Journal of Information Technology & Software Engineering
Open Access

Multi-mode Detection Testing based with IoT Enabled Wireless Sensor Networks

Timothy Julius^{* *}Correspondence: Timothy Julius, Department of Information Technology, Bauman Moscow State Technical University, Moscow, Russia, Email:

Author info »

Description

A sensor network is a system that consists of multiple sensors connected to each other through wireless communication protocols. These networks can be used for a wide variety of applications, such as environmental monitoring, surveillance, and healthcare. Sensor networks typically consist of three main components: sensors, wireless communication modules, and a central processing unit. Sensor networks can also be used to improve safety and security. For example, sensors can be used to detect the presence of hazardous materials, monitor for leaks or other safety hazards, or track the movement of people or vehicles in a secure area. This can help organizations prevent accidents, respond quickly to emergencies, and protect assets.

Sensors are the devices that collect data from the environment. They can be of various types, such as temperature sensors, humidity sensors, light sensors, and motion sensors. The data collected by these sensors are transmitted wirelessly to the central processing unit for analysis. Wireless communication modules enable the sensors to communicate with each other and with the central processing unit. There are various wireless communication protocols available for sensor networks, such as Zigbee, Bluetooth, and Wi-Fi [1]. The choice of wireless communication protocol depends on the application requirements, such as the range of communication, the data rate, and the power consumption.The central processing unit is responsible for processing the data collected by the sensors. It can perform various tasks such as data aggregation, data fusion, and event detection. The processed data can be visualized or stored for further analysis. While sensor networks offer many benefits, there are also several challenges that organizations must overcome when deploying these networks. One of the biggest challenges is the cost of deploying and maintaining the network [2]. Sensors can be expensive, especially when they need to be designed to withstand harsh environments. Additionally, the cost of installing and maintaining the communication infrastructure required to transmit data can also be significant [3].

One of the key advantages of sensor networks is their ability to collect data in real-time. This enables quick decision-making based on the data collected. For example, in environmental monitoring applications, sensor networks can be used to monitor air pollution levels in real-time [4]. This can help authorities take timely action to control pollution levels. Sensor networks can also be used for surveillance applications. They can be used to monitor areas such as airports, seaports, and borders. The data collected by the sensors can be used to detect suspicious activities and alert the authorities. In healthcare applications, sensor networks can be used to monitor the health of patients. For example, wearable sensors can be used to monitor vital signs such as heart rate and blood pressure [5]. The data collected by these sensors can be transmitted wirelessly to healthcare professionals for analysis. This can enable early detection of health problems and prompt medical intervention. As sensor networks become more widespread, it is essential that they can work together seamlessly. This requires standardization of communication protocols and data formats, which can be difficult to achieve [6].

Sensor networks can also be used for precision agriculture [7]. They can be used to monitor soil moisture levels, temperature, and humidity. This data can be used to optimize irrigation and fertilization. This can lead to increased crop yields and reduced water and fertilizer usage. However, sensor networks also have some limitations [8]. One of the main limitations is the limited battery life of the sensors. The sensors need to be powered by batteries, and the battery life can be limited, depending on the application. This can be overcome by using energy harvesting techniques or by using rechargeable batteries [9]. Another limitation is the limited range of wireless communication protocols. The range of communication depends on the wireless communication protocol used [10]. For example, Zigbee has a range of up to 100 meters, while Wi-Fi has a range of up to 100 meters indoors and up to 300 meters outdoors [11]. This can be overcome by using range extenders or by deploying more sensors.

Conclusion

Sensor networks are an emerging technology with a wide range of applications. They have the potential to revolutionize various industries such as healthcare, agriculture, and surveillance.

While there are some limitations to sensor networks, ongoing research is working towards overcoming these limitations. Organizations must implement robust security measures to protect the data and ensure the integrity of the network.

References

1. Akkaya K, Younis M. A survey on routing protocols for wireless sensor networks. Ad Hoc Networks. 2005;3(3):325-349.

   [Google Scholar]

2. Akyildiz IF, Su W, Sankarasubramaniam Y, Cyirci E. Wireless sensor networks: A survey. Comput Netw. 2002;38(4):393-422.

   [Google Scholar]

3. Besg J. On the Statistical Analysis of Dirty Pictures. J R Stat Soc Series B: Stat Methodol. 1986;48(3):259-302.

   [Crossref] [Google Scholar]

4. Dempster AP, Laird N, Rubin D. Maximum Likelihood Estimation from Incomplete Data via the EM Algorithm. J R Stat Soc Series B: Stat Methodol. 1977;39:1-38.

   [Google Scholar]

5. Gu D. Distributed EM Algorithm for Gaussian Mixtures in Sensor Networks. IEEE trans neural netw. 2008;19:1154-1166.

   [Crossref] [Google Scholar]

6. McGrory CA, Titterington DM. Variational Approximations in Bayesian Model Selection for Finite Mixture Distributions. Comput Stat D Analy. 2007;51(11):5352-5367.

   [Crossref] [Google Scholar]

7. Nowak RD.  Distributed EM Algorithms for Density Estimation and Clustering in Sensor Networks. IEEE Trans Signal Process. 2003;51(8):2245-2253. 

   [Crossref] [Google Scholar]

8. Mika I, Masaki A. The Reliability Performance of Wireless Sensor Networks Configured by Power-Law and Other Forms of Stochastic Node Placement. IEICE Trans Commun. 2004;87(9):2511-2520.

   [Google Scholar]

9. Saffre F, Jovanovic H, Hoile C, Nicolas S. Scale-free topology for pervasive networks. BT Techno J. 2004;22(3):200-208.

   [Crossref] [Google Scholar]

10. Dai F, Wu J. On constructing k-connected k-dominating set in wireless ad hoc and sensor networks. J Parallel Distrib Comput. 2006;66(7):947-958.

    [Crossref] [Google Scholar]

11. Frezzetti A, Manfredi S, Pagano M. A design approach of the solar harvesting control system for wireless sensor node. Control Eng Pract. 2015;44:45-54.

    [Crossref] [Google Scholar]