Abstract—Periodic Sensor Networks (PSNs) represent one
of the essential elements of emerging Cyber-Physical System
(CPS) designs because of their using in multiple applications.
PSNs are one of the major contributors of the big data in the
future. One fundamental challenge in PSNs is to periodically
collect the large volume of data in an energy efficient way and
then transmit them to the sink so as to enhance the network
lifetime. Since sensor batteries have a limited lifetime, therefore,
adaptive sampling method to periodic data collection is required
to support energy-efficient data gathering and fusion of CPS. In
this research, we suggest a protocol, called Distributed Adaptive
DAta Collection protocol (DADAC), which collects periodically
sensor readings and prolong the lifetime of a Periodic Sensor
Network (PSN). The lifetime of DADAC protocol is divided
into cycles. Each cycle is composed of four stages. First, data
collection. Second, dimensionality reduction using Piecewise Aggregate
Approximation (PAA) technique to reduce the amount
of data collected by each sensor. Third, Frequency Reduction
using SAX (Symbolic Aggregate approXimation) approach in
order to remove the redundant data before send them to
sink. Fourth, sampling rate adaptation based PAA similarity
to acclimate its rate of sampling according to the dynamic
modification of the monitored environment. DADAC allows each
sensor to remove the redundant collected data and adapts its
sampling rate in accordance with the monitored environment
conditions.
We conduct extensive simulation experiments on real sensor
data by applying OMNeT++ network simulator to explain the
effectiveness of the DADAC protocol in comparison with two
other existing methods.