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

Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture

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

When we compress a large amount of data, we face the problem of the time it takes to compress it. Moreover, we cannot predict how effective the compression performance will be. Therefore, we are not able to choose the best algorithm to compress the data to its minimum size. According to the Kolmogorov complexity, the compression performances of the algorithms implemented in the available compression programs in the system differ. Thus, it is impossible to deliberately select the best compression program before we try the compression operation. From this background, this paper proposes a method with a principal component analysis (PCA) and a deep neural network (DNN) to predict the entropy of data to be compressed. The method infers an appropriate compression program in the system for each data block of the input data and achieves a good compression ratio without trying to compress the entire amount of data at once. This paper especially focuses on lossless compression for image data, focusing on the image blocks. Through experimental evaluation, this paper shows the reasonable compression performance when the proposed method is applied rather than when a compression program randomly selected is applied to the entire dataset.

/pdf/adaptive-lossless-image-data-compression-method-inferring-1ayvmkm2.pdf

Adaptive Lossless Image Data Compression Method Inferring Data Entropy by Applying Deep Neural Network

Data resilience system for fog computing

In the recent years, Internet of things (IoT) has become an integral part of the modern digital ecosystem. It has the ability to handle the tasks smartly for many different situations. Therefore, it is one of the main technologies for autonomous systems. These IoTs deal with a lot of information. As the resources of the IoT are limited, data compression is an essential need. Some of the information transmitted over the IoTs cannot be compromised at all. Any loss of such sensitive data may cause serious consequences. Therefore, lossless data compression techniques are preferred for such data so that the integrity can be maintained. The low bandwidth IoTs are very popular in the recent times. They provide services over large coverage area with limited resources. These networks are known as low power wide area networks (LPWANs). In the 3GPP framework, there are some popular LPWANs such as narrowband IoT (NBIoT), and LTE machine-type communication (LTE-M). This article focuses on the lossless compression techniques employed in these popular LPWANs. This research work shows the reasons why lossless compression techniques are needed in NBIoT and LTE-M. It also goes through the challenges posed by the low bandwidth IoTs. Further, the recently used compression techniques for these low bandwidth IoTs are also discussed.

Lossless Compression Techniques for Low Bandwidth Io Ts

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

Data compression is one of the fundamental processes in the modern communication and computing world. It provides essential reduction in the amount of data before it is sent to a remote destination. In the communication networks some data is very much redundant and it can be compressed by either lossless of lossy techniques. However, for some data any minor loss may result in serious short-comings. For that kind of data, lossy compression methods are not at all suitable. The modern communication networks are dealing with a significant amount of the desired data. In those cases, lossless compression techniques are the only methods to reduce the amount of data. More number of low bandwidth networks are observed these days. For instance, the early generation mobile cellular networks, IoT networks, signaling networks, and even some satellite networks are very much limited in bandwidth. In such cases, sensitive data is compressed using lossless compression techniques. The proposed research work focuses more on the common low bandwidth networks. Very commonly, it has been found that the low power wide area networks (LPWANs) have low bandwidth. However, in many applications the LPWANs deal with sensitive data in which any loss results in serious consequences. This article analyzes the compression techniques used in LPWANs and other low bandwidth networks.

M. Pappa

Papers

Lossless Compression Techniques for Low Bandwidth Io Ts

Narrowband IoT Based Support Functions in Smart Cities

Narrowb and IoT (NBIoT) for Smart Cities

Lossless Compression Techniques for Low Bandwidth Networks