The head of IoT2US Lab, Stefan Poslad has been recognised as being in the top 2% of the world’s scientists

A comprehensive list was recently released by Stanford University and Elsevier which examined the work of more than seven million scientists across 22 subject fields between 1996 and 2019. The list considered various factors including; number of citations; h-index and co-authorship.

The recognised academics in the School of Electronic Engineering and Computer Science, Queen Mary University of London are:

  • Andrea Cavallaro
  • Simon Dixon
  • Maged Elkashlan
  • Shaogang Gong
  • Yang Hao
  • Ebroul Izquierdo 
  • Simon Lucas
  • Pasquale Malacaria 
  • Arumugam Nallanathan
  • Clive G Parini
  • Stefan Poslad
  • Steve Uhlig

We are proud that the head of the IoT2US Lab, Stefan Poslad is on the list!

The full details could be found at “Updated science-wide author databases of standardized citation indicators” https://data.mendeley.com/datasets/btchxktzyw/2 or https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000918.

Posted in News

IOT2US Lab Won the 2nd Place in IPIN2020 Competition

Introduction of IPIN Competition

Indoor Positioning and Indoor Navigation (IPIN) is the largest international academic conference in the field of indoor positioning over the world. It was initiated by Federal Institute of technology, Zurich in 2010. Additionally, IPIN also holds indoor positioning competition every year. This competition and another two indoor positioning competitions held by National Institute of Standards and Technology (NIST) and Microsoft (MILC), respectively, are the most authoritative international indoor positioning competitions, which represents the most cutting-edge level of indoor positioning technologies.

Competition Tracks

IPIN2020 is the 7th international indoor positioning competition. Scholars from academia and industry are all able to participate. This year, the competition consists of 5 tracks:

  • Track 3: Smartphone (11 teams participated)
  • Track 4: Foot-mounted IMU (3 teams participated)
  • Track 5: xDR in manufacturing (2 teams participated)
  • Track 6: On-vehicle smartphone (3 teams participated)
  • Track 7: Channel impulse response (1 team participated)

Track #3 is the most competitive one since it attracts the largest number of teams to participate. This year, there are 11 teams in this track #3.

Here is a brief introduction on track #3. The experiment for this competition was conducted in the library at Universidad Jaume I in Spain. This library has five floors. The data is collected by a generic smartphone that has many built-in sensors, e.g., WiFi, accelerometer, gyroscope, magnetometer, barometer, light sensor, sound sensor, temperature sensor, etc. There is much training data provided for participators to train their methods. The evaluation data is a long path that an experimenter, holding a smartphone to record the data, walks freely in this library for about 20 minutes. So each participator should use their algorithms to estimate the location of this path. There are no restrictions on estimation methods for all teams.

Teams

11 teams from the world’s top universities, research institutes or high-tech companies participated in this competition. Here is a list of the teams.

IOT2US is a team combined by IOT2US Lab of Queen Mary University (QMUL), Smart City Institute of Shenzhen University (SZU) and Electronic Engineering Department of University College London (UCL). Team members include Wu Bang (QMUL), Ma Chengqi (UCL), Zhang Wei (SZU), Yankun Wang (SZU), fan Yonglei (QMUL), Stefan Poslad (supervisor, QMUL), Weixi Wang (supervisor, SZU), David Selviah (supervisor, UCL). After one month’s hard work, IOT2US team won the second place in the competition.

The results are as follows:

Previous Achievements of IOT2US

The 6th IPIN competition held in Pisa, Italy on September 30, 2019. 15 teams from the world’s top universities, research institutes or high-tech companies (such as Intel, Tencent, line) and indoor positioning professional companies (such as Xihe technology, fineway, arards) took part in the competition, which is very competitive.

The IOT2US Team, of which members are from the IOT2US Laboratory of Queen Mary University of London (QMUL) and the Department of electronic engineering, University College London(UCL), won the 3rd place. The team members include Bang Wu(QMUL), Chengqi Ma(UCL), Stefan Poslad (Supervisor, QMUL), David Selviah (Supervisor, UCL), Wei Wu(WHU), Xiaoshuai Zhang(QMUL), Guangyuan Zhang(QMUL) and Zixiang Ma(QMUL).

Edited by Yonglei Fan, Bang Wu, and Guangyuan Zhang.

Posted in News

QMUL IoT2US Lab CSC PhD Recruitment 2021

Who may be interested to do a CS PhD at Internet of Things Lab of QMUL with Stefan Poslad, Associated Professor, websites:

http://iot.eecs.qmul.ac.uk/

http://iot.eecs.qmul.ac.uk/people/academic/stefan-poslad/

Interests:

Internet of Things (IoT), Ubiquitous Computing or Pervasive Computing: Smart Devices, Environments and Interaction (SmartDEI – search for the book and company) between services, devices (low power computing, M2M, Semantic Web, Distributed AI), people (personalization, social networking), the physical environment (spatial- awareness, physical world sensing and context-awareness, pervasive games), and IoT security and privacy.

In terms of research topics, he is interested in:

  1. Electronics for IoT, including building new smart devices based upon micro-controller cores, low-power computing and communication, energy harvesting, Battery less sensing, etc.
  2. Security for low-resource IoT devices, wearables, M2M, including access control, cryptography and secure communication protocols, Blockchain.
  3. User-centered privacy protection, e.g., against spatial-temporal tracking, smart environment sensing, etc.
  4. Security and privacy in fog/edge computing
  5. Mobility profiling using hybrid sensor data (Indoor Positioning/Navigation; LPWAN GPS-free outdoor positioning)
  6. Seamless indoor and outdoor map systems and location-based services
  7. Big data processing of mobility data (GIS/RS)
  8. Citizen science and sensing: health, road vehicles, air and water quality; sensing in harsh environments, smart agriculture, Internet of Green things.
  9. Smart transport, smart home, smart health, smart utilities, etc. Activity during Daily Life recognition using sensors

 

“If a student has a different idea. I am also open to hear about this.”

欢迎攻博/联合培养/交流的学生申请,要求入学前可获得研究生学位/或为优秀本科生,英语成绩满足QMUL EECS博士生入学要求(详见官网

欢迎有相关研究经历与兴趣的同学邮件咨询Dr. Stefan: stefan.poslad@qmul.ac.uk

或联系:bang.wu@qmul.ac.uk/ guangyuan.zhang@qmul.ac.uk

 

Posted in Uncategorized Tagged with:

IoT2USLab Research on “Bio-Internet of Things” was Reported by MIT Technology Review

IoT2USLab Research on “Bio-Internet of Things” was Reported by MIT Technology Review: The scientists who are creating a bio-internet of things

Imagine designing the perfect device for the internet of things. What functions must it have? For a start, it must be able to communicate, both with other devices and with its human overlords. It must be able to store and process information. And it must monitor its environment with a range of sensors. Finally, it will need some kind of built-in motor.

There is no shortage of devices that have many of these features. Most are based on widely available, low-cost devices such as Raspberry Pis, Arduino boards, and the like.

But another set of machines with similar functions is much more plentiful, say Raphael Kim and Stefan Poslad at Queen Mary University of London in the UK. They point out that bacteria communicate effectively and have built-in engines and sensors, as well as powerful information storage and processing architecture.

And that raises an interesting possibility, they say. Why not use bacteria to create a biological version of the internet of things? Today, in a call to action, they lay out some of the thinking and the technologies that could make this possible.

The way bacteria store and process information is an emerging area of research, much of it focused on the bacterial workhorse Escherichia coli. These (and other) bacteria store information in ring-shaped DNA structures called plasmids, which they transmit from one organism to the next in a process called conjugation.

Last year, Federico Tavella at the University of Padua in Italy and colleagues built a circuit in which one strain of immotile E. coli transmitted a simple “Hello world” message to a motile strain, which carried the information to another location.

This kind of information transmission occurs all the time in the bacterial world, creating a fantastically complex network. But Tavella and co’s proof-of-principle experiment shows how it can be exploited to create a kind of bio-internet, say Kim and Poslad.

E. coli make a perfect medium for this network. They are motile—they have a built-in engine in the form of waving, thread-like appendages called flagella, which generate thrust. They have receptors in their cell walls that sense aspects of their environment—temperature, light, chemicals, etc. They store information in DNA and process it using ribosomes. And they are tiny, allowing them to exist in environments that human-made technologies have trouble accessing.

E. coli are relatively easy to manipulate and engineer as well. The grassroots movement of DIY biology is making biotechnology tools cheaper and more easily available. The Amino Lab, for example, is a genetic engineering kit for schoolchildren, allowing them to reprogram E. coli to glow in the dark, among other things.

This kind of biohacking is becoming relatively common and shows the remarkable potential of a bio-internet of things. Kim and Poslad talk about a wide range of possibilities. “Bacteria could be programmed and deployed in different surroundings, such as the sea and ‘smart cities’, to sense for toxins and pollutants, gather data, and undertake bioremediation processes,” they say.

Bacteria could even be reprogrammed to treat diseases. “Harbouring DNA that encode useful hormones, for instance, the bacteria can swim to a chosen destination within the human body, [and] produce and release the hormones when triggered by the microbe’s internal sensor,” they suggest.

Of course, there are various downsides. While genetic engineering makes possible all kinds of amusing experiments, darker possibilities give biosecurity experts sleepless nights. It’s not hard to imagine bacteria acting as vectors for various nasty diseases, for example.

It’s also easy to lose bacteria. One thing they do not have is the equivalent of GPS. So tracking them is hard. Indeed, it can be almost impossible to track the information they transmit once it is released into the wild.

And therein lies one of the problems with a biological internet of things. The conventional internet is a way of starting with a message at one point in space and re-creating it at another point chosen by the sender. It allows humans, and increasingly devices, to communicate with each other across the planet.

Kim and Poslad’s bio-internet, on the other hand, offers a way of creating and releasing a message but little in the way of controlling where it ends up. The bionetwork created by bacterial conjugation is so mind-bogglingly vast that information can spread more or less anywhere. Biologists have observed the process of conjugation transferring genetic material from bacteria to yeast, to plants, and even to mammalian cells.

Evolution plays a role too. All living things are subject to its forces. No matter how benign a bacterium might seem, the process of evolution can wreak havoc via mutation and selection, with outcomes that are impossible to predict.

Then there is the problem of bad actors influencing this network. The conventional internet has attracted more than its fair share of individuals who release malware for nefarious purposes. The interest they might have in a biological internet of things is the stuff of nightmares.

Kim and Poslad acknowledge some of these issues, saying that creating a bacteria-based network presents fresh ethical issues. “Such challenges offer a rich area for discussion on the wider implication of bacteria driven Internet of Things systems,” they conclude with some understatement.

That’s a discussion worth having sooner rather than later.

Ref: arxiv.org/abs/1910.01974  : The Thing with E. coli: Highlighting Opportunities and Challenges of Integrating Bacteria in IoT and HCI

https://www.technologyreview.com/s/614629/the-scientists-who-are-creating-a-bio-internet-of-things/

Posted in News Tagged with:

IOT2US LAB WON 3RD PLACE IN THE PREMIER COMPETITION OF Indoor Positioning and Indoor Navigation (IPIN)

IoT2US Lab won 3rd place in the premier competition of the 10th International Conference on Indoor Positioning and Indoor Navigation (IPIN). There are fifteen teams in all from top universities, institutes and high-tech companies all over the world. This is one of the most world famous two indoor positioning competitions, IPIN[1] and Microsoft Indoor Localization Competition (IPSN)[2].

Team information

The work is a product of IoT2US Lab, in the School of (EECS), QMUL, in collaboration with UCL, Electronic Engineering department. Our team members are Bang Wu(QMUL), Chengqi Ma (UCL), Stefan Poslad (supervisor, QMUL), David Selviah (supervisor, UCL), Wei Wu (WHU), Xiaoshuai Zhang (QMUL) , Guangyuan Zhang (QMUL) , Zixiang Ma (QMUL).

Competition Goal

The goal of this competition track is to evaluate the performance of different indoor localization solutions based on the signals available to a smartphone (such as WiFi readings, inertial measurements, etc…) and received while a person is walking along several regular unmodified multi-floor buildings. The mobility modes include ascending stairs, descending stairs, stationary, walking and stationary walking etc. This track is done off-site, so all data for calibration and evaluation is provided by competition organizers before the celebration of the IPIN conference. The competition teams can calibrate their algorithmic models with several databases containing readings from sensors typically found in modern mobile phones and some ground-truth positions. Finally, each team will compete using additional database files, but in this case, the ground-truth reference is not given and must be estimated by the competitors. This is an off-line competition where all competitors have the same data of the testing environment, so custom on-site calibration is not allowed.

More informtion can be found at http://blogger.youraisemeup920616.com/2019/10/smartphone-based-activity-recognition.html?m=1

[1]. http://ipin2019.isti.cnr.it/competition

[2]. https://www.microsoft.com/en-us/research/event/microsoft-indoor-localization-competition-ipsn-2018/

Posted in News Tagged with:

QMUL IoT2US Lab CSC PhD Recruitment 2020

Who may be interested to do a CS PhD at Internet of Things Lab of QMUL with Stefan Poslad, Associated Professor, websites:

http://iot.eecs.qmul.ac.uk/

http://iot.eecs.qmul.ac.uk/people/academic/stefan-poslad/

Interests:

Internet of Things (IoT), Ubiquitous Computing or Pervasive Computing: Smart Devices, Environments and Interaction (SmartDEI – search for the book and company) between services, devices (low power computing, M2M, Semantic Web, Distributed AI), people (personalization, social networking), the physical environment (spatial- awareness, physical world sensing and context-awareness, pervasive games), and IoT security and privacy.

In terms of research topics, he is interested in:

  1. Electronics for IoT, including building new smart devices based upon micro-controller cores, low-power computing and communication, energy harvesting, Battery less sensing, etc.
  2. Security for low-resource IoT devices, wearables, M2M, including access control, cryptography and secure communication protocols, Blockchain.
  3. User-centered privacy protection, e.g., against spatial-temporal tracking, smart environment sensing, etc.
  4. Security and privacy in fog/edge computing
  5. Mobility profiling using hybrid sensor data (Indoor Positioning/Navigation; LPWAN GPS-free outdoor positioning)
  6. Seamless indoor and outdoor map systems and location-based services
  7. Big data processing of mobility data (GIS/RS)
  8. Citizen science and sensing: health, road vehicles, air and water quality; sensing in harsh environments, smart agriculture, Internet of Green things.
  9. Smart transport, smart home, smart health, smart utilities, etc. Activity during Daily Life recognition using sensors

 

“If a student has a different idea. I am also open to hear about this.”

欢迎攻博/联合培养/交流的学生申请,要求入学前可获得研究生学位,英语成绩满足QMUL EECS博士生入学要求(详见官网

欢迎有相关研究经历与兴趣的同学邮件咨询Dr. Stefan: stefan.poslad@qmul.ac.uk

或联系:bang.wu@qmul.ac.uk/ guangyuan.zhang@qmul.ac.uk

Posted in News Tagged with:

Magnetic-field (MF) IPS

  • Objectives: Create an IPS that is unaffected by moving humans, providing more time-invariant location information, unlike Wi-Fi, Bluetooth
  • Method: Use smart phone to create a radiomap of known MF patterns, then detect a new unknown RF pattern & derive the location
  • Results: Validate in a library, retail-like building, with multiple metal shelves & pillars, positioning error is 1.8 m. Scalability issues:  MF IPS pattern location accuracy drops as size of space increases
  • Conclusions: use of magnetic field (MF), unlike typical Wi-Fi or Bluetooth positioning measurements, are unaffected by moving humans, providing more time-invariant location information. We proposed a method to do detect the location quicker.
Posted in Research interests, Spatial Intelligence Tagged with:

Prediction of People Density Distribution

  • Objectives: using deep learning method to predict spatial-temporal distribution of people based on the Call Detail Record (CDR) dataset
  • Method: Use CDR to map the dynamic people density distribution by kernel density estimation (KDE) method, and then input to a convolution long short-term memory (ConvLSTM) model
  • Results: The mean absolute error of the predicted results of ConvLSTM ranged from 0.6 to 1.8 over 17 February 2015, which means that the model was much more stable and accurate than the other two baseline methods. Moran’s I index for the error distribution was still lower than that of the other baseline methods in space
  • Conclusions: the predicted density correlated much better with the original data at the temporal and spatial scales used when using ConvLSTM as compared to the other two methods, which do not consider the spatial autocorrelation.
Posted in Research interests, Spatial Intelligence Tagged with:

Wi-Fi RTT Positioning System

RTT Measurement Approach

  • An RTT range system requires an initiator to send an FTM request and a responder to respond to it with an acknowledgement (ACK). A complete callback will return four time points t1 ~ t4.
  • The RTT is calculated from n groups of callbacks according to the equation

Test bed configuration

  • Initiator Google Pixel 2: Android OS version 9.0 (Android Pie), with RTT process supported wireless card and chipset.
  • Responder WILD fit2: 802.11 mc protocol supported. Can be configured to work in both 2.4 GHz and 5 GHz channels with channel bandwidths of 20 MHz, 40 MHz, 80 MHz.

RTT positioning result

  • The average error of all these tests is 0.54 m, which is far more precise then all existing Wi-Fi fingerprinting and propagation model based methods (generally considered to be 1.5 ~ 2 m).
Posted in Research interests, Spatial Intelligence Tagged with:

Millimetre Wave Radar based HAR System

  • Objectives: use millimetre wave radar to build a HAR system that can recognize human activities and vital signs.
  • Method: Signal processing algorithms (e.g. FFT, Wavelet transform) are used to get feature map and machine learning and deep learning algorithms are employed to recognize activity.
  • Results&conclusions: Recognition accuracy: overall 0.978
Posted in Research interests, Spatial Intelligence Tagged with: