Modeling the Use of an Airborne Platform for Cellular Communications Following Disruptions

In the wake of a disaster, infrastructure can be severely damaged, hampering telecommunications. An Airborne Communications Network (ACN) allows for rapid and accurate information exchange that is essential for the disaster response period. Access to information for survivors is the start of returning to self-sufficiency, regaining dignity, and maintaining hope. Real-world testing has proven that such a system can be built, leading to possible future expansion of features and functionality of an emergency communications system. Currently, there are no airborne civilian communications systems designed to meet the demands of the public following a natural disaster. A system allowing even a limited amount of communications post-disaster is a great improvement on the current situation, where telecommunications are frequently not available. It is technically feasible to use an airborne, wireless, cellular system quickly deployable to disaster areas and configured to restore some of the functions of damaged terrestrial telecommunications networks. The system requirements were presented, leading to the next stage of the planned research, where a range of possible solutions were examined. The best solution was selected based on the earlier, predefined criteria. The system was modeled, and a test ii system built. The system was tested and redesigned when necessary, to meet the requirements. The research has shown how the combination of technology, especially the recent miniaturizations and move to open source software for cellular network components can allow sophisticated cellular networks to be implemented. The ACN system proposed could enable connectivity and reduce the communications problems that were experienced following Hurricane Sandy and Katrina. Experience with both natural and man-made disasters highlights the fact that communications are useful only to the extent that they are accessible and useable by the population

Developing a Framework for Stigmergic Human Collaboration with Technology Tools: Cases in Emergency Response

Information and Communications Technologies (ICTs), particularly social media and geographic information systems (GIS), have become a transformational force in emergency response. Social media enables ad hoc collaboration, providing timely, useful information dissemination and sharing, and helping to overcome limitations of time and place. Geographic information systems increase the level of situation awareness, serving geospatial data using interactive maps, animations, and computer generated imagery derived from sophisticated global remote sensing systems. Digital workspaces bring these technologies together and contribute to meeting ad hoc and formal emergency response challenges through their affordances of situation awareness and mass collaboration. Distributed ICTs that enable ad hoc emergency response via digital workspaces have arguably made traditional top-down system deployments less relevant in certain situations, including emergency response (Merrill, 2009; Heylighen, 2007a, b). Heylighen (2014, 2007a, b) theorizes that human cognitive stigmergy explains some self-organizing characteristics of ad hoc systems. Elliott (2007) identifies cognitive stigmergy as a factor in mass collaborations supported by digital workspaces. Stigmergy, a term from biology, refers to the phenomenon of self-organizing systems with agents that coordinate via perceived changes in the environment rather than direct communication. In the present research, ad hoc emergency response is examined through the lens of human cognitive stigmergy. The basic assertion is that ICTs and stigmergy together make possible highly effective ad hoc collaborations in circumstances where more typical collaborative methods break down. The research is organized into three essays: an in-depth analysis of the development and deployment of the Ushahidi emergency response software platform, a comparison of the emergency response ICTs used for emergency response during Hurricanes Katrina and Sandy, and a process model developed from the case studies and relevant academic literature is described

SDN Testbed for Evaluation of Large Exo-Atmospheric EMP Attacks

Large-scale nuclear electromagnetic pulse (EMP) attacks and natural disasters can cause extensive network failures across wide geographic regions. Although operational networks are designed to handle most single or dual faults, recent efforts have also focused on more capable multi-failure disaster recovery schemes. Concurrently, advances in software-defined networking (SDN) technologies have delivered highly-adaptable frameworks for implementing new and improved service provisioning and recovery paradigms in real-world settings. Hence this study leverages these new innovations to develop a robust disaster recovery (counter-EMP) framework for large backbone networks. Detailed findings from an experimental testbed study are also presented

Device-to-device based path selection for post disaster communication using hybrid intelligence

Public safety network communication methods are concurrence with emerging networks to provide enhanced strategies and services for catastrophe management. If the cellular network is damaged after a calamity, a new-generation network like the internet of things (IoT) is ready to assure network access. In this paper, we suggested a framework of hybrid intelligence to find and re-connect the isolated nodes to the functional area to save life. We look at a situation in which the devices in the hazard region can constantly monitor the radio environment to self-detect the occurrence of a disaster, switch to the device-to-device (D2D) communication mode, and establish a vital connection. The oscillating spider monkey optimization (OSMO) approach forms clusters of the devices in the disaster area to improve network efficiency. The devices in the secluded area use the cluster heads as relay nodes to the operational site. An oscillating particle swarm optimization (OPSO) with a priority-based path encoding technique is used for path discovery. The suggested approach improves the energy efficiency of the network by selecting a routing path based on the remaining energy of the device, channel quality, and hop count, thus increasing network stability and packet delivery

Promoting Resiliency in Emergency Communication Networks: A Network Interdiction Stylized Initial Case Study Model of a Miami-Dade County Network

Police, fire, and emergency personnel rely on wireless networks to serve the public. Whether it is during a natural disaster, or just an ordinary calendar day, wireless nodes of varying types form the infrastructure that local, regional, and even national scale agencies use to communicate while keeping the population served safe and secure. We present a network interdiction modeling approach that can be utilized for analyzing vulnerabilities in public service wireless networks subject to hacking, terrorism, or destruction from natural disasters. We develop a case study for the wireless network utilized by the sheriff’s department of Miami-Dade County in Florida in the United States. Our modeling approach, given theoretical budgets for the “hardening” of wireless network nodes and for would-be destroyers of such nodes, highlights parts of the network where further investment may prevent damage and loss of capacity

A Multiple Case Study Investigating Principles of Design and Implementation of Operational Safety Plans for Crises at Colleges, Universities, and Affiliated Institutions

In the wake of the 2007 Virginia Tech tragedy, the Virginia state legislature mandated that all college-affiliated institutions create an operational safety plan for natural and manmade crises. Previous empirical research has mostly focused on documenting faculty and students’ perceptions of campus safety, preparations for manmade crises over natural disasters, and enhancing specific aspects of emergency responses for future incidents. Thus, design and implementation “best practices” for higher education operational safety plan protocols is an understudied, yet burgeoning area of inquiry. To address this literature gap, a comparative case study of five institutions was conducted using a novel document analysis protocol and interviews to analyze current operational safety plans through the lenses of open systems, rational choice, and chaos theories. Data revealed three guiding principles for design, four factors identified for successful implementation, and five candidate “best practices,” which aligned with the training, emergency threat assessment, emergency management, resources, communication and coordination protocol themes. A practical implication from this study was that the selected institutions placed more attention on operational safety plan response elements than on preparation components, which may mean less than optimal plan execution. Moreover, a significant theoretical implication was the discovery that emergency managers rationalized their operational safety plan decisions based on perceived costs and benefits, which may deprive these plans of the most up-to-date, peer reviewed information. Future studies will contribute additional novel practical and theoretical guidance regarding the essential components for effective operational safety planning that would increase the capability of senior administrators and campus safety personnel to manage risks associated with emergency disasters

Intergrating the Fruin LOS into the Multi-Objective Ant Colony System

Building evacuation simulation provides the planners and designers an opportunity to analyse the designs and plan a precise, scenario specific instruction for disaster times. Nevertheless, when disaster strikes, the unexpected may happen and many egress paths may get blocked or the conditions of evacuees may not let the execution of emergency plans go smoothly. During disaster times, effective route-finding methods can help efficient evacuation process, in which the directors are able to react to the sudden changes in the environment. This research tries to integrate the highly accepted human dynamics methods proposed by Fruin into the Ant-Colony optimisation route-finding method. The proposed method is designed as a multi-objective ant colony system, which tries to minimize the congestions in the bottlenecks during evacuations, in addition to the egress time, and total traversed time by evacuees. This method embodies the standard crowd dynamics method in the literature, which are Fruin LOS and pedestrian speed. The proposed method will be tested against a baseline method, that is shortest path, in terms of the objective functions, which are evacuation time and congestion degree. The results of the experiment show that a multi-objective ant colony system performance is able to reduce both egress time and congestion degree in an effective manner, however, the method efficiency drops when the evacuee population is small. The integration of Fruin LOS also produces more meaningful results, as the load responds to the Level of Service, rather than the density of the crowd, and the Level of Service is specifically designed for the sake of measuring the ease of crowd movement

Energy sustainability of next generation cellular networks through learning techniques

The trend for the next generation of cellular network, the Fifth Generation (5G), predicts a 1000x increase in the capacity demand with respect to 4G, which leads to new infrastructure deployments. To this respect, it is estimated that the energy consumption of ICT might reach the 51% of global electricity production by 2030, mainly due to mobile networks and services. Consequently, the cost of energy may also become predominant in the operative expenses of a Mobile Network Operator (MNO). Therefore, an efficient control of the energy consumption in 5G networks is not only desirable but essential. In fact, the energy sustainability is one of the pillars in the design of the next generation cellular networks. In the last decade, the research community has been paying close attention to the Energy Efficiency (EE) of the radio communication networks, with particular care on the dynamic switch ON/OFF of the Base Stations (BSs). Besides, 5G architectures will introduce the Heterogeneous Network (HetNet) paradigm, where Small BSs (SBSs) are deployed to assist the standard macro BS for satisfying the high traffic demand and reducing the impact on the energy consumption. However, only with the introduction of Energy Harvesting (EH) capabilities the networks might reach the needed energy savings for mitigating both the high costs and the environmental impact. In the case of HetNets with EH capabilities, the erratic and intermittent nature of renewable energy sources has to be considered, which entails some additional complexity. Solar energy has been chosen as reference EH source due to its widespread adoption and its high efficiency in terms of energy produced compared to its costs. To this end, in the first part of the thesis, a harvested solar energy model has been presented based on accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. The typical HetNet scenario involves dense deployments with a high level of flexibility, which suggests the usage of distributed control systems rather than centralized, where the scalability can become rapidly a bottleneck. For this reason, in the second part of the thesis, we propose to model the SBS tier as a Multi-agent Reinforcement Learning (MRL) system, where each SBS is an intelligent and autonomous agent, which learns by directly interacting with the environment and by properly utilizing the past experience. The agents implemented in each SBS independently learn a proper switch ON/OFF control policy, so as to jointly maximize the system performance in terms of throughput, drop rate and energy consumption, while adapting to the dynamic conditions of the environment, in terms of energy inflow and traffic demand. However, MRL might suffer the problem of coordination when finding simultaneously a solution among all the agents that is good for the whole system. In consequence, the Layered Learning paradigm has been adopted to simplify the problem by decomposing it in subtasks. In particular, the global solution is obtained in a hierarchical fashion: the learning process of a subtask is aimed at facilitating the learning of the next higher subtask layer. The first layer implements an MRL approach and it is in charge of the local online optimization at SBS level as function of the traffic demand and the energy incomes. The second layer is in charge of the network-wide optimization and it is based on Artificial Neural Networks aimed at estimating the model of the overall network.Con la llegada de la nueva generación de redes móviles, la quinta generación (5G), se predice un aumento por un factor 1000 en la demanda de capacidad respecto a la 4G, con la consecuente instalación de nuevas infraestructuras. Se estima que el gasto energético de las tecnologías de la información y la comunicación podría alcanzar el 51% de la producción mundial de energía en el año 2030, principalmente debido al impacto de las redes y servicios móviles. Consecuentemente, los costes relacionados con el consumo de energía pasarán a ser una componente predominante en los gastos operativos (OPEX) de las operadoras de redes móviles. Por lo tanto, un control eficiente del consumo energético de las redes 5G, ya no es simplemente deseable, sino esencial. En la última década, la comunidad científica ha enfocado sus esfuerzos en la eficiencia energética (EE) de las redes de comunicaciones móviles, con particular énfasis en algoritmos para apagar y encender las estaciones base (BS). Además, las arquitecturas 5G introducirán el paradigma de las redes heterogéneas (HetNet), donde pequeñas BSs, o small BSs (SBSs), serán desplegadas para ayudar a las grandes macro BSs en satisfacer la gran demanda de tráfico y reducir el impacto en el consumo energético. Sin embargo, solo con la introducción de técnicas de captación de la energía ambiental, las redes pueden alcanzar los ahorros energéticos requeridos para mitigar los altos costes de la energía y su impacto en el medio ambiente. En el caso de las HetNets alimentadas mediante energías renovables, la naturaleza errática e intermitente de esta tipología de energías constituye una complejidad añadida al problema. La energía solar ha sido utilizada como referencia debido a su gran implantación y su alta eficiencia en términos de cantidad de energía producida respecto costes de producción. Por consiguiente, en la primera parte de la tesis se presenta un modelo de captación de la energía solar basado en un riguroso modelo estocástico de Markov que representa la energía capturada por paneles solares para exteriores. El escenario típico de HetNet supondrá el despliegue denso de SBSs con un alto nivel de flexibilidad, lo cual sugiere la utilización de sistemas de control distribuidos en lugar de aquellos que están centralizados, donde la adaptabilidad podría convertirse rápidamente en un reto difícilmente gestionable. Por esta razón, en la segunda parte de la tesis proponemos modelar las SBSs como un sistema multiagente de aprendizaje automático por refuerzo, donde cada SBS es un agente inteligente y autónomo que aprende interactuando directamente con su entorno y utilizando su experiencia acumulada. Los agentes en cada SBS aprenden independientemente políticas de control del apagado y encendido que les permiten maximizar conjuntamente el rendimiento y el consumo energético a nivel de sistema, adaptándose a condiciones dinámicas del ambiente tales como la energía renovable entrante y la demanda de tráfico. No obstante, los sistemas multiagente sufren problemas de coordinación cuando tienen que hallar simultáneamente una solución de forma distribuida que sea buena para todo el sistema. A tal efecto, el paradigma de aprendizaje por niveles ha sido utilizado para simplificar el problema dividiéndolo en subtareas. Más detalladamente, la solución global se consigue de forma jerárquica: el proceso de aprendizaje de una subtarea está dirigido a ayudar al aprendizaje de la subtarea del nivel superior. El primer nivel contempla un sistema multiagente de aprendizaje automático por refuerzo y se encarga de la optimización en línea de las SBSs en función de la demanda de tráfico y de la energía entrante. El segundo nivel se encarga de la optimización a nivel de red del sistema y está basado en redes neuronales artificiales diseñadas para estimar el modelo de todas las BSsPostprint (published version