# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 2 Significance of Energy Efficiency in IoT Use Cases
### Simulation Oriented Characterization  
In this section, recent technical developments in energy-oriented architectures and protocols for IoT environments are explored experimentally in a realistic simulation environment. The comparative investigation focuses on the performance monitoring of state-of-the-art techniques considering energy-oriented network evaluation metrics. The simulations have been performed on a NS2 network simulator considering a 100 m \(\times\) 100 m square experimental area with 500 nodes for a total simulation duration of 60 min to represent an IoT-based monitoring scenario. For robust experimental verification a standard simulator has been used considering its usage in a wide range of experiments. The major simulation parameters listed in Table 1 are used to conduct 25 rounds of repeated experiments considering different arbitrary seed numbers to acquire a 95% certainty interval. In this section, various routing protocols, namely AODV, ER-RPL, REL and FFSC, are simulated and analyzed using the mentioned simulation parameters. The focus of this simulation-based evaluation is to realize a resource-constrained scenario in an IoT environment. To this end, lower transmission range limit, lower power consumption rate in signal transmission and reception, a restricted area for  
Figure 5: Real time CoT-based automated irrigation system. monitoring are the major considered parameter settings. As initial results, two metrics are considered for evaluating some data dissemination techniques under an IoT centric parameter settings in the simulation environment, namely packet delivery ratio and network lifetime. The precise definition of network lifetime in sensor-enabled networks depends on the application scenario. For example, security-oriented monitoring applications require continuous operation of each node in the network, i.e., energy exhaustion of even 1% of the nodes is considered the network lifetime limit, whereas in normal environmental monitoring applications, energy exhaustion of even 10% of the nodes is not considered the network lifetime limit assuming that 90% of the network nodes can still perform their tasks smoothly. In our simulation-based network evaluation case, the energy exhaustion of 2% of the nodes is considered the network lifetime considering the connectivity-oriented IoT environment target application scenario. Considering a 2% dead nodes situation as a monitoring threshold, Figure 6a shows the network lifetime of various protocols (AODV, ER-RPL, REL and FFSC) under various node densities According to Figure 6a, network lifetime is defined as the number of rounds until 2% of the nodes die. In all simulation scenarios with 100, 200 and 500 nodes, FFSC outperforms other protocols, in terms of lifetime, then ER-RPL and REL have equal lifetimes, and AODV performs the worst. Figure 6b shows the percentage of alive nodes as the simulation time increases. Two measurement metrics were used to assess the energy efficiency: network lifetime and the saturation time of the IoT network. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulation area & 100 m \(\times\) 100 m \\ \hline Simulation time & 70 m \\ \hline Number of Nodes & 500 \\ Topology & random \\ \hline Initial Energy & 18,535 J (2 AA batteries) \\ Power for transmission & 0511 W \\ Power for reception & 0588 W \\ \hline Base station Location & (60, 60) \\ \hline Transmission range & 0.2 m \\ Transmission limit & 5 \\ \hline The radius of network & Source and destination specific \\ The traffic rate & 3 p/s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 6: _Cont._  
According to Figure 6b, the network lifetime is defined as the time spent by device nodes in the IoT network until the moment when only 1% of the nodes remain alive. The saturation time is like the network lifetime, except its measurement begins from the point when the first node dies.