Green radios systems

Essential overview of GRS implementation scenarios. The needs of GRS and some concrete cases exampleope

MAC protocols with wake-up radio for wireless sensor networks: A review

The use of a low-power wake-up radio in wireless sensor networks is considered in this paper, where relevant medium access control solutions are studied. A variety of asynchronous wake-up MAC protocols have been proposed in the literature, which take advantage of integrating a second radio to the main one for waking it up. However, a complete and a comprehensive survey particularly on these protocols is missing in the literature. This paper aims at filling this gap, proposing a relevant taxonomy, and providing deep analysis and discussions. From both perspectives of energy efficiency and latency reduction, as well as their operation principles, state-of-the-art wake-up MAC protocols are grouped into three main categories: (1) duty cycled wake-up MAC protocols; (2) non-cycled wake-up protocols; and (3) path reservation wake-up protocols. The first category includes two subcategories: (1) static wake-up protocols versus (2) traffic adaptive wake-up protocols. Non-cycled wake-up MAC protocols are again divided into two classes: (1) always-on wake-up protocol and (2) radio-triggered wake-up protocols. The latter is in turn split into two subclasses: (1) passive wake-up MAC protocols versus (2) ultra low power active wake-up MAC protocols. Two schemes could be identified for the last category, (1) broadcast based wake-up versus (2) addressing based wake-up. All these classes are discussed and analyzed in this paper, and canonical protocols are investigated following the proposed taxonomy

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application