IntroductionWireless Sensor Network (WSN) leverages miniaturization enabled by advanced integrated circuit design to couple complete wireless subsystems to sophisticated sensors, allowing people and businesses to measure myriad things in the physical world and act on that information through computer monitoring and control systems [1]. In particular, WSNs used for remote area monitoring usually include a large number of small static sensing devices, which are used to forward data to a sink in an ad hoc manner, over a geographically large area to detect parameters of interest. Sink is a data collection and information processing center, which receives and collects data from a network of sensing devices that are usually distributed randomly. However, sensor nodes are constrained in terms of power supply and bandwidth since each sensor node is tightly constrained in terms of power and, once, the lifetime of the WSN is limited [2]. Such constraints, combined with the typical deployment of a large number of sensor nodes, have posed many challenges to the design and management of sensor networks. Problem Statement Radio transmission or reception is the most energy-costly activity performed by a node, so these operations have a more significant impact on node energy consumption and lifetime than other activities performed by a node [3]. Mainly two measures are adopted for energy saving achieved by reducing radio communications: duty-cycling and network processing [4]. Unlike many high-performance data networks, wireless sensor networks do not require high bit rates: 10-100 Kbps of raw network bandwidth is sufficient for many applications but not all systems, such as surveillance systems . Routing in sensor networks is very challenging due to several characteristics that distinguish them from c...... middle of paper ...... which act as node scheduling protocols over other protocols. This research on node aggregation performance will also be addressed on multiple-sink networks to evaluate performance and energy efficiency issues. Since the data from different sampled groups will vary, the redundancy problem will decrease, which will help reduce the data volume where data compression protocols on sinks will be appropriate requiring fewer resources for computation and transmission. Since the problem of data routing and hopping will be reduced by mobile sinks or multiple static sinks, an energy expenditure test will be carried out to evaluate the best possible movement method of the mobile sink and the location of the static sink. In this research, a further possibility of using similar data collection techniques with minimal data hopping in the WSN scenario will also be studied in depth instead of using the robotic locomotive model...