# Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems
## 2 Related Works  
The possibility to dynamically and remotely control LED streetlights has been investigated mainly in the last decade. Starting from the first papers on this topic [15; 16; 17; 18; 19; 20], different remote-control systems have been proposed, which rely on the exchange of information between light fixtures and a Control Center, the latter being the management headquarter for the lighting service. An overview of early researches concerning smart street lighting systems is presented in [13]. The ability to adapt the light intensity to actual conditions was considered since the pioneering era of smart lighting. For instance, the intelligent control of luminaries based on the detection of moving objects was proposed in 2013 [21]. In the same year, an intelligent streetlight management was presented in [22], which was jointly based on wireless communication technologies and environmental monitoring. Another investigation on intelligent street lighting based on the sensing of vehicles appeared in 2014 [23], which stated that energy savings could exceed 50% on roads with low traffic. In the meanwhile, the smart city paradigm gained ground and smart lighting infrastructures were increasingly considered as a pillar of such concept [24; 25; 26]. In particular, they were meant (and are meant) to provide additional services, such as predicting traffic flows and regulate traffic lights [27]. Smart lighting infrastructures are thus assimilated to wireless sensor networks (WSNs), as in [28], where the performances of a WSN-based solution for the conversion of a standard public lighting network into a smart lighting system are reported and discussed. As in [29; 30; 31; 32; 33; 34; 35; 36], smart lighting infrastructures can also be included in the IoT framework. In recent years, the topic of urban smart lighting gained attention and several aspects of this technology were investigated separately. Some papers deal with the design of the hardware (e.g., light controller, lamp driver) [37; 38; 39; 40; 41], whereas other papers deal with the automatic, context aware, control of illumination [42; 29; 43]. There are papers concerning software/cloud-computing issues in smart city scenarios [44; 45; 46], others focusing on energy efficiency [47; 48; 49; 50; 51; 52], as well as paper presenting smart lighting solutions based on specific communication technologies [53; 54; 55; 56; 57]. Photometric computations have been also addressed to achieve substantial power savings [58; 59], whereas a formal model based on graph theory aimed at incorporating knowledge regarding multiple sensors into the lighting control model is proposed in [60]. As for significant testbeds, one of the largest is Smart Santander [61], where around 3000 IEEE 802.15.4 devices were deployed in the city of Santander (Spain), which makes the size of such testbed similar to the ones we discuss in our manuscript. However, in contrast to this paper [61] does not deal with smart lighting, thus no specific insight is provided on such application. As a matter of fact, current literature dealing with complete public-lighting infrastructures either shows simulation results, such as [62; 63] or presents the results of limited testbeds with few nodes [24; 30; 64] or campus-wide installations [65; 66]. To the best of the authors' knowledge, the scientific literature is lacking a complete discussion of all the aspects that have an impact on possible energy savings, which must be taken into account when planning a smart lighting infrastructure. In fact, different choices, in terms of adopted communications technologies and light controllers, generates different costs (e.g., for deployment and maintenance) and different energy savings as well as different opportunities to use the same communication network for other smart-city services. This paper aims at filling this gap, also considering future developments of smart lighting infrastructures, such as their integration with 5G networks.