Combining Fog and Cloud Computing to Support Spatial Analytics in Smart Cities

Internet of Things (IoT) is an emerging technology that consists of tiny sensors embedded with IoT devices used in e-Health, smart cities and assisted living. IoT devices are equipped with constrained power, memory, and computing capabilities, which creates new security challenges. By integrating sensors and wireless technologies, this cutting-edge technology automates numerous routine tasks in different domains. The main goal of advancing IoT in the existing healthcare system is to automate the monitoring of hospitals and patients’ health by spreading goodwill toward the IoT vision. IoT has gained popularity on practically all platforms that are available. This paper presents a remote patient health monitoring and diagnosis system that uses Message Queuing Telemetry Transport protocol (MQTT) and Internet of Things (IoT) devices with one time pad cryptography. The proposed plan offers end-to-end data secrecy for an IoT system using MQTT that enables e-health and mobility. Patients’ movements inside nursing homes and healthcare facilities are also managed safely without the need for periodic reconfigurations. Prior to communicating through the MQTT protocol, this secure crypto scheme encrypts the message on both the publisher and subscriber ends. According to the results, the proposed scheme is more efficient than delegation-based architecture in terms of security and lightweight communication. The proposed system established communication between patients and medical experts using IoT technologies to alleviate the hospital strain caused by congestion in medical treatment and enable an expressway of medical responses. According to our results the proposed scheme has low computational overhead compared to other solutions that use conventional encryption to guarantee robust protection against most of the IoT security attacks.

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

In the current ongoing world of IoT (Internet of things) devices, it is vital to have a safe, secure and reliable cyberspace. Cyberspace or network is free from all sorts of unethical activities like hacked systems, data breaches and stolen data. For that goal to be accomplished, we need to have a modern, solid and rigid cybersecurity system to stay safe from harm's way. Our research includes recent IoT data security issues and a robust security model that ensures the IoT devices' data security based on a smart city's perspective. According to the recent research data on the vulnerable IoT devices we propose an intelligent and effective security approach to tackle the modern IoT security issues because our data needs to be protected as the IoT devices or “things” as we call it needs to have proper security. A few areas of future research indicate the use of blockchain to resolve the cybersecurity issues of IoT devices are the most promising and exciting.

A smart city refers to an intelligent environment obtained by deploying all available resources and recent technologies in a coordinated and smart manner. Intelligent sensors (Internet of Things (IoT) devices) along with 5G technology working mutually are steadily becoming more pervasive and accomplish users' desires more effectively. Among a variety of IoT use cases, e-voting is a considerable application of IoT that relegates it to the next phase in the growth of technologies related to smart cities. In conventional applications, all the devices are often assumed to be cooperative and trusted. However, in practice, devices may be disrupted by the intruders to behave maliciously with the aim of degradation of the network services. Therefore, the privacy and security flaws in the e-voting systems in particular lead to a huge problem where intruders may perform a number of frauds for rigging the polls. Thus, the potential challenge is to distinguish the legitimate IoT devices from the malicious ones by computing their trust values through social optimizer in order to establish a legitimate communication environment. Further, in order to prevent from future modifications of data captured by smart devices, a Blockchain is maintained where blocks of all legitimate IoT devices are recorded. This article has introduced a secure and transparent e-voting mechanism through IoT devices using Blockchain technology with the aim of detecting and resolving the various threats caused by an intruder at various levels. Further, in order to validate the proposed mechanism, it is analyzed against various security parameters such as message alteration, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack and authentication delay.

The key technological developments in the current era include Blockchain and the Internet of Things in varied application fields. There’s a difference between the above two technologies but the unison of the above two has a wider and practical acceptability in today’s technological scenario. The IoT is responsible for generating a large amount of data in varied fields like healthcare, retail, real-time forecasting, and smart city development. The data is quite useful in terms of big data analytics whereas the Blockchain will promote secure transactions of data along with varied application integration. Smart city can be considered as a prime application area for IoT and there are a lot of progressions among IoT devices, sensors, and actuators. IoT devices are capable enough of communicating among themselves via the Internet using lightweight protocols which results in making them more and more user-friendly. Though the Internet connectivity of IoT devices makes them more vulnerable in regards to data scalability, consistency, and safety, blockchain confirms the integrity of data as it is able in trailing and synchronizing connected devices. As it is based on a decentralized approach, which restricts the condition of a single point of failure thereby forming a steady interoperable safe system. Internet of Things (IoT) requires various tools and techniques for the purpose of information security which necessitates the operative expansion of any IoT cantered smart city. Various security threats are always looking for any ambiguities or loopholes, which can be exploited in any network; security threats are a perilous contest, which needs some action to be taken and implemented instantaneously. The book chapter will be discussing the implications of Blockchain and IoT in a metropolitan urban scenario.KeywordsBlockchainIoTSmart Metropolitan Development

Internet of Things (IoT) devices has limited resource capacity. They have limited power supply, CPU capacity, memory, constrained bandwidth, throughput etc. However, these IoT devices can share the resources. Hence, they can be used in variety of applications like smart city, smart energy, and healthcare etc. Doctor-patient ratio in India is well below the global average. So, IoT can have profound effect on healthcare in India. Healthcare organizations must be prepared to play critical role in managing and analyzing huge volumes of data received from all health and fitness devices. In healthcare, several companies offer remote healthcare services through their platforms. However, most these platforms are proprietary. They can provide data and services to the specific devices and their proprietary servers. But these platforms cannot provide integration services, as they cannot communicate each other. They follow different standards or use different communication protocols. Management and interoperability of data is very important in healthcare. To identify issues in today's solutions and to improve the healthcare platforms this paper reviews different IoT platforms.

The Internet of Things (IoT) has recently received significant attention. An IoT device may possess an array of sensors that for example monitors the air temperature, carbon monoxide level, wifi signals, and sound intensity. IoT data is initially created on the device, then sent over to a central database system (e.g., the cloud) that organizes and prepares such data for the ongoing use by myriad applications, which include but are not limited to smart home, smart city, the industrial internet, connected cars, and connected health. Data generated by IoT devices is inherently spatial and temporal. For instance, an audio signal represents the variation of the sound intensity (retrieved by a sound sensor) over the time dimension. Furthermore, IoT devices are either installed in a static location (e.g., a building, a traffic intersection) or can be attached to moving objects such as a connected vehicle or a wearable device. In this article, we argue that existing IoT data systems do not properly consider the SpatioTemporal aspect of such data. Hence, the article represents a call for action to the SIGSPATIAL community in order to conduct research on building systems and applications that treat both the spatial and temporal dimensions of IoT data as first class citizens.

A huge amount of data is being generated in Internet of Things (IoT) devices used in smart homes, traffic sensors, smart cities and various connected appliances. Fog computing is one area which is quite popular in processing this huge amount of IoT data. However, there are challenges in these models for performing real time analytics in such data for quick analytics and insights. Fog analytics pipeline is one such area which can be a possible solution to address these challenges. In this chapter, an overview of using Fog computing platform for analyzing the data generated by IoT devices is provided. Fog computing platforms will be compared with the state-of-the-art to differentiate its impact in terms of analytics.

Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions

Non-orthogonal multiple access (NOMA) is a promising radio access technique that enables massive connectivity and increased spectral efficiency. The deployment of aerial base stations (ABSs) as a relay is also an optimistic goal that fairly serves a large number of internet of things (IoT) devices. On one side, ABS-assisted communication leverages effective communication services for secondary IoT devices in smart cities. On the other hand, NOMA allows several IoT devices to concurrently acquire the same frequency-time resource. To this end, weighted sum-rate (WSR) is an essential goal because it allows numerous trade-offs between user fairness and sum-rate efficiency. Therefore, this work aims to investigate the WSR for an integrated aerial terrestrial network subject to cellular power and delay constraints in downlink NOMA. Herein, a theoretical insight-based low-complexity iterative solution is provided for optimal power and blocklength allocation to achieve maximum sum-rate. For this purpose, the mixed-integer non-linear problem is formulated and a low-complexity near-optimal solution is proposed. Numerical results show that the proposed scheme achieves a near-optimal solution and outperforms baseline techniques, i.e., the performance gain of 5.18% over the legacy OMA system for NOMA with two IoT devices per subcarrier.

Internet of Things (IoT) is growing too fast and it is being deployed in various domains such as Smart Homes, Smart City, Healthcare, etc. Security in IoT environments is critical, as information between IoT devices and end-users needs to be shared securely. Therefore, IoT systems require protecting two critical aspects, which are confidential data and identity management. However, IoT devices have many constraints, including limitations in computation, power, memory and energy. In this paper, we present a fast and secure authenticated key agreement protocol based on elliptic curve cryptography. The proposed protocol is used for distributed IoT applications. Therefore, a Trusted Third Party (TTP) is used to perform the registration phases for users and IoT devices. With respect to distributed networks, and after the registration phases are accomplished, the end-user can communicate and obtain information straightaway from the sensor nodes. The informal security of the proposed protocol is analyzed, and the analysis shows that our protocol can resist a variety of attacks. Our proposed protocol uses ECDigital Signature Algorithm (EC–DSA). It offers significantly improved performance, in terms of the required computational amount and energy usage, compared to many other authenticated protocols.

The Internet of Things (IoT) devices is able to communicate with another IoT devices in heterogeneous environments. They are also able to create a network by their own using wireless capabilities without a centralized approach. This network is called wireless mobile ad hoc network (MANET). The MANET facilitate IoT devices to communicate with each other in an infrastructure less network of the Internet of Things environment. The IoT devices can dynamically join, create connection and exchange information among them. The role of cloud in this framework is to store and process the data by IoT devices. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Our purpose of this study is to create a framework for providing the communication in an integrated framework of Cloud and MANET among IoT devices. Our main contribution links a new study that integrates cloud computing to the MANET and provides communication to the internet of things. This study can be used in the framework of the Internet of Things in future.

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.

The explosive proliferation of Internet of Things (IoT) devices is generating an incomprehensible amount of data. Machine learning plays an imperative role in aggregating this data and extracting valuable information for improving operational and decision-making processes. In particular, emerging machine intelligence platforms that host pre-trained machine learning models are opening up new opportunities for IoT industries. While those platforms facilitate customers to analyze IoT data and deliver faster and accurate insights, end users and machine learning service providers (MLSPs) have raised concerns regarding security and privacy of IoT data as well as the pre-trained machine learning models for certain applications such as healthcare, smart energy, etc. In this paper, we propose a cloud-assisted, privacypreserving machine learning classification scheme over encrypted data for IoT devices. Our scheme is based on a three-party model coupled with a two-stage decryption Paillier-based cryptosystem, which allows a cloud server to interact with MLSPs on behalf of the resource-constrained IoT devices in a privacy-preserving manner, and shift load of computation-intensive classification operations from them. The detailed security analysis and the extensive simulations with different key lengths and number of features and classes demonstrate that our scheme can effectively reduce the overhead for IoT devices in machine learning classification applications.