# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 3 Network Model  
The IoT network consists of 'N' number of nodes and one DODAG root. The nodes are randomly deployed in the network. The EDADA-RPL follows the tree-based approach. The sensor node generates the data and transmits it to the parent node. Each parent node collects the data packets and performs the data aggregation using compressed sensing (CS) theory [29]. Likewise, the data aggregation operation performs from a downward parent to the root of the DODAG. Finally, the DODAG root obtains the compressed data and it performs the data recovery using the matching pursuit algorithm. Figure 1 shows the network model of EDADA-RPL.  
The Figure 2 shows the data collection and aggregation process using CS theory. In this, the path1 contains the subset of nodes in the DODAG, which are represented the data aggregation process. The parent node (PN) generates the aggregated data \(d_{i}\) multiplied by the random weight value \(r_{i}\). In each level in DODAG, the current parent node (PN) obtains the aggregated data in their child nodes along with downward parent transmitted data \(PN_{k}=\sum\limits_{i=1}^{k}r_{\text{1i}}\times d_{i}\). The detailed discussions of the data aggregation process are given in Section 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S.No** & **Protocol** & **Author’s** & **Proposed Technique** & **Improvement** & **Limitations** \\ \hline \multirow{3}{*}{\(1\)} & \multirow{3}{*}{LA-RPL} & [PERSON] & learning automata-based & \multirow{3}{*}{Extends the network lifetime} & \multirow{3}{*}{It does not consider the trickle timer} \\  & & [PERSON] et al. & & dynamic data aggregation & & lifetime \\ \hline \multirow{2}{*}{\(2\)} & \multirow{2}{*}{A-RPL} & [PERSON] and & data aggregation & \multirow{2}{*}{Increased the network lifetime} & \multirow{2}{*}{congestion occurs in a particular situation} \\  & & [PERSON] & based RPL & & \\ \hline \multirow{3}{*}{\(3\)} & \multirow{2}{*}{RECOUP-RPL} & [PERSON] et al. & cluster-based & Increased the packet & \multirow{2}{*}{It takes more energy consumption, as it} \\  & & multicast routing & delivery ratio & & \\ \hline \multirow{3}{*}{\(4\)} & \multirow{3}{*}{CCR-RPL} & [PERSON] et al. & content-centric routing & better performance in & \\  & & & terms of latency, energy & & \\ \hline \multirow{2}{*}{\(5\)} & \multirow{2}{*}{C-RPL} & [PERSON] et al. & Cluster parent routing & Increased the reliability & \multirow{2}{*}{It takes more time to choose the cluster} \\  & & & & & parent \\ \hline \multirow{2}{*}{\(6\)} & \multirow{2}{*}{FLWP} & [PERSON] & fuzzy-based data fusion & It provides superior & \multirow{2}{*}{It takes a longer time to predict the parent node} \\  & & et al. & technique & the AODV & \\ \hline \multirow{2}{*}{\(7\)} & \multirow{2}{*}{C-RPL} & [PERSON] et al. & cooperative interaction & Increased the & \multirow{2}{*}{It takes additional time to choose the parent node present in the multiple DODAG} \\  & & & & & multiple DODAG \\ \hline \multirow{2}{*}{\(8\)} & \multirow{2}{*}{FC-RPL} & [PERSON]. [PERSON] and [PERSON] & \multirow{2}{*}{cluster routing} & Extended the & \multirow{2}{*}{It forms the more number of clusters in} \\  & & [PERSON] & network lifetime & the network. \\ \hline \multirow{2}{*}{\(9\)} & \multirow{2}{*}{MUCRR-RPL} & [PERSON] et al. & cluster routing & Improved the network & \multirow{2}{*}{Initially, it takes time to form the cluster.} \\  & & & & delivery ratio & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature review of Data aggregation on Routing Protocol for Low Power and Lossy Networks (RPL).  
Assumptions:  
1. Nodes are deployed randomly on the network.
2. All nodes are having the same energy.
3. The rate of data transfer is one packet a minute.