Introducing reinforcement learning in the Wi-Fi MAC layer to support sustainable communications in e-Health scenarios

The crisis of energy supplies has led to the need for sustainability in technology, especially in the Internet of Things (IoT) paradigm. One solution is the integration of Energy Harvesting (EH) technologies into IoT systems, which reduces the amount of battery replacement. However, integrating EH technologies within IoT systems is challenging, and it requires adaptations at different layers of the IoT protocol stack, especially at Medium Access Control (MAC) layer due to its energy-hungry features. Since Wi-Fi is a widely used wireless technology in IoT systems, in this paper, we perform an extensive set of simulations in a dense solar-based energy-harvesting Wi-Fi network in an e-Health environment. We introduce optimization algorithms, which benefit from the Reinforcement Learning (RL) methods to efficiently adjust to the complexity and dynamic behaviour of the network. We assume the concept of Access Point (AP) coordination to demonstrate the feasibility of the upcoming Wi-Fi amendment IEEE 802.11bn (Wi-Fi 8). This paper shows that the proposed algorithms reduce the network&amp;#x2019;s energy consumption by up to 25% compared to legacy Wi-Fi while maintaining the required Quality of Service (QoS) for e-Health applications. Moreover, by considering the specific adjustment of MAC layer parameters, up to 37% of the energy of the network can be conserved, which illustrates the viability of reducing the dimensions of solar cells, while concurrently augmenting the flexibility of this EH technique for deployment within the IoT devices. We anticipate this research will shed light on new possibilities for IoT energy harvesting integration, particularly in contexts with restricted QoS environments such as e-Healthcare.</p

Multiple-polynomial LFSR based pseudorandom number generator for EPC Gen2 RFID tags

International audienceWe present a lightweight pseudorandom number generator (PRNG) design for EPC Gen2 RFID tags. It is based on a linear feedback shift register (LFSR) configured with multiple feedback polynomials that are selected by a physical source of randomness. The proposal successfully handles the inherent linearity of LFSR based PRNGs and satisfies the statistical requirements imposed by the EPC Gen2 standard. Statistical analysis of the sequences generated by our generator confirms the validity of the proposed technique.We show that our proposal has, moreover, a simpler hardware implementation and energy consumption than previous designs reported in the literature

Analysis and improvement of a pseudorandom number generator for EPC Gen2 tags

International audienceThe EPC Gen2 is an international standard that proposes the use of Radio Frequency Identification (RFID) in the supply chain. It is designed to balance cost and functionality. The development of Gen2 tags faces, in fact, several challenging constraints such as cost, compatibility regulations, power consumption, and performance requirements. As a consequence, security on board of Gen2 tags is often minimal. It is, indeed, mainly based on the use of on board pseudorandomness. This pseudorandomness is used to blind the communication between readers and tags; and to acknowledge the proper execution of password-protected operations. Gen2 manufacturers are often reluctant to show the design of their pseudorandom generators. Security through obscurity has always been ineffective. Some open designs have also been proposed. Most of them fail, however, to prove their correctness. We analyze a recent proposal presented in the literature and demonstrate that it is, in fact, insecure.We propose an alternative mechanism that fits the Gen2 constraints and satisfies the security requirements