scholarly journals A Study on the Impact of Nodes Density on the Energy Consumption of LoRa

2021 ◽  
Vol 15 (14) ◽  
pp. 157
Author(s):  
Aizat Faiz Ramli ◽  
Muhammad Ikram Shabry ◽  
Mohd Azlan Abu ◽  
Hafiz Basarudin
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Low Power ◽  
Large Scale ◽  
Sensor Nodes ◽  
Delivery Ratio ◽  
Area Network ◽  
Wide Area Network ◽  
Consumption Ratio ◽  
The Impact

LoRaWAN is one of the leading Low power wide area network (LPWAN) LPWAN technologies that compete for the formation of big scale Internet of Things (IoT). It uses LoRa protocol to achieve long range, low bit rate and low power communication. Large scale LoRaWAN based IoT deployments can consist of battery powered sensor nodes. Therefore, the energy consumption and efficiency of these nodes are crucial factors that can influence the lifetime of the network. However, there is no coherent experimental based research which identifies the factors that influence the LoRa energy efficiency at various nodes density. In this paper, results on measuring the packet delivery ratio, packet loss, data rate and energy consumption ratio ECR to gauge the energy efficiency of LoRa devices at various nodes density are presented. It is shown that the ECR of LoRa is inversely proportional to the nodes density and that the ECR of the network is smaller at higher traffic indicating better network energy efficiency. It is also demonstrated that at high node density, spreading factor SF of 7 and 9 can improve the energy efficiency of the network by 5 and 3 times, respectively, compare to SF 11.

Evaluation of Energy Consumption of LPWAN Technologies

2021 ◽  
Author(s):  
Husam Rajab ◽  
Tibor Cinkler ◽  
Taoufik Bouguera
Keyword(s):  
Energy Consumption ◽  
Low Power ◽  
Sensor Node ◽  
Minimum Energy ◽  
Sensor Nodes ◽  
Sleep Mode ◽  
Area Network ◽  
Wide Area Network ◽  
Battery Lifetime ◽  
Minimum Energy Consumption

Abstract The modern technological innovations provide small radios with ability to broadcast over vast areas with minimum energy consumption that will significantly influence the future of the Internet of Things (IoT) communications. The majority of IoT implementations demand sensor nodes run reliably for an extended time. Furthermore, the radio settings can endure a high data rate transmission while optimizing the energy-efficiency. The LoRa/LoRaWAN is one of the primary Low-Power Wide Area Network (LPWAN) technology that has highly enticed much concentration recently from the community. The energy limits is a significant issue in wireless sensor networks since battery lifetime that supplies sensor nodes have a restricted amount of energy and neither expendable nor rechargeable in most cases. A common hypothesis in previous work is that the energy consumed by sensors in sleep mode is negligible. With this hypothesis, the usual approach is to consider subsets of nodes that reach all the iterative targets. These subsets also called coverage sets, are then put in the active mode, considering the others are in the low-power or sleep mode. In this paper, we address this question by proposing an energy consumption model based on LoRa and LoRaWAN, that model optimizes the energy consumption of the sensor node for different tasks for a period of time. The proposed analytical approach permits considering the consumed power of every sensor node element; furthermore, it can be used to analyse different LoRaWAN modes to determine the most desirable sensor node design to reach its energy autonomy.

scholarly journals Efficient Data Collection using Mobile Sink Scheme in IoT

2019 ◽  
Vol 9 (1) ◽  
pp. 1360-1371
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Data Collection ◽  
Large Scale ◽  
Performance Metrics ◽  
Route Planning ◽  
Mobile Sink ◽  
Sensor Nodes ◽  
Delivery Ratio ◽  
Energy Hole

Wireless sensor networks (WSNs) have become increasingly important in the informative development of communication technology. The growth of Internet of Things (IoT) has increased the use of WSNs in association with large scale industrial applications. The integration of WSNs with IoT is the pillar for the creation of an inescapable smart environment. A huge volume of data is being generated every day by the deployment of WSNs in smart infrastructure. The collaboration is applicable to environmental surveillance, health surveillance, transportation surveillance and many more other fields. A huge quantity of data which is obtained in various formats from varied applications is called big data. The Energy efficient big data collection requires new techniques to gather sensor-based data which is widely and densely distributed in WSNs and spread over wider geographical areas. In view of the limited range of communication and low powered sensor nodes, data gathering in WSN is a tedious task. The energy hole is another considerable issue that requires attention for efficient handling in WSN. The concept of mobile sink has been widely accepted and exploited, since it is able to effectively alleviate the energy hole problem. Scheduling a mobile sink with energy efficiency is still a challenge in WSNs time constraint implementation due to the slow speed of the mobile sink. The paper addresses the above issues and the proposal contains four-phase data collection model; the first phase is the identification of network subgroups, which are formed due to a restricted range of communication in sensor nodes in a wide network, second is clustering which is addressed on each identified subgroup for reducing energy consumption, third is efficient route planning and fourth is based on data collection. The two time-sensitive route planning schemes are presented to build a set of trajectories which satisfy the deadline constraint and minimize the overall delay. We have evaluated the performance of our schemes through simulation and compared them with the generic enhanced expectation-maximization (EEM) mobility based scenario of data collection. Simulation results reveal that our proposed schemes give much better results as compared to the generic EEM mobility approach in terms of selected performance metrics such as energy consumption, delay, network lifetime and packet delivery ratio.

scholarly journals Design, Implementation, and Empirical Validation of an IoT Smart Irrigation System for Fog Computing Applications Based on LoRa and LoRaWAN Sensor Nodes

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6865
Author(s):  
Iván Froiz-Míguez ◽  
Peio Lopez-Iturri ◽  
Paula Fraga-Lamas ◽  
Mikel Celaya-Echarri ◽  
Óscar Blanco-Novoa ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Irrigation System ◽  
Power Level ◽  
Fog Computing ◽  
Sensor Nodes ◽  
Sensitivity Threshold ◽  
Area Network ◽  
Planning Tool ◽  
The Impact ◽  
Radio Planning

Climate change is driving new solutions to manage water more efficiently. Such solutions involve the development of smart irrigation systems where Internet of Things (IoT) nodes are deployed throughout large areas. In addition, in the mentioned areas, wireless communications can be difficult due to the presence of obstacles and metallic objects that block electromagnetic wave propagation totally or partially. This article details the development of a smart irrigation system able to cover large urban areas thanks to the use of Low-Power Wide-Area Network (LPWAN) sensor nodes based on LoRa and LoRaWAN. IoT nodes collect soil temperature/moisture and air temperature data, and control water supply autonomously, either by making use of fog computing gateways or by relying on remote commands sent from a cloud. Since the selection of IoT node and gateway locations is essential to have good connectivity and to reduce energy consumption, this article uses an in-house 3D-ray launching radio-planning tool to determine the best locations in real scenarios. Specifically, this paper provides details on the modeling of a university campus, which includes elements like buildings, roads, green areas, or vehicles. In such a scenario, simulations and empirical measurements were performed for two different testbeds: a LoRaWAN testbed that operates at 868 MHz and a testbed based on LoRa with 433 MHz transceivers. All the measurements agree with the simulation results, showing the impact of shadowing effects and material features (e.g., permittivity, conductivity) in the electromagnetic propagation of near-ground and underground LoRaWAN communications. Higher RF power levels are observed for 433 MHz due to the higher transmitted power level and the lower radio propagation losses, and even in the worst gateway location, the received power level is higher than the sensitivity threshold (−148 dBm). Regarding water consumption, the provided estimations indicate that the proposed smart irrigation system is able to reduce roughly 23% of the amount of used water just by considering weather forecasts. The obtained results provide useful guidelines for future smart irrigation developers and show the radio planning tool accuracy, which allows for optimizing the sensor network topology and the overall performance of the network in terms of coverage, cost, and energy consumption.

scholarly journals Analysis of the Impact of Residential Property and Equipment on Building Energy Efficiency and Consumption—A Data Mining Approach

2020 ◽  
Vol 10 (10) ◽  
pp. 3589 ◽  
Author(s):  
Mahsa Nazeriye ◽  
Abdorrahman Haeri ◽  
Francisco Martínez-Álvarez
Keyword(s):  
Data Mining ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Large Scale ◽  
Building Energy Efficiency ◽  
Data Set ◽  
Residential Homes ◽  
Improve Energy Efficiency ◽  
Data Mining Approach ◽  
The Impact

Human living could become very difficult due to a lack of energy. The household sector plays a significant role in energy consumption. Trying to optimize and achieve efficient energy consumption can lead to large-scale energy savings. The aim of this paper is to identify the equipment and property affecting energy efficiency and consumption in residential homes. For this purpose, a hybrid data-mining approach based on K-means algorithms and decision trees is presented. To analyze the approach, data is modeled once using the approach and then without it. A data set of residential homes of England and Wales is arranged in low, medium and high consumption clusters. The C5.0 algorithm is run on each cluster to extract factors affecting energy efficiency. The comparison of the modeling results, and also their accuracy, prove that the approach employed has the ability to extract the findings with greater accuracy and detail than in other cases. The installation of boilers, using cavity walls, and installing insulation could improve energy efficiency. Old homes and the usage of economy 7 electricity have an unfavorable effect on energy efficiency, but the approach shows that each cluster behaved differently in these factors related to energy efficiency and has unique results.

scholarly journals Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios

2020 ◽  
Vol 10 (22) ◽  
pp. 7964
Author(s):  
David Todoli-Ferrandis ◽  
Javier Silvestre-Blanes ◽  
Víctor Sempere-Payá ◽  
Ana Planes-Martínez
Keyword(s):  
Energy Consumption ◽  
Low Power ◽  
Packet Loss ◽  
Industry 4.0 ◽  
Smart Cities ◽  
Data Rate ◽  
Wide Area ◽  
Area Network ◽  
Wide Area Network ◽  
Class B

Low-power wide-area network (LPWAN) technologies are becoming a widespread solution for wireless deployments in many applications, such as smart cities or Industry 4.0. However, there are still challenges to be addressed, such as energy consumption and robustness. To characterize and optimize these types of networks, the authors have developed an optimized use of the adaptative data rate (ADR) mechanism for uplink, proposed its use also for downlink based on the simulator ns-3, and then defined an industrial scenario to test and validate the proposed solution in terms of packet loss and energy.

scholarly journals Compression-Aware Aggregation and Energy-Aware Routing in IoT–Fog-Enabled Forest Environment

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4591
Author(s):  
Srividhya Swaminathan ◽  
Suresh Sankaranarayanan ◽  
Sergei Kozlov ◽  
Joel J. P. C. Rodrigues
Keyword(s):  
Energy Consumption ◽  
Low Power ◽  
Forest Fire ◽  
Routing Protocol ◽  
Fire Management ◽  
Sensor Nodes ◽  
Delivery Ratio ◽  
Lossy Networks ◽  
Packet Delivery ◽  
Forest Fire Management

Forest fire monitoring is very much needed for protecting the forest from any kind of disaster or anomaly leading to the destruction of the forest. Now, with the advent of Internet of Things (IoT), a good amount of research has been done on energy consumption, coverage, and other issues. These works did not focus on forest fire management. The IoT-enabled environment is made up of low power lossy networks (LLNs). For improving the performance of routing protocol in forest fire management, energy-efficient routing protocol for low power lossy networks (E-RPL) was developed where residual power was used as an objective function towards calculating the rank of the parent node to form the destination-oriented directed acyclic graph (DODAG). The challenge in E-RPL is the scalability of the network resulting in a long end-to-end delay and less packet delivery. Additionally, the energy of sensor nodes increased with different transmission range. So, for obviating the above-mentioned drawbacks in E-RPL, compressed data aggregation and energy-based RPL routing (CAA-ERPL) is proposed. The CAA-ERPL is compared with E-RPL, and the performance is analyzed resulting in reduced packet transfer delay, less energy consumption, and increased packet delivery ratio for 10, 20, 30, 40, and 50 nodes. This has been evaluated using a Contiki Cooja simulator.

scholarly journals UAV-Assisted Low-Consumption Time Synchronization Utilizing Cross-Technology Communication

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5134
Author(s):  
Ziyi Tan ◽  
Xu Yang ◽  
Mingzhi Pang ◽  
Shouwan Gao ◽  
Ming Li ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Low Power ◽  
Large Scale ◽  
Time Synchronization ◽  
Resource Constraints ◽  
Vital Role ◽  
Sensor Nodes ◽  
Zero Energy ◽  
Synchronization Errors ◽  
Low Consumption

Wireless sensor networks (WSNs) have been used in many fields due to its wide applicability. In this kind of network, each node is independent of each other and has its own local clock and communicates wirelessly. Time synchronization plays a vital role in WSNs and it can ensure accuracy requirements for coordination and data reliability. However, two key challenges exist in large-scale WSNs that are severe resource constraints overhead and multihop time synchronization errors. To address these issues, this paper proposes a novel unmanned aerial vehicle (UAV)-assisted low-consumption time synchronization algorithm based on cross-technology communication (CTC) for a large-scale WSN. This algorithm uses a UAV to send time synchronization data packets for calibration. Moreover, to ensure coverage and a high success rate for UAV data transmission, we use CTC for time synchronization. Without any relays, a high-power time synchronization packet can be sent by a UAV to achieve the time synchronization of low-power sensors. This algorithm can achieve accurate time synchronization with almost zero energy consumption for the sensor nodes. Finally, we implemented our algorithm with 30 low-power RF-CC2430 ZigBee nodes and a Da Jiang Innovations (DJI) M100 UAV on a 1 km highway and an indoor site. The results show that time synchronization can be achieved accurately with almost zero energy consumption for the sensor nodes, and the time synchronization error is less than 30 μs in 99% of cases.

scholarly journals Exposing End-to-End Delay in Software-Defined Networking

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Ting Zhang ◽  
Bin Liu
Keyword(s):  
Large Scale ◽  
Packet Delay ◽  
Software Defined Networking ◽  
Area Network ◽  
Delay Model ◽  
Wide Area Network ◽  
Worst Case ◽  
End To End Delay ◽  
End To End ◽  
The Impact

Software-Defined Networking (SDN) shows us a promising picture to deploy the demanding services in a fast and cost-effective way. Till now, most SDN use cases are deployed in enterprise/campus networks and data center networks. However, when applying SDN to the large-scale networks, such as Wide Area Network (WAN), the end-to-end delay of packet traversal is suspected to be very large and needs to be further investigated. Moreover, stringent time constraint is the cornerstone for real-time applications in SDN. Understanding the packet delay in SDN-based large networks is crucial for the proper design of switch architecture and the optimization of network algorithms such as flow control algorithms. In this paper, we present a thorough systematic exploration on the end-to-end delay in SDN which consists of multiple nodes, fully exposing the components which contribute to the long delay. We disclose that SDN switches cannot completely avoid the generation of flow setup even in proactive mode and conduct data mining on the probability of flow setup. We propose an analytical model for the end-to-end delay. This model takes into account the impact of the different rule installation time consumption on different switches. Considering the delay in switches contributes a large proportion to the entire delay, we conduct various measurements on the delay of a single switch. Results for the delay at different flow setup rates and with different rule priority patterns are presented. Furthermore, we study the impact on packet delay caused by ternary content addressable memory (TCAM) update. We measure parameters in the delay model and find that if SDN is deployed in all segments of WAN, the delay of packet traversal will be increased up to 27.95 times in the worst case in our experimental settings, compared with the delay in conventional network. Such high delay may eventually lead the end-to-end connections fail to complete if no additional measures are taken.

scholarly journals K-Means Spreading Factor Allocation for Large-Scale LoRa Networks

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4723 ◽  
Author(s):  
Muhammad Asad Ullah ◽  
Junnaid Iqbal ◽  
Arliones Hoeller ◽  
Richard Souza ◽  
Hirley Alves
Keyword(s):  
Low Power ◽  
Long Range ◽  
Large Scale ◽  
Research Work ◽  
Wide Area ◽  
Area Network ◽  
Wide Area Network ◽  
Spreading Factor ◽  
Worst Case ◽  
Simulation Results

Low-power wide-area networks (LPWANs) are emerging rapidly as a fundamental Internet of Things (IoT) technology because of their low-power consumption, long-range connectivity, and ability to support massive numbers of users. With its high growth rate, Long-Range (LoRa) is becoming the most adopted LPWAN technology. This research work contributes to the problem of LoRa spreading factor (SF) allocation by proposing an algorithm on the basis of K-means clustering. We assess the network performance considering the outage probabilities of a large-scale unconfirmed-mode class-A LoRa Wide Area Network (LoRaWAN) model, without retransmissions. The proposed algorithm allows for different user distribution over SFs, thus rendering SF allocation flexible. Such distribution translates into network parameters that are application dependent. Simulation results consider different network scenarios and realistic parameters to illustrate how the distance from the gateway and the number of nodes in each SF affects transmission reliability. Theoretical and simulation results show that our SF allocation approach improves the network’s average coverage probability up to 5 percentage points when compared to the baseline model. Moreover, our results show a fairer network operation where the performance difference between the best- and worst-case nodes is significantly reduced. This happens because our method seeks to equalize the usage of each SF. We show that the worst-case performance in one deployment scenario can be enhanced by 1 . 53 times.

scholarly journals A Sigfox Energy Consumption Model

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 681 ◽  
Author(s):  
Carles Gomez ◽  
Juan Carlos Veras ◽  
Rafael Vidal ◽  
Lluís Casals ◽  
Josep Paradells
Keyword(s):  
Energy Consumption ◽  
Transmission Rate ◽  
Energy Performance ◽  
Area Network ◽  
Wide Area Network ◽  
Development Organizations ◽  
Current Consumption ◽  
Energy Consumption Model ◽  
Device Lifetime ◽  
The Impact

Sigfox has become one of the main Low-Power Wide Area Network (LPWAN) technologies, as it has attracted the attention of the industry, academy and standards development organizations in recent years. Sigfox devices, such as sensors or actuators, are expected to run on limited energy sources; therefore, it is crucial to investigate the energy consumption of Sigfox. However, the literature has only focused on this topic to a very limited extent. This paper presents an analytical model that characterizes device current consumption, device lifetime and energy cost of data delivery with Sigfox. In order to capture a realistic behavior, the model has been derived from measurements carried out on a real Sigfox hardware module. The model allows quantifying the impact of relevant Sigfox parameters and mechanisms, as well as frame losses, on Sigfox device energy performance. Among others, evaluation results show that the considered Sigfox device, powered by a 2400 mAh battery, can achieve a theoretical lifetime of 1.5 or 2.5 years while sending one message every 10 min at 100 bit/s or 600 bit/s, respectively, and an asymptotic lifetime of 14.6 years as the message transmission rate decreases.

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