# A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing
## 4 The Extreme-LT Routing Protocol  
As a means of validating and testing the performance of the Cookie platform in multi-hop distributed deployment contexts, the design and implementation of a dynamic and adaptive routing strategy is proposed in this work, seeking reliability yet lightweight operation for the Extreme Edge. The presented routing algorithm is based on the self-composition of the network topology based on the deployment conditions of the wireless nodes in the target scenario, to find and update the best possible routes for the given circumstances in a lightweight and dynamic fashion, so as to achieve an optimized data delivery from the sensing nodes to the Edge of the IoT layers. There are some studies in the literature exploring the diverse options for the choice of IoT routing protocols. In [29], the authors focus on ad-hoc routing and study several protocols based on different mechanisms such as distance vector or link state. Another IoT routing protocol is RPL (Routing Protocol for Low-Power and Lossy Networks, [30]), which is widely used and supported by many IoT platforms and operating systems. This is reflected in the existence of many adaptations and variations for it, to enhance its performance in certain scenarios ([31; 32; 33]), as well as reviews of RPL-based protocols such as the one in [34]. The Extreme Edge Lightweight Transmission protocol (Extreme-LT) is a lightweight routing protocol developed at the Center of Industrial Electronics (CEI-UPM) for its use on the Cookie modular platform. It is a distance vector routing protocol for IoT networks, focused on simplicity, robustness and reduced processing load. It pursues the goals of reliability, robustness, efficiency and hardware independence set by CTP (Collection Tree Protocol, [35]), adapting and simplifying some mechanisms used by other protocols. The protocol distinguishes between two node types: sending nodes and a root node. A network will always be composed of a root node, acting as a sink, and a variable number of sending nodes, \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **AES-ECB (1 Block)** & **AES-CBC (16 Blocks)** & **AES-CTR (16 Blocks)** \\ \hline EFR32 MG12 & 31 us & 210 us & 219 us \\ \hline ATECC608A & 7 us & 165 us & 183 us \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental comparison of the two security modules included in the Cookie platform, considering the computational time for different AES modesestablishing a tree topology. In this sense, Extreme-LT builds a Destination Oriented Directed Acyclic Graph (DODAG, [30]), similar to the ones used in other IoT routing protocols such as RPL. The protocol is designed as a simple solution to route messages from the sending nodes to the sink, and since it is conceived to tackle the scenarios where the majority of traffic is directed to the sink node, and having in mind that the environment is lossy and routes are expected to change frequently, there is no necessity to store the whole upstream route in the node using routing tables, as seen in other protocols. Instead, each node only needs to know the route to its parent. Because of this, the choice of the best parent among the candidates is of utmost importance to the establishment of the tree and the optimization of the network topology. For the construction of the DODAG, the protocol uses the rank information and Received Signal Strength Indication (RSSI) as the metric to determine the best parent node from all the potential ones. The procedure to assign the rank of a node follows the following expression:  
\[Rank(child)=Rank(parent)+HopIncre \tag{1}\]  
With a rank increase per hop of 1 by default, the node rank is equal to the hop count from the root, resulting in the same metric that RPL implements for its Objective Function Zero (OF0, [36]). According to [PERSON] et al. [37], OF0 is not inferior to MRHOF (Minimum Rank with Hysteresis Objective Function, [38]) in terms of Packet Delivery Ratio (PDR) and power consumption, and even outperforms it in some scenarios.