# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 2 Related Works  
In this section, the various techniques related to IoT-based routing in energy constrained environment are discussed. [PERSON] et al. proposed an enhanced model for data aggregation for IoT environments [17]. In the RPL-based routing, the number of child nodes under a parent decides the load of the entire network. Sometimes, if the child nodes are more for a single parent, it will create congestion towards connecting the parent node. Dynamic aggregation of data is done at the parent node using learning automata. Every node in the network has its own learning automata (LA), which does the operation of aggregation and forwarding data. Learning automata RPL (LA-RPL) is implemented using a Contiki Cooja simulator. The challenge in this work is network congestion due to the absence of a trickle timer for synchronization. The authors in this work proposed a resource-constrained routing path by utilizing an aggregator node [18]. The routing protocol RPL changes its route periodically based on available resources. Dynamic routing has been proposed where the DODAG formation is fast, and the sink node collects the data from the lower layer nodes. The objective function of adaptive RPL (A-RPL) is decided based on the environmental behavior and network overload parameters. The challenge in this work is also the absence of a dynamic trickle timer and the congestion of aggregated packets during the forwarding process. [PERSON] et al. suggested a group-based routing to improve the performance of the RPL. Multi-cast routing is done among the sensor nodes from each cluster. Since clustering and combining data packets are done at the parent node, network load is reduced. In addition, the parent node is chosen in a way that is capable of performing group communication [19]. The main improvement needed in this work is that energy is not a metric for selecting a parent node. [PERSON] et al. [20] proposed an improved metric for calculating the rank of the destination-oriented directed acyclic graph (DODAG). The rank of the node is determined based on the context of the respective sensor node. Since it depends on context awareness, packet loss is gradually reduced in the routing protocol. The drawback of this work is that it has taken only the context for the calculation of the RANK metric in the objective function. None of the link-oriented metrics are considered in this work that will have more impact on the network load. In [21], the authors recommended two different MAC-based metrics for routing. First, R-metric improves the standard expected transmission range objective function (ETX-OF) by taking packet drops at each node into account. The next Q-metric will manage the load of the network by considering the number of packets involved in transmission and reception. Hence, an optimized modified routing for RPL is the solution for the challenges listed above for forest fire. This work has combined both ETX and load metrics to strengthen the routing and compared the performance on delay constraints versus reliability constraints. The challenge in this work is that the scalability of the network is not taken for the performance of the work. Different data-centric routing algorithms are proposed for IoT. Content centric routing for RPL (CCRPL) is proposed in [22]. Whenever the content is available at the routing node, it will consolidate and forward the data towards the destination. Data aggregation is done at the immediate node before forwarding. CCRPL is compared with traditional RPL in terms of metrics such as reliability, latency, power efficiency, etc. Though aggregation is incorporated here, properties and efficiency on aggregation are not specified, which is a major drawback. Cluster-based RPL (C-RPL) was implemented by [PERSON] et al. for IoT in the real-time environment [23]. C-RPL introduced a forwarding scheme with optimal selection of parent nodes towards reducing the delay in the communication between source and destination. For data transfer, scheduling based on priority is integrated into RPL.