Journal of Computer Science Applications and
Information Technology

Abstract: Wireless Sensor Networks (WSNs) have emerged as a pivotal technology, enabling a wide array of applications across diverse domains. This study aimed to evaluate the performance of different aspects within WSNs using the Wireless Power Management (WPM) method. The analysis focused on crucial metrics, including Coverage and Connectivity, Data Accuracy and Reliability, Latency and Throughput, and Fault Tolerance and Robustness.The results revealed that Wireless Communication excelled in Coverage and Connectivity, while Data Aggregation exhibited remarkable performance in Data Accuracy and Reliability. Routing Protocols demonstrated strong capabilities in Latency and Throughput, and the Fault Tolerance category received a significant boost. Notably, the Internet of Things (IoT) registered exceptional performance across all metrics, particularly in Coverage and Connectivity.The evaluation process involved assigning equal weightages to each performance metric, ensuring a balanced consideration. The Weighted Normalized Decision Matrix indicated that IoT attained a perfect score, signifying optimal performance across all considered metrics. Conversely, Routing Protocols exhibited slightly lower scores, particularly in Latency and Throughput and Fault Tolerance categories.The Preference Score and Rank analysis provided further insights into the relative performance of each aspect. Wireless Communication secured the highest Preference Score and ranked first, indicating its superior performance. Data Aggregation and Internet of Things (IoT) followed closely, while Routing Protocols and Fault Tolerance attained lower scores and ranks, suggesting areas for improvement within the WSNs under the WPM method.The findings underscore the strengths and potential areas for enhancement within WSNs when employing the WPM method. Wireless Communication and IoT demonstrated exceptional performance, particularly in Coverage and Connectivity, indicating their suitability for applications prioritizing these metrics. However, aspects such as Routing Protocols and Fault Tolerance exhibited relatively lower performance, highlighting the need for further optimization.It is important to note that the WPM method is one of several approaches to evaluating the performance of WSNs. Future research could explore alternative methods or integrate additional metrics to gain a more comprehensive understanding of WSNs’ performance in diverse scenarios and applications. Overall, this study provides valuable insights into the performance of WSNs using the WPM method, serving as a foundation for further research and optimization efforts, facilitating the widespread adoption and unlocking the full potential of WSNs across various domains.

Keywords: Wireless Communication, Data Aggregation, Routing Protocols, Fault Tolerance and Internet of Things (IoT).

Wireless Sensor Networks (WSNs) have gained significant global interest in recent years, particularly with advancements in micro-Electro-Mechanical Systems (MEMS). The widespread adoption of technology has enabled the creation of smart sensors, which are compact and equipped with limited processing and computing capabilities. Moreover, these sensors are more cost effective compared to their traditional counterparts. Operating within these networks, sensor nodes can detect and gather information from the surrounding environment, making localized decisions based on this data before transmitting it to end-users [1]. Since the turn of the third millennium, Wireless Sensor Networks (WSNs) have garnered increasing attention from both industry and research communities. WSNs, which consist of individual nodes or computers dispersed throughout an environment to facilitate communication between themselves and the surrounding surroundings, offer a unique perspective. They form a network of nodes that collaborate and control operations within their domain.On one hand, WSNs introduce novel applications and open up new potential markets. On the other hand, they present challenges that demand innovative paradigms in design due to inherent constraints. Operating under limited energy, processing capabilities, and communication functions, WSNs require a cross-layer design approach. This approach typically involves the distributed handling of signal/ data processing, medium access, and control and communication protocols, necessitating a holistic consideration across these layers [2]. Sensor nodes are often at risk and frequently experience critical conditions when deployed in various environments. These vulnerabilities stem from hardware complications, physical damage, or depletion of energy supply, resulting in node failure. Compared to traditional assumptions in wireless networks, the rate of node failures is expected to be higher. To mitigate this, sensor network protocols should swiftly detect failures to uphold overall network performance, even in the face of multiple failures. Ethical design principles emphasize the necessity of establishing alternative routes to adapt to changing circumstances. Different environments necessitate varying levels of fault tolerance, highlighting the importance of tailoring strategies accordingly [3].

In essence, the sensor node typically consists of three main components: a compact design encompassing essential elements such as data collection from the physical environment, a sensory subsystem for reception, local processing, and storage of data, as well as a processing subsystem and wireless support for communication and data exchange. Furthermore, the device requires energy for its scheduled operations, which is supplied by a power source. However, the energy source is often constrained, commonly being a battery with limited capacity. Recharging the battery can pose challenges, especially in environments where nodes face conflicts or practical limitations [4]. The Internet, when viewed as a physical network, undergoes constant evolution across various sectors such as healthcare, defense, transportation, entertainment, emergency response, national security, and urban infrastructure. This evolving technology holds immense potential for diverse applications, which is particularly intriguing due to the significant shift in foundational assumptions guiding its development.Historically, research primarily focused on distributed systems built upon wired connections, with abundant energy resources, lacking real-time capabilities, and relying on conventional user interfaces like screens and mice. These systems operated on fixed resource allocations, treating each node as equally significant and independent.In stark contrast, modern wireless sensor networks operate in dynamic environments, characterized by limited power, real-time demands, and the utilization of sensors and actuators as interfaces. These networks exhibit emergent behavior due to the aggregation of resources and emphasize the significance of node locations. Additionally, they often utilize low-capacity devices, challenging conventional solutions and necessitating innovative approaches [5]. Simultaneously, Wireless Sensor Networks (WSNs) in the healthcare sector encounter numerous technical hurdles that need to be addressed to realize their full potential. These challenges encompass restricted network capacity, processing and memory limitations, as well as limited energy resources. These constraints are inherent to all WSNs but are exacerbated in healthcare applications. Unlike other domains, healthcare apps demand heightened system reliability, quality of service, and stringent privacy and security measures [6]. A recent original study titled “Wireless Ad-Hoc Sensor Networks” has emerged, focusing on their significant impact on time, economy, and lifestyle transformation. These networks pose various challenges in system development, introducing new concepts and optimization issues. Fundamental problems such as placement, deployment, and monitoring arise, alongside applications that heavily rely on the information they provide. Quality of service, particularly in monitoring, plays a crucial role in ensuring satisfactory coverage and answering pertinent questions [7]. Ten years ago, the initial papers on Wireless Sensor Networks (WSN) discussed their potential applications in diverse environments such as forests, waterways, buildings, defense, and even on the battlefield, for monitoring purposes. These early writings clearly highlighted the technological promise they held. However, over the past decade, progress has not been as rapid as initially anticipated. WSN technology, characterized by affordable costs, powerful microcontrollers, and single-chip radio transceivers, has experienced a hype cycle largely driven by its increasing availability [8]. Over the recent years, the research community has shown significant interest in wireless sensor networks (WSNs) due to the multitude of theoretical and practical hurdles they present. This growing research interest stems from WSNs’ capability to gather data from the surroundings through numerous sensor nodes deployed at scale. WSNs have been instrumental in exploring novel applications like processing sensed data and transmitting it remotely to designated locations. These networks are predominantly utilized in scenarios where bandwidth is limited, and latency can be tolerated, spanning across both civilian and military domains, including environmental and health monitoring [9]. Low-power micro sensors, actuators, and embedded systems are deployed across distributed applications with accompanying apps and radios. This setup facilitates environmental monitoring, smart spaces, medical applications, and wireless connectivity for various purposes, including precision agriculture. Applied sensor networks, although relatively compact, encompass a diverse array of sensors, all transmitting signals to a central processing unit where data processing takes place [10]. One of the key research areas in sensor network networking revolves around intriguing challenges, with a primary focus on the power limitations faced by individual nodes. Despite this challenge, the continuous evolution of sensor networks is crucial. These networks typically rely on battery-powered nodes and are deployed across diverse terrains. To address the power constraints, future deployments are anticipated to incorporate alternative energy sources such as solar power and other environmental resources. For instance, sensor nodes could be utilized in climate monitoring within canopies. However, a significant energy drain arises from radio communication, highlighting the need for efficient energy management strategies [11]. Wireless Sensor Networks (WSNs) find utility across diverse sectors like military, homeland security, healthcare, environmental monitoring, agriculture, and manufacturing. Envisioning their future deployment involves the creation of large-scale networks comprising thousands of small, self-organizing sensor nodes communicating wirelessly. However, ensuring security within such networks poses a significant challenge. Unlike regular desktop computers, sensor nodes have restricted processing power, storage, and energy reserves, as well as limited bandwidth for wireless connections [12]. A remote subtropical lake, secluded from human activity by its location, remains largely untouched each year. Despite its isolation, it faces the impact of hurricanes, nestled amidst ancient Cypress forests. Scientists specializing in limnology, botany, and climatology regard its fertile ecosystem as an ideal setting for conducting extensive long-term studies on environmental systems. During a tornado, debris may plummet into the lake, reaching depths of one meter in its 4.5-meter depth.

Additionally, the lake experiences rapid purification when rainfall doubles its volume in a single day [13]. Wireless Sensor Networks (WSNs) have emerged as a focal point of research in recent times due to their efficient design and implementation. This surge is partly attributable to the evolution of sensor technology, facilitating connectivity across various applications. This connectivity offers a vast spectrum of operational possibilities, bridging the gap between the physical and virtual realms. By interconnecting numerous small sensors through networking, WSNs enable the acquisition of data on events that were previously inaccessible via conventional means. Over the forthcoming years, advancements in microfabrication and sensor technology are anticipated to maintain node manufacturing costs at a steady level. This, in turn, will pave the way for the proliferation of reduced-cost wireless sensor deployments, leading to the expansion of networks and the eventual proliferation of nodes [14]. For sensor networks, in many scenarios, energy resources are constrained due to the prevalent usage of battery-powered nodes. These nodes often require manual intervention for battery recharge or replacement, which can be challenging or even impossible in certain situations. Consequently, when a node exhausts its energy, it ceases to perform data sensing and routing tasks, leading to a reduction in network coverage and connectivity. Therefore, efficient utilization of energy resources is imperative for optimal operation of sensor networks [15].

Cell phones and satellites are utilized for a plethora of purposes within mobile communication. They facilitate the connection of various devices, enable the functioning of IoT, aid in traffic management, and support wireless environmental monitoring sensor networks. Additionally, they contribute to weather forecasting and television broadcasting through wireless and satellite communication channels. Thus, the applications of communication through these means span across diverse fields.

Data are sought out, gathered, and condensed into a report format. This process, known as data collection, serves distinct business goals, aiding companies in achieving their objectives and facilitating analysis of processes and human behavior at any magnitude.

There are several options for routing protocols, such as EGP, BGP, RIP, RIPng, HELLO, OSPF, ICMP/Router Discovery, and IS-IS. These protocols enable communication between network devices. Additionally, if you need to modify management information for a particular element or wish to view it, you can utilize the SNMP protocol.

Fault tolerance refers to the capability of a system to persist through disruptions or failures without interruption. Whether it’s a computer system, cloud infrastructure, or a network setup, fault tolerance ensures uninterrupted operation even if individual components crash. This resilience remains steadfast whether the system operates as a cluster or a network, exemplifying its ability to function regardless of adverse circumstances.

The Internet of Things, or IoT, embodies a network of interconnected devices, encompassing both hardware and cloud-based systems. It enables seamless communication among these devices, facilitating technology that fosters connectivity

Coverage and connectivity are crucial aspects of Wireless Sensor Networks (WSNs), determining their performance. These criteria prioritize prolonged operational duration and the extent to which sensors cover their designated area of interest, while also addressing factors that may obscure or hinder sensor functionality.

Data precision refers to the accuracy of data values, emphasizing their correctness. It revolves around ensuring that data is free from errors and reflects the real-world scenario accurately, thereby guaranteeing the precise representation of companies. On the other hand, data integrity pertains to the consistency and reliability of data over its lifespan. It signifies the trustworthiness and dependability of the data, ensuring its stability and reliability over time.

The factors affecting both latency and performance, whether in network or computer contexts, were discussed previously. In terms of computer speed, latency refers to the amount of time it takes to process data, impacting how much data can be transmitted within a given timeframe.

In the suggested study, WPM and PROMETHEE emerged as the most suitable methods for determining the optimal underground mining method for mineral deposits. These techniques are noted for their high precision in estimation and consistently yield optimal outcomes. Decision-making methodologies across diverse disciplines such as engineering and technology have been extensively explored by researchers. Notably, Nicholas introduced a pioneering technique in underground mining systems examination in 1981 [16].
The weighted sum model (WSM) is straightforward and commonly applied in multi-criteria decision-making systems. In this approach, arithmetic methods relying on weighted averages are utilized to replace attribute values. Decision makers assign weights to each attribute’s measured value based on their relative importance. These weights are then multiplied with the measured values for each attribute to calculate an evaluation score. One advantage of the WSM method is its ability to provide a proportional linear transformation of the raw data [17]. How much weight does each criterion carry? The decision-maker has the authority to prioritize them according to importance, striving for optimal decisions whenever possible. In essence, this entails assessing the significance of each criterion in the decision-making process. By scrutinizing changes in the current weighting, we can ascertain the actual ranking of alternatives. This sensitivity analysis delves into the subtle intricacies of the criteria’s weights, revealing how even slight adjustments can significantly impact the ranking of alternatives. Furthermore, in a separate scenario, we explore the significance of various performance measures, employing a similar approach to prioritize criteria and rank alternatives one decision at a time. These two distinct types of sensitivity analyses will be further examined in subsequent sections [18]. In 1922, Bridgman introduced the concept of the weighted production method (WPM). This method has been extensively tested and proven effective for multi-criteria decisionmaking scenarios. It involves assigning weights to different criteria, enabling a systematic approach to decision-making even when faced with numerous factors to consider.Numerous studies have demonstrated the reliability of WPM across various domains, including choosing the right food in a boarding house or selecting the most suitable option from multiple criteria. Researchers have reported success in utilizing WPM for decision-making tasks in learning platforms, as well as in applications such as Goosebumps detection and similar endeavors.This study primarily focuses on applying WPM to decision-making processes, particularly aimed at assisting individuals facing challenges in making decisions.
The goal is to leverage WPM’s capabilities to address such difficulties effectively. Furthermore, efforts have been made to integrate WPM into internet-based systems for calculation and implementation, enhancing its accessibility and usability [19]. Traditional data mining techniques are employed to achieve optimal performance and have been extensively studied in the context of wireless sensor network (WSN) environments. However, they face limitations due to ongoing research on WSNs. In response, an Adaptive Data Processing Framework has been proposed by Izadi and others. They introduced a fuzzy-based approach for data fusion in WSNs, which selectively collects and integrates actual data values. This selective approach reduces the processing load on the base station (BS), distinguishing and combining data more effectively [20].A wavelet transform, employed for timefrequency domain analysis, operates by organizing data into packets within the frequency domain. Furthermore, it enables segregation within the time domain, effectively isolating signals from background noise like impulse interference in Time Division Multiplexing (TDM) or disturbances in Orthogonal Frequency Division Multiplexing (OFDM) reception. In scenarios where multiple codes are compromised, Wavelet Packet Modulation (WPM) excels in excluding problematic codes and structuring packets accordingly. Substituting WPM for OFDM in selected frequency ranges enhances balance properties, rendering it a compelling alternative for multicarrier communication [21]. The Simple Additive Weighting (SAW) method and the Weighted Product Method (WPM) are both techniques used in decision making processes, particularly in the context of cyber-attack modeling.

SAW involves assigning weights to different criteria and then calculating a score for each option by multiplying the weights by the performance of each criterion and summing them up. On the other hand, WPM computes a similar score by multiplying the performance values of each option across all criteria, raising them to the corresponding weights, and then taking their product. In terms of pros and cons, SAW is relatively straightforward to understand and implement, making it accessible for users. However, it may struggle with nonlinear relationships between criteria and can be cumbersome when defining appropriate scales for evaluation. WPM, while more complex, can capture nonlinear relationships better but may be harder to use due to its intricacies.Both methods encounter difficulties in handling characteristics and scoring across different criteria, especially in WPM where nonlinear relationships can complicate scoring. To address these challenges, a new evaluation strategy is proposed. This strategy integrates decision-making processes and enhances the modeling assessment of cyber-attacks by introducing a new set of evaluation criteria inspired by Schmitt’s criteria.In summary, while SAW and WPM offer different approaches to decision-making, they both have their strengths and weaknesses in the context of cyber-attack modeling. The proposed strategy aims to overcome some of these limitations and improve the accuracy and efficiency of the assessment process [22]. Creating a fresh text input for system settings involves significant effort and cost due to its intricate nature. To streamline this process, a systematic approach needs to be devised. This entails crafting a prototype and subjecting it to real-world testing with actual users. Despite the time-consuming nature of mastering text input methods, thorough testing within system environments is crucial. Nonetheless, conducting longitudinal user studies poses considerable challenges [23]. Despite the numerous benefits, Industry 5.0 prioritizes enhancing security, efficiency, and adaptability within various sectors. Smart Manufacturing, a cornerstone of Industry 5.0, revolves around the integration of manufacturing processes, warehousing, mechanical equipment, and facility centers, alongside diverse elements like distribution networks. This integration fosters interconnectedness throughout the entire supply chain, promoting intellectual synergy and enabling a cohesive network [24]. These four methods share some similarities but also exhibit differences. For instance, while STA operates as a mathematical programming method, the other three methods are heuristic in nature. STA employs a restricted sequential optimization approach, ensuring solutions within specified test specifications. It guarantees full compliance but is limited in flexibility. Conversely, the other three methods tailor test specifications to specific objectives, treating them as guidelines rather than strict rules. Tests are optimized to achieve target values, allowing for flexibility until certain thresholds are exceeded. However, for WDM, WPM, and MPI, while potential solutions can be identified, meeting all test specifications may not be feasible [25]. Essentially, the method involves abstaining from touching the screen directly, instead relying on detection of maximum touch proximity for interaction. Various strategies and systems are employed for opportunities and monitoring. This includes inputting text via audio feedback and utilizing 6-bit Braille encoding for single and double character input.
It facilitates manual input for individuals proficient in Braille, particularly benefiting blind users. Through a longitudinal study involving participants, Perk input demonstrated notably faster and accurate input compared to iPhone’s Voiceover, especially for onehanded operation. A case study assessing expert performance revealed an average session speed of 17.56 words per minute (WPM) with a low error rate of 0.14% using one-handed input [26]. Alternative and Augmentative Communication (AAC) is a method designed to assist individuals with speech impairments in communicating effectively. With a focus on improving communication accessibility, AAC seeks to explore various means to facilitate interaction. In the United States, approximately 2 million people encounter challenges in communication due to various factors. AAC encompasses electronic mediums and devices, including artificial speech output, to aid in communication. However, a notable challenge with communication devices is their slower pace compared to natural speech [27]. Various options have been suggested, rendering the selection of robots a significant and challenging endeavor. This task is not confined to a single instance and has been tackled through several proposed methods. One such approach, the Polygons Area Method (PAM), is employed in this study to address the multi-attribute nature of the robot selection problem. PAM facilitates decision-making by utilizing Radar Charts derived from robot attributes, with the maximum polygon area serving as a criterion [28]. As an expansion of cyberspace, these gadgets liberate us from the confines of traditional computers, paving the way for a future saturated with readily available information. However, the advancement of Pervasive Computing encounters a hurdle in the form of text input barriers. For instance, a recent study clearly demonstrated the usefulness of tablet computers in households. Nevertheless, the inefficiency of text input methods on tablets, such as for chatting, emailing, or entering URLs, has made typical applications more challenging to use [29]. In this paper, we delve into forthcoming examinations aimed at fostering a deeper engagement between researchers and our subject matter. We adopt a systematic approach to explore the realm of mid-air text entry, scrutinizing its design landscape. Our study furnishes significant contributions in terms of enhanced clarity and comprehensibility through the integration of high-resolution visuals tailored to the pertinent usage contexts. We investigate three distinct mid-air text entry techniques, offering valuable insights into their assessment and presentation modalities. These approaches draw inspiration from varied domains, encompassing adaptations of successful methodologies such as game controller text entry, mobile phone text entry, and established mid-air text entry methods. Additionally, we outline prospective advancements in mid-air text entry techniques, laying a robust foundation for future comparisons and evaluations [30].

The provided table illustrates the performance metrics of various aspects within Wireless Sensor Networks (WSNs) using the Wireless Power Management (WPM) method. Each category, including Coverage and Connectivity, Data Accuracy and Reliability, Latency and Throughput, and Fault Tolerance and Robustness, is assessed with numerical values indicating performance levels.Wireless Communication boasts a high score in Coverage and Connectivity at 102.25, while Data Aggregation excels in Data Accuracy and Reliability with a score of 132.16. Routing Protocols demonstrate strong Latency and Throughput capabilities, scoring 66.15. Fault Tolerance category sees a significant boost with Fault Tolerance itself obtaining a score of 105.56. Lastly, Internet of Things (IoT) registers remarkable performance across all metrics, particularly in Coverage and Connectivity with a score of 108.85. These metrics provide insights into the interoperability and efficiency of WSNs under the WPM method.

its Table 2 presents the performance values of various aspects within Wireless Sensor Networks (WSNs) using the Wireless Power Management (WPM) method. Each category, including Wireless Communication, Data Aggregation, Routing Protocols, Fault Tolerance, and Internet of Things (IoT), is evaluated with performance values indicating their effectiveness. For instance, IoT achieves the highest overall performance value of 1.00000, reflecting its superior performance across all evaluated metrics, while Routing Protocols show a relatively lower performance value, particularly in Latency and Throughput and Fault Tolerance categories. These values provide a nuanced understanding of WSNs’ operational efficiency under the WPM method.

Figure 1 displays the performance metrics of Wireless Sensor Networks (WSNs) using the Wireless Power Management (WPM) method. It evaluates Coverage, Connectivity, Data Accuracy, Reliability, Latency, Throughput, Fault Tolerance, and Robustness. These metrics offer insights into the interoperability and effectiveness of WSNs under the WPM method.

Figure 2 illustrates the performance values of Wireless Sensor Networks (WSNs) under the Wireless Power Management (WPM) method. Each category, including Wireless Communication, Data Aggregation, Routing Protocols, Fault Tolerance, and Internet of Things (IoT), is assigned a performance value, reflecting their effectiveness in various aspects.

Table 3 delineates the weightages assigned to different performance metrics within the Wireless Power Management (WPM) method. Each metric, such as Coverage and Connectivity, Data Accuracy and Reliability, Latency and Throughput, and Fault Tolerance and Robustness, is assigned an equal weight of 0.25. This uniform distribution of weights ensures a balanced consideration of all aspects when evaluating the performance of Wireless Sensor Networks (WSNs) under the WPM method, allowing for a comprehensive assessment of their operational efficiency and effectiveness.

Table 4 depicts the Weighted Normalized Decision Matrix within the Wireless Power Management (WPM) method, showcasing the aggregated performance metrics of Wireless Sensor Networks (WSNs). Each aspect, including Wireless Communication, Data Aggregation, Routing Protocols, Fault Tolerance, and Internet of Things (IoT), is evaluated based on their weighted normalized values. For instance, IoT attains a perfect score of 1.00000, indicating its optimal performance across all considered metrics. Meanwhile, Routing Protocols exhibit slightly lower scores, particularly in Latency and Throughput and Fault Tolerance categories. This matrix enables a comprehensive comparison of WSNs’ performance under the WPM method, facilitating informed decision-making for system optimization and enhancement.

Figure 3 presents the Weighted Normalized Decision Matrix under the Wireless Power Management (WPM) method for Wireless Sensor Networks (WSNs). Each aspect, such as Wireless Communication, Data Aggregation, Routing Protocols, Fault Tolerance, and Internet of Things (IoT), is represented by its respective weighted normalized values, offering insights into their performance and effectiveness.

Figure 4 illustrates the Preference Score and corresponding Rank of various aspects within Wireless Sensor Networks (WSNs) under the Wireless Power Management (WPM) method. Wireless Communication achieves the highest Preference Score and ranks first, followed by Data Aggregation, Internet of Things (IoT), Routing Protocols, and Fault Tolerance, respectively.

Table 5 outlines the Preference Score and Rank of various aspects within Wireless Sensor Networks (WSNs) using the Wireless Power Management (WPM) method. Each aspect, including Wireless Communication, Data Aggregation, Routing Protocols, Fault Tolerance, and Internet of Things (IoT), is assigned a Preference Score and corresponding rank based on its performance. Wireless Communication secures the highest Preference Score and ranks first, indicating its superior performance, while Fault Tolerance and Routing Protocols attain lower scores and ranks, suggesting areas for improvement within the WSNs under the WPM method.

Figure 5 presents the ranks of different aspects within Wireless Sensor Networks (WSNs) using the Wireless Power Management (WPM) method. Each aspect, including Wireless Communication, Data Aggregation, Routing Protocols, Fault Tolerance, and Internet of Things (IoT), is assigned a rank based on its performance. Wireless Communication secures the top rank, followed by Data Aggregation, Internet of Things (IoT), Routing Protocols, and Fault Tolerance, respectively, offering insights into their relative effectiveness within the WSNs under the WPM method.

Wireless Sensor Networks (WSNs) have garnered significant attention due to their diverse applications and potential to revolutionize various domains. This study aimed to evaluate the performance of different aspects within WSNs using the Wireless Power Management (WPM) method. The analysis encompassed crucial metrics such as Coverage and Connectivity, Data Accuracy and Reliability, Latency and Throughput, and Fault Tolerance and Robustness.The results demonstrated that Wireless Communication excelled in Coverage and Connectivity, achieving a high score of 102.25, while Data Aggregation exhibited remarkable performance in Data Accuracy and Reliability with a score of 132.16. Routing Protocols demonstrated strong capabilities in Latency and Throughput, scoring 66.15. Additionally, the Fault Tolerance category received a significant boost, with the Fault Tolerance aspect itself obtaining a score of 105.56. Notably, the Internet of Things (IoT) registered remarkable performance across all metrics, particularly in Coverage and Connectivity with a score of 108.85.The evaluation process involved assigning equal weightages to each performance metric, ensuring a balanced consideration of all aspects. The Weighted Normalized Decision Matrix revealed that IoT attained a perfect score of 1.00000, indicating its optimal performance across all considered metrics. Conversely, Routing Protocols exhibited slightly lower scores, particularly in Latency and Throughput and Fault Tolerance categories.The Preference Score and Rank analysis provided further insights into the relative performance of each aspect. Wireless Communication secured the highest Preference Score and ranked first, indicating its superior performance. Data Aggregation and Internet of Things (IoT) followed closely, while Routing Protocols and Fault Tolerance attained lower scores and ranks, suggesting areas for improvement within the WSNs under the WPM method.The findings of this study underscore the strengths and potential areas for enhancement within WSNs when employing the WPM method. Wireless Communication and IoT demonstrated exceptional performance, particularly in Coverage and Connectivity, indicating their suitability for applications that prioritize these metrics. However, aspects such as Routing Protocols and Fault Tolerance exhibited relatively lower performance, highlighting the need for further optimization to enhance their effectiveness within WSNs.It is important to note that the WPM method is just one of several approaches to evaluating the performance of WSNs. Future research could explore alternative methods or integrate additional metrics to gain a more comprehensive understanding of WSNs’ performance in diverse scenarios and applications.Overall, this study provides valuable insights into the performance of WSNs using the WPM method, serving as a foundation for further research and optimization efforts. By identifying strengths and areas for improvement, researchers and industry professionals can make informed decisions to enhance the efficiency, reliability, and adaptability of WSNs, facilitating their widespread adoption and unlocking their full potential across various domains.

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