The Internet of Things (IoT) paradigm plays a vital role for improving smart city applications by tracking and managing city processes in real-time. One of the most significant issues associated with smart city applications is solid waste management, which has a negative impact on our society’s health and the environment. The traditional waste management process begins with waste created by city residents and disposed of in garbage bins at the source. Municipal department trucks collect garbage and move it to recycling centers on a fixed schedule. Municipalities and waste management companies fail to keep up with outdoor containers, making it impossible to determine when to clean them or when they are full. This work proposes an IoT-enabled solid waste management system for smart cities to overcome the limitations of the traditional waste management systems. The proposed architecture consists of two types of end sensor nodes: PBLMU (Public Bin Level Monitoring Unit) and HBLMU (Home Bin Level Monitoring Unit), which are used to track bins in public and residential areas, respectively. The PBLMUs and HBLMUs measure the unfilled level of the trash bin and its location data, process it, and transmit it to a central monitoring station for storage and analysis. An intelligent Graphical User Interface (GUI) enables the waste collection authority to view and evaluate the unfilled status of each trash bin. To validate the proposed system architecture, the following significant experiments were conducted: (a) Eight trash bins were equipped with PBLMUs and connected to a LoRaWAN network and another eight trash bins were equipped with HBLMUs and connected to a Wi-Fi network. The trash bins were filled with wastes at different levels and the corresponding unfilled levels of every trash bin were monitored through the intelligent GUI. (b) An experimental setup was arranged to measure the sleep current and active current contributions of a PBLMU to estimate its average current consumption. (c) The life expectancy of a PBLMU was estimated as approximately 70 days under hypothetical conditions.

https://www.mdpi.com/2624-6511/4/3/53/pdf

IoT-Enabled Solid Waste Management in Smart Cities

Deep Learning-Based Convolutional Neural Network with Cuckoo Search Optimization for MRI Brain Tumour Segmentation

Narrowband Internet of Things (NB-IoT) is one of the low-power wide-area network (LPWAN) technologies that aim to support enormous connections, featuring wide-area coverage, low power consumption, and low costs. NB-IoT could serve a massive number of IoT devices, but with very limited radio resources. Therefore, how to enable a massive number of IoT devices to transmit messages periodically, and with low latency, according to transmission requirements, has become the most crucial issue of NB-IoT. Moreover, IoT devices are designed to minimize power consumption so that the device battery can last for a long time. Similarly, the NB-IoT system must configure different power-saving mechanisms for different types of devices to prolong their battery lives. In this study, we propose a persistent periodic uplink scheduling algorithm (PPUSA) to assist a plethora of Internet of Things (IoT) devices in reporting their sensing data based on their sensing characteristics. PPUSA explicitly considers the power-saving mode and connection suspend/resume procedures to reduce the IoT device’s power consumption and processing overhead. PPUSA allocates uplink resource units to IoT devices systematically so that it can support the periodic–uplink transmission of a plethora of IoT devices while maintaining low transmission latency for bursty data. The simulation results show that PPUSA can support up to 600,000 IoT devices when the NB-IoT uplink utilization is 80%. In addition, it takes only one millisecond for the transmission of the bursty messages.

/pdf/persistent-periodic-uplink-scheduling-algorithm-for-massive-2ozwatez.pdf

Persistent Periodic Uplink Scheduling Algorithm for Massive NB-IoT Devices

Analysis and optimization of downlink energy in NB-IoT

Pandemics create a lot of disruptions in the society and we are directly affected. Covid-19 pandemic has changed a lot of aspects of human lives since the beginning of 2020. Several countries have gone for complete lockdown for several months to protect their citizens. Other countries too have restricted many common activities to some extent. In the majority of the countries, traditional classes in the schools, colleges and universities were not possible. Smart classrooms have emerged as a main alternative for the traditional classes. These smart classrooms are very much different from the video broadcasting. Several immersive and interactive technologies have been proposed for smart classrooms. In this article, we provide the basic principles of smart classrooms, their enabling technologies, and their impact on the education sector. We also provide specific pandemic centric initiatives for effective education in a virtual environment and their effects on the students, teachers and other stakeholders.

Smart Classroom-Based Innovative Solution Toward Uninterrupted Education

This paper shows the main supporting roles of narrowband Internet of things (NBIoT) in the smart cities. This research work will consider the cases in which there already exists some infrastructure for the smart city operations. NBIoT is used to improve the overall functions and to ease the process of resource management. Smart city initiatives are extremely popular in the developed world. In the developing countries too, it gains popularity in the recent years. There are a lot of tasks in the smart city initiatives. It covers the basic services such as water and electricity distribution, maintenance of streetlights, city traffic management, garbage management, and support in effective policing. It also provides the advanced services such as the safety and surveillance monitoring, smart healthcare, location and tracking based services, and smart vehicular networking services. Several information and communication technology-based services are used to assist in these functions. It has been found that IoT is exemplary in these service provision operations. For large scale deployment of IoT a lot of resources are needed. Now, it is well known that NBIoT is one of the resource efficient versions of IoT. It is suitable for large scale deployment in projects like smart cities and smart grids. This article shows that, NBIoT can assist in the smart city functions to a large extent.

Narrowband IoT Based Support Functions in Smart Cities

Smart cities are being developed across the world to improve the services and facilities in the urban areas In the smart city initiatives, all the traditional infrastructures are linked with the information and communication technologies (ICT) networks Through the ICT support, qualities of all the basic services and public facilities are improved Basically, the emerging technologies such as the Internet of things (IoT) are pivotal in the provision of high quality services in the modern urban areas In this article, the basic goals of smart city projects are demonstrated and their requirements are analyzed Research work demonstrates that smart city projects are large and need a large scale deployment of IoT devices and components This implies that smart cities need energy efficient forms of IoT for their long term success and sustainability Narrowband IoT (NBIoT) is one of such energy and resource efficient version of the IoT available for deployment In this article, the suitability of NBIoT for smart cities in the long term is demonstrated and the advanced features and standards of NBIoT are presented Comparing with the smart city requirements research work validated the use of NBIoT for smart cities For long term sustainability of smart cities, the ICT support networks should be energy efficient and easily deployable over large areas NBIoT provides all these features and thus can be a natural choice for smart cities

Narrowb and IoT (NBIoT) for Smart Cities

Since the last decade, classification methods are useful in a wide range of applications. Classification is a task to segregate the sample into different groups. This capability can be introduced in the computer system by designing various types of classifiers. The methodology known as the neural network in artificial intelligence (AI) offers so many approaches for classification problem. In this aspect, the hybrid classifier is created and tested for the application of the multi-class classification problem on the basis of Binary Neural Network (BNN) and Fuzzy Ant Colony Optimization (FACO). ACO algorithm has proven to be a powerful tool for optimal control of continuous-state dynamic systems. This hybrid Fuzzy ACO algorithm combines the multi-agent optimization and fuzzy partitioning of the state space of the system. This classifier is used to classify any type of data whether it is labelled or unlabeled or both. FACO can monitor the continuous data type and eliminate the complexity and allowing it to outperform the standard algorithm. FACO employs a rule-based feature selection concept to eliminate the least influential features, which increases computational time. To begin, it preprocesses the data set to generate binary values. Then, preprocessed data is used as an input for the hidden layer in order to reduce preparation time across all multiple classes. The hidden layer training is performed in parallel, which employs the geometrical learning principle. This system has been evaluated on a variety of benchmark datasets and compared by varying the learning parameters.

Eisha Akanskha

Papers

Narrowband IoT Based Support Functions in Smart Cities

Narrowb and IoT (NBIoT) for Smart Cities

Hybrid Classifier Based on Binary Neural Network and Fuzzy Ant Colony Optimization Algorithm