The Long Road to Sobriety: Estimating the Operational Power Consumption of Cellular Base Stations in France Arsalan Ahmed † and Marceau Coupechoux § † Faculté des Sciences et Technologies, Université de Lorraine, 54506, Vandoeuvre-lès-Nancy, France § LTCI, Télécom Paris, Institut Polytechnique de Paris, 91120, Palaiseau, France Email: ahmed46u@etu.univ-lorraine.fr, marceau.coupechoux@telecom-paris.fr

Abstract-As the Information and Communication Technology (ICT) sector represents 1.8% to 3.9% of the global Green House Gas (GHG) emissions, it is of upmost importance to know how much energy is spent annually in mobile networks and how this consumption is evolving. It is quite likely that the huge energy efficiency gains achieved by technology evolution have at least been compensated by the surge in data traffic. Therefore, in this paper, we estimate the operational power consumption of cellular Base Stations (BSs) deployed in France from 2015 to 2022. However, unfortunately, the lack of openly available data hinders the estimation process. In order to work around this issue, we rely on a public dataset on radio electric installations, on widely adopted power consumption models and on a set of assumptions backed by the scientific literature. We demonstrate that, over the considered period, the numbers of BSs and transceivers have grown at a sustained Compound Annual Growth Rate (CAGR) of 7.55% and 18.27%, respectively. Within the same period, the average BS power consumption has increased at a CAGR of 9.89% while the total operational power consumption of BSs has grown at a CAGR of 18.18%. We further show that the introduction of 5G has accelerated this trend despite the recent decommissioning of 2G and 3G transceivers. These alarming figures advocate for proactive digital sobriety policies.

Index Terms-Mobile Network, 5G, Base Station, Power Consumption, Digital Sobriety, France.

I. INTRODUCTION

In recent years, the widespread proliferation of wireless mobile devices have prompted tremendous growth in the cellular communications industry [START_REF]Ericsson Mobility Report[END_REF]. Such industry expansions are carefully guided and encouraged by the mobile operators who continually strive to provide more capacity through their networks. This addition of capacity is not only to cater the increasing user demands but it is also an incentive for further traffic growth which eventually translates into higher profits. For this purpose, mobile operators deploy additional physical Base Stations (BSs) or reinforce the current ones. Several studies have investigated the Information and Communication Technology (ICT) sector's global climate impact. ICT represents between 1.8% and 3.9% of the global Green House Gas (GHG) emissions * [START_REF] Freitag | The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations[END_REF]. Despite the huge energy efficiency gains achieved with the arrival of newer cellular generations of 4G and 5G, it is clear that these gains have at least been compensated by the increase in data traffic [START_REF] Lange | Digitalization and energy consumption. Does ICT reduce energy demand?[END_REF], [5]. Between 2002 and 2012, GHG emissions directly related to ICT, grew twice as fast as the overall global emissions. From 2012 till 2015, studies lead to contrasting conclusions [START_REF] Freitag | The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations[END_REF]. However, all authors agree on the fact that this share will not significantly reduce if the business keeps running as usual. As mobile networks constitute a large part of the ICT sector, they are subject to ambitious targets of cutting down on GHG emissions, see e.g. the International Telecommunication Union (ITU) recommendation [START_REF]Recommendation L.1470: Greenhouse gas emissions trajectories for the information and communication technology sector compatible with the UNFCCC Paris Agreement[END_REF] which, by 2030, envisions a 45% reduction in the emissions of mobile network operators. But the rise in BS installations and deployments are a serious hindrance to this goal as BSs alone are responsible for approximately 80% of the total energy consumption of mobile networks [START_REF] Nokia | White Paper: 5G network energy efficiency[END_REF], [START_REF] Gabriel | Green 5G: Building a Sustainable World[END_REF]. Due to such trends, the increased energy consumption of BSs has become a major issue which should gain more attention from the researchers.

The modern end-user devices with newer mobile generations like 5G are becoming more and more energy-efficient in terms of energy per transmitted bit. However, these energy gains can easily be nullified due to the so called rebound effect [START_REF] Bieser | Next generation mobile networks: Problem or opportunity for climate protection?[END_REF], [START_REF] Coroamȃ | Digital rebound-why digitalization will not redeem us our environmental sins[END_REF]. Direct rebound effect in mobile networks occurs when the changing consumer behaviour increases the usage of energy-efficient end-user devices to the point that the aggregate usage outweighs the potential energy savings. Hence, it is necessary to evaluate the total energy consumption in J or Wh to be able to quantify the overall impact and not only the energy efficiency in J/bit. However, historically, energy consumption at the BSs has been difficult to evaluate due to the lack of openly available data from the mobile operators and ICT equipment providers. On the other hand, users are expected to remain aware and conscious of their mobile usage in light of the growing environmental impact of mobile networks, especially if digital sobriety policies were to be implemented. This serious contradiction can partially be resolved with reliance on models and estimates which have their own share of advantages and drawbacks.

There exists a number of previous studies that try to estimate the aggregate energy consumption of mobile networks [START_REF] Malmodin | The electricity consumption and operational carbon emissions of ICT network operators 2010-2015[END_REF], [START_REF] Lundén | Electricity consumption and operational carbon emissions of European telecom network operators[END_REF], [START_REF] Pihkola | Energy consumption of mobile data transfer-Increasing or decreasing? Evaluating the impact of technology development & user behavior[END_REF], [START_REF] Golard | Evaluation and projection of 4G and 5G RAN energy footprints: The case of Belgium for 2020-2025[END_REF]. The data used in these studies is mostly provided upon request by telecom network operators. Malmodin et al. investigate the electricity consumption and operational carbon emissions of telecom operators from 2010 to 2015 [START_REF] Malmodin | The electricity consumption and operational carbon emissions of ICT network operators 2010-2015[END_REF]. The same authors in [START_REF] Lundén | Electricity consumption and operational carbon emissions of European telecom network operators[END_REF], present the operational electricity consumption and GHG emissions for selected European telecom network operators from 2015 to 2018. A similar study is conducted for Finland where the current and future energy consumption trends for mobile networks are discussed [START_REF] Pihkola | Energy consumption of mobile data transfer-Increasing or decreasing? Evaluating the impact of technology development & user behavior[END_REF]. Golard et al. present a method to evaluate and project the total energy consumption of radio access networks using on-site measurements provided by operators. This method is applied to come up with 4G and 5G BS models which are then used to predict the energy footprints for Belgium [START_REF] Golard | Evaluation and projection of 4G and 5G RAN energy footprints: The case of Belgium for 2020-2025[END_REF]. However, since most of these works use collective values from telecom operators, the break down of energy consumption is not quite clear and visible especially in terms of location, BSs, operators and mobile generations. Furthermore, the data collection methodology, calculation details and information about continuous historical data remain unknown.

Hence, this work attempts to estimate the national operational power consumption of cellular BSs in France between April 2015 and September 2022 using a public dataset on radioelectric installations, some specific power consumption models from the literature and accompanying assumptions related to the BS equipment. The key findings of this study are: We provide a brief description of the dataset and data processing in Section II. Section III presents the BS power consumption models. The numerical results are discussed in Section IV while Section V is for discussion. We conclude the paper in Section VI.

II. ANFR DATASET AND DATA PROCESSING

A. ANFR Dataset

This study is based on the public dataset on radioelectric installations above 5 W [START_REF]Données sur les installations radioélectriques de plus de 5 watts[END_REF] from the Agence Nationale des FRéquences (ANFR) which is a governmental agency managing all radio frequencies in France (https://www.anfr.fr/en). The ANFR is tasked with the authorization of all transmission site deployments above 5 Watts in mainland France as well as in the overseas French territories. This publicly accessible dataset provides a unique opportunity for researchers to conduct studies on a national scale. The dataset contains data from all public and private operators of radio installations. It has been maintained from April 2015 and is updated on a monthly basis. Each of the data files can be considered as a table containing some specific information. The dataset gathers information about different elements of the radio installations, namely physical supports, BSs, antennas, transceivers and frequency bands.

There exists a hierarchy in the dataset that defines the relationship between these elements as depicted in Fig. 1. It can be seen that a support (also known as a site) is at the top of the hierarchy. Each support or site consists of several BSs which can belong to the same or different operators. In some rare cases, a single BS is attached to multiple supports. For example, when a BS uses two antennas that are more than 90 meters apart, then the BS is said to be supported by two different physical supports. A BS is owned by a single operator and utilizes one or more antennas. It is also possible that several BSs share the same antenna. This is due to the sharing of telecom infrastructure where competitors become partners to lower costs and investments. In France, leading mobile operators have signed mutual strategic agreements to share infrastructure among each other [START_REF]Bouygues Telecom and SFR conclude a strategic agreement to share a part of their mobile access networks[END_REF]. From the dataset, we observe that as of September 2022, around 17.86% of the antennas are shared between BSs belonging to several different operators. An antenna is associated with one or more transceivers. However, one transceiver can only use a single antenna. Lastly, a transceiver can operate multiple frequency bands but those bands belong to only a single type of wireless communications standard/technology.

B. Data Processing

We use python as the programming language for dataset processing. Specifically, python's Pandas library is mainly employed because of its support for cleaning, manipulating and analyzing tabular data.

First, we preprocess the dataset before performing the analysis and computations related to power consumption. This is to filter out irrelevant information from it and reduce the data size. For this preprocessing stage, we only consider the cellular technologies/standards. These include GSM 900, GSM 1800, GSM 900/1800, UMTS 900, UMTS 2100, UMTS 2100/900, LTE 700, LTE 800, LTE 1800, LTE 2100, LTE 2600, NR 700, NR 2100 and NR 3500. Overall, all the mobile communication generations are taken into account while excluding their experimental bands such as LTE 1400 Expe. Furthermore, we only consider the four leading French operators as they hold the majority of the market share and hence, their data samples are representative of the entire dataset. In addition, only the in service BSs are taken into consideration, i.e., authorized BSs not yet in service are excluded from the analysis. We then utilize the compact preprocessed dataset for faster data analysis and power consumption related calculations. The complete source code of the project with further details on the dataset and data processing is publicly available on GitHub [START_REF] Ahmed | ANFR Dataset Processing Codes -GitHub Repo[END_REF].

III. BS POWER CONSUMPTION MODELLING

We rely on accepted models from the literature for performing our power consumption estimations.

A. Power Consumption Models

1) EARTH Model: The Energy Aware Radio and Network Technologies (EARTH) project defines a widely used BS power consumption model [START_REF] Auer | How much energy is needed to run a wireless network?[END_REF]. The EARTH model assumes that all BSs can be generalized such that they consist of multiple transceivers where each transceiver serves a single antenna element. Each transceiver chain includes a power amplifier (PA), a RF small-signal transceiver module, baseband boards (BB), a DC-DC power supply, an active cooling system and an AC-DC unit (mains supply) for connection to the electrical power grid [START_REF] Auer | How much energy is needed to run a wireless network?[END_REF]. The mathematical formulation of this model is the following:

P BS = N T RX × P P A + P RF + P BB (1 -σ DC )(1 -σ M S )(1 -σ cool ) (1)
where P BS is the total BS power consumption, P P A is the PA power consumption, P RF is the power consumption of the transceiver module and P BB is the baseband power consumption. In addition the σ DC , σ M S and σ cool are the power losses in the DC-DC power supply, mains supply and active cooling system, respectively. Furthermore, N T RX is the number of transceiver chains present in the BS. The power consumption of the PA is calculated as follows:

P P A = P out η P A × (1 -σ f eed ) (2)
where P out is the output RF transmit power, η P A is the power efficiency of the PA and σ f eed is the feeder/cable loss arising due to the physical distance between the BS and the antenna. Note that P out is generally an increasing function of the cell load and of the signal bandwidth. We consider two types of BS models namely, the integrated BS model and the Remote Radio Head (RRH) based BS model [19]. These models are shown in Fig. 2. An integrated BS exists as a single unit placed at a distance from the antennas. The PAs require active cooling within the BS and are connected to the antennas via long feeder cables. This results in power losses of σ cool and σ f eed which are already taken into account by Equation 1. On the other hand, in RRH based BSs, a RRH is introduced for hosting the PAs at the same physical location as that of the antennas. The RRH is connected to the BS using optical fibers while the antennas are connected to the RRH through small flexible jumpers. As a consequence, the feeder loss is reduced to a lower value. The cooling loss is completely avoided as the PAs inside the RRH are now cooled through natural air circulation.

2) 5G NR 3500 specific Model: The EARTH model is applicable to a wide range of BS types. However, it does not accurately model 5G BSs operating at the 3500 MHz band. This is because at this frequency, Massive MIMO is employed which requires the presence of several active antenna elements inside an Active Antenna Unit (AAU). Hence, we select a specific power consumption model that models 5G BSs considering the impact of this massive beam-forming functionality [START_REF] Tombaz | Energy performance of 5G-NX wireless access utilizing massive beamforming and an ultra-lean system design[END_REF]. This model estimates the total BS power consumption as follows:

P BS = N T RX × P out η P A + N T XRU P C + P Base (3)
where P out is the output RF transmit power of the AAU, η P A is the power efficiency of the PA, N T XRU is the number of transceiver units inside the AAU, P C represents the additional digital and RF processing needed for each antenna branch and P Base is the baseline power consumption. Furthermore, N T RX corresponds to the number of complete transceiver chains utilizing a single AAU present in the BS. In practice, N T RX is equal to the number of sectors at the BS (typically 3).

It is to be noted that only a few models take Massive MIMO into account. Another one is described in [START_REF] López-Pérez | A survey on 5g energy efficiency: Massive mimo, lean carrier design, sleep modes, and machine learning[END_REF]. However, numerical values of [START_REF] Tombaz | Energy performance of 5G-NX wireless access utilizing massive beamforming and an ultra-lean system design[END_REF] provide better estimates when compared to publicly available data, like in [START_REF] Han | Energy-efficient 5G for a greener future[END_REF].

3) Dependence on Load: The output power P out is an increasing linear function of the load, i.e., the average proportion of radio resources used for the transmission. Assuming that the BS has a maximum transmit power P max , we compute the output power as follows: P out = ρ × P max , where ρ is the BS load. This linear model is sufficiently accurate to be well accepted by the literature, see e.g. [START_REF] Auer | How much energy is needed to run a wireless network?[END_REF], [START_REF] Lorincz | On interdependence among transmit and consumed power of macro base station technologies[END_REF], [START_REF] López-Pérez | A survey on 5g energy efficiency: Massive mimo, lean carrier design, sleep modes, and machine learning[END_REF].

B. Performance Improvements

The variable values in equations ( 1), ( 2) and (3) do not remain fixed but are updated with time in order to take into account the continuous technological improvements resulting in increased BS power efficiencies. In general, the analog RF circuitry and the digital baseband circuitry are subject to technology scaling on the basis of Moore's law [START_REF] Debaillie | A flexible and future-proof power model for cellular base stations[END_REF]. Moreover, a reduction of 2% per year is observed in the power lost inside a PA [START_REF] Debaillie | A flexible and future-proof power model for cellular base stations[END_REF]. Therefore, the values of P RF , P BB and η P A are updated as follows:

P n RF = P RF L ⌊ n 2 ⌋ P RF (4) P n BB = P BB L ⌊ n 2 ⌋ P BB (5)
η n P A = η P A × L n η P A (6)
where P n RF is P RF at year n, P RF is at year 0 and L P RF is the constant scaling factor for P RF . Similarly, P n BB is P BB at year n, P BB is at year 0 and L P BB is the constant scaling factor for P BB . Lastly, η n P A is η P A at year n, η P A is at year 0 and L η P A is the constant per year scaling factor for η P A . In ( 4) and ( 5), n 2 is subject to integer division since Moore's law is applied every two years.

Equations ( 4), ( 5) and ( 6) are applied for state-of-the-art BSs at year n. We assume that, according to the dataset, when a new BS is put in service at year n, such a state-of-the-art equipment is installed. This particular BS will keep the same parameters until year n + d LT , where d LT is the average BS lifetime duration in years. At year n+d LT , the values of P RF , P BB and η P A are updated to P n+d LT RF , P n+d LT BB and η n+d LT P A respectively, as if a current state-of-the-art BS were deployed. Note that we apply these performance improvements to the EARTH model only and not to the 5G NR 3500 specific model. This is because the 5G NR is a relatively new technology and its models do not yet require time based scaling.

IV. NUMERICAL RESULTS

A. Numerical Assumptions

In order to obtain the variable values of the BS consumption models, we make appropriate assumptions since the information regarding BS equipment is proprietary and kept confidential by the operators as well as the equipment manufacturers.

The information coming directly from the preprocessed dataset includes N T RX that is the number of complete transceiver chains in each BS, total bandwidth associated with each transceiver, the duplex mode of each transceiver and the year in which each BS becomes in service. In order to get the value of N T RX at a BS, we group together and count all transceivers with the same characteristics. In addition, we calculate the total bandwidth of each transceiver by using the starting and ending frequency fields provided in the dataset. Furthermore, for determining the duplex mode, we observe their associated frequency bands and the related regulation. Lastly, for each BS, a date of service field is provided in the dataset which we use for getting the in service date.

Tab. I presents all the assumed values for each transceiver signal chain for power consumption related calculations with the EARTH model. We use the values measured (in 2011) and not estimated (for 2020) by the EARTH model but then apply the improvement procedure as described in Section III-B. The improvement figures for L P RF , L P BB and L η P A are taken from [START_REF] Debaillie | A flexible and future-proof power model for cellular base stations[END_REF]. From Tab. I, it can be seen that the BSs are [START_REF] Auer | How much energy is needed to run a wireless network?[END_REF]. Specifically, the value of σ f eed is reduced from 0.5 to 0.2 for RRH based BSs in comparison with integrated BSs. This is because the general feeder loss value for integrated BSs is 3 dB while for RRH based BSs is 1 dB [START_REF] Micallef | Mobile operators have set ambitious targets-is it possible to boost network capacity while reducing its energy consumption?[END_REF]. In addition, the maximum output power P max depends on the bandwidth of the transceiver. We follow here the simple 4 W/MHz rule as in [START_REF] Golard | Evaluation and projection of 4G and 5G RAN energy footprints: The case of Belgium for 2020-2025[END_REF], where the considered bandwidth is only for downlink.

For Frequency Division Duplexing (FDD), this is exactly the transceiver bandwidth. For Time Division Duplexing (TDD), the considered bandwidth is 2/3 of the transceiver bandwidth. We assume the average BS lifetime duration to be 8 years after which it is replaced with up-to-date equipment. This value based on the fact that the lifetime of a typical BS is between 5 to 10 years [START_REF] Nokia | Sustainability and the life of a base station[END_REF]. We also assume a realistic load of ρ = 30% which is close to the average load in busy hours [START_REF] Golard | Evaluation and projection of 4G and 5G RAN energy footprints: The case of Belgium for 2020-2025[END_REF].

Tab. II presents all the assumed values for the 5G NR 3500 specific model. The values in Tab. II are largely taken from [START_REF] Tombaz | Energy performance of 5G-NX wireless access utilizing massive beamforming and an ultra-lean system design[END_REF]. The P max is now significantly higher at a value of 240 W as compared to the EARTH model due to the presence of a 5G NR 3500 AAU in the signal chain [START_REF] Han | Energy-efficient 5G for a greener future[END_REF].

B. Equipment Count

In this section, we discuss the insights extracted from the preprocessed dataset. These insights do not depend upon the selected power consumption models nor on our assumptions. Fig. 3 shows the total count of in service BSs with respect to the cellular technology in France from 2015 to 2022. From the most recent data point, it is revealed that there are over 100, 000 in service cellular BSs in the country. Furthermore, it is observed that the majority of the BSs support multiple cellular generations. Since 2G and 3G mobile networks are gradually becoming obsolete, a sharp downward trend is observed in the numbers of only 2G, only 3G and 2G/3G BSs. On the contrary, during the same time period, BSs of the type 2G/3G/4G and 3G/4G have been increasing due to the addition of 4G LTE transceivers to the BS sites. Furthermore, with the authorization and commissioning of 5G transceivers in November 2020 as indicated by the vertical line, 2G/3G/4G/5G and 3G/4G/5G BSs have started to appear which are now quickly increasing in number. We determine that between April 2015 and September 2022, the number of BSs has grown at a sustained CAGR of 7.55%. Even if only the period after the introduction of 5G BSs is considered, the CAGR value does not deviate much and is 7.2%. From this, we deduce that the newer generations reuse existing infrastructure instead of dedicated BS deployments. Hence, BSs are now supporting an increased number of cellular standards and frequency bands. This trend is also exemplified by the fact that in 2020, 97% of the 5G towers in China were constructed on existing sites [START_REF] Ding | Carbon emissions and mitigation potentials of 5G base station in China[END_REF].

Fig. 4 presents the number of transceivers by cellular technology. Currently, the total number of transceivers exceeds more than 1, 600, 000. As expected, we observe that the growth of 2G and 3G transceivers has stagnated in the recent past whereas 4G and 5G transceivers have been rising. A CAGR of 18.27% is calculated for transceivers from April 2015 to September 2022. Since the introduction of 5G, the transceivers' CAGR for only the next 12 months is found to be 21.65%. However, when we consider the complete duration since the introduction of 5G, the CAGR drops to 17.09%. This is due to the increase in decommissioning of several 2G and 3G transceivers in 2022.

C. Operational Power Consumption

Fig. 5 depicts the calculated total BS operational power consumption according to the cellular technology. As BSs generally include several generations of the technology, we only represent the most significant combinations. It can be seen that there is an almost 3.5-fold increase in the aggregate BS power consumption over the span of 7 years. Currently, the total BS power consumption crosses 350 MW which translates into 3 TWh energy consumption per year (approximately 0.7% of the total electricity consumption in 2021 in France [START_REF]Bilan énergétique de la France en[END_REF]). Since the introduction of 5G transceivers with high power demand at the end of 2020, there has been a slight acceleration in energy consumption. We compute that between April 2015 and September 2022, the total BS power consumption has increased at a sustained CAGR of 18.18%. Moreover, since the introduction of 5G BSs, we found the power consumption to have grown at a higher CAGR of 19.75%. This exhibits the presence of a direct rebound effect where increasing BS installations nullify the energy efficiency gains attributed with newer cellular generations such as 5G.

We also observe that, from April 2015 to September 2022, the average BS power consumption went from 1.75 kW to 3.53 kW. This translates into a sustained CAGR of 9.89%. Considering only the period after the introduction 5G, the average BS power consumption CAGR has escalated to 11.7%. This is caused by the continuous reinforcement of current BSs with new cellular technologies. Fig. 6 demonstrates how the average BS power consumption rises with the number of supported frequency systems as of September 2022. As expected, the lower the number of frequency systems, the lower is the average power consumption at the BS. Fig. 7 shows the average BS power consumption with respect to the highest technology present at the BS as of September 2022. Here, a highest BS technology type includes all systems before it. For example, 3G BSs include 2G, 3G and 2G/3G BSs. We make a distinction between the lower bands and the 3500 MHz band of 5G in order to clearly demonstrate the effect of using 5G AAUs on the average BS power consumption. The general trend is that the average BS power consumption rises with the existence of higher cellular generations at the BS. Specifically, BSs with the 5G 3500 MHz transceivers consume the highest power due to the presence of Massive MIMO supporting AAUs.

V. DISCUSSION

In this section, we check the coherence of our results with other sources and perform a sensitivity analysis related to some model parameters.

A. Coherence with other sources

Since we don't have access to private data of the operators, it is difficult to verify the accuracy of the obtained results over the years. We thus crosscheck our estimates with other sources of data. Lundén et al. present the operational electricity consumption for European telecom network operators between 2015 to 2018 [START_REF] Lundén | Electricity consumption and operational carbon emissions of European telecom network operators[END_REF]. In this study, values for mobile access networks are provided for a number of mobile subscriptions ranging between 255 millions to 265 millions. In the same period, the number of mobile subscriptions in France went from 66.68 million to 70.42 million [START_REF] Itu-T | Mobile Cellular Subscriptions 2000 -2020[END_REF]. We thus scale down (French subscriptions represent 26%) the values of [START_REF] Lundén | Electricity consumption and operational carbon emissions of European telecom network operators[END_REF] and provide the comparison in Tab. III. From the second column of the table, it can be seen that our estimate accounts for 40% to 70% of Lundén et al. evaluation. The difference can be explained by the scope of [START_REF] Lundén | Electricity consumption and operational carbon emissions of European telecom network operators[END_REF] which includes the whole mobile network while we are focusing on BSs. BSs are indeed Another important source of comparison is a recent report from the French telecom regulator ARCEP on the data collected in 2020 [START_REF]Enquête annuelle "Pour un numérique soutenable[END_REF], see Tab. IV. This data is related to both fixed and mobile networks of the operators (all equipment in the access, back-haul and core networks). If we apply a "rule of thumb" of 60% for the mobile networks [START_REF] Universfreebox | Découvrez combien consomment en électricité les réseaux fixe et mobile de Free[END_REF] and 80% for the BSs [START_REF] Nokia | White Paper: 5G network energy efficiency[END_REF], we obtain the third column of the table. Again, we obtain the same order of magnitude without relying on proprietary data: differences of 29%, 6%, 6%, 16% and 17% can be observed from 2016 to 2020.

In [START_REF] Universfreebox | Découvrez combien consomment en électricité les réseaux fixe et mobile de Free[END_REF], it is mentioned that the French mobile operator "Free" consumed 456 GWh of electricity in 2017, out of which 60% was for its mobile network. Taking into account a 80% share only for BSs [START_REF] Nokia | White Paper: 5G network energy efficiency[END_REF], leads to an installed power of 24 MW in 2017. By filtering the BSs deployed by "Free" in the dataset, we are able to estimate an average power consumption of 26.6 MW in 2017. This shows that our estimate is very close to the reported value.

According to [START_REF] Ding | Carbon emissions and mitigation potentials of 5G base station in China[END_REF], a 64T64R three-sectorized 5G macro BS consumes approximately 3-4 kW which is 2 to 3 times higher than 4G equipment. The factor of 3 between 4G and 5G BSs is also given by an Executive Vice-President of China Mobile in [START_REF] Jones | Power Consumption: 5G Basestations Are Hungry, Hungry Hippos[END_REF]. The president of EJL Wireless Research mentioned in [START_REF] Jones | Power Consumption: 5G Basestations Are Hungry, Hungry Hippos[END_REF] that a typical 5G site consumes 10 kW. The ITU-T Recommendation [START_REF]Recommendation L.1210: Environment and ICTS, Climate Change, e-Waste, Energy Efficiency; Construction, Installation and Protection of Cables and Other Elements of Outside Plant[END_REF] reports a typical consumption of 8 kW for a 2G/3G/4G/5G BS. In [START_REF] Han | Energy-efficient 5G for a greener future[END_REF], a 64T64R 5G NR BS with transmit power of 240 W operating on 100 MHz bandwidth is said to consume 4297 W of power (about four times the consumption of a 8T8R 4G BS with 40 W on 20 MHz). These sources do not precise the corresponding load values. Based on our results shown in Fig. 7, the average power consumption of a 4G (highest technology) BS is 1.96 kW. Furthermore, on average, BSs equipped with only the lower bands of 5G consume 3.91 kW while BSs running the 3500 MHz band consume 6.47 kW at 30% load. Our results verify that 5G BSs consume 2 to 3 times more power than 4G BSs. In addition, for 64T64R 5G BSs, our value of average power consumption lies between the reported values of 4.23 -10 kW.

B. Sensitivity analysis

Given the nature of our employed methodology to calculate power consumption, it is possible that small changes in some model parameters may cause a large deviation in the final results. Hence, we present here a brief sensitivity analysis by varying two parameters one by one. It is to be noted that except for the varied parameter, all other values are the same as before. Fig. 8 depicts the total BS power consumption in France at loads varying from 10% to 40% with a step of 5%. Similarly, Fig. 9 shows the total BS power consumption curve at several different average BS lifetime (LT) duration. These curves suggest that the assumption on the average BS lifetime duration does not significantly impact the results. On the contrary, the average load has a determining influence on the computed total power consumption. This means that more accurate load estimations, taking into account the space and time dynamics, are required to continue the work presented in this paper.

Fig. 1 :

 1 Fig. 1: Hierarchy in the ANFR dataset.

  RRH based BS Model.

Fig. 2 :

 2 Fig. 2: Two types of BS models along with their sub-units.

Fig. 3 :

 3 Fig. 3: Number of in service BSs by technology in France (the vertical line indicates the introduction of 5G in France).

Fig. 4 :

 4 Fig. 4: Number of transceivers by technology in France.

Fig. 5 :

 5 Fig. 5: Total BS power consumption in France.

Fig. 6 :

 6 Fig. 6: Average BS power consumption at 30% by the number of frequencies (France, Sept. 2022).

TABLE I :

 I Assumption set for the EARTH model.

		Integrated BS	RRH based BS
	Tech.	2G, 3G and 2G/3G	4G, 5G, 3G/4G, 4G/5G, 2G/4G,
			2G/5G, 3G/5G, 2G/3G/4G,
			3G/4G/5G, 2G/4G/5G,
			2G/3G/5G and 2G/3G/4G/5G
	MIMO	Single transmitter	For 4G and 5G transceivers only.
			• 2T2R MIMO for lower bands
			including LTE 700, LTE 800 and
			NR 700.
			• 4T4R MIMO for higher bands
			including LTE 1800, LTE 2100,
			LTE 2600 and NR 2100.
	σ f eed	0.5	0.2
	σ cool	0.1	0
	σ DC		0.075
	σ M S		0.09
	P RF		12.9 W
	P BB		29.6 W
	η P A		0.311
	Pmax L P RF	4 W per downlink MHz. √ 2
	L P BB		2
	Lη P A		1.02
	d LT		8 years
	ρ		0.3

TABLE II :

 II Assumption set for the 5G NR 3500 model. GHz use 4T4R MIMO. Furthermore, the values for σ f eed , σ cool , σ DC , σ M S , P RF , P BB and η P A are adopted from the original work of the EARTH model

	MIMO	64T64R MIMO with the use of an AAU.
	P Base	260 W
	η P A	0.25
	Pmax	240 W
	N T XRU	64
	P C	1 W
	ρ	0.3
	classified as either integrated or RRH based BSs. This is
	determined based on the general industry practice where BSs
	belonging to the recent cellular generations of 4G and 5G
	are using RRHs. Similarly, the Multiple-Input Multiple-Output
	(MIMO) configurations are also assumed. Mainly, the lower
	bands of 4G and 5G employ 2T2R MIMO while the higher
	bands up to 2.6

TABLE III :

 III Comparison with Lundén et al.'s average annual power consumption (2015-2018) [12].

	Year	Lundén et al. scaled	Lundén	et	al.	Our Work
		(whole mobile net-	scaled×80% [MW]	(BSs) [MW]
		work) [MW]
	2015	261		208		107
	2016	267		213		126
	2017	273		218		160
	2018	282		225		197
	known to represent approximately 80% of the consumption
	of mobile networks [7]. If we apply this factor to the scaled
	values, we obtain the third column of the table. With these
	rough approximations, our approach provides values at 48%
	and 41% from the estimation in 2015 and 2016, respectively,
	but at only 26% and 12% in 2017 and 2018. Note that the
	linear scaling of the European values to just France, ignores
	the country-wise dynamics that define this contribution to the
	total consumption

TABLE IV :

 IV Comparison with ARCEP collected data (2016-2020)[START_REF]Enquête annuelle "Pour un numérique soutenable[END_REF].

	Year	ARCEP (fixed and	ARCEP×60%×80%	Our Work
		mobile	networks)	[MW]	(BSs) [MW]
		[MW]
	2016		340	163	126
	2017		353	169	160
	2018		387	186	197
	2019		408	196	234
	2020		434	208	250

* There is an inherent complexity in evaluating the carbon footprint of ICT due to, e.g., the lack of accurate data or the difficult definition of the sector's boundary, see[START_REF] Stephens | Carbon impact of video streaming[END_REF] for a discussion. The range provided by[START_REF] Freitag | The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations[END_REF] is rather pessimistic among literature's estimates.

VI. CONCLUSION

In this paper, we propose an estimation of the operational power consumption of cellular BSs in France from 2015 to 2022. We base our methodology on a public dataset on radioelectric installations, widely adopted BS power consumption models and realistic assumptions from the literature. We highlight the fact that the number of BSs, the number of transceivers, the average BS power consumption and the national power consumption are growing at sustained CAGRs of 7.55%, 18.27%, 9.89% and 18.18% respectively. So far, the introduction of 5G has not significantly modified the rate of increase in the numbers of BSs and transceivers thanks to the practice of co-siting and the decommissioning of 2G/3G transceivers, respectively. However, it has caused an acceleration in growth rates of the average and the national BS power consumption. Without access to proprietary data, it is difficult to check the accuracy of our results. However, the comparisons with recent publications show that we obtain the right order of magnitude. Certainly, our estimates are intended to be preliminary in nature with the major goal of starting a discourse around the topic. These estimates can be further refined in the future to make them more accurate. For example, the space and time heterogeneity of the traffic could be taken into account in place of an average load per BS, or a model for sleep modes in 5G could be introduced. However, if our estimates are confirmed, the alarming figures we show here would call for proactive digital sobriety policies.


Stefan Daniel Dumitrescu
email: sdumitrescu@racai.ro

Stefan Trausan-Matu
email: stefan.trausan@cs.pub.ro

Mihaela Brut
email: mihaela.brut@thalesgroup.com

Ontology-based flexible topic classification of crowdsourcing textual resources

Keywords: I.2.4 [Knowledge Representation Formalisms and Methods]: Semantic networks, ontologies, I.2.7 [Natural Language Processing]: Text analysis, I.7 [Document and text processing]: Document preparation, Index generation Algorithms, Experimentation, Theory Ontology, text classification, concept, knowledge engineering, graph algorithm

The paper presents a solution to the problem of capitalizing in different contexts and by different stakeholders the time-stamped new documents produced by social Web sites (including news, blog entries, and uploaded documents). The solution core includes an ontology-based method to express the interest topics and to automatically classify them. For such textual content obtained in real-time, we propose an unsupervised text classification system based on general YAGO ontology, graph algorithms and a custom scoring method. The system shows good performance using only ontology information and the ontology structure itself. We compare our system against a SVM-based (Support Vector Machine) classic text classification approach. For determining the relevance of a specific document for a specific topic, our approach develops and compares the ontology sub graphs corresponding to the query and to the document. It leads to a high flexibility in terms of capitalizing the already classified documents when refining and changing the interest topic: a graph-based matching of the already obtained ontology-based document representation against the new query representation is enough to assess the document relevance.

INTRODUCTION

Information extracted and inferred from social networks is deemed of utmost relevance as a valuable input for multiple stakeholders acting at a city level. Journalists, economic analysts, sociologists, police, city municipality want to be informed or to easily locate by themselves the latest news concerning a specific topic of interest for the current moment. Moreover, it is very important to scan the social websites and assess in real-time the population feedback and reactions to a public/private institution initiative. Actually, the ontology-based semantic description of texts provides a very good basis for a more efficient and pertinent exploitation of them for different functionalities such as information retrieval or various functionalities tailored to the current user needs. This article presents a solution for automatically classifying this type of content according to the YAGO general ontology and for determining documents' relevance for different topics that dynamically become of interest for different stakeholders. Our solution approach is to delimitate the extended set of interest topics corresponding to the user query (through a query enrichment process), and to consider only the ontology concepts associated with these topics among the document annotations, as well as their maximum two-degree related concepts. Thus, the document annotation process is alleviated while non-obvious semantic relations between topics and documents are detected.

The proposed method is based on an implemented system that adopts YAGO as a support ontology in order to extend an initial query topic and to perform unsupervised text classification given an initial topic list. It uses the ontology as a knowledge source on which it applies graph algorithms to detect and create a partial sub-graph illustrating the relations between the concepts that characterize each document. Thus, our solution avoids the use of machine learning algorithms in the main processing phase, while only employing such algorithms like a Maximum Entropy model for sentence identification and token splitting in the document pre-processing phase.

On the other hand, once analyzed, the textual resources keep their acquired YAGO ontology-based representation. When different stakeholders express new interest topics, these topics are as well processed and enhanced based on the YAGO ontology, while documents annotations are only performed based on the new topics. The relevant documents are detected further to a graphmatching algorithm.

We also engage in a discussion on the benefits and problems of using ontologies for such a task.

LITERATURE REVIEW 2.1 Watching and Gathering Data from Web

With the increase of the Web content volume and types, the methods and tools for gathering data from Web are more and more efficient. The Web crawlers, among which we could mention the open-source Heritrix or DataSparkSearch1 , are focused on specific issues and efficient due to the multiple policies they are able to handle, such as for page selection and revisit, for politeness (in order to avoid the Web sites overloading), for crawling parallelization (in the case of distributed Web sites). The Social Web contributes as well to the Web data gathering alleviation due to their techniques to notify about the Web content evolution. The site updates and new articles are signaled through, for example, the RSS feeds. The specific content that becomes popular is highlighted, for example, through the featured articles or through articles signaled as most popular. The private or public institutions are notified about the users' reactions to their published initiative (through comments, responses, scoring).

Crowdsourcing is a powerful method to gather useful information by directly involving voluntary users. As an example in the context of smart city innovation, crowdsourcing methods for idea generation and idea selection are investigated and applied [START_REF] Schuurman | Smart Ideas for Smart Cities: Investigating Crowdsourcing for Generating and Selecting Ideas for ICT Innovation in a City Context[END_REF]. The effort to actively engage people in systems as participants is fully rewarded by crowdsourcing contributions capitalizing their knowledge and expertise [START_REF] Erickson | Geocentric Crowdsourcing and Smarter Cities: Enabling Urban Intelligence in Cities and Regions[END_REF]. As an example, the campaign "Investigate your MP"2 organized by the newspaper The Guardian gathered multiple tens of thousands of people to review the 458.832 pages of the report on the scandal about excessive expense submissions. Concerning the topic on how to make smarter a city, ideas from people are gathered through an impressive number of Web sites3 , whose focused crawling could be very interesting for specific city stakeholders.

The main issue to handle such data published on the Web is not its gathering, but its automatic processing according to some predefined (and still evolving) interest topics.

Big Data Analytics is the emerging technology to support the storage and processing of large volumes of information and to make the information available when needed to the systems demanding it. In this context, Hadoop technology is specialized for massive processing in a distributed infrastructure and HBase Database for the storage of large amount of data. Additionally, STORM supports data processing in real time with any programming language and the easy integration with different database management systems. Existing SmartCities solutions, such as the IBM, Intelligent Operations Centre for Smarter Cities, are more transversal solutions (emergency management, public safety, social services, monitoring transportation or water networks) that provide executive dashboards for city leaders, but do not cover the previous processing demands.

Nevertheless, semantic-oriented data analysis functionality is not supported by the current Big Data Analytics solutions, whereas they could substantially capitalize in the context of smart cities, as illustrated above. In order to expose the text analysis context of our solution, we further present an overview of the topic classification approaches.

Topic Classification Approaches

The domain of text classification is, at present, dominated by machine learning methods, statistical methods, with knowledge engineering methods trailing behind [START_REF]Intelligent Text Categorization and Clustering[END_REF]. While a large variety of approaches could be noticed, the best performing systems consistently use algorithms like SVM (Support Vector Machine) to achieve consistent and good results. Machine learning algorithms like SVM, Naïve Bayes, Maximum Entropy are relatively simple to understand and use, and unlike knowledge engineering methods, they do not require large knowledge-bases to be pre-defined manually by engineers. Also, this kind of systems is not domain related, unlike most engineering approaches that are focused on restricted domains or even subdomains (as it happens, for example, in the medical domain where compact parts of the consecrated ontologies are adopted for certain medical specializations). The good functioning of these algorithms usually requires the "translation" of the documents into feature vectors. Common feature vector construction involves term frequency, document frequency, term frequency and inverse document frequency combined, information gain, term strength, and chi-statistic [START_REF] Ikonomakis | Text Classification Using Machine Learning Techniques[END_REF], [START_REF] Sebastiani | Machine Learning in Automated Text Categorization[END_REF].

Latent Semantic Indexing (LSI) has been used in conjunction with WordNet or other domain ontologies to reduce the dimensionality of feature vectors [START_REF] Moravec | wordnet ontology in dimension reduction for information retrieval[END_REF] [START_REF] Lv | Realizing English text classification with semantic set index method[END_REF]. The main idea of LSI is that there is a semantic structure between the words in a document that can be discovered and used to group similar documents into similar space structures using statistical analysis. Using LSI means that after document preprocessing, the document vector is obtained (in the form of d={(keyword i ,weight i )|i=1..n}, its dimensionality is reduced using LSI and then it is compared to every category vector topic. The category vector that is closest to the document vector is the topic assigned to that document. [START_REF] Yang | The Application of Latent Semantic Indexing and Ontology in Text Classification[END_REF] showed an slight increase in performance when using LSI and an ontology as opposed to simply using a Naïve Bayes classifier (or equivalent) and an ontology. In [START_REF]A Semantic-Oriented Approach for Organizing and Developing Annotation for E-learning[END_REF] we developed a text classification method where the LSI technique was combined with a WordNet-based text analysis. However, while LSI is effective in mitigating word similarities, it is quite difficult to maintain such a system when the document size varies and any modification of the initial set of documents requires that the entire semantic space to be reconstructed [START_REF] Lee | Use of Ontology to Support Concept-Based Text Categorization[END_REF].

Concerning the ontology-based approaches to text classification, it can be noticed that most often domain ontologies are used [START_REF] Gu | Text Classification Based on Domain Ontology[END_REF]. Domain ontologies are usually small and contain very specific facts about a domain, like certain group of illnesses for the medical domain, names and hierarchy of wines for the oenological domain or car parts for the automotive domain. When applied to a collection of texts from a certain area, a domain ontology focusing on that area will be much more effective than a general ontology. However, for diverse collections of documents, the use of domain ontologies is no longer possible.

For the purpose of this paper we will use the YAGO ontology [START_REF] Suchanek | YAGO -A Core of Semantic Knowledge[END_REF]. YAGO is a general, light-weight and extensible ontology with high coverage and quality. YAGO was built as a very large, accurate (95+ accuracy) and simple to use ontology for machines including WordNet entities and hierarchy, and information extracted from Wikipedia like named entities (people, organizations, geographic locations, books, songs, products, etc.), and also relations among these entities. In the YAGO model all objects are entities and any two such entities can stand in a relation. YAGO uses two sources of information: WordNet and Wikipedia. From WordNet it borrows the hypernym hierarchy, while from Wikipedia it borrows entities and uses them as arguments to the relations implemented in YAGO. Each WordNet synset is translated in a YAGO class. Thus, WordNet defines the upper hierarchy, while Wikipedia contributes to the lower, most specific branches. These are also linked up using the subClassOf relation. WordNet synsets have words with similar meaning. In summary, YAGO stores more than 2 million entities with 20 million facts about them.

Our solution is destined to multiple stakeholders acting at the city level (e.g. journalists, economic analysts, sociologists, police, city municipality representatives), providing them access to a commonly developed knowledge base where the emerging social Web resources are indexed based on the YAGO ontology.

The first provided support concern the interest topic formulation, since for a specific stakeholder it is very difficult to exhaustively express it. For an initial topic (e.g. "pesticides in the baby food"), a list of YAGO concepts accompanied by their corresponding weight is provided, while a supplementary manual topic refinement of the topic is facilitated, based on the YAGO structure: either to enrich or reduce the topic, either to reconsider the weights associated with the composing concepts.

SYSTEM IMPLEMENTATION

This section discusses system architecture and implementation. The system can be logically divided into three major modules: Processor, Analysis and Evaluator.

A quick overview of the way the system works: First, at initialization phase, the topic list is constructed. Then a document is fed to the Processor where it is parsed and words are extracted from it, along other useful information, as word frequency and word type, form, etc. The words are then associated to classes or entities from the ontology. Next, in the Analysis module, each word's associated class is found in the ontology, and then a score is added to each topic's overall score, depending on the influence of the word to that respective topic. After all words have been processed, the topics are sorted by their descending scores in the Evaluator module. The topic with the biggest score is the document's proposed topic. Below, we present each module, starting with the initial topic list creation.

The topic list creation

The data set for each topic is created at program initialization, before analyzing any document. For the current system we had a collection of 848 news articles that are placed into 50 topics. For each topic an array of 'concepts' is created. A concept is in itself an array of similar words, much like the senses in a WordNet synset. However, we do not store simple words in the concepts arrays, but YAGO classes corresponding to the words. We assign each class in a concept array a weight, and to each concept in a topic another weight. This allows us a fine-grained control over word/concept influence.

For example, let us consider the topic 'Pesticides in baby food'. We create the concepts of 'pesticide', 'baby' and 'food', assigning each a weight of 1.0, meaning full importance. The concept 'baby' has, for example, the classes of 'wordnet_baby_109827683' with a weight of 1.0 and 'wordnet_child_109918554' with a weight of 0.7, meaning we prefer baby over child if possible, but child should also be considered relevant if found in a document.

The topic list was created semi-automatically, meaning that we wrote a side-program that took each topic, extracted words from it and located their probable classes; our work was only to remove or to add some words to better define a topic and adjusted weights in three degrees of separation (1.0, 0.7 and 0.4 -heuristically chosen). This is the only part of the entire system that depends on human intervention. For each word in each topic the list of possible concepts was found automatically. We could have used some flavor of Word Sense Disambiguation [START_REF] Navigli | Word sense disambiguation: A survey[END_REF] (even basic Lesk could work to a degree) to differentiate the weights between each word's possible classes, but due to time constraints as well as not wanting to introduce a new error source (WSD is far from perfect) we chose to select the weights ourselves (not perfect either, but closer) and better evaluate the ontology-based algorithm by itself.

Processor Module

This module takes as input a raw text document and outputs a list of words, each of the words having a set of probable classes in the ontology.

The document is first split into sentences using a Maximum Entropy (ME) model with very good results. Then, for each sentence, it is split into individual words (tokens), using another ME model. The tokens are then analyzed and their part of speech is determined, their form (singular or pluralif the word is in its plural form, it is inflected to its singular form) and whether the token is a Named Entity or a simple common word. All this information is needed to determine which words are searched for in the ontology (we restricted to only common nouns and Named

Processor Analysis Evaluator

Ontology

Raw doc. files

List of topics

Figure 1. System architecture

Entities), and what classes are assigned to each of them. Also, if a word is found more than 1 time then a) its frequency count is increased, and b) it is considered already 'disambiguated' -we consider that a repeated word always means the same thing.

For example, the word 'governments' is searched for in the ontology and it yields the following classes: 'wordnet_government_108050678', 'wordnet_government_ 101124794', 'wordnet_government_ 105663671' and 'wordnet_politics_106148148'. The search in the ontology is performed using the means relation. We ask YAGO to tell us all the classes it knows where that class means 'government'. Thus, for each word we obtain a set of classes that we keep.

Each document is defined as a set of words, each with their determined classes. This output is further fed to the Analysis Module.

Analysis Module

The module takes as input the list of words with their associated classes and as output it assigns a score to each topic concept. For each class in the set of each word, it first finds it in the ontology. Then, it performs a graph walk starting with this class as a center, limited to two levels of depth. Every node it reaches is analyzed. If the node belongs to one of the concepts in a topic, that concept's score is increased accordingly. The weight of the concept is increased by I: This is a heuristically chosen formula, where d wc is the distance from the word w being analyzed to the matched concept c. We restrict the distance to maximum 2, so the distance modifier will either be 1 (when the distance is zeromeaning the word w is exactly the concept c), 0.5 (direct link between w and c -distance 1) or 0.25 (for distance 2). Further distances in the ontology usually yield very poor semantic connections, so we argue that a maximum distance of 2 is sufficient. The distance modifier is then multiplied by the squared logarithm of the frequency of the word w. We have used a logarithm to dampen the influence effect of exceedingly repeated words. For example, we analyze the word 'vegetable', with two YAGO classes, one of which is 'wordnet_vegetable_107707451'. We start the graph search from this class, up to a distance of maximum 2. The system's console displays: meaning than it has found three matches, two of them being an identical match (distance 0) and thus increasing the score by 0.69 of the 'vegetable' concepts of topics 71 and 90. It also increases the score of concept 'food' (topic 41) by only 0.1725 because class 'wordnet_food_107555863' is at distance 2 from the starting point of 'wordnet_vegetable_107707451'.

As such, for every word in the list it performs the graph walk. For every class in the ontology it encounters, if that class belongs to a concept of a topic, it increases the concept's score accordingly.

In the example below, the score composition of concept 'food' from topic 41 'Pesticides in Baby Food' is shown, as each matching concept adds a small increase I to the final score of 8.5574: It should be noted that in the examples above we give only WordNet related classes (e.g. class wordnet_beef_107663592) from YAGO because the relations between them are easier to understand and there are more links between them than between named entities (e.g. class France, class Tunnel_Channel, etc). However, YAGO's imported WordNet hierarchy contains only around 65.000 classes from the more than 1 million entities known. Named entities contribute just as much as (and in some cases even more than) the common entities that our system uses.

Evaluator Module

The evaluator module takes as input the topic lists with their scores, and evaluates them. The output is a sorted list of relevant topics.

The scoring method we used here is to add the scores of each topic's individual concepts, taking into consideration the number of non-zero concepts: first, we calculate the general score for each topic that is composed of the simple sum of the scores of its concepts. Then we evaluate the first 3-5 best topics, and we calculate the average score difference between each topic, which will call the error margin. Then, for that document, if the general simple score of the best topic is greater that the score of the second topic plus the error margin, we assume that the first topic is the correct topic. If the second topic is within the error margin of the first, we count for each topic the number of concepts that have a score greater than 0. The topic we believe is correct becomes the topic that has the highest 'coverage'. For example, if we have two topics, even if the first topic has a higher score than the second, if the second topic (being within the error margin) has a greater percent of concepts with a score above zero, we will choose the second topic as correct (as a concrete example, if topic 1 has 4 out of 5 concepts greater than zero, and topic 2 has 4 out of 4 concepts greater than zero, then we choose topic 2, because topic 1 has only 4/5 = 0.8 coverage while topic 2 has 4/4 = 1.0).

Even though this is a heuristically chosen metric, it provides better results than just simple concept addition. The 'coverage' idea was introduced to simulate the human tendency to assign a topic to a document if that document has more words that are found in the topic rather than just a few common words with the topic but repeated several times.

For the purposes of this article, the scoring method provides relatively good performance. However, for larger collections of data (above 10.000 documents) we believe that a linear formula with adjustable parameters will provide an even better topic differentiating metric. The adjustable parameters could be determined in a training phase to estimate either a better document-individual error margin, or even a global margin.

EVALUATION

Before evaluating the results, a quick description of the data collection on which we tested is needed. We used the LA94 TREC Information-Retrieval Text Research Collection, representing a sampling of news articles published by the Los Angeles Times in 1994. The collection includes 828 such articles, which are classified over 50 topics. The articles propose news on different topics such as entertainment, movies, television, music, politics, business, health, technology, etc.

In order to compare our results against a standard method of text classification used today, we have implemented a SVM based system for text classification. The system is built in Java and uses core functionality from WEKA [START_REF] Hall | The WEKA Data Mining Software: An Update[END_REF]. Each document is parsed, and a feature vector is extracted. The vector is further elaborated upon, eliminating stop-words, using lowercase tokens, setting a minimum term frequency for allowed terms, pruning periodically, using a stemmer, and finally applying a TF*IDF transform. The SVM is then trained on the document collection, and evaluated using a random-seed, 10-fold cross validation. We have tried to build the evaluator system as best as possible using the latest feature vector techniques and the best classifier for this job, the SVM.

Table 1. Comparison between the proposed system and a standard SVM state-of-the-art method

System

Performance (correctly classified documents) Our ontology-centric system 529 / 828 (63.88%) SVM comparison system 661 / 828 (79.83%)

The SVM comparison system at this moment performs better, by a margin of around 16%. However, our proposed system achieves a respectable performance of 63.88% using only the ontology as a source of information. We designed this system as proof-ofconcept, to test the possibility of using ontologies as the core of a text classification system, and to see the performance degree of such an approach.

While the system proves effective even at this stage, we believe that its performance can be improved, especially concerning the topic list creation. Performance is affected by the description of each the topic (meaning the 'concepts' of each topic).

Table 2 shows some of the performance gains after manually tweaking some of the topic's concepts. For example, while adding context to topic 50, performance is very slightly improved by almost 1%, while for topic 80 the correct topic classification rate is doubled to 34%. By concept tweaking we mean editing individual concepts. For example, for topic 80 we had the initial concepts of 'hunger' and 'strike'. After adding context, meaning concepts 'government', 'demonstration' and 'cause' (each with a slightly lower weight than the initial two concepts), the detection rate greatly increased. However, after also tweaking topic 58, the performance negatively affected topic 70's recognition rate. This is due to the adding to topic 58 of the concept 'kill' which was already present in more topics, including topic 70. This means that the word kill will now score for topic 58 also. The multiplicity of the same concept in many topics, while unavoidable, does negatively impact performance. It should also be noted after tweaking, each topic has grown from 2-3 concepts to an average of 4 concepts with only few topics having more than 6 concepts.

Another valuable insight from this before / after comparison is related to the ontology information content. We have found out that in some places there are lacks in information in the ontology, while in other places there is an abundance of it. For example, we had trouble finding YAGO classes to describe the concepts for topic 'Solar Energy': while we have 'wordnet_energy_ 111452218' for the 'energy' concept, for the 'solar' concept there is no solar class, just classes like 'wordnet_solar_cell_ 104257986', 'wordnet_solar_dish_104258138' or 'wordnet_ solar_house_104258438'. While there is the direct class of 'wordnet_solar_energy_111509697' which we have also used, because it is multi-word, for us to match it we need to have the expression in the text solar energy. This means that in documents where the word solar is used, if it is not followed by cell, dish, house or energy, it will not be counted.

Another topic list related point is that the list needs to be created partially by hand. While usually this is not desired due to the human intervention that is required, we argue that the number of possible topics for any classification is manageable, ranging from a few tens to usually no more than a few hundreds, a relatively simple task for even one person.

Another perspective work aspect concern the fact that we currently consider the document as a bag of words. Improvement can be achieved if words would be analyzed in contextwe could consider sentences and relations between words in a sentence and even more across sentence boundaries. For example, for the sentence '…genetically modified apples may lead to illness …' the noun apples would contribute more to topic 'Vegetables, Fruit and Cancer' due to the close proximity to illness that itself is closely related to topic concept 'cancer', than it contributes to topic 'Pesticides in Baby Food', because there is no pesticide or baby related word in apples's proximity. This would be implemented in the Analysis module, introducing a proximity factor to the score awarded to a concept based on the closeness to other related words for each topic. Another improvement that can be somewhat easily employed is to consider word modifiers. For example, for the sentence fragment '… thus avoiding war in the region.' , war should contribute to the topic 'Peace-Keeping Forces in Bosnia' because 'avoiding war' implies the 'peace' concept in this topic. A list of antonyms could be used. For example, the negation 'not dry' directly implies wet, which would score for rain-related topics due to rain-water-wet conceptual ontology links. Also, a list of negative verbs could be used, such as for the fragment '... to avoid war …', avoid could trigger war's antonympeace. Furthermore, we could use the ontology as not only a semantic similarity map, but also use the relations themselves as useful information. That would mean identifying subjectobject entities and then match the verb that links them to a specific relation in the ontology. This would provide a stronger link between concepts, and an algorithm could judge whether to take into account certain entities or not based on the relations between them. However, the task of Relation Detection and Identification is in itself a difficult problem, requiring a dedicated system of its own. At the time of writing we could not find such a system easily implementable on our YAGO-centric structure.

CONCLUSIONS

Our solution proposes a different approach in exploiting the Web and social Web data in the context of smart cities. While technologies exist for data crawling and crowdsourcing, as well as for big data analytics, a semantic-oriented analysis of these data could significantly contribute to knowledge management. We believe that ontologies, especially general ontologies represent a powerful yet somewhat underused tool for the text classification problem in the context of smart cities. The structure of the ontology itself contains information that can be used in the form of concept closeness, synonymy, hypernymy, relation types, etc. As time passes, general ontologies will become larger, thus providing better results even using the same algorithms.

However, the use of ontologies does impose some limitations For example, information density in an ontology varies greatly, meaning some concepts will be defined in more detail than others, that in turn leading to uneven topic recognition accuracy. This problem is usually addressed by using domain ontologies. However, for example, for news articles a domain ontology is mostly useless considering the method we have applied in this article, where we do not use the ontology as a simple hierarchical taxonomy, but as a concept semantic-similarity map. We propose a system that achieves a good performance rating using only an ontology as its information source, and graph algorithms with a custom scoring method. The system uses the links available in the ontology to assign a score to the semantic similarity between concepts. Future work on the subject will include implementing some of the suggestions in the evaluation section, as well as implementing supervised and unsupervised algorithms to generate the topic list's concepts instead of manually tweaking them, and evaluate performance between this system's versions.

We've chosen to compare our solution with the SVM approach since it is proved by the state of the art as the most efficient method for text classification; however, two issues diminish its efficiency in the case of the presented "smart city" context: firstly, SVM approach provide better results when classifying a fixed document corpus (where the space is determined), but has performance issues when new documents, new topics and thus new keywords are added in real-time (retraining takes time; even online training for machine-learning algorithms like SVM still have problems working in real-time with large scale data, an issue that does not exists in unsupervised approaches); secondly, the SVM approach is not "deterministic", in the sense that for the same input data, after several training iterations (for incoming new data), the same output will be presented (the same classification category for example), while in the semantic Web age the "control" of semantics is an emerging and important issue to be handled; An YAGO-based solution for text classification enable not only to handle the text semantics when new documents, new topics and thus new keywords appear continuously. In addition, the proposed graph-based method enables to handle the frequent changing of this topics and keywords' space dimension. While at present machine learning algorithms take center stage in the performance, we believe that knowledge engineering methods will slowly gain momentum, and aided by machine learning techniques as well as larger knowledge repositories in the form of ontologies, will provide the better results in this field.

  69 (d:0) e: wordnet_vegetable_107707451 TOP[71 Vegetables, Fruit and Cancer] > CON[vegetable] Match 0.69 (d:0) e: wordnet_vegetable_107707451 TOP[90 Vegetable Exporters] > CON[vegetable] Match 0.1725 (d:2) e: wordnet_food_107555863 TOP[41 Pesticides in Baby Food] > CON[food]

  concept [food/1.0] 8.5574: wordnet_food_107555863/1.0:8.5574 >> Add 1.0*3.76=3.76 to 3.76 from wordnet_food_107555863/42 >> Add 0.25*0.69=0.1725 to 3.9324 from wordnet_game_107650449/1 >> Add 0.5*2.89=1.445 to 5.3774 from wordnet_meat_107649854/17 >> Add 0.5*1.1=0.55 to 5.9274 from wordnet_fish_107775375/2 >> Add 0.5*0.69=0.345 to 6.2724 from wordnet_cheese_107850329/1 >> Add 0.5*1.95=0.975 to 7.2474 from wordnet_seafood_107776866/6 >> Add 0.25*2.48=0.62 to 7.8674 from wordnet_shellfish_107783210/11 >> Add 0.25*0.69=0.1725 to 8.04 from wordnet_beef_107663592/1 >> Add 0.25*0.69=0.1725 to 8.2124 from wordnet_delicatessen_107594406/1 >> Add 0.25*0.69=0.1725 to 8.384 from wordnet_vegetable_107707451/1 >> Add 0.25*0.69=0.1725 to 8.5574 from wordnet_pork_107668702/1

Table 2 . A short comparison between overall system performance grouped by topic, before and after topic tweaking, for 5 out of the 50 total topics

 2

	Topic id	Topic	# of docs	Performance before tweaking	Performance after tweaking
	50	Revolt_in_ Chiapas	105	98 / 105 (93.3%)	99 / 105 (94.28%)
	43	El_Nino_and _the_Weather	11	4 / 11 (36.36%)	6 / 11 (54.54%)
	80	Hunger_Strikes	56	9 / 56 (16.07%)	18 / 32 (34.61%)
	70	Death_of_ Kim_Il_Sung	33	28 / 33 (84.84%)	22 / 33 (66.66%)
	58	Euthanasia	49	14 / 49 (28.57%)	31 / 49 (63.26%)

https://webarchive.jira.com/wiki/display/Heritrix/Heritrix, http://www.dataparksearch.org/

http://www.guardian.co.uk/politics/mps-expenses

a few examples: http://www.smartcities.info, http://www.citymayors.com, http://www.smart-cities.eu, http://www.smartcitiesineurope.com, etc.