# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 4 Energy Efficient Techniques in the IoT
### Energy Efficient Techniques at the Application Layer  
A green and reliable communication modeling for industrial internet of things [55], named Hybrid Transmission Protocol (HTP) is a combination of a send-wait automatic repeat request approach in hotspot areas and a network coding-based redundant transmission approach in non-hotspot areas for industrial IoT applications. HTP has been proposed to maximize lifetime, to save energy and to minimize delays in the network. If \(E^{in}\) is initial energy of node, \(d\) is data size and \(k\) is maximum time for retransmission under send-wait automatic repeat request approach. Then lifetime of whole network under HTP technique is expressed as:  
\[L=\left\{ \begin{array}{l}\frac{E^{in}}{kdE^{in}+k(d+1)E^{in}+k(d+1)\in_{f}d ^{2}}\,\ if\ l<l_{0}\\ \frac{E^{in}}{kdE^{in}+k(d+1)E^{in}+k(d+1)\in_{m}d^{2}},\ if\ l>l_{0}\end{array} \right. \tag{30}\]  
and end to end reliability can be formulated as:  
\[R^{2\ell e}=\prod_{i=1}^{n+1}\left(1-(1-R)^{k_{i}}\prod_{i=n+2}^{N}\sqrt{1-R_{ l}^{h_{i}+2}-(h_{i}+2)R_{l}^{h_{i}+1}(1-R_{l})}\right) \tag{31}\]  
where \(k_{i}\) is maximum time for retransmission under send-wait automatic repeat request approach for node \(i\) and \(h_{i}\) is redundancy level of node \(i\) under network coding based redundant transmission approach. Energy-Efficient Dynamic Packet Downloading for Medical IoT Platforms (EEDPD) [56] utilizes dynamic energy efficient technique to compute amount of power allocated in every access point which is based on channel state and buffer backlog size considering buffer stability to enhance power/energy management. The medical cloud computing architecture (Figure 19) consists of various key element: medical users (mobile phone, tablet etc. ), access points and medical cloud. The medical cloud containing its own processing unit, can generate relational maps and find meaningful patient information while performing data mining process. Related medical information is downloaded by medical users from central medical cloud platforms. In this technique quality-ware operations are not considered in medical imagining cloud networks. Optimized Day-Ahead Pricing with Renewable Energy Demand-Side Management for Smart Grids (ODAP) [57] has been suggested while considering renewable energy buying-back technique with dynamic pricing to make energy efficient system for smart grid enabled IoT. In this technique, a day-ahead time-dependent pricing scheme has been proposed distributively to enhance user privacy, after formulating dynamic pricing problem as a convex optimization dual problem for providing benefit to both user and electric  
Figure 19: Medical cloud computing system architecture. companies. According to Figure 20, a power delivery system is composition of an electric company and users having energy storage devices, and renewable energy generation devices (wind turbine). From the user's perspective, if \(l_{k}\) is electricity consumed by each user with charged/discharged rate \(r_{k}\) and \(m_{k}\) is renewable energy sold by each user in time slot \(k\), then welfare of every user is formulated as:  
\[W\Big{(}l_{k}^{i},n_{k}^{i}\Big{)}=\sum_{k}^{K}\Big{(}U\Big{(}l_{k}^{i},n_{k}^{ i}\Big{)}-R_{s}\Big{(}r_{k}^{i}\Big{)}-R_{r}\Big{(}m_{k}^{i}\Big{)}-P_{k}\times \Big{(}l_{k}^{i}+r_{k}^{i}\Big{)}+E_{k}\times m_{k}^{i}\Big{)} \tag{32}\]  
where \(U\big{(}l_{k}^{i},n_{k}^{i}\big{)}\) is the utility function of user, \(R_{s}\big{(}r_{k}^{i}\big{)}+R_{r}\big{(}m_{k}^{i}\big{)}+P_{k}\times\big{(}l_ {k}^{i}+r_{k}^{i}\big{)}\) is the total cost of every user and \(E_{k}\times m_{k}^{i}\) is the profit after selling renewable energy back to grid. A summary of related literatures on energy efficient techniques at the application layer is given in Tables 16 and 17, which comprises characteristics/protocol, issues, contributions, techniques, simulation tools used, metrics and research limitations of each paper reviewed.