IOT Based Wastewater Management in Smart Cities

Wastewater management is a mechanism that is used to extract and refine pollutants from wastewater or drainage that can be recycled to the water supply with minimal environmental effects. New methods and techniques are required to ensure safe and smart wastewater management systems in smart cities because of the present deteriorating environmental state. Wireless sensor networks and the Internet of Things (IoT) represent promising wastewater treatment technologies. The elaborated literature survey formulates a conceptual framework with an Internet of Things (IoT)-based wastewater management system in smart cities (IoT-WMS) using blockchain technology. Blockchain technology is now being used to store information to develop an incentive model for encouraging the reuse of wastewater. Concerning the quality and quantity of recycled wastewater, tokens are issued to households/industries in smart cities. Nevertheless, this often encourages tampering with the information from which these tokens are awarded to include certain rewards. Anomaly detector algorithms are used to identify the possible IoT sensor data which has been tampered with by intruders. The model employs IoT sensors together with quality metrics to measure the amount of wastewater produced and reused. The simulation analysis shows that the proposed method achieves a high wastewater recycling rate of 96.3%, an efficiency ratio of 88.7%, a low moisture content ratio of 32.4%, an increased wastewater reuse of 90.8%, and a prediction ratio of 92.5%.

The Internet of Things (IoT), a solely original computer paradigm, first appeared years ago. Its significance is demonstrated by the way it links the physical and digital worlds to achieve a significant benefit. The IoT is a network of internet-connected devices that enables widespread connections and interactions with other embedded devices, services, and people. With the growing need for smart services in smart cities, this paper focuses on the development of intelligent vehicular management systems in these cities. Here, the devices used in IoT have embedded systems that take the data from the environment. The data is transferred through a wireless network and a gateway to a carrier network. All the data will be stored at Application Enablement Platforms (AEP) which seem like web servers. The data is analyzed through big data analytics platforms. Finally, the devices and gateway remote management platforms distribute the new versions and upgrades to all devices. The primary parameters for determining road conditions include traffic flow rates, air quality, meteorological characteristics, and signal status as indicated by the received signal intensity for both road and intra-vehicular settings. Bluetooth Low Energy (BLE) beacons and mobile applications are employed for smart parking and navigation system, public transportation system, traffic size monitoring, and health-related issues for passengers.

Technologies are constantly evolving and finding applications in both business and government organizations for various purposes such as improving operational efficiency, increasing customer satisfaction, and reducing the cost of transactions. Technology adoption is a well-researched phenomenon in the academic world. Based on the current state of e-governance in Saudi cities and the potential of Artificial Intelligence (AI), Internet of Things and Blockchain in e-governance, this research study aims to study factors influencing the adoption intention of AI and Blockchain technologies in citizen services delivery within a smart city in Saudi. This article explores AI, IoT and Blockchain technologies adoption intention in the context of smart cities within a developing country like Saudi. Finally, the study provides a set of practical recommendations for smart city officials while planning to launch service applications using AI and Blockchain technologies. More specifically, the study emphasizes the importance of ethics, data privacy and security, and transparency in building trust between smart city systems.

Internet of Things (IoT) lays the foundation for the various applications in smart cities, yet resource-constrained IoT devices are prone to suffer from devastating cyberattacks and privacy leak threats, thus are inevitability supposed as the weakest link of the systems in smart cities. Mitigating the security risks of data and the computing limitation of edge devices, especially identity authentication and key validity management of group devices are essential for IoT system security. In order to tackle the issues of anonymity, traceability, unforgeability of expiry time as well as efficient membership revocation for life-cycle management of devices in IoT setting, we presented a dynamic time-bound group signature with unforgeability of expiry time. Unforgeability of expiry time disables a revoked signer to create a valid signature by means of associating the signing key with an expiry time. The anonymity and traceability of the proposed scheme contribute to the identity privacy of the entities and supervision for authority agency. Moreover, our proposal is feasible in the resource-constrained setting for efficient computational cost of signing and verification algorithms.

Abstract: Wireless Sensor Networks (WSNs) play a key role in the Internet of Sensor Things (IoST).IoST helps collect data from environments and is used in energy trading, monitoring, smart grids, and more. Connect to the Internet and automate your surveillance system without third-party involvement. An IoST network consists of Sensor nodes that perform environmental monitoring. Wireless Sensor Networks (WSN) and the Internet of Things (I.o.T) have gained popularity in recent years as the underlying infrastructure for connected devices and sensors in a variety of sectors. The data generated by these sensors are used in smart cities, agriculture, transportation systems, healthcare systems, toll collection systems, automatic identification of road data, automatic identification of vehicle license plates, and more. It has become a proposed blockchain mechanism. The main problems and challenges of WSN are effectively reduced by using the LEACH protocol with efficient cluster head selection.

In this paper, we consider a novel Internet of Things (IoT) system in smart city called unmanned aerial vehicle‐ (UAV‐) assisted cognitive backscatter network, where a UAV is employed as both a relay and a radio frequency source to help the data transmission between ground IoT backscatter devices (BDs) and a remote data center (DC). However, since the IoT applications are usually not assigned dedicated spectrum resource in smart cities, these data transmissions from BDs to the DC should share the licensed spectrum of cellular users (CUs). Therefore, we aim to maximize the minimum uplink throughput among all BDs while avoiding severe interference to CUs via joint spectrum management and UAV trajectory design. To solve the problem, we propose an iterative method utilizing block coordinated decent to partition the variables into two blocks. For the spectrum management problem, we first prove its convexity with the transmit power and time scheduling and then propose a two‐step method to solve the two variables sequentially. For the UAV trajectory design problem, we resort to the fractional programming method to handle it. Simulation results demonstrate that the proposed algorithm can significantly increase the average max‐min rate of the BDs while guaranteeing the acceptable interference to CUs with a fast convergence speed.

Pragmatic Internet of Everything (IOE) has emerged as a powerful paradigm for representing and solving complex problems. This reference demonstrates how to coordinate behaviour among a collection of semi-autonomous problem-solving agents: how they can coordinate their knowledge, goals and plans to act together, to solve joint problems, or to make individually or globally rational decisions in the face of uncertainty and multiple, conflicting perspectives. The book presents a collection of articles surveying several major recent developments in Pragmatic Internet of Everything (IOE). The book focuses on issues and challenges that arise in building IOE systems for smart cities in real-world settings. It also presents solutions to the issues faced by system architects. The synthesis of recent thinking, both theoretical and applied, on major IOE problems makes this essential reading for anyone involved in the design and planning of IOT systems for smart cities. Key Features - Summarizes available literature and practical ventures with references - Merges different perspectives on IoT technology thereby giving a 360-degree perspective to the reader - Gives some tips for implementation of practical ventures in this space - Includes an analysis of information gathered from citizens of smart cities

Chapter 5 - Blockchain-based energy-efficient smart green city in IoT environments

With the advent of ubiquitous sensors and Internet of Things (IoT) applications, research and development initiatives on smart cities are ramping up worldwide. It enables remote monitoring, management, and control of devices and the generation of fresh and actionable insight from huge quantities of real-time data. Real-time noise and emissions monitoring of vehicles remain indispensable in a smart city context. Effective management and control of noise and emissions of vehicles on the road are necessary and possible through analyzing lots of sensor data in real-time to take an actionable insight. To contribute to this, as part of an ongoing effort of the European Union project called “NEMO: Noise and Emissions Monitoring and Radical Mitigation”, in this paper, we present the design and development of an IoT-based real-time noise and emissions monitoring system for vehicles in a smart city context. Real-world sensor data of the vehicles in some European cities are collected during the pilot tests. We have developed a complete application for infrastructure managers and analysts to monitor the sensor data related to noise and emissions of vehicles in real-time. The data of the individual road vehicles and trains in selected EU cities and from trains on a track in the Netherlands are collected in the cloud and analyzed with artificial intelligence (AI) algorithms for classification such as high emitter, medium emitter, and normal emitters. We present the development of a complete software solution that can be integrated with existing intelligent transportation systems in smart cities. Finally, we report the initial vehicle classification results from the Rotterdam (Netherlands) pilot test as a representative example for the NEMO monitoring system.

The rapid uptake of blockchain and artificial intelligence (AI) technologies like machine learning (ML) in particular has brought about a paradigm change that is elevating the digital ecosystem for smart cities. The development of new technology can be used to create intelligent societies within smart cities in this era of rapid digital communication. Uses of blockchain technology and ML technologies are proliferating, guaranteeing solutions for issues in a multitude of sectors, such as financial services, cryptocurrency, threat intelligence, social and public services, and the Internet of Things. These “smart cities” are constructed using various technologies, including artificial intelligence and blockchain. As a result, the way these technologies are applied in smart cities both present and future will alter not only the character of governance and human interaction but also the way business is done. The research suggests doing an experimental study to find out how blockchain technology and artificial intelligence are affecting the creation of smart cities. It tries to set the scene for queries like how traditional business models are getting ready for this disruption, what obstacles they might encounter, and what effects both technologies might have on the organization's growth. Blockchain technology and artificial intelligence have a lot of potential to help create smart cities. The results of the research will provide corporations with the rationale to start concentrating on these technologies and putting early adoption strategies into place, which will eventually cause them to change their smart city business models advance.

One of the characteristics of the modern world is the rapid growth of the human population. This population growth is strongly connected with the growth of urban communities. As a result, the world is facing a problem with the increase in air and noise pollution mainly in cities, which is largely caused by road traffic. Numerous discussions are currently underway to define optimal routes for the special conditions of important traffic corridors in the Republic of Serbia. In particular, discussing the problems of their position in the vicinity of urban areas, hospitals, kindergartens, schools, cultural monuments, etc. The rapid development of wireless technologies facilitates the development and implementation of IoT (Internet of Things) systems in Smart cities. A model of IoT system designed for monitoring the noise in the cities caused by the traffic is presented in this paper. The collected data are used for routing the traffic to reduce noise in sensitive parts of the city. The system used LoRaWAN, one of the most applied Low-Power Wide Area Network (LP-WAN) technologies. The system architecture is based on micro-services and MQTT protocol. Besides the system model, the variations of the Dijkstra shortest path algorithm are used to calculate alternative routes for navigating vehicles. The system model is presented by the widely accepted IoT-layered system architecture.

Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities' efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people's standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.

Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.

Internet of Things (IoT) is now serving as a vehicle to a huge number of applications, resulting in innovative and smart solutions in many fields. While providing abundant benefits which improve the quality of our lives, the IoT environment has also created new challenges, especially to users' privacy. Many IoT applications use location-based services (LBS), where service provider (SP) trust cannot be taken for granted. Sensitive information available to SPs could be used to cause considerable loss or damage to users' property or even endanger their lives. There are several methods to preserve the privacy of users' data from SPs but they all suffer from one or more anomalies. This research presents a new method, known as the Swapping of Peers and Fogs (SPF), to protect users' privacy from SPs. By exploiting the features of fogs and smart dummies, the SPF approach offers remarkable improvements to the level of protection of users' identity, which can be used to extract personal information. The SPF method does not compromise accuracy and, by using a pair of caches and fogs, provides greater efficiency for applications as compared to the existing approaches. To demonstrate the effectiveness and efficiency of the proposed method, detailed comparisons with current methods are presented via simulations based on different scenarios. Finally, an application of the SPF method to connected street systems in smart cities is also discussed.

The concept of smart cities is getting popularity day by day. Many countries have started adopting the idea of smart city to improve the quality of life by achieving the recommended level of sustainable development. The smart city is a technologically advanced area which could understand the world by analyzing the data in order to improve living conditions. The underlying technology infrastructure of smart cities are wireless sensor network (WSN), Internet of thing (IoT), RFID, and 6G are among others. Along with the technology, the role of machine learning and data analytics can’t be ignored. The smart cities generate massive amount of data from the monitoring equipments and sensors. Big data analytics is one of the important technologies which is capable of improving intelligent urban facilities. In smart cities large amount of data is continuously received from many sensors, autonomous machines or intelligent IoT devices. The accurate prediction depends on the approaches of data analytics and machine learning techniques. This chapter presents fundamental of smart cities, vertical in smart cities and data analytics approaches.