# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 4 Energy Efficient Techniques in the IoT
### Energy Efficient Techniques at Sensing Layer
#### 4.1.1 Energy Efficient Sleep/Wakeup Techniques  
Toward QoI and Energy-Efficiency in Internet-of-Things Sensory Environments (TQEE) [32] has been proposed to manage long term energy consumption under appropriate QoI constraints in diverse applications like environmental monitoring, etc. In this technique, first a novel approach of QoI-aware sensor-to-task relevancy is introduced considering the sensing capabilities of sensors. Then, after \begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(c^{n}\) & Sensing capability of sensor \(n\) \\ \(q^{m}\) & Desired QoI of task \(m\) \\ \(r^{n}\) & Sensory radius \\ \(\partial^{m}\) & Variance for sensor \(n\) \\ \(\beta^{m}\) & Accuracy requirement of task \(m\) \\ \(r(c^{n},q^{m})\) & Sensor-task relevancy function \\ \(\mathbb{E}\left(s^{i},m\right)\) & Maximum residual energy of all possible subset of \(m\) activated sensors \\ \(v^{i}\) & Normalized residual energy of sensor \(s^{i}\) \\ \(E^{b}\) & Energy per information bit \\ \(R\) & System data rate \\ \(E^{\text{tot}}(t)\) & Total energy utilization of the network at time \(t\) \\ \(\partial\) & Actual coverage area \\ \(K(t)\) & Active sensors quantity at time \(t\) \\ \(p\left[E^{h}=k\right]\) & Probability of harvesting \(k\) energy units during \(T\) time slots of a frame \\ \(E^{h}\) & Amount of harvested energy of a node during a frame \\ \(E\left[\frac{p^{h}}{E}\right]\) & Average harvested energy \\ \(E^{\text{dir}}\) & Energy consumption by sensor node \\ \(\underset{\text{Do}mR_{\text{old}}}{\text{C}S^{i}}\) & Energy consumed to operate electrical circuit in the sensor node \\ \(E^{\text{s}}\) & Relative dominance of candidate service \\ \(E^{\text{s}}\) & Total energy spent in awake state \\ \(p^{\text{s}}\) & Outage probability \\ \(P^{\text{s}}_{h}\) & Maximum outage probability threshold \\ \(L^{i}\) & Lifetime of a node \\ \(p\) & Forwarding probability of a node \\ \(E_{i}\) & Residual energy of node \(i\) \\ \(E_{max}\) & Maximum residual energy of node \(i\) \\ \(E_{th}\) & Energy threshold \\ \(f^{i,s}\) & Traffic generation rate at node \\ \(f^{i,d}\) & Traffic arrival rate at node \\ \(r^{ij}\) & Traffic departure rate from node \(i\) to node \(j\) \\ \(T^{xy}\) & Delay between two node \(x\) and \(y\) \\ \(T^{M-sink}\) & Iterative delay \\ \(D^{H}_{t}\) & Expected delay of node \\ \(D^{\prime 0}\) & Total delay of entire network \\ \(E^{\prime}_{b}\) & Energy consumption of a node \(X\) distance away from the sink \\ \(E^{\prime\prime}_{b}\) & Total energy consumption in entire network \\ \(\delta\left(C^{i}\right)\) & Energy cost of a component \(C^{i}\) \\ \(\delta\left(D^{i}\right)\) & Energy consumption of a device \\ \(F\left(a^{k},\beta\right)\) & Lagrangian function of optimization problem \\ \(\mathbb{R}^{2\alpha 2}\) & End to end reliability \\ \(L\) & Lifetime of whole network under HTP technique \\ \(W\left(\frac{t^{i}}{k^{i}},n^{i}_{k}\right)\) & Welfare of every user \\ \(U\left(\frac{t^{i}}{k^{i}},n^{i}_{k}\right)\) & Utility function of user \\ \(z\) & Conflict parameter \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Nomenclature. proposing the technique of critical coverage set of a given task to select the sensors, dynamic decisions for energy consumption optimization are made instantly (Figure 8). For this model sensor-task relevancy function is formulated as:  
\[r(c^{n},q^{m})=1\{dis(X^{n},Y^{m})\leq r^{n}\}\cdot\min\left\{\frac{2\beta^{m}} {Q\Big{(}\frac{\beta^{m}}{\sqrt{y^{m}}}\Big{)}}\right\} \tag{1}\]  
where \(c^{n},\ q^{m},\ r^{n},\ y^{n},\ q^{m}\) and \(\beta^{m}\) are the sensing capability of sensor \(n\), desired QoI of task \(m\), sensory radius, variance for sensor \(n\) and accuracy requirement of task \(m\), respectively.