Wireless Sensor Network

Introduction

A wireless sensor network comprises interconnected nodes. Each node possesses processing capabilities (such as microcontrollers, CPUs, or DSP chips), memory (including program, data, and flash memories), an RF transceiver (typically with a single omni-directional antenna), a power source (such as batteries and solar cells), as well as sensors and actuators (Nack, 2010). The nodes are deployed in an ad hoc manner, enabling wireless communication and frequent self-organization. It is typical to anticipate the presence of systems comprising tens of thousands of nodes. Systems of this nature possess the capacity to fundamentally transform our quotidian existence. This innovative technology exhibits potential applications across diverse domains including the environment, medicine, the military, transportation, entertainment, crisis management, homeland defense, and smart spaces.

Elements of WSN

A typical wireless sensor network can be divided into two elements. They are:

– Sensor Node

– Network Architecture

A Sensor Node in a WSN consists of four basic components. They are:

– Power Supply

– Sensor

– Processing Unit

– Communication System

The analog data from the outside environment is gathered by the sensor and then converted to digital by an ADC. A microprocessor or microcontroller is the brains of the device, processing and manipulating data intelligently. Short-range radios are used to send and receive data as part of the communication infrastructure. One single battery, such as a CR-2032, is utilized to provide power to the complete system because all of the components are low-power electronics.  A Sensor Node is made up of not just the sensing component, but includes other vital aspects such as processing, communication, and storage components as well. A Sensor Node is equipped with all these parts and pieces to perform data gathering from the physical environment, network analysis, data correlation, and data fusion with data from other sensors.

Architecture of WSN

The architectural framework employed in WSNs is commonly referred to as sensor network architecture. This architectural approach demonstrates versatility in its applicability across various domains, including but not limited to healthcare facilities, educational institutions, transportation infrastructure, and commercial structures (Rahman & Ikeura, 2012). The architectural framework of the WSN comprises a total of five layers, each serving distinct functions, as well as three cross layers that facilitate communication and coordination across the network. The layers of the WSN are employed to achieve network functionality and facilitate sensor collaboration, thereby enhancing overall network efficiency.

Layered Network Architecture uses hundreds of sensor nodes as well as a base station. Here the arrangement of network nodes can be done into concentric layers. It comprises five layers as well as 3 cross layers which include the following.

Wireless Sensor Network Architecture

These three cross layers are mainly used for controlling the network as well as to make the sensors function as one in order to enhance the overall network efficiency.

Applications of WSN surveillance

Wireless sensor network (WSN) surveillance has been widely implemented across diverse domains and industries, effectively mitigating many challenges through the integration of interconnected wireless sensors. Wireless sensor networks (WSNs) are now integral to modern monitoring and data collection systems due to their versatility. The subsequent instances illustrate various use cases.

Environmental Monitoring

WSNs are extensively employed in environmental monitoring to acquire real-time data concerning the quality of air and water, weather patterns, and natural phenomena (Jaladi et al., 2017). These networks enable monitoring pollution levels, detecting environmental hazards, and investigating climate pattern changes. This enables informed decision-making and proactive measures for environmental protection and preservation.

Home Security

WSN surveillance is crucial for augmenting residential security by facilitating various applications such as intrusion detection, fire detection, and home automation. Intelligent sensors strategically positioned within residential premises possess the capability to promptly identify instances of unauthorized entry, suspicious activities, or potential fire risks, thereby promptly notifying homeowners or pertinent authorities (Ramson & Moni, 2017). Moreover, the integration of these networks with home automation systems enables the remote control of appliances, lighting, and security devices, thereby enhancing safety and convenience.

Industrial Monitoring

WSN surveillance plays a crucial role in monitoring equipment, machinery, and critical infrastructure within industrial settings. As per Alyousuf (2020), by strategically distributing sensor nodes throughout factories or industrial sites, it becomes possible to gather real-time data pertaining to a range of parameters, including temperature, pressure, and vibrations. This feature allows for prompt identification of equipment malfunctions, potential failures, or deviations from normal operating conditions, thereby facilitating the implementation of predictive maintenance strategies, minimizing downtime, and ensuring maximum productivity.

Wildlife Tracking

Wireless sensor network surveillance presents a non-intrusive and efficient approach to investigating wildlife behavior and migration patterns. Miniature and lightweight sensors can be affixed to animals, facilitating the monitoring of their locomotion patterns, migratory pathways, and habitat selection (Aravind et al., 2017). This data enhances comprehension of ecological dynamics, wildlife conservation endeavors, and the ramifications of human activities on diverse species and ecosystems.

Smart Agriculture

With the increasing demand for sustainable agriculture, WSN surveillance has found application in smart farming practices. By deploying sensors in agricultural fields, farmers can monitor crucial parameters such as soil moisture levels, temperature, and crop health in real-time. This data-driven approach empowers farmers to optimize irrigation, apply precise amounts of fertilizers and pesticides, and make informed decisions regarding crop management, ultimately leading to higher yields, reduced resource wastage, and more sustainable farming practices (Gomathi & Jagtap, 2021).

Fundamentals of WSN Surveillance

WSN surveillance pertains to the utilization of WSNs for the purpose of monitoring and surveillance (Dalal & Kukami, 2021). WSN surveillance involves the strategic deployment of sensor nodes equipped with various sensors such as cameras, acoustic sensors, motion detectors, and temperature sensors, among others. The sensor nodes are strategically deployed in targeted areas to enable efficient monitoring and surveillance. The sensor nodes exhibit collaborative behavior to establish a network, enabling wireless communication with a central base station or gateway. The base station serves as the central hub responsible for processing and analyzing data collected from the sensor nodes.

Sensor Technologies in WSN Surveillance

Various sensor types can be strategically employed in WSNs for surveillance objectives. Each sensor is meticulously designed to detect particular physical conditions in the observed environment. By integrating a variety of specialized sensors, surveillance systems can effectively gather real-time data from the environment, enhancing situational awareness. Currently, a multitude of WSNs are being deployed in various environments such as terrestrial, subterranean, and aquatic. Various sensor types employed in WSNs for surveillance encompass:

Mechanical sensors

Mechanical sensors are utilized to detect and measure various mechanical properties and actions (Heikenfeld et al., 2018). This encompasses various sensors such as pressure, velocity, vibration, and accelerometers. The employed sensors comprise pressure sensors, specifically piezoresistive pressure sensors, capacitive pressure sensors, and optical pressure sensors.

Position and Motion Sensors

Position sensors are of significant importance in a diverse range of applications. Various position detection methods exist, encompassing basic contact sensors and advanced contact-free alternatives. Position measurement can be categorized into two types: relative, which involves using displacement sensors, and absolute, which can be further classified as linear or angular (Rakočević). The sensors employed encompass accelerometers, including resistive, capacitive, and piezoelectric variants.

Temperature Sensor

Temperature sensors detect variations in physical parameters, such as resistance or output voltage, which are indicative of alterations in temperature (Kassal et al., 2018). There exist three fundamental categories of temperature sensors, namely electromechanical, electronic, and thermo-resistive.

Humidity Sensor

Humidity refers to the quantity of water vapor in a particular substance, typically a gas. This parameter holds significance in various domains, such as patient monitoring for room air humidity, perseveration in museum exhibits, meteorological observations, soil humidity in agriculture, and process control in industrial applications (Rathinam, 2019). This parameter holds significance in various domains, such as monitoring patient humidity in healthcare settings, perseveration of exhibits in museums, meteorological observations, soil humidity in agriculture, and process control in industrial applications (Rakočević).

Image sensors

Image sensors are devices renowned for their ability to capture visual information and have been extensively utilized across diverse domains. They have a significant impact on surveillance cameras, as they can monitor designated areas, detect motion, and identify objects or individuals, thereby revolutionizing security systems. According to Zang et al., (2017), image sensors play a crucial role in facilitating the transmission and analysis of visual data by converting light signals into electronic signals. This capability empowers surveillance systems to respond promptly to threats and safeguard public and private spaces.

Chemical sensors

Chemical sensors detect the presence or concentration of specific chemical elements or compounds within a given sample (Kassal et al., 2018). A chemical sensor typically comprises a chemically sensitive film or membrane in conjunction with a transducer.

A chemical process taking place within a chemically sensitive film or membrane results in the generation of a signal at the transducer. Commonly employed mechanisms include host-guest binding, catalytic reactions, and redox processes. As per Park et al., (2015), chemical sensors possess a diverse range of applications spanning from medical diagnostics and nutritional sciences to security and the automotive industry.

Radiation sensors

Ionizing radiation comprises subatomic particles or waves with sufficient energy to induce ionization by detaching electrons from atoms or molecules. Radiation exposure induces microscopic harm to biological tissue, leading to cutaneous burns and radiation-induced illness at elevated doses, as well as the development of cancer, tumours, and genetic impairments at lower doses (Rakočević). Hence, it is crucial to monitor radiation levels in various industrial applications involving human contact with radioactive materials, as well as to prevent intentional or accidental radiation exposure to the general population. Wireless sensor networks offer an optimal infrastructure for such systems.

Biosensors

The detection of bacteria, viruses, and various molecules and molecular complexes such as proteins, enzymes, antibodies, and DNA is crucial for a diverse array of applications. Historically, the process has involved laborious chemical analysis techniques that necessitate controlled laboratory environments and utilize costly reagents and equipment (Park et al., 2015). The integration of micro-sensor technology in this field has facilitated the development of biosensors. Similar to a chemical sensor, a biosensor comprises three essential components, namely a sensitive layer, a transducer, and electronic circuitry responsible for signal processing (Kassal et al., 2018). The sensitive layer in a biosensor refers to a sensitive biological component, such as enzymes, antibodies, cell membrane receptors, or tissue slices.

WSN Communication Protocols

Wireless sensor networks utilize diverse communication protocols to facilitate effective data transmission between sensor nodes and the central base station or gateway. The protocols are tailored to address the unique requirements and limitations of WSNs, including minimizing power usage, ensuring scalability, and enhancing resilience (Ketshabetswe et al., 2019). Commonly employed communication protocols in WSNs encompass Zigbee, Bluetooth, and LoRaWAN.

References

Alyousuf, R. S. M. (2020, August). Analysis and comparison on algorithmic functions of leach protocol in wireless sensor networks [WSN]. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1349-1355). IEEE.

Aravind, D., Anupriya, S., Aarthi, A. T., & Kumar, C. S. (2017). An automatic wildlife tracking system using GPS and wireless sensor networks. In National Conference on Electronics, Communication and Computing (NCECC-2017).

Dalal, B., & Kukarni, S. (2021). Wireless sensor networks: Applications. Wireless Sensor Networks: Design, Deployment and Applications, 3.

Gomathi, N., & Jagtap, M. A. M. (2021). Smart agriculture system towards Iot based wireless sensor network. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6), 4133-4150.

Heikenfeld, J., Jajack, A., Rogers, J., Gutruf, P., Tian, L., Pan, T., … & Wang, J. (2018). Wearable sensors: modalities, challenges, and prospects. Lab on a Chip, 18(2), 217-248.

Jaladi, A. R., Khithani, K., Pawar, P., Malvi, K., & Sahoo, G. (2017). Environmental monitoring using wireless sensor networks (WSN) based on IOT. Int. Res. J. Eng. Technol, 4(1), 1371-1378.

Kassal, P., Steinberg, M. D., & Steinberg, I. M. (2018). Wireless chemical sensors and biosensors: A review. Sensors and Actua Rathinam, D. D. K., Surendran, D., Shilpa, A., Grace, A. S., & Sherin, J. (2019, March). Modern agriculture using wireless sensor network (WSN). In 2019 5th international conference on advanced computing & communication Systems (ICACCS) (pp. 515-519). IEEE. tors B: Chemical, 266, 228-245.

Ketshabetswe, L. K., Zungeru, A. M., Mangwala, M., Chuma, J. M., & Sigweni, B. (2019). Communication protocols for wireless sensor networks: A survey and comparison. Heliyon, 5(5).

Nack, F. (2010). An overview on wireless sensor networks. Institute of Computer Science (ICS), Freie Universität Berlin, 6.

Park, M. C., & Ha, O. K. (2015). Development of effective cattle health monitoring system based on biosensors. Adv. Sci. Tech, 117, 180-185.

Rahman, S. M., & Ikeura, R. (2012). International symposium on robotics and intelligent sensors 2012 (IRIS 2012) estimating and validating relationships between actual and perceived weights for lifting objects with a power assist robot: The psychophysical approach. Procedia Engineering, 41, 685-693.

Rakočević, G. Overview of Sensors for Wireless Sensor Networks. The IPSI BgD Transactions on Internet Research, 13.

Ramson, S. J., & Moni, D. J. (2017, February). Applications of wireless sensor networks—A survey. In 2017 international conference on innovations in electrical, electronics, instrumentation and media technology (ICEEIMT) (pp. 325-329). IEEE.

Rathinam, D. D. K., Surendran, D., Shilpa, A., Grace, A. S., & Sherin, J. (2019, March). Modern agriculture using wireless sensor network (WSN). In 2019 5th international conference on advanced computing & communication Systems (ICACCS) (pp. 515-519). IEEE.

Valero, E., Sivanathan, A., Bosché, F., & Abdel-Wahab, M. (2017). Analysis of construction trade worker body motions using a wearable and wireless motion sensor network. Automation in Construction, 83, 48-55.

Zhang, J., Xiang, Q., Yin, Y., Chen, C., & Luo, X. (2017). Adaptive compressed sensing for wireless image sensor networks. Multimedia Tools and Applications, 76, 4227-4242.