Traditionally, clustering of multivariate data aims at grouping objects described with multiple heterogeneous attributes based on a suitable similarity (conversely, distance) function. One of the main challenges is due to the fact that it is not straightforward to directly apply mathematical operations (e.g., sum, average) to the feature values, as they stem from heterogeneous contexts.

We present CACSE – a system for Context Aware Clustering of Stellar Evolution – for datasets corresponding to temporal evolution of stars, which are multivariate time series, usually with a large number of attributes (e.g., ≥ 40). Typically, the datasets are obtained by simulation and are relatively large in size (5 ∼ 10 GB per certain interval of values for various initial conditions). Investigating common evolutionary trends in these datasets often depends on the context – i.e., not all the attributes are always of interest, and among the subset of the context-relevant attributes, some may have more impact than others. To enable such context-aware clustering, our CACSE system provides functionalities allowing the domain experts to dynamically select attributes that matter, and assign desired weights/priorities. Our system consists of a PostgreSQL database, Python-based middleware with RESTful and Django framework, and a web-based user interface as frontend. The user interface provides multiple interactive options, including selection of datasets and preferred attributes along with the corresponding weights. Subsequently, the users can select a time instant or a time range to visualize the formed clusters. Thus, CACSE enables a detection of changes in the the set of clusters (i.e., convoys) of stellar evolution tracks. Current version provides two of the most popular clustering algorithms – k-means and DBSCAN.

The Transport Layer Security (TLS) protocol has been considered as a promising approach to secure Internet of Things (IoT) applications. The different cipher suites offered by the TLS protocol play an essential role in determining communication security level. Each cipher suite encompasses a set of cryptographic algorithms, which can vary in terms of their resource consumption and significantly influence the lifetime of IoT devices. Based on these considerations, in this paper, we present a comprehensive study of the widely used cryptographic algorithms by annotating their source codes and running empirical measurements on two state-of-the-art, low-power wireless IoT platforms. Specifically, we present fine-grained resource consumption of the building blocks of the handshake and record layer algorithms and formulate tree structures that present various possible combinations of ciphers as well as individual functions. Depending on the parameters, a path is selected and traversed to calculate the corresponding resource impact. Our studies enable IoT developers to change cipher suite parameters and immediately observe the resource costs. Besides, these findings offer guidelines for choosing the most appropriate cipher suites for different application scenarios.