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The Wireless Sensor Network: Optimizing Watchdog Selection

Summary

The selected article – Hasan and Mouftah (2017) – presents three models for optimizing watchdog selection within a wireless sensor network (WSN). The authors note that given the rapidly expanding WSN application areas and heightened security concerns in extensive deployment, it has become critical to enhance watchdog identification in the network. They define a watchdog system as a security improvement that commissions several sensor nodes to monitor their single hop neighbors and report findings to the base station (BS). While these security operations are essential in combating distrust, the authors lament, they consume additional resources and reduce the system’s efficiency and effectiveness. Thus, Hasan and Mouftah (2017) suggest a watchdog selection optimization model that focuses on the nodes’ coverage and overlapping issues. Notably, the overlapping of nodes is inevitable due to a wireless network’s propagation characteristics. The authors believe their recommendations will ensure limited resource consumption by networks without compromising their security features.

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Hasan and Mouftah (2017) also use case studies for realistic network topologies to evaluate the suggested improvement models. They note that full coverage occurs when each existing node is either working as a watchdog or at least one watchdog node in the wireless sensor network monitors it. The article has five parts. In the first section – the introduction – Hasan and Mouftah (2017) provide background information about the paper and its intended purpose. In section two, they present the respective parameters for the three models, which include a limited overlapping (LO) model, a full coverage heuristic (FCH) model, and a linear resource minimization (LRM) model. In the third part, Hasan and Mouftah (2017) optimize the models and present and discuss numerical results from realistic random topologies’ case studies in section four. Notably, the authors did not design the network. Instead, they used the “Generator, Sensor Network” (GenSeN) tool to create three WSN topologies for an area measuring 40 meters by 40 meters. Lastly, the authors offer their concluding remarks in the fifth section of the paper, where they note that the LO and FCH models were the best for high and reduced sensing ranges, respectively.

Description of the Wireless Sensor Network from the Paper

The bulk of the Hasan and Mouftah (2017) paper discuss the three models for watchdog selection optimization in WSN, with a small portion of it – section IV – examining the outcomes of three realistic random topologies created using the GenSeN tool, which they adopt from Camilo et al. (2007). The three topologies that Hasan and Mouftah (2017) generated were unique in that they overlapped in two critical ways. First, one could select two of them and group them into one as they had the same sensor node number. However, this selection will mean that the network topologies had different ranges (one’s range was six meters, and the other’s was eight meters). The basis of the other possible combination was the range; that is, one could select two of the network topologies and put them into a group for their range similarity. However, for the second combination, the issue was that the nodes would have a different number of nodes; one had 100 nodes, and the other had 70 nodes.

After generating the three network topologies, Hasan and Mouftah (2017) compared the three optimization models’ performance. They implemented the by running the MATLAB-CPLEX integrated solver on a core i3 desktop computer whose central processing unit utilizes speeds of up to 3.30 gigahertz and a random access memory of 4 gigabytes. The results showed that, ironically, the LRM model, which was supposed to minimize resources’ utilization, actually demanded even more. They also found that the resources that the FCH model required relative to the LO model depended on the sensing ranges. The shorter the sensing range, the lesser the FCH model’s resource requirements than the LO model’s, and vice versa. In terms of comparative coverage, all three models offer full coverage. The authors concluded that the LO model was the best than the other two for increased sensing ranges. For higher sensing ranges, FCH was better. LMR was the most resource-intensive model in any sensing range.

Personal Analysis of the Wireless Sensor Network

Without a doubt, Hasan and Mouftah (2017) present three attractive models for optimizing the selection of watchdogs in a wireless sensor network. The authors use an appropriate theoretical methodology and mathematical derivations to select the three models. However, they do not conduct a real-life assessment of an existing wireless sensor network. Instead, they use a tool developed by Camilo et al. (2007) to generate the network topologies upon which they base their conclusion. The network topologies the authors generated assume perfect situations within a network. In real life, networks have imperfect rangers and overlap in different ways. Buildings and tall trees also block or jam networks, affecting their overall efficiency.

Most importantly, real-life wireless sensor networks cover extensive ranges and contain innumerable nodes. Therefore, while I agree with the authors regarding their design and mathematical model, a more realistic investigation is needed for better outcomes. More research is required to identify the real-life consequences of limiting sensor overlapping, achieving full coverage without overlapping, and resource minimization in non-linear terms on watchdog selection.

References

Camilo, T., Silva, J. S., Rodrigues, A., & Boavida, F. (2007). Gensen: A topology generator for real wireless sensor networks deployment. In IFIP International Workshop on Software Technolgies for Embedded and Ubiquitous Systems (pp. 436-445). Springer, Berlin, Heidelberg. Web.

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Hasan, M. M., & Mouftah, H. T. (2016). Optimization of watchdog selection in wireless sensor networks. IEEE Wireless Communications Letters, 6(1), 94-97. Web.

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StudyCorgi. "The Wireless Sensor Network: Optimizing Watchdog Selection." September 29, 2022. https://studycorgi.com/the-wireless-sensor-network-optimizing-watchdog-selection/.

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StudyCorgi. 2022. "The Wireless Sensor Network: Optimizing Watchdog Selection." September 29, 2022. https://studycorgi.com/the-wireless-sensor-network-optimizing-watchdog-selection/.

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StudyCorgi. (2022) 'The Wireless Sensor Network: Optimizing Watchdog Selection'. 29 September.

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