LoRaWAN Real Life Issues
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1. The document discusses various practical considerations for implementing a LoRaWAN network including choosing antennas, cables, frequencies and LoRa modules based on factors like SWR and internal circuitry. 2. It also provides information on developing LoRaWAN nodes using libraries like LMIC and decoding LoRaWAN packets by analyzing the PHYPayload, FHDR, FPort and other fields. 3. Recommendations are given on starting a LoRaWAN learning journey including suggested hardware, software and costs starting at around $200.
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How can you configure Wireshark to always recognize port 444 as an SSL/TLS port?
Solution
General configuration :
How to Configure Wireshark
To configure Wireshark, follow these general steps:
Step 1 Define, modify or delete a capture point.
Step 2 Activate or deactivate a capture point.
Default Wireshark Configuration
Table 58-1 shows the default Wireshark configuration.
Table 58-1
Feature
Default Wireshark Configuration
Duration
Packets Packet-length
File size
Ring file storage Buffer storage mode
Default Setting
No limit
No limit
No limit (full packet)
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No
Linear
Software Configuration Guide—Release IOS XE 3.5.0E and IOS 15.2(1)E
OL_28731-01
58-11
How to Configure Wireshark
Chapter 58
Configuring Wireshark
Defining, Modifying, or Deleting a Capture Point
Step 1 Step 2
Step 3 Step 4 Step 5 Step 6
Step 7
Although listed in sequence, the steps to specify values for the options can be executed in any
order. You can also specify them in one, two, or several lines. Except for attachment points,
which can be multiple, you can replace any value with a more recent value by redefining the
same option, in the following order:
Define the name that identifies the capture point.
Specify the attachment point with which the capture point is associated.
Multiple attachment points can be specified. Range support is also available both for adding and
removing attachment points.
Define the core system filter, defined either explicitly, through ACL or through a class map.
Specify the session limit (in seconds or packets captured).
Specify the packet segment length to be retained by Wireshark.
Specify the file association, if the capture point intends to capture packets rather than merely
display them.
Specify the size of the memory buffer used by Wireshark to handle traffic bursts.
To filter the capture point, use the following commands:
Command
Purpose
[no] monitor capture mycap match {any | macmac-match-string | ipv4ipv4-match-string |
ipv6ipv6-match-string}
Defines an explicitly in-line core filter.
To remove the filter, use the no form of this command.
[no] monitor capture mycap matchmac {src-mac-addr src-mac-mask | any | hostsrc-mac-addr} |
{dest-mac-addr dest-mac-mask | any | hostdest-mac-addr}
Specifies use of a filter for MAC.
To remove the filter, use the no form of this command.
[no] monitor capture mycap match {ipv4 | ipv6} [src-prefix/length | any | hostsrc-ip-addr] [dest-
prefix/length | any | hostdest-ip-addr]
[no] monitor capture mycap match {ipv4 | ipv6} proto {tcp | udp} [src-prefix/length | any |
hostsrc-ip-addr] [eq | gt | lt | neq <0-65535>] [dest-prefix/length | any | hostdest-ip-addr] [eq | gt |
lt | neq <0-65535>]
Specifies a filter for IPv4/IPv6, use one of the formats. To remove the filters, use the no form of
this command.
To define a capture point, use the following commands:
Command
Purpose
monitor capturename [{interfacename | vlannum | control-plane} {in | out | both}
Specifies one or more attachment points with direction..
Ccna exploration ch4 crossword
Cisco Networking Academy provides courses on networking fundamentals. This document contains a crossword puzzle with networking terms as clues and answers. Some of the crossword clues include terms like acknowledgment, checksum, flow control, netstat, http, flags, and socket. The crossword contains common networking concepts and protocols.
Debugging Ruby
This document provides information on various debugging and profiling tools that can be used for Ruby including:
- lsof to list open files for a process
- strace to trace system calls and signals
- tcpdump to dump network traffic
- google perftools profiler for CPU profiling
- pprof to analyze profiling data
It also discusses how some of these tools have helped identify specific performance issues with Ruby like excessive calls to sigprocmask and memcpy calls slowing down EventMachine with threads.
.NET Fest 2019. Łukasz Pyrzyk. Daily Performance Fuckups
.NET has accustomed us to writing code quickly and without thinking about what is going on underneath. Unfortunately, convenience comes with additional cost. It is very easy to lose the performance of our component through simple statement or code block which behaves differently than we thought. I will focus on the everyday performance traps, which can spoil your hard effort.
Day2
The document discusses various network security tools including TCP/IP headers, tcpdump, ethereal, ntop, MRTG, network scanners like Nmap and Nessus. It provides examples of using these tools to analyze network traffic, scan for open ports, detect operating systems, and monitor network usage.
Send me your echolocation
When you operate a globally distributed set of datacenters, the network can break at any time between any pair of hosts. For most tools, you need to log onto the right machine at the right time in hopes that you'll find what's wrong. Instead of doing what network engineers are wont to do - suffer with the tools provided - we turned to a programmatic approach. In this talk, I'll introduce some networking concepts, then dive into how we (at Fastly) replaced these tools with an API to monitor an ever-growing fleet of hosts. In doing so, we could reap the benefits of making these tools available to the network-layman, data collection, and automated decision-making.
customization of a deep learning accelerator, based on NVDLA
This document discusses customizing a deep learning accelerator. It begins with a demonstration of object detection using a Tiny YOLO v1 model on an FPGA-based prototype. It then discusses designing a high-efficiency accelerator with three steps: 1) increasing MAC processing elements and utilization, 2) increasing data supply, and 3) improving energy efficiency. Various neural network models are profiled to analyze memory bandwidth and computational power tradeoffs. The document proposes a customizable architecture and discusses solutions like layer fusion, quantization-aware training, and post-training quantization. Performance estimates using an equation-based profiler for sample models are provided to demonstrate the customized accelerator design.
Qualcomm lte-performance-challenges-09-01-2011
This document discusses key factors impacting LTE network performance including expected performance metrics, dependencies, and challenges. It provides an overview of call setup times and throughputs expected under ideal conditions, then discusses how factors like deployment issues, RF interference, backhaul limitations, scheduler configuration, and mobility parameters can negatively influence performance and result in increased call setup times, lower throughputs, and handover failures. The document aims to help network operators identify areas to focus on for optimizing LTE network performance at launch.
FIWARE Global Summit - Connecting to LoRa networks: Practical Demo
Presentation by Daniel Calvo
R&D Engineer – Energy Sector, Atos
FIWARE Global Summit
27-28 November 2018
Malaga, Spain
PLNOG 13: Piotr Głaska: Quality of service monitoring in IP networks
Piotr Głaska – Senior Product Manager at Huawei, Enterprise Networking department. Experienced in management, design and deployment of IP solutions, for 17 years worked for various companies as service provides, through the end-user, integrator, up to device producer. The Huawei Certified Datacom Proffesional HCDP, Cisco CCIE #15966 and HP MASE.
Topic of Presentation: Quality of service monitoring in IP networks
Language: Polish
Abstract: TBD
2010 03 papi_indiana
The document discusses PAPI (Performance API), a tool for collecting hardware performance counter data from processors. PAPI provides a consistent interface for accessing performance counters across platforms and defines platform-neutral events. It supports many modern processors and operating systems and comes with utilities for collecting, analyzing, and visualizing performance data. An example shows how reordering loops in a matrix multiplication algorithm can improve data cache and TLB behavior.
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ES'HAIL 2/QO-100 Reception
Es'hail 2 / QO-100 is a geostationary satellite carrying amateur radio transponders that was launched in November 2018. It provides the first amateur radio communication capability from Brazil to Thailand using two transponders - a 250 kHz linear transponder for conventional analogue operations and an 8 MHz transponder primarily for DVB amateur television. The presentation discusses receiving signals from the satellite using various hardware and software, as well as transmitting to it. It also includes a quiz about the satellite.
LoRaWAN Antenna Measurement
The document describes measuring the standing wave ratio (SWR) of various LoRaWAN antennas using low-cost network analyzers and software-defined radios. Common LoRaWAN antennas like rubber ducky whips, ground planes, and fiberglass are tested. Their SWR is measured using network analyzers connected to SWR bridges or using noise generators, SWR bridges, and software on RTL-SDR devices. Specifications for the antennas and testing equipment are provided.
DTH (DVB-S2) Reception
This document provides information about DD Free Dish, India's free direct-to-home satellite service. It discusses what DTH and DD Free Dish are, how to receive DD Free Dish signals, the satellite and parameters used, how to assemble a Ku band dish antenna, types of cables and connectors used, and DVB digital satellite receivers. The document contains detailed instructions and specifications for setting up a DD Free Dish DTH system to receive free television channels via satellite.
DVB-T2 Reception using RTL SDR
This document provides a user guide for a DVB-T2 USB dongle and TVR software. It describes installation and setup of the dongle and software, shows screenshots of various TV channels received, and provides advanced information on signal diagnostics and testing of the DVB-T2 transmission in Bangalore, India.
Decoding LoRaWAN Packet
The document discusses decoding a LoRaWAN packet received at a gateway. It provides steps to:
1) Use an online tool to decode the base64 encoded packet and obtain the PHYPayload
2) Use a Python script along with the PHYPayload, dev address, and key to decrypt the payload and obtain a string of decimal values
3) Convert the decimal values to hexadecimal to reveal the original message of "hello world".
GSM Triangulation (GPS Denied Condition)
The document discusses cell tower triangulation and how it can be used to determine the approximate location of a mobile phone. It describes how triangulation works by measuring the signal strength and transmission time from multiple cell towers to calculate the phone's position. It also provides specifications for the SIM800 GSM module hardware used and shows sample output from a network scan command identifying nearby cell towers and their parameters. Finally, it outlines how to calculate the phone's position using the cell tower coordinates, signal strengths, and triangulation algorithm.
ADS-B Out Experiment
The document discusses setting up a simulation of an ADS-B signal on a Windows system using Python and HackRF software. It describes modifying a Python ADS-B encoding script to fix an error, using the script to generate an IQ file, increasing the file size for HackRF transmission, transmitting the file to HackRF using hackrf_transfer, receiving the signal using dump1090, and viewing the simulated radar target in virtual radar software. The key steps are modifying the Python script, generating and resizing the IQ file, transmitting it to HackRF, receiving it using dump1090, and viewing it in virtual radar.
Software Defined Radio Workshop
This document summarizes a workshop on software defined radio held by the Bangalore Amateur Radio Club on July 9th, 2017. It provides information on different software defined radio hardware options such as HackRF One, Ettus B200, BladeRF, and RTL-SDR. It discusses SDR software for Windows, MAC, Linux and Android devices. It also gives examples of using SDRs for applications like receiving AM/FM radio, decoding digital signals, receiving GPS and weather satellite data, and acting as a spectrum analyzer. Diagrams provide explanations of direct down conversion receivers and examples of decoding ADS-B signals from aircraft and building AM/FM receivers with an SDR and VFO.
RF Experiments in Raspberry Pi
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Hamfest,Bangalore 2018 presentation
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官方认证美国密歇根州立大学毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国密歇根州立大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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7:个人信誉贷款加10分
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
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represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
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geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
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ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
For International shipping and maritime laws all wood must contain the ISPM 15 Stamp. Here is how and why.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
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留信网认证的作用:
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5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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LoRaWAN Real Life Issues
- 2. Typical LoRaWAN Based Network ANTENN A CABLE LoRaWA N Node LoRaWA N Gateway LoRaWA N Server Cloud LoRaWA N Server
- 3. `Which Antenna? And WHY???
- 4. SWR? What the heck? SWR READIN G % OF LOSS ERP* WATTS AVAILAB LE 1.0:1 0.0% 100.0% 4.00 1.1:1 0.2% 99.8% 3.99 1.2:1 0.8% 99.2% 3.97 1.3:1 1.7% 98.3% 3.93 1.4:1 2.8% 97.2% 3.89 1.5:1 4.0% 96.0% 3.84 1.6:1 5.3% 94.7% 3.79 1.7:1 6.7% 93.3% 3.73 1.8:1 8.2% 91.8% 3.67 2.0:1 11.1% 88.9% 3.56 2.2:1 14.1% 85.9% 3.44 2.4:1 17.0% 83.0% 3.32 2.6:1 19.8% 80.2% 3.21 3.0:1 25.0% 75.0% 3.00 4.0:1 36.0% 64.0% 2.56 5.0:1 44.4% 55.6% 2.22 6.0:1 51.0% 49.0% 1.96 7.0:1 56.3% 43.8% 1.75 8.0:1 60.5% 39.5% 1.58 9.0:1 64.0% 36.0% 1.44 10.0:1 66.9% 33.1% 1.32
- 6. OUT DUT Antenna IN NWT4000-1 Network Analyzer RF OUT RF IN SWR Bridge OUT DUT Antenna IN Noise Generator RF OUT RF IN SWR Bridge RTL Software Defined Radio OUT DUT Antenna IN Noise Generator RF OUT RF IN SWR Bridge RTL Software Defined Radio SWR measurement?
- 7. Which Cable and Why?
- 8. Which Frequency and Why?
- 9. Which Frequency and Why? 866 MHz 433 MHz
- 10. Which LoRa Modules(How to Choose)? RFM95W DRF1272 F LORA1276- C1 DRF1276G Lora1276F30 RFM95PW
- 11. Which LoRa Modules(How to Choose)? Murata RisingHF Microchip Rakwireless Internal Circuit Murata Module
- 12. Which LoRa Development Board?
- 13. Is LoRaWAN node dead/alive? Spectrum Analysis
- 14. SN Phase Repository Type of software Remark 1 Gateway https://github.com/Lora-net/lora_gateway Open source 2 Gateway https://github.com/Lora-net/packet_forwarder Open source 3 LoRa Node(Semtech) https://github.com/Lora-net/LoRaMac-node Open source 4 Arduino Node LMIC https://github.com/matthijskooijman/arduino- lmic Open source 5 WiringPi git://git.drogon.net/wiringPi 6 Raspberry Pi Node LMIC https://github.com/ernstdevreede/lmic_pi Open source 7 I-CUBE-LRWAN(STM) https://www.st.com/en/embedded-software/i- cube-lrwan.html 8 Physical Message to Frame Message decoding https://LoRaWAN-packet-decoder- 0ta6puiniaut.runkit.sh 9 LoRaWAN packet decryption https://github.com/jieter/python-lora Open source 10 ASCII to BASE64 https://www.branah.com/ascii-converter 11 Signal Sniffing over Air https://github.com/rpp0/gr-lora/wiki/Capturing- LoRa-signals-using-an-RTL-SDR-device Open source 12 Signal Sniffing over Air https://revspace.nl/DecodingLora Open source Which LoRaWAN Library to start with?
- 15. Assuming base64-encoded packet QNIfASaABAABA/uB+N3FPLnND5ufMW6z Message Type = Data PHYPayload = 40D21F01268004000103FB81F8DDC53CB9CD0F9B9F316EB3 ( PHYPayload = MHDR[1] | MACPayload[..] | MIC[4] ) MHDR = 40 MACPayload = D21F01268004000103FB81F8DDC53CB9CD0F9B MIC = 9F316EB3 ( MACPayload = FHDR | FPort | FRMPayload ) FHDR = D21F0126800400 FPort = 01 FRMPayload = 03FB81F8DDC53CB9CD0F9B ( FHDR = DevAddr[4] | FCtrl[1] | FCnt[2] | FOpts[0..15] ) DevAddr = 26011FD2 (Big Endian) FCtrl = 80 FCnt = 0004 (Big Endian) FOpts = Message Type = Unconfirmed Data Up Direction = up FCnt = 4 FCtrl.ACK = false FCtrl.ADR = true How to decode LoRaWAN packet?
- 16. How to decode LoRaWAN packet?
- 17. The data received is In hex =68, 65, 6C, 6F, 20, 77, 6F, 72, 6C, 64 In decimal=104, 101,108,111,32,119,114,108,100 In ascii=h,e,l,l,o, ,w,o,r,l,d How to decode LoRaWAN packet?
- 18. 1. Get a development board Arduino Uno/STM Nucleo Board/Raspberry Pi(Lowest Cost Rs 500/-) 2. Get an SPI based Lora module (SX1276 or RFM95W) pair (Lowest Cost Rs 1500/- ) 3. Breakout Board, Antenna , (Connector Lowest Cost Rs 250/-) ------------------------------------------------------------------------------------------------------------ Total Cost Rs 1750/- How can I start my LoRaWAN journey/leraning?
- 19. LoRaWAN CLASSES
- 20. LoRaWAN SPEED
- 22. LoRaWAN Range Check A LORAWAN network was simulated in Canara Engineering , Mangalore •SX1301 Based Gateway(100 mW) •SX1276 Based Node(500 mW) •Arduino Environment for Node •Raspberry Pi 3 Environment for Gateway •Frequency Range(865-867 MHz) •Modified Semtech Packet Forwarder for Gateway •TTN(The Things Network Server) •Modified LMIC (LoraMAC-in-C) library •SF 12, Data Rate 250 Kbps