# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
## 2 Literature Review  
With the development of the power grid, the concept of microgrids came into being. The microgrids is placed at the end of the entire grid-connected system. The WSN is engaged in the management of power generation, power consumption, transmission and distribution. In terms of power generation, the WSN detects the power generated by the microgrid for large-scale applications. In addition, the WSN needs to monitor the transmission status of the microgrid to transmit power effectively. However, the energy of sensor nodes is limited. Under the condition of limited energy, how to improve the efficiency of data transmission and the lifetime of the sensor network is a crucial concern. A three-layer framework is proposed based on joint rate-aware fuzzy clustering and stable sensor association that considers various factors of sensor energy efficiency [10]. In [11], fog computing was utilized to optimize the WSN connected to the microgrid. In the grid-connected community (GCC), an energy model was modeled to evaluate the energy consumption of the WSN and its performance in microgrids. The collected information of sensor nodes is used to calculate the optimal value of data transmission by utilizing the whale optimization algorithm, which reduces the energy consumption of the sensor network and improves the throughput of the network. In [12], a load balancing and routing strategy using the Glowworm swarm optimization approach (LBR-GSO) is adopted to handle the energy consumption of sensor nodes. In the WSN, the CHs consume more energy than other nodes, so the lifetime of the CHs is shorter than other cluster members (CMs). Therefore, to extend the sensor network's lifetime, the load of the sensor nodes connected to the CHs must be balanced. This is the load balancing clustering problem, which is an NP-hard. In [13], a routing algorithm was proposed. The routing algorithm reduces and balances the energy consumption in the network by finding a suitable path between each CH and the aggregation end. In [14], a user association and resource allocation algorithm was investigated based on the Stackelberg game. In [15], a distributed WSN scheme based on grid clustering and fuzzy reinforcement learning was proposed to maximize the network life cycle while achieving energy-efficient data aggregation. In [16], path operator calculus centrality was utilized to optimize the centrality problem in routing, the harmony search algorithm was used to find the main routing path, and then the particle swarm algorithm was used to estimate high centrality nodes. At last, optimal routing with reduced energy consumption was found. As a result, the hybrid algorithm reduced time delay and increased residual energy, effectively balancing energy consumption. Considering that different transmission protocols have an impact on the energy consumption of sensor nodes. It is a challenging task to implement complex service quality protocols and security mechanisms in sensor networks. In addition, the WSN should not only focus on reducing the power consumption of sensor devices but also maintain the high reliability and high throughput required for smart grids applications [17]. In [18], a new data acquisition routing scheme was proposed based on bionic self-optimizing butterfly matching optimization, a self-optimizing intelligent routing protocol. In [19], an \"Efficient Power Grid Link Routing Protocol\" was proposed in WSN to expand the network's lifetime. First, the grid algorithm was utilized to divide the nodes into virtual units, and then the intra-cluster and inter-cluster chains are used for data transmission, and each node communicates with its neighboring nodes. The protocol can improve the stability of the network and reduce energy consumption more than the low-energy adaptive clustering hierarchy (LEACH) protocol.