1,289 research outputs found
Bounding inconsistency using a novel threshold metric for dead reckoning update packet generation
Human-to-human interaction across distributed applications requires that sufficient consistency be maintained among participants in the face of network characteristics such as latency and limited bandwidth. The level of inconsistency arising from the network is proportional to the network delay, and thus a function of bandwidth consumption. Distributed simulation has often used a bandwidth reduction technique known as dead reckoning that combines approximation and estimation in the communication of entity movement to reduce network traffic, and thus improve consistency. However, unless carefully tuned to application and network characteristics, such an approach can introduce more inconsistency than it avoids. The key tuning metric is the distance threshold. This paper questions the suitability of the standard distance threshold as a metric for use in the dead reckoning scheme. Using a model relating entity path curvature and inconsistency, a major performance related limitation of the distance threshold technique is highlighted. We then propose an alternative time—space threshold criterion. The time—space threshold is demonstrated, through simulation, to perform better for low curvature movement. However, it too has a limitation. Based on this, we further propose a novel hybrid scheme. Through simulation and live trials, this scheme is shown to perform well across a range of curvature values, and places bounds on both the spatial and absolute inconsistency arising from dead reckoning
Mobile Online Gaming via Resource Sharing
Mobile gaming presents a number of main issues which remain open. These are
concerned mainly with connectivity, computational capacities, memory and
battery constraints. In this paper, we discuss the design of a fully
distributed approach for the support of mobile Multiplayer Online Games (MOGs).
In mobile environments, several features might be exploited to enable resource
sharing among multiple devices / game consoles owned by different mobile users.
We show the advantages of trading computing / networking facilities among
mobile players. This operation mode opens a wide number of interesting sharing
scenarios, thus promoting the deployment of novel mobile online games. In
particular, once mobile nodes make their resource available for the community,
it becomes possible to distribute the software modules that compose the game
engine. This allows to distribute the workload for the game advancement
management. We claim that resource sharing is in unison with the idea of ludic
activity that is behind MOGs. Hence, such schemes can be profitably employed in
these contexts.Comment: Proceedings of 3nd ICST/CREATE-NET Workshop on DIstributed SImulation
and Online gaming (DISIO 2012). In conjunction with SIMUTools 2012.
Desenzano, Italy, March 2012. ISBN: 978-1-936968-47-
Dead Reckoning Using Play Patterns in a Simple 2D Multiplayer Online Game
In today’s gaming world, a player expects the same play experience whether playing on a local network or online with many geographically distant players on congested networks. Because of delay and loss, there may be discrepancies in the simulated environment from player to player, likely resulting in incorrect perception of events. It is desirable to develop methods that minimize this problem. Dead reckoning is one such method. Traditional dead reckoning schemes typically predict a player’s position linearly by assuming players move with constant force or velocity. In this paper, we consider team-based 2D online action games. In such games, player movement is rarely linear. Consequently, we implemented such a game to act as a test harness we used to collect a large amount of data from playing sessions involving a large number of experienced players. From analyzing this data, we identified play patterns, which we used to create three dead reckoning algorithms. We then used an extensive set of simulations to compare our algorithms with the IEEE standard dead reckoning algorithm and with the recent “Interest Scheme” algorithm. Our results are promising especially with respect to the average export error and the number of hits
Using Neural Networks to Reduce Entity State Updates in Distributed Interactive Applications
Dead reckoning is the most commonly used predictive
contract mechanism for the reduction of network traffic in
Distributed Interactive Applications (DIAs). However,
this technique often ignores available contextual
information that may be influential to the state of an
entity, sacrificing remote predictive accuracy in favour of
low computational complexity. In this paper, we present a
novel extension of dead reckoning by employing neuralnetworks
to take into account expected future entity
behaviour during the transmission of entity state updates
(ESUs) for remote entity modeling in DIAs. This
proposed method succeeds in reducing network traffic
through a decrease in the frequency of ESU transmission
required to maintain consistency. Validation is achieved
through simulation in a highly interactive DIA, and results
indicate significant potential for improved scalability
when compared to the use of the IEEE DIS Standard dead
reckoning technique. The new method exhibits relatively
low computational overhead and seamless integration with
current dead reckoning schemes
An Information-Theoretic Framework for Consistency Maintenance in Distributed Interactive Applications
Distributed Interactive Applications (DIAs) enable geographically dispersed users
to interact with each other in a virtual environment. A key factor to the success
of a DIA is the maintenance of a consistent view of the shared virtual world for
all the participants. However, maintaining consistent states in DIAs is difficult
under real networks. State changes communicated by messages over such networks
suffer latency leading to inconsistency across the application. Predictive Contract
Mechanisms (PCMs) combat this problem through reducing the number of messages
transmitted in return for perceptually tolerable inconsistency. This thesis examines
the operation of PCMs using concepts and methods derived from information theory.
This information theory perspective results in a novel information model of PCMs
that quantifies and analyzes the efficiency of such methods in communicating the
reduced state information, and a new adaptive multiple-model-based framework for
improving consistency in DIAs.
The first part of this thesis introduces information measurements of user behavior
in DIAs and formalizes the information model for PCM operation. In presenting the
information model, the statistical dependence in the entity state, which makes using
extrapolation models to predict future user behavior possible, is evaluated. The
efficiency of a PCM to exploit such predictability to reduce the amount of network
resources required to maintain consistency is also investigated. It is demonstrated
that from the information theory perspective, PCMs can be interpreted as a form
of information reduction and compression.
The second part of this thesis proposes an Information-Based Dynamic Extrapolation
Model for dynamically selecting between extrapolation algorithms based on
information evaluation and inferred network conditions. This model adapts PCM
configurations to both user behavior and network conditions, and makes the most
information-efficient use of the available network resources. In doing so, it improves
PCM performance and consistency in DIAs
Using User Perception to Determine Suitable Error Thresholds for Dead Reckoning in Distributed Interactive Applications
Entity state update mechanisms are readily employed in Distributed
Interactive Applications (DIAs), particularly in networked games.
These mechanisms use prediction techniques in order to reduce the
number of update packets sent across the network, while
maintaining a high level of consistency from the remote user’s point
of view. These mechanisms only send update packets when the local
user’s actual behaviour differs from the predictive behaviour by a
certain value, often referred to as the error threshold. In practice,
this value is arbitrarily chosen and typically reflects what ‘appears’
to be suitable. It has been illustrated in various other media that
psycho-perceptual measures can be used to greatly improve
compression techniques, while maintaining satisfactory end-user
experience. The best example of this is the MP3 compression used in
audio. This paper describes a preliminary study designed to collect
information relating to a subject’s perception of a networked
computer game. The main motivation behind this work is to
investigate if psycho-perceptual measures can be used to obtain
appropriate error threshold measures for entity state prediction
mechanisms. Here, we employ Dead Reckoning as it is the simplest
and most commonly used of these mechanisms in distributed
gaming. The experiment outlined in this paper attempts to
determine if an error threshold can be chosen from the users’
perception, where user feedback is determined via linguistic
variables. Furthermore, the effects of convergence, the speed of the
entity and the shape of the entity trajectory are also examined from
a psycho-perceptual viewpoint. The results are presented within
Multistep-Ahead Neural-Network Predictors for Network Traffic Reduction in Distributed Interactive Applications
Predictive contract mechanisms such as dead reckoning are widely employed to support scalable
remote entity modeling in distributed interactive applications (DIAs). By employing a form of
controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the
distribution of instantaneous derivative information, dead reckoning trades remote extrapolation
accuracy for low computational complexity and ease-of-implementation. In this article, we present
a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of
instantaneous velocity information with predictive velocity information in order to improve the
accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning
approach, each controlling host employs a bank of neural network predictors trained to estimate
future changes in entity velocity up to and including some maximum prediction horizon. The effect
of each estimated change in velocity on the current entity position is simulated to produce an
estimate for the likely position of the entity over some short time-span. Upon detecting an error
threshold violation, the controlling host transmits a predictive velocity vector that extrapolates
through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such
an approach succeeds in reducing the spatial error associated with remote extrapolation of entity
state. Consequently, a further reduction in network traffic can be achieved. Simulation results
conducted using several human users in a highly interactive DIA indicate significant potential
for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our
proposed neuro-reckoning framework exhibits low computational resource overhead for real-time
use and can be seamlessly integrated into many existing dead reckoning mechanisms
Multistep-Ahead Neural-Network Predictors for Network Traffic Reduction in Distributed Interactive Applications
Predictive contract mechanisms such as dead reckoning are widely employed to support scalable
remote entity modeling in distributed interactive applications (DIAs). By employing a form of
controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the
distribution of instantaneous derivative information, dead reckoning trades remote extrapolation
accuracy for low computational complexity and ease-of-implementation. In this article, we present
a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of
instantaneous velocity information with predictive velocity information in order to improve the
accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning
approach, each controlling host employs a bank of neural network predictors trained to estimate
future changes in entity velocity up to and including some maximum prediction horizon. The effect
of each estimated change in velocity on the current entity position is simulated to produce an
estimate for the likely position of the entity over some short time-span. Upon detecting an error
threshold violation, the controlling host transmits a predictive velocity vector that extrapolates
through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such
an approach succeeds in reducing the spatial error associated with remote extrapolation of entity
state. Consequently, a further reduction in network traffic can be achieved. Simulation results
conducted using several human users in a highly interactive DIA indicate significant potential
for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our
proposed neuro-reckoning framework exhibits low computational resource overhead for real-time
use and can be seamlessly integrated into many existing dead reckoning mechanisms
An Information-Based Dynamic Extrapolation Model for Networked Virtual Environments
Various Information Management techniques have been developed to help maintain a consistent shared virtual world in a
Networked Virtual Environment. However, such techniques have to be carefully adapted to the application state dynamics and
the underlying network. This work presents a novel framework that minimizes inconsistency by optimizing bandwidth usage to
deliver useful information. This framework measures the state evolution using an information model and dynamically switches
extrapolation models and the packet rate to make the most information-efficient usage of the available bandwidth. The results
shown demonstrate that this approach can help optimize consistency under constrained and time-varying network conditions
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