IoTiSS - Internet of Things in Smart Streetlighting

Duration: 15.04.2017 - 15.07.2019
Project Leader: Prof. Dr.-Ing Horst Hellbrück
Staff: Angelos Oikonomopoulos, Marco Cimdins


Streetlamps are ideal as an infrastructure of the Internet of Things, as they are present in large numbers within cities and have their own power supply. Existing solutions are currently controllable via powerline. Unfortunately, most of the systems do not provide a backward-channel, and, as a consequence, are not ready for the Internet of Things


In this research and development project, we develop a radio network module. With this module, street lamps are enabled for communication within a smart city. To avoid costly civil engineering, the module will be integrated into existing light fixtures. By employing standardized wireless communications within the ISM-band, a backward-channel is created for the street lamps. In contrast to many existing light systems, we develop an open platform for the street light applications. In addition, an important goal is to develop a standardized interface for future applications and integration of additional wireless devices. The challenge is to design a radio network module and a transport network to support a large number of devices (e.g. 40,000) as well as a large area (e.g. over 100 km²). The applications should be able to run in parallel and without distortions.

Projekt partners



Förderkennzeichen: ZF4186102ED6



Other Publications
[2019] Phase Error Correction for IEEE 802.11 Channel State Information (C. Trejo, M. Cimdins, H. Hellbrück), (T. M. Buzug et. al., ed.), 2019. [bib] [abstract]
Channel State Information (CSI) capture is a new technology, which measures the phase and magnitude of WLAN signals. In this paper, we investigate the accuracy and predictability of CSI phase measurements for potential use in geometric localization. We determine known sources of linear and non-linear error. The known sources of error are then correct for by utilizing a curve fitting method. An ideal mathematical model is created and compared to the corrected phase results. The conclusion is that the phase correction method was effective in some cases, however, the results were inconsistent with additional unaccounted sources of error.
[2019] Investigation of the Message Delay in Wireless Sensor Networks (M. Hadler, M. Cimdins, H. Hellbrück), (T. M. Buzug et. al., ed.), 2019. [bib] [abstract]
Wireless sensor networks (WSN) can be implemented across entire cities to connect devices and sensors with each other. The example used in this paper to represent a WSN is that street lamps (nodes) build a transport network, which will be investigated to find the message delay. Furthermore, each street lamp is connected to a garbage can (sensor) via a second network. So that, a garbage-can sends data to a street lamp and the street lamp forwards the data through the transport network until the destination receives the message. Each hop of the message occurs a delay which sums up to a multi-hop delay. In this paper, we simulate the transport network by using Contiki-NG with Cooja and calculate the delay of a message with an algorithm. The algorithm finds the routing path of the received message and saves the calculated multihop delay. Our results demonstrate that the delay of the messages becomes smaller if the throughput to the nodes decreases. For the garbage bin example, the delay would not be a problem, however, keep in mind that a delay of 1s is too much for many applications.
[2019] Recurrent Neural Network for Sequence Classification of mmWave Radar Data (T. Geiger, M. Constapel, H. Hellbrück), (T. M. Buzug et. al., ed.), 2019. [bib] [abstract]
This paper unveils the potential and utilization of Recurrent Neural Network (RNN) in radar applications for sequence classification. We focus on human activity recognition, which inputs are multichannel time series signals acquired by a frequency modulated continuous wave (FMCW) millimeter wave (mmWave) radar. We designed a proof of concept implementation for a system where a mmWave radar records the movement of a human body. The proposed RNN predicts and classifies the direction the human is heading. Such systems are valuable in numerous applications ranging from security and safety applications like detection and assessment of human activity at airports to patient monitoring in hospitals. Applied to the dataset recorded by the mmWave radar the implemented RNN provides satisfying classification results.
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