# A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context
## 3 Related Works
Indeed, the central node will be conceived bearing in mind the availability of the electric grid. This condition will be easily met due to the logistic of the application scenario. Adopting such a network topology, most of the solutions described in these papers are of few interest for the proposed architecture. With the purpose of reducing energy consumption, it is fundamental to revise the single components of the node architecture. Usually, the main building blocks are: a microcontroller (or a microprocessor), a communication module and some sensors. The choice of each part should be done in such a way to limit its individual energy requirement. For instance, it is far better to use passive sensors instead of the active ones, or it is better to use a low power transmission module capable of covering only the transmission distance needed. An interesting review of this topic is reported in [57], while in [58] some techniques to decrease power needs and increase energy efficiency of a wireless sensor node within the Internet of Things (IoT) context are proposed. Both of those studies provided interesting suggestions and advice during the design phase of our sensor node, especially for what concerns the alternatives from which we chose its building blocks. In the design phase of a WSN sensor node that has to be employed in all these contexts, one of the most important concepts to take into account is power management. A possible approach is to optimize each component of the node: the elaboration device (microcontroller, SoC), sensors and their conditioning circuits as well as the transceiver module for wireless communication. Due to the fact that sensor nodes are often powered through constrained power sources, like batteries, their consumption needs to be reduced. For this reason, the components of the node architecture have to be investigated so as to identify all their energy requirements needed for achieving both the sensing operations (with the related data processing) and the radio transmission. Indeed, a viable solution consists of equipping nodes with low power technologies: BLE (Bluetooth Low Energy), 6 LoWPAN (IPv6 over Low power Wireless Personal Area Networks) or the emerging LPWAN solutions like the LoRa and SigFox protocols. In the following section, an optimized sensor node architecture based on one of these last technologies, i.e., the LoRa protocol, will be proposed. Eventually, this section is concluded by highlighting some common aspects as well as some improvements in comparison with some related works, which span among different application scenarios, belonging to the literature. Let us start by citing [59] which shows how the sand transportation due to wind on a coastal scenario could be measured via a WSN having heterogeneous sensor nodes. In particular, one of those nodes is a wireless battery-powered scale placed on the bottom of a sediment trap whose estimated lifetime is of 14 days. Its main components are: a 3000 mAh battery pack, a linear voltage regulator, a load cell and its conditioning circuit and a ZigBee transmission module. Since sediment measurements are periodically performed and transmitted, the sleep mode for the transmission module is enabled whenever data are not required. Moreover, since data processing is remotely accomplished, then a microcontroller is not necessary. In spite of the fact that our sensor node is equipped with a microcontroller, it has a noticeably higher lifetime (as it will be seen in Section 5.2) mainly due to these reasons that involve a consumption reduction: a step-down voltage regulator is employed in place of a linear one (see Section 4.4), an energy-saving policy capable of turning off the energy-hungrier parts of the system is adopted rather than exploiting a simple sleep mode (see Section 5.1) and a higher amount of energy is available thanks to the usage of bigger capacity batteries (see Section 5.2). In [60] a sensor node for the measurement of the water level within the context of irrigation networks is presented.