Energy storage is the dominating factor in Wireless Sensor Networks (WSN). It is important to choose a routing strategy for the appropriate delivery of packages. This research provided an analysis of energy-efficient chain-based routing, focusing on routing methods in WSN and analyzing previous methods. The inspiration came from gateway nodes instead of chain head nodes and a new method was discussed to improve energy efficiency and the lifespan of networks. In the proposed methods, the gateway nodes were provided with rechargeable solar batteries. Moreover, there was a gateway node in each chain for collecting data and directly sending it to the sink node or Base Station (BS). Therefore, these methods could reduce energy consumption as well as the end-to-end and overall delay of network and increased the network lifespan, compared to previous methods. The simulation results superlatively showed that in the new algorithm, the energy in the nodes was conserved more than 50% and the networks' lifespan was prolonged at least 46.42% more than previous methods.

Mobile sink nodes play a very active role in wireless sensor network (WSN) routing. Because hiring these nodes can decrease the energy consumption of each node, end-to-end delay, and network latency significantly. Therefore, mobile sinks can soar the network lifetime dramatically. Generally, there are three movement paths for a mobile sink, which are as follows: (1) Random/ stochastic, (2) controlled, and (3) fixed/ predictable/predefined paths. In this paper, a novel movement path is introduced as a fourth category of movement paths for mobile sinks. This path is based on deep learning, so a mobile sink node can go to the appropriate region that has more data at a suitable time.Thereupon, WSN routing can improve very much in terms of end-to-end delay, network latency, network lifetime, delivery ratio, and energy efficiency. The new proposed routing suggests a reinforcement learning movement path (RLMP) for multiple mobile sinks. The network in the proposed work consists of a couple of regions; each region can be employed for a special purpose, so this method is hired for any application and any size of the network. All simulations in this paper are done by network simulator 3 (NS-3). The experimental results clearly show that the RLMP overcomes other approaches by at least 32.48% in the network lifetime benchmark.