Optimizing the Performance of Pure ALOHA for LoRa-Based ESL

被引:19
作者
Khan, Malak Abid Ali [1 ]
Ma, Hongbin [1 ]
Aamir, Syed Muhammad [1 ]
Jin, Ying [1 ]
机构
[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
基金
中国国家自然科学基金;
关键词
LoRa; machine learning; electronic shelf labels; aloha; spreading factor; bandwidth;
D O I
10.3390/s21155060
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
(1) Background: The scientific development in the field of industrialization demands the automization of electronic shelf labels (ESLs). COVID-19 has limited the manpower responsible for the frequent updating of the ESL system. The current ESL uses QR (quick response) codes, NFC (near-field communication), and RFID (radio-frequency identification). These technologies have a short range or need more manpower. LoRa is one of the prominent contenders in this category as it provides long-range connectivity with less energy harvesting and location tracking. It uses many gateways (GWs) to transmit the same data packet to a node, which causes collision at the receiver side. The restriction of the duty cycle (DC) and dependency of acknowledgment makes it unsuitable for use by the common person. The maximum efficiency of pure ALOHA is 18.4%, while that of slotted ALOHA is 36.8%, which makes LoRa unsuitable for industrial use. It can be used for applications that need a low data rate, i.e., up to approximately 27 Kbps. The ALOHA mechanism can cause inefficiency by not eliminating fast saturation even with the increasing number of gateways. The increasing number of gateways can only improve the global performance for generating packets with Poisson law having a uniform distribution of payload of 1 similar to 51 bytes. The maximum expected channel capacity usage is similar to the pure ALOHA throughput. (2) Methods: In this paper, the improved ALOHA mechanism is used, which is based on the orthogonal combination of spreading factor (SF) and bandwidth (BW), to maximize the throughput of LoRa for ESL. The varying distances (D) of the end nodes (ENs) are arranged based on the K-means machine learning algorithm (MLA) using the parameter selection principle of ISM (industrial, scientific and medical) regulation with a 1% DC for transmission to minimize the saturation. (3) Results: The performance of the improved ALOHA degraded with the increasing number of SFs and as well ENs. However, after using K-mapping, the network changes and the different number of gateways had a greater impact on the probability of successful transmission. The saturation decreased from 57% to 1 similar to 2% by using MLA. The RSSI (Received Signal Strength Indicator) plays a key role in determining the exact position of the ENs, which helps to improve the possibility of successful transmission and synchronization at higher BW (250 kHz). In addition, a high BW has lower energy consumption than a low BW at the same DC with a double-bit rate and almost half the ToA (time on-air).
引用
收藏
页数:15
相关论文
共 26 条
[1]   Understanding the Limits of LoRaWAN [J].
Adelantado, Ferran ;
Vilajosana, Xavier ;
Tuset-Peiro, Pere ;
Martinez, Borja ;
Melia-Segui, Joan ;
Watteyne, Thomas .
IEEE COMMUNICATIONS MAGAZINE, 2017, 55 (09) :34-40
[2]   A Study of LoRa: Long Range & Low Power Networks for the Internet of Things [J].
Augustin, Aloys ;
Yi, Jiazi ;
Clausen, Thomas ;
Townsley, William Mark .
SENSORS, 2016, 16 (09)
[3]  
Ayele E.D., 2017, INTERNET THINGS GLOB, P1, DOI 10.1109/IoTGC.2017.8008973
[4]   Measuring a LoRa Network: Performance, Possibilities and Limitations [J].
Carlsson, Anders ;
Kuzminykh, Ievgeniia ;
Franksson, Robin ;
Liljegren, Alexander .
INTERNET OF THINGS, SMART SPACES, AND NEXT GENERATION NETWORKS AND SYSTEMS, NEW2AN 2018, 2018, 11118 :116-128
[5]  
Cesar J., 2018, P IEEE 17 ANN MED AD P IEEE 17 ANN MED AD, P55
[6]   Adaptive dynamic network slicing in LoRa networks [J].
Dawaliby, Samir ;
Bradai, Abbas ;
Pousset, Yannis .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 98 :697-707
[7]   A Review on Human-Centered IoT-Connected Smart Labels for the Industry 4.0 [J].
Fernandez-Carames, Tiago M. ;
Fraga-Lamas, Paula .
IEEE ACCESS, 2018, 6 :25939-25957
[8]  
Harwahyu Ruki, 2019, IOP Conference Series: Earth and Environmental Science, V248, DOI 10.1088/1755-1315/248/1/012088
[9]   LoRa Scalability: A Simulation Model Based on Interference Measurements [J].
Haxhibeqiri, Jetmir ;
Van den Abeele, Floris ;
Moerman, Ingrid ;
Hoebeke, Jeroen .
SENSORS, 2017, 17 (06)
[10]  
Hosseinzadeh S, 2017, Big Data Cogn. Comput., V1, P7, DOI [10.3390/bdcc1010007, DOI 10.3390/BDCC1010007]