Adaptive Duty Cycle Medium Access Control Protocol for Wireless Sensor Networks

2016 ◽  
Vol 5 (2) ◽  
pp. 79
Author(s):  
Anand Babu
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Duty Cycle ◽  
Consumption Rate ◽  
Medium Access Control Protocol ◽  
Sleep Mode ◽  
Medium Access ◽  
Low Energy Consumption ◽  
Idle Listening ◽  
Energy Consumption Rate

<p>To increase the network lifetime of WSNs is a major concern. Network lifetime can be increased by reducing energy consumptions through MAC protocols periodic and a- periodic sleep mode mechanisms. The short duty cycle makes sensors have low energy consumption rate but increases the transmission delay and long duty cycle makes the sensor to increase the energy consumption and reduce the delay. Duty cycle need to be adaptively varied to reduce the idle listening. In the proposed Adaptive Duty cycle MAC (ADMAC) protocol, duty cycle is varied by taking nodes rate of energy consumption and filled queue length in account. It reduces the delay and energy spent by reducing the idle listening. ADMAC is realized in NS2 and its performance is compared with SMAC.</p>

scholarly journals Improving Duty Cycle-based MAC Protocol in Wireless Networks using AI and Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 3011-3017
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Duty Cycle ◽  
Mac Protocol ◽  
Selection Function ◽  
Medium Access ◽  
Sleep Phase ◽  
Agricultural Applications ◽  
Time Division ◽  
Wireless Node

Duty cycle of a Medium Access Control (MAC) protocol is made up of sleep phase, wake-up phase and listen phase. MAC protocols usually proposes to optimize the duration of the wake-up and listen phases, in order to increase the duration of the sleep phase, thereby reducing the unwanted energy consumption of the wireless node. In this paper, we propose an Artificial Intelligence (AI) and machine learning (ML) based approach, which uses a hybrid combination of Time Division Multiple Access (TDMA), Bitmap Assisted MAC (BMA) and Sensor MAC (SMAC). The machine learning layer utilizes the duty cycle in the MAC layer, and generates multiple solutions for a given wireless communication. The AI layer then selects the best solution from the generated solutions by incorporating a duty cycle factor in the selection function, thereby optimizing the duty cycle of the protocol. The proposed system shows a 15% improvement in communication speed, and a 10% reduction in energy consumption across multiple communications. We plan to further extend this work for rural India, and apply it to real time agricultural applications.

An Advanced Wireless Sensor Networks Design for Energy-Efficient Applications Using Condition-Based Access Protocol

2021 ◽  
pp. 171-186
Author(s):  
Vijendra Babu D. ◽  
K. Nagi Reddy ◽  
K. Butchi Raju ◽  
A. Ratna Raju
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Network Throughput ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Medium Access ◽  
Access Protocol ◽  
Battery Lifetime

A modern wireless sensor and its development majorly depend on distributed condition maintenance protocol. The medium access and its computing have been handled by multi hope sensor mechanism. In this investigation, WSN networks maintenance is balanced through condition-based access (CBA) protocol. The CBA is most useful for real-time 4G and 5G communication to handle internet assistance devices. The following CBA mechanism is energy efficient to increase the battery lifetime. Due to sleep mode and backup mode mechanism, this protocol maintains its energy efficiency as well as network throughput. Finally, 76% of the energy consumption and 42.8% of the speed of operation have been attained using CBI WSN protocol.

scholarly journals ANALYSIS OF THE ENERGY CONSUMPTION RATE OF OFFICE BUILDINGS BY THE TYPE OF HEAT SOURCE EQUIPMENTS

2013 ◽  
Vol 19 (43) ◽  
pp. 1017-1022
Author(s):  
Yorimasa IKI ◽  
Satoshi SUDO ◽  
Hiroshi YOSHINO ◽  
Shuzo MURAKAMI ◽  
Kazuaki BOGAKI ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Heat Source ◽  
Consumption Rate ◽  
Office Buildings ◽  
Energy Consumption Rate
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