# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 1 Introduction
### Contributions  
With years of development, the routing protocol of WSNs has become an intense research field. However, as mentioned above, many studies have only proposed \"special effect\" algorithms; that is, some performance indicators are very prominent, while other aspects are difficult to compare with different algorithms. Therefore, based on the original intention of providing comprehensive and proper routing protocols, this paper designs a new algorithm termed multiparameter fusion decision routing (MPFDR). First, our algorithm establishes a multidimensional routing model, which lays a foundation for subsequent routing. Second, a new routing strategy based on contribution is proposed for the selection of the next hop. Furthermore, the main contributions of this paper are summarized as follows:  
(1) We develop a routing evaluation model. Comprehensively integrating the residual energy and forward distance of neighbor node, single-hop transmission energy consumption, queue cache, energy equilibrium degree, and other indicators, we calculate the routing evaluation parameters of each forward neighbor.  
(2) The routing selection algorithm based on the parameter contribution degree is proposed. The algorithm makes a concurrent contribution to enhancing the energy balance and effectiveness, alleviating node congestion, and improving transmission delay. According to the change of the relevant network state indicators of the current node and its forward neighbor region, this proposed strategy dynamically adjusts the reliability coefficient of each parameter to coordinate the decision influence force in the process of selecting the next hop. Moreover, after considering the parameter contribution degree of the forward neighbors and the fuzzy contribution degree, it calculates the fusion contribution degree of multiple scenes under the condition of ensuring at least one deterministic parameter contribution degree. Finally, the result of the superposition is used as the final routing decision criterion.  
(3) In addition, we analyze the algorithm. Through theoretical analysis, the interval of the adjustment factor in the cache queue index and the influence of different values are determined. We provide the computational time complexity of our algorithm.  
(4) Combined with the experimental simulation and comparison algorithms, we analyze the influence of the adjustment factor and network size on the network performance. The network performance metrics include the network lifetime, average energy consumption, packet loss ratio, and average hops. By contrast with the comparison algorithms, the MPFDR algorithm has the lowest packet loss ratio, which reflects good congestion control ability. Although the network lifetime is lower than that of MRE, and the average energy consumption and average hops are slightly higher than those of MPEC, other aspects of the proposed algorithm are better than those of the other two algorithms, and the overall performance has more obvious advantages.  
The rest of the paper is organized as follows. Section 2 gives the system model and some relevant definitions. Section 3 describes the proposed routing optimization algorithm in detail and provides the implementation and analysis of our algorithm. Next, in Section 4, the effects of the adjustment factor and network size on performance are analyzed based on the simulation results and other algorithms. Finally, Section 5 summarizes the full text and future work.