# 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  
#### 4.3.1 Energy Efficient Scheduling Techniques  
Energy-Centered and QoS-Aware Services Selection for Internet of Things (EQSA) [40] utilizes lexicographic optimization and QoS constraints relaxation approaches to preselect QoS-aware services required for the user's level and the technique of relative dominance of services in the Pareto sense to minimize energy usage for a composite service in the IoT environment. The relative dominance of candidate services is dependent on QoS attributes, energy profile (EP) and user's preferences and calculated as:  
\[Dom_{Rel}^{CSj}=EP-Dom^{CSj}\sum\limits_{q=1}^{QoS}Dom^{(CSj,\ q)}\times Pref^{q} \tag{9}\]  
where \(\sum\limits_{q=1}^{QoS}Dom^{(CSj,\ q)}\times Pref^{q}\) is QoS-based dominance of the service \(CS_{j}^{i}\). Energy Efficient Context Aware Traffic Scheduling for IoT applications (EECATS) [41] has been proposed to realize energy-efficient resource allocation in Wi-Fi-based devices in heterogeneous traffic demand and distinct weighted quality classes-based multi-hop IoT infrastructures. In this technique, service quality requirements and a context priority-based optimization model by sub-gradient projection method is introduced. In the awake state, the total energy spent is formulated as:  
\[E^{anwake}=P^{tx}\times T^{raw,I}=P^{tx}\times\left(\sum\limits_{i=1,j=1}^{n}A_{ i,j}\Big{(}2\times n\times T^{t,i}+3\times T^{sfs}+T^{pf}+T^{wait}\Big{)}\right) \tag{10}\]  
QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems (QEECS) [42] has been suggested to provide tradeoff between energy efficiency and QoS provisioning by formulating a multi objective optimization problem, while using network lifetime and outage probability as metric, respectively. This technique considers two phases for data transmission: setup phase and steady state phase. During the setup phase a combination of quantum-inspired particle swarm optimization and exhaustive search for selecting optimum cooperative coalition for cluster head (CH) and cooperative nodes (Coop) in capillary network of industrial IoT systems. In steady state phase, collection and transmission of data by all nodes is divided into three stages using TDMA scheduling, data collection, local broadcasting and long-haul cooperative transmission. The multi objective optimization problem is solved using improved non-dominated sorting genetic algorithm (NSGA-2). Finally, the combination of QPSO and NSGA-2 algorithms obtains the Pareto-optimal solution first. For QoS provisioning, formula for the outage probability is mentioned as:  
\[P^{out}=p_{th}^{out}-P^{out,\frac{Cll}{cap}} \tag{11}\]  
where \(p_{th}^{out}\) is the maximum outage probability threshold. Lifetime of a node is formulated as:  
\[L^{i}=\frac{E^{res}}{E} \tag{12}\]  
\(E\) is the energy consumption of a node, if \(p_{dc}\), \(p_{lb}\) and \(p_{lh}\) are power consumption in data collection, local broadcasting and local-haul cooperative transmission phase respectively, then \(E\) is described by a formula as:  
\[E=\frac{1}{\alpha}\times p_{dc}+\frac{1}{2\alpha}\times p_{lb}+\frac{1}{2 \alpha}\times p_{lh} \tag{13}\]  
Figure 13 illustrates the system model of QEECS technique. A summary of related literatures on energy efficient scheduling techniques is given in Tables 8 and 9, which comprises characteristics/protocol, issues, contributions, techniques, simulation tools used, metrics and research limitations of each paper reviewed. In Table 9, the characteristics of each paper reviewed are categorized into three classes, including protocol/author, approach, and metrics. The protocol/author column comprises abbreviation of techniques and reference. The approach column is categorized into five namely, MAC, CLT, CA, DC and QoS. Last column represents metrics including DL, EE, OPM, RD, NL, OP, DR, COL and THP. Considering Table 9, the approaches in column five demonstrate that more contributions have been made by QoS based techniques, followed by MAC and duty cycle based techniques.