# Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks
## 1 Introduction  
With the development of efficient wireless communication and the progress of electronic information technology, the wireless sensor network (WSN) is widely used in various fields because of its low cost, miniaturization and multi-function characteristics [1; 2; 3; 4]. However, in most cases, the nodes in WSN are powered by batteries and are usually deployed in unmanned outdoor or more dangerous environments, which make it inconvenient to replenish energy. The cost of redundant deployment and node replacement is also usually high. Therefore, an efficient routing strategy is needed to minimize network energy consumption and prolong the network lifetime. Since the energy of the sensor node is mainly used for data reception and transmission [5], the traditional routing strategy mainly considers how to utilize the shortest path to transfer data from the source node to the destination as quickly as possible. However, in the energy-constrained sensor network, a large amount of data is transmitted from the source node to sink by \"many-to-one\" mode, which easily causes serious \"funnel effect\" and \"energy hole\" problems. As a result, the energy consumption of nodes located around the shortest path or the sink node is much faster than that of others, resulting in energy imbalance and lower network lifetime. In addition, the \"many-to-one\" data transmission mode may also cause congestion in the network. For example, when a key event is triggered, the nodes need to transmit a large amount of data to the sink node in a short time, and congestion may occur at this time. Congestion can cause a large number of data packets to be discarded, reducing the reliability of data transmission. Meanwhile,the network will generate unnecessary energy consumption and increase transmission delay due to data retransmission, which will reduce energy utilization. In some research on congestion, once congestion is found, certain measures are usually used to control the data rate of the inflow [6], and it is difficult for the upstream node to control the data rate flowing into the downstream to avoid congestion. There are also many routing strategies that prefer to idle nodes to reduce congestion, but the nodes may lose more energy due to detours [7; 8]. Therefore, in order to improve the energy efficiency, prolong the network lifetime and increase the reliability of data transmission, we propose an energy-efficient and reliable routing algorithm based on Dempster-Shafer (DS) evidence theory (DS-EERA) by adopting multi-attribute decision-making method. The main contributions of this paper are as follows:  
* From the perspective of energy consumption reduction, DS-EERA establishes the node evaluation function. That is, the attributes of the nodes are considered comprehensively and abstracted into three indexes: transmission energy efficiency ratio, idleness degree and energy density factor. * Considering the operation of each round of the network, we objectively determine the weight of each index based on the difference coefficient of entropy values. * The application of DS evidence theory in node attributes fusion as routing is very few in WSN. DS-EERA regards three indexes as evidence, and determines whether the node can become the next hop as the recognition target. We innovatively apply DS fusion rules to the fuse the node indexes and take the fusion results as the basis of routing decisions. * Significantly, the simulation results show that DS-EERA can not only reduce energy consumption effectively and prolong network lifetime in energy-constrained networks but also achieve lower packet loss rate and increase the reliability of data transmission. The rest of the paper is organized as follows: Section 2 reviews the related research works. The network model and DS evidence rule are introduced in Section 3. We describe the DS-EERA algorithm in detail in Section 4. The performance of our algorithm is analyzed and discussed according to relevant simulation results in Section 5. Finally, the conclusion is summarized in Section 6.