## 1 Introduction  
Wireless sensor networks (WSNs) have continued to generate a great deal of interest over the past two decades due to the wide range of applications they allow [1, 2]. Their autonomy, simplicity of implementation, and miniature appearance make it possible to integrate them into a wide range of small and large-scale applications. Smart cities where information and communication technologies are increasingly used in everyday life offer many opportunities for Large Scale Wireless Sensor Networks (LS-WSNs) applications [3]. Indeed, in the face of strong demographic growth, the main challenge for these cities is to offer high-performance and innovative services that should improve the living environment. More often than not, these cities use technological means such as Internet of Things (IoT), Big Data, Open Data, and Web/mobile applications. The development and the deployment of electronic sensors and smart IoT devices on street furniture make it possible to interconnect infrastructures, to provide useful information to public and private services and citizens, or to adapt energy expenditure, among other things [4; 5; 6]. Moreover, data is at the center of the digital development of services. The new models of data collection are becoming more and more efficient (IoT in particular) and the amount of collected data is exponential. This has led to problems concerning the sharing, storage, analysis, and presentation of data. Therefore, _Big Data_ is the term used for massive volumes of data. Also, older models of data storage and processing are no longer suitable. This has led to this phenomenon and new models have been emerging for some years now [7]. Furthermore, the concept of _Open Data_ refers to data that is open to the entire population. Anyone can access, use, distribute, redistribute, mix, or share it without restriction or commercial contribution, but also contribute by adding or creating new data. The essence of Open Data is interoperability, i.e., the ability to freely mix different data sets. This allows the creation of large and complex systems [8]. Finally, internet access and smartphones or tablets allow access to a large number of digital services. These services can be used to inform citizens, conduct research, give opinions, or sometimes take action (e.g., feedback on information, etc.). The energy consumption of his or her household can enable a citizen to take action to reduce its bill and consequently to carry out a \"green\" act. Web and mobile applications also allow public services to interact with citizens, soliciting their opinions and actions. In addition, these applications also allow to restitute and sometimes act on the information collected and analyzed on the sensors located on the street furniture. Furthermore, in the day-to-day management of smart cities, general data play an important role. However, big data remains a problem in its own right. Therefore, this mass of data constitutes one of the major challenges of moderate computing [9; 10; 11; 12; 13]. It is almost impossible to collect and then process this data using conventional computer systems. However, for smart cities, most of the literature [14; 15] agrees that once collected and processed, these data can support or improve most of the services offered in smart cities (operation of renewable electricity production facilities, real-time status of public distribution networks, monitoring of road traffic, measurement of pollution levels, etc.). Although LS-WSNs are a preferred solution for data collection and processing, these networks are little or not used to our knowledge for collecting big data across smart cities. This remains an interesting challenge that we wish to take up through our work. However, the design and implementation of LS-WSNs pose several important and interesting challenges [16; 17; 18; 1; 19]. These low-cost, low-power devices are characterized by limited computing, memory storage, communication, and battery power capabilities.