# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 2 Related Works
Simulations were done using NS3 and C-RPL performed better than RPL. The challenge in this work is that the overall packet transmission time was affected in terms of packet loss in C-RPL. Multiple unequal cluster-based routing is done for LLN [24]. The entire network is divided into multiple clusters of unequal sizes. The residual energy of the node is taken for choosing the cluster head. Data aggregation is done at the cluster head, and finally, it is transmitted to sink. There are some time delays in the formation of clusters initially. Simulations are done using the COOJA simulator, and the performance of the protocol is compared with RPL in terms of network lifetime and scalability. The main challenge found in this work is that the power taken for cluster formation and delay in the control message transfer have not been analyzed. In [25], the authors detailed the need for aggregation in WSN. An elaborated survey with analysis is done for aggregations and lists the advantages of aggregation. Aggregation minimizes the size and amount of data transfer. Few challenges related to aggregation, such as limited communication based on an interval basis, data compression rate, energy optimization, choosing aggregation based on application scenarios, etc., are summarized by the authors. In [26], authors have proposed an aggregator-based RPL for IoT-Fog-based power distribution system. The authors here have improvised the Contiki RPL by introducing the concept of an aggregator among smart meters based on ETX as an objective function. The aggregator-based RPL is under the assumption that energy meters are connected to power, and so, energy constraint is not taken into consideration. The challenge in this work is that other metrics such as CPU utilization, number of parent changes, and Qos metric are not taken into consideration in this work. Additionally, only ETX was taken for aggregator selection in forming the DODAG, and other metrics were not considered. In [27], authors have developed an energy-aware routing protocol (EAGDA-RPL) using grid-based data aggregation scheme. This has been developed for agricultural IoT applications. The routing protocol here forms equal-sized grid on a square network to reduce congestion among grid head nodes. Additionally, grid head is selected on a rotational basis. Each grid head uses ETX as a metric for selecting the optimal parent for data transmission. The challenge in this work is that energy is not taken into consideration for DODAG formation with grid head, where these sensor nodes are resource constrained. Additionally, the unequal grid size is not taken into consideration, which is another challenge. From the related works, it is observed that some improvements have been carried out on RPL towards extending the network lifetime and improving the packet delivery ratio and reliability. Though there are improvements, the role of aggregation is not considered in RPL with energy as an objective function in the previous work, which is one of the main drawbacks. There are also other limitations in the previous work on RPL such as energy consumption, predicting parent node, time taken for forming DODAG structure, etc. These works were not focused on forest fire application, in particular, where energy constraint is a big challenge. These limitations have motivated the proposed work by involving the aggregator with energy as an objective function and data compression in RPL to balance the network load and reduce the number of nodes to be involved for the DODAG structure. This has resulted in an increased packet delivery ratio, reduced power consumption, and less end-to-end delay. The details are explained in the forthcoming section.