68 research outputs found
A Modelling and Experimental Framework for Battery Lifetime Estimation in NB-IoT and LTE-M
To enable large-scale Internet of Things (IoT) deployment, Low-power
wide-area networking (LPWAN) has attracted a lot of research attention with the
design objectives of low-power consumption, wide-area coverage, and low cost.
In particular, long battery lifetime is central to these technologies since
many of the IoT devices will be deployed in hard-toaccess locations. Prediction
of the battery lifetime depends on the accurate modelling of power consumption.
This paper presents detailed power consumption models for two cellular IoT
technologies: Narrowband Internet of Things (NB-IoT) and Long Term Evolution
for Machines (LTE-M). A comprehensive power consumption model based on User
Equipment (UE) states and procedures for device battery lifetime estimation is
presented. An IoT device power measurement testbed has been setup and the
proposed model has been validated via measurements with different coverage
scenarios and traffic configurations, achieving the modelling inaccuracy within
5%. The resulting estimated battery lifetime is promising, showing that the
10-year battery lifetime requirement specified by 3GPP can be met with proper
configuration of traffic profile, transmission, and network parameters.Comment: submitted to IEEE Internet of Things Journal, 12 pages, 10 figure
NarrowBand IoT Data Transmission Procedures for Massive Machine Type Communications
Large-scale deployments of massive Machine Type Communications (mMTC)
involve several challenges on cellular networks. To address the challenges of mMTC, or
more generally, Internet of Things (IoT), the 3rd Generation Partnership Project has
developed NarrowBand IoT (NB-IoT) as part of Release 13. NB-IoT is designed to
provide better indoor coverage, support of a massive number of low-throughput devices,
with relaxed delay requirements, and lower-energy consumption. NB-IoT reuses Long
Term Evolution functionality with simplifications and optimizations. Particularly for small
data transmissions, NB-IoT specifies two procedures to reduce the required signaling:
one of them based on the Control Plane (CP), and the other on the User Plane (UP). In
this work, we provide an overview of these procedures as well as an evaluation of their
performance. The results of the energy consumption show both optimizations achieve
a battery lifetime extension of more than 2 years for a large range in the considered
cases, and up to 8 years for CP with good coverage. In terms of cell capacity relative to
SR, CP achieves gains from 26% to 224%, and UP ranges from 36% to 165%. The
comparison of CP and UP optimizations yields similar results, except for some specific
configurations.This work is partially supported by the Spanish
Ministry of Economy and Competitiveness and
the European Regional Development Fund (Projects TIN2013-46223-P, and TEC2016-76795-
C6-4-R), and the Spanish Ministry of Education,
Culture and Sport (FPU Grant 13/04833)
Analytical Modeling and Experimental Validation of NB-IoT Device Energy Consumption
The recent standardization of 3GPP Narrowband
Internet of Things (NB-IoT) paves the way to support low-power
wide-area (LPWA) use cases in cellular networks. NB-IoT design
goals are extended coverage, low power and low cost devices,
and massive connections. As a new radio access technology, it is
necessary to analyze the possibilities NB-IoT provides to support
different traffic and coverage needs. In this paper, we propose and
validate an NB-IoT energy consumption model. The analytical
model is based on a Markov chain. For the validation, an experimental
setup is used to measure the energy consumption of two
commercial NB-IoT user equipments (UEs) connected to a base
station emulator. The evaluation is done considering three test
cases. The comparison of the model and measurements is done
in terms of the estimated battery lifetime and the latency needed
to finish the control plane procedure. The conducted evaluation
shows the analytical model performs well, obtaining a maximum
relative error of the battery lifetime estimation between the model
and the measurements of 21% for an assumed interarrival time
(IAT) of 6 min.This
work was supported in part by the Spanish Ministry of Economy and
Competitiveness and the European Regional Development Fund under
Project TEC2016-76795-C6-4-R and in part by the H2020 European Project
TRIANGLE under Grant 688712
Energy efficiency in short and wide-area IoT technologies—A survey
In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions
Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions
The ever-increasing number of resource-constrained Machine-Type Communication
(MTC) devices is leading to the critical challenge of fulfilling diverse
communication requirements in dynamic and ultra-dense wireless environments.
Among different application scenarios that the upcoming 5G and beyond cellular
networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the
unique technical challenge of supporting a huge number of MTC devices, which is
the main focus of this paper. The related challenges include QoS provisioning,
handling highly dynamic and sporadic MTC traffic, huge signalling overhead and
Radio Access Network (RAN) congestion. In this regard, this paper aims to
identify and analyze the involved technical issues, to review recent advances,
to highlight potential solutions and to propose new research directions. First,
starting with an overview of mMTC features and QoS provisioning issues, we
present the key enablers for mMTC in cellular networks. Along with the
highlights on the inefficiency of the legacy Random Access (RA) procedure in
the mMTC scenario, we then present the key features and channel access
mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT.
Subsequently, we present a framework for the performance analysis of
transmission scheduling with the QoS support along with the issues involved in
short data packet transmission. Next, we provide a detailed overview of the
existing and emerging solutions towards addressing RAN congestion problem, and
then identify potential advantages, challenges and use cases for the
applications of emerging Machine Learning (ML) techniques in ultra-dense
cellular networks. Out of several ML techniques, we focus on the application of
low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss
some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future
publication in IEEE Communications Surveys and Tutorial
Coverage measurements of NB-IoT technology
Abstract. The narrowband internet of things (NB-IoT) is a cellular radio access technology that provides seamless connectivity to wireless IoT devices with low latency, low power consumption, and long-range coverage. For long-range coverage, NB-IoT offers a coverage enhancement (CE) mechanism that is achieved by repeating the transmission of signals. Good network coverage is essential to reduce the battery usage and power consumption of IoT devices, while poor network coverage increases the number of repetitions in transmission, which causes high power consumption of IoT devices. The primary objective of this work is to determine the network coverage of NB-IoT technology under the University of Oulu’s 5G test network (5GTN) base station. In this thesis work, measurement results on key performance indicators such as reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), and signal to noise plus interference (SINR) have been reported. The goal of the measurement is to find out the NB-IoT signal strength at different locations, which are served by the 5GTN cells configured with different parameters, e.g., Tx power levels, antenna tilt angles.
The signal strength of NB-IoT technology has been measured at different places under the 5GTN base station in Oulu, Finland. Drive tests have been conducted to measure the signal strength of NB-IoT technology by using the Quectel BG96 module, Qualcomm kDC-5737 dongle and Keysight Nemo Outdoor software. The results have shown the values of RSRP, RSRQ, RSSI, and SINR at different locations within several kilometres of the 5GTN base stations. These values indicate the performance of the network and are used to assess the performance of network services to the end-users.
In this work, the overall performance of the network has been checked to verify if network performance meets good signal levels and good network coverage. Relevant details of the NB-IoT technology, the theory behind the signal coverage and comparisons with the measurement results have also been discussed to check the relevance of the measurement results
Internet of Things and Sensors Networks in 5G Wireless Communications
This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors
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