BIGM: A Biogeography Inspired Group Mobility Model for Mobile Ad Hoc Networks

Verma, Jyotsna; Kesswani, Nishtha

doi:10.1007/s10776-018-0410-7

Cited by 10 publications

(8 citation statements)

References 19 publications

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“…Verma and Kesswani 28 introduced a Biogeography Inspired Group Mobility model for Mobile Ad Hoc Networks (MANETs) depending on the Biogeography Based Optimization (BBO) algorithm. BBO is based on the migration behavior of species among the islands and how they develop into extinct and new species happens.…”

Section: Related Workmentioning

confidence: 99%

A novel optimized resource management model for software defined future mobile networks

Sureshkumar¹,

Surendran

2022

Int J Communication

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Extreme densities are an important factor in future mobile networks that can provide very high data rate to mobile users. It also increases complexity in handover decisions and resource management. However, SDNs (softwaredefined networks) and mobility models can provide seamless mobility and efficient resource management in heterogeneous mobile environment, and ensure QoS (quality of service) is achieved. The paper proposes SDN based on GMM (Group Mobility Model) called MoMo, and resource management using MLOAs (mutation lion optimization algorithms) are used to alleviate handover and addressing issues in network congestion. The proposed method is based on SDN controllers working in global perspectives for achieving required network conditions and end user QoSs. SDN with GMM-MoMo-MLOA offer transparent and dynamic support for sessions during handoffs and thus eliminates congestion overheads and packet losses related to mobile traffic, resulting in improved QoS for mobile users. The performance analysis through experiments depicts that the proposed model gives enhanced performance when compared to that of the other conventional methods and also has proven efficiency in terms of other parameters such as handover latency, signaling cost, throughput, and packet loss. The results from the simulation show that the proposed method SDN-GMM-MoMo-MLOA greatly improves the performance of network and also maintains optimum resource utilization and efficiency. K E Y W O R D Sfuture mobile networks, group mobility management (GMM), handover, MoMo model, mutation lion optimization algorithm (MLOA), quality of service (QoS), software defined network (SDN)

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Section: Related Workmentioning

confidence: 99%

A novel optimized resource management model for software defined future mobile networks

Sureshkumar¹,

Surendran

2022

Int J Communication

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“…One more approach [70] exists in the literature that maintains the continuity of services of the migrating sensor nodes in a framework which is based on the concept of a Web of Things. Several other group mobility models for mobile ad hoc networks [71][72][73] were proposed that manage the mobility pattern of the nodes, inspired by the social behavior of the natural systems. 5.…”

Section: Research Challenges and Constraints For Ensembling Iot Through Sensor Cloudmentioning

confidence: 99%

Enabling Internet of Things through Sensor Cloud: A Review

Verma

2021

SCPE

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With the inception of the Internet of Things (IoT), wireless technology found a new outlook where the physical objects can interact with each other and can sense the environment. The IoT has found its way in the real world and has connected billions of devices throughout the world. However, its limitations, such as limited processing capability, storage capability, security and privacy issues, and energy constraints prevent the IoT system to be properly utilized by the real-world applications. Hence, the integration of IoT with various emerging technologies like big data, software defined networks, machine learning, fog computing, sensor cloud, etc., will make the IoT system a more powerful technology. The sensor cloud provides an open, secure, flexible, large storage and a computational capable infrastructure which makes the ensemble architecture of IoT and sensor cloud more efficient. An extensive review of the IoT system enabled sensor cloud is presented in the paper, and with this context, the paper attempts to summarize the sensor cloud infrastructure along with its challenges. In addition, the paper presents the possible integrated architecture of the IoT and the sensor cloud which enables the network to be properly utilized. Further, the importance of integrating these two promising technologies and research challenges associated with it is also identified. Finally, the paper analyses and discusses the motivation behind the ensemble system along with future research direction.

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“…The random m destination or target d 1 , d 2 , , d m is defined for each group of nodes G i for visiting within the simulation environment. The visiting matrix V i,j of the group of nodes G i is given in equation (11). If the nodes in the group G i reach or visit the randomly selected destination d j the visiting matrix V i,j will be:…”

Section: Animal Migration Inspired Group Mobility Model This Sectionmentioning

confidence: 99%

“…There are many mobility models [8,9,10,11] in the literature that use nature inspired techniques to model the realistic scenarios and till date, to the best of our knowledge, there is no generalized mobility model for all the application scenarios. The approach of our proposed mobility model is based on the animal migration behavior ecology which can be found in all animal groups, like fish, reptiles, amphibians, insects, etc.…”

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confidence: 99%

AMIGM: Animal Migration Inspired Group Mobility Model for Mobile Ad hoc Networks

Verma

Kesswani

2019

SCPE

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The most widespread notion of mobility model is the representation of mobile node’s movement pattern in the wireless ad hoc networks which has a significant impact on the performance of the network protocols. In this paper, we have proposed an Animal Migration Inspired Group Mobility (AMIGM) model for mobile ad hoc networks based on the migration behavior of animals like, insects, flock of birds, schools of fishes, reptiles, amphibians, etc. The propound model tries to overcome the limitations of the existing mobility models, such as temporal dependencies, spatial dependencies, geographical restrictions and migration of nodes between the group of nodes so that it can realistically model the real world application scenarios. The proposed AMIGM model is based on Animal Migration Optimization (AMO) algorithm, in which each group of nodes has two phases namely, Migration phase and Population updating phase. In the first phase, the model simulates the movement of nodes in the group from one position to another by obeying the swarming laws. In the second phase, the model simulates joining and leaving of the nodes in the group during migration. The protocol dependent and independent performance metrics of the proposed model are compared with Random Waypoint Mobility model (RWP) and Reference Point Group Mobility model (RPGM) through ns-2 simulator.

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BIGM: A Biogeography Inspired Group Mobility Model for Mobile Ad Hoc Networks

Cited by 10 publications

References 19 publications

A novel optimized resource management model for software defined future mobile networks

A novel optimized resource management model for software defined future mobile networks

Enabling Internet of Things through Sensor Cloud: A Review

AMIGM: Animal Migration Inspired Group Mobility Model for Mobile Ad hoc Networks

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