What Can Wireless Cellular Technologies Do about the Upcoming Smart Metering Traffic?

The introduction of smart electricity meters with cellular radio interface puts an additional load on the wireless cellular networks. Currently, these meters are designed for low duty cycle billing and occasional system check, which generates a low-rate sporadic traffic. As the number of distributed energy resources increases, the household power will become more variable and thus unpredictable from the viewpoint of the Distribution System Operator (DSO). It is therefore expected, in the near future, to have an increased number of Wide Area Measurement System (WAMS) devices with Phasor Measurement Unit (PMU)-like capabilities in the distribution grid, thus allowing the utilities to monitor the low voltage grid quality while providing information required for tighter grid control. From a communication standpoint, the traffic profile will change drastically towards higher data volumes and higher rates per device. In this paper, we characterize the current traffic generated by smart electricity meters and supplement it with the potential traffic requirements brought by introducing enhanced Smart Meters, i.e., meters with PMU-like capabilities. Our study shows how GSM/GPRS and LTE cellular system performance behaves with the current and next generation smart meters traffic, where it is clearly seen that the PMU data will seriously challenge these wireless systems. We conclude by highlighting the possible solutions for upgrading the cellular standards, in order to cope with the upcoming smart metering traffic.Comment: Submitted; change: corrected location of eSM box in Fig. 1; May 22, 2015: Major revision after review; v4: revised, accepted for publicatio

Enabling LTE RACH Collision Multiplicity Detection via Machine Learning

The collision resolution mechanism in the Random Access Channel (RACH) procedure of the Long-Term Evolution (LTE) standard is known to represent a serious bottleneck in case of machine-type traffic. Its main drawbacks are seen in the facts that Base Stations (eNBs) typically cannot infer the number of collided User Equipments (UEs) and that collided UEs learn about the collision only implicitly, through the lack of the feedback in the later stage of the RACH procedure. The collided UEs then restart the procedure, thereby increasing the RACH load and making the system more prone to collisions. In this paper, we leverage machine learning techniques to design a system that outperforms the state-of-the-art schemes in preamble detection for the LTE RACH procedure. Most importantly, our scheme can also estimate the collision multiplicity, and thus gather information about how many devices chose the same preamble. This data can be used by the eNB to resolve collisions, increase the supported system load and reduce transmission latency. The presented approach is applicable to novel 3GPP standards that target massive IoT, e.g., LTE-M and NB-IoT.Comment: Submitted to IEEE GLOBECOM 201

Smart Grid Traffic Modeling in GSM and LTE using Multidimensional Markovian Process

The traditional electrical grid has provided electricity over a decades. Since then it has been of static nature without significant update and offers limited control. But as of now with rise in demand of power the traditional grid is not capable of meeting them. Also, several blackouts have occurred which raises the serious Questions on grid reliability and efficiency. Recently smarter solution has been suggested to address the above problem. It will take the advantage of advanced Information and Communication technology (ICT) and transform the grid into Smart grid

Survey on Wi-Fi and Cellular Communication Technology for Advanced Metering Infrastructure (AMI) in a Developing Economy

Traditional energy meters have suffered from a lack of automated analysis and inaccuracy in reading energy consumption, which has brought about smart metering systems. Developing economies such as in Africa. still experience a setback in electricity monitoring and load distribution because of existing traditional meter systems in use. Communication technologies play an important role to improve the monitoring of energy consumption and ensure a road map toward a smart grid. This paper reviews communication technologies used for Advanced Metering Infrastructure (AMI) emphasizing Wi-Fi and Cellular technologies. Metrics used to evaluate their performance include cost, energy efficiency, coverage, deployment, latency, payload, and scalability. The review presents a benchmark for research on AMI communication technologies in developing economies. When adopted, the expected AMI benefits are reduced energy theft, cost efficiency, real-time analysis, security, and safety of energy supply in developing economies

A Heterogeneous Communications Network for Smart Grid by Using the Cost Functions

Smart Grids (SG) is an intelligent power grid in which the different SG node types with different communication requirements communicates different types of information with Control Stations (CS). Radio Access Technologies (RATs) due to its advantages are considered as the main access method to be used in order to have bidirectional data transferring between different node types and CS. Besides, spectrum is a rare source and its demand is increasing significantly. Elaborating a heterogeneous in order to fulfill different SG node types communication requirements effectively, is a challenging issue. To find a method to define desirability value of different RAT to support certain node types based on fitness degree between RAT communication characteristics and node type communication requirements is an appropriate solution. This method is implemented by using a comprehensive Cost Function (CF) including a communication CF (CCF) in combination with Energy CF (ECF). The Key Point Indicators which are used in the CCF are SG node type communication requirements. The existing trade of between Eb/N0 and spectral efficiency is considered as ECF. Based on the achieved CCF and ECF and their tradeoffs, SG node types are assigned to different RATs. The proposed assigning method is sensitive to the SG node types densities. The numerical results are achieved by using MATLAB simulation. The other different outcomes of the research output such as cognitive radio in SG and collectors effect number on data aggregation are discussed as well

Channel Access in Wireless Networks: Protocol Design of Energy-Aware Schemes for the IoT and Analysis of Existing Technologies

The design of channel access policies has been an object of study since the deployment of the first wireless networks, as the Medium Access Control (MAC) layer is responsible for coordinating transmissions to a shared channel and plays a key role in the network performance. While the original target was the system throughput, over the years the focus switched to communication latency, Quality of Service (QoS) guarantees, energy consumption, spectrum efficiency, and any combination of such goals. The basic mechanisms to use a shared channel, such as ALOHA, TDMA- and FDMA-based policies, have been introduced decades ago. Nonetheless, the continuous evolution of wireless networks and the emergence of new communication paradigms demand the development of new strategies to adapt and optimize the standard approaches so as to satisfy the requirements of applications and devices. This thesis proposes several channel access schemes for novel wireless technologies, in particular Internet of Things (IoT) networks, the Long-Term Evolution (LTE) cellular standard, and mmWave communication with the IEEE802.11ad standard. The first part of the thesis concerns energy-aware channel access policies for IoT networks, which typically include several battery-powered sensors. In scenarios with energy restrictions, traditional protocols that do not consider the energy consumption may lead to the premature death of the network and unreliable performance expectations. The proposed schemes show the importance of accurately characterizing all the sources of energy consumption (and inflow, in the case of energy harvesting), which need to be included in the protocol design. In particular, the schemes presented in this thesis exploit data processing and compression techniques to trade off QoS for lifetime. We investigate contention-free and contention-based chanel access policies for different scenarios and application requirements. While the energy-aware schemes proposed for IoT networks are based on a clean-slate approach that is agnostic of the communication technology used, the second part of the thesis is focused on the LTE and IEEE802.11ad standards. As regards LTE, the study proposed in this thesis shows how to use machine-learning techniques to infer the collision multiplicity in the channel access phase, information that can be used to understand when the network is congested and improve the contention resolution mechanism. This is especially useful for massive access scenarios; in the last years, in fact, the research community has been investigating on the use of LTE for Machine-Type Communication (MTC). As regards the standard IEEE802.11ad, instead, it provides a hybrid MAC layer with contention-based and contention-free scheduled allocations, and a dynamic channel time allocation mechanism built on top of such schedule. Although this hybrid scheme is expected to meet heterogeneous requirements, it is still not clear how to develop a schedule based on the various traffic flows and their demands. A mathematical model is necessary to understand the performance and limits of the possible types of allocations and guide the scheduling process. In this thesis, we propose a model for the contention-based access periods which is aware of the interleaving of the available channel time with contention-free allocations