# No Free Lunch--Characterizing the Performance of 6 TiSCH When Using Different Physical Layers
## 7 Conclusions  
Table 5 summarizes the key performance indicators of our network measured in the experimental campaign. FSK 868 MHz exhibits the best network formation time, end-to-end reliability and end-to-end latency, at the cost of a battery lifetime roughly 10 times lower than O-QPSK 2.4 GHz. OFDM 868 MHz shows balanced results, between FSK 868 MHz and O-QPSK 2.4 GHz. There is no single PHY layer that exhibits the best performance over all KPIs. _So which PHY should I use?_ If choosing a single PHY, which to pick depends on the application. If it is acceptable to change batteries every year or so, FSK 868 MHz appears to be the most appropriate. If battery lifetime is of utmost importance, Table 5 suggests O-QPSK 2.4 GHz is the most appropriate. Finally, for an \"in between\" performance, choose OFDM 868 MHz. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **FSK 868 MHz** & **OFDM 868 MHz** & **O-QPSK 2.4 GHz** \\ \hline DC & 2.100\% & 0.750\% & 0.650\% \\ \(DC_{TX}\) & 0.250\% & 0.038\% & 0.050\% \\ \(DC_{RX}\) & 1.850\% & 0.713\% & 0.600\% \\ \(I_{TX}\) & 62 mA & 62 mA & 24 mA \\ \(I_{RX}\) & 28 mA & 28 mA & 20 mA \\ Supply voltage & 2.5 V & 2.5 V & 3.0 V \\ energy per day & 0.0141 Wh & 0.0033 Wh & 0.0015 Wh \\ battery lifetime & 1.6 years & 6.9 years & 15.1 years \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \"Ideal battery\" lifetime for each configuration. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Network Formation** & **End-to-End Reliability** & **End-to-End Latency** & **Battery Lifetime** \\ \hline  & **(Section 6.1)** & **(Section 6.2)** & **(Section 6.3)** & **(Section 6.4)** \\ \hline FSK 868 MHz & **7 min** & **100.00\%** & **10 s** & 1.6 years \\ \hline OFDM 868 MHz & 9 min & 99.84\% & 25 s & 6.9 years \\ \hline O-QPSK 2.4 GHz & 11 min & 98.08\% & 35 s & **15.1 years** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the Key Performance Indicator (KPI) measured in our testing. The best values are shown in bold. Figure 9: Evolution of the mote’s radio duty cycle over time. The problem is that the reasoning in the paragraph above is flawed in at least two ways. First, the results presented in this article, while conducted and presented in a rigorous fashion, only hold for use cases very similar to the deployment shown in Figure 2. There are undoubtedly cases (deeply sparse network, heavily unbalanced deployment,...) where our results do not hold, and where the \"ranking\" of the different PHYs is different. Second, even if every precaution is taken to pick the right PHY during the design phase of the network, it is entirely possible that conditions or requirements change during the operation of the network. This could lead to operating with the \"wrong\" PHY layer, leading to sub-optimal performance. The real outcome of this paper is not the absolute numbers presented in Table 5. What that table does indicate is that no PHY is the best for all metrics, and that best performance is achieved when _combining_ the PHY layers, rather than _picking_ one. This result holds when contemplating additional PHY layer, one obvious candidate being LoRa [32]. As alluded to in Section 1, we are at an exciting stage where we have both radio chips which are able to switch PHY on a frame-by-frame basis, and we have the scheduling technology to orchestrate this PHY-layer agility. We argue for technology-agile network, in which each more keeps track of the quality of its link to its neighbors _for each PHY layer_, and uses the PHY layer most appropriate for each frame. This means a more may use a different PHY to communicate with different neighbors or to send frames belonging to different classes of traffic. This also means that, in a multi-hop scenario, a packet can travel from source to destination using a different PHY at each hop. In that context, energy efficient neighbor discovery and network consistency are the main challenges, elements we are working on in our current research. Conceptualization, methodology, M.R., Q.L., D.B. and T.W. ; software, data curation, M.R.