An efficient hand-off optimization based RPL routing protocol for optimal route selection in mobility enabled LLNs

Sneha, K; Prasad, B. G.

doi:10.1109/icgtspicc.2016.7955284

Cited by 7 publications

(15 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, the authors in Reference proposed an energy‐balanced routing method based on forward‐aware factor. In the work, link weight and forward energy density derived from the residual energy and distance between the source node and the targeted node were used to determine the next‐hop node.…”

Section: Related Workmentioning

confidence: 99%

“…In the work, link weight and forward energy density derived from the residual energy and distance between the source node and the targeted node were used to determine the next‐hop node. However, these two protocols in References and relied upon the neighbouring nodes to provide their residual energy. Apart from this, Reference assumed that the locations of nodes and sink are fixed, which is against the characteristics of MWSN's nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Although several research efforts had shown that residual energy and mobility factor of nodes are the major parameters needed for determining effective routing in MWSNs, however, the major issue is how to obtain these parameters. Are they to be requested from the targeted nodes (direct approach) or predicted by the sink based on some intrinsical factors (predictive approach)?.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, for all of these to be feasible, predictive approach requires an effective predictive model capable of predicting optimal route selection parameters using intrinsical information from nodes. However, most of the existing predictive models either provide energy level or mobility factor of nodes for optimal route path selection, and some of them still require targeted nodes to supply their residual energies to compute link weight and other parameters as being done in References and , which will definitely affect the ability of the protocol to determine optimal route paths for data routing.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mobility and energy prediction models: Approach toward effective route management in mobile wireless sensor networks

Olakanmi

Odeyemi

Abbas

2020

Engineering Reports

View full text Add to dashboard Cite

Mobile wireless sensor network (MWSN), as a backbone of most communication and embedded systems, is characterized by low data delivery as a result of the energy constraint and in‐deterministic movement of the nodes. To select optimal route path for a reliable data routing, protocols need to select neighbouring nodes with low mobility and residual energy parameters along the route. However, obtaining these parameters from the targeted nodes may encourage mischievous nodes to manipulate their energy and mobility parameters in their favor, leading to the selection of non‐optimal route path and/or exposing the network to other attacks. Rather than depending on the targeted nodes for these parameters, they can be accurately and securely obtained by sink node or base station using effective predictive models. However, most of the existing predictive models engaged by MWSN routing protocols either provide only the energy level or mobility factor of the nodes for optimal route path selection. This, therefore, reduces the reliability of the obtained optimal route paths in terms of data delivery. In this article, we propose predictive models for an accurate prediction of mobility and residual energy parameters of nodes in MWSN for efficient route management. The models are simulated on an existing MWSN protocol to determine their performance. The simulation results show that the adoption of these models in the development of routing protocol will not only lead to optimal route paths' selection but also enhance data delivery rate in MWSN.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mobility and energy prediction models: Approach toward effective route management in mobile wireless sensor networks

Olakanmi

Odeyemi

Abbas

2020

Engineering Reports

View full text Add to dashboard Cite

show abstract

“…Sneha et al [6] proposed a mobility-enabled RPL protocol to enhance the hand-off process, optimize control messages and improve best parent selection scheme. In their context, they considered that mobile nodes and static nodes co-exist within the network.…”

Section: Related Workmentioning

confidence: 99%

Study of Mobility Enhancements for RPL in Convergecast Scenarios

Wang

Chalhoub

2017

Future Internet

View full text Add to dashboard Cite

Abstract:In recent years, mobility support has become an important requirement in various wireless sensor network (WSN) applications. However, due to the strict resource constraints of power, memory, and processing resources in WSNs, routing protocols are mainly designed without considering mobility. Low-Power and Lossy Networks (LLNs) are a special type of WSNs that tolerate data loss. The Routing Protocol for Low-Power and Lossy Networks (RPL) is a routing protocol for LLNs that adapts IPv6 (Internet Protocol version 6) and runs on top of the IEEE (Institute of Electrical and Electronics Engineers) 802.15.4 standard. RPL supports multipoint-to-point traffic and point-to-multipoint traffic. In this paper we propose a mobility enhancement mechanism in order to improve data collection applications in highly mobile scenarios. The enhancement is based on signal strength monitoring and depth updating in order to improve the routing protocol performance in mobile scenarios. This enhancement helps routing protocols to cope better with topology changes and makes proactive decisions on updating next-hop neighbours. We integrated this mechanism into the RPL and compared it with other existing RPL mobility support enhancements. Results obtained through simulation using Cooja show that our work outperforms other existing RPL mobility supports on different performance metrics. Results also prove the efficiency of our proposal in highly mobile scenarios.

show abstract

Investigation of Next Generation Internet Protocol Mobility-Assisted Solutions for Low Power and Lossy Networks

Vidhya

Mathi

2018

Procedia Computer Science

View full text Add to dashboard Cite

An efficient hand-off optimization based RPL routing protocol for optimal route selection in mobility enabled LLNs

Cited by 7 publications

References 9 publications

Mobility and energy prediction models: Approach toward effective route management in mobile wireless sensor networks

Mobility and energy prediction models: Approach toward effective route management in mobile wireless sensor networks

Study of Mobility Enhancements for RPL in Convergecast Scenarios

Investigation of Next Generation Internet Protocol Mobility-Assisted Solutions for Low Power and Lossy Networks

Contact Info

Product

Resources

About