Algorithm 1:

Input:$p_{\mathrm{th}}$, $r_s$, and $\lambda$, where $p_{\mathrm{th}}$ is the threshold, $r_s$ is the sensing range, and  $\lambda$ is the sensor-dependent parameter given in (1). Output: $\frac{r_s}{\sqrt{3}}$ if $p_{\mathrm{th}}\le p_{\mathrm{th}}^{\min }$; $r_1^{*}$ or an $ϵ$-approximation of $r_1^{*}$ if $p_{\mathrm{th}}>p_{\mathrm{th}}^{\min }$. 1:if$p_{\mathrm{th}}\le p_{\mathrm{th}}^{\min }$then$r_1\leftarrow \frac{r_s}{\sqrt{3}}$ 2:else 3:    found$\leftarrow$ false; 4:    $ϵ\leftarrow {10}^{-6}$; 5:    lower $\leftarrow 1-{(1-p_{\mathrm{th}})}^{\frac{1}{3}}$; 6:    upper $\leftarrow {(1-{(1-p_{\mathrm{th}})}^{\frac{1}{3}})}^{\frac{1}{\sqrt{3}}}$; 7:    while ((found = false) and (upper − lower) ≥ ϵ)) do 8:        $mid\leftarrow \frac{lower+upper}{2}$; 9:        $val\leftarrow 1-(1-mid)(1-mi{d}^{\sqrt{3}})(1-mi{d}^{\sqrt{3}})$; 10:        if $(val=p_{\mathrm{th}})$ then $found\leftarrow$ true; 11:        else if $(val<p_{\mathrm{th}})$ then $lower\leftarrow mid$; 12:        else $upper\leftarrow mid$; 13:        end if 14:    end while 15:    if ($found=$ true) then 16:        $r_1\leftarrow \frac{lnmid}{-\lambda }$;   //$r_1^{*}$ 17:    else 18:        $r_1\leftarrow \frac{lnupper}{-\lambda }$;   //an $ϵ$-approximation of $r_1^{*}$ 19:    end if 20:end if 21:return $r_1$;