55 research outputs found
Scalability Analysis of a LoRa Network under Imperfect Orthogonality
Low-power wide-area network (LPWAN) technologies are gaining momentum for
internet-of-things (IoT) applications since they promise wide coverage to a
massive number of battery-operated devices using grant-free medium access.
LoRaWAN, with its physical (PHY) layer design and regulatory efforts, has
emerged as the widely adopted LPWAN solution. By using chirp spread spectrum
modulation with qausi-orthogonal spreading factors (SFs), LoRa PHY offers
coverage to wide-area applications while supporting high-density of devices.
However, thus far its scalability performance has been inadequately modeled and
the effect of interference resulting from the imperfect orthogonality of the
SFs has not been considered. In this paper, we present an analytical model of a
single-cell LoRa system that accounts for the impact of interference among
transmissions over the same SF (co-SF) as well as different SFs (inter-SF). By
modeling the interference field as Poisson point process under duty-cycled
ALOHA, we derive the signal-to-interference ratio (SIR) distributions for
several interference conditions. Results show that, for a duty cycle as low as
0.33%, the network performance under co-SF interference alone is considerably
optimistic as the inclusion of inter-SF interference unveils a further drop in
the success probability and the coverage probability of approximately 10% and
15%, respectively for 1500 devices in a LoRa channel. Finally, we illustrate
how our analysis can characterize the critical device density with respect to
cell size for a given reliability target
Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks
Long Range (LoRa) wireless technology, characterized by low power consumption
and a long communication range, is regarded as one of the enabling technologies
for the Industrial Internet of Things (IIoT). However, as the network scale
increases, the energy efficiency (EE) of LoRa networks decreases sharply due to
severe packet collisions. To address this issue, it is essential to
appropriately assign transmission parameters such as the spreading factor and
transmission power for each end device (ED). However, due to the sporadic
traffic and low duty cycle of LoRa networks, evaluating the system EE
performance under different parameter settings is time-consuming. Therefore, we
first formulate an analytical model to calculate the system EE. On this basis,
we propose a transmission parameter allocation algorithm based on multiagent
reinforcement learning (MALoRa) with the aim of maximizing the system EE of
LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED
to better learn how much ''attention'' should be given to the parameter
assignments for relevant EDs when seeking to improve the system EE. Simulation
results demonstrate that MALoRa significantly improves the system EE compared
with baseline algorithms with an acceptable degradation in packet delivery rate
(PDR).Comment: 6 pages, 3 figures, This paper has been accepted for publication in
IEEE Global Communications Conference (GLOBECOM) 202
Energy Efficient Uplink Transmissions in LoRa Networks
LoRa has been recognized as one of the most promising low-power wide-area (LPWA) techniques. Since LoRa devices are usually powered by batteries, energy efficiency (EE) is an essential consideration. In this paper, we investigate the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. Specifically, our objective is to maximize the corresponding EE by jointly exploiting user scheduling, spreading factor (SF) assignment, and transmit power allocations. To solve them efficiently, we first propose a suboptimal algorithm, including the low-complexity user scheduling scheme based on matching theory and the heuristic SF assignment approach for LoRa users scheduled on the same channel. Then, to deal with the power allocation, an optimal algorithm is proposed to maximize the SEE. To maximize the MEE of LoRa users assigned to the same channel, an iterative power allocation algorithm based on the generalized fractional programming and sequential convex programming is proposed. Numerical results show that the proposed user scheduling algorithm achieves near-optimal EE performance, and the proposed power allocation algorithms outperform the benchmarks. © 2020 IEEE
A Survey on Long-Range Wide-Area Network Technology Optimizations
Long-Range Wide-Area Network (LoRaWAN) enables flexible long-range service communications with low power consumption which is suitable for many IoT applications. The densification of LoRaWAN, which is needed to meet a wide range of IoT networking requirements, poses further challenges. For instance, the deployment of gateways and IoT devices are widely deployed in urban areas, which leads to interference caused by concurrent transmissions on the same channel. In this context, it is crucial to understand aspects such as the coexistence of IoT devices and applications, resource allocation, Media Access Control (MAC) layer, network planning, and mobility support, that directly affect LoRaWAN’s performance.We present a systematic review of state-of-the-art works for LoRaWAN optimization solutions for IoT networking operations. We focus on five aspects that directly affect the performance of LoRaWAN. These specific aspects are directly associated with the challenges of densification of LoRaWAN. Based on the literature analysis, we present a taxonomy covering five aspects related to LoRaWAN optimizations for efficient IoT networks. Finally, we identify key research challenges and open issues in LoRaWAN optimizations for IoT networking operations that must be further studied in the future
Improving Age of Information with Interference Problem in Long-Range Wide Area Networks
Low Power Wide Area Networks (LPWAN) offer a promising wireless communications technology for Internet of Things (IoT) applications. Among various existing LPWAN technologies, Long-Range WAN (LoRaWAN) consumes minimal power and provides virtual channels for communication through spreading factors. However, LoRaWAN suffers from the interference problem among nodes connected to a gateway that uses the same spreading factor. Such interference increases data communication time, thus reducing data freshness and suitability of LoRaWAN for delay-sensitive applications. To minimize the interference problem, an optimal allocation of the spreading factor is requisite for determining the time duration of data transmission. This paper proposes a game-theoretic approach to estimate the time duration of using a spreading factor that ensures on-time data delivery with maximum network utilization. We incorporate the Age of Information (AoI) metric to capture the freshness of information as demanded by the applications. Our proposed approach is validated through simulation experiments, and its applicability is demonstrated for a crop protection system that ensures real-time monitoring and intrusion control of animals in an agricultural field. The simulation and prototype results demonstrate the impact of the number of nodes, AoI metric, and game-theoretic parameters on the performance of the IoT network
A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN
The number of IoT devices is predicted to reach 125 billion by 2023. The
growth of IoT devices will intensify the collisions between devices, degrading
communication performance. Selecting appropriate transmission parameters, such
as channel and spreading factor (SF), can effectively reduce the collisions
between long-range (LoRa) devices. However, most of the schemes proposed in the
current literature are not easy to implement on an IoT device with limited
computational complexity and memory. To solve this issue, we propose a
lightweight transmission-parameter selection scheme, i.e., a joint channel and
SF selection scheme using reinforcement learning for low-power wide area
networking (LoRaWAN). In the proposed scheme, appropriate transmission
parameters can be selected by simple four arithmetic operations using only
Acknowledge (ACK) information. Additionally, we theoretically analyze the
computational complexity and memory requirement of our proposed scheme, which
verified that our proposed scheme could select transmission parameters with
extremely low computational complexity and memory requirement. Moreover, a
large number of experiments were implemented on the LoRa devices in the real
world to evaluate the effectiveness of our proposed scheme. The experimental
results demonstrate the following main phenomena. (1) Compared to other
lightweight transmission-parameter selection schemes, collisions between LoRa
devices can be efficiently avoided by our proposed scheme in LoRaWAN
irrespective of changes in the available channels. (2) The frame success rate
(FSR) can be improved by selecting access channels and using SFs as opposed to
only selecting access channels. (3) Since interference exists between adjacent
channels, FSR and fairness can be improved by increasing the interval of
adjacent available channels.Comment: 14 pages, 12 figures, 8 tables. This work has been submitted to the
IEEE for possible publication. Copyright may be transferred without notice,
after which this version may no longer be accessibl
Towards Energy-Fairness in LoRa Networks
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordLoRa has become one of the most promising networking technologies for Internet-of-Things applications. Distant end devices have to use a low data rate to reach a LoRa gateway, causing long in-the-air transmission time and high energy consumption. Compared with the end devices using high data rates, they will drain the batteries much earlier and the network may be broken early. Such an energy unfairness can be mitigated by deploying more gateways. However, with more gateways, more end devices may choose small spreading factors to reach closer gateways, increasing the collision probability. In this paper, we propose a networking solution for LoRa networks, EF-LoRa, that can achieve energy fairness among end devices by carefully allocating network resources, including frequency channels, spreading factors and transmission power. We develop a LoRa network model to study the energy consumption of the end devices, considering the unique features of LoRa networks such as LoRaWAN MAC protocol and the capacity limitation of a gateway. We formulate the energy fairness allocation as an optimization problem, and propose a greedy allocation algorithm to achieve max-min fairness of energy efficiency. Simulation results show that EF-LoRa can improve the energy fairness of the state-of-the-art works by 177.8%
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