a) Select a suitable frequency band for WSN and give reasons for your selection.
There has been a rapid emergence of new modern technologies all over the world in the last decade. An example of such technologies is the introduction of Internet of things (IoT). This technology has been highly used in the in industrial environments and critical infrastructure due to its ability to deliver good performance and its efficiency. The Smart Grid (SG) has often been seen as a more perfect host for IoT technology, as compared to other similar hosts. This is mainly because, SG has a potential of becoming the engine to all other electrical-dependent infrastructures in the setup.
In order to make most SG-oriented IoT cost-effective, employment of the unlicensed ISM bands (such as the 2400 MHz one) is inevitable. This is mainly because of its extended communication bandwidth as compared to the ones with the lower bandwidth. This has been the most used band by the two technologies from which the IoT inherits, that is, the Wireless Sensor Network(WSN) and the Mobile Ad-hoc Network (MANET). Despite of the efficiency and sufficiency of this band, there has been a series of questions and doubts raised by personnel wanting to know if such ‘default’ communication band is more perfect in the real world as compared to the 915MHz ISM band. This has therefore led to a comprehensive quantitative comparison of the two bands in terms of power, average network delays and the rate of packet reception Musaloiu, (2008). This comparison has been enhanced by the existence of a special model developed for the SG. This model has been derived, tested and incorporated into an appropriate simulator (known as TOSSIM).
The simulation outcomes acquired show that the 2400MHz band only works best in when there is little or no interferences from other 2400 MHz meddling devices- such as Bluetooth or WiFi, or in small networks. On other hand, SG-oriented IoT operating in the 915 MHz band works best in all other environments. With the rapid increase in the growth of technology, working in Bluetooth or WiFi free environments is a thing close to impossible. Therefore, that makes the 915 MHz band a more suitable frequency band for WSN setup or rather implementation.
solution
One sensing device generates 5kb per minute.
Therefore, to get the minimum data transferred to the center channel, we must convert the data generated by one device per minute into the units of measure of the data rate, which is kb/s. The assumption is that, there is no redundancy in the data transmission. Therefore, the amount of data generated is equal to the amount of data that reaches the destination.
The conversion of kB/minute to kb/s
1kB = 1024B. Therefore, 5kB = (1024 × 5) = 5,120B
Therefore, each device transfers 5,120B/min.
To convert B to b,
8b = 1B therefore, 5,120B = (5,120 × 8) = 40,960b.
40,960b/1024 = 40kb therefore, each device transfer 40kb/min. divide this by 60 to make it kb/s.
40/60= 0.67
therefore, minimum data rate for one sensing device = 0.67kb/s.
solution
To calculate the minimum data rate required for control center to ISP channel, you multiply the minimum data rate for one sensing device with the number of devices transferring to the center channel per second.
The number of devices = number of animals = 2500.
Therefore, 0.67× 2500 = 1,675kb/s
solution
Noise power is proportional to the band width. Therefore, it is possible to derive a generalized equation for the noise voltage within a given bandwidth.
Where:
V = integrated RMS voltage between f1 and f2
R = resistive component of impendence in ?
T =temperature degrees’ kelvin (Celsius + 273.16)
f1 and f2 = lower and upper limits of required band width.
Since the resistance remains constant in many cases, it is possible to simplify the equation to:
Where:
B = bandwidth in Hz
With this equation, it is possible to calculate the thermal noise level for room temperature,20o C or 290o K. This is mainly calculated for a 1 Hz bandwidth for ease scaling since noise power is proportional to the bandwidth. The most commonly used resistance is 50 ?.
since the thermal noise in a 50 ? system at room temperature is -174dB/Hz (from the equation P = V2/4R), the expression, 9nV = -174 dB/Hz counts. Thus,
the thermal noise at 20 oC = -174dB/Hz.
From the case study given, the thermal noise contributes to only 5% of the total noise experienced.
Therefore,
if -174dB/Hz = 5% then, 100% = (-174×100) / 5 = -3,480 dB/Hz
From the question, signal power divided by noise power gives 63.
Therefore, S/N = 63. But the total noise = -3480 dB/Hz.
Therefore, S/-3480=63.
Thus, the total signal power S = -3480 dB/Hz × 63 = – 219,240 db/Hz.
To get the signal power for one sensing device, divide the total signal power by the number of devices present.
That is, (-219,240dB/Hz)/ 2500 = -87.696 dB/Hz
Bandwidth is the difference between the lower and upper frequencies of a channel.
Capacity (C) =.332bandwidth(B)(SNR). To get B, divide both sides by B.
C/B=(.332B*63)/B, then divide both sides by C.
1/B=20.926/C then find the reciprocal of the both sides.
Thus, B=1/(20.926/C)
III. What will be the bandwidth of the multiplexed channel if FDM scheme is used?
From Shannon’s Law, C = .332 (Bandwidth) ( SNR), but bandwidth = B and SNR =63
Therefore, C = (.332B × 63) = 20.926B kb/s.
The FSPL formula expresses a loss value that is the reciprocal of gain and assumes the directivity for the transmit and receive antennas are isotropic and therefore unity. Both modifications simplify the equation to {displaystyle {begin{aligned}{mbox{FSPL}}=left({frac {4pi d}{lambda }}right)^{2}end{aligned}}}:
FSPL = ( 4πd/?)2
{displaystyle lambda }where:
is the signal wavelength in m,
{displaystyle d}is the distance between the antennas in m,
? = frequency but frequency =1.675kb/s
d=1.5km=1500m
therefore, FSPL = (4*3.142/1.675) =7.503
solution
The signal loss due to attenuation and fading is the same as the maximum free space loss experienced by the signals sent from sensors.
This signal lost accounts for 30% of the total signal.
From the question ‘e’ above, the signal lost during the data transfer = 7.503.
Therefore, if 7.503 = 30%,
The total signal required = (7.503*100)/30 = 25.01 dBµV/m
With the introduction of WSN in the agricultural and business sectors, there is an increase I the amount of resources that are required for the efficiency in the operation of such devices. The need for enough storage for the data collected from the fields. But should the agricultural or business sectors involved be worried about the issue? No, they shouldn’t. This is due to the fact that there has been development of new technologies that can handle that issue for them. The cloud computing for instance, has evolved over the recent years and has merged to be one of the most sufficient and efficient technologies that can be used to remotely store bulky and confidential data from different locations and organizations for considerably fair prices Rao, (2012).
The issue of data security is also taken care of by the service providers from the involved cloud teams. It is therefore right to say that, business and agricultural organizations shouldn’t stress themselves over the issue of data security of going for individual storage devices that they aren’t sure enough, that they are truly in a position to provide sufficient, efficient and safe means for storing their data. Instead, they should go for cloud computing as it provides all that, and at lower prices.
The Internet of Things (IoT) is known to provide a virtual way of viewing things. Through the use of IoT technologies, on is able to view a large variety of objects in the real-life, through the internet protocol. This means that through this technology, monitoring of objects or ones property is eased in a big way. Wireless Sensor Networks are therefore the most suitable devices that are used hand in hand with the IoT technologies. The IoT applications therefore utilizes the compatibility of WSN in their day to day operations, in a large way Jin, (2014).
References
Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An information framework for creating a smart city through internet of things. IEEE Internet of Things Journal, 1(2), 112-121.
Musaloiu-E, R., & Terzis, A. (2008). Minimising the effect of WiFi interference in 802.15. 4 wireless sensor networks. International Journal of Sensor Networks, 3(1), 43-54.
Rao, B. P., Saluia, P., Sharma, N., Mittal, A., & Sharma, S. V. (2012, December). Cloud computing for Internet of Things & sensing based applications. In Sensing Technology (ICST), 2012 Sixth International Conference on (pp. 374-380). IEEE.
Rappaport, T. S. (1996). Wireless communications: principles and practice (Vol. 2). New Jersey: prentice hall PTR.
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