IntroductionThe Wireless Sensor Network (WSN) uses miniaturization enabled by advanced integrated circuit design to couple fully wireless subsystems with sophisticated sensors, enabling individuals and businesses to measure myriad things in the physical world and act on this information via computer-based monitoring and control systems [1]. In particular, WSNs deployed for remote area monitoring typically comprise a large number of tiny static sensing devices, which are deployed to transmit data to a receiver in an ad hoc manner, over a wide geographic area to detect data parameters. 'interest. Sink is a data collection and information processing center, which receives and aggregates data from a network of typically randomly deployed sensing devices. However, sensor nodes are limited in terms of energy supply and bandwidth, because each sensor node is tightly power limited and unique, the lifetime of the WSN is limited [2]. Such constraints, combined with a 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-expensive task a node performs, so these operations have a more significant impact on the node. energy consumption and lifetime than other tasks performed by a node [3]. Two main measures are taken to save energy by reducing radio communications: duty cycle and network processing [4]. Unlike many high-performance data networks, wireless sensor networks do not require high bit rates: 10 to 100 Kbps of raw network bandwidth is sufficient for many applications, but not for all systems like monitoring systems. Routing in sensor networks is very difficult due to several characteristics that set them apart from the middle of paper...... and act as a node scheduling protocol over other protocols. This research on node aggregation performance will also be targeted on multi-sink networks to evaluate performance and energy efficiency issues. As the data from different sampled groups varies, the redundancy problem will decrease, helping to reduce the data volume when the data compression protocols on the receivers are appropriate, requiring fewer resources for computation and transmission. Since the problem of routing and data hopping will be reduced by the moving sink(s) or multiple static sinks, an energy expenditure test will be performed to evaluate the best possible method of moving the moving sink, the position of the static sink. The possibility of using similar data collection techniques with the least data skips on the WSN scenario instead of using a robotic locomotive model will also be investigated extensively in this research..