12 research outputs found
Demo : WiSCoP : wireless sensor communication prototyping platform
To enhance system performance of future heterogeneous wireless networks the
co-design of PHY, MAC, and higher layer protocols is inevitable. In this work,
we present WiSCoP - a novel embedded platform for experimentation, prototyping
and implementation of integrated cross-layer network design approaches. WiSCoP
is built on top of a Zynq hardware platform integrated with FMCOMMS1/2/4 RF
front ends. We demonstrate the flexibility of WiSCoP by using it to prototype a
fully standard compliant IEEE 802.15.4 stack with real-time performance and
cross-layer integration.Comment: 2 pages, 2 figures, to be published in the EWSN'17 Proceedings of the
2017 International Conference on Embedded Wireless Systems and Networks,
Uppsala, Sweden - February 20-22, 201
Towards a cognitive MAC layer: Predicting the MAC-level performance in dynamic WSN using machine learning
Predictable network performance is key in many low-power wireless sensor
network applications. In this paper, we use machine learning as an effective
technique for real-time characterization of the communication performance as
observed by the MAC layer. Our approach is data-driven and consists of three
steps: extensive experiments for data collection, offline modeling and
trace-driven performance evaluation. From our experiments and analysis, we find
that a neural networks prediction model shows best performance.Comment: 2 pages, 3 figures, accepted for publication in the EWSN'17
Proceedings of the 2017 International Conference on Embedded Wireless Systems
and Networks, Uppsala, Sweden - February 20-22, 201
Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices
Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals’ modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI’s probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access