Context aware Topology Control in wireless sensor networks

Bhajantri, Lokesh B.; H.C, Pavithra; Patil, Meera

doi:10.1109/icatcct.2016.7912114

Cited by 1 publication

(2 citation statements)

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“…Nearly all smart devices are using sensor nodes for obtaining data from real-world scenarios and provides services according to user mode and according to the context. WSN brings all these devices in a uniform layer and arrange them in a context [12] [13]. Due to the resource-constrained nature and unique characteristics, WSNs are always at risk of faults and failures [14].…”

Section: Introductionmentioning

confidence: 99%

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Improving Energy Efficiency With Content-Based Adaptive and Dynamic Scheduling in Wireless Sensor Networks

Khan¹,

Rahman²,

Almaiah³

et al. 2020

IEEE Access

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Wireless Sensor Networks (WSNs) have revolutionized the era of conventional computing into a digitized world, commonly known as "The Internet of Things". WSN consists of tiny low-cost sensing devices, having computation, communication and sensing capabilities. These networks are always debatable for their limited resources and the most arguable and critical issue in WSNs is energy efficiency. Sensors utilize energy in broadcasting, routing, clustering, on-board calculations, localization, and maintenance, etc. However, primary domains of energy consumption at node level are three i.e. sensing by sensing-module, processing by microprocessor and communication by radio link. Extensive sensing, over-costs processing and frequent communication not only minimize the network lifetime , but also affects the availability of these resources for other tasks. To increase lifetime and provide an energy-efficient WSN, here we have proposed a new scheme called "A Content-based Adaptive and Dynamic Scheduling (CADS) using two ways communication model in WSNs". CADS dynamically changes a node states during data aggregation and each node adapts a new state based on contents of the sensed data packets. Analyzer module at the Base-Station investigates contents of sensed data packets and regulates functions of a node by transmitting control messages in a backward direction. CADS minimizes energy consumption by reducing unnecessary network traffic and avoid redundant message-forwarding. Simulation results have been shown that it increases energy-efficiency in terms of network lifetime by 9.65% in 100 nodes-network, 11.36% in 150 nodes-network and 0.94% in 300 nodes. The proposed scheme is also showing stability in terms of increasing cluster life by 87.5% for a network of 100 nodes, 94.73% for 150 nodes and 53.9% in 300 nodes.

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Section: Introductionmentioning

confidence: 99%

“…will remain the same, E (Receive) (L) = L * E (Electric) While D (Threshold) is calculated as; D (Threshold) = √ [E (Final) / ɛ amp …………………….….. (13). For each node energy consumption will beE (Residual) = E (Supply) -( E (Transmit) (L, D) + E (Receive) (L)) ……………………………………………………….…”

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confidence: 99%