18 research outputs found
Cross-layer framework and optimization for efficient use of the energy budget of IoT Nodes
Both physical and MAC-layer need to be jointly optimized to maximize the
autonomy of IoT devices. Therefore, a cross-layer design is imperative to
effectively realize Low Power Wide Area networks (LPWANs). In the present
paper, a cross-layer assessment framework including power modeling is proposed.
Through this simulation framework, the energy consumption of IoT devices,
currently deployed in LoRaWAN networks, is evaluated. We demonstrate that a
cross-layer approach significantly improves energy efficiency and overall
throughput. Two major contributions are made. First, an open-source LPWAN
assessment framework has been conceived. It allows testing and evaluating
hypotheses and schemes. Secondly, as a representative case, the LoRaWAN
protocol is assessed. The findings indicate how a cross-layer approach can
optimize LPWANs in terms of energy efficiency and throughput. For instance, it
is shown that the use of larger payloads can reduce up to three times the
energy consumption on quasi-static channels yet may bring an energy penalty
under adverse dynamic conditions
Delay and energy consumption analysis of frame slotted ALOHA variants for massive data collection in internet-of-things scenarios
This paper models and evaluates three FSA-based (Frame Slotted ALOHA) MAC (Medium Access Control) protocols, namely, FSA-ACK (FSA with ACKnowledgements), FSA-FBP (FSA with FeedBack Packets) and DFSA (Dynamic FSA). The protocols are modeled using an AMC (Absorbing Markov Chain), which allows to derive analytic expressions for the average packet delay, as well as the energy consumption of both the network coordinator and the end-devices. The results, based on computer simulations, show that the analytic model is accurate and outline the benefits of DFSA. In terms of delay, DFSA provides a reduction of 17% (FSA-FBP) and 32% (FSA-ACK), whereas in terms of energy consumption DFSA provides savings of 23% (FSA-FBP) and 28% (FSA-ACK) for the coordinator and savings of 50% (FSA-FBP) and 24% (FSA-ACK) for end-devices. Finally, the paper provides insights on how to configure each FSA variant depending on the network parameters, i.e., depending on the number of end-devices, to minimize delay and energy expenditure. This is specially interesting for massive data collection in IoT (Internet-of-Things) scenarios, which typically rely on FSA-based protocols and where the operation has to be optimized to support a large number of devices with stringent energy consumption requirementsPeer ReviewedPostprint (published version
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%
Fast and reliable LoRa-based data transmissions
LoRaWAN is a recently proposed MAC layer protocol which manages communications between LoRa-based gate-ways and end-devices. It has attracted much scientific attention due its physical layer characteristics, but mainly due to its versatile configuration parameters. However, it is known that LoRaWAN-based transmissions suffer from extensive collisions due to the unregulated access to the medium. For this reason, various techniques that alleviate the burst of collisions have been proposed in the literature. In this paper, we deal with the problem of fast data delivery in LoRa-based networks. We model a network where transmissions follow a Poisson process. We compute the average packet success probability per Spreading Factor (SF) assuming orthogonal transmissions. We, then, formulate an SF optimization problem to maximize the success probability given an amount of data per node and a maximum data collection time window. We show - both theoretically and using simulations - that the overall success probability can be improved by approximately 100% using optimal SF assignments. We validate our findings using a 10-node testbed and extensive experiments. Despite that experiments reveal the existence of inter-SF interference, our solution still provides the best performance compared to other LoRaWAN configurations
LoRa-E: Overview and Performance Analysis
International audienceLong Range-Frequency Hopping Spread Spectrum(LR-FHSS) is the new physical layer designed to addressextremely long-range and large-scale communication scenarios,such as satellite IoT. At its core is a fast frequency hoppingtechnique designed to offer higher network capacity whileoffering the same radio link budget as LoRa. Additionally,LR-FHSS finely manages packet transmission thanks to its designprinciples, enabling QoS policies on a per-packet basis. Giventhe notorious adoption of LoRaWAN in the IoT applicationlandscape, this article is a reference for understanding howexactly LR-FHSS works, the performance it can offer, and itslimitations and research opportunities