Recharging of Flying Base Stations using Airborne RF Energy Sources

This paper presents a new method for recharging flying base stations, carried by Unmanned Aerial Vehicles (UAVs), using wireless power transfer from dedicated, airborne, Radio Frequency (RF) energy sources. In particular, we study a system in which UAVs receive wireless power without being disrupted from their regular trajectory. The optimal placement of the energy sources are studied so as to maximize received power from the energy sources by the receiver UAVs flying with a linear trajectory over a square area. We find that for our studied scenario of two UAVs, if an even number of energy sources are used, placing them in the optimal locations maximizes the total received power, while achieving fairness among the UAVs. However, in the case of using an odd number of energy sources, we can either maximize the total received power, or achieve fairness, but not both at the same time. Numerical results show that placing the energy sources at the suggested optimal locations results in significant power gain compared to nonoptimal placements.Comment: 6 pages, 5 figures, conference pape

Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI

There are significant milestones in modern human's civilization in which mankind stepped into a different level of life with a new spectrum of possibilities and comfort. From fire-lighting technology and wheeled wagons to writing, electricity and the Internet, each one changed our lives dramatically. In this paper, we take a deep look into the invasive Brain Machine Interface (BMI), an ambitious and cutting-edge technology which has the potential to be another important milestone in human civilization. Not only beneficial for patients with severe medical conditions, the invasive BMI technology can significantly impact different technologies and almost every aspect of human's life. We review the biological and engineering concepts that underpin the implementation of BMI applications. There are various essential techniques that are necessary for making invasive BMI applications a reality. We review these through providing an analysis of (i) possible applications of invasive BMI technology, (ii) the methods and devices for detecting and decoding brain signals, as well as (iii) possible options for stimulating signals into human's brain. Finally, we discuss the challenges and opportunities of invasive BMI for further development in the area.Comment: 51 pages, 14 figures, review articl

Online Learning Adaptation Strategy for DASH Clients

In this work, we propose an online adaptation logic for Dynamic Adaptive Streaming over HTTP (DASH) clients, where each client selects the representation that maximize the long term expected reward. The latter is defined as a combination of the decoded quality, the quality fluctuations and the rebuffering events experienced by the user during the playback. To solve this problem, we cast a Markov Decision Process (MDP) optimization for the selection of the optimal representations. System dynamics required in the MDP model are a priori unknown and are therefore learned through a Reinforcement Learning (RL) technique. The developed learning process exploits a parallel learning technique that improves the learning rate and limits sub-optimal choices, leading to a fast and yet accurate learning process that quickly converges to high and stable rewards. Therefore, the efficiency of our controller is not sacrificed for fast convergence. Simulation results show that our algorithm achieves a higher QoE than existing RL algorithms in the literature as well as heuristic solutions, as it is able to increase average QoE and reduce quality fluctuations

Mobile agents for route planning in Internet of Things using Markov decision process

Bokani, A ORCiD: 0000-0001-5160-7724Using mobile agents for data aggregation in ad-hoc networks is a promising approach and gains more popularity every day. However, these agents need an efficient rout planning to optimize the quality of service (QoS) which is a very challenging task in such uncertain environment. Numerous previous works have presented different schemes for route planning of mobile agents in wireless sensor networks. Similarly, some other approaches have proposed the use of mobile agents for data aggregation in the Internet of Things (IoT). However, current approaches for route planning of mobile agents do not satisfy the requirements of the internet of things, due to the mobile and heterogeneous IoT nodes. In this paper, we propose an intelligent rout planning that enables mobile agents in IoT systems to make the best decision for selecting the next node in different moments. We use Markov Decision Process (MDP) as the underlying optimization model, which is well-known on its effectiveness to optimize decision making under uncertainty. In this model, we consider the distance between the nodes from each other, the distance between the nodes and the sink, residual energy of the nodes and the priority of them as the MDP parameters. Our proposed method could improve the energy consumption of IoT nodes and the life time of the system. Furthermore, our proposed method tries to maximize the reliability of the network and enhances data transmission delay

Overleaf LaTeX: An online tool for synchronous, collaborative scholarly writing

Bokani, A ORCiD: 0000-0001-5160-7724; Hassan, JA ORCiD: 0000-0002-0939-2106; Islam, N ORCiD: 0000-0002-5469-8104Collaborative writing represents a big portion of all writings done in the academic environment, and is considered a core skill in graduates. Around 85% of produced documents in office and university settings had at least two authors. Interestingly, the face-to-face settings of collaborations are being supplemented by various on-line tools, such as Zoom, Dropbox, Microsoft Teams, etc., due to the availability of such feature-rich tools and our need for working flexibly. For on-line collaborative writing tools, the ability of supporting interactions during the writing process through real-time feedback, co-editing, and problem solving with the team is a must. Such interactions in the face-to-face sessions occur naturally, however not so in the on-line environments. While the majority of the on-line collaborative writing tools are inadequate in supporting this requirement, Overleaf LATEX is a welcomed exception. Overleaf is an online LATEX editor, which facilitates writers to contribute collaboratively in scholarly articles, large reports, thesis, journal articles within high quality templates. Writers can work on the article concurrently, and hence it facilitates real time collaborations. Additionally, it eliminates the need of installing any software packages for the desired templates as it has a library of all the latest packages for all templates. Overleaf allows automatic real-time preview by compiling the project in the background, and displaying the PDF output right away. Other useful features such as real-time track changes and commenting, reviewing, providing feedback through the review option, high quality mathematical equation editor, a chat box for communicating with contributors while writing, etc, make it a very effective tool for on-line collaborative writing. Based on our experiences of using the tool, we will highlight the tool’s ability to fulfil the need of on-line collaborative writing in the tertiary education setting in this presentation

Recharging of flying base stations using airborne RF energy sources

Bokani, A ORCiD: 0000-0001-5160-7724; Hassan, JA ORCiD: 0000-0002-0939-2106This paper presents a new method for recharging flying base stations, carried by Unmanned Aerial Vehicles (UAVs), using wireless power transfer from dedicated, airborne, Radio Frequency (RF) energy sources. In particular, we study a system in which UAVs receive wireless power without being disrupted from their regular trajectory. The optimal placement of the energy sources are studied so as to maximize received power from the energy sources by the receiver UAVs flying with a linear trajectory over a square area. We find that for our studied scenario of two UAVs, if an even number of energy sources are used, placing them in the optimal locations maximizes the total received power, while achieving fairness among the UAVs. However, in the case of using an odd number of energy sources, we can either maximize the total received power, or achieve fairness, but not both at the same time. Numerical results show that placing the energy sources at the suggested optimal locations results in significant power gain compared to nonoptimal placements

Recharging of flying base stations using airborne RF energy sources

This paper presents a new method for recharging flying base stations, carried by Unmanned Aerial Vehicles (UAVs), using wireless power transfer from dedicated, airborne, Radio Frequency (RF) energy sources. In particular, we study a system in which UAVs receive wireless power without being disrupted from their regular trajectory. The optimal placement of the energy sources are studied so as to maximize received power from the energy sources by the receiver UAVs flying with a linear trajectory over a square area. We find that for our studied scenario of two UAVs, if an even number of energy sources are used, placing them in the optimal locations maximizes the total received power, while achieving fairness among the UAVs. However, in the case of using an odd number of energy sources, we can either maximize the total received power, or achieve fairness, but not both at the same time. Numerical results show that placing the energy sources at the suggested optimal locations results in significant power gain compared to nonoptimal placements

Enabling efficient and high quality zooming for online video streaming using edge computing

High quality zooming function for online video streaming using cloud content servers remains a challenge due to the intertwined relationships among video chunk lengths, viewer's fast changing Region of Interest (RoI), and network latency. It is possible to utilize tiled Video technique and store picture tiles in separate files with their unique URLs on the media server with smaller chunk sizes, however it introduces a significant burden on the network core due to increased total video length contributed by combined non-video bits from too many smaller chunks. To overcome this, in this paper we propose the use of edge computing to achieve high quality zooming function for video steaming. Our proposal includes the system architecture using Tiled-DASH (T-DASH) video encoding on edge servers, and a novel ROI prediction method combining three different prediction models: online, offline and object-level prediction models on the client side. Our evaluations show that a high level of ROI prediction accuracy is achieved by our approach, fulfilling a core condition for making the zooming function a reality

Mobile agents for route planning in Internet of Things using Markov decision process

Using mobile agents for data aggregation in ad-hoc networks is a promising approach and gains more popularity every day. However, these agents need an efficient rout planning to optimize the quality of service (QoS) which is a very challenging task in such uncertain environment. Numerous previous works have presented different schemes for route planning of mobile agents in wireless sensor networks. Similarly, some other approaches have proposed the use of mobile agents for data aggregation in the Internet of Things (IoT). However, current approaches for route planning of mobile agents do not satisfy the requirements of the internet of things, due to the mobile and heterogeneous IoT nodes. In this paper, we propose an intelligent rout planning that enables mobile agents in IoT systems to make the best decision for selecting the next node in different moments. We use Markov Decision Process (MDP) as the underlying optimization model, which is well-known on its effectiveness to optimize decision making under uncertainty. In this model, we consider the distance between the nodes from each other, the distance between the nodes and the sink, residual energy of the nodes and the priority of them as the MDP parameters. Our proposed method could improve the energy consumption of IoT nodes and the life time of the system. Furthermore, our proposed method tries to maximize the reliability of the network and enhances data transmission delay