# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 4 Energy Efficient Techniques in the IoT
### Energy Efficient Techniques at Sensing Layer
#### 4.1.3. Energy Efficient Self-Organized Things (SOT)-Based Techniques  
An energy-efficient next generation network management (SOT-EENGNM) [35] has been proposed to minimize the total energy consumption of things and to stabilize the whole coverage area by using an optimization procedure. In this technique, the next generation self-configuration, self-healing and self-optimization procedure of self-organizing network structure of long term evaluation systems is redefined to overcome human dependency in SOT (Figure 9). The spatial distribution of devices to determine active things' distribution in 2D space, and the intersection of device's coverage areas using a conflict parameter are also derived analytically in the self-management process of SOT. If \(E^{tot}(t)\) is the total energy utilization of the network at time \(t\), \(K(t)\) is the active sensors quantity at time \(t\), \(r\) is coverage radius and \(z\) is conflict parameter, then actual coverage area \(\partial\) is formulated as:  
\[\partial=\frac{z\times K(t)\times\pi\times r^{2}}{E^{tot}(t)} \tag{5}\]  
Figure 9(a) illustrates the energy utilization at particular time \(t\) w.r.t. actual coverage area (\(\partial\)), while keeping various coverage radius (\(r\)) according to Equation (5), while Figure 9(b) shows how when \(E^{tot}(t)\) varies the number of active sensor nodes varies. Summaries of related literatures on energy efficient techniques at the sensing layer are given in Tables 4 and 5, which comprises characteristics/protocol, issues, contributions, techniques, simulation tools used, metrics and research limitations of each paper reviewed. Figure 10: Representation of Equation (5): (a) total energy utilization via actual coverage area; (b) total energy utilization via number of active sensor nodes. Figure 9: Network topology using SOT. Table 3 presents the major abbreviations used in particular in our comparison tables. Tables 4 and 5 which shows analysis of energy efficient techniques at LPSL. In Table 5, the characteristics of each paper reviewed are categorized into three groups, including protocol/author, approach, and metrics. The protocol/author column comprises abbreviation of techniques and reference. The approach column is categorized into five classes, namely QoI, CS, CL, DC and SOT. The last column represents metrics including DL, EE, DR, SNR, NL, CP, ACA, and DB. Considering Table 5, the approaches in column four demonstrate that more contributions have been made by duty cycle-based techniques. Further, the metrics column shows that EE is the most considered metric, followed by DR, NL and then ACA for measuring performance of energy efficient techniques at sensing layer. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Abbreviation** & **Description** & **Abbreviation** & **Description** \\ \hline CL & Cross-layer & PCV & Photovoltaic cell voltage \\ CA & Context aware & PDR & Packet delivery ratio \\ DL & Delay & CLT & Clustering \\ EC & Energy consumption & ETX & Expected transmission count \\ EE & Energy efficiency & BU & Bandwidth Usage \\ DB & Durability & LQ & Link quality \\ CS & Compressive sensing & EH & Energy harvesting \\ NL & Network lifetime & OP & Outage probability \\ MAC & Medium access control & RD & Relative dominance \\ CPL & Composite lifetime & OPM & Optimality \\ DC & Duty cycle & ST & Selection time \\ ND & Network distortion & THP & Throughput \\ ACA & Actual coverage area & L-Ratio & Largest energy cost ratio \\ DPR & Droped packet ratio & PLR & Packet loss rate \\ QoS & Quality of service & T-Ratio & Total saved energy cost ratio \\ CP & Conflict parameter & CO & Control overhead \\ QoI & Quality of information & HC & Hop count \\ NDIB & Number of delivered information bits & PD & Processing density \\ EUS & Energy used per server & AR & Acceptance rate \\ NoM & Number of migrations & PC & Power consumption \\ CU & CPU utilization & PAR & Peak to average ratio \\ GL & Grid loading & GV & Grid variance \\ SOT & Self-Organized Things & & \\ \hline \hline \end{tabular}
\end{table}