Technological development and improvements have accelerated
the Sensor node design in terms of low power consumption, and low cost and also
exhibit multifunctional and untethered communication in short distances. The
capabilities of the sensor nodes like sensing, data collecting and processing,
transferring, etc have assisted and greatly transformed the design, advancement
and deployment strategies of Wireless Sensor Networks, where all the nodes
collectively collaborate for the target application. The sensor nodes with the
help of the sensors attached to them monitor various parameters and transmit
the acquired data via wireless medium to a distant node which is called a sink
or base station. The main function of the sensor network is to gather sensor
data from the area/region of event occurrence and transmit it to the sink node.
The nodes in the sensor network work in a collective manner, thus making it
different from the ad-hoc networks.
A node is able to sense an event within a specified range.
The strength of the event signal is also deterministic of which sensor nodes
can sense the event. To ensure that no event is missed being reported, sensors
in a WSN are densely deployed. The sensor node’s position is generally not
fixed; the nodes might be randomly distributed around the phenomenon. So there
is a need for protocols to have self-organizing capabilities, also the nodes
must work in a co-operating manner to achieve effective information transfer.
Sensor nodes have processing capabilities of their own; they locally process
the data and then transfer it. The sensor nodes deployed in WSN have limited
computational capacities, memory and power compared to ad-hoc networks. Sensor
nodes in WSN may not have unique global identification as it can cause overhead
in communication, also owing to a high density of sensor nodes, global
identification can be challenging. In WSN there exist challenges in areas viz.,
scalability, cost, power, self-organization, interoperability, data
compression, etc.
Sensor nodes aggregate the sensed data before transmitting
it. Typically aggregation is performed at representative nodes or in the
gateway nodes while the data packets are in transit to sink. Data aggregation
protocols aim to remove redundant data, thus enhancing the lifetime of the
sensor network. In a typical WSN, data is transmitted in a multi-hop fashion;
nodes send data to their neighbors which are nearer to the sink. Nodes that are
closely placed are likely to sense the same data and thus cause redundancy.
Based on the application requirement, sensors either
transmit the data whenever an event is detected or periodically. These WSN
characteristics and varied application areas motivate a sensor MAC which is
operationally different from existing traditional MACs. Sensor networks MAC
have node self-organization and energy conservation as their primary goal. The
wireless channel access plays an important role in forwarding the data frames
to the sink. Many MAC protocols are proposed for efficient channel access. WSN
MAC techniques control and coordinate the radio component so that the network
is energy efficient thereby improving lifetime considerably.
There are problems associated with the existing WSN systems.
The existing schemes for data aggregation mechanisms are weakly related to data
correlation and data redundancy, leading to poor data quality. The majority of
the research work emphasizes that the clusterhead be elected based on node
energy, which may not prove fruitful for data correlation-based aggregation.
Data aggregation schemes employed in current techniques fail to perform data
analysis cost-effectively. MAC schemes result in congestion in the nodes
surrounding the base station, which should be eased to achieve better network
performance.
To overcome these challenges we present a framework to
enhance the data quality during the aggregation process. It is a novel and
simple clustering algorithm that performs the selection of the clusterhead
based on the data correlation factor. We also propose a novel hybrid MAC
technique called Improved Funneling MAC for effective resource management. Both
the protocols are implemented in MATLAB and simulation results are presented.
Implementations are compared with the existing schemes and it is found that our
implementations contribute to improved performance.
Author(s) Details
Dr. Anand Gudnavar
Department of Computer Science and Engineering, Jain College of Engineering
and Research, Belagavi, India.
Dr. Prakash Sonwalkar
Department of Computer Science and Engineering (AIML), Jain College of
Engineering and Research, Belagavi, India.
Dr. Keerti Narega
Department of Computer Science and Engineering, Graphic Era Deemed to be
University, Dehradun, India.
Please
see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48006-44-8
