971 research outputs found
Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks
This article is posted here with permission of IEEE - Copyright @ 2010 IEEEIn recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.This work was supported by the Engineering
and Physical Sciences Research Council of U.K. underGrant EP/E060722/
Impact of network structure on the capacity of wireless multihop ad hoc communication
As a representative of a complex technological system, so-called wireless
multihop ad hoc communication networks are discussed. They represent an
infrastructure-less generalization of todays wireless cellular phone networks.
Lacking a central control authority, the ad hoc nodes have to coordinate
themselves such that the overall network performs in an optimal way. A
performance indicator is the end-to-end throughput capacity.
Various models, generating differing ad hoc network structure via differing
transmission power assignments, are constructed and characterized. They serve
as input for a generic data traffic simulation as well as some semi-analytic
estimations. The latter reveal that due to the most-critical-node effect the
end-to-end throughput capacity sensitively depends on the underlying network
structure, resulting in differing scaling laws with respect to network size.Comment: 30 pages, to be published in Physica
Deep Reinforcement Learning for Scheduling and Power Allocation in a 5G Urban Mesh
We study the problem of routing and scheduling of real-time flows over a
multi-hop millimeter wave (mmWave) mesh. We develop a model-free deep
reinforcement learning algorithm that determines which subset of the mmWave
links should be activated during each time slot and using what power level. The
proposed algorithm, called Adaptive Activator RL (AARL), can handle a variety
of network topologies, network loads, and interference models, as well as adapt
to different workloads. We demonstrate the operation of AARL on several
topologies: a small topology with 10 links, a moderately-sized mesh with 48
links, and a large topology with 96 links. For each topology, the results of
AARL are compared to those of a greedy scheduling algorithm. AARL is shown to
outperform the greedy algorithm in two aspects. First, its schedule obtains
higher goodput. Second, and even more importantly, while the run time of the
greedy algorithm renders it impractical for real-time scheduling, the run time
of AARL is suitable for meeting the time constraints of typical 5G networks
A Distributed Scheduling Algorithm to Provide Quality-of-Service in Multihop Wireless Networks
Control of multihop Wireless networks in a distributed manner while providing
end-to-end delay requirements for different flows, is a challenging problem.
Using the notions of Draining Time and Discrete Review from the theory of fluid
limits of queues, an algorithm that meets delay requirements to various flows
in a network is constructed. The algorithm involves an optimization which is
implemented in a cyclic distributed manner across nodes by using the technique
of iterative gradient ascent, with minimal information exchange between nodes.
The algorithm uses time varying weights to give priority to flows. The
performance of the algorithm is studied in a network with interference modelled
by independent sets
Delay-aware Backpressure Routing Using Graph Neural Networks
We propose a throughput-optimal biased backpressure (BP) algorithm for
routing, where the bias is learned through a graph neural network that seeks to
minimize end-to-end delay. Classical BP routing provides a simple yet powerful
distributed solution for resource allocation in wireless multi-hop networks but
has poor delay performance. A low-cost approach to improve this delay
performance is to favor shorter paths by incorporating pre-defined biases in
the BP computation, such as a bias based on the shortest path (hop) distance to
the destination. In this work, we improve upon the widely-used metric of hop
distance (and its variants) for the shortest path bias by introducing a bias
based on the link duty cycle, which we predict using a graph convolutional
neural network. Numerical results show that our approach can improve the delay
performance compared to classical BP and existing BP alternatives based on
pre-defined bias while being adaptive to interference density. In terms of
complexity, our distributed implementation only introduces a one-time overhead
(linear in the number of devices in the network) compared to classical BP, and
a constant overhead compared to the lowest-complexity existing bias-based BP
algorithms.Comment: 5 pages, 5 figures, submitted to IEEE ICASSP 202
Routing Design Issues in Heterogeneous Wireless Sensor Network
WSN has important applications such as habitat monitoring, structural health monitoring, target tracking in military and many more. This has evolved due to availability of sensors that are cheaper and intelligent but these are having battery support. So, one of the major issues in WSN is maximization of network life. Heterogeneous WSNs have the potential to improve network lifetime and also provide higher quality networking and system services than the homogeneous WSN. Routing is the main concern of energy consumption in WSN. Previous research shows that performance of the network can be improve significantly using protocol of hierarchical HWSN. However, the appropriateness of a particular routing protocol mainly depends on the capabilities of the nodes and on the application requirements. This study presents different aspects of Heterogeneous Wireless Sensor network and design issues for routing in heterogeneous environment. Different perspectives from different authors regarding energy efficiency based on resource heterogeneity for heterogeneous wireless sensor networks have been presented
Performance Analysis of On-Demand Routing Protocols in Wireless Mesh Networks
Wireless Mesh Networks (WMNs) have recently gained a lot of popularity due to their rapid deployment and instant communication capabilities. WMNs are dynamically self-organizing, self-configuring and self-healing with the nodes in the network automatically establishing an adiej hoc network and preserving the mesh connectivity. Designing a routing protocol for WMNs requires several aspects to consider, such as wireless networks, fixed applications, mobile applications, scalability, better performance metrics, efficient routing within infrastructure, load balancing, throughput enhancement, interference, robustness etc. To support communication, various routing protocols are designed for various networks (e.g. ad hoc, sensor, wired etc.). However, all these protocols are not suitable for WMNs, because of the architectural differences among the networks. In this paper, a detailed simulation based performance study and analysis is performed on the reactive routing protocols to verify the suitability of these protocols over such kind of networks. Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR) and Dynamic MANET On-demand (DYMO) routing protocol are considered as the representative of reactive routing protocols. The performance differentials are investigated using varying traffic load and number of source. Based on the simulation results, how the performance of each protocol can be improved is also recommended.Wireless Mesh Networks (WMNs), IEEE 802.11s, AODV, DSR, DYMO
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