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Energy-Efficient Channel Coding Strategy

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Now a days, precision crop management in agriculture is widely used in association with information technology to improve quality and quantity of production.An example of such technology is Wireless Sensor Networks(WSN) that are used by farmers in agriculture to provide large amount of information like sound, pressure, pollution level, humidity, temperature, wind etc. that are helpful in good crop production. Using these Wireless sensors wide area of farm can be monitored easily as they consists of network of sensor nodes that are distributed spatially. These sensors collect and process all the data like information regarding physical conditions of an environment, preventing wastage of pesticides, providing appropriate quantity of nutrients for crops etc. centrally. These wireless sensor networks contains various sensors, processors , RF modules that are all battery powered.Today these networks are used in all fields of society like sport, industry , medicine but in agriculture it is the most favorable and cost effective activity to improve crop production yield and help farmers.

As in this case study, farm chosen is 3 km by 3 km which is too wide area in such case Long range radio (LoRa) wireless protocol is used that uses long range radio frequency band i.e. 433, 868, 915 Mhz ISM bands. It consume less power, small size, easy implantation of networks, scalability, low latency and can be easily deployed in vast agriculture field. In this LoRa uses star topology in which all the data from nodes are collected and communicated using cloud server over long distance with high scalability. It was used for improving Corn production in Italy by improving fertilization methods (Jawad et al., 2017).

Channel capacity means the upper bound on the rate at which information can be transmitted over channel. The capacity of channel can be increased by reducing the transmission power used by wireless sensor networks. The MIMO antenna are used to increase the capacity of channels of wireless sensors networks. In this method efficient space time is provided.

Centre channel controlled by one sensing device

In this case the channel capacity of centre channel is 100 bits/s which is controlled by one sensing device.

Control centre to ISP channel

For ISP the channel capacity is 90 bits/s which is controlled by centre.

Noise -level calculations

Thermal noise is also called as white noise. Thermal noise power is directly proportional to bandwidth. It spread over spectrum.

Temperature = 20 degree Celsius

Resistance = 10 Ohms

Bandwidth = 10 Hz


Noise Voltage = 0.00127238 microvolt RMS

Noise power = -163.9 dBm

Total noise calculations depend on the thermal noise.As it is given only 5% of total noise experienced is thermal noise in wireless sensor networks (Adelantado et al., 2017).


In this wireless sensors use radio frequency bands to transfer data. The below equation is used to calculate received power. In case central frequency is high then received signal power will be good.

The signal received is -40 dBm to -50 dBm. The higher signal strength are received by LoRa receiver modules.

The Rate of data sampling for sensors should be higher than 10 Hz . They are referred as high data sensing devices. For low power wireless sensor networks the frequency bandwidth is 100 Kbits.s - 1 Mbits/s. The quantity of data taken from user is called bandwidth. The minimum bandwidth is 250 KBps(Ruizhong Lin, Zhi Wang and Youxian Sun, 2004).

Wireless sensor network uses wide range of frequencies. All the frequencies can be multiplexed using FDM technique. As a result all the wireless nodes can communicate without RF interference between them. It allows multiple signals to be transmitted on single channel using fiber optic fibres(Barreto et al., 2018) .  

The maximum free space calculation

The free space path loss is based on distance, frequency, transmitter and receiver gain of sensors. The radio frequency strength of wireless network sensors using the free space path loss  ( Guoqiang Mao, Brian D.O. Anderson, Baris Fidan, 2006).

30% of signal strength fade and attenuated while travelling from sensors to control centre.Therefore, -10 dBm is the transmission signal strength in wireless sensor networks.

 Introduction of Information technologies in agriculture and business has improved the performance or an efficiency of that sector. Using these technologies communication is improved and information sharing is very easy. Using cloud computing helps in :

  • Initial costs are reduced
  • Resources are allocated on demand.
  • Global service development is possible.
  • Maintenance and Upgradation is at back-end.

Internet is moving slowly from Internet of people to Internet of things. By 2020 millions of things will be connected to internet. Such an example of IoT implementation is wireless sensor networks in which there are number of nodes or sensors are connected and communicate with each other for information sharing. So Internet of things implements four layers - sensors, processors, gateways and Application. Through gateways WSNs connect things to internet. IoT are used in varied fields like weather sensing, smart grids, automation etc. In smart grids the WSN keep track of usage of power and its production for optimize usage of energy. IoT execute smart algorithms on information to interact intelligently with other things. Two things are important for integration of WSN and IoT - First is IPv6 should be deployed over low power wireless personal area networks. Second standardization of machine to machine communications (Flammini and Sisinni, 2014).



Jawad, H., Nordin, R., Gharghan, S., Jawad, A. and Ismail, M. (2017). Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review. Sensors, 17(8), p.1781.

Adelantado, F., Vilajosana, X., Tuset-peiro, P., Martinez, B., Melià-Seguí, J. and Watteyne, T. (2017). Understanding the Limits of LoRaWAN. IEEE Communication magzine. [online] Available at: [Accessed 11 Jun. 2018].

Augustin, A., Yi, J., Clausen, T. and Townsley, W. (2016). A Study of LoRa: Long Range & Low Power Networks for the Internet of Things. Sensors, [online] 16(9), p.1466. Available at: [Accessed 11 May 2018].

Barreto, G., Simão, D., Pellenz, M., Souza, R., Jamhour, E., Penna, M., Brante, G. and Chang, B. (2018). Energy-Efficient Channel Coding Strategy for Underwater Acoustic Networks. [online] Available at: [Accessed 11 Jun. 2018].

Lin, R., Wang, Z. and Sun, Y., 2004, May. Energy efficient medium access control protocols for wireless sensor networks and its state-of-art. In Industrial Electronics, 2004 IEEE International Symposium on (Vol. 1, pp. 669-674). IEEE.

Mao, G., Anderson, B.D. and Fidan, B., 2007. Path loss exponent estimation for wireless sensor network localization. Computer Networks, 51(10), pp.2467-2483.

Hori, M., Kawashima, E. and Yamazaki, T., 2010. Application of cloud computing to agriculture and prospects in other fields. Fujitsu Sci. Tech. J, 46(4), pp.446-454.

 Flammini, A. and Sisinni, E. (2014). Wireless Sensor Networking in the Internet of Things and Cloud Computing Era. Procedia Engineering, [online] 87, pp.672-679. Available at:


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