Analysis Of Wireless Sensor Networks In Smart Grid And IoT With ISM Radio Frequency Bands

Use of ISM Radio Frequency Bands in Wireless Sensor Networks

Discuss about the Wireless Sensor Networks for Personal Health Monitoring.

Modern, Scientific and Medical (ISM) recurrence band is the most reasonable for Wireless sensor arrange in the flag transmission. It works in free groups of radio electric range which give best execution in WSN condition because of recurrence decent variety that builds the quality against flag misfortune multipath, condition blurring and impedances. For these chose condition the Smart Grid exceeds expectations is the best space for the innovation utilized, this is significantly a direct result of its capacity to wind up the engine to the rest electrical ward frameworks regardless examination. Keeping in mind the end goal to influence it to savvy, the vast majority will decide on unlicensed ISM groups, particularly the 2400MHz as a result of the broad correspondence data transmission in relationship with bring down groups. With 915MHz it speaks to a possibility for Internet of Things worth examining, it has some calculable advantages, and it has longer scope with less power utilization. A far-reaching quantitative examination of these groups has achieved as far as power utilization and normal deferral and parcel gathering rate. For this investigation with creature tag with controls a double proliferation model will be fruitful. The outcome will uncover that utilizing 2400MHz gadget work best with choice of utilizing remote associations. Be that as it may, SG-arranged Internet of Things quantitative performs better in the event that it works with 915MHz band. With a particular ultimate objective to make most SG-arranged IoT sharp, work of the unlicensed ISM gatherings, (for instance, the 2400 MHz one) is certain. This is generally an immediate consequence of its extended correspondence information exchange limit when diverged from the ones with the lower transmission limit. This has been the most used band by the two developments from which the IoT procures, that is, the Wireless Sensor Network (WSN) and the Mobile Ad-hoc Network (MANET). Regardless of the viability and ampleness of this band, there has been a movement of request and inquiries raised by work constraint having to know whether such ‘default’ correspondence band is more perfect as a general rule when appeared differently in relation to the 915MHz ISM band. This has thusly incited a comprehensive quantitative examination of the two gatherings to the extent control, ordinary framework delays and the rate of package gathering. This relationship has been enhanced by the nearness of an unprecedented model created for the SG. This model has been resolved, attempted and joined into an appropriate test framework

Quantitative Analysis of Different Bands

For this situation examine the sensor labels on the creatures with focal hub, the ISM is practical, the 2400MHZ is more reasonable motivation of expanded correspondence transfer speed make the investigation effective (Musaloiu, 2008).

1. Calculate the channel capacity (minimum data rate) required for:
2. One sensing device to control Centre channel

In order to calculate the minimum data we have to convert 5kB into byte, here were taking

5kB * 1024= 5120bytes. Then we know IB = 8b, therefore

1B = 8b

5120B =?

5120 * 8 = 40960b but data rate is measured in kb/s

We convert 40960b into kb by dividing with 1024 which result into

40960/1024 =40.11731832b

Finally, we divide it by 60 to make it in seconds

40.11731832/60 = 0.6686255305

 = 0.6686kb/s which is the minimum data rate

Control centre to ISP channel.

Since we had a total of 2500 animals, each animal generated 0.06686kb/s as calculated above. To get the channel capacity we have to multiply minimum data generated by one animal by the total number of animals which is 2500 animals.

Therefore, 2500 * 0.6686 = 1671.5 kb/s

1671.5kb/s will be required by the control centre to ISP channel.

(Snr, 2000).

1. For both the channel types calculate the following noise level experienced
2. Thermal noise

Thermal noise is efficiently white noise that can be extended over a wide spectrum. Noise power is proportional to the bandwidth. The equation for noise is given by

Where V= is the integrated RMS voltage between the given two frequencies

    R= resistive with the symbol Ω

    T= is the temperature in degree Kelvin

For the case study the ambient temperature is 20°C which is the same as the room temperature, converting it into Kelvin we have 2900k which is calculated for a 1 HZ bandwidth since noise power is proportional to the bandwidth with the impedance of 50Ω.

Relating these to the thermal noise the system has 50Ω. At the study environment is -174dBm / Hz

(Harrison, 2003).

Total noise experienced

From above we know that 5% of the total noise was thermal noise therefor to get the total noise we have to find 100% which will be experienced

 5% = -174dBz

100% =?

(-174 * 100)/5 = 3480dBz

Therefore the total noise experienced is -3480dB/Hz

1. It is expected to maintain SNR of 63 at the control centre for sensor signals.
2. Calculate the signal power received at control centre from one sensing device

We know that SNR is the difference between the signal power and total noise produced, therefore, to get the signal power we need to add the noise and the SNR,

S/N = 63, and the total noise is -3480dB/Hz

Signal power is therefore -3480*63 = -219,240db/Hz

The total signal power produced is -219,240db/Hz

(Akyildiz, 2002)

Calculate the bandwidth of the sensing device to control centre channel.

Bandwidth is the ratio of signal power to noise, we have the signal power -3480dBz and signal power as 3543dBm. Therefor -3480/-3543 which gives 0.98221 that leads to approximately 1

Calculation of Minimum Data and Channel Capacity

What will be the bandwidth of the multiplexed channel if FDM scheme is used?

Using  Shannon’s channel capacity criteria we have the formula as

C = Blog (1 + S/N)

But we have a bandwidth of 1hz and the value of S/N value of the 3543dBz

C = 1 * log (1 + 3543)

 = 3.3549 bps

Calculate the maximum free space loss experienced by the signals sent from sensors. (Assume control centre is located exactly at the centre of the landscape)

The formula is given by

Signal = 1/d*d

While the free space path loss is given by

Free space = (4*3.142*d/wavelength)2

Also, we can use the formula

FSPL = (4πd/?)2

Frequency we got 1.667kb/s

The distance we will take 1.5km which is from the center of the farm

Thus = (4*3.142/1.667) =7.5132

Determine the required transmission signal strength if other impairments such as attenuation and fading cause loss of 30% in the signal power during the propagation from sensors to the control centre.

Fading is the variation of signals with many variables, we were given the 200C

the signal power was 3543 but we want 30% of it

thus (3543*30)/100 = 1062.9bps

With your general knowledge in the agriculture sector and business suggest functionalities for the cloud application to effectively use sensor data.

The agricultural sector in Australia has created hotspots for agricultural farmers where they can be able to access current information which will be helpful in the production. The existence of mobile sensing technologies that always help in measuring agricultural produces, storing data and calculation of simple mathematics.

Cloud computing has also simplified the accessibility and sharing of techniques by the experts leading to better products being produced. Most Australian has appreciated the impact of WSN where market accessibility is an issue like the recent past.

With the evolution of video conferencing and smartphone which records video makes learning easier as many farmers are thought new technology which keeps on changing every time. This approach has also minimized the time wasted in traveling.

In the business sector, it has led efficiency at work can every staff is monitored and the workload measured. Many businesses now days employ automation of the services which increases delivery in the production department.

WSN is used in communications in business without good communication challenges the business experiences challenges and may end up closing down in the long run.

It is also applicable to management by helping in making vital decisions. With the ability to access information within a short period, important decisions are made within a short time because data are obtained in real time also with help of teleconferencing it minimizes time wasted in movement.

Thermal Noise

 It is also used in monitoring the business, For security purposes, the sensor data can the used in line with the CCVT which helps in recording all activities taking place within the firm. It can also help to single out lazy or stuff who operate illegal activity by installing video recording devices that interconnect with the wireless sensor network.

The main objective of the Internet of Things is to create a wide network that will facilitate the transfer of information across all devices. WSN technology helps many users obtain information within a short period and is also affordable, it connects the sensing and network layers. We have a gateway that facilitates the transfer of data within the layers

With the current improvement in the technology sector, Scientists are a task with developing efficient and improved systems to meet the expectation of dynamic economy. The Internet has become the source of information that many people across the world access with the objective of getting best result to their problems. The Internet is the source for interconnection of how people communicate, things interconnect and how human beings interconnect with things. The invention of the Internet of Things (IoT) has form the basis of Wireless Sensor Network, the IoT structure is composed of different layers where each layer interconnect effectively for efficient flow of information.

The IoT protocols form the important part in Wireless Sensor Network, these protocols include the WiFi which is a wireless network that enables many devices to use the available Internet through wireless connections. The Internet is established on an access point that enables many users within a specified perimeter to use the internet, Current WiFi is the most widely used and it is affordable to most people. There are also shortcomings that arise from wifi including the security of the data is not guaranteed and experience hacking, despite these challenges WiFi play a major role in Wireless Sensor network since it covers a wider area than Bluetooth by creating the hotspot.

Bluetooth is another wireless technology that facilitates the transfer of data within a short range. It uses short wavelength UHF radio wave in ISM bands of about 2.45GHz. The headsets and watches contain chips that enable Bluetooth connections though pairing. With advancement in technology, there are two types of Bluetooth that help in the wireless sensor network, the Bluetooth basic rate used in speakers and headphones while Bluetooth low energy used for new modern types of equipment such as the smart home. The Bluetooth is helpful in Wireless Sensor network as it enables transfer of data within a short range hence it can be more economical. (Milenkovi?,2006).

Another protocol in IoT that helps WSN is the mobile phones, the mobile phones use wireless communication by using electromagnetic waves signals. Many satellites are set up to enable transfer of signals; they magnify the strength of signals. The satellites are used to boost the strength of the Wireless Sensor network which enables it to cover a larger area

References

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer networks, 38(4), 393-422.

Harrison, R. R., & Charles, C. (2003). A low-power low-noise CMOS amplifier for neural recording applications. IEEE Journal of solid-state circuits, 38(6), 958-965.

Huang, J. H., Amjad, S., & Mishra, S. (2005, November). Cenwits: a sensor-based loosely coupled search and rescue system using witnesses. In Proceedings of the 3rd international conference on Embedded networked sensor systems (pp. 180-191). ACM.

Milenkovi?, A., Otto, C., & Jovanov, E. (2006). Wireless sensor networks for personal health monitoring: Issues and an implementation. Computer communications, 29(13-14), 2521-2533.

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.

Snr, D. E. D. (2000). Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. International Journal of Machine Tools and Manufacture, 40(8), 1073-1098.