The need to enhance information processing has often been deemed as the main reason for the emergence of Wireless Sensor Networks (WSN). Since most information is relayed and received via the internet, WSN were preceded by wireless communication. Later, WSN emerged and superseded the effectiveness levels availed by wireless communication (Mahmood, Seah & Welch, 2015). According to Rashid and Rehmani (2016), WSN has taken over electronic communication due to the uniqueness of its sensor node element. Unlike any other communication and information processing platform, WSN’s unique sensing ability draws its capacity to sense environmental conditions using the sensor nodes. This ability makes information processing easier because the dedicated sensors can organize data and execute central storage. According to Al Ameen, Liu and Kwak (2012), WNS emerged owing to the need to augment the performance of military applications. Since then, WSN usage has permeated numerous consumer and industrial applications. An accurate understanding of these networks can be achieved by taking into account their functionality.
The Functionality of WSN’s
The functionality of WSN’s relies on the spatial and dispersed distribution of their dedicated sensors. As explained by Rault, Bouabdallah, and Challal (2014), this means that they need wireless connectivity to function. However, this is not the only functionality necessity. Rashid and Rehmani (2016) further enlightened that the networks also need impulsive construction of networks to help them transport collected sensor information without wired connections. More importantly, the spatial distribution aspect ensures that their autonomous sensors can monitor environmental and physical conditions like pressure, temperature, and sound (Guo et al., 2014). After acquiring the relevant data, the sensors cooperate and transmit individual data via wireless networks to a location of choice. The efficiency of the networks stems from the fact that their autonomous sensor nodes can be deployed using minimal infrastructure. Nonetheless, the networks exhibit several operational constraints.
Mostly, operational limitations associated with WSN’s have to do with optimization. Explaining the phenomenon, Rault, Bouabdallah, and Challal (2014) argued that while deploying their autonomous nodes, environments often present challenges that are linked to link quality, mobility, event coverage, robustness, and connectivity. However, coverage issues exert more significant impacts when it comes to optimization. The layout/coverage issues are also most common because users have to place and distribute the nodes appropriately for effective performance. However, this is seldom the case. According to Guo et al. (2014), users often fail when it comes to placement of the autonomous nodes, which minimizes connectivity and creates less room for the deployment of the required number of the autonomous nodes. Issues related to sensor placement and location hinder optimization by reducing the ability of the deployed notes as it relates to environment detection.
Physical Resource Limitations
Batteries used for WSN’s place constraints on their effectiveness. According to Rashid and Rehmani (2016), the effectiveness and operational duration of the autonomous nodes are directly related to power supply. Such an assertion strongly suggests that WSN’s are often unable to deliver the expected results because WSN’s handlers face the issue of battery life. Rault, Bouabdallah, and Challal (2014) affirmed this argument is asserting that the handler’s choices at it relate to the network’s physical layer exert significant influences on the energy consumption of the deployed nodes. When this happens, the devices automatically designate higher-level protocols. Eventually, the protocol changes combined with power limitations place embargos on WSN’s computational capabilities. This places constraints on the device’s memory size and affects data storage by the autonomous nodes. Such a perspective introduces the need to take into account the main components of the network’s sensory/autonomous nodes.
Sensor Nodes Main Components
Power Source Options
WSN’s autonomous/sensory nodes rely on primary batteries as the chief power source. As explained by Han et al. (2016), this is because it has proven difficult to use mains supply on deployment sites. Unfortunately, enhancements in WSN utilization has not been matched by improvements in battery technology. This explains why over-reliance on batteries has often been deemed as the most significant constraint for these networks. Notably, attempts have been made to counter this limitation through the deployment of energy-harvesting methodologies. However, the resulting small-sized devices developed have been unable to provide direct power for the sensory/autonomous nodes. Still, experts have been able to develop secondary elements for storing energy. According to Guo et al. (2014), these elements preserve energy using chemical bonds that allow for recharging. Despite the improvements, primary batteries are still WSN’s chief power source.
The communication range for the networks has not improved significantly over the years. According to Han et al. (2016), range improvements have presented a hefty challenge because improving communication range would require significant developments as it relates to WSN’s power source. However, the range for indoor mechanisms has undergone several improvements. Rashid and Rehmani (2016) pointed out that such systems have been made possible by the fact that indoor systems like smoke detectors and residential systems involve applications that require fewer data. Moreover, experts have been able to augment communication ranges for some outdoor systems like weather systems, municipal lighting, smart meters, and asset tracking. In cases where experts are required to ensure long-range communication, they have to overlook battery life and deploy more sensors than is often the case.
Examples of Existing Models
One of the WSN models that are currently being used is the Ultra-narrowband model. For example, the Sigfox company has been using an Ultra-narrowband model, which was founded upon the need to enhance communication range by leaning on the positive influences associated with the Internet of Things (IoT) (Rault, Bouabdallah & Challal, 2014). Notably, this model also relies on re-transmission mechanisms, which ensure that WSN’s analyze, transmit and store all acquired information. An increasing number of experts also prefer the systems model. According to Han et al. (2016), the efficiency of this WSN model stems from the fact that the behavior of the entire system is understood based on its parameters. As such, the model ensures that sensory nodes capture, analyze, and transmit information after incorporating physical constant and design-time parameters.
Al Ameen, M., Liu, J., & Kwak, K. (2012). Security and privacy issues in wireless sensor networks for healthcare applications. Journal of medical systems, 36(1), 93-101.
Guo, S., Zhang, H., Zhong, Z., Chen, J., Cao, Q., & He, T. (2014). Detecting faulty nodes with data errors for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 10(3), 40.
Han, G., Jiang, J., Zhang, C., Duong, T. Q., Guizani, M., & Karagiannidis, G. K. (2016). A survey on mobile anchor node assisted localization in wireless sensor networks. IEEE Communications Surveys & Tutorials, 18(3), 2220-2243.
Mahmood, M. A., Seah, W. K., & Welch, I. (2015). Reliability in wireless sensor networks: A survey and challenges ahead. Computer Networks, 79, 166-187.
Rashid, B., & Rehmani, M. H. (2016). Applications of wireless sensor networks for urban areas: A survey. Journal of Network And Computer Applications, 60, 192-219.
Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104-122.