Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior

Poultry farms are a major contributor to the human food chain. However, around the world, there have been growing concerns about the quality of life for the livestock in poultry farms; and increasingly vocal demands for improved standards of animal welfare. Recent advances in sensing technologies and machine learning allow the possibility of monitoring birds, and employing the lessons learned to improve the welfare for all birds. This task superficially appears to be easy, yet, studying behavioral patterns involves collecting enormous amounts of data, justifying the term Big Data. Before the big data can be used for analytical purposes to tease out meaningful, well-conserved behavioral patterns, the collected data needs to be pre-processed. The pre-processing refers to processes for cleansing and preparing data so that it is in the format ready to be analyzed by downstream algorithms, such as classification and clustering algorithms. However, as we shall demonstrate, efficient pre-processing of chicken big data is both non-trivial and crucial towards success of further analytics.Comment: arXiv admin note: text overlap with arXiv:1811.0314

Situation-aware Edge Computing

Future wireless networks must cope with an increasing amount of data that needs to be transmitted to or from mobile devices. Furthermore, novel applications, e.g., augmented reality games or autonomous driving, require low latency and high bandwidth at the same time. To address these challenges, the paradigm of edge computing has been proposed. It brings computing closer to the users and takes advantage of the capabilities of telecommunication infrastructures, e.g., cellular base stations or wireless access points, but also of end user devices such as smartphones, wearables, and embedded systems. However, edge computing introduces its own challenges, e.g., economic and business-related questions or device mobility. Being aware of the current situation, i.e., the domain-specific interpretation of environmental information, makes it possible to develop approaches targeting these challenges. In this thesis, the novel concept of situation-aware edge computing is presented. It is divided into three areas: situation-aware infrastructure edge computing, situation-aware device edge computing, and situation-aware embedded edge computing. Therefore, the concepts of situation and situation-awareness are introduced. Furthermore, challenges are identified for each area, and corresponding solutions are presented. In the area of situation-aware infrastructure edge computing, economic and business-related challenges are addressed, since companies offering services and infrastructure edge computing facilities have to find agreements regarding the prices for allowing others to use them. In the area of situation-aware device edge computing, the main challenge is to find suitable nodes that can execute a service and to predict a node’s connection in the near future. Finally, to enable situation-aware embedded edge computing, two novel programming and data analysis approaches are presented that allow programmers to develop situation-aware applications. To show the feasibility, applicability, and importance of situation-aware edge computing, two case studies are presented. The first case study shows how situation-aware edge computing can provide services for emergency response applications, while the second case study presents an approach where network transitions can be implemented in a situation-aware manner

Terminal cooperation in next generation wireless networks: aerial and regional access networks

Throughout the years, progress of humankind has depended on the power of communication and over the decades, the ways of communication has witnessed mammoth changes. Specifically wireless communication in the last decade has completely revolutionized the way we communicate with each other. Smartphones have become an ubiquitous part of our life. With most operators throughout the world deploying fourth generation wireless communication systems, peculiar use cases and scenarios are being envisioned such as public safety networks, aerial networks, etc. to be addressed by the next generation wireless systems. Moreover, as urban areas are becoming saturated commercial network operators are looking for business cases to move towards the untapped regional areas. However, to deploy networks in regional areas economically, novel technologies and architectures need to be developed and investigated. In this thesis, we study the novel concept of terminal cooperation in the context of next generation wireless communication systems especially looking into aerial and regional access networks. In the first part of the thesis, we investigate the physical radio channel for device-to-device (D2D) communication which would help in enabling terminal cooperation in wireless networks. Specifically, we propose propagation model for D2D in rural areas using 922 MHz and 2466 MHz, a channel model for vehicular communications using 5.8 GHz and a propagation model for D2D using millimetre wave frequencies. In the second part of the thesis, we evaluate the coverage performance of aerial access networks using different technologies and develop algorithms to enhance the coverage using terminal cooperation in regional access networks. Specifically, we evaluate the performance of two different technologies, LTE and WiFi, in aerial access networks. We propose game-theoretic algorithms to enable terminal cooperation to enhance coverage in regional access networks and perform system level simulation to evaluate the proposed algorithms. In the last part of this thesis, we analyse and develop techniques to enhance energy efficiency in aerial access networks using terminal cooperation. Specifically, we propose a clustering algorithm called EECAN which improves the energy efficiency of the terrestrial nodes accessing the aerial base-station, a clustering algorithm based on Matern Hardcore Point Process which allows us to optimize cluster head spacing analytically and we further enhance this algorithm by including impairments introduced by the wireless channel. Throughout this thesis, we verify and validate our analytic results, algorithms and techniques with Monte-Carlo simulations of the considered scenarios. Most of the work presented in this thesis was published in-part or as a whole in conferences, journals, book-chapters, project reports or otherwise undergoing a review process. These publications and reports are highlighted in the course of the thesis. Lastly, we invite the reader to enjoy exploring this thesis and we hope that it will add more understanding to this promising new technology of terminal cooperation in aerial and regional access networks

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Radio Resource Allocation in Wireless OFDM Systems

Ph.DDOCTOR OF PHILOSOPH

QoS-aware Adaptive Resource Management in OFDMA Networks

PhDOne important feature of the future communication network is that users in the network are required to experience a guaranteed high quality of service (QoS) due to the popularity of multimedia applications. This thesis studies QoS-aware radio resource management schemes in different OFDMA network scenarios. Motivated by the fact that in current 4G networks, the QoS provisioning is severely constrained by the availability of radio resources, especially the scarce spectrum as well as the unbalanced traffic distribution from cell to cell, a joint antenna and subcarrier management scheme is proposed to maximise user satisfaction with load balancing. Antenna pattern update mechanism is further investigated with moving users. Combining network densi fication with cloud computing technologies, cloud radio access network (C-RAN) has been proposed as the emerging 5G network architecture consisting of baseband unit (BBU) pool, remote radio heads (RRHs) and fronthaul links. With cloud based information sharing through the BBU pool, a joint resource block and power allocation scheme is proposed to maximise the number of satisfi ed users whose required QoS is achieved. In this scenario, users are served by high power nodes only. With spatial reuse of system bandwidth by network densi fication, users' QoS provisioning can be ensured but it introduces energy and operating effciency issue. Therefore two network energy optimisation schemes with QoS guarantee are further studied for C-RANs: an energy-effective network deployment scheme is designed for C-RAN based small cells; a joint RRH selection and user association scheme is investigated in heterogeneous C-RAN. Thorough theoretical analysis is conducted in the development of all proposed algorithms, and the effectiveness of all proposed algorithms is validated via comprehensive simulations.China Scholarship Counci

DR9.3 Final report of the JRRM and ASM activities

Deliverable del projecte europeu NEWCOM++This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.Postprint (published version

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin

Resource Management and Backhaul Routing in Millimeter-Wave IAB Networks Using Deep Reinforcement Learning

Thesis (PhD (Electronic Engineering))--University of Pretoria, 2023..The increased densification of wireless networks has led to the development of integrated access and backhaul (IAB) networks. In this thesis, deep reinforcement learning was applied to solve resource management and backhaul routing problems in millimeter-wave IAB networks. In the research work, a resource management solution that aims to avoid congestion for access users in an IAB network was proposed and implemented. The proposed solution applies deep reinforcement learning to learn an optimized policy that aims to achieve effective resource allocation whilst minimizing congestion and satisfying the user requirements. In addition, a deep reinforcement learning-based backhaul adaptation strategy that leverages a recursive discrete choice model was implemented in simulation. Simulation results where the proposed algorithms were compared with two baseline methods showed that the proposed scheme provides better throughput and delay performance.Sentech Chair in Broadband Wireless Multimedia Communications.Electrical, Electronic and Computer EngineeringPhD (Electronic Engineering)Unrestricte