Internet of Things (IoT) is one of the most significant recent developments in the field of networking. Energy efficiency and scalability are two important technical issues that must be fully addressed in order to build high-performance IoTs. Topology control (TC) plays a crucial role in scalable and energy-efficient IoTs. In this chapter, we first give an overview of TC technologies and their applications in IoTs. Then we describe a systematic approach for topology construction in IoT to achieve scalability and energy efficiency. Such an approach includes a hierarchical system framework for IoT deployment, an optimization model for realizing energy efficiency, and an algorithm for solving the optimization mode. Experimental results are also presented in the chapter to show effectiveness of this topology construction approach.

Memory optimization, real-time scheduling, energy conservation, reprogramming, context awareness, and fault tolerance are the critical research challenges in the wireless sensor network (WSN). To address these challenges, a new WSN operating system (OS) LiveOS is designed and implemented in this chapter. Compared with the other WSN OSes, LiveOS has two typical features. On the one hand, the new OS design concepts such as the hybrid scheduling, the shared-stack scheduling, the reactive-defragmentation allocation, and the pre-linked native-code middleware are implemented. By doing this, the memory cost of the real-time WSN OS can be decreased. Moreover, the reprogramming performance of the WSN nodes can be improved. On the other hand, the new research approach, which addresses the WSN challenges by combining both the software technique and the multi-core hardware technique, is applied in LiveOS. By means of the multi-core hardware infrastructure, the lifetime of the LiveOS node can be prolonged. Moreover, the context-aware ability, the real-time performance, and the fault-tolerant capability of the WSN nodes can be improved. With the implementation of the above concepts, LiveOS becomes the WSN OS which can be applied on the resource-constrained WSN nodes and can be used to execute the real-time WSN applications with high reliability.

With cloned tag attacks being a serious problem in radio frequency identification (RFID) supply chains, tracing event records and inspecting them are one of the notably effective means to defense such attacks. Unfortunately, current event formulations are unable to characterize the variety and complexness of the events in real-world RFID supply chains. This chapter identifies the problem of the existing tag event models, reviews the RFID supply chain architecture, proposes a new tag event model, and devises the ensuing event-tracing algorithm. It is our belief that the newly proposed event model together with the event-tracing algorithm based on this model furnish a foundation for future more sophisticated event-record-based clone detection techniques.

Internet of Things (IoT) aims to provide ubiquitous connectivity and services for individual persons and machines. The related applications cover various areas in industry, healthcare, city management, etc. The efficient information collection is very critical for the success of these applications. Without enough number of data, it is difficult for a system to provide high-quality services. Participatory sensing network (PSN) is a promising paradigm to efficiently collect information/sensing data. Meanwhile, the incentive mechanism design plays a key role in achieving the collection of enough number of sensing data in PSN, where the sensor-equipped mobile devices are owned and controlled by individual users. Most of existing works on incentive mechanism design focus on the participation of smartphone users, rather than the quality of sensing data. However, data quality is also an important factor for a data collector since smartphone users may submit the erroneous or unreliable data. Low-quality data will impact the accuracy of data analysis result and degrade the provided service. Therefore, data quality should be considered in the incentive mechanism design. Reputation is one way to evaluate the quality of data provided by a mobile user. In this chapter, we introduce the significance of incentive mechanism design for the applications of IoT, then we design a reputation-aware incentive mechanism. Taking the quality of sensing data into account, the proposed mechanism can maximize the weighted social welfare of the whole system and guarantee the nice features of truthfulness and individual rationality. Extensive simulations have been conducted to demonstrate the better performance of the proposed incentive mechanism compared with other existing methods in terms of the weighted social welfare and the average reputation.

The rapid growth in the population density in urban cities demands additional, smart, and fast provision of services and infrastructure. Countries and metropolitan authorities are really interested to provide smart and intelligent environment and real-time facilities to their citizens. Citizens also want to be facilitated by the provision of real-time information regarding anything like traffic, flood, security, pollution, etc. To meet the requirements of both the metropolitan authorities and the citizens, we proposed the use of Internet of Things (IoT)-based smart systems for smart city establishment and the urban planning as well. In this chapter, we propose an IoT-based system that uses the massive volume of data, termed as big data, generated by the smart systems to establish smart city and to do urban planning for the bright future. The data are generated from the smart home sensors, vehicular networks, weather and water sensors, smart parking systems, surveillance objects, etc. A four-tier architecture is proposed which include (1) bottom tier-1: responsible for the management and deployment of IoT sources, data generations, and collections; (2) intermediate tier-1: handles all type of communication between sensors, relays, base stations, the Internet, etc.; (3) intermediate tier-2: levers the data management and processing using Hadoop framework; and (4) top tier: is responsible for application and usage of the data analysis, results generation, and smart decisions. The system implementation consists of various phases including data generation and collecting, aggregating, filtration, classification, preprocessing, computing, and decision making. The proposed system implementation is done in Hadoop ecosystem with spark, voltDB, storm or S4 for real-time processing to generate results in order to establish the smart city. For urban planning or city future development, the offline historical data are used using MapReduce programming in Hadoop environment. IoT-based datasets generated by smart homes, smart parking, weather, pollution, and vehicle datasets are used for analysis and evaluation. The system is evaluated with respect to efficiency in terms of throughput and processing time.