Bio-inspired group mobility model for mobile ad hoc networks based on bird-flocking behavior

Misra, Sudip; Agarwal, Prateek

doi:10.1007/s00500-011-0728-x

Cited by 29 publications

(15 citation statements)

References 23 publications

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“…Inspired from the mobility of a flock of birds, Misra and Agarwal (2012) proposes a group mobility model. Wanalertlak et al (2011) presents a behavior-based mobility prediction scheme in IEEE 802.11 networks, which captures short-term and periodic behavior of mobile users to provide next-cell predictions, and eliminates the incurred scanning overhead to reduce the handoff latency.…”

Section: Related Workmentioning

confidence: 99%

SmartHO: mobility pattern recognition assisted intelligent handoff in wireless overlay networks

et al. 2015

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Handoff management providing continuous mobility services is important for end-users in wireless overlay networks. The existing studies focus on improving the handoff performance with little attention to determine the current mobility pattern to provide different advanced services for end-users, and the handoff process is not intelligent as well. Due to the randomness and fuzziness of human mobility, predicting the mobility is still an open issue. In this paper, a mobility pattern recognition assisted intelligent handoff (SmartHO) is proposed. By exploring the regularity and rationality in the seemingly random daily itinerary and analyzing the typical mobility patterns, a spatial-temporal mobility pattern model is firstly constructed, and the mobility pattern recognition algorithm based on the joint space-time correlation then is designed. In different handoff cases, the specific handoff optimization strategies corresponding to the mobility patterns are employed to make the handoff process more intelligent. With the configurations from the authors real life, the simulations conducted on the NS-2 platform Communicated by

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Section: Related Workmentioning

confidence: 99%

SmartHO: mobility pattern recognition assisted intelligent handoff in wireless overlay networks

et al. 2015

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“…Therefore, following a known map of the area could not be sufficient to successfully perform search and rescue operations. The bird-flocking behaviour inspired group mobility (BFBIGM) [15] takes inspiration from the mobility of bird flocks, flying in group coordination. In this model, several distinct groups of nodes may exist (flocks), but all have the same destination, which is a common randomly defined target.…”

Section: Group Mobility Modelsmentioning

confidence: 99%

Modelling Mobility Based on Obstacle-Aware Human Behaviour in Disaster Areas

Conceição

Curado

2014

Wireless Pers Commun

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Mobility models are used to mimic the realistic movement of entities. Mobility models for wireless networks feature different objectives and characteristics, most based on random behaviour. However, random based mobility models, such as the random waypoint, are often not suitable to represent the reality of node mobility, particularly in disaster areas where the search time for victims is a critical factor. This work studies the main existent mobility models able to suit disaster environments, according to specifically identified requirements. Moreover, a formal specification of the human behaviour for disaster areas (HBDA) mobility model is presented and an obstacle avoidance mechanism is introduced. Obstacle-aware HBDA is evaluated and compared with two existent mobility models, regarding its movement and network performances in different types of scenarios with variable network size and obstacle density. Obtained results show that obstacle-aware HBDA provides an even distribution of nodes, a efficient area coverage and a good transmission rate.

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“…They found out that human mobility is characterized by a time-independent travel distance and that people travel to a few highly frequented locations. In [10], the authors propose a mobility model based on the flocking behavior of birds in order to model a realistic movement of groups of mobile entities in mobile ad-hoc networks (MANETs). Similar to that Morlot et al [11] propose an interaction-based mobility model for hot spots, i.e., their Fig.…”

Section: Mobility Modelsmentioning

confidence: 99%

Trajectory estimation based on mobile network operator data for cellular network simulations

Ostermayer

Kieslich

Lindorfer

2016

J Wireless Com Network

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In this paper, we present a framework for estimating trajectories of cellular networks users based on mobile network operator data. We use handover and location area update events of both speech and packet data users captured in the core network of the Austrian MNO A1 to estimate the subscribers' mobility behavior. By utilizing publicly available data, i.e., environmental information, road infrastructure data, transmitter power ranges and antenna characteristics, our approach allows the estimation of subscriber trajectories for both urban and semi-rural environments with a good accordance to the actual trajectories. Additionally, we present a method to estimate a particular subscriber's movement velocity, on the basis of mentioned data. Furthermore, we propose a methodology to estimate when a particular user started or ended a speech or packet data session during his journey, based on mobility-related network events. With this, our framework enables the creation of reproducible mobility situations for cellular network simulations at system level.

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Bio-inspired group mobility model for mobile ad hoc networks based on bird-flocking behavior

Cited by 29 publications

References 23 publications

SmartHO: mobility pattern recognition assisted intelligent handoff in wireless overlay networks

SmartHO: mobility pattern recognition assisted intelligent handoff in wireless overlay networks

Modelling Mobility Based on Obstacle-Aware Human Behaviour in Disaster Areas

Trajectory estimation based on mobile network operator data for cellular network simulations

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