MITS5003 Wireless Networks And Communication Case Study 1wir
MITS5003 Wireless Networks And Communicationcase Study 1wireless Senso
Mits5003 Wireless Networks and Communication Case Study 1 Wireless Sensor Network (WSN) can be defined as a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. WSNs measure environmental conditions like temperature, sound, pollution levels, humidity, wind, and so on. There has been tremendous research on how to use WSN technology in Agriculture and Livestock industry - one of the key sectors of Australian exports. Sense-T is one such initiative based in Tasmania and the network has proven it could help farmers by providing useful information in decisions making. Observing these positive results, recently a livestock giant VicStock came up with an idea to use a WSN in their farms to monitor the animals' health and behaviour.
The proposed pilot project will be implemented in one of their farms located in Gippsland area called MaffraStock. This farm is a 3 km x 3 km square shaped land and has got various types of animals including cattle, sheep and camel. The total number of animals is 2,500. The plan is to tag each animal with a device equipped with sensors, a GPS tracker and an accelerometer that monitors the motion. One device generates 5 kB data per minute and there is a central node at the middle of the farm to collect data from all the sensors.
This central node multiplexes data from all the sensors and uploads it to a cloud-based IoT platform through a wireless ISP link so that VicStock can analyse trends and detect anomalies. To avoid licensing costs, the WSN uses ISM frequency bands for signal transmission. The ambient temperature around the farm is 20°C, which causes a small amount of thermal noise. It is found that thermal noise is only 5% of the total noise experienced by WSN channels.
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Introduction
Wireless Sensor Networks (WSN) are fundamental to modern precision agriculture and livestock management systems. These networks facilitate continuous monitoring of environmental and animal health parameters, providing valuable data that can optimize farming practices, increase productivity, and improve animal welfare. The deployment of WSNs in livestock farms, such as the MaffraStock farm in Gippsland, exemplifies their utility in real-world agricultural applications. This paper explores the technical considerations for implementing such a system, including frequency band selection, capacity calculations, noise considerations, signal strength requirements, and the broader implications facilitated by IoT platforms in agriculture.
Frequency Band Selection for WSN
The selection of an appropriate frequency band for the WSN is critical for ensuring reliable communication, minimal interference, and compliance with regulatory standards. The Industrial, Scientific, and Medical (ISM) bands are globally available license-free frequencies suitable for WSN applications, notably 2.4 GHz, 868 MHz (Europe), and 915 MHz (North America). Given the farm's environment and typical sensor data rates, the 2.4 GHz band is an apt choice due to its widespread use, mature technology, and available off-the-shelf modules. The 2.4 GHz band offers a good balance between data rate capacity and propagation characteristics, providing sufficient bandwidth for sensor data and supporting multiple simultaneous channels via Channel hopping and spread spectrum techniques, reducing interference and enhancing reliability.
Capacity Calculations
Part I: Sensor to Control Center Channel Data Rate
Each device generates 5 kB of data per minute, which translates to approximately 83.33 bytes/sec (since 5,000 bytes / 60 seconds). To compute the minimum data rate, convert bytes to bits: 83.33 × 8 = 666.67 bits/sec. Considering overheads such as protocol headers and error correction, an estimated safe minimum is roughly 1 kbps per device.
Part II: Control Center to ISP Channel Data Rate
All 2,500 animals' sensors transmit data to the central node. Assuming simultaneous transmission or time-division multiplexing, the aggregate data rate would be 2,500 × 1 kbps ≈ 2.5 Mbps. However, given practical constraints like duty cycling and available bandwidth, the control center-to-ISP uplink must support at least this aggregate data rate with some margin, recommending an uplink capacity of approximately 3 Mbps or higher for reliable operation.
Noise Level Analysis
Thermal Noise
Thermal noise, Nthermal, in a resistor is calculated by the Johnson-Nyquist equation: Nthermal = kTB, where k is Boltzmann's constant (1.38×10-23 J/K), T is temperature in Kelvin, B is bandwidth in Hz.
At 20°C (293K), for a bandwidth of 1 MHz (common for WSN channels), Nthermal = 1.38×10-23 × 293 × 1×106 ≈ 4.04×10-15 W, which equates to about -174 dBm/Hz thermal noise floor.
Total Noise
Total noise includes thermal noise plus other noise sources (e.g., interference). Given the ambient thermal noise accounts for only 5% of the total noise, total noise Ntotal = Nthermal / 0.05 = 20× Nthermal ≈ 8.08×10-14 W, or about -161 dBm, reflecting the combined environmental and system noise.
Sensor Signal Power at Control Center
Assuming a Signal-to-Noise Ratio (SNR) of 63 (approx. 18 dB), the received signal power Psignal can be found using the relation: SNR = Psignal/Ntotal. Therefore, Psignal = SNR × Ntotal = 63 × 8.08×10-14 W ≈ 5.09×10-12 W, or about -172.9 dBm.
Bandwidth Calculations
The minimum bandwidth necessary for the sensing device to control center communication is obtained via the Shannon-Hartley theorem: C = B × log2(1 + SNR). Rearranged, B = C / log2(1 + SNR).
Using C ≈ 1 kbps (from earlier calculations) and SNR = 63 (or 18 dB), B ≈ 1,000 / log2(1 + 63) ≈ 1,000 / 6.0 ≈ 167 Hz.
For the multiplexed channel employing Frequency Division Multiplexing (FDM), if each of 2,500 channels uses 167 Hz, total bandwidth = 2,500 × 167 Hz ≈ 417.5 kHz.
Free Space Path Loss and Transmission Power
Free space path loss (FSPL) in dB is given by:
FSPL = 20 log10(d) + 20 log10(f) - 147.55, where d is distance in meters, f is frequency in Hz.
Given the farm is 3 km x 3 km, the maximum distance from sensors at the corner to the center is approximately 4.24 km (~4240 meters). Using f = 2.4 GHz (2.4 × 109 Hz):
FSPL ≈ 20 log10(4240) + 20 log10(2.4×109) - 147.55 ≈ 75.54 + 187.6 - 147.55 ≈ 115.59 dB.
Required Transmission Power
Assuming 30% loss due to attenuation and fading, so only 70% of the transmitted power arrives at the control center. To maintain the received power Preceived (~ -173 dBm), the transmit power must account for path loss and losses:
Ptransmit = Preceived + FSPL + system margin + loss factors.
Calculating roughly, if Preceived is -173 dBm, and total path loss is approximately 115.59 dB, then for 70% efficiency, Ptransmit ≈ -173 + 115.59 + 10 (system margin) ≈ -47 dBm.
Functionality of Cloud Application in Agriculture
The cloud platform can aggregate sensor data from multiple farms, providing real-time dashboards to monitor environmental parameters, animal health, and farm conditions. It can incorporate analytics to detect anomalies, predict disease outbreaks, optimize resource allocation, and support decision-making for feed, water, and medication management. The platform can also facilitate remote alerts, report generation, and integration with farm management systems, enhancing operational efficiency and animal welfare.
Role of IoT Platforms in WSN Implementation
IoT platforms serve as the backbone for managing complex sensor networks by providing centralized data collection, storage, and analysis. They enable seamless integration of sensor data with cloud computing, facilitate scalable device management, support secure data transmission, and allow advanced analytics using artificial intelligence and machine learning. Moreover, IoT platforms enable remote configuration, firmware updates, and maintenance, reducing operational costs and improving system reliability, thereby enabling efficient and effective deployment of WSN in agriculture.
Conclusion
The deployment of a WSN for monitoring livestock health on the MaffraStock farm demonstrates the blend of technical and practical considerations essential for successful implementation. From selecting suitable frequency bands, calculating capacity, understanding noise impacts, determining necessary power levels, to leveraging IoT solutions, each step plays a vital role in optimizing system performance. Such integration supports sustainable and intelligent farming practices aligned with modern agricultural advancements.
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