## 3 Review of Previous Works
### Throughput Estimation Model
#### 3.1.1 Received Signal Strength Estimation  
First, the Euclidean distance \(d\) (m) is calculated for each link (AP/host pair) by:  
\[d= \sqrt{(AP_{x}-H_{x})^{2}+(AP_{y}-H_{y})^{2}} \tag{1}\]  
where \(AP_{x}\) and \(H_{x}\) represent the \(x\) coordinate of the AP and the host; and \(AP_{y}\) and \(H_{y}\) represent the \(y\) coordinate, respectively. After that, the received signal strength (RSS) \(P_{d}\) (\(-\)dBm) is estimated at the host by:  
\[P_{d}=P_{1}-10\alpha log_{10}d-\sum_{k}n_{k}W_{k} \tag{2}\]  
where \(P_{1}\) represents \(RSS\) from the AP to the host at the 1 m distance when there is no obstacle; \(\alpha\) represents the path loss exponent; \(n_{k}\) is the number of \(type\_k\) obstacles or walls along the route between the AP and the host; and \(W_{k}\) represents the signal attenuation ratio (dBm) for the \(type\_k\) (\(k=1,2,\ldots,6\)) obstacle. It is noted that in a building, there are several types of walls that have different thicknesses and materials, which make the different signal attenuation factors.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference** & **Goal** & **Method** & **Satisfy USER Requirement** & **Dense Wi-Fi** & **Dual Interface** & **Mininize AP Cost** & **Evaluation** \\ \hline \multirow{2}{*}{proposal} & minimize the number of active APs & greedy algorithm and local search method & yes & yes & yes & yes & simulation and testbed experiment \\ \hline \multirow{2}{*}{[37]} & optimizes the APs & & & & & & \\  & placement & & & & & & \\ \hline \multirow{2}{*}{[38]} & adjust the AP & & & & & & \\  & deployment & & & & & & \\ \hline \multirow{2}{*}{[39]} & minimize the number & & & & & & \\  & of APs & & & & & & \\ \hline \multirow{2}{*}{[40]} & optimal usersassociation & & & & & & \\  & and maximizes the total & & & & & & \\  & network throughput & & & & & & \\ \hline \multirow{2}{*}{[41]} & maximizes the network & rounding and bandwidth & & & & & \\  & throughput and AP utilization & & allocation algorithm & & & & \\ \hline \multirow{2}{*}{[42]} & minimize the number of APs & & genetic algorithm & no & yes & no & yes & simulation \\ \hline \multirow{2}{*}{[43]} & minimize the number of APs & & fruit fly optimization algorithm & no & yes & no & yes & simulation \\ \hline \multirow{2}{*}{[44]} & throughput analysis task into & & & & & & \\  & account the effects of & & airtime concept & no & yes & no & simulation \\  & inter-network interference & & & & & & \\ \hline \multirow{2}{*}{[45]} & minimize the number & & multi-objective particle swarm & & & & \\  & of APs & optimization (MOPSO) algorithm and & & yes & & \\  & & greedyalgorithm & & & & \\ \hline \multirow{2}{*}{[46]} & minimize the number & & polynomial time & & & & \\  & of APs & & heuristic algorithm & yes & yes & no & yes & simulation \\ \hline \multirow{2}{*}{[47]} & investigate the ability of & & & & & & & \\  & IEEE 802.11n dual-band APs & - & no & no & no & no & \\ \hline \multirow{2}{*}{[48]} & maximize the performance & & QoS differentiation & & & & \\  & of real-time application & & scheme (Slickfi) & no & no & yes & yes & testbed experiment \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between our proposal and existing works.  
The value of \(\alpha\) depends on the specific environment in which the radio signal is propagating. It represents the amount of loss that the signal will experience as it propagates through the environment. \(type\_k\) obstacles represent the obstacles or walls that have the k-th signal attenuation factor \(W_{k}\). In this paper, we consider six types of obstacles, namely, the corridor wall for \(W_{1}\), the partition wall for \(W_{2}\), the intervening wall for \(W_{3}\), the glass wall for \(W_{4}\), the elevator wall for \(W_{5}\), and the door for \(W_{6}\)[24].