M
Marko Hännikäinen
Researcher at Tampere University of Technology
Publications - 128
Citations - 3172
Marko Hännikäinen is an academic researcher from Tampere University of Technology. The author has contributed to research in topics: Wireless sensor network & Wireless network. The author has an hindex of 27, co-authored 128 publications receiving 3061 citations.
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Proceedings ArticleDOI
Design and Implementation of Low-Area and Low-Power AES Encryption Hardware Core
TL;DR: This paper presents an AES encryption hardware core suited for devices in which low cost and low power consumption are desired and constitutes of a novel 8-bit architecture and supports encryption with 128-bit keys.
Journal ArticleDOI
A survey of application distribution in wireless sensor networks
TL;DR: A framework in which a middleware distributes the application processing to a WSN so that the application lifetime is maximized is recommended, and an approach providing a complete distributed environment for applications is absent.
Proceedings ArticleDOI
Genetic Algorithm to Optimize Node Placement and Configuration for WLAN Planning
TL;DR: A novel algorithm to rapidly create a high quality network plan for IEEE 802.11 based WLAN according to assigned design requirements was used in WLAN planning for a suburb, which is under development in Tampere-Lempaala area in Finland.
Proceedings ArticleDOI
Performance analysis of IEEE 802.15.4 and ZigBee for large-scale wireless sensor network applications
TL;DR: This paper analyses the performance of IEEE 802.15.4 Low-Rate Wireless Personal Area Network (LR-WPAN) in a large-scale Wireless Sensor Network (WSN) application and finds that the minimum device power consumption is as low as 73 μW, when beacon interval is 3.93 s, and data are transmitted at 4 min intervals.
Proceedings ArticleDOI
Experiments on local positioning with Bluetooth
TL;DR: The design and implementation of the Bluetooth local positioning application based on received power levels, which is converted to distance estimates according to a simple propagation model, and the extended Kalman filter computes a 3D position estimate on the basis of distance estimates.