Energy Efficient Multichannel MAC Protocol for High Traffic Applications in Heterogeneous Wireless Sensor Networks

Author(s): Sunil Kumar*, Priya Ranjan, Ramaswami Radhakrishnan, Malay Ranjan Tripathy.

Journal Name: Recent Advances in Electrical & Electronic Engineering

Volume 10 , Issue 3 , 2017

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Abstract:

Background: Wireless Sensor Networks (WSNs) have become an important part in recent years with a wide range of applications from low traffic to high traffic applications (e.g. Multimedia, VoIP, IOT, IOE). These applications require collection and transmission of the high amount of data in a timely manner. In WSNs, sensors are randomly deployed in ad hoc fashion and as per application requirement are inactive for a long time and quickly activate when any event is detected. Designing a good MAC is important to optimize the energy at the data link layer of the communication model. Proposed protocol EE-MMAC is an energy-efficient multichannel MAC for high traffic applications.

Methods: The primary objective of EE-MMAC is to cut energy consumption at the data link layer based on following multi-channel attributes: Idle listening, elimination of collisions, overhearing and reducing the control packet overhead. EE-MMAC uses periodically sleep technique in the multi-channel environment based on the directional antenna, which is different from the traditional IEEE 802.11 MAC and IEEE 802.15.4. When a node is transmitting data, during that time other nodes must be in sleep mode motivated by S-MAC, PAMAS. Nodes must exchange control packets on CC (control channel) to choose DC (data channel) for communication and to decide the beam direction of the flow.

Results: The proposed algorithm results show that the EE-MMAC achieves significant energy gains (30% to 45% less than comparable to IEEE 802.11, and MMAC) based on mainly energy efficiency, PDR (packet delivery ratio) and throughput.

Keywords: Energy efficient, multichannel, MAC, wireless sensor networks, protocol, noetes.

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Article Details

VOLUME: 10
ISSUE: 3
Year: 2017
Page: [223 - 232]
Pages: 10
DOI: 10.2174/2352096510666170601090202
Price: $58

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