Internet of Things (IoT) generates a huge amount of data in real-time. Utilization of such data requires appropriate data storage, analytics, and computation techniques so that valuable information can be extracted and immediate actions could be taken place. In this chapter, we present real-time traffic applications, typical IoT-enabled big data applications in smart city context, but for unreliable and fragmented infrastructures in developing countries. We analyze a case study to show challenges that we need to address when implementing big IoT data applications in such infrastructures. Based on that we outline our key design principles and techniques. We present several examples to particularly discuss how we achieve our designs and lesson learned.

This chapter briefly overviews robust machine-learning solutions for wearable IoT applications. Furthermore, it presents one of the earliest attempts in presenting an autonomous learning framework for wearables. The focus, in particular, is on cases where a new sensor is added to the system and the new (untrained) sensor is worn/used on various body locations. The process of autonomous learning automatically leads to a new collaborative decision-making algorithm. Addressing the problem of expanding pattern-recognition capabilities from a single setting algorithm with a predefined configuration to a dynamic setting where sensors can be added, displaced, and used unobtrusively is challenging. In such cases, successful knowledge transfer is needed to improve the learning performance by avoiding expensive data collection and labeling efforts. In this chapter, a novel and generic approach to transfer learning capabilities of an existing static sensor to a newly added dynamic sensor is described.

This chapter assesses the efficiency of smart city facilities in developing countries to demonstrate how smart solutions are used to address day-to-day problems and to evaluate the efficiency of these solutions in a smart city context. We start by clarifying the smart city concept in terms of characteristics, evolution, dimensions, definitions, applications, and evaluation before studying the city of Yaoundé, its problems, the solutions, and an assessment of its performance in a smart city context. Yaoundé is the densely populated city in Cameroon with an important economic life which faces daily security, environmental, infrastructural, and administrative challenges. These challenges are addressed by different institutions with solutions using information and communications technologies (ICTs) especially to be reachable. For the evaluation of smart city facilities in Yaoundé, we use the revised triple helix framework with the actors and components of smart city. The main result is the fact that Yaoundé is moving to be a smart city, but there are several things that governments and actors in the process may adopt such as the coordination that seems to be the main obstacle at the moment.

This chapter addresses data collection and real-time analysis of massive IoT data using a horizontally distributed fog -computing architecture. We explain how data -analysis problem of IoT can be handled in a layered fog architecture so that information extraction can be achieved in both local and global scales. We provide a deep investigation of existing fog-computing-based IoT solutions in a categorical manner and discuss the open-research problems. In terms of big data analysis, stream data mining and CEP techniques are proven to be quite promising solutions in the literature. Currently, stream data processing tools and techniques are mainly developed for cloud -based systems. However, there is an urgent need for adapting these techniques to horizontally distribute IoT-based data collection and analysis systems. We perform an extensive survey on stream data processing techniques by focusing on their ability to work on fog-computing -based IoT systems. We document a significant survey of CEP techniques in IoT systems by providing pros and cons of each scheme. An example scenario was also provided to show that the problem of collection and real-time analysis of massive IoT data can be solved using fog-computation-based distributed architecture and CEP techniques.

Emerging technologies that generate a huge amount of data such as the Internet of Things (IoT) services need latency-aware computing platforms to support time critical applications. Due to the on-demand services and scalability features of cloud computing, Big Data application processing is done in the cloud infrastructure. Managing Big Data applications exclusively in the cloud is not an efficient solution for latency -sensitive applications related to smart transportation systems, healthcare solutions, emergency response systems and content delivery applications. Thus, the fog -computing paradigm that allows applications to perform computing operations in-between the cloud and the end devices has emerged. In fog architecture, IoT devices and sensors are connected to the fog devices which are located in close proximity to the users, and it is also responsible for intermediate computation and storage. Most computations will be done on the edge by eliminating full dependencies on the cloud resources. In this chapter, we investigate and survey fog-computing architecture which have been proposed over the past few years. Moreover, we study the requirements of IoT applications and platforms, and the limitations faced by cloud systems when executing IoT applications. Finally, we review current research works that particularly focus on Big Data application execution on fog and address several open challenges as well as future-research directions.

The discussion of supporting Big Data at the vehicular edge will focus on a simple case of parked vehicles. A model will be created to evaluate processing Big Data using a datacenter comprising these parked vehicles. The model will simulate a datacenter implemented on the vehicles in the parking lot of a business that operates 24 h a day, 7 days a week. The employees of the business work in staggered 8-h shifts. This provides a pool of vehicles that can serve as the basis for a datacenter for the business. The vehicles in the parking lot are provided a standard power outlet for charging their vehicles in return for the use of their computing resources. The challenge of facing the implementation of the datacenter is to maintain high availability and reliability.

As Big Data and Internet of Things (IoT) keep evolving and play an increasingly important role in the digitization, it is crucial to crystalize how these two are related to each other. This article investigates the correlation between Big Data and IoT systematically and introduces a comprehensive relationship model to synergize IoT and Big Data. The overarching model is broken down into pillars to manage the complexity: independent, interconnecting, interacting, and intertwined (i4). The independent pillar is concerned with stand-alone operations and distinct functionalities, composed of four components: difference, implementation, similarity, and capability (DISC). The interconnecting pillar deals with the structural connections with four units: composition, realization, atomicity, and multiplicity (CRAM). The interacting pillar covers four behavioral styles with four modules: control, association, range, and dependency (CARD). Lastly, the intertwined pillar is in regard to the intermesh of working together cohesively, consisting of four parts: touchpoints, integration, mapping, and enablement (TIME). We delve into each individual element in greater detail, followed by the guidelines and best practices on how to effectively apply the i4 model in real-life initiatives.

This book chapter represents an overall idea of the Internet of Things (IoT)-based smart transportation system under real-time environment. IoT technology is growing very fast in recent years in the industry and business areas with lots of opportunities. IoT is used to connect between different objects or things in the real world to the internet. IoT is a new concept area, which is also known as a riskless, powerful and precise architectural infrastructure which is very much related to our daily life. IoT is continuously reducing cost and also power consumption. IoT is known as an emerging technological world where anyone or anything can be connected, interacted and communicated with each other anytime and anywhere in a perceptive way through different types of gadgets like smartphones and computers. In IoT, physical objects like actuators and sensors are wirelessly or physically connected to the internet. An enormous amount of data is produced by the network to the analytical devices to scrutinize it. IoT is known as an infrastructural environment where communication and computing system consistently embedded to achieve some specified targets. In the IoT, multiple physical objects of the real world associated with each other to collect and interchange the data. The application areas of IoT spread out at various segments, including transportation, health-care environments, home automation, agriculture, smart appliances and smart city. The IoT technology along with the Big Data framework is created a huge change in the transportation sector, especially in motorways. Nowadays, traffic congestion is a very serious problem throughout the world. Moreover, day by day the number of vehicles is also increased in a terrible way to create an inescapable traffic condition. After that, this disorganized and chaotic traffic situation is also responsible to increase the pollution level and wastage of natural resources because most of the vehicles keep their engine active at any traffic congestion. Apart from this, it is also seen that using the same lane for heavy and light vehicles, the number of accidents is also increased day by day. So those aforesaid issues are very much responsible to create terrible congestion on the road, which is the prime suspect for several crashes of vehicles and the violation of safety environment on road. The performance of most of the existing techniques is not good enough to provide the solution of the aforesaid problem because those techniques are itself very costly and always face various maintenance issues. So to overcome the aforesaid problems and to give more safety on the road, intelligent transportation system (ITS) or smart transportation system performs an active role to increase the efficiency of the transportation system on road. This smart transportation system is developed with the help of lots of sensors such as pressure sensors, proximity sensors, light sensors, humidity sensors, temperature sensors and wireless sensor network (WSN) such as Zigbee, Xbee and Wi-Fi. This book chapter presents an overview of the data acquisition (DAQ), data-processing and data-analysis technique, various work, existing trends, challenges and future scope of this smart transportation system.

This chapter presents the detailed discussion on EC-based architecture for big data enabled IoT with their advantages and disadvantages. The EC-based architecture provide closer proximity-based solutions avoiding latency and limited bandwidth issues. However, to support big data handling and management of IoT sensors and smart city infrastructure, load balancing and resource sharing features are required. We can conclude from the earlier discussion that increased number of devices and data generated by them have posed resource-scarcity challenges at edge devices. In the worst case, the edge device is forced to forward the request to a distant remote cloud eliminating the beneficial features of reduced latency and Internet dependency offered by EC architecture.

The Internet of Things (IoT) is proving to be an essential part of the fourth industrial revolution (4IR). In highly critical environments, IoT systems are required to be secure, reliable and available. Although there are many investigations on the security and privacy of IoT systems, there are relatively far fewer efforts expended on their dependability. The individual `things' of an IoT system may be unfailing, but that does not make the complete IoT system - from sensor to server - dependable. In this chapter, a technique is proposed to evaluate the dependability of a complete IoT ecosystem by using a modified version of the classical fault tree analysis (FTA) and Monte Carlo simulation techniques. The propose technique is applied to a hypothetical case study, and the results are promising.