# Optimal Energy Consumption Tasks Scheduling Strategy for Multi-Radio WSNs
## 2 Related Work  
It is critical to research how to reduce the energy consumption for MR-WSNs, since MR-WSNs consist of low power devices that are generally equipped with un-rechargeable/un-replaceable batteries and are generally distributed in geographically isolated areas. Therefore, many researchers have paid more attention to this topic in recent years. In [11], the authors proposed an optimal routing method based on convex theory for multi-radio multi-channel wireless mesh networks. [PERSON] et al. [12] formulated the routing, scheduling, channel assignment and power control problem in multi-power-level multi-radio WSNs as an integer linear programming problem, and then designed a distributed routing protocol which could significantly reduce energy consumption. In order to meet the data-intensive requirements of the structural health monitoring (SHM) field, a novel binary hybrid meta-heuristic algorithm was proposed for multi-radio multi-channel multi-power WSNs [13; 14]. [PERSON] et al. [15] improved the cluster head selection method and then proposed an enhanced schedule approach based on the modified Time Division Multiple Access (TDMA) scheme in order to minimize the energy dissipation during network communications. In [16], [PERSON] et al. proposed an optimal model of energy consumption for nodes, in which the residual energy and energy consumption of communication are considered. Moreover, they applied the Nash Equilibrium of cooperative game theory to solve the optimal solutions of the built model. [PERSON] et al. [17] proposed a new self-healing scheme based on a single flow-controlled mobility within a cluster to make a trade-off between self-healing and energy consumption in mobile Unattended Wireless Sensor Networks. [PERSON] et al. [18] proposed a two-step fuzzy logic system to enhance the cluster heads selection, further reduce the energy consumption, and prolong the lifetime of the WSNs. [PERSON] et al. [19] proposed a novel energy efficient connected coverage scheduling to maximize the lifetime of the WSNs. [PERSON] et al. [20] presented a novel secret confusion-based energy saving and privacy preserving data aggregation algorithm to reduce data traffic and energy consumption in WSNs. [PERSON] et al. [21; 22] applied the interval type-2 fuzzy logic system (IT2 FLS) to achieve the multi-radio resource scheduling problem. Moreover, in [23], [PERSON] et al. took the advantage of dealing with uncertainties of IT2 FLS to handle the transmission power control problem and gave the power allocation strategy for maximization the lifetime of WSNs. In [24], [PERSON] et al. proposed an energy efficient genetic algorithm based approach with the concept of virtual grid based dynamic routes adjustment which enhanced the overall performance of WSNs. The complex energy consumption optimization problems in WSNs, especially when the objective function contains many variables, have been proven to be a non-deterministic polynomial-complete problem (NP-complete). Hence, several heuristic algorithms, such as the differential evolution (DE) algorithm [25; 26; 27; 28; 29], the genetic algorithm (GA) [30; 31; 32; 33], PSO [34; 35; 36; 37; 38], etc., are usually preferred to search the optimal result. In order to maximize the coverage and to prolong network lifetime, the DE algorithm and the PSO algorithm were, separately, proposed to solve clustering the problem in WSNs [25; 26]. In [27], the DE algorithm was presented for the situation in which the communication range value was unknown in order to deal with the non-convex optimization problem. In [28], the authors applied the multi-objective DE algorithm to jointly optimize the sensor distribution over diverse area shapes, increase the coverage area and reduce the network energy at the same time. For the path optimization problem in WSN, the DE algorithm was used to solve the minimum energy consumption and obtain the optimal routing path [29].