Mobility Model Characteristics

Roy, Radhika Ranjan

doi:10.1007/978-1-4419-6050-4_2

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…is model is not suitable for aircraft due to the rapid change in speed and destination. It is not based on the history of the previous movement and neighbor UAVs [29]. (b) Random walk Random walk (RW) mobility model was first time introduced in 1905 by Pearson [30].…”

Section: Pure Randomized Mobility Modelsmentioning

confidence: 99%

“…is model is memory less and independent from the previous movement and neighbor behavior of UAVs. In some environments, it fails like during mission, the mobile UAVs movement pattern may be constrained due to CHs and its member UAVs [29]. (c) Random direction e mobility model, random direction (RD), was introduced to resolve the issue in the random 4 Complexity waypoint mobility model, i.e., only focusing UAVs in the center part of simulation area [31].…”

Section: Pure Randomized Mobility Modelsmentioning

confidence: 99%

“…e mobility to each member UAV is assigned with a reference point (i.e., CH) that follows the group movement with a predefined path. e member UAVs are randomly placed in cluster range [29]. e applications of this category are environmental sensing and search and rescue operations.…”

Section: Reference Point Group Mobility Modelsmentioning

confidence: 99%

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Bioinspired Mobility-Aware Clustering Optimization in Flying Ad Hoc Sensor Network for Internet of Things: BIMAC-FASNET

2020

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Flying ad hoc sensor network (FASNET) for Internet of Things (IoT) consists of multiple unmanned aerial vehicles (multi-UAVs) with high mobility, quick changes in topology, and diverse direction. The flying multi-UAVs were operated remotely by human beings or automatically by an onboard system. The applications of multi-UAVs are remote sensing, tracking, observing, and monitoring. It has a different nature compared to ordinary ad hoc network. The speed and diverse directions of multi-UAVs make it harder to route information in a desired way. Different issues may arise due to differences in unmanned aerial vehicle mobility, speed, diverse direction, and quick changes in topology. The researchers proposed conventional ad hoc routing protocols which has poor aspects for the flying ad hoc networks. They tried to tackle the issue by using the clustering approach that divides the network structure into multiple clusters, each with its own cluster head (CH). During the selection of CH and balance cluster formation, they consider only location awareness, neighborhood range, residual energy, and connection to the base station (BS) while ignoring the multi-UAVs distance, speed, direction, degree, and communication load. In this paper, we proposed bioinspired mobility-aware clustering optimization scheme based on bee intelligence foraging behavior for routing, considering relative mobility, residual energy, degree, and communication load during CH selection and balanced cluster formation. First, the clustering problem in network is formulated to dynamic optimization problem. An algorithm is designed based on bee intelligence, applied to select optimal UAVs CH and balanced cluster. The simulation results show that the proposed BIMAC-FASNET scheme performs better among existing clustering protocols in terms of link-connection lifetime, reaffiliation rate, communication load, number of UAVs per cluster, CH lifetime, and cluster formation time.

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Section: Pure Randomized Mobility Modelsmentioning

confidence: 99%

Section: Pure Randomized Mobility Modelsmentioning

confidence: 99%

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Bioinspired Mobility-Aware Clustering Optimization in Flying Ad Hoc Sensor Network for Internet of Things: BIMAC-FASNET

2020

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“…It allows interconnection of mobile nodes within communication range without the aid of permanent infrastructure. A mobility model dictates the movement of mobile nodes in terms of node position, speed, and direction over a certain period [2]. The mobility model describes the characteristics of mobile nodes in the dynamic network topology.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Performance of Geographical Routing Protocol in Highly Dynamic Mobility and Mobility Patterns

Palanisamy¹,

Muralibhaskaran²

2014

IJCA

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In geographical routing, each node takes the data forwarding decision based on the location information of its one-hop neighbors. Dissemination of location information is the key function of the geographic routing. The location error due to the node mobility models has direct impact on the geographical routing. Node mobility is a great challenge to the beacon based location broadcasting schemes. The general mechanism to optimize the local topology information according to the node mobility is Proactive beaconing schemes support specific mobility model, but it does not perform well under various mobility models. However, it is difficult to capture the changes in the network topology and fit the beaconing approach correctly to the various mobility models. This paper proposes the Context Aware Mobility Adaptive Location Update (CAALU) scheme which contains Distance and Traffic based Location update (DTL), Band Prediction (BP), and Conditional Location Update on Overhearing (CLUO). These schemes make CAALU more flexible to various mobility models. For supporting the mobility models, the DTL dynamically adjusts the beaconing interval based on the node distance and traffic. For simplification, it divides the node transmission area into inband and out-band region, and each node continuously exchange its location information with the out-band nodes. The BP mechanism predicts the mobile node region and broadcast the beacon only if the predicted distance error is greater than the threshold range. The CLUO mechanism employs the MAC layer overhearing concept whereby a node sends the beacon packet, when it overhears the data/beacon packet transmission from a new neighbor. Simulated results reveal the adaptability of CAALU on various mobility models.

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Adaptability of Various Mobility Models for Flying AdHoc Networks—A Review

Singh

Verma

2017

Networking Communication and Data Knowledge Engineering

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Mobility Model Characteristics

Cited by 12 publications

References 8 publications

Bioinspired Mobility-Aware Clustering Optimization in Flying Ad Hoc Sensor Network for Internet of Things: BIMAC-FASNET

Bioinspired Mobility-Aware Clustering Optimization in Flying Ad Hoc Sensor Network for Internet of Things: BIMAC-FASNET

Enhancing the Performance of Geographical Routing Protocol in Highly Dynamic Mobility and Mobility Patterns

Adaptability of Various Mobility Models for Flying AdHoc Networks—A Review

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