A highly adaptive distributed routing algorithm for mobile wireless networks

Park, V.D.; Corson, M. Scott

doi:10.1109/infcom.1997.631180

Cited by 1,497 publications

(747 citation statements)

References 17 publications

Supporting

Mentioning

726

Contrasting

Unclassified

Order By: Relevance

“…The basic PSO uses a real-valued multidimensional space as search space, and evolves the position of each particle in that space using ( 7) and ( 8).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…In order to allow the communication between stations that aren't directly connected, the networks establish routing protocols ad hoc as such LMR (Land Mobile Radio) [3], Link Reversal [4], DSR (Dynamic Source Routing) [5], OLRS (Optimized Link State Routing) [6], DSDV (Dynamic Destination Sequenced Distance-Vector Routing) [7], TORA (Temporally-Ordered Routing Algorithm) [8], or DFCN (Delayed Flooding with Cumulative Neighborhood) [9] to search routes towards a receptor. Hence, having a well-tuned broadcasting strategy will produce a major impact in network performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks

Perez-Perez

Luque

Cervantes

et al. 2007

2007 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

Abstract-A mobile adhoc network (MANETs) is a selfconfiguring network of mobile routers (and associated hosts). The routers tend to move randomly and organize themselves arbitrarily; thus, the network's wireless topology may change fast and unpredictably. Nowadays, these networks are having a great influence due to the fact that they can create networks without a specific infrastructure. In MANETs message broadcasting is critical to network existence and organization. The broadcasting strategy in MANETs can be optimized by defining a multiobjective problem whose inputs are the broadcasting algorithm's parameters and whose objectives are: reaching as many stations as possible, minimizing the network utilization, and reducing the makespan. The network can be simulated to obtain the expected response to a given set of parameters. In this paper, we face this multiobjective problem with two algorithms: Multiobjective Particle Swarm Optimization and ESN (Evolution Strategy with NSGAII). Both algorithms are able to find an accurate approximation to the Paretooptimal front that is the solution of the problem. ESN improves the results of MOPSO in terms of the set coverage and hypervolume metrics used for comparison.

show abstract

“…The basic PSO uses a real-valued multidimensional space as search space, and evolves the position of each particle in that space using ( 7) and ( 8).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks

Perez-Perez

Luque

Cervantes

et al. 2007

2007 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…Routing: The loop-free, distributed, and adaptive routing algorithm presented in [7] is designed to operate in highly dynamic environments. This source-initiated, temporally ordered, routing protocol focuses its route maintenance messages to only the few nodes next to the occurrence of a topological change.…”

Section: Transition From Wired To Wireless Networkmentioning

confidence: 99%

Accommodating Transient Connectivity in Ad Hoc and Mobile Settings

Handorean

Gill

Roman

2004

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Much of the work on networking and communications is based on the premise that components interact in one of two ways: either they are connected via a stable wired or wireless network, or they make use of persistent storage repositories accessible to the communicating parties. A new generation of networks raises serious questions about the validity of these fundamental assumptions. In mobile ad hoc wireless networks connections are transient and availability of persistent storage is rare. This paper is concerned with achieving communication among mobile devices that may never find themselves in direct or indirect contact with each other at any point in time. A unique feature of our contribution is the idea of exploiting information associated with the motion and availability profiles of the devices making up the ad hoc network. This is the starting point for an investigation into a range of possible solutions whose essential features are controlled by the manner in which motion profiles are acquired and the extent to which such knowledge is available across an ad hoc network.

show abstract

“…The temporally ordered routing algorithm (TORA) [3] uses a link-reversal algorithm [6] to maintain loop-free multipaths that are created by a query-reply process similar to that used in DSR and AODV. The limitation with TORA and similar approaches is that they require reliable exchanges among neighbors and coordination among nodes over multiple hops, which incurs more signaling overhead compared to AODV and DSR.…”

Section: Introductionmentioning

confidence: 99%

On-demand loop-free routing with link vectors

Garcia-Luna-Aceves

Roy

2005

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

Abstract-We present the on-demand link vector (OLIVE) protocol, a routing protocol for ad hoc networks based on linkstate information that is free of routing loops and supports destination-based packet forwarding. Routers exchange routing information reactively for each destination in the form of complete paths, and each node creates a labeled source graph based on the paths advertised by its neighbors. A node originates a broadcast route request (RREQ) to obtain a route for a destination for which a complete path does not exist in its source graph. When the original path breaks, a node can select an alternative path based on information reported by neighbors, and a node can send a unicast RREQ to verify that the route is still active. A node that cannot find any alternate path to a destination sends route errors reliably to those neighbors that were using it as next hop to the destination. Using simulation experiments in ns2, OLIVE is shown to outperform dynamic source routing, ad hoc on-demand distance vector, optimized link-state routing protocol, and topology broadcast based on reverse-path forwarding, in terms of control overhead, throughput, and average network delay, while maintaining loop-free routing with no need for source routes.Index Terms-Ad hoc networks, on-demand, link-state routing, loop-freedom.

show abstract

A highly adaptive distributed routing algorithm for mobile wireless networks

Abstract: We

Cited by 1,497 publications

References 17 publications

Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks

Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks

Accommodating Transient Connectivity in Ad Hoc and Mobile Settings

On-demand loop-free routing with link vectors

Contact Info

Product

Resources

About