Introduction
The future of internet will include a huge number of interconnected nodes expressing
various things from tiny sensor nodes and portable devices to large web servers and
supercomputer clusters. This type of a worldwide network is called the internet of things
(IoT) (Jun et al., 2011; Li et al., 2013). The effective data collection techniques are
necessary for IoT in order to gather and process the data at IoT nodes (Jun et al., 2011;
Ulusoy et al., 2011). In IoT, every sensor node has low cost, power supply, a speed of
processing, bandwidth, and memory capacity. Sensor nodes are spatially deployed in the
region of interest in order to monitor the physical or environmental phenomena like
temperature, humidity, light, pollution, pressure and sound. They collect the sensed data
from the monitored environment, manipulate the data locally, and transmit them to the
sink for further analysis. These sensor nodes work in a collaborative manner and
constitute a wireless sensor network (WSN) (Akyildiz and Vuran, 2010; Idrees et al.,
2014, 2015; Abdelaal et al., 2016). WSN represents one of the big contributors in the IoT
because of their widespread use in many applications such as agricultural, healthcare,
transportation, environment, industry, and military (Wang et al., 2012a; Idrees
et al., 2016).
One of the most critical constraints of the sensor node is the battery life. Due to the
environment or cost restrictions, it is difficult or impossible to change or recharge the
sensor batteries (Abdelaal et al., 2016; Wang et al., 2012a). Thus, the sensor nodes are
deployed with high density in order to enhance the network lifetime. In sensor node, the
radio unit represents the principal source of energy consumption. Therefore, it is
important to remove redundant sensed data before reporting them to the sink to save the
energy and improve the lifetime of sensor node (Tang and Xu, 2008). It is necessary to
take into consideration data capturing, communication, and routing problems in order to
design energy-saving protocol for PSN. Data collection approaches determine the way of
sensor’s work in data collection and sending to the base station. Therefore, data
collection represents the crucial function in PSNs (Campobello et al., 2016; Jon, 2016).
In WSN, the collection of data can be categorised into two models: time-driven and
event-driven (Abdelaal et al., 2016; Jon, 2016). This work considers time-driven data
collection model which known as periodic sensor networks (PSNs). In PSN, every sensor
node transmits the sensed data of the monitored area to the sink periodically. Several
PSNs applications use the periodic way to monitor certain conditions regularly such as
pressure, humidity, temperature, etc. Two main challenges in PSN, first, PSN has to
provide adequate lifetime in order to satisfy application’s needs. Second, data
management is more difficult due to the huge amount of collected data by this network.