## 2 Background Information
### Application Scenarios and Data Characteristics  
A critical objective of this effort concerns the performance enhancement offered by an efficient compression module to the state-of-the-art WSN platforms targeting highly demanding applications. Epilepsy monitoring based on IoT platform, represents the main targeted application scenario of this paper. From an engineering point of view, the respective application scenarios are based on acquiring excessive amounts of data (digitized physiological measurements), for extended periods of time, which must be either stored locally or transmitted to an aggregation point. The most demanding case, comprising the main objective of this paper, concerns the real-time monitoring of an epileptic person. This represents a critical requirement since epileptic events can occur unexpectedly and unpredictably, while the triggering event is highly personalized to the specific person. For that reason, periodic-based monitoring leads to myopic and unreliable results and conclusions. However, in both cases highly accurate signal monitoring is also required, which represents also a critical requirement towards effective compression. WSNs comprise a rapidly evolving research area proposing a new communication paradigm making them appealing for such cases. However, in realistic scenarios the respective platforms suffer from critical resource limitations especially in areas such as energy availability, communication bandwidth and processing power that, at the same time, are interdependent resource consuming factors. More specifically, the operation of state-of-the-art WSN platforms is based on small batteries, typically offering energy capacity from 450 mAh (e.g., Shimmer platform) up to 3300 mAh (e.g., two AA batteries). Consequently, all components must offer low-power characteristics resulting in processing units with low processing capabilities (provided by 16-bit-based Micro-Controller Units) and limited available memory (in the area of 10 Kbyte RAM). Additionally, the data transfer is based on low-power wireless interfaces such as IEEE 802.15.4 and Bluetooth offering bandwidth capabilities at physical layer from 250 Kbps up to a few Mbps. Taking into consideration the aforementioned application scenarios, the two modalities of paramount importance in epileptic seizure study, resulting into significant amount of accumulated data, are EEG and ECG measurements. Typical acquisition devices produce samples represented as 16-bit numbers. Furthermore, a wired EEG setup is usually comprised of 64 sensors with sampling frequency up to 2.5 kHz, while ECG typically requires 4 sensors with adequate sampling frequency of a few hundreds of Hertz. Given that the main application scenario targeted is the real-time monitoring of epileptic persons, even with some rough calculations, it can be derived that a setup of 64 EEG sensors requires higher wireless bandwidth (not considering packet headers and control data) than the one prominent communication technologies can provide. Specifically, assuming typical wireless technologies such as IEEE 802.15.4 and Bluetooth, such scenarios pose overwhelming burden to WSN platforms typically offering extremely limited resources [9; 13; 14; 15; 16; 17; 18]. Such workload scenarios demand that power-hungry components (such as the wireless interfaces or/and the processing unit) are continuously operating at their maximum power consumption operational state. Such behavior, however, contradicts to the main objective of typical WSNs of residing such components to low-power states for extended periods of time enabling drastic extension of the network lifetime and thus meet demanding requirements of many hours or days of monitoring. Therefore, in such cases the reduction of the amount of data (i.e., by compressing them) can yield significant and multifaceted benefits to the system performance.