tinyLTE: Lightweight, Ad-Hoc Deployable Cellular Network for Vehicular Communication

The application of LTE technology has evolved from infrastructure-based deployments in licensed bands to new use cases covering ad hoc, device-to-device communications and unlicensed band operation. Vehicular communication is an emerging field of particular interest for LTE, covering in our understanding both automotive (cars) as well as unmanned aerial vehicles. Existing commercial equipment is designed for infrastructure making it unsuitable for vehicular applications requiring low weight and unlicensed band support (e.g. 5.9 GHz ITS-band). In this work, we present tinyLTE, a system design which provides fully autonomous, multi-purpose and ultra-compact LTE cells by utilizing existing open source eNB and EPC implementations. Due to its small form factor and low weight, the tinyLTE system enables mobile deployment on board of cars and drones as well as smooth integration with existing roadside infrastructure. Additionally, the standalone design allows for systems to be chained in a multi-hop configuration. The paper describes the lean and low-cost design concept and implementation followed by a performance evaluation for single and two-hop configurations at 5.9 GHz. The results from both lab and field experiments validate the feasibility of the tinyLTE approach and demonstrate its potential to even support real-time vehicular applications (e.g. with a lowest average end-to-end latency of around 7 ms in the lab experiment)

Self-Evolving Integrated Vertical Heterogeneous Networks

6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight on network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table

An Emergent Architecture for Scaling Decentralized Communication Systems (DCS)

With recent technological advancements now accelerating the mobile and wireless Internet solution space, a ubiquitous computing Internet is well within the research and industrial community's design reach - a decentralized system design, which is not solely driven by static physical models and sound engineering principals, but more dynamically, perhaps sub-optimally at initial deployment and socially-influenced in its evolution. To complement today's Internet system, this thesis proposes a Decentralized Communication System (DCS) architecture with the following characteristics: flat physical topologies with numerous compute oriented and communication intensive nodes in the network with many of these nodes operating in multiple functional roles; self-organizing virtual structures formed through alternative mobility scenarios and capable of serving ad hoc networking formations; emergent operations and control with limited dependency on centralized control and management administration. Today, decentralized systems are not commercially scalable or viable for broad adoption in the same way we have to come to rely on the Internet or telephony systems. The premise in this thesis is that DCS can reach high levels of resilience, usefulness, scale that the industry has come to experience with traditional centralized systems by exploiting the following properties: (i.) network density and topological diversity; (ii.) self-organization and emergent attributes; (iii.) cooperative and dynamic infrastructure; and (iv.) node role diversity. This thesis delivers key contributions towards advancing the current state of the art in decentralized systems. First, we present the vision and a conceptual framework for DCS. Second, the thesis demonstrates that such a framework and concept architecture is feasible by prototyping a DCS platform that exhibits the above properties or minimally, demonstrates that these properties are feasible through prototyped network services. Third, this work expands on an alternative approach to network clustering using hierarchical virtual clusters (HVC) to facilitate self-organizing network structures. With increasing network complexity, decentralized systems can generally lead to unreliable and irregular service quality, especially given unpredictable node mobility and traffic dynamics. The HVC framework is an architectural strategy to address organizational disorder associated with traditional decentralized systems. The proposed HVC architecture along with the associated promotional methodology organizes distributed control and management services by leveraging alternative organizational models (e.g., peer-to-peer (P2P), centralized or tiered) in hierarchical and virtual fashion. Through simulation and analytical modeling, we demonstrate HVC efficiencies in DCS structural scalability and resilience by comparing static and dynamic HVC node configurations against traditional physical configurations based on P2P, centralized or tiered structures. Next, an emergent management architecture for DCS exploiting HVC for self-organization, introduces emergence as an operational approach to scaling DCS services for state management and policy control. In this thesis, emergence scales in hierarchical fashion using virtual clustering to create multiple tiers of local and global separation for aggregation, distribution and network control. Emergence is an architectural objective, which HVC introduces into the proposed self-management design for scaling and stability purposes. Since HVC expands the clustering model hierarchically and virtually, a clusterhead (CH) node, positioned as a proxy for a specific cluster or grouped DCS nodes, can also operate in a micro-capacity as a peer member of an organized cluster in a higher tier. As the HVC promotional process continues through the hierarchy, each tier of the hierarchy exhibits emergent behavior. With HVC as the self-organizing structural framework, a multi-tiered, emergent architecture enables the decentralized management strategy to improve scaling objectives that traditionally challenge decentralized systems. The HVC organizational concept and the emergence properties align with and the view of the human brain's neocortex layering structure of sensory storage, prediction and intelligence. It is the position in this thesis, that for DCS to scale and maintain broad stability, network control and management must strive towards an emergent or natural approach. While today's models for network control and management have proven to lack scalability and responsiveness based on pure centralized models, it is unlikely that singular organizational models can withstand the operational complexities associated with DCS. In this work, we integrate emergence and learning-based methods in a cooperative computing manner towards realizing DCS self-management. However, unlike many existing work in these areas which break down with increased network complexity and dynamics, the proposed HVC framework is utilized to offset these issues through effective separation, aggregation and asynchronous processing of both distributed state and policy. Using modeling techniques, we demonstrate that such architecture is feasible and can improve the operational robustness of DCS. The modeling emphasis focuses on demonstrating the operational advantages of an HVC-based organizational strategy for emergent management services (i.e., reachability, availability or performance). By integrating the two approaches, the DCS architecture forms a scalable system to address the challenges associated with traditional decentralized systems. The hypothesis is that the emergent management system architecture will improve the operational scaling properties of DCS-based applications and services. Additionally, we demonstrate structural flexibility of HVC as an underlying service infrastructure to build and deploy DCS applications and layered services. The modeling results demonstrate that an HVC-based emergent management and control system operationally outperforms traditional structural organizational models. In summary, this thesis brings together the above contributions towards delivering a scalable, decentralized system for Internet mobile computing and communications

Battery Pack Cells Mon itoring for Intelligent Charging

This dissertation intends to create a system capable of cell charging, cell balancing or both at the same time for batteries with multiple cells connected in series. It also tries to understand why there is only few literature connected with cell balancing and cell charging at the same time. For that purpose, this dissertation presents a review on the state of the art of many concepts related both to balancing and charging in order to pick the right methods and equipment to achieve the objectives of this work. This dissertation includes literature review on batteries, cell balancing methods and topologies, cell charging methods and a small review on state of charge estimation methods. Later on, this document studies and explains hardware and software requirements and choices in order to understand the final developed circuit. Lastly, development difficulties, results and conclusions are presented.Esta dissertação pretende criar um sistema capaz de carregar, balancear ou ambos em simultâneo num pack com diversas células ligadas em série. Tenta ainda perceber a razão de haver tão pouca bibliografia que junte em simultâneo carregamento e balanceamento de baterias. Para alcançar estes objetivos, esta dissertação conta com uma revisão do estado da arte de vários temas relacionados tanto com balanceamento como com carregamento de forma a perceber os métodos e equipamentos mais adequados para implementar. A dissertação inclui revisão bibliográfica em baterias, métodos de balanceamento e suas topologias, métodos de carregamento de baterias e ainda alguma revisão sobre métodos de estimação de estado de carga. Posteriormente, este documento estuda e explica os requisitos de software e hardware e as escolhas feitas para o desenvolvimento do circuito. Finalmente apresentam-se as dificuldades de desenvolvimento encontradas, os resultados e ainda algumas conclusões

Reconfigurable middleware architectures for large scale sensor networks

Wireless sensor networks, in an effort to be energy efficient, typically lack the high-level abstractions of advanced programming languages. Though strong, the dichotomy between these two paradigms can be overcome. The SENSIX software framework, described in this dissertation, uniquely integrates constraint-dominated wireless sensor networks with the flexibility of object-oriented programming models, without violating the principles of either. Though these two computing paradigms are contradictory in many ways, SENSIX bridges them to yield a dynamic middleware abstraction unifying low-level resource-aware task reconfiguration and high-level object recomposition. Through the layered approach of SENSIX, the software developer creates a domain-specific sensing architecture by defining a customized task specification and utilizing object inheritance. In addition, SENSIX performs better at large scales (on the order of 1000 nodes or more) than other sensor network middleware which do not include such unified facilities for vertical integration

Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)

The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17–19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field

Implications of Consumer Lifestyle Changes and Behavioral Heterogeneity on U.S. Energy Consumption and Policy

Understanding the relationship between consumer lifestyle and energy use is essential to solving many of the energy and sustainability challenges. By studying shifts in consumer lifestyle over time and behavior heterogeneity, this dissertation provides valuable insights into understanding energy consumption trends and improving energy efficiency programs. Technologies continue to change our daily lifestyles, influencing energy demand. In the first part of the dissertation, changes in how people spend their time (time-use) patterns are used as an indicator of lifestyle shifts. Using decomposition analysis changes in energy use due to these lifestyle shifts are measured. The results show that for an average American, time spent in residences increased at the rate of 3.1 minutes per day per year while time spent for travel and other non-residential activities decreased (-0.4 min/day/year and -2.7 min/day/year respectively). The time-use shifts induced a net energy change of -1,722 trillion BTU, 1.8% of national primary energy consumption in 2012. The lifestyle/energy shifts are interpreted as primarily driven by information and communication technology: people are spending more time at home with online entertainment and services. Information provided to consumers and energy efficiency rebate programs generally assume characteristics of an average consumer. There is, however, substantial heterogeneity in behavior, energy prices and impacts of electricity use. To understand the impact of heterogeneity on rebate programs, in the second part, the economic and carbon benefits of efficient choices of three household technologies (television, clothes washer and dryer) are assessed for different locations and usage patterns. For some households, an efficient energy washers and dryers do not save money, but brings substantial economic benefits to others. Viewing utility appliance rebate programs as tools for carbon abatement, abatement cost of carbon was assessed. At current rebate levels, for an average household, the abatement cost for carbon exceeds social cost of carbon (SCC). However, subpopulations with abatement cost less than SCC exists: 4%, 6%, and 41% for televisions, washers and dryers respectively. Therefore, abatement programs can benefit from targeted intervention. For targeted intervention, it would be useful to identify groups with high energy use and characterize their demographics. To achieve this, in the third analysis, time-use survey data is used to characterize patterns of TV watching. Using cluster analysis, the population was divided into three groups, the high-energy use cluster has 14% of the population and spends an average of 7.7 hours per day on TV. This relatively small group, due to high use, accounts for 34% of total television energy consumption. This group tends to be older, not in the work force and/or poorly educated. A high-use household purchasing an efficient television saves more than three times the energy of an average household. The main policy implications of these results are that more targeted information and policies have potential to enhance adoption by household who will benefit the most economically as well as reduce more carbon. In the management of utility efficiency programs, the results make a case for variable rebates or tiered communication programs

SHORT TERM TRAVEL BEHAVIOR PREDICTION THROUGH GPS AND LAND USE DATA

The short-term destination prediction problem consists of capturing vehicle Global Positioning System (GPS) traces and learning from historic locations and trajectories to predict a vehicle’s destination. Drivers have predictable trip destinations that can be estimated through probabilistic modeling of past trips. This dissertation has three main hypotheses; 1) Employing a tiered Markov model structure will permit a shorter learning period while achieving similar accuracy results, 2) The addition of derived trip purpose information will increase accuracy of the start of trip and in-route models as a whole, and 3) Similar methodologies of travel pattern inference can be used to accurately predict trip purpose and socio-economic factors. To study these concepts, a database of GPS driving traces (120 participants for 70 days) is collected. To model the user’s trip purpose, a new data source was explored: Point of Interest (POI)/land use data. An open source land use/POI dataset is merged with the GPS dataset. The resulting database includes over 20,000 trips with travel characteristics and land use/POI data. From land use/POI data, and travel patterns, trip purpose is calculated with machine learning methods. A new model structure is developed that uses trip purpose when it is available, yet falls back on traditional spatial temporal Markov models when it is not. The start of trip model has an overall increase of accuracy over other start of trip models of 2%. This comes quickly, needing only 30 days to reach this level of accuracy compared to nearly a year in many other models. When adding trip purpose and the start of trip model to in-route prediction methods, the accuracy of the destination prediction increases significantly: 15-30% improvement of accuracy over similar models between 0-50% of trip progression. Certain trips are predicted more accurately than others: work and home based trips average of 90% correct prediction, whereas shopping and social based trips hover around the 50% mark. In all, the greatest contribution of this dissertation is the trip purpose methodology addition and the tiered Markov model structure in gaining fast results in both the start of trip and in-route models

Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial

This paper presents a tutorial on stochastic geometry (SG)-based analysis for cellular networks. This tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks. This paper starts by modeling and analyzing the baseband interference in a baseline single-tier downlink cellular network with single antenna base stations and universal frequency reuse. Then, it characterizes signal-to-interference-plus-noise-ratio and its related performance metrics. In particular, a unified approach to conduct error probability, outage probability, and transmission rate analysis is presented. Although the main focus of this paper is on cellular networks, the presented unified approach applies for other types of wireless networks that impose interference protection around receivers. This paper then extends the unified approach to capture cellular network characteristics (e.g., frequency reuse, multiple antenna, power control, etc.). It also presents numerical examples associated with demonstrations and discussions. To this end, this paper highlights the state-of-the-art research and points out future research directions