# Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks
## 2 Related Works  
Since sensor nodes are mostly powered by batteries, once some nodes are exhausted, the network may not work properly. Therefore, energy-saving has always been one of the keys to the efficient operation of WSN. Some traditional methods mainly reduce the transmission distance and energy consumption of the path, thus prolonging the overall lifetime of the network [9; 10]. Authors in [9] select the neighbor node with the fewest hops from the source node to the sink node as the relay, and when there are multiple paths with the fewest hops, the remaining energy of the node is used as the determinant. [PERSON] et al. [10] proposed a ladder diffusion algorithm based on ant colony optimization (ACO) to solve the problem of energy consumption in routing. The algorithm mainly uses the ACO mechanism to determine transmit paths, which effectively reduces energy consumption. In some way, these algorithms based on minimum hop count are equivalent to the minimum energy routing [11]. Although this kind of method can reduce the energy consumption of the path, it has obvious disadvantages of only some nodes undertake the data transmission in a period of time while other nodes are idle. That is to say, when they choose the forwarding node, they do not consider the residual energy, which is very easy to make some nodes run out of energy prematurely and result in uneven energy distribution between nodes. Thus the network lifetime is always at a lower level. In response to the above problems, in some algorithms for solving load balancing [12; 13; 14], [PERSON] et al. [12] proposed a forward-aware factor-based energy balance routing protocol (FAF-EBRM), which uses the energy density of the forward region and the traffic on the link to alleviate congestion. However, without minimizing the energy consumption of the path, it will lead to energy loss due to detours. For this reason, some algorithms minimize the path energy consumption as much as possible while ensuring the energy balance of nodes [13; 14]. The ACHCMproposed by [PERSON] et al. [13] combines the advantages of the ant colony optimization algorithm and minimum hop routing strategy to effectively reduce network energy consumption. The ESRA proposed by [PERSON] et al. [14] first constructs the minimum energy consumption tree and then propose the \"cut edge\" strategy to balance the load between nodes, which can effectively utilize the energy and extend the network lifetime. However, the computational complexity and communication overhead required by the two algorithms are large and may consume too many resources in practical applications. Therefore, it is unreasonable to only consider single routing information or adopt a simple mathematical model in routing decisions, and it is also necessary to comprehensively consider various attributes of the network. Among them, the method of multi-attribute decision-making (MADM) can be well applied to routing. The most widely used multi-attribute decision-making methods include the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy theory and DS evidence theory. In some routing protocols [11; 15; 16] that use TOPSIS, such as the Multi-criteria based centralities measures routing protocol (MCRP) [15], it is mainly to sort multiple network attribute matrices to obtain the optimal solution. However, the routing decision using the TOPSIS can only reflect the relative closeness of each attribute to its ideal solution, which does not reflect the closeness to the overall ideal solution. In addition, in other algorithms that adopt fuzzy theory for route selection [17; 18; 19], such as fuzzy-logic-based energy optimized routing (FLEOR) algorithm [18], the determination of attribute weight has a strong subjectivity, that is, the fuzzy rules are determined artificially, and the lack of objectivity may lead to the final decision not be the optimal. The DS evidence theory is simple to calculate and has low demand for prior knowledge.