WSN-HaDaS: A Cross-Layer Handoff Management Protocol for Wireless Sensor Networks, a Practical Approach to Mobility

Buenrostro-Mariscal, Raymundo; Cosio-Leon, Maria; Hipólito, Juan Iván Nieto; Guerrero-Ibáñez, J.; Vazquez-Briseno, Mabel; Sanchez-Lopez, Juan de Dios

doi:10.1587/transcom.e98.b.1333

Cited by 5 publications

(9 citation statements)

References 13 publications

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“…And more very few of them are considered handling handoff from MN side by accepting its limitations. Authors of WSN-HaDaS [1], most of them are also member of this work, designed an algorithm which can manage handoff from CN side with the following contributions:…”

Section: Related Workmentioning

confidence: 99%

“…The tradition schemes have mainly suffered from this problem. There most important information collected through researchers that provides the weaknesses or errors that may occur during handoffs [1,2,3,4]. 1.…”

Section: Iiimnh-wsn (Mobile Node Based Handoff For Wsn) a Backgroundmentioning

confidence: 99%

“…In MNH-WSN, MAC layer protocol, is initiated when an MN is moving away from its current CN and getting near to another CN. In other words, when the connectivity of MN with its current CN is dropped below a predefined threshold of signal strength (<-85dbm) [1] then it initiates a handoff process. Therefore, the handoff processes is based on the LQ measurement from current CN it is connected.…”

Section: B General Description Of Mnh-wsnmentioning

confidence: 99%

“…The line where MNH-WSN will react communication and direct to handoff process .The lowest limit to process MNH-WSN is when the link quality is -85dbm, which is studded and decided by paper [1,6,5]. Here MNH-WSN has two important mechanisms used for handoff action, soft and hard handoff.…”

Section: Handoff Limitmentioning

confidence: 99%

“…Finally it will decide depending on the result, as designed by the algorithm in fig-3. Evaluation is done by compering Performance of MNH-WSN with the handoff mechanism of IEEE 802.15.4 standard, Soft-Handoff WSN-HaDaS [1], and MIPv6 Soft-Handoff [13]. For evaluation we set all transmission ranges of MN equal to 100 m, and use random topologies with different numbers of MNs in each experiment .…”

Section: Handoff Limitmentioning

confidence: 99%

See 4 more Smart Citations

MNH-WSN (Mobile Node Based Handoff for Wireless Sensor Network)

Besher¹,

Hipólito²,

Buenrostro-Mariscal³

et al. 2016

IJERT

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in this paper we analyze and implement a MAC layer handoff mechanism for WSN by introduce best algorithm for reducing handoff time. More we introduce the best mechanisms to manage handoff from Mobile node (MN) side. At the end of the paper we will show how, with our approach, it is possible to reduce the mobile node handoff delay effectively. Every result shown in the analysis is done both in simulation and real-time implementation of our algorithm. As our result shows that, the total handoff delay time we have is the best time duration achieved in handoff handling of mobile sensors. Physical environment test is done for sensor applications working in hospital, considering that sensors in such environment are used for very sensitive data communication, that's why it's very important to analyze our algorithm both in real environment and simulation. That's what we did.

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Section: Related Workmentioning

confidence: 99%

Section: Iiimnh-wsn (Mobile Node Based Handoff For Wsn) a Backgroundmentioning

confidence: 99%

Section: B General Description Of Mnh-wsnmentioning

confidence: 99%

Section: Handoff Limitmentioning

confidence: 99%

Section: Handoff Limitmentioning

confidence: 99%

See 3 more Smart Citations

MNH-WSN (Mobile Node Based Handoff for Wireless Sensor Network)

Besher¹,

Hipólito²,

Buenrostro-Mariscal³

et al. 2016

IJERT

View full text Add to dashboard Cite

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Is Cognitive Radio an Opportunity to Improve the Performance of the IEEE 802.15.4 in Mobile e-Health Scenarios?

Calixto-Sanchez

Cosío-León

Buenrostro-Mariscal

et al. 2019

IEEE Latin Am. Trans.

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A Review of Deep Learning Applications for the Next Generation of Cognitive Networks

et al. 2022

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Intelligence capabilities will be the cornerstone in the development of next-generation cognitive networks. These capabilities allow them to observe network conditions, learn from them, and then, using prior knowledge gained, respond to its operating environment to optimize network performance. This study aims to offer an overview of the current state of the art related to the use of deep learning in applications for intelligent cognitive networks that can serve as a reference for future initiatives in this field. For this, a systematic literature review was carried out in three databases, and eligible articles were selected that focused on using deep learning to solve challenges presented by current cognitive networks. As a result, 14 articles were analyzed. The results showed that applying algorithms based on deep learning to optimize cognitive data networks has been approached from different perspectives in recent years and in an experimental way to test its technological feasibility. In addition, its implications for solving fundamental challenges in current wireless networks are discussed.

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WSN-HaDaS: A Cross-Layer Handoff Management Protocol for Wireless Sensor Networks, a Practical Approach to Mobility

Cited by 5 publications

References 13 publications

MNH-WSN (Mobile Node Based Handoff for Wireless Sensor Network)

MNH-WSN (Mobile Node Based Handoff for Wireless Sensor Network)

Is Cognitive Radio an Opportunity to Improve the Performance of the IEEE 802.15.4 in Mobile e-Health Scenarios?

A Review of Deep Learning Applications for the Next Generation of Cognitive Networks

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