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

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

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

An optimal multi-UAV deployment model for UAV-assisted smart farming

Next-generation wireless networks will deploy UAVs dynamically as aerial base stations (UAV-BSs) to boost the wireless network coverage in the out-of-reach areas. To provide an efficient service in stochastic environments, the optimal number of UAV-BSs, their locations, and trajectories must be specified appropriately for different scenarios. Such deployment requires an intelligent decision-making mechanism that can deal with various variables at different times. This paper proposes a multi UAV-BS deployment model for smart farming, formulated as a Multi-Criteria Decision Making (MCDM) method to find the optimal number of UAV-BSs to monitor animals’ behavior. This model considers the effect of UAV-BSs’ signal interference and path loss changes caused by users’ mobility to maximize the system’s efficiency. To avoid collision among UAV-BSs, we split the considered area into several clusters, each covered by a UAV-BS. Our simulation results suggest up to 11x higher deployment efficiency than the benchmark clustering algorithm

UAV-based Smart Agriculture: a Review of UAV Sensing and Applications

One of the main problems in agriculture is the lack of timely, accurate data. Farmers require real-time farm management that can reduce production costs while increasing production per unit area. Precision agriculture could be very helpful for farmers in many ways. One of the latest precision agriculture tools is unmanned aerial vehicles (UAVs). UAVs are used for a variety of purposes, including imaging, monitoring biotic and abiotic stresses, foliar spraying, pollination, livestock management, monitoring natural resources, and more. At a low cost, farmers can get the same results as with expensive treatments. The vast amounts of data collected by UAVs equipped with the on-board sensors can help improve agricultural production by providing accurate information about the fields and environment. This paper provides an overview of how agricultural production can be improved through the use of UAVs, including the various on-board sensors in use and their application areas in smart farming. Discussions on challenges and portunities in UAV-based smart farming that will guide subsequent research are provided

Energy-efficient and QoS-aware UAV communication using reactive RF band allocation

Next generation mobile communication systems propose the use of Unmanned Aerial Vehicles (UAVs) in providing wireless communication services. Emerging bandwidth-demanding applications such as real-time video streaming could also be satisfied by the next generation UAVs while exploiting the unoccupied bandwidth available at millimetre Wave (mmWave) frequency ranging from 30 to 300 GHz. However, mmWave UAVs suffer from high attenuation loss and Line Of Sight (LOS) communication. To combat the attenuation, UAVs must transmit using higher transmission power which results in higher energy consumption. MmWave, however, incurs shorter communication sessions implying shorter flight duration and less energy consumption than Long-Term Evolution (LTE) band for delivering the same service. Furthermore, a wide range of applications are delay sensitive and unable to be served by LTE. Since mmWave UAVs require continuous LOS and are unable to serve concurrent multiple nodes, we explore the concept of dual-mode UAV-assisted service delivery in which the UAV switches to mmWave band for serving bandwidth-hungry applications, and back to LTE for all other applications. The aim is to achieve a trade-off between Quality of Service (QoS) and energy consumption for Air2Ground (A2G) service delivery. Our evaluation results show the feasibility of such dual-mode system for next generation UAVs while achieving higher QoS compared to the current mono-band UAVs

Invigilated final exams: An outdated or a proper testing scheme for tertiary education?

In the past few years, the global higher education system has witnessed significant changes and transformation such as changes in the teaching mode, curriculum design, assessment pattern, financing and governance patterns, evaluation and accreditation mechanisms, etc. With this evolution, the practice of invigilated final examination is becoming a questionable practice. Studies conducted in the literature show that the invigilated exam is a least efficient way of assessing deep conceptual understanding of students. Rather, it is considered as a process of overstuffing the student’s brain with the unit content in the night before the exam day, and dumping that stuff in the exam booklet on the exam day, with very little knowledge retention afterwards. In the defence of keeping final exam, people may argue that invigilated final examinations are inevitable way of preventing students from cheating. However, final exams can be replaced with comprehensive final assessment items in such a way that the scope for cheating/plagiarism can be minimised. These alternative assessments have proven to be more effective in engaging the student in learning. In this presentation, we present some arguments for and against the practice of having closed book invigilated final exams in tertiary education. Our argument will be supported by practical evidence taken from some ICT units coordinated and taught by the authors at Central Queensland University, Australia

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

Collaborative 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

AETD: An application-aware, energy-efficient trajectory design for flying base stations

Recent developments in consumer Unmanned Aerial Vehicles (UAVs) technology have created unprecedented opportunities for their applications in various civil domains. These ubiquitous vehicles of different shapes and sizes with easy and user-friendly configurations are favorite choices for providing different services such as wireless communications, emergency medical deliveries, disaster handling and many more. However, the limited battery life of UAVs pose a challenge to their service continuity, thus mechanisms to extend the UAVs’ battery life are required. For service delivery, UAVs consume energy for mechanical functionalities as well as for communicating with other network nodes. To reduce the mechanical energy consumption, the shortest flying path can be considered while selecting a right radio frequency level for UAV’s communications can effectively reduce the remaining required energy. In this paper, we analyze energy requirements for providing different communication services using different radio frequency bands. We propose an application-aware, energy-efficient trajectory design method which dynamically adapts the UAV’s communication radio frequency to the requested services in the best flying trajectory while considering service level priorities as well. Our simulation results show that our approach can save up to 14% energy while providing even higher Quality of Service (QoS) in a given trajectory

Trajectory optimization of flying energy sources using Q-Learning to recharge hotspot UAVs

Despite the increasing popularity of commercial usage of UAVs or drone-delivered services, their dependence on the limited-capacity on-board batteries hinders their flighttime and mission continuity. As such, developing in-situ power transfer solutions for topping-up UAV batteries have the potential to extend their mission duration. In this paper, we study a scenario where UAVs are deployed as base stations (UAV-BS) providing wireless Hotspot services to the ground nodes, while harvesting wireless energy from flying energy sources. These energy sources are specialized UAVs (Charger or transmitter UAVs, tUAVs), equipped with wireless power transmitting devices such as RF antennae. tUAVs have the flexibility to adjust their flight path to maximize energy transfer. With the increasing number of UAV-BSs and environmental complexity, it is necessary to develop an intelligent trajectory selection procedure for tUAVs so as to optimize the energy transfer gain. In this paper, we model the trajectory optimization of tUAVs as a Markov Decision Process (MDP) problem and solve it using Q-Learning algorithm. Simulation results confirm that the Q-Learning based optimized trajectory of the tUAVs outperforms two benchmark strategies, namely random path planning and static hovering of the tUAVs