In Periodic Sensor Network (PSN), the periodic energy
efficient collection of a large amount of sensed data by the
sensor nodes is considered as one of the main challenges in
this type of network. An adaptive sampling approach to
periodic data collection is needed for energy optimization
due to the limited lifetime nature of the sensor batteries. In
this paper, we propose a protocol, called an Adaptive Data
Collection protocol (ADaC), which collects periodically
sensor readings and prolong the lifetime of a Periodic
Sensor Network (PSN). The lifetime of ADaC protocol is
divided into cycles. Each cycle is composed of two stages.
First, data collection. Second, sampling rate adaptation
based on the similarity between periods of one cycle using
Euclidean distance measure to adapt its rate of sampling
according to the dynamic modification of the monitored
environment. ADaC allows each sensor to adapt 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 ADaC
protocol in comparison with a method without using
adaptive sampling.