82,116 research outputs found
Hybrid performance modelling of opportunistic networks
We demonstrate the modelling of opportunistic networks using the process
algebra stochastic HYPE. Network traffic is modelled as continuous flows,
contact between nodes in the network is modelled stochastically, and
instantaneous decisions are modelled as discrete events. Our model describes a
network of stationary video sensors with a mobile ferry which collects data
from the sensors and delivers it to the base station. We consider different
mobility models and different buffer sizes for the ferries. This case study
illustrates the flexibility and expressive power of stochastic HYPE. We also
discuss the software that enables us to describe stochastic HYPE models and
simulate them.Comment: In Proceedings QAPL 2012, arXiv:1207.055
On Leveraging Partial Paths in Partially-Connected Networks
Mobile wireless network research focuses on scenarios at the extremes of the
network connectivity continuum where the probability of all nodes being
connected is either close to unity, assuming connected paths between all nodes
(mobile ad hoc networks), or it is close to zero, assuming no multi-hop paths
exist at all (delay-tolerant networks). In this paper, we argue that a sizable
fraction of networks lies between these extremes and is characterized by the
existence of partial paths, i.e. multi-hop path segments that allow forwarding
data closer to the destination even when no end-to-end path is available. A
fundamental issue in such networks is dealing with disruptions of end-to-end
paths. Under a stochastic model, we compare the performance of the established
end-to-end retransmission (ignoring partial paths), against a forwarding
mechanism that leverages partial paths to forward data closer to the
destination even during disruption periods. Perhaps surprisingly, the
alternative mechanism is not necessarily superior. However, under a stochastic
monotonicity condition between current v.s. future path length, which we
demonstrate to hold in typical network models, we manage to prove superiority
of the alternative mechanism in stochastic dominance terms. We believe that
this study could serve as a foundation to design more efficient data transfer
protocols for partially-connected networks, which could potentially help
reducing the gap between applications that can be supported over disconnected
networks and those requiring full connectivity.Comment: Extended version of paper appearing at IEEE INFOCOM 2009, April
20-25, Rio de Janeiro, Brazi
Adaptive stochastic radio access selection scheme for cellular-WLAN heterogeneous communication systems
This study proposes a novel adaptive stochastic radio access selection scheme for mobile users in heterogeneous cellular-wireless local area network (WLAN) systems. In this scheme, a mobile user located in dual coverage area randomly selects WLAN with probability of ω when there is a need for downloading a chunk of data. The value of ω is optimised according to the status of both networks in terms of network load and signal quality of both cellular and WLAN networks. An analytical model based on continuous time Markov chain is proposed to optimise the value of ω and compute the performance of proposed scheme in terms of energy efficiency, throughput, and call blocking probability. Both analytical and simulation results demonstrate the superiority of the proposed scheme compared with the mainstream network selection schemes: namely, WLAN-first and load balancing
Incremental Stochastic Subgradient Algorithms for Convex Optimization
In this paper we study the effect of stochastic errors on two constrained
incremental sub-gradient algorithms. We view the incremental sub-gradient
algorithms as decentralized network optimization algorithms as applied to
minimize a sum of functions, when each component function is known only to a
particular agent of a distributed network. We first study the standard cyclic
incremental sub-gradient algorithm in which the agents form a ring structure
and pass the iterate in a cycle. We consider the method with stochastic errors
in the sub-gradient evaluations and provide sufficient conditions on the
moments of the stochastic errors that guarantee almost sure convergence when a
diminishing step-size is used. We also obtain almost sure bounds on the
algorithm's performance when a constant step-size is used. We then consider
\ram{the} Markov randomized incremental subgradient method, which is a
non-cyclic version of the incremental algorithm where the sequence of computing
agents is modeled as a time non-homogeneous Markov chain. Such a model is
appropriate for mobile networks, as the network topology changes across time in
these networks. We establish the convergence results and error bounds for the
Markov randomized method in the presence of stochastic errors for diminishing
and constant step-sizes, respectively
The Strength of Arcs and Edges in Interaction Networks: Elements of a Model-Based Approach
When analyzing interaction networks, it is common to interpret the amount of
interaction between two nodes as the strength of their relationship. We argue
that this interpretation may not be appropriate, since the interaction between
a pair of nodes could potentially be explained only by characteristics of the
nodes that compose the pair and, however, not by pair-specific features. In
interaction networks, where edges or arcs are count-valued, the above scenario
corresponds to a model of independence for the expected interaction in the
network, and consequently we propose the notions of arc strength, and edge
strength to be understood as departures from this model of independence. We
discuss how our notion of arc/edge strength can be used as a guidance to study
network structure, and in particular we develop a latent arc strength
stochastic blockmodel for directed interaction networks. We illustrate our
approach studying the interaction between the Kolkata users of the myGamma
mobile network.Comment: 23 pages, 5 figures, 4 table
On Space-Time Capacity Limits in Mobile and Delay Tolerant Networks
We investigate the fundamental capacity limits of space-time journeys of
information in mobile and Delay Tolerant Networks (DTNs), where information is
either transmitted or carried by mobile nodes, using store-carry-forward
routing. We define the capacity of a journey (i.e., a path in space and time,
from a source to a destination) as the maximum amount of data that can be
transferred from the source to the destination in the given journey. Combining
a stochastic model (conveying all possible journeys) and an analysis of the
durations of the nodes' encounters, we study the properties of journeys that
maximize the space-time information propagation capacity, in bit-meters per
second. More specifically, we provide theoretical lower and upper bounds on the
information propagation speed, as a function of the journey capacity. In the
particular case of random way-point-like models (i.e., when nodes move for a
distance of the order of the network domain size before changing direction), we
show that, for relatively large journey capacities, the information propagation
speed is of the same order as the mobile node speed. This implies that,
surprisingly, in sparse but large-scale mobile DTNs, the space-time information
propagation capacity in bit-meters per second remains proportional to the
mobile node speed and to the size of the transported data bundles, when the
bundles are relatively large. We also verify that all our analytical bounds are
accurate in several simulation scenarios.Comment: Part of this work will be presented in "On Space-Time Capacity Limits
in Mobile and Delay Tolerant Networks", P. Jacquet, B. Mans and G. Rodolakis,
IEEE Infocom, 201
Modelling and performance analysis of mobile ad hoc networks
PhD ThesisMobile Ad hoc Networks (MANETs) are becoming very attractive and useful in many kinds of communication and networking applications. This is due to their efficiency, relatively low cost, and flexibility provided by their dynamic infrastructure. Performance evaluation of mobile ad hoc networks is needed to compare various architectures of the network for their performance, study the effect of varying certain network parameters and study the interaction between various parameters that characterise the network. It can help in the design and implementation of MANETs.
It is to be noted that most of the research that studies the performance of MANETs were evaluated using discrete event simulation (DES) utilising a broad band of network simulators. The principle drawback of DES models is the time and resources needed to run such models for large realistic systems, especially when results with a high accuracy are desired. In addition, studying typical problems such as the deadlock and concurrency in MANETs using DES is hard because network simulators implement the network at a low abstraction level and cannot support specifications at higher levels.
Due to the advantage of quick construction and numerical analysis, analytical modelling techniques, such as stochastic Petri nets and process algebra, have been used for performance analysis of communication systems. In addition, analytical modelling is a less costly and more efficient method. It generally provides the best insight into the effects of various parameters and their interactions. Hence, analytical modelling is the method of choice for a fast and cost effective evaluation of mobile ad hoc networks.
To the best of our knowledge, there is no analytical study that analyses the performance of multi-hop ad hoc networks, where mobile nodes move according to a random mobility model, in terms of the end-to-end delay and throughput. This work
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presents a novel analytical framework developed using stochastic reward nets and mathematical modelling techniques for modelling and analysis of multi-hop ad hoc networks, based on the IEEE 802.11 DCF MAC protocol, where mobile nodes move according to the random waypoint mobility model. The proposed framework is used to analysis the performance of multi-hop ad hoc networks as a function of network parameters such as the transmission range, carrier sensing range, interference range, number of nodes, network area size, packet size, and packet generation rate.
The proposed framework is organized into several models to break up the complexity of modelling the complete network and make it easier to analyse each model as required. This is based on the idea of decomposition and fixed point iteration of stochastic reward nets. The proposed framework consists of a mathematical model and four stochastic reward nets models; the path analysis model, data link layer model, network layer model and transport layer model. These models are arranged in a way similar to the layers of the OSI protocol stack model.
The mathematical model is used to compute the expected number of hops between any source-destination pair; and the average number of carrier sensing, hidden, and interfering nodes. The path analysis model analyses the dynamic of paths in the network due to the node mobility in terms of the path connection availability and rate of failure and repair. The data link layer model describes the behaviour of the IEEE 802.11 DCF MAC protocol. The actions in the network layer are modelled by the network layer model. The transport layer model represents the behaviour of the transport layer protocols. The proposed models are validated using extensive simulations
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