Wireless, power line communication (PLC), fiber optic, Ethernet, and so forth are among the communication technologies on which smart grid communication infrastructure is envisioned to be built. Among these, wireless and PLC-based solutions are attractive considering the cost of initial deployment. Wireless communication deployment in smart grid covers a variety of environments such as indoor, outdoor, and electric-power-system facilities. Similar diversity is expected in PLC deployment as well covering low voltage (LV), medium voltage (MV), and high voltage (HV) segments of the grid. In spite of being attractive, wireless and PLC channels are very harsh posing great challenges to performance of communication systems. In proposing solutions to smart grid communication needs, two approaches are likely to be followed. One is based on the use of existing wireless and PLC technologies with some modifications, and the other relies upon developing novel communication protocols particularly addressing the smart grid needs. Both of these approaches require an in-depth knowledge of communication channel characteristics. The aim of this study is to reveal the wireless and PLC channel characteristics of smart grid environments in terms of several parameters such as path loss and attenuation, time dispersion, time selectivity, amplitude statistics, and noise characteristics.
Smart grid is a challenging project that requires the establishment of a very extensive communication infrastructure. PLC and wireless-based solutions seem very attractive considering the cost of initial investment. Being cost-effective solutions, two approaches are likely to emerge: integration of already existing PLC and wireless technologies into the grid with some modifications regarding QoS, latency, reliability, power consumption, and so forth, or developing novel communication protocols particularly addressing the smart grid communication needs. No matter what approach is taken, a deep understanding of the communication channel characteristics of smart grid environments is essential. In this study, communication channel characteristics of both PLC and wireless environments were discussed in details as summarized in Table 2. Smart grid wireless deployment options were classified roughly as indoor, outdoor, and electric-power-system environments. Similar methodology was followed in PLC environments as well by classifying them as LV, MV, and HV.
Principles Of Mobile Communication Stuber Solution 67
Mobile network data has been proven to provide a rich source of information in multiple statistical domains such as demography, tourism, urban planning, etc. However, the incorporation of this data source to the routinely production of official statistics is taking many efforts since a diversity of highly entangled issues (access, methodology, IT tools, quality, skills) must be solved beforehand. To do this, one-off studies with concrete data sets are not enough and a standard statistical production process must be put in place. We propose a concrete modular process structured into evolvable modules detaching the strongly technological layer underlying this data source from the necessary statistical analysis producing outputs of interest. This architecture follows the principles of the so-called ESS Reference Methodological Framework for Mobile Network Data. Each of these modules deals with a different aspect of this data source. We apply hidden Markov models for the geolocation of mobile devices, use a Bayesian approach on this model to disambiguate devices belonging to the same individual, compute aggregate numbers of individuals detected by a telecommunication network using probability theory, and model hierarchically the integration of auxiliary information from the telco market and official data to produce final estimates of the number of individuals across different territorial regions in the target population. A first simple illustrative proposal has been applied to synthetic data providing preliminary software tools and accuracy indicators monitoring the performance of the process. Currently, this exercise has been applied to the estimation of present population and origin-destination matrices. We present an illustrative example of the execution of these production modules comparing results with the simulated ground truth, thus assessing the performance of each production module.
There exist two important issues which raise immediate rightful concerns when using mobile network data for statistical purposes. These are (i) privacy and confidentiality of network subscribers and (ii) access conditions to data by NSOs. We shall not be dealing with these issues in the next sections, but we mention the general principles for the context in which our proposed process is to be considered. Privacy and confidentiality of any statistical information collected, processed, and disseminated by NSOs have been, are, and will be a priority for any kind of data source. Traditional survey data is indeed identified personal data and concerns about its protection are duly accounted for with a specific production phase known as statistical disclosure control [32, 33]. All kind of survey and administrative data about personal habits, causes of death, business revenues, VAT and personal taxes, etc. are collected, processed, and aggregated and official statistics are disseminated under a negligible risk of reidentification of statistical units, whatever their nature is. Not only is this commitment present with new digital data sources in general and mobile network data in particular, but is it also reinforced.
Regarding access, this is an intricately complex unsolved issue where many, many facets need to be considered simultaneously. Currently, there exist concrete agreements between some NSOs/research centres/universities and Mobile Network Operators (MNOs) for research on limited data sets, but the conditions for routinely production of official statistics are yet to be found. By and large, in our view, MNOs will need to become an active part of the official statistical production process and this brings novel challenges. We identify at least the following restrictions to be jointly satisfied to arrive at a feasible solution. Firstly, security, confidentiality, and privacy must be legally and technically assured during the whole process, involving the approval by the national Data Protection Authorities. In this sense, we underline the traditional role of NSOs in collecting and processing sensitive information. Currently, we consider that any kind of mobile network data processing must be undertaken in the original information systems of MNOs. However, notice that further research needs to be conducted. For example, there exists both theoretical and empirical evidence [34, 35] that privacy is not preserved even after aggregating data under certain conditions. Secondly, appropriate territorial and time breakdowns for target indicators and aggregates for the social good, potentially to be included in sectorial legal regulations, must be identified so that valuable information for data-based policy making and decision taking can be produced and disseminated for free. Thus, the relevant role of statistical offices in society according to the Fundamental Principles of Official Statistics [36] would be strengthened. Thirdly, a new branch of economic activity is growing on the basis of digital data and data analytics [37]. This is usually substantiated in the so-called monetization of data generated by enterprises during their business activities. MNOs are not an exception and due to the technologically complex data ecosystem of telecommunication networks, investments are needed (mobile network data for statistical purposes do not exist, a preprocessing stage is needed). Thus, a trade-off between public and private interests must be found. In this line of thoughts, as we have expressed elsewhere [38], public-private partnerships arise as an optimal solution, in which win-win agreements are indeed feasible. The present methodological proposal, beyond the statistical contents included hereafter, provides also an insight on aspects to be taken into account when finding these agreements.
We make a proposal for an end-to-end statistical process going from the raw telco data generated at the mobile telecommunication networks to the final target population count estimates. The proposal follows the principles of functional modularity adapted to statistical production [41] focusing on input and output data as well as the throughput of each production step. The proposal so far focuses on a single-MNO scenario. The next sections describe each of the functional modules of the statistical process. In Sect. 2 we provide a description of the (synthetic) data used to illustrate the proposal. In Sect. 3 we describe the module to geolocate mobile devices. In Sect. 4 we propose a method to disambiguate devices carried by the same individual. In Sect. 5 we include general considerations to identify devices pertaining to the target population under analysis. In Sect. 6 we suggest a method to aggregate data from the device level to the territorial unit level. In Sect. 7 we propose to use hierarchical modelling to infer population counts in the target population from the population counts in the network, integrating at the same time auxiliary information. In Sect. 8 we integrate all modules in a production chain. Finally, in Sect. 9 we close with some conclusions and future prospects.
Our proposed approach for the statistical filtering of target populations is strongly based on the geolocation outputs obtained from the preceding process modules. Different aspects are to be taken into account. As before, the target mobile network data is assumed to be basically some form of signalling data so that time frequency and spatial resolution are high enough as to allow us to analyse movement data in a meaningful way. In this sense, for example, CDR data only provides up to a few records per user in an arbitrary day which makes virtually impossible any rigorous data-based reasoning in this line. Next, the use of hidden Markov models, as described in Sect. 3, implicitly incorporates a time interpolation which will be very valuable for this statistical filtering exercise. In this way we avoid the issues arising from noncontinuous traces approaches (see e.g. [60] for home location algorithms). However, a wider analysis is needed to find the optimal time scope. The spatial resolution issue is dealt with by using the reference grid introduced in Sect. 3. This releases the analyst from spatial techniques such as Voronoi tessellation, which introduces too much noise for our purposes. Nonetheless, the uncertainty measures computed from the underlying probabilistic approach for geolocation must be taken into account to deal with precision issues in different regions (e.g. high-density populated vs. low-density populated). The algorithms to be developed to statistically filter the target population will be mainly based on quantitative measures of movement data. In particular, from the HMMs fitted to the data (especially the location probabilities) we shall derive a probability-based trajectory per device which will be the basis for these algorithms. 2ff7e9595c
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