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The Internet of Things (IoT) is increasingly a reality today. Nevertheless, some key challenges still need to be given particular attention so that IoT solutions further support the growing demand for connected devices and the services offered. Due to the potential relevance and sensitivity of services, IoT solutions should address the security and privacy concerns surrounding these devices and the data they collect, generate, and process. Recently, the Blockchain technology has gained much attention in IoT solutions. Its primary usage scenarios are in the financial domain, where Blockchain creates a promising applications world and can be leveraged to solve security and privacy issues. However, this emerging technology has a great potential in the most diverse technological areas and can significantly help achieve the Internet of Things view in different aspects, increasing the capacity of decentralization, facilitating interactions, enabling new transaction models, and allowing autonomous coordination of the devices. The paper goal is to provide the concepts about the structure and operation of Blockchain and, mainly, analyze how the use of this technology can be used to provide security and privacy in IoT. Finally, we present the stalker, which is a selfish miner variant that has the objective of preventing a node to publish its blocks on the main chain.

Abstract-Layered transmission of data is often recommended as a solution to the problem of varying bandwidth constraints in multicast video applications. Multilayered encoding, however, is not sufficient to provide high video quality and high network utilization, since bandwidth constraints frequently change over time. Adaptive techniques capable of adjusting the rates of video layers are required to maximize video quality and network utilization. We define a class of algorithms known as source-adaptive multilayered multicast (SAMM) algorithms. In SAMM algorithms, the source uses congestion feedback to adjust the number of generated layers and the bit rate of each layer. We contrast two specific SAMM algorithms: an end-to-end algorithm, in which only end systems monitor available bandwidth and report the amount of available bandwidth to the source, and a network-based algorithm, in which intermediate nodes also monitor and report available bandwidth. Using simulations that incorporate multilayered video codecs, we demonstrate that SAMM algorithms can exhibit better scalability and responsiveness to congestion than algorithms that are not source-adaptive. We also study the performance trade-offs between end-to-end and network-based SAMM algorithms.

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