With the proliferation of portable and mobile IoT devices and their increasing processing capability, we witness that the edge of network is moving to the IoT gateways and smart devices. To avoid Big Data issues (e.g. high latency of cloud based IoT), the processing of the captured data is starting from the IoT edge node. However, the available processing capabilities and energy resources are still limited and do not allow to fully process the data on-board. It calls for offloading some portions of computation to the gateway or servers. Due to the limited bandwidth of the IoT gateways, choosing the offloading levels of connected devices and allocating bandwidth to them is a challenging problem. This paper proposes a technique for managing computation offloading in a local IoT network under bandwidth constraints. The existing bandwidth allocation and computation offloading management techniques underutilize the gateway's resources (e.g. bandwidth) due to the fragmentation issue. This issue stems from the discrete coarse-grained choices (i.e. offloading levels) on the IoT end nodes. Our proposed technique addresses this issue, and utilizes the available resources of the gateway effectively. The experimental results show on average 1 hour (up to 1.5 hour) improvement in battery life of edge devices. The utilization of gateway's bandwidth increased by 40%. 1

Todays prevalent solutions for modern embedded systems and general computing employ many processing units connected by an on-chip network leaving behind complex superscalar architectures In this paper, we couple the concept of distributed computing with parallel applications and present a workload-aware distributed run-time framework for malleable applications on many-core platforms. The presented framework is responsible for serving in a distributed way and at run-time, the needs of malleable applications, maximizing resource utilization avoiding dominating effects and taking into account the type of processors supporting platform heterogeneity, while having a small overhead in overall inter-core communication. Our framework has been implemented as part of a C simulator and additionally as a runtime service on the Single-Chip Cloud Computer (SCC), an experimental processor created by Intel Labs, and we compared it against a state-of-art run-time resource manager. Experimental results showed that our framework has on average 70% less messages, 64% smaller message size and 20% application speed-up gain.

Internet-of-Things (IoT) envisions an infrastructure of ubiquitous networked smart devices offering advanced monitoring and control services. Current art in IoT architectures utilizes gateways to enable application-specific connectivity to IoT devices. In typical configurations, an IoT gateway is shared among several IoT devices. However, given the limited available bandwidth and processing capabilities of an IoT gateway, the quality of service (QoS) of IoT devices must be adjusted over time not only to fulfill the needs of individual IoT device users, but also to tolerate the QoS needs of the other IoT devices sharing the same gateway.In this paper, we address the problem of QoS management for IoT devices under bandwidth, battery, and processing constraints. We first formulate the problem of resourceaware QoS tailored to the IoT paradigm and then propose an efficient problem decomposition that enables the adoption of a recurrent dynamic programming approach with reduced execution time overhead. We evaluate the efficiency of the proposed approach with a case study and through extensive experimentation over different IoT system configurations regarding to the number and type of the employed IoTdevices. Experiments show that our solution improves the overall QoS by 50% compared to an unsupervised system while both meet the constraints.

Healthcare is one of the most rapidly expanding application areas of the Internet of Things (IoT) technology. IoT devices can be used to enable remote health monitoring of patients with chronic diseases such as cardiovascular diseases (CVD). In this paper we develop an algorithm for ECG analysis and classification for heartbeat diagnosis, and implement it on an IoT-based embedded platform. This algorithm is our proposal for a wearable ECG diagnosis device, suitable for 24-hour continuous monitoring of the patient. We use Discrete Wavelet Transform (DWT) for the ECG analysis, and a Support Vector Machine (SVM) classifier. The best classification accuracy achieved is 98.9%, for a feature vector of size 18, and 2493 support vectors. Different implementations of the algorithm on the Galileo board, help demonstrate that the computational cost is such, that the ECG analysis and classification can be performed in real-time.

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Strong evidence is provided for significant far from equilibrium complex processes in the seismogenic layer of the North Aegean region (Greece), after applying modern nonlinear methods to various seismicity time series. The data used are subsets of the regional catalogue compiled in the central Seismological Station of Geophysics Department, Aristotle University of Thessaloniki and concern 4367 earthquakes of magnitude greater than 3.8, which took place during the period of 1968–2008. We present results, derived from the application of nonlinear algorithms, concerning the estimation of correlation dimension, mutual information, largest Lyapunov exponent, flatness coefficient and q-value which correspond to Tsallis nonextensive statistics. These quantities are estimated for two seismic time series corresponding to the basic focal parameters of earthquakes, namely origin time and magnitude. The obtained results can be associated with novel far from equilibrium complex dynamics such as low dimensional chaos, Self Organized Criticality (SOC) and intermittent turbulence. Furthermore, in this study, new information is provided about the nonlinear turbulent character of the Hellenic lithospheric dynamics related to the Tsallis nonextensive statistical theory. Our analysis indicates the coexistence of two different lithospheric processes, one low dimensional (chaotic) and the other high dimensional (SOC), revealing the strongly turbulent character of the Greek lithospheric system. In particular, the low dimensional chaotic process corresponds to the temporal manifestation of earthquakes, whereas the high dimensional nonlinear (SOC) process corresponds to the burst energy releases, a result that has significant implications concerning the ability of earthquake prediction.

Image processing algorithms are dominating contemporary digital systems due to their importance and adoption by a large number of application domains. Despite their significance, their computational requirements often limit their usage, especially in deeply embedded designs. Heterogeneous computing systems offer a promising solution for this performance gap, leading to their ever increasing utilization by designers. This work targets the acceleration of an image registration pipeline on a System-on-Chip (SoC) including both general purpose and re-configurable computing elements. The evaluation of our proposed HW/SW co-designed image registration application on a state-of-the-art FPGA based SoC showcases its ability to outperform software designs leading to orders of performance speedup (up to 67x) against embedded CPUs. CCS CONCEPTS• Computing methodologies → Image processing; • Hardware → Hardware accelerators; Hardware-software codesign.

As technology constantly strengthens its presence in all aspects of human life, computing systems integrate a high number of processing cores, whereas applications become more complex and greedy for computational resources. Inevitably, this high increase in processing elements combined with the unpredictable resource requirements of executed applications at design time impose new design constraints to resource management of many-core systems, turning the distributed functionality into a necessity. In this work, we present a distributed runtime resource management framework for many-core systems utilizing a network-on-chip (NoC) infrastructure. Specifically, we couple the concept of distributed management with parallel applications by assigning different roles to the available computing resources. The presented design is based on the idea of local controllers and managers, whereas an on-chip intercommunication scheme ensures decision distribution. The evaluation of the proposed framework was performed on an Intel Single-Chip Cloud Computer, an actual NoC-based, many-core system. Experimental results show that the proposed scheme manages to allocate resources efficiently at runtime, leading to gains of up to 30% in application execution latency compared to relevant state-of-the-art distributed resource management frameworks.