# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 4 Energy Efficient Techniques in the IoT
### Energy Efficient Techniques at Network/Communication Layer
#### 4.3.2 Energy Efficient Routing Techniques
\(R_{a}\) energy \(>R_{b}\) energy, \(R_{a}\) energy \(\leq R_{b}\) energy \(+E_{th}\), \(R_{a}\) hopcount \(>R_{b}\) hopcount \(+HCdiffer^{\max\_allow}\) and \(R_{a}\) weak links \(\geq R_{b}\) weak links REL selects a route with good link quality and efficient energy consumption, but has the disadvantage of less routing success probability and more route set-up delay. This protocol is applicable only for static environments. For energy saving the radio transmission technique can be used. Energy-Efficient Content-Based Routing (EECBR) [45] is a routing technique for publishing/subscribing approaches that comprises routing based on context and content. The EECBR protocol tackles the routing problem while balancing the consumption of energy in efficient manner across an IoT network. First of all, a virtual topology is created, which can be a tree or the set of clusters depending upon the network's topology. In this algorithm, the sensor which covers the maximum sets of sensors of interest that are located in the communication range, is selected as a leader from a set of sensors of interest for a particular event. Compared with many other protocols it schedules and decides sleep/awake sensors by monitoring a certain area by using some leaders. The virtual topology helps in organizing sensors and reducing their communication cost across the IoT network. While making decisions on the selection of the leaders, the contribution of every sensor is evaluated, which depends on the quantity of uncovered sensors that are interested in a particular event/uncovered leaders of \((i-1)\)th level located within the range of the sensors, and the residual energy of sensors. The sensor with the largest contribution is selected as a leader. Intended receivers are broadcasted by enabling it. An Energy-Efficient Region-Based RPL Routing Protocol for Low-Power and Lossy Networks (ER-RPL) [46] has been suggested to achieve energy-efficient point to point data delivery while considering reliability in machine to machine communication for static, low-power and lossy networks. ER-RPL utilizes only a subset of nodes from some regions, in place of all nodes, for finding optimal route in terms of efficient energy consumption and reliability. ER-RPL has been described in two stages: network initialization stage and route discovery stage. In the network initialization stage, the first coordinates of each reference node are computed, then based on reference node's coordinate values and hop counts, every node estimates its distance to every reference node. By using a distributed self-regioning technique, the region of a node in the network is computed. In the route discovery stage, based on the region of the source node and destination node, route discovery is processed among the subset of nodes using region-based route discovery. Then, region-based route discovery is enhanced, by designing region to region routing without route discovery. Besides, the region to region route is further shortened by implementing a route adjustment on-the-fly algorithm and the dead zone problem is solved by an adaptive region selection algorithm. If \(f^{i,s}\) is traffic generation rate at node \(i\), \(f^{i,d}\) is traffic arrival rate at destination node \(i\), \(r^{ij}\) is traffic departure rate from node \(i\) to node \(j\). Then, in ER-RPL, formula for point to point traffic model of node is mentioned below:  
\[f^{i,s}+\sum_{j\in NN(i)}\partial^{ij}r^{ji}-\sum_{j\in NN(i)}\partial^{ij}r^{ ij}p^{ji}=f^{i,d} \tag{15}\]  
where \(0\leq r^{ji}\leq C^{ji},\ \forall_{i,j\in u}\), and \(i\
eq j\).