Software defined media: Virtualization of audio-visual services

More buildings are becoming smart day by day which play a key role in development of smart cities and Internet of Things is essential in the development of such smart buildings. The incorporation of smart infrastructure is taken care right from the design phase of smart buildings itself. One of the crucial smart infrastructures in the development of smart buildings and smart cities is indoor localization. Being aware of the location or movement of people within a building can be very useful in several ways like energy management, location aware marketing services etc. and there are so many such IoT services. So the research problem here is to locate people within a building without using any additional infrastructure. Many research proposals have already been made over the last few years with the goal of predicting location in smart buildings but the real challenge lies with the accuracy of predicted location. User privacy and energy efficiency are also major challenges of indoor localization. Here we propose a random forest based machine learning algorithm that concentrates on improving the location accuracy in indoor localization as an IoT service for smart buildings. The obtained experimental results show 14% better success test prediction percentage in terms of overall deviation.

The concept of "smart cities" The Internet of Things (IoT) is a multi-layered framework that allows cities to employ urban networks to enhance economic performance, generate innovative technological solutions, and more. Smart Cities are the result of a new age in information technology and smart economy growth, based on Internet mesh networks, communication networks, transmission networks, wireless networks, and other end-to-end sensor networks. The Internet of Things (IoT) is at the heart of it. The Internet of Things incorporates sensors into common things and links them through particular protocols for sharing data and communications over the Internet. Smart detection leads, location, monitoring, and administration are all provided by this modular architecture. A smart city of excellence must overcome three main factors: instrumented, linked, and intelligent, with the help of IoT technology. At this point of IoT development, smart cities can only arise through the interconnectedness of all of these smart elements. The goal of this essay is to learn how the Internet and sensors may assist in the development of smart cities.
 The study's main goal is to figure out how the Internet of Things, or IoT, is being used in smart cities. We also want to establish the necessity for IoT systems in these smart cities, as well as the issues that they may pose to individuals and society, via the research.
 Review secondary research papers, reviews, dissertations, and online documents from the EBSCO, Scopus, and Emerald databases to analyse and comprehend the usage of IoT in smart cities and the possible problems that its implementation may provide in order to accomplish the following goals.
 Municipal requirements are getting more complicated, necessitating the adoption of new technology like the Internet of Things to ensure that all of these cities' duties are completed. Although more cities are becoming smart via the usage of IoT in a number of businesses, the study shows that this IoT system is also creating worries about the security of citizens' data.

The study aims to avoid the phenomenon that thousand cities seem the same in the construction of smart cities and the efforts of all walks of life are jointed to implement the construction of smart cities and the creation of city brands. First, the basic theory of smart city construction is introduced. Second, the restricting and promoting factors influencing smart city construction and development are analyzes, and the evaluation system of smart city development is established. Then, the model for smart city construction and development based on the neural network is implemented. Finally, some domestic cities are selected as the dataset to build a training model, and the city brand optimization strategy is proposed. On this basis, an evaluation system for smart city development based on the intelligent neural network and Grey relational analysis Back Propagation Neural Network (GRA-BPNN) is obtained. The entropy weight method (EWM), grey relational analysis (GRA) method and the evaluation method based on Technique for Order Preference by Similarity to an Ideal Solutionv (TOPSIS) are regarded as the members of the control group, and the results of different methods for evaluating the development of smart cities are compared. The results show that the modeling of smart city construction based on neural networks can help to implement an evaluation model for smart city construction and development, and it can help evaluate smart city construction and development accurately. And then the corresponding strategies are proposed to speed up the construction of smart cities with its local characteristics, and then city brands are built. Compared with other evaluation algorithms, the performance of the algorithm proposed is better and more stable and the evaluation results are more reasonable than those of the others, which can prove that the evaluation algorithm is feasible and scientific. This study provides a new idea for the application of deep learning to smart city construction and city brand building.

Internet of Things (IoT) "The smart city concept has become a dream that big cities in Indonesia want to achieve, basically the smart city concept focuses on developing the human element using technology. IoT opens up many opportunities for new services by connecting the physical world and the virtual world to various electronic devices. IoT aims to leverage cutting-edge technology to support sustainable services for governments and their citizens. Systematic Literature Review (SLR) is one method in conducting an overview of previous interrelated researchers. The purpose of the literature study in this research is to understand trending research topics, methods, and architecture in the development of smart cities with IoT. In this study, various interesting information was found in various research journals regarding the role of IoT in building a smart city that has a role to serve smart city infrastructure, identify and analyze trends, develop smart cities and become innovations for IoT. Based on the research, there are things that need to be done to improve the study to focus more on the role of IoT that can be more utilized. IoT is also one of the technologies that are widely used in several countries in the development of IoT in the future. IoT in smart cities will become an inseparable technology because humans will increasingly depend on IoT in their daily lives

Big data and artificial intelligence based early risk warning system of fire hazard for smart cities

With the emergence of smart devices and the declining market value of sensing systems, the implementation of the internet of things (IoT) is gaining momentum. IoT has become an integral part of many smart systems such as smart buildings, smart cities, and smart heath, etc. Despite many advantages, from its inception, IoT system is vulnerable to many threats. Such systems are more likely to be attacked and breached, by denial of service (DoS) or distributed denial of service (DDoS) attacks. DDoS attacks are one of the Internet security's most disruptive threats. The towering volume of smart devices with a greater level of heterogeneity increases the severity of this attack. To date, there is no protocol that guarantees complete protection of IoT systems. To enable the resiliency, continuous monitoring, along with an adaptive decision-making system, is essential. These challenges can be addressed with the help of software-defined networking (SDN), which can effectively handle the IoT devices. Moreover, the centralized provisioning of SDN enables a secured and adaptive IoT environment that can effectively handle the DDoS attack. The software-defined internet of things (SDIoT) framework based on SDx paradigm consists of a controller pool, SD-IoT switches integrated with 190an IoT gateway, and IoT devices. In this chapter, a broad range of various DDoS attacks and detection methods for the IoT environment used in the SDN paradigm have been analyzed. Finally, different future scopes of this evolving field have been discussed.

A smart city is an urban policy innovation that can improve the community's quality of life by promoting environmental, social, and economic sustainability issues. The involvement and participation of civil society in developing smart cities is a major issue in implementing inclusive smart cities. However, gender issues are often forgotten in developing sustainable smart cities. This article examines two issues. First, the importance of gender issues integration in the development of sustainable smart cities in Indonesia. Second, the importance of gender mainstreaming policy in the development of smart cities in Indonesia. This is a desk study or literature study. Data were obtained from research results published in national and international journals. Data was obtained through accredited journal search engines, namely google scholar, SagePub and Researchgate. The research results show that development of smart city in Indonesia is not yet gender responsive, causing gender inequality gender and excluding women from development. To respond to this, thus there is a need for gender mainstreaming policies in developing smart cities through an overview of the challenges of gender mainstreaming in developing smart cities. Thus, this research recommends a gender mainstreaming strategy as a policy in developing smart cities in Indonesia. Gender mainstreaming policy in smart city development need to be to create a smart city life that is just, inclusive and involves women in development.

Smart Building (SB) is an Internet of Things (IoT) application that aims to manage buildings without human intervention. SB interconnects almost all IoT-enabled devices in the home, such as lights, car parking, temperature sensors, AC, and so on. Another important component of SB is that can provide real-time information. On the other hand, Software Defined Networking (SDN) is a networking technology that can easily transfer and modify information from the data layer to the control layer through the SDN protocol. Currently, intelligent cities, offices, and large organizations are being developed based on SDN and IoT. Among them, many researchers have completed their work on this technology and they have faced various problems. Based on the above discussion, we propose an SB secure architecture model where multiple controllers are used to protecting the building from problems like a single point of failure. Moreover, the southbound protocol is used between the data layer and the controller layer to protect the security and privacy of the building, and through the northbound protocol, customer data is stored in cloud storage. Finally, the author evaluates the performance of the proposed model with various network metrics (Throughput, Round Trip Time) and CPU, Memory and Bandwidth Utilization rate.

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

Smart City and IoT

The extensive use of Internet of Things (IoT) technology has recently enabled the development of smart cities. Smart cities operate in real-time to improve metropolitan areas’ comfort and efficiency. Sensors in these IoT devices are immediately linked to enormous servers, creating smart city traffic flow. This flow is rapidly increasing and is creating new cybersecurity concerns. Malicious attackers increasingly target essential infrastructure such as electricity transmission and other vital infrastructures. Software-Defined Networking (SDN) is a resilient connectivity technology utilized to address security concerns more efficiently. The controller, which oversees the flows of each appropriate forwarding unit in the SDN architecture, is the most critical component. The controller’s flow statistics are thought to provide relevant information for building an Intrusion Detection System (IDS). As a result, we propose a five-level classification approach based on SDN’s flow statistics to develop a Smart Attacks Learning Machine Advisor (SALMA) system for detecting intrusions and for protecting smart cities from smart threats. We use the Extreme Learning Machine (ELM) technique at all levels. The proposed system was implemented on the NSL-KDD and KDDCUP99 benchmark datasets, and achieved 95% and 99.2%, respectively. As a result, our approach provides an effective method for detecting intrusions in SDNs.

The large deployment of Internet of Things (IoT) is actually enabling Smart City projects and initiatives all over the world. Objects used in daily life are being equipped with electronic devices and protocol suites in order to make them interconnected and connected to the Internet. According to a recent Gartner study, 50 billion connected objects have been deployed in smart cities by 2020. These connected objects will make our cities smart. However, they will also open up risks and privacy issues. As various smart city initiatives and projects have been launched in recent years, we have witnessed not only the expected benefits but the risks introduced. The current and future trends of smart city with respect to IoT have been described. It also discussed the interaction between smart cities and IoT. The drivers behind the evolution & development of IoT and smart cities have been explained. Finally, the IoT weaknesses and how they can be addressed when used for smart cities has been discussed

A smart city is an intelligent space, in which large amounts of data are collected and analyzed using low-cost sensors and automatic algorithms. The application of artificial intelligence and Internet of Things (IoT) technologies in electronic health (E-health) can efficiently promote the development of sustainable and smart cities. The IoT sensors and intelligent algorithms enable the remote monitoring and analyzing of the healthcare data of patients, which reduces the medical and travel expenses in cities. Existing deep learning-based methods for healthcare sensor data classification have made great achievements. However, these methods take much time and storage space for model training and inference. They are difficult to be deployed in small devices to classify the physiological signal of patients in real time. To solve the above problems, this paper proposes a micro time series classification model called the micro neural network (MicroNN). The proposed model is micro enough to be deployed on tiny edge devices. MicroNN can be applied to long-term physiological signal monitoring based on edge computing devices. We conduct comprehensive experiments to evaluate the classification accuracy and computation complexity of MicroNN. Experiment results show that MicroNN performs better than the state-of-the-art methods. The accuracies on the two datasets (MIT-BIH-AR and INCART) are 98.4% and 98.1%, respectively. Finally, we present an application to show how MicroNN can improve the development of sustainable and smart cities.

The Internet of Things (IoT) environment is constantly evolving. Many IoT services have emerged, improving living conditions. Smart homes were among the first developments, and smart buildings, smart factories, and smart cities are attracting increasing attention. Smart cities represent the ultimate convergence of the IoT, the Cloud, big data, and mobile technology. Smart homes, buildings, and factories create smart cities. In addition, the IoT finds applications in traffic control, public safety, and medical services, permitting group-based communication. As the scale of service grows, the number of things (devices) constituting the service also increases. However, security vulnerabilities arise in group-based communication environments. A device may require authentication when entering a gateway; to secure environments with large numbers of devices (such as those featuring IoT smart metering), the gateways bear heavy loads. Therefore, efficient authentication of group leaders and devices is essential. Here, we develop a dynamic group authentication and key exchange scheme for group-based IoT smart metering environments which enables efficient communication among secure IoT services. Our group authentication scheme increases the computational efficiency of the group leader and the participating devices, based on a threshold secret sharing technique.

Over the last decade, the evolution of the Internet of Things (IoT) has resulted in a drastic increase in the development of smart cities, including smart parking and intelligent transportation systems (ITS). Smart cities combine a variety of sensors (such as traffic, parking and weather sensors) deployed within these cities. These sensors are used for various applications, such as transportation, parking and weather forecasting. We propose an approach for the mapping of traffic sensors with route coordinates in order to analyze traffic conditions (e.g., level of congestion) on the roadways. We present an algorithm and provide two illustrative examples that cover all of the possible mapping scenarios. We also evaluate the performance of our proposed approach in terms of sensors' correct detection, missed detection and false detection on the routes. Our work can be used for the development of various smart city applications, such as traffic management and smart parking.