49 research outputs found
QoS multicast routing protocol oriented to cognitive network using competitive coevolutionary algorithm
The human intervention in the network management and maintenance should be reduced to alleviate the ever-increasing spatial and temporal complexity. By mimicking the cognitive behaviors of human being, the cognitive network improves the scalability, self-adaptation, self-organization, and self-protection in the network. To implement the cognitive network, the cognitive behaviors for the network nodes need to be carefully designed. Quality of service (QoS) multicast is an important network problem. Therefore, it is appealing to develop an effective QoS multicast routing protocol oriented to cognitive network.
In this paper, we design the cognitive behaviors summarized in the cognitive science for the network nodes. Based on the cognitive behaviors, we propose a QoS multicast routing protocol oriented to cognitive network, named as CogMRT. It is a distributed protocol where each node only maintains local information. The routing search is in a hop by hop way. Inspired by the small-world phenomenon, the cognitive behaviors help to accumulate the experiential route information. Since the QoS multicast routing is a typical combinatorial optimization problem and it is proved to be NP-Complete, we have applied the competitive coevolutionary algorithm (CCA) for the multicast tree construction. The CCA adopts novel encoding method and genetic operations which leverage the characteristics of the problem. We implement and evaluate CogMRT and other two promising alternative protocols in NS2 platform. The results show that CogMRT has remarkable advantages over the counterpart traditional protocols by exploiting the cognitive favors
A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems
Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been
applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of
evolutionary algorithms to solve optimization problems related to a type of complex network likemobilemultihop ad hoc networks.
Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular
ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we
review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and
open challenges to guide further research in this topic
Self-Organized Coverage and Capacity Optimization for Cellular Mobile Networks
Die zur Erfüllung der zu erwartenden Steigerungen übertragener
Datenmengen notwendige größere Heterogenität und steigende Anzahl von
Zellen werden in der Zukunft zu einer deutlich höheren Komplexität bei
Planung und Optimierung von Funknetzen führen. Zusätzlich erfordern
räumliche und zeitliche Änderungen der Lastverteilung eine dynamische
Anpassung von Funkabdeckung und -kapazität
(Coverage-Capacity-Optimization, CCO). Aktuelle Planungs- und
Optimierungsverfahren sind hochgradig von menschlichem Einfluss abhängig,
was sie zeitaufwändig und teuer macht. Aus diesen Grnden treffen Ansätze
zur besseren Automatisierung des Netzwerkmanagements sowohl in der
Industrie, als auch der Forschung auf groes
Interesse.Selbstorganisationstechniken (SO) haben das Potential, viele der
aktuell durch Menschen gesteuerten Abläufe zu automatisieren. Ihnen wird
daher eine zentrale Rolle bei der Realisierung eines einfachen und
effizienten Netzwerkmanagements zugeschrieben. Die vorliegende Arbeit
befasst sich mit selbstorganisierter Optimierung von Abdeckung und
Übertragungskapazität in Funkzellennetzwerken. Der Parameter der Wahl
hierfür ist die Antennenneigung. Die zahlreichen vorhandenen Ansätze
hierfür befassen sich mit dem Einsatz heuristischer Algorithmen in der
Netzwerkplanung. Im Gegensatz dazu betrachtet diese Arbeit den verteilten
Einsatz entsprechender Optimierungsverfahren in den betreffenden
Netzwerkknoten. Durch diesen Ansatz können zentrale Fehlerquellen (Single
Point of Failure) und Skalierbarkeitsprobleme in den kommenden heterogenen
Netzwerken mit hoher Knotendichte vermieden werden.Diese Arbeit stellt
einen "Fuzzy Q-Learning (FQL)"-basierten Ansatz vor, ein einfaches
Maschinenlernverfahren mit einer effektiven Abstraktion kontinuierlicher
Eingabeparameter. Das CCO-Problem wird als Multi-Agenten-Lernproblem
modelliert, in dem jede Zelle versucht, ihre optimale Handlungsstrategie
(d.h. die optimale Anpassung der Antennenneigung) zu lernen. Die
entstehende Dynamik der Interaktion mehrerer Agenten macht die
Fragestellung interessant. Die Arbeit betrachtet verschiedene Aspekte des
Problems, wie beispielsweise den Unterschied zwischen egoistischen und
kooperativen Lernverfahren, verteiltem und zentralisiertem Lernen, sowie
die Auswirkungen einer gleichzeitigen Modifikation der Antennenneigung auf
verschiedenen Knoten und deren Effekt auf die Lerneffizienz.Die
Leistungsfähigkeit der betrachteten Verfahren wird mittels eine
LTE-Systemsimulators evaluiert. Dabei werden sowohl gleichmäßig verteilte
Zellen, als auch Zellen ungleicher Größe betrachtet. Die entwickelten
Ansätze werden mit bekannten Lösungen aus der Literatur verglichen. Die
Ergebnisse zeigen, dass die vorgeschlagenen Lösungen effektiv auf
Änderungen im Netzwerk und der Umgebung reagieren können. Zellen stellen
sich selbsttätig schnell auf Ausfälle und Inbetriebnahmen benachbarter
Systeme ein und passen ihre Antennenneigung geeignet an um die
Gesamtleistung des Netzes zu verbessern. Die vorgestellten Lernverfahren
erreichen eine bis zu 30 Prozent verbesserte Leistung als bereits bekannte
Ansätze. Die Verbesserungen steigen mit der Netzwerkgröße.The challenging task of cellular network planning and optimization will
become more and more complex because of the expected heterogeneity and
enormous number of cells required to meet the traffic demands of coming
years. Moreover, the spatio-temporal variations in the traffic patterns of
cellular networks require their coverage and capacity to be adapted
dynamically. The current network planning and optimization procedures are
highly manual, which makes them very time consuming and resource
inefficient. For these reasons, there is a strong interest in industry and
academics alike to enhance the degree of automation in network management.
Especially, the idea of Self-Organization (SO) is seen as the key to
simplified and efficient cellular network management by automating most of
the current manual procedures. In this thesis, we study the self-organized
coverage and capacity optimization of cellular mobile networks using
antenna tilt adaptations. Although, this problem is widely studied in
literature but most of the present work focuses on heuristic algorithms for
network planning tool automation. In our study we want to minimize this
reliance on these centralized tools and empower the network elements for
their own optimization. This way we can avoid the single point of failure
and scalability issues in the emerging heterogeneous and densely deployed
networks.In this thesis, we focus on Fuzzy Q-Learning (FQL), a machine
learning technique that provides a simple learning mechanism and an
effective abstraction level for continuous domain variables. We model the
coverage-capacity optimization as a multi-agent learning problem where each
cell is trying to learn its optimal action policy i.e. the antenna tilt
adjustments. The network dynamics and the behavior of multiple learning
agents makes it a highly interesting problem. We look into different
aspects of this problem like the effect of selfish learning vs. cooperative
learning, distributed vs. centralized learning as well as the effect of
simultaneous parallel antenna tilt adaptations by multiple agents and its
effect on the learning efficiency.We evaluate the performance of the
proposed learning schemes using a system level LTE simulator. We test our
schemes in regular hexagonal cell deployment as well as in irregular cell
deployment. We also compare our results to a relevant learning scheme from
literature. The results show that the proposed learning schemes can
effectively respond to the network and environmental dynamics in an
autonomous way. The cells can quickly respond to the cell outages and
deployments and can re-adjust their antenna tilts to improve the overall
network performance. Additionally the proposed learning schemes can achieve
up to 30 percent better performance than the available scheme from
literature and these gains increases with the increasing network size
Uavs path planning under a bi-objective optimization framework for smart cities
Unmanned aerial vehicles (UAVs) have been used extensively for search and rescue operations, surveillance, disaster monitoring, attacking terrorists, etc. due to their growing advantages of low-cost, high maneuverability, and easy deployability. This study proposes a mixed-integer programming model under a multi-objective optimization framework to design trajectories that enable a set of UAVs to execute surveillance tasks. The first objective maximizes the cumulative probability of target detection to aim for mission planning success. The second objective ensures minimization of cumulative path length to provide a higher resource utilization goal. A two-step variable neighborhood search (VNS) algorithm is offered, which addresses the combinatorial optimization issue for determining the near-optimal sequence for cell visiting to reach the target. Numerical experiments and simulation results are evaluated in numerous benchmark instances. Results demonstrate that the proposed approach can favorably support practical deployability purposes
Control of free-ranging automated guided vehicles in container terminals
Container terminal automation has come to the fore during the last 20 years to improve their efficiency. Whereas a high level of automation has already been achieved in vertical handling operations (stacking cranes), horizontal container transport still has disincentives to the adoption of automated guided vehicles (AGVs) due to a high degree of operational complexity of vehicles. This feature has led to the employment of simple AGV control techniques while hindering the vehicles to utilise their maximum operational capability. In AGV dispatching, vehicles cannot amend ongoing delivery assignments although they have yet to receive the corresponding containers. Therefore, better AGV allocation plans would be discarded that can only be achieved by task reassignment. Also, because of the adoption of predetermined guide paths, AGVs are forced to deploy a highly limited range of their movement abilities while increasing required travel distances for handling container delivery jobs. To handle the two main issues, an AGV dispatching model and a fleet trajectory planning algorithm are proposed. The dispatcher achieves job assignment flexibility by allowing AGVs towards to container origins to abandon their current duty and receive new tasks. The trajectory planner advances Dubins curves to suggest diverse optional paths per origin-destination pair. It also amends vehicular acceleration rates for resolving conflicts between AGVs. In both of the models, the framework of simulated annealing was applied to resolve inherent time complexity. To test and evaluate the sophisticated AGV control models for vehicle dispatching and fleet trajectory planning, a bespoke simulation model is also proposed. A series of simulation tests were performed based on a real container terminal with several performance indicators, and it is identified that the presented dispatcher outperforms conventional vehicle dispatching heuristics in AGV arrival delay time and setup travel time, and the fleet trajectory planner can suggest shorter paths than the corresponding Manhattan distances, especially with fewer AGVs.Open Acces
Performance analysis of biological resource allocation algorithms for next generation networks.
Masters Degree. University of KwaZulu-Natal, Durban.Abstract available in PDF.Publications listed on page iii
Coordinating Coupled Self-Organized Network Functions in Cellular Radio Networks
Nutzer der Mobilfunknetze wünschen und fordern eine Steigerung des
Datendurchsatzes, die zur Erhöhung der Netzlast führt. Besonders seit der
Einführung von LTE erhöht sich daher die Anzahl und Dichte der Zellen in
Mobilfunknetzen. Dies führt zusätzlich zur Zunahme der Investitions- und
Betriebskosten, sowie einer höheren Komplexität des Nerzbetriebs. Der
Einsatz selbstorganisierter Netze (SONs) wird vorgeschlagen, um diese drei
Herausforderungen zu bewältigen. Einige SON-Funktionen (SF) wurden sowohl
von Seiten der Netzbetreiber als auch von den Standardisierungsgremien
vorgeschlagen. Eine SF repräsentiert hierbei eine Netzfunktion, die
automatisiert werden kann. Ein Beispiel ist die Optimierung der Robustheit
des Netzes (Mobility Robustness Optimization, MRO) oder der Lastausgleich
zwischen Funkzellen (Mobility Load Balancing, MLB).
Die unterschiedlichen SON-Funktionen werden innerhalb eines Mobilfunknetzes
eingesetzt, wobei sie dabei häufig gleiche oder voneinander abhängige
Parameter optimieren. Zwangsläufig treten daher beim Einsatz paralleler
SON-Funktionen Konflikte auf, die Mechanismen erfordern, um diese
Konflikte aufzulösen oder zu minimieren. In dieser Dissertation werden
Lösungen aufgezeigt und untersucht, um die Koordination der SON-Funktionen
zu automatisieren und, soweit möglich, gleichmä{\ss}ig zu verteilen.
Im ersten Teil werden grundsätzliche Entwürfe für SFs evaluiert, um die
SON-Koordination zu vereinfachen. Basierend auf der Beobachtung, dass die
Steurung der SON-Funktion sich ähnlich dem generischen Q-Learning Problem
verhält, werden die SFs als Q-Learning-Agenten entworfen. Dieser Ansatz
wurde mit sehr positiven Ergebnissen auf zwei SFs (MRO und MLB) angewandt.
Die als Q-Learning-Agenten entworfenen SFs werden für zwei
unterschiedliche Ansätze der SON-Koordination evaluiert. Beide
Koordinierungsansätze betrachten dabei die SON-Umgebung als ein
Multi-Agenten-System. Der erste Ansatz basierend auf einer
räumlich-zeitlichen Entkoppelung separiert die Ausführung von
SF-Instanzen sowohl räumlich als auch zeitlich, um die Konflikte zwischen
den SF-Instanzen zu minimieren. Der zweite Ansatz wendet kooperatives
Lernen in Multi-Agenten-Systemen als automatisierten Lösungsansatz zur
SON-Koordination an. Die einzelnen SF-Instanzen lernen anhand von
Utility-Werten, die sowohl die eigenen Metriken als auch die Metriken der
Peer-SF-Instanzen auswerten. Die Intention dabei ist, durch die erlernte
Zustands-Aktions-Strategie Aktionen auszuführen, die das beste Resultat
für die aktive SF, aber auch die geringste Auswirkung auf Peer-SFs
gewährleisten. In der Evaluation des MRO-MLB-Konflikts zeigten beide
Koordinierungsansätze sehr gute Resultate.Owing to increase in desired user throughput and to the subsequent increase
in network traffic, the number and density of cells in cellular networks
have increased, especially starting with LTE. This directly translates into
higher capital and operational expenses as well as increased complexity of
network operation. To counter all three challenges, Self-Organized
Networks (SON) have been proposed. A number of SON Functions (SFs) have
been defined both from the network operator community as well as from the
standardization bodies. In this respect, a SF represents a network
function that can be automated e.g. Mobility Robustness Optimization (MRO)
or Mobility Load balancing (MLB).
The different SFs operate on the same radio network, in many cases
adjusting the same or related parameters. Conflicts are as such bound to
occur during the parallel operation of such SFs and mechanisms are required
to resolve or minimize the conflicts. This thesis studies the solutions
through which SON functions can be coordinated in an automated and
preferably distributed manner.
In the first part we evaluate the design principles of SFs that aim at
easing the coordination. With the observation that the SON control loop is
similar to a generic Q-learning problem, we propose designing SFs as
Q-learning agents. This framework is applied to two SFs (MRO and MLB) with
very positive results. Given the designed QL based SFs, we then
evaluate two SON coordination approaches that consider the SON environment
as a Multi-Agent System (MAS). The first approach based on
Spatial-Temporal Decoupling (STD) separates the execution of SF instances
in space and time so as to minimize the conflicts among instances. The
second approach applies multi-agent cooperative learning for an automated
solution towards SON coordination. In this case individual SF instances
learn based on utilities that aggregate their own metrics as well as the
metrics of peer SF instances. The intention in this case is to ensure that
the learned state-action policy functions apply actions that guarantee the
best result for the active SF but also have the least effect on the peer
SFs. Both coordination approaches have been evaluated with very positive
results in simulations that consider the MRO - MLB conflict
Recommended from our members
Adaptive Multiagent Traffic Management for Autonomous Robotic Systems
There is growing commercial interest in the use of unmanned aerial vehicles (UAVs) in urban environments, specifically for package delivery applications. However, the size, complexity and sheer numbers of expected UAVs makes conventional air traffic management that relies on human air traffic controllers infeasible. To enable UAVs to safely and efficiently operate in congested environments, it is essential to develop autonomous UAV management strategies.
We introduce a dynamic hierarchical traffic control model that reacts to traffic conditions instantaneously to reduce congestion in the airspace. An obstacle-filled airspace lends itself to a modelling as a graph structure similar to a road network. We introduce controller agents, which set costs across the airspace. These agents control traffic similarly to adaptive metering lights in highway traffic. UAVs then plan their paths based on the costs (e.g. conflicts, or delays) they see for traversing particular parts of the airspace. This provides us a decentralized method for reducing traffic in an airspace
Our hierarchical structure allows us to separate the traffic reduction problem from the individual robot navigation problem. Each robot does not explicitly coordinate with others in the airspace. Instead, robots execute their own individual internal cost-based planner to travel between locations. We then use neuro-evolution to provide incentives to these cost-based planners to reduce traffic in the environment.
Traffic quality can be expressed in several different ways. We first evaluate traffic our traffic reduction policies in terms of `conflicts', which characterizes situations where an aircraft comes too close to another for safety in a physical space. We then examine traffic in terms of the amount of `delay' that all agents incur, which assumes that there is a structure to ensure only a safe number of UAVs occupy the same area. Finally, we look at the total travel time that a UAV can expect to take from the moment it enters the airspace until the time it gets to its destination.
To facilitate an exploration of the UTM problem without waiting for a full simulation of UAVS running with A* , we develop an abstraction of the UTM domain that preserves the core UTM problem. We then investigate performance under differing levels of traffic, a well as two different agent structures. Our results show similar performance for both agent definitions, with delay reduction of up to 68% in high traffic cases.
With a fast version of the UTM problem, we explore the effect of redefining the control structure such that links, or edges of the UTM graph, set costs individually. This shifts the control paradigm toward controlling directional travel rather than areas in the space, as was the case with sector agents used in previous approaches. Due to our graph structure, we find that there are far more control elements in the link agent approach than in the sector agent approach. We identify a tradeoff; link agents give finer control, but the coordination problem for the sector agents is easier because there are fewer sector agents. This indicates that we can improve performance out of a more distributed link-based setup if we address the challenges of multiagent coordination. However, the UAV traffic management domain presents a uniquely difficult coordination problem; each agent's action can affect the perceived value of every other agent's actions. This means that there is an excessive amount of noise in the system, as another agent's action can have a lot of impact on the reward an agent receives.
We reduce the amount of multiagent noise by reducing the number of agents that are capable of learning. We identify that some agents have more ability to influence traffic based on the topology and traffic profile of the graph. This metric we call impactfulness. We use this metric to improve the learning by removing less impactful agents from the learning process, making a more stationary system in which the impactful agents can learn.
The contributions of this work are to:
- Introduce a cost-based traffic management approach that is platform-agnostic and fast to implement.
- Develop a multiagent approach to setting costs in this traffic management system that is adaptive to traffic conditions and learns long-term effects of management decisions.
- Create an abstraction of UAV traffic that captures key physical attributes, creating a fast and flexible simulation method.
- Quantify agent contributions to system performance by experimenting with single agent learning, single agent exclusion, and a sliding number of agents learning in the system.Keywords: Planning, UAV, Multiagen
A Multiobjective Computation Offloading Algorithm for Mobile Edge Computing
In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This paper studies the trade-off between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e. ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed.1) The problem-specific population initialization scheme uses a latency-based execution location initialization method to initialize the execution location (i.e. either local SMD or MEC server) for each task. 2) The dynamic voltage and frequency scaling based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and meta-heuristics in terms of the convergence and diversity of the obtained nondominated solutions
Optimizing city-scale traffic through modeling observations of vehicle movements
The capability of traffic-information systems to sense the movement of
millions of users and offer trip plans through mobile phones has enabled a new
way of optimizing city traffic dynamics, turning transportation big data into
insights and actions in a closed-loop and evaluating this approach in the real
world. Existing research has applied dynamic Bayesian networks and deep neural
networks to make traffic predictions from floating car data, utilized dynamic
programming and simulation approaches to identify how people normally travel
with dynamic traffic assignment for policy research, and introduced Markov
decision processes and reinforcement learning to optimally control traffic
signals. However, none of these works utilized floating car data to suggest
departure times and route choices in order to optimize city traffic dynamics.
In this paper, we present a study showing that floating car data can lead to
lower average trip time, higher on-time arrival ratio, and higher
Charypar-Nagel score compared with how people normally travel. The study is
based on optimizing a partially observable discrete-time decision process and
is evaluated in one synthesized scenario, one partly synthesized scenario, and
three real-world scenarios. This study points to the potential of a "living
lab" approach where we learn, predict, and optimize behaviors in the real
world