Internet of Things (IoT) has gained increasing visibility among emerging technologies and undoubtedly changing our daily life. Its adoption is strengthened by the growth of connected devices (things) as shown in recent statistics. However, as the number of connected things grows, responsibility related to security aspects also needs to increase. For instance, cyberattacks might happen if simple authentication mechanisms are not implemented on IoT applications, or if access control mechanisms are weakly defined. Considering the relevance of the subject, we performed a systematic literature review (SLR) to identify and synthesize security issues in IoT discussed in scientific papers published within a period of 8 years. Our literature review focused on four main security aspects, namely authentication, access control, data protection, and trust. We believe that a study considering these topics has the potential to reveal important opportunities and trends related to IoT security. In particular, we aim to identify open issues and technological trends that might guide future studies in this field, thus providing useful material both to researchers and to managers and developers of IoT systems. In this paper, we describe the protocol adopted to perform the SLR and present the state-of-the-art on the field by describing the main techniques reported in the retrieved studies. To the best of our knowledge, ours is the first study to compile information on a comprehensive set of security aspects in IoT. Moreover, we discuss the placement, in terms of architectural tiers, for deploying security techniques, in an attempt to provide guidelines to help design decisions of security solution developers. We summarize our results showing security trends and research gaps that can be explored in future studies.

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The Internet of things (IoT) has recently transformed the internet, enabling the communication between every kind of objects (things). The growing number of sensors and smart devices enhanced data creation and collection capabilities and led to an explosion of generated data in the form of Data Streams. Processing these data streams is complex and presents challenges and opportunities in the stream processing field. Due to the inherent lacking of accuracy and completeness of sensor generated data, the quality of raw data is often poor. Data cleaning tasks are required to help increasing the quality of the data being processed in an IoT application. This work proposes a data stream processing workflow for IoT to be deployed at the edge of the network. It performs a fast data cleaning with low power consumption from edge and sensor nodes. The edge computing paradigm is used to bring the data cleaning task closer to the data sources and allow actions to be triggered immediately. In addition, an energy-aware data collection component is designed to reduce the network traffic and, as a consequence, decrease the power consumption of the network devices. The proposed workflow enables the deployment of long running real-time processing systems on remote outdoor environments.

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