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Variation in Beacon Packets' RSSI Measurements due to RF Interference


WiFi Beacon packets are transmitted periodically and contain information for announcing the presence of a WiFi enabled devices. Beacon Packets RSSI (Received Signal Strength Indicator) measurements indicate the power of signal that is received at the receiver. RSSI measurements are extensively used for ranging and localization purposes, meaning that they should be accurate and reliable. However, previous studies show that RSSI measurements are influenced by RF interference, which influences their values, and consequently their accuracy and reliability. In order to analyze to which extent the RSSI values vary due to different types of interference, we have designed and performed a set of experiments. Experiments consist of measuring the WiFi beacon packets’ RSSI values on different locations and in three artificially generated RF interference scenarios in our testbed. The generated interference scenarios are as follows. In the first scenario no artificial interference was generated and this scenarios served as a reference scenario for comparison to other two interference scenarios. The other two scenarios used IEEE 802.11 and IEEE 802.15.4 jamming as the source of artificially generated interference. The results of our experiments yielded a large set of measurements. In order to easily analyze the collected measurements, a web-based visualization tool was implemented. This tool was then used for analyzing the measurements and calculating various statistics that served for comparison between the RSSI measurements in different interference scenarios. Finally, by analyzing the obtained results   we showed that the change in RSSI measurements due to RF interference exists. This change is visible firstly in the higher loss-rate of WiFi beacon packets and secondly in the increase in the RSSI values of the received beacon packets.


Except of the ranging and localization purposes mentioned before, beacon packets’ RSSI measurements are also used for other purposes where accuracy and reliability should be guaranteed, e.g. for finding the closest neighbouring node. From previous studies, we assumed that RSSI values can be influenced by RF interference in the same frequency band and we aimed on evaluating that influence.


RSSI is an indicator of the power level of the signal on a receiving node and it is expressed in dBm. RSSI is used by Network Interface Card (NIC) to determine the amount of radio energy in the channel. It is known that power dissipates from a point source as it moves further out and the relationship between power and distance is that power is inversely proportional to the square of the distance traveled. In this way the distance from the sender to receiver can be calculated for localization purpose. However, in n order to be able to do that, the RSSI measurements have to accurate estimates of the received power, which does not hold entirely in practice. Namely, different studies show that RSSI values have significant variations due to various factors.


  • Values are reported significantly different by different hardwares,
  • values vary with change in temperature,
  • Values are affected by RF interference in the same frequency band.

There is no standardized relationship of any particular physical parameter to the RSSI reading. The 802.11 standard does not define any relationship between RSSI value and power level in mW or dBm. Vendors and chipset makers provide their own accuracy, granularity, and range for the actual power (measured as mW or dBm) and their range of RSSI values (from 0 to RSSI Max). As an example, Figure shows the variance of RSSI values due to different hardwares and vendors. In is also well known that the RSSI values change with the changes in the temperature of both transmitting and receiving nodes. Also, previous experimental studies show that RSSI values could be influenced by various interferences such as data traffic in the same channel, full power transmissions or jamming signals, architectural obstructions causing reflections and scattering.

We selected the latest problem for our study, i.e. we aimed on evaluating the influence of RF- interference in the same frequency band on the WiFi beacon packets’ RSSI measurements.

Project Workflow

A workflow of our research is given as follows:

  • Previously completed works were studied, which mainly included  the EVARILOS results that gave gave guideline and tools for evaluation of different RF based indoor localization algorithms and solutions.
  • The knowledge regarding the testbed infrastructure is to be used for our experiments was obtained. That included the knowledge about controlling a robot for automation of collecting measurements, devices for generating artificial interference and a device for collecting measurements.
  • Experiments for obtaining measurements were designed and executed. This included a selection of appropriate measurement points and interference parameters according to the guidelines provided by the EVARILOS Benchmarking Handbook.
  • A visualization tool for visualizing the collected measurements and calculating various statistics of the measurements  was implemented.
  • Collected measurements were processed using the developed visualization tool. The obtained results were a statistical analysis of the collected measurements and a set of graphs showing spatial distributions of the measurements in our testbed.
  • The results of the project and all other relevant information were well documented.

Experiments Description

In the following we describe the execution of the experiments and provide details about the generated interference scenarios.

Experiments Execution

At each measurement point a measuring device was requested to provide a set of beacon packets RSSI measurements. The device was carried to the each measurement location using the robotic platform. The robotic platform knows its location with high accuracy, so we were using this location for labeling the locations of our collected measurements.

The experiments were performed at the weekend afternoon, so the influence of the uncontrolled RF interference in the testbed environment was minimized. Furthermore, the wireless spectrum was measured using the WiSpy device attached to the robotic platform and all measurements with the uncontrolled interference threshold above certain level were discarded and repeated. Finally, before each experiment a more detailed measurement of the spectrum was taken with a spectrum analyzer at a predefined location. Used infrastructure is given in the following figure.


Interference scenarios

Reference scenario was instantiated on the 2nd floor of the TWIST testbed. It is called a “Reference scenario” because no artificial interference was generated and the presence of uncontrolled interference was minimized. According to the EVARILOS Benchmarking Handbook (EBH), this scenario is an instance of the “Small office” type of scenarios. In this scenario and in all others the same 20 measurement points were defined, with their locations given in the figure below.

First interference scenario instantiated in TWIST testbed used the testbeds Wireless Fidelity (WiFi) node as interference sources. Interference type is jamming on one IEEE 802.11 channel with the maximum transmission power (20 dBm). The jamming node is located at a predefined location in the testbed environment.

Second interference scenario instantiated in TWIST testbed defines interference types that is usual for the office and home environments. Namely, interference was emulated using 4 WiFi embedded Personal Computers (PCs), namely a server, email client, data client, and video client. The server acted as a WiFi Access Point (AP) and a gateway for the emulated services. The email client was checking “check email” once every 15 seconds for a duration of one second. The data client was emulated via TCP streams one starting at 45 seconds for a duration of 22.5 seconds and the other starting at 105 seconds for a duration of 45 seconds. The video client was emulated as a UDP stream of 100 kbps for half the experiment cycle and was starting at the middle of the experiment. In total, one cycle of the interference generation took approximately 150 seconds and then it was repeated.


Tool for Visualizing Collected Measurements

Measurements yielded large amounts of raw data which contained the complete information of experiments such as testbed measurement locations, access points, RSSI, channel, SSID, BSSID, etc. In order to track collected raw data, a raw data visualization tool was implemented.

This tools helped us to list and visualize the available databases and collections of experiments under one hood. It is a web-based standalone software which was implemented using Javascript and various libraries. This tool can be simply started by opening it on a web browser. It needs an Internet connection to retrieve data from the backend servers. It makes use of asynchronous (AJAX) ability of Javascript and processes large array of raw data. This tool consists of 4 panels which showcases the work flow in an user friendly manner.


Evaluation and Results

From the obtained measurements the graphs between distance and mean of RSSI values, variance of RSSI values and group variances were generated. The data has been grouped based on  access point from which the measurements were obtained. As learned from the previous experiments under environment with minimal interferences , distance and RSSI show a linear relationship such that the RSSI value decreases with increase in distance. However, the experiments with various interference sources exhibit a non linear relationship of RSSI values with distance from the source and influenced significantly around the interference sources. These results show that there is variation in RSSI values due to interferences. The following sections show plotted graphs.



In this work, we experimentally evaluated the influence of RF interference on the WIFi beacon packets RSSI measurements. In order to process and analyze a large set of experimentally collected measurements, we have implemented a tool for visualization. This tool helped us to obtain statistical presentations of the collected measurements, which gave some insights about the change of WiFi beacon packets’ RSSI values due to RF interference, and this change seems to be additive.

Zusatzinformationen / Extras


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Aravinth Panchadcharam


Filip Lemic
Dr. Arash Behboodi