# Energy Efficient IoT Data Collection in Smart Cities Exploiting D2D Communications
## 4 The Novel D2D-Based Energy Efficient IoT Data Collection  
The D2D-based solution we propose in this paper, hereafter also referred to as D2D-EE, is based on the possibility for two or more UEs to cluster together and _cooperatively upload_ their data in a unique transmission to the eNodeB. In order to enhance the overall energy efficiency, we refer to an _aggregator_ as the IoT device that is elected as a \"cluster head\" and collects all the data from the IoT devices within the cluster. Then, all the received data are aggregated into a unique payload and the aggregator itself sends them towards the LTE BS.  
Since D2D links cover short-distances, the channel quality is typically good even if lower transmission power is used [49]. Consequently, short-rage communications implicitly introduce energy savings. Nonetheless, similarly to a standard uplink transmission to the eNodes, also on a D2D link further energy efficient techniques can be implemented. This means that in our scenario where IoT devices are clustered together, a more robust MCS can be used both on the cellular link from the aggregator to the eNodeB and on the D2D transmissions within the cluster. Based on this observation, the objective of the proposed energy efficient solution for the IoT data uploading is based on the following three aspects: (i) the adoption of low transmission power over short-range D2D links; (ii) an optimal energy efficient MCS selection on every D2D link within a cluster; and (iii) optimal energy efficient MCS selection in the uplink from the aggregator to the eNodeB. Specifically, for the objectives listed in (ii) and (iii), the approach presented in [14] has been extended in order to make it compliant to the specific scenario and data communication adopted in our work.  
To this aim, the eNodeB will implement the algorithm described in the rest of this section. A radio resource management (RRM) scheme is implemented to configure (i) the set of UEs acting as _aggregator_; (ii) the cluster configuration for the D2D data collection; (iii) the MCS and the RBs assigned to the aggregators; and (iv) the MCS and the RBs for supporting the D2D transmissions in each cluster. In particular, when the data collection in the IoT is triggered in a single TTI, a single execution of the listed steps is executed to collect the data. Whenever significant variations in the channel conditions are observed (e.g., due to UEs' mobility), the algorithm should be repeated to update the service  
Figure 2: Energy efficient IoT data collection uploading solution. The overhead refers to the link layer (_i.e._, L2) and varies according to the LTE Modulation and Coding Scheme adopted to guarantee a BLER of 10%. (**a**) Fixed number of RBs; (**b**) Variable number of RBs.  
configuration. However, when focusing on almost static scenarios where channel conditions and devices positions do not significantly change over time, also the formed clusters are rarely to be updated. Moreover, the additional energy consumption for the cluster formation algorithm at the LTE BS is not critical as the LTE BS is typically connected to a wired power supply system, which reduces the importance of energy consumption for the BS.