# A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing
## 1 Introduction
In this case, the authors were able to infer the key that protects the firmware updates of the lamps, by measuring the patterns on the power consumption of the main chip while making cryptographic tasks. Then, they could upload new custom versions of the firmware to the lamps by just requesting it to the chip, once a minimum distance from the node is reached. Upon this update being accepted, the new firmware can be programmed to request the same update to other lamps on the network, propagating itself such as an infection. The ability to change the software that controls the nodes is the key to allow the attackers to cause several problems to the users. Looking at those lamps, the authors remark the possibility of causing epileptic attacks to users by generating stroboscopic lights. In this context, some solutions are being proposed by the community [6] in different ways, software-wise as well as hardware-wise. The security issue in IoT is certainly gaining more attention presently, and new approaches are being proposed to improve this aspect. For instance, some authors present testbeds to approach the difficult task of assessing security in IoT deployments [7]. In this work, these main concerns related to the security on the Edge of IoT are addressed, by creating a hardware platform that combines a main processing core with a Hardware Security Module in a modular and flexible architecture, so as to foster protection strategies for current and future sensor network deployments. Different techniques are used to guarantee privacy and integrity in the data collected by sensor nodes, as well as mechanisms to join the network in a trustable manner. The design and implementation of the hardware layer have been conceived to produce a trade-off solution between computational performance, power consumption awareness and high degree of protection with dedicated hardware resources, particularly considering the increasingly importance of the active operations of the nodes in IoT dynamic contexts. The runtime self-diagnosis management of the Edge node by providing power and functional islands, real-time current consumption monitoring and an extended range of operational modes for advanced power profiles are key features that this work takes into account to provide dynamic adaptation for the target application scenarios. Secondly, in order to validate and provide a baseline hardware and software platform for supporting distributed IoT Extreme Edge applications, a lightweight and robust multi-hop communication strategy for the Extreme Edge of IoT is proposed in this work (called Extreme-LT Routing) that allows verifying the dynamic deployment, discovery, data processing and dissemination of IoT devices in a reliable yet low-power resource-constrained fashion. The presented routing algorithm is based on the self-composition of the network topology based on the deployment conditions of the wireless nodes to find the best possible routes for the given circumstances, so as to achieve an optimized data delivery from the sensing nodes to the Edge of the IoT layers. The design and implementation of this technique is included as an embedded software component of the proposed IoT platform, and it has been used as the support communication capability to analyze its behavior and performance in real IoT Extreme Edge deployments, through the realization of intensive experimental tests, as shown in the outcomes of the work. The main contributions of this work can be summarized as follows:  
* A modular hardware platform for the edge and extreme edge of the IoT, with HW enhancements for security, trustability and protection against hacking. It includes the implementation of enhanced low-power profiles to provide a trade-off solution between more demanding processing capabilities yet reduced energy consumption. * An extreme lightweight transmission protocol for multi-hop packet routing in resource-constrained IoT edge sensor networks. Its main features are simplicity, robustness, efficiency and hardware Independence.