Smart City and sensing platforms are considered some of the most significant topics in the Internet of Things (IoT). Sensors are at the heart of the IoT, and their development is a key issue if such concepts are to achieve their full potential. This book addresses the major challenges in realizing smart city and sensing platforms in the era of the IoT and the Cloud. Challenges vary from cost and energy efficiency to availability and service quality. To tackle these challenges, sensors must meet certain expectations and requirements such as size constraints, manufacturing costs and resistance to environmental factors. This book focuses on both the design and implementation aspects for smart city and sensing applications that are enabled and supported by IoT paradigms. Attention is also given to data delivery approaches and performance aspects.
Inspec keywords: wireless sensor networks; Big Data; smart cities; Internet of Things; computer network security
Other keywords: sensor registering; mesh networking; big data; low-power sensor motes; security; trust; access control; Internet of Things; smart cities; statistical analysis; evolutionary decision tree model; smart urban drainage systems; sensory E-Health applications; healthcare services; IoT safety; cybersecurity attacks; HaLow; Contiki-OS IoT data analytics; IoT-based smart water
Subjects: Wireless sensor networks; General and management topics; Computer communications; Computer networks and techniques; Data security; General electrical engineering topics
As the world population grows along with increase in urbanization, cities are getting more and more populated and increasing demands on various natural and man-made resources. Making cities smart through the appropriate application of the plethora of innovative new technologies and paradigms would help mollify potential environmental problems/constraints in such cities. Besides their significance in solving environmental problems, smart cities also aim to improve citizen's quality of life and efficiency of public services by optimizing the costs and resources involved. Smart city applications are founded on various enabling technologies and processes related to communication and networking, real-time control and big data analytics, to name a few. These constituents of a smart city infrastructure need to be integrated appropriately and seamlessly for provisioning efficient services to citizens. Each enabling technology has its own unique properties. The interplay and interaction among smart city constituents and their unique properties raise unique security and privacy challenges. In this chapter, we overview enabling and emerging technologies and security and privacy challenges for smart cities. Ensuring authorized accesses to these constituents is critical for ensuring the security of smart cities. We present existing solutions to these challenges mainly from the perspective of access control (AC) with a special focus on risk management, trust, insider threats and secure interoperation. Finally, we present future research directions.
A smart city uses various technologies to make the lives more easy and simple fulfilling the demands of the increasing population. Internet of Things (IoT) plays a major role in making the city smart. It takes the required input and helps to make the things associated with it smart. Some of the smart operations include water and energy management which is becoming scarce. The system can be deployed in the cities to make things work and save future resources. A smart city can be empowered to increase the quality of life of the people and improve the environment to sustain for a long time. Implementing a smart city with IoT and connected technology helps enhance the quality, performance and interactivity of urban services, optimize resources and reduce costs. The chapter briefly discusses what is the role of IoT in smart cities describing the basics of what is IoT and what comprises a smart city followed by smart city segments. Benefits of IoT and their impact on the smart city along with the national and international case studies. At the end of the chapter, some of the challenges associated with the IoT with respect to smart cities.
This chapter primarily focuses on the role of the Internet of Things (IoT) in the smart water (SW) conversion process. The several problems like leakage detection, efficient water distribution, remote water monitoring, etc., can be addressed by using IoT with the combination of information and communication technology (ICT). In this line of thought, we propose a smart water system (SWS) layer architecture to ensure the proper utilization of natural or man-made resources. The hygiene water is the birthright of every human being and to ensure it for a future generation the SWS is a proven model.
Advancements in sensor network have evolved rapidly in recent years, and devices are smart enough to build and manage their network and route optimization referred to as Internet of Things (IoT). Numerous IoT operating systems (OSs) are developed for resource-constrained IoT devices. Contiki IoT OS is a widely used IoT OS by researchers and practitioners. Contiki-OS Cooja emulator is recognized as one of the favourite tools of researchers for running large-scale simulations and observing the results before the real-time deployment. Cooja generates execution logs for all the activities of the network simulation. However, there are no tools or programs available to summarize and analyse the big log files generated by Cooja. This slows down the research pace for complex network scenarios and makes it difficult to compare with existing bench marks and research work. In order to help researchers, an evaluation tool which gathers information, analyses and develops simulation log results is required. It provides detailed individual mote statistics as well as complete IoT network statistics. In this chapter, we discuss three algorithms and their merits and demerits. First, the proposed scheme scans the generated log file and provides summary of all the IoT motes in separate files. This technique is useful for very large files and complex operation, although it requires more hard disc space for temporary files. Second, the proposed algorithm scans log file to summarize data in memory. This algorithm requires additional space for temporary files and scans source files many times consequently, and it requires more time to complete the evaluation. Third algorithm scans log file exactly once, does not require any additional space for temporary files and computes summaries in memory. It makes processing really fast, and can work without temporary files generated. All three algorithms are helpful in different IoT deployment scenarios; therefore, researcher can choose according to their preference of memory requirements, file sizes, and time constraints.
The Internet of Things (IoT) is an idea according to which uniquely identifiable gadgets can directly or indirectly assemble, process or transfer data via the KONNEX quick electrical installation (KNX) or a computer network. With the advent of IoT, there were also fears that IoT is growing too fast, without due consideration of the significant security challenges and regulatory changes that may be necessary. This chapter analyses the security aspects of the mesh IoT. After entering the IoT mesh theme, a general review of IoT network security is presented. Next, security aspects in the IEEE 802.15.4 standard were analysed. Significant attention was also paid to the technical guidelines that enable secure transmission of information between selected IoT mesh points. The safety of implementing the mesh IoT network has been analysed in detail. Finally, the security aspects of different systems were compared.
Recently, as an alternative method for monitoring of drainage systems, Internet of Things (IoT) technology is initiated in smart cities. IoT is used for detection of the location of the sediment deposition within the drainage pipe system to alert for repairing before complete blocking. However, from the hydraulic point of view, it is reasonable to design the drainage and sewer pipes to prevent the deposition of the sediment based on the physical parameters. To this end, instead of detection of blockage location, monitoring the flow characteristics is of more importance to keep pipe bottom clean from sediment deposition. Accordingly, smart sensors mounted in the drainage and sewer pipes should read the flow velocity and alert once the flow reaches a velocity in which sediment deposition is occurred. In order to determine the sediment deposition velocity, this study models sediment transport in drainage systems by means of evolutionary decision tree (EDT) technique. EDT results are compared with conventional decision tree (DT) and evolutionary genetic programming (GP) techniques. A large number of experimental data covering wide ranges of sediment and pipe size were used for the modeling. Evaluation of the developed models in terms of verity of statistical indices showed the outperformance of the proposed EDT model. The EDT, DT and GP models were found superior to their traditional corresponding regression models existing in the literature. Results are helpful for determination of the flow characteristics at sediment deposition condition in drainage systems maintained using IoT technology in smart cities.
The population of people whose ages are over 60 is expected to be more than double by 2050. As they live alone or without any healthcare professional, it is a complicated job to understand emergency situations or mild symptoms of diseases. On the other hand, smart home environments offer an unrivalled opportunity to collect data for monitoring to understand human behaviours so that healthcare institutions intervene such cases. Furthermore, thanks to the availability of large smart home data sets on the Internet with different features such as different number of residents, house plans and different use cases (activities of daily living (ADL), work activities), it is possible to work on different aspects in this area. Hence, forthcoming smart cities can highly benefit from those electronic health (E-Health) systems to enhance Ambient Assisted Living (AAL).This chapter presents the opportunities and the challenges that come with this type of data and the applications while emphasizing the importance of statistical analysis on these topics. In addition, it shows the steps of analysis of the data from binary sensors deployed in a smart home.
Smart city is an emerging concept whose main goal is to improve the quality of life of its citizens by leveraging Information and Communications Technologies (ICTs) as the key medium. In this context, smart city healthcare can play a pivotal role toward the improvement of citizens' quality of life, since it can allow citizens to be provided with personalized e-health services, without limitations on time and location. In smart city healthcare, medical Internet of Things (IoT) devices constitute a key underlying technology for providing personalized e-health services to smart city patients. However, despite the significant advantages that IoT medical device technology brings into smart city healthcare, medical IoT devices are vulnerable to various types of cybersecurity threats and thus, they pose a significant risk to smart city patient safety. Based on that and the fact that the security is a critical factor for the success of smart city healthcare services, novel security mechanisms against cyberattacks of today and tomorrow on IoT medical devices are required. Toward this direction, the first step is the comprehensive understanding of the existing cybersecurity attacks on IoT medical devices. Thus, in this chapter, we will provide a categorization of cybersecurity attacks on medical IoT devices which have been seen in the wild and can cause security issues and challenges in smart city healthcare services. Moreover, we will present security mechanisms, derived from the literature, for the most common attacks, as well as highlight emerging good practice and approaches that manufacturers can take to improve medical IoT device security throughout its life cycle. In this chapter, the authors' intent is to provide a foundation for organizing research efforts toward the development of the proper security mechanism against cyberattacks targeting IoT medical devices.
IEEE 802.11ah working group (WG) introduced Wi-Fi HaLow (or simply HaLow which is a marketing name of Wi-Fi for low-power devices) as a revision of the long-range Wi-Fi technology for the Internet of Things (IoT) applications in smart cities. Such applications often involve thousands of wireless devices (typically sensors and actuators) connected to a shared wireless channel. Channel access for these thousands of IoT devices significantly affects the performance of the network. Several mechanisms have been proposed to register thousands of low-power sensors and actuators by handling the contention between them. Two of which known as the centralized authentication control (CAC) and the distributed authentication control (DAC), aimed to address the contention reduction during the link set-up process in HaLow. In HaLow, a link set-up process requires much more interests from the researchers because the access point (AP) knows nothing about the connected devices and the mean of control at these stations is very limited. DAC is a self-adaptive device authentication mechanism, whereas CAC requires an algorithm to dynamically control critical parameters, such as transmission slots and channel access period. However, the existing IoT devices registration mechanism in HaLow is based on carrier-sense multiple access with collision avoidance (CSMA/CA), which is not very efficient for the registration of large-scale IoT devices due to its limited binary exponential contention mechanism. In this chapter, we explain both of the device authentication mechanisms, i.e. CAC and DAC, in detail. Later, we discuss one of the authentication mechanism known as hybrid slotted-CSMA/CA-time-division multiple access (TDMA) (HSCT) as our case study that is proposed to overcome the aforementioned issues in current authentication techniques. The HSCT mechanism allows IoT systems in smart cities to register thousands of low-power IoT devices (sensors and actuators). This chapter also comes up with the analyses of the access period in a single HSCT time slot.
Nowadays, Internet of Things (IoT) applications with low-power sensor motes are becoming more and more popular. Environment monitoring and disaster surveillance applications make use of low-power sensors. Energy is one of the most important metrics in such applications. Low-power sensor motes are used to create more energy efficient applications. Many new architectures and platforms are proposed to support low-power IoT. Although there are many platforms and approaches, the research in the area to analyze low-power wireless sensor networks (WSNs) in terms of energy consumption is not sufficient. Studies from literature propose methods to estimate lifetime, yet statistical analysis with observed data is missing. To analyze such systems, we apply statistical analysis to the data set from “Intel Berkeley Research Lab”. Data set includes 35 days of Mica2Dot sensor data including sensor readings and voltage values. The main objective is to analyze effects of environmental variables such as temperature and humidity on lifetime of a sensor node. To understand the data, descriptive analysis is conducted. Some statistical models like linear regression and ordered logit regression are used and results are discussed in detail.
Smart cities have emerged as one of the most promising wireless sensor networks (WSNs) applications in the Internet of Things (IoT) era because of their agile nature and significant impact in almost everyday activity. Toward more efficient smart cities implementations, in this work, we proposed and evaluated the use of sensors and WSNs in large-scale IoT applications. We focused on key design aspects in the sensor node and WSNs performance, deployment and data readings trends. In the present Chapter is a summary of our conclusions and recommendations for the future relevant work.
