Delay based approach to support low priority users in preemptive wireless networks

Title from PDF of title page, viewed on January 27, 2012Thesis advisor: Cory C. BeardVitaIncludes bibliographic references (p. 39)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2011At times of serious disasters (natural or man-made), wireless networks are quickly congested due to the sheer volume and stress on network resources, and, preferential treatment is necessary for National Security/Emergency Preparedness (NS/EP) users to combat the disaster by responding effectively and potentially save many lives. Under such circumstances, with scarce resources, the new request for sessions are denied and worse even, active sessions are dropped for general public whilst they have come to rely on these resources and depend on them especially during distressed times. Prior research has been conducted to examine upper limit (UL) and preemptive approaches to support emergency users but the traditional approach of blocking the capacity for emergency users is, from one perspective, restrictive to the general public. In this thesis, we propose the delay-based soft preemptive approach to support the low priority users and provide an alternative to several preemptive policies by further examining them. We provide a queuing algorithm in the network that warns the low priority users with an active session of scarce resources thereby giving them an opportunity to complete their session prior to reducing the quality of service (QoS) of their session and moving their bandwidth to emergency users, if blocked. The emergency users in turn wait for the resources to become available and are on hold until resources become available. By creating a queuing modeling system for this algorithm, we present simulation model in C with results of our delay-based soft preemptive approach and examine other preemptive approaches to provide a comparative analysis. The results demonstrate that increasing the warning time also increases the number of sessions blocked for emergency users as well as general public due to further constraining the resources, however, this reduces the inconvenience of preemption caused to the low priority users.Introduction -- Related work -- Algorithm and simulations -- Analysis and results -- Conclusio

VSRS: Variable Service Rate Scheduler for Low Rate Wireless Sensor Networks

This paper proposes a variable service rate scheduler VSRS for heterogeneous wireless sensor and actuator networks (WSANs). Due to recent advancement, various applications are being upgraded using sensor networks. Generally, traffic consists of delay sensitive and delay tolerant applications. Handling such traffic simultaneously is a critical challenge in IEEE 802.15.4 sensor network. However, the standard CSMA/CA does not focus on traffic-based data delivery. Therefore, this paper presents a solution for prioritybased traffic over no-priority i.e. regular traffic using CSMA/CA IEEE 802.15.4 MAC sublayer. The VSRS scheduler uses a queuing model for scheduling incoming traffic at an actor node using a dual queue. The scheduler updates priority of each incoming packet dynamically using network priority weight metric. The VSRS scheduler scans queues and picks the highest network priority packet. A packet weight is updated after selection from the respective queue. This core operation of an actor node offers good packet delivery ratio, throughput, and less delay experience of long distance traveled packets against no priority traffic. The work is validated using theoretical analysis and computer generated network simulators; proves that the priority based approach using weight factor works better over the First-Come-First-Serve (FCFS) mechanism

Learning and Risk Perception Mechanisms in Route Choice and Activity Scheduling Dynamics

This dissertation explores the learning and risk mechanisms underlying the dynamics of route choice and activity scheduling decisions. With respect to route choice dynamics, the study models decision mechanisms related to travel time perception, learning, and risk attitudes, exploring their implications on system performance over time. This objective is accomplished by performing experiments using a network performance model, in this case an agent-based simulation model of individual experience given the collective effects arising from the interaction of the agents' route choice decisions. In regards to activity scheduling decisions, the study examines the range of behavioral insights obtained from a modeling framework that views the individual scheduling process as a single-server queuing system, introducing the concept of activity stress. The study presents numerical experiments on this framework using a discrete event simulation of an M/G/1 queuing system. Furthermore, an operational model of activity participation is estimated using observed activity schedules. The results indicate that travel time uncertainty and user perception of this uncertainty greatly affect the performance of the system over time, in particular the convergence of traffic flows. With respect to activity scheduling, the results overall indicate the significance of activity stress in motivating activity scheduling and participation decisions over time, with particular importance placed on the evolution of activity queue and activity schedule states over time. Results from studies investigating both route choice and activity scheduling behavior indicate the important role of decision dynamics for determining the behavior of users in complex information-rich environments

Reliability and Quality of Service in Opportunistic Spectrum Access

RÉSUMÉ Les réseaux radio-cognitif constituent une des meilleures options technologiques pour les réseaux sans-fil futurs. Afin d’étudier comment la fiabilité devrait être redéfinie dans ces réseaux, nous étudions d'abord les sources les plus fréquentes de panne dans les réseaux sans-fil et fournissons une procédure systématique de classement des pannes. Il est ensuite expliqué comment les radios cognitives peuvent profiter de leur propre capacité à mettre en œuvre des mécanismes efficaces de prévention et de récupération contre les pannes et ainsi assurer des communications sans-fil fiables et de qualité de service constante. En considérant des normes arrivantes sur la base de l'OSA, ce qui distingue un réseau radio-cognitif de ses prédécesseurs est des changements fréquents de canal ainsi que de nouvelles exigences telles la détection de disponibilité et la décision d'utilisation du spectre. Nous nous concentrons sur cet aspect et modélisons la remise du spectre comme une panne. Par conséquent, améliorer la fiabilité est équivalent à augmenter le temps moyen entre pannes, à rendre plus efficace le processus de récupération et à réduire le temps moyen de réparation. Nous étudions donc d'abord l'impact du temps de récupération sur la performance du réseau radio-cognitif. En classifiant les pannes en dures et souples, il est examiné comment la disponibilité, le temps moyen entre pannes et le temps moyen jusqu'à la réparation sont touchés par le procès de récupération. Nous observons que le temps dépensé pour la récupération empêche le réseau d'atteindre le maximum de disponibilité. Par conséquent, pour obtenir un temps plus élevé entre pannes et un temps de réparation plus court, une option disponible est d'augmenter le nombre de canaux pouvant être utilisés par le réseau radio-cognitif, de sorte que, avec une haute probabilité, un utilisateur qui a raté le canal puisse trouver bientôt un nouveau canal. De l'autre côté, un mécanisme de récupération efficace est nécessaire pour mieux profiter de ce grand nombre de canaux; l'amélioration de la récupération est donc indispensable. Pour étudier l'impact de la récupération sur les couches plus hautes (e.g., la couche liaison et réseau), l’approche de l’analyse de file d'attente est choisie. Compte tenu des périodes de récupération comme une interruption de service, un modèle général de file d'attente de M/G/1 avec des interruptions est proposé. Différents paramètres de fiabilité et de qualité de service peuvent être trouvés à partir de ce modèle de file d'attente pour étudier comment la spécification des canaux, tels la distribution des périodes de disponibilité et d'indisponibilité, et la spécification de l'algorithme de récupération, tels la durée de récupération, affectent les paramètres de performance comme la perte de paquets, de retard et de gigue, et aussi le temps entre pannes. Pour soutenir la différenciation des classes de trafic, nous proposons une approche de file d'attente avec priorité. Nous proposons une extension des résultats du modèle de file d'attente générale et présentons quatre différentes disciplines de file d'attente de priorité, allant d'un régime préemptif absolu à un régime complètement non préemptif. Les nouvelles disciplines augmentent la flexibilité et la résolution de décision et permettent au noeud CR de contrôler l'interaction des différentes classes de trafic avec plus de précision.---------- ABSTRACT Cognitive-radio based wireless networks are a technology of choice for incoming wireless networks. To investigate how reliability should be redefined for these networks, we study the most common sources of failure in wireless networks and provide a systematic failure classification procedure. It is then explained how cognitive radios can use their inherent capabilities to implement efficient prevention and recovery mechanisms to combat failures and thereby provide more reliable communications and consistent quality of service in wireless networks. Considering incoming OSA-based standards, what distinguishes a cognitive radio network from its predecessors is the frequent spectrum handovers along with new requirements such as spectrum sensing and spectrum usage decision. We thus focus on this aspect and model the spectrum handover as a failure, so improving the reliability is equivalent to increasing the mean time to failure, improving the recovery process and shortening the mean time to repair. We first study the impact of the recovery time on the performance of the cognitive radio network. By classifying the failures into hard and soft, it is investigated how the availability, mean time to failure and mean time to repair are affected by the recovery time. It is observed that the time spent for recovery prevents the network from reaching the maximum availability. Therefore, to achieve a high mean time to hard failure and low mean time to repair, an available option is to increase the number of channels, so that with a high probability, a user who missed the channel can soon find a new channel. On the other side, an efficient recovery scheme is required to better take advantage of a large number of channels. Recovery improvement is thus indispensable. To study the impact of recovery on higher communication layers, a queueing approach is chosen. Considering the recovery periods as a service interruption, a general M/G/1 queueing model with interruption is proposed. Different reliability and quality of service parameters can be found from this queueing model to investigate how channel parameters, such as availability and unavailability periods, and the recovery algorithm specifications, such as the recovery duration, affect packet loss, delay and jitter, and also the MTTF and MTTR for hard and soft failures. To support traffic differentiation, we suggest a priority queueing approach. We extend the results of the general queueing model and discuss four different priority queueing disciplines ranging from a pure preemptive scheme to a pure non-preemptive scheme. New disciplines increase the flexibility and decision resolution and enable the CR node to more accurately control the interaction of different classes of traffic. The models are solved, so it can be analyzed how the reliability and quality of service parameters, such as delay and jitter, for a specific class of traffic are affected not only by the channel parameters, but also by the characteristics of other traffic classes. The M/G/1 queueing model with interruptions is a foundation for performance analysis and an answer to the need of having closed-form analytical relations. We then extend the queueing model to more realistic scenarios, first with heterogeneous channels (heterogeneous service rate for different channels) and second with multiple users and a random medium access model

Integrating Context-Awareness in the IP Multimedia Subsystem for Enhanced Session Control and Service Provisioning Capabilities

The 3GPP-defined IP Multimedia Subsystem (IMS) is becoming the de-facto standard for IP-based multimedia communication services. It consists of an overlay control and service layer that is deployed on top of IP-based mobile and fixed networks. This layer encompasses a set of common functions (e.g. session control functions allowing the initiation/modification/termination of sessions) and service logics that are needed for the seamless provisioning of IP multimedia services to users, via different access technologies. As it continues to evolve, the IMS still faces several challenges including: the enabling of innovative and personalized services that would appeal to users and increase network operators' revenues; its interaction with other types of networks (e.g. wireless sensor networks) as means to enhance its capabilities; and the support of advanced QoS schemes that would manage the network resources in an efficient and adaptive manner. The context-awareness concept, which comes from the pervasive computing field, signifies the ability to use situational information (or context) in support to operations and decision making and for the provision of relevant services to the user. Context-awareness is considered to enhance users' experience and is seen as an enabler to adaptability and service personalization - two capabilities that could play important roles in telecommunication environments. This thesis focuses on the introduction of the context-awareness technology in the IMS, as means to enhance its session control and service provisioning capabilities. It starts by presenting the necessary background information, followed by a derivation of requirements and a review of the related work. To ensure the availability of contextual information within the network, we then propose an architecture for context information acquisition and management in the IMS. This architecture leverages and extends the 3GPP presence framework. Building on the capabilities of this architecture, we demonstrate how the managed information could be integrated in IMS operations, at the control and service levels. Showcasing control level integration, we propose a novel context-aware call differentiation framework as means to offer enhanced QoS support (for sessions/calls) in IMS-based networks. This framework enables the differentiation between different categories of calls at the IMS session control level, via dynamic and adaptive resource allocation, in addition to supporting a specialized charging model. Furthermore, we also propose a framework for enhanced IMS emergency communication services. This framework addresses the limitations of existing IP-based emergency solutions, by offering three main improvements: a QoS-enhanced emergency service; a context-aware personalized emergency service; and a conferencing-enhanced emergency service. We demonstrate the use of context awareness at the IMS service level using two new context-aware IMS applications. Finally, to validate our solutions and evaluate their performance, we build various proof-of-concept prototypes and OPNET simulation model

Mathematical optimization techniques for demand management in smart grids

The electricity supply industry has been facing significant challenges in terms of meeting the projected demand for energy, environmental issues, security, reliability and integration of renewable energy. Currently, most of the power grids are based on many decades old vertical hierarchical infrastructures where the electric power flows in one direction from the power generators to the consumer side and the grid monitoring information is handled only at the operation side. It is generally believed that a fundamental evolution in electric power generation and supply system is required to make the grids more reliable, secure and efficient. This is generally recognised as the development of smart grids. Demand management is the key to the operational efficiency and reliability of smart grids. Facilitated by the two-way information flow and various optimization mechanisms, operators benefit from real time dynamic load monitoring and control while consumers benefit from optimised use of energy. In this thesis, various mathematical optimization techniques and game theoretic frameworks have been proposed for demand management in order to achieve efficient home energy consumption scheduling and optimal electric vehicle (EV) charging. A consumption scheduling technique is proposed to minimise the peak consumption load. The proposed technique is able to schedule the optimal operation time for appliances according to the power consumption patterns of the individual appliances. A game theoretic consumption optimization framework is proposed to manage the scheduling of appliances of multiple residential consumers in a decentralised manner, with the aim of achieving minimum cost of energy for consumers. The optimization incorporates integration of locally generated and stored renewable energy in order to minimise dependency on conventional energy. In addition to the appliance scheduling, a mean field game theoretic optimization framework is proposed for electric vehicles to manage their charging. In particular, the optimization considers a charging station where a large number of EVs are charged simultaneously during a flexible period of time. The proposed technique provides the EVs an optimal charging strategy in order to minimise the cost of charging. The performances of all these new proposed techniques have been demonstrated using Matlab based simulation studies