Probabilistic Rateless Multiple Access for Machine-to-Machine Communication

Future machine to machine (M2M) communications need to support a massive number of devices communicating with each other with little or no human intervention. Random access techniques were originally proposed to enable M2M multiple access, but suffer from severe congestion and access delay in an M2M system with a large number of devices. In this paper, we propose a novel multiple access scheme for M2M communications based on the capacity-approaching analog fountain code to efficiently minimize the access delay and satisfy the delay requirement for each device. This is achieved by allowing M2M devices to transmit at the same time on the same channel in an optimal probabilistic manner based on their individual delay requirements. Simulation results show that the proposed scheme achieves a near optimal rate performance and at the same time guarantees the delay requirements of the devices. We further propose a simple random access strategy and characterized the required overhead. Simulation results show the proposed approach significantly outperforms the existing random access schemes currently used in long term evolution advanced (LTE-A) standard in terms of the access delay.Comment: Accepted to Publish in IEEE Transactions on Wireless Communication

Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.Comment: To be published in IEEE Communications Surveys and Tutorial

Deriving Machine to Machine (M2M) Traffic Model from Communication Model

© 2018 IEEE. The typical traffic models proposed in literature can be considered as heuristic models since they only reflect the stochastic characteristic of the generated traffic. In this paper, we propose a model for M2M communications that generates the traffic. Therefore, the proposed model is able to capture a wider picture than the state-of-the-art traffic models. The proposed model illustrates the behaviour of M2M uplink communication in a network with multiple-access limited information capacity shared channels. In this paper, we analyzed the number of transmitted packets using the traffic model extracted from our proposed communication model and compared it with the state-of- the-art traffic models using simulations. The simulation results show that the proposed model has a significantly higher accuracy in estimating the number of transmitted packets compared with the liteature model

A survey of smart grid architectures, applications, benefits and standardization

The successful transformation of conventional power grids into Smart Grids (SG) will require robust and scalable communication network infrastructure. The SGs will facilitate bidirectional electricity flow, advanced load management, a self-healing protection mechanism and advanced monitoring capabilities to make the power system more energy efficient and reliable. In this paper SG communication network architectures, standardization efforts and details of potential SG applications are identified. The future deployment of real-time or near-real-time SG applications is dependent on the introduction of a SG compatible communication system that includes a communication protocol for cross-domain traffic flows within the SG. This paper identifies the challenges within the cross-functional domains of the power and communication systems that current research aims to overcome. The status of SG related machine to machine communication system design is described and recommendations are provided for diverse new and innovative traffic features

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe

Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.Peer reviewe

White Paper for Research Beyond 5G

The documents considers both research in the scope of evolutions of the 5G systems (for the period around 2025) and some alternative/longer term views (with later outcomes, or leading to substantial different design choices). This document reflects on four main system areas: fundamental theory and technology, radio and spectrum management; system design; and alternative concepts. The result of this exercise can be broken in two different strands: one focused in the evolution of technologies that are already ongoing development for 5G systems, but that will remain research areas in the future (with “more challenging” requirements and specifications); the other, highlighting technologies that are not really considered for deployment today, or that will be essential for addressing problems that are currently non-existing, but will become apparent when 5G systems begin their widespread deployment

Impacto das comunicações M2M em redes celulares de telecomunicações

Mestrado em Engenharia Electrónica e de TelecomunicaçõesAs comunicações Máquina-Máquina (M2M) apresentam um crescimento muito significativo e algumas projeções apontam para que esta tendência se acentue drasticamente ao longo dos próximos anos. O tráfego gerado por este tipo de comunicações tem caraterísticas muito diferentes do tráfego de dados, ou voz, que atualmente circula nas redes celulares de telecomunicações. Assim, é fundamental estudar as caraterísticas dos tipos de tráfego associados com comunicações M2M, por forma a compreender os efeitos que tais caraterísticas podem provocar nas redes celulares de telecomunicações. Esta dissertação procura identificar e estudar algumas das caraterísticas do tráfego M2M, com especial enfoque na sinalização gerada por serviços M2M. Como resultado principal deste trabalho surge o desenvolvimento de modelos que permitem a construção de uma ferramenta analítica de orquestração de serviços e análise de rede. Esta ferramenta permite orquestrar serviços e modelar padrões de tráfego numa rede UMTS, possibilitando uma análise simultânea aos efeitos produzidos no segmento core da mesma rede. Ao longo deste trabalho procura-se que a abordagem aos problemas apresentados permita que os resultados obtidos sejam válidos, ou adaptáveis, num âmbito mais abrangente do que apenas as comunicações M2M.Machine to Machine (M2M) communications present significant growth and some projections indicate that this trend is going to increase dramatically over the coming years. The traffic generated by this type of communication has very different characteristics when compared to data or voice traffic currently going through cellular telecommunications networks. Thus, it is essential to study the characteristics of traffic associated with M2M communications in order to understand the effects that its features can imply to cellular telecommunications networks. This dissertation tries to identify and study some of the characteristics of M2M traffic, with particular focus on signaling generated by M2M services. A number of models, that enable the development of an analytic tool for service orchestration and network analysis, are presented. This tool enables service orchestration and traffic modeling on a UMTS network, with simultaneous visualization of the impacts on the core of such network. The work presented in this document seeks to approach the problems at study in ways ensuring that its outcomes are valid for a wider scope than just M2M communications

Modelling IoT devices communication employing representative operation modes to reveal traffic generation characteristics

Several traffic models for the Internet of Things (IoT) have been proposed in the literature. However, they can be considered as heuristic models since they only reflect the stochastic characteristic of the generated traffic. In this paper, we propose a model to represent the communication of IoT devices. The model was used to obtain the traffic generated by the devices. Therefore, the proposed model is able to capture a wider understanding of device behaviour than existing, state-of-the-art traffic models. The proposed model illustrates the behaviour of Machine-to-Machine uplink communication in a network with multiple-access limited information capacity shared channels. In this paper, we analysed the number of transmitted packets using the traffic model extracted from our proposed communication model and compared it with the state-of-the-art traffic models using simulations. The simulation results show that the proposed model has significantly higher accuracy in estimating the number of transmitted packets compared with the current models in the literature