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Selected Topics in Sustainable Communication Networks

October 2021


October 20th


16:00 - 17:00





Protall (An Intelligent, multi-sensor, comprehensive obstacle avoidance system for automobiles and UAVs)

Speaker: Manav Dhelia

In this era of Artificial Intelligence and Automation, manufacturing and testing of self- driving cars and autonomous delivery of parcels to the desired location with the help of UAVs have a considerable amount of growth in the industry. This advancement in technology also raises safety issues due to the failure of sensors to detect the object or sometimes because of the dynamic environment. Protall (Protect-all) is an integrated solution for UAV and automobile vehicles to provide ultimate safety to both itself and its surroundings. With the strategic sensor integration and its intelligent processing, it aims to produce controlled output and hence, ensures to prevent any possible failures from occurring. The system constantly reacts to the environment and maintains comprehensive interaction with the user thus, enabling it to handle any dynamic situation and hence, it emerges as an optimal solution for Vehicles and UAVs.



October 13th


16:00 - 17:00



A Multi-agent-based simulation system for crowd evacuation in the fire emergency environment

Speaker: Gayamini Shanmuganathan

Evacuation and personal safety are major concerns from wide areas or indoors under emergency. Every disaster, whether man-made or natural, we face many unexpected problems, for instance, fatal accidents, and property damage. In some moments like this, panic among pedestrians beings is frequent. Everyone wants to save their lives. As a result of panic, some people lose the energy of thinking. It can also trigger conflicts among pedestrians. In emergencies there is less time to react; This can lead to a large loss of life, since many people who are caught may unaware of the exit to get out from there and are more likely to run in the wrong direction. To prove this, several investigations indicate that a substantial number of deaths occur due to wrong decisions residents make within the available evacuation time. Besides, the conclusion from the past analysis relieves guiding residents during evacuation proves to be more effective because it decreases the average escape time thereby increasing the chance of survival in a fire emergency. However, widespread fire disaster has the highest occurrence of frequency in several concentrated short periods among disasters. To provide a better solution to the issue, systems have been developed to alert or warn pedestrians in the presence of a fire emergency, so they can act promptly. Several approaches are done by past researchers to simulate the crowd evacuation, Nevertheless, the concept of autonomous agents has been bringing into play successfully to investigate collective human behavior during an emergency evacuation over the past decades. Similarly, artificial intelligence is strongly making its footprint in all disciplines. Our first and foremost objective is to evacuate residents safely in the shortest possible time in the event of a fire. With the support of reinforcement learning, which is a subfield of AI, we have done the pedestrian evacuation simulation system in the fire emergency environment. Several steps were taken to develop our system to achieve efficiency in an evacuation, in other words, safely evacuate residents within a short time. When considering a large-scale environment, it has many features, and only a few selected features are designed by ArcMap to enforce as the foundation of the system environment. We adopt the multi-agent concept to train the agents in the NetLogo environment. IIOne important point is that the moment we start training, any resident can only attend the training if they become pedestrians, and they will then share their experience with
others. Also, the deep reinforcement learning algorithm leads them to achieve the optimal policy. Besides, the A* algorithm directed the pedestrians to designated shelters. As well as fire dynamics are created by fire agencies. These results demonstrate empirically that the proposed simulation system is effective with time efficiency and the system has a strong capability to describe, represent, and explain the reality of evacuation.


July 2021

Wednesday, July 21st


16:00 - 17:00



Early warning system for landslides using Wireless Sensor Networks

Speaker: Piumika Karunanayake

Landslide is a natural disaster which causes a considerable damage to the natural habitat,
environment, economy and other resources. Due to the randomness of the event, monitoring,
predicting and controlling are major challenges associated with landslides. Yet, developing an
accurate prediction mechanism with an effective early warning system has become a need of
the hour since the damages and the losses occurred due to the landslides are intolerable. There
exist expensive, advanced mechanisms deployed to predict the possibility of occurring
landslides by using satellites and radar systems with artificial intelligence capabilities.
Comparing with the existing high-end systems, a cost effective wireless sensor network which is
capable of identifying the underground movements and soil conditions is introduced as a
practical solution. Yet, dealing with a large number of sensor data and identifying the
correlation of the variables and the occurrence of a landslide is difficult. Hence in this work,
machine learning is used to predict the occurrence of landslide with a set of sensor data
gathered for a period of two years on a land which is identified as prone for land slide in Sri
Lanka. After developing three models for prediction, one model was selected as its performance
measurements are better compared to other two including an accuracy of 99.8%. An prototype
of warning system is also built which takes the model output and display a web based warning
message. Although the developed machine learning model is site specific, similar approach can
be implemented in other landslide prone areas to improve the efficiency of the disaster
management system in Sri Lanka.


Wednesday, July 7th


16:00 - 17:00





Collecting Data in Ubiquitous Infrastructures: How to Engage Communities and Make Sense of Large Volumes of Data

Speaker: Catia Prandi

In this talk, I will present some case studies where crowdsourcing/crowdsensing, and participatory sensing were applied to ubiquitous infrastructures to investigate how to gather data regarding relevant issues (such as urban accessibility and environmental sustainability). In presenting these studies, I will focus on how the systems have been designed and evaluated using HCI methodologies, and I will point to strategies for involving users, such as gamification/gameful experiences and data visualization, in collecting data and making sense of large volumes of data to benefit the different communities.


June 2021

Wednesday, June 9th


17:00 - 18:00



LDACS: Self-organizing air-to-air communication

Speaker: Sebastian Lindner (Research Fellow at TUHH)

The Single European Sky Air Traffic Management Research (SESAR) program is a joint undertaking to overhaul and modernize European air traffic management. During all phases of flight, modern digital data links shall realize the Future Communications Infrastructure (FCI). The air-to-air (A2A) component of the FCI is the L-band Digital Aeronautical Communications System (LDACS) A2A mode, which is currently in early stages of development.
In this talk, we would like to present the challenges such a mobile network must tackle and give an overview on the self-organizing medium access control (MAC) that is being researched. Also, an offshoot project towards a Machine Learning-based predictive MAC that realizes coexistence with legacy communication systems in the same frequency band will be discussed.