# Energy-Aware Security Adaptation for Low-Power IoT Applications
## 2 Related Work
This component monitors join request traffic between end nodes, in order to detect jamming attacks. In [13], it is proposed to use two activation keys. This method is very similar to the one specified by the LoRa Alliance; however, it uses yet another preconfigured key to segregate the key management functions of the network and application layers. This approach uses a key (AppKey) to generate a session key between the end device and theAS, and another key (NwkKey) to generate the session key to be used between the end device and the NS. This approach discusses the exposure of the network to attacks, due to the fact that the NS is able to access application-level session keys. A mechanism for updating session keys is also proposed to increase system security, using the session keys currently used at each layer to create new session keys. A review of vulnerabilities, threats and common defense strategies in some LPWAN protocols, in particular LoRaWAN and SigFox, is performed in [14]. This work discusses critical aspects of security in LPWAN technologies and identifies attack vectors, while also proposing defense strategies to mitigate the identified risks in the context of relevant IoT technologies. In [15], the authors propose a new method for multihop data routing, which employs Q-learning in the context of data transfers. The main goal is to improve the performance of the network regarding quality of service and energy consumption, a core concern when referring to IoT applications. The results of the experiment are compared against existing modules, and the new proposal is verified to be very efficient in terms of energy savings, with lower data interference and improved quality of service. The authors in [16] discuss the importance of managing energy and fast data transfers in wireless sensor networks (WSN) and propose a new data routing method optimizing the energy cost of the links. This strategy allows one to generate uniform energy consumption and promote faster data transfers. The evaluation considers two strategies: simulation, with exhaustive analysis of network lifetime, energy and data latency, and deployment over a test bed, which demonstrated that the proposed cognitive small world model is more efficient regarding energy balancing and consumption, data latency and network lifetime, when compared with different state-of-the-art approaches. From our analysis on related work, we may observe that, although various proposals address security weaknesses in IoT LoRaWAN environments, there is a lack of proposals addressing the dynamic management of security and energy, as per the goals of our proposal in this article. As previously discussed, our main motivation is to design and evaluate a solution to address, in tandem, these two important and (from the point of view of the resources available in constrained IoT devices) conflicting aspects. As we address throughout our discussion in this article, the dynamic management of energy and security in the context of an IoT application may provide the required security to protect the network against threats being detected at the moment, while allowing for a more careful and intelligent management of energy during the life cycle of the sensing devices employed--a valuable resource in providing acceptable lifetime to the IoT application at hand.