Novel adaptive random access window (raw) slot adjustment model for iot network using an integrated scheme.
By: Nwogu, UchennaObioma
.
Publisher: Haryana IOSR - International Organization of Scientific Research 2022Edition: Vol.12(6), Nov-Dec.Description: 21-29p.Subject(s): Computer EngineeringOnline resources: Click here
In:
IOSR journal of VLSI and signal processing (IOSR-JVSP)Summary: The RAW (Restricted Access Window) component of the IoT network is deployed to reduce
traffic and channel contention in dense and heterogeneous sensor network environment. It divides sensor nodes
into groups and slots, allowing channel access only to one RAW slot at a time. Several algorithms and improved
channel utilization optimization models have been proposed to optimize the RAW parameters, to ensure a
contention free network or at least, minimally reduce it. These techniques often rely on previous traffic demands
schedules, collision analysis and send/receive matrices to accurately predict the future of stations’ interactions
in an IoT environment. Thus systematically adjusting its operations to reduce contention among the stations and
the Access Point(AP), thereby ensuring a flexible transmission even in a dense network environment. This paper
critically investigated, analysed and proposed a novel approach to RAW size adjustment. The new approach will
invoke data mining instruments and optimisation algorithms to improve performance of stations in an IoT
environment and thereafter simulated (with respect to some specified QoS parameters) to ascertain it’s (output)
performance index with the already existing models.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-0322 |
The RAW (Restricted Access Window) component of the IoT network is deployed to reduce
traffic and channel contention in dense and heterogeneous sensor network environment. It divides sensor nodes
into groups and slots, allowing channel access only to one RAW slot at a time. Several algorithms and improved
channel utilization optimization models have been proposed to optimize the RAW parameters, to ensure a
contention free network or at least, minimally reduce it. These techniques often rely on previous traffic demands
schedules, collision analysis and send/receive matrices to accurately predict the future of stations’ interactions
in an IoT environment. Thus systematically adjusting its operations to reduce contention among the stations and
the Access Point(AP), thereby ensuring a flexible transmission even in a dense network environment. This paper
critically investigated, analysed and proposed a novel approach to RAW size adjustment. The new approach will
invoke data mining instruments and optimisation algorithms to improve performance of stations in an IoT
environment and thereafter simulated (with respect to some specified QoS parameters) to ascertain it’s (output)
performance index with the already existing models.
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