The Internet of Things (IoT) brings together a multitude of technologies, with a vision of creating an interconnected world. This will benefit both corporations as well as the endusers. However, a plethora of security and privacy challenges need to be addressed for the IoT to be fully realized. In this paper, we identify and discuss the properties that constitute the uniqueness of the IoT in terms of the upcoming security and privacy challenges. Furthermore, we construct requirements induced by the aforementioned properties. We survey the four most dominant IoT architectures and analyze their security and privacy components with respect to the requirements. Our analysis shows a mediocre coverage of security and privacy requirements. Finally, through our survey we identify a number of research gaps that constitute the steps ahead for future research.

Complex event processing (CEP) is a fundamental paradigm for a software system to self-adapt to environmental changes. CEP provides efficient means to detect (complex) events corresponding to environmental changes by performing a realtime analysis on many, possibly heterogeneous, data sources. The way current CEP systems detect events is determined at design time without accounting for dynamic changes of the environment monitored by the CEP system. This can lead to situations where the performance, quality and reliability of event detection significantly drop (e.g., due to mobility) since initial assumptions of the environment are violated or stated too general. In this paper, we propose ADAPTIVECEP, a CEP system that is able to self-adapt to detected changes in environmental conditions. We propose a CEP query language that allows specifying changes in the behavior of the CEP system and its mechanisms in detecting events dependent on environmental conditions. This way, ADAPTIVECEP can select the best-suited configurations for given quality demands. In our evaluation, we show by means of a reference concept how the flexibility exposed by the query language helps to achieve significant performance gains.

No abstract

No abstract

Distributed Complex Event Processing (DCEP) is a commonly used paradigm to detect and act on situational changes of many applications, including the Internet of Things (IoT). DCEP achieves this using a simple specification of analytical tasks on data streams called operators and their distributed execution on a set of infrastructure. The adaptivity of DCEP to the dynamics of IoT applications is essential and very challenging in the face of changing demands concerning Quality of Service. In our previous work, we addressed this issue by enabling transitions, which allow for the adaptive use of operator placement mechanisms. In this article, we extend the transition methodology by optimizing the costs of transition and analyzing the behavior using multiple operator placement mechanisms. Furthermore, we provide an extensive evaluation on the costs of transition imposed by operator migrations and learning, as it can inflict overhead on the performance if operated uncoordinatedly.