# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 1 Introduction  
Forest or wildfire has a huge impact on environmental losses that damage the flora and fauna. These fires happen due to seasonal variations such as high temperature, high humidity, and so forth. Every year all over the world, the percentage of forest fire is increasing. There have been a lot of developments made in the field of wildfire fighting regarding disaster response such as early warning systems and real-time exchange of data for forest fire monitoring. Recent developments in information and communication technology are already having a huge impact, specifically in forest fire detection systems [1]. Internet of Things (IoT) is the latest buzzword that allows objects or devices to be connected wireless to the internet. Mostly, the devices deployed in the remote area are resource constrained. These devices also called sensor nodes are deployed in a remote area that is constrained in terms of processing power, power allocation, and radio resources. Due to the drastic increase in the evolution of smart devices, many remote-based applications, such as military surveillance, smart agriculture, and forest management, need a supportable solution for power-constrained environments [2]. In most IoT-based applications [3], energy is very scarce. In applications such as forest fire where there is no power backup, energy conservation is a big challenge where regular monitoring is required for preventing forest fires. There is a lot of research going on in terms of routing, preserving the energy of the node states, redundancy-based routing,clustering, compressed routing, optimized duty cycling [4], latency [5], etc. Most of the existing protocols and approaches are targeted towards improving the network lifetime. The IoT-enabled environment is made up of low power and lossy networks [6]. Low Power and Lossy Networks (LLNs) are collectively made of sensor nodes with limited energy, computing capacity, and memory storage. These constraints form the network communication with packet loss, lower data transmission rates, disconnectivity in the network, etc. Applications specific to environment monitoring need development by the way of better-enhanced routing in LLNs. Recently, the development of the Internet of Things makes its application more extensive; the scope of application extended to the location [7], vehicle networking [8], and other related fields. Currently, researchers concentrate on routing [9; 10; 11], energy-based clustering [12], Medium Access Control (MAC)-oriented protocols [13; 14; 15], and related issues. To improve the performance of the routing protocol in forest fire management, a power-saving routing is needed to increase the overall network lifetime. The routing protocol for low power lossy networks (RPL) is proposed as an IPv6 routing protocol for LLNs by the ROLL Working Group in the Internet Engineering Task Force (IETF). RPL is intended to support User Datagram Protocol (UDP) communication for a proper tradeoff between power consumption and timely communication. Energy is limited to routing in forest fire. Therefore, the conservation of energy of the sensor nodes is a significant challenge in the real-time scenario. Routing plays a vital role to optimize the utilization of nodes' energy. The objective function used in routing protocol for low power lossy network (RPL) rank calculation usually depends on hop count. In energy-efficient RPL (E-RPL) [16], the objective function is the residual power (RP) of the node to calculate the rank of the parent node. The highest rank node is assigned as the parent node in the Destination Oriented Directed Acyclic Graph (DODAG) tree. However, energy is not the only parameter to be included. The optimized root id is framed by considering the parameters such as scalability, the distance between the nodes, transmission range, etc. The energy-efficient RPL is validated using the Contiki Cooja simulator. The challenge in energy-efficient RPL (E-RPL) is the scalability of nodes.