The Here East Digital Twin was a six month trial of a real-time 3D data visualisation platform, designed for the purpose of supporting operational management in the built environment. 


1. View the project video for context:

2. Review details of the Envirosensor implementation:

3. One option for analysing the data is using Spark and Python in a Jupyter Notebook as outlines in the following example GitHub repository:


Truth discovery techniques, which can obtain accurate aggregation results based on the weighted sensory data of users, are widely adopted in industrial sensing systems. However, there are some privacy matters that cannot be ignored in truth discovery process. While most of the existing privacy preserving truth discovery methods focus on the privacy of sensory data, they may neglect to protect the privacy of another equally important information, the tagged location information.


Internet of Things (IoT) make the world easy, with healthcare applications being the most important. In general, IoT is used to interconnect advanced medical resources and provide smart and effective healthcare services to people. Advanced sensors can either be worn or embedded in patients' bodies, so that their health can be monitored remotely. Information collected in this way can be analyzed, collected, and mined to make an early prediction of diseases. Processing algorithms help physicians to personalize treatment and it helps to make healthcare affordable, with better results.


With the increasing popularity of the Internet of Things (IoT), security issues in the IoTnetwork have become the focus of research. Since the number of IoT devices connected to the networkhas increased, the conventional network framework faces several problems in terms of network latencyand resource overload. Fog computing is intended to construct a new network framework. However, fogcomputing has also caused new security challenges, such as authentication, authorization, and securecommunication.




Supplementary material for TII.


This RSSI Dataset is a comprehensive set of Received Signal Strength Indicator (RSSI) readings gathered from three different types of scenarios. Three wireless technologies were used which consisted of:

  • Zigbee (IEEE 802.15.4),
  • Bluetooth Low Energy (BLE), and
  • WiFi (IEEE 802.11n 2.4GHz band).

The scenarios took place in three rooms with different sizes and inteference levels. For the experimentation, the equipment utilized consisted of Raspberry Pi 3 Model Bs, Gimbal Series 10 Beacons, and Series 2 Xbees with Arduino Uno microcontrollers.



A set of tests was conducted to determine the accuracy between multiple types of system designs including: Trilateration, Fingerprinting with K-Nearest Neighbor (KNN) processing, and Naive Bayes processing while using a running average filter. For the experiments, all tests were done on tables which allowed tests to be simulated at a height where a user would be carrying a device in their pocket. Devices were also kept in the same orientation throughout all the tests in order to reduce the amount of error that would occur in the measuring of RSSI values.


Three different experimental scenarios were utilized with varying conditions in order to determine how the proposed system will function according to the environmental parameters.

Scenario 1 was a 6.0 x 5.5 m wide meeting room. The environmental area was cleared of all transmitting devices to create a clear testing medium where all the devices can transmit without interference. Transmitters were placed 4 m apart from one another in the shape of a triangle. Fingerprint points were taken with a 0.5 m spacing in the center between the transmitters. This created 49 fingerprints that would comprise the database. For testing, 10 points were randomly selected.

Scenario 2 was a 5.8 x 5.3 m meeting room. This area was a high noise environment as additional transmitting devices were placed around the environment in order to create interference in the signals. There were 16 fingerprints gathered with a larger distance selected between the points. In this Scenario, 6 testing points were randomly selected to be used for comparing the algorithms.

Scenario 3 was a 10.8 x 7.3 m computer lab. This lab was a large area with a typical amount of noise occurring due to the WiFi and BLE transmitting that were in the area. The large space also allowed for signals to experience obstructions, reflections, and interference. Transmitters were placed so Line-of-Sight (LoS) was available between the transmitters to the receiver. In total, 40 fingerprints were gathered with an alternating pattern occurring between the points. Points were taken to be 1.2 m apart in one direction, and 0.6 m apart in the other. For testing 16 randomly selected points were taken.


In the testing environment, fingerprints were gathered to be used in the creation of a database, while test points were selected to be used against the database for the comparison. The figures of each topology can be found inside the dataset folder. In the figures, the black dots represent the location of the transmitters and the red dots represent the locations where fingerprints and test points were gathered where appropriate. 

Related Publication

S. Sadowski, P. Spachos, K. Plataniotis, "Memoryless Techniques and Wireless Technologies for Indoor Localization with the Internet of Things", IEEE Internet of Things Journal.


The RSSI dataset contains a folder for each experimental scenario and furthermore on wireless technology (i.e. Zigbee, BLE, and WiFi). Each folder contains three additional folders where the data was gathered (Pathloss, Database, and Tests). Pathloss contains 18 files measuring the RSSI at varying distances from the devices. The number of files located in Database and Tests varies based on the scenario.

For each technology, the file name corresponds to the point as to where the data was gathered. For specific locations, the (x,y) coordinates can be seen in the appropriate .xlsx file.

For the files in the Database and Tests folders, there are approximately 300 reading. In the Pathloss folder, there are approximately 50 only occurring from a single node. Readings appear in the format "Node LetterValue" where:

Letter corresponds to the transmitter that signal was sent from, represented by 'A', 'B', or 'C'.

Value is the RSSI reading.


RPL is the de-facto IPv6-based routing protocol for the Internet of Things (IoT), where networks are mainly formed by sensors and other low capacity devices. However, RPL lacks scalability and is inefficient in any-to-any communications. In this article, we present IoTorii, a layer-two hierarchical protocol that creates routes based on a probe frame, instead of the computation of a distance vector, as in RPL.


To evaluate IoTorii, we implemented RPL and IoTorii in the OMNeT++ 5.2.1 simulator, with the INET 3.6.3 library. After validating IoTorii in OMNeT++, we developed both routing protocols using Contiki-NG which is a real Operating System (OS) for IoT devices. This Dataset includes data collected by OMNeT++ and Cooja simulators.

The Dataset includes two folders to store raw and chart data. The folder of raw data includes all data collected by the simulators. The folder of chart data includes data selected/aggregated by the raw data. Sub folders in this folder are categorized and named based on the simulations. Each sub folder includes three excel files to aggregate the raw data, and 3_plotData.xlsx includes the final data in the excel format, and all text files include the final data to be used as data tables in the TiKZ library of LaTeX.


[17-APR-2020: WE ARE STILL UPLOADING THE DATASET, PLEASE WAIT UNTIL IT IS COMPLETED] -The dataset comprises a set of 11 different actions performed by 17 subjects that is created for multimodal fall detection. Five types of falls and six daily activities were considered in the experiment. Data collection comes from five wearable sensors, one brainwave helmet sensor, six infrared sensors around the room and two RGB-cameras. Three attempts per action were recorded. The dataset contains raw signals as well as three windowing-based feature sets.


We will upload the instructions in the following days.


Dataset of V2V (vehicle to vehicle communication), GPS, inertial and WiFi data collected during a road vehicle trip in the city of Porto, Portugal. Four cars were driven along the same route (approx. 12 km), facing everyday traffic conditions with regular driving behavior. No special environments or settings were chosen, other than keeping the vehicles in communication reach of each other for as long as possible while being safe and compliant with the road rules.


There is a folder with data collected by each of the vehicles.

ID1 - Seat Leon
ID2 - Audi A4
ID3 - Nissan Micra
ID4 - Fiat Punto

Equipment collecting data:
- NEC LinkBird MX
- GPS receiver (rooftop, connected to the LinkBird)
- Smartphone Nexus 4 running SensorReader (SR) fixed on the windshield
- Smartphone Nexus 4 or Nexus 5 running SenseMyCity (SMC) fixed on the dashboard (different positions in each vehicle)


In this paper presented method for reducing the amount of data transmitted and stored in IoT systems. Instead of expensive and complex network devices, developers can apply cheap and proven low-speed solutions (ZigBee, NB IoT, BLE). This approach is focused on on-the-sensor data processing. Correlation and autocorrelation methods for detecting events, depending on shape of a wave, were described in details and proposed endpoint architecture implementation.