M
Matti Siekkinen
Researcher at Aalto University
Publications - 109
Citations - 2566
Matti Siekkinen is an academic researcher from Aalto University. The author has contributed to research in topics: Energy consumption & Mobile device. The author has an hindex of 25, co-authored 104 publications receiving 2186 citations. Previous affiliations of Matti Siekkinen include University of Helsinki & Helsinki University of Technology.
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Proceedings ArticleDOI
How low energy is bluetooth low energy? Comparative measurements with ZigBee/802.15.4
TL;DR: This work studies the energy consumption of BLE by measuring real devices with a power monitor and derive models of the basic energy consumption behavior observed from the measurement results, and investigates the overhead of Ipv6-based communication over BLE, relevant for future IoT scenarios.
Journal ArticleDOI
Energy Efficient Multimedia Streaming to Mobile Devices — A Survey
TL;DR: This survey examines solutions that have been proposed during the last few years, to improve the energy efficiency of wireless multimedia streaming in mobile hand-held devices and provides evidence of the fact that some of these tactics already exist in modern smaprtphones and provide energy savings with real measurements.
Journal ArticleDOI
Modeling, Profiling, and Debugging the Energy Consumption of Mobile Devices
TL;DR: This article introduces the terminologies and describes the power modeling and measurement methodologies applied in model-based energy profiling, classify the profilers according to their implementation and deployment strategies, and compare the profiling capabilities and performance between different types.
Proceedings ArticleDOI
Practical power modeling of data transmission over 802.11g for wireless applications
TL;DR: A simple and practical power model for data transmission over an 802.11g WLAN is presented and its accuracy against physical data measured from three popular mobile platforms, Maemo, Android and Symbian is shown.
Journal ArticleDOI
Edge Computing Assisted Adaptive Mobile Video Streaming
TL;DR: An optimized solution for network assisted adaptation specifically targeted to mobile streaming in multi-access edge computing (MEC) environments is presented, designed a heuristic-based algorithm with minimum need for parameter tuning and having relatively low complexity.