minimization of resource waste. The cost of manual interpretation is minimized in the intelligent smart waste management system in comparison to the traditional approach, as shown in the experiments.