## 3 Problem Formulation
Therefore, the cost of fault indicator can be expressed by  
\[\mathit{FIC}=\mathit{FIC}_{\text{acquisition}}+\mathit{FIC}_{\text{collection}} \tag{11}\]  
where  
\[\mathit{FIC}_{\text{acquisition}}=\mathit{FIC}_{\text{LoRa-acquisition}}+ \mathit{FIC}_{\text{Other-acquisition}} \tag{12}\]  
\[\mathit{FIC}_{\text{collection}}=\mathit{FIC}_{\text{LoRa-collection}}+ \mathit{FIC}_{\text{NB-IoT}}+\mathit{FIC}_{\text{Other-collection}} \tag{13}\]  
As Equations (12) and (13) indicates, the cost of acquisition unit consists of the cost for LoRa module of acquisition and other parts of acquisition, the cost of collection unit consists of the cost for LoRa module of collection, NB-IoT module, and other parts of collection. Furthermore,  
\[\mathit{FIC}_{\text{LoRa-acquisition}}=n_{1}\cdot\mathit{Price}_{\text{LoRa}} /m_{\text{LoRa}}+\mathit{PWF}\cdot n_{1}\cdot\mathit{Maintain}_{\text{LoRa}} \tag{14}\]  
\[\mathit{FIC}_{\text{LoRa-collection}}=n_{2}\cdot\mathit{Price}_{\text{LoRa}} /m_{\text{LoRa}}+\mathit{PWF}\cdot n_{2}\cdot\mathit{Maintain}_{\text{LoRa}} \tag{15}\]  
\[\mathit{FIC}_{\text{NB-IoT}}=n_{2}\cdot\mathit{Price}_{\text{NB-IoT}}/m_{ \text{NB-IoT}}+\mathit{PWF}\cdot n_{2}\cdot\mathit{Maintain}_{\text{NB-IoT}}+ \mathit{PWF}\cdot n_{2}\cdot\mathit{Com}_{\text{NB-IoT}} \tag{16}\]  
where \(n_{1}\) is the number of acquisition units that are installed, \(n_{2}\) is the number of collection units that are installed, \(\mathit{Price}_{\text{LoRa}}\) is the installing price of a LoRa module, \(\mathit{Price}_{\text{NB-IoT}}\) is the installing price of an NB-IoT module, \(\mathit{m}_{\text{LoRa}}\) is the service life of a LoRa module, \(\mathit{m}_{\text{NB-IoT}}\) is the service life of an NB-IoT module, \(\mathit{Maintain}_{\text{LoRa}}\) is the cost of maintaining a LoRa module, \(\mathit{Maintain}_{\text{NB-IoT}}\) is the cost of maintaining an NB-IoT module. \(\mathit{Com}_{\text{NB-IoT}}\) is communication fee per year. \(\mathit{PWF}\) is present worth factor, which can be expressed by  
\[\mathit{PWF}=\frac{1+\eta_{\text{inf}}}{1+\eta_{\text{int}}} \tag{17}\]  
where \(\eta_{\text{inf}}\) is inflation rate and \(\eta_{\text{int}}\) is interest rate. Moreover, the relationship between the number and location of installed acquisition units and collection units can be expressed by  
\[\sum\limits_{i\in\mathit{AF}_{\text{acquisition}}}\varphi_{i}=n_{1} \tag{18}\]  
As shown in \[IC=\sum\limits_{l=1}^{n}\left[(T_{\text{seek-}l}\cdot(1+p)+T_{recect})\cdot \lambda\cdot Price\cdot(Load_{l}+\sum\limits_{k=l+1}^{n}Load_{k})\right] \tag{10}\]0 (a) According to the original geographical location map, the distance coordinate system between nodes is established, and the nodes are labeled. (b) According to the relationship between nodes, the load matrix after power failure and the tabu domain searched after power failure are established. (c) Initialize whether the node has collecting devices or not, and initialize the number of collecting devices according to the limiting conditions. (d) The fitness of each individual is calculated according to the load and the cost of the device, and the optimal individual is recorded. (e) Judge whether the optimal individual has not changed within several times or reaches the maximum iteration times. (f) If yes, output the optimal individual data and stop the cycle. (g) If not, the population is copied, crossed, and mutated, and the useful individuals are screened according to the restriction conditions to ensure the constant population size and recycle (d)-(g). In this algorithm, we update the individual in the population through replication, crossover, and mutation in each cycle. Through this circulation, the optimized configuration scheme set is continuously updated until the cost obtained has almost no change after 10 evolutions. Therefore, we can get the optimal results.