An Adaptive Data Gathering Scheduler Based on Data Variance for Energy Efficiency in Mobile Social Networks

Abstract

Mobile Social Networks (MSNs) consist of numerous mobile nodes that exhibit social characteristics such as gender, age, and more. Nowadays, with the rise in popularity of smartphones, these devices serve as nodes in mobile social networks, inheriting their users’ characteristics. These networks utilize a “store-carry-and-forward” mechanism for transmitting and delivering packets. New applications, like smart city monitoring, necessitate the deployment of sensors in urban areas to gather relevant information. The sensed data must be collected through mobile nodes (in this case, smartphones) and transmitted to a base station or other interested nodes. In these applications, if the data collection period is brief, smartphones will experience high energy consumption, and a significant amount of redundant data will be produced. Conversely, if the collection period is extended, some data may be lost. Several schemes have been proposed for data collection in wireless sensor networks. Unfortunately, in these schemes, nodes are either constantly in the data collection phase or gather data at fixed time intervals through simple scheduling. It appears that adaptive data collection based on the differences in the collected data could be more effective. This paper proposes a new Data Gathering Scheduler based on the Differences in collected data, DGSD. In this method, if the difference between the last two data points is low, the next time slot is set to be longer; as the difference between the last two data points increases, the time slot is shortened. Simulation results indicate that energy consumption with DGSD improves when compared to related works in discovering the same number of events.

Introduction

Mobile Social Network [1] is a type of mobile network in which nodes are wireless and have social characteristics [2, 3]. Users in such networks are people equipped with smartphones, who carry data, move around the city, and forward it to others for information distribution [4, 5]. Mobile social networks consist of social networks [6] and mobile communications networks [7]. Applications of MSN include data exchange, sharing, healthcare services [8, 9], and delivery services. MSNs are classified into two categories: web- based and decentralized. This network has three major components: content provider, mobile user, and network infrastructure. Due to the network architecture of MSN, the challenges include routing, limited content, intermittent connections, security, data gathering, etc. The data-gathering challenge means that sensed data from every sensor node in the network is aggregated and sent for processing to a base station [10, 11]. Data collection schemas are categorized into two types: (i) aggregated data and (ii) raw data. Aggregate schemas are suitable when a strong spatial association exists in the data or when the purpose is to gather summarized information, such as the average of sensed data. Raw data is collected when every piece of sensed data is uniformly important. The increasing use of smartphones, combined with the mobility of their users, sensing data through wireless sensor network nodes, internet access in most locations, and communication between sensor nodes and smartphones, will facilitate the use of smartphones to collect data in mobile social networks [12]– [13]. We propose the use of smartphones to collect data from sensor nodes opportunistically. The goal of data collection algorithms [14] is to minimize energy consumption while ensuring high event coverage. Numerous algorithms have been proposed for this purpose [15, 16]. Unfortunately, most of them are implemented in WSN without considering the social characteristics [8]– [17] of networks [18]. If data from sensor nodes is collected continuously, energy consumption [19] will reach maximum levels, and duplicate data will be collected. Data collection based on events causes coordination overhead. To address these issues, in this paper, we introduce an adaptive data gathering schema based on the differences in collected data. If the difference between the two most recently collected data points is less than the threshold, then the next data gathering interval is set to be longer; if the difference is high, the interval for data collection is set to be shorter.

The rest of the paper is organized as follows: Sect. 2 describes related works on data gathering. In Sect. 3, we present the proposed method, DGSD. Sect. 4 evaluates the data gathering efficiency of smartphones on MSNs. Finally, Sect. 5 concludes the paper.

Related Works

The data gathering challenge in mobile social networks is driven by earlier efforts. In this section, we review some of these works. In [20], data collection via smartphones in the vicinity of rarely visited regions (islands) in an urban environment was discussed. Researchers demonstrated that the size and connectivity of components influence data collection efficiency. Data collection protocols perform better on small network components around which users are consistently traveling. In [15], PEGASIS is discussed. This algorithm is a chain-based data-gathering schema for sensor networks. PEGASIS improves LEACH by utilizing only one transmission to the BS per round. In this manner, all data is aggregated before collection. Each node is connected only to a nearby neighbor, with all nodes transmitting data to the base station in turn. Tree-based data aggregation is an effective technique for applications with an aggregative nature. Researchers in [21] addressed the challenges of tree-based data collection by incorporating a secondary parent. To provide a fault-tolerant mechanism in cases where sudden node breakdown might occur due to unexpected power loss, the overlapping range of sensor nodes is utilized. In [11], a survey of data gathering techniques is provided. All these techniques aim to conserve energy at a node. In 2000, Heinzelman and colleagues proposed low-energy adaptive hierarchical clustering (LEACH) to collect data. Accordingly, the network is divided into multiple clusters, with cluster heads gathering the data and sending it to the sink. CH is selected randomly. This clustering-based protocol minimizes energy consumption in Wireless Sensor Networks [22]. Tiny aggregation (TAG) for aggregation in low-power, distributed, wireless environments was introduced in [16]. This method collects data in a tree manner, where the sink node serves as the tree root and source nodes are leaf nodes. The leaf nodes collect data and relay it to the root node or sink. Researchers in [23] presented an efficient algorithm for retransmitting lost packets by discovering alternative routes. They introduced a data gathering model for wireless sensor networks that offers a reasonable trade-off between robustness and efficiency in terms of energy savings. A data gathering approach that relies on a spanning tree provides alternative paths. Researchers in [10] proposed a hybrid unequal clustering with layering protocol (HUCL). In this method, a combination of static and dynamic clustering is employed for data gathering. In HUCL, the network is organized into layers and clusters of various sizes, and cluster heads are selected based on available energy, distance to the sink, and the number of neighbors. Once the cluster is formed, the same structure is maintained for several rounds. Data is transmitted to the sink through multi-hop layer-based communication with an in-network data compression algorithm. HUCL promotes energy balance, a good distribution of clusters, enhanced network lifetime, and mitigates the energy hole problem. In [24], a data gathering approach is introduced in which some mobile collectors visit only specific sojourn points (SPs) or data collection points instead of all sensor nodes. The mobile collectors gather data from the sensors and transfer it to the sink. The Mobile Collector Path Planning (MCPP) algorithm addresses this issue for both obstacle-free and obstacle-resisting environments. This method reduces energy consumption and enhances network lifetime compared to other data-gathering algorithms. In [25], an energy-efficient clustering routing data gathering scheme is introduced, which first develops an energy consumption model to determine the optimal number of clusters. Second, an efficient deterministic dynamic clustering scheme is designed to ensure that all cluster heads are uniformly distributed. The proposed BECDA algorithm in [26] presents a solution for effective data gathering with in-network aggregation. BECDA reduces the number of data packet transmissions from nodes to the mobile sink and focuses on symmetric aggregation functions at the cluster head. It utilizes the correlation of data within the packet to apply the aggregation function to the data generated by nodes. The method proposed in [27] describes an efficient data collection protocol for wireless sensor networks (WSNs) that combines TDMA techniques with multichannel communication. This protocol employs optimized time slot scheduling [28] in TDMA to improve throughput while decreasing energy consumption. To enhance synchronization and downward communication, the method uses a constructive interference (CI)-based flooding technique. MULE [29], MCPP [30], and MoVe [31] are noteworthy. The MULE (Mobile Ubiquitous LAN Extension) protocol employs mobile agents, such as smartphones or vehicles, to periodically collect data from stationary sensor nodes and relay it to a base station, significantly decreasing energy consumption at the sensor level. MCPP (Mobile Collector Path Planning) emphasizes optimizing the movement path of mobile collectors to minimize delay and energy use while maximizing data gathering efficiency, particularly in dense sensor environments. MoVe (Mobile Vehicle-based data collection) utilizes existing vehicular movement patterns, such as buses or taxis, to opportunistically gather sensor data without requiring additional infrastructure. These protocols represent effective solutions for mobile environments, and their inclusion in the experimental results ensures a fair and comprehensive comparison with the proposed DGSD method.

An adaptive sampling method for static WSNs using clustering and local prediction to reduce redundant transmissions. It requires centralized coordination and is unsuitable for mobile or decentralized environments [32].

A statistical sampling approach using control charts (CUSUM/EWMA) for anomaly detection in industrial data streams. It is centralized and not designed for energy-efficient or mobile scenarios [33].

Data Gathering Scheduler Based on the Difference of Collected Data (DGSD)

The proposed method consists of two parts: data gathering and identifying the time range for data collection. Each section will be explained in detail below:

The Proposed Method: Data Collection Steps

In this part of the proposed method, we address data gathering, which includes the following stages: node deployment, sending and receiving data from the environment, transmitting the data to smart nodes, and uploading the collected data to the relevant database. Each of these steps will be discussed below.

• Node Deployment: This step explains how to operate the network for effective data sensing. Based on functional needs, this issue can be categorized into regional and location coverage sections. In the proposed method, sensor nodes are randomly distributed in specific areas of a city according to the functional program’s requirements. The deployment of these nodes is conducted by humans. Each location within the sensor region is covered by k sensor nodes, where k is greater than or equal to 1.

• Sensing and Receiving Data from the Environment: In this step, the sensor nodes deployed in the city consistently or intermittently sense data according to their functional specifications, and receive and store the data based on needs.

• Sending the Data to Smart Nodes (Data Collection): After sensory nodes receive data from the environment, it should be collected by mobile nodes, which are smart mobile phones. To facilitate data collection via smart mobile phones, certain applications must be installed on them. Smart mobile phones implement a data collection protocol based on demand, utilizing the data spider algorithm. When a person passes through the intended area, their smart mobile phone sends a message to the sensor nodes. During this message flood, a tree structure for dynamic data routing forms within the nodes. The nodes transmit the data to smart mobile phones using a dynamic tree routing method. The root of the dynamic data routing tree is the node closest to the smart mobile phone.

• Sending the Collected Data to the Appropriate Databases: The data is sent to the relevant databases to prepare for actions after being collected by smart mobile phones. Sending the collected data to the appropriate databases requires routing. Various data routing methods have been proposed in mobile social networks. One routing method in MSN is epidemic routing. This method is based on distributing message transfers within the network. In the proposed method, after collecting the data via smart mobile phones, it is sent to the databases in an epidemic format.

The Proposed Method: Determining the Time Slot for Data Collection

The time range for data collection in the proposed method is identified dynamically and comparatively based on the differences in the collected data. If a significant difference is observed in the data collected by smart mobile phones, the data collection program on these devices gathers data at shorter intervals. Conversely, if the difference in the collected data is minor, the program collects data at longer intervals. This approach not only saves energy but also minimizes data loss. The key question is how we can dynamically identify the time interval for data collection. To achieve this, we need to compute the difference between the collected data. It is assumed that the data received from the environment by the sensor nodes and smart mobile phones consists of numerical values. In this article, the data necessary for functional programs pertains to temperature measurements in a particular city. Sensor nodes transmit a number to smart mobile phones after gathering data from the environment, and this number indicates the temperature in that city. In the proposed method, time is denoted by the letter t, the data collected through smart mobile phones over a time period is represented by the letter v, and the maximum temperature difference in a normal environment is represented by the letter a, which is set to 10 (a = 10). To normalize the values of v ́, which represent the data collected by smart mobile phones, the range of data variation is first determined by calculating the minimum and maximum values of v ́ within a specific time period. Each value of v ́ is then transformed into a normalized value of v in the range [0, 1] using the equation This process makes the data scalable and comparable while reducing the impact of extreme fluctuations or outliers. Normalization is crucial for optimizing the time intervals for data collection. The difference between the collected data is calculated using Eq. (1).

1

Where vt is the data collected by the smart mobile phone at time t0, and vt1 is the data collected at time t1. The time interval of data collection (∆T) represents the difference between the collected data. After calculating the difference between the data collected through smart mobile phones, the time interval for data collection will be derived from Eq. (2).

2

The difference between the collected data and the highest temperature is represented as ∆V, which indicates the possible difference in an environment. To determine the next data collection interval , a linear mapping function is used to convert the normalized difference into a valid time interval with actual units as Eq. (3) (e.g., seconds):

3

Where:

is the minimum allowable data collection interval (e.g., 10 s),

is the maximum allowable interval (e.g., 120 s).

This mapping ensures that when the variation in data is high (), the collection interval becomes shorter, enabling rapid detection of significant changes. Conversely, when data variation is low (ΔV→0), the interval is extended to conserve energy by avoiding redundant sampling. The actions performed by the sensor nodes situated in the environment are illustrated in the flowchart shown in Fig. 1.

Figure 2 of the flowchart represents the stages of activities done by smart mobile phones as collection devices.

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Fig. 1

Data collecting through sensors

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Fig. 2

Smart phones flowchart

The scalability of the DGSD algorithm in larger networks with high node density and varying mobility is a crucial aspect of its performance. As the number of nodes increases, DGSD dynamically adjusts the data collection intervals to maintain efficiency and prevent excessive energy consumption. Higher node density leads to an increase in the number of detected events since more data points are available for collection; however, this also results in greater energy consumption due to the higher frequency of transmissions.

Different DTN routing protocols are supported. Additionally, the ONE simulator provides real-time visualization of node movement and message passing, which is highly beneficial for researchers and developers in the fields of DTNs and opportunistic communications. This tool also allows the importing of real-world mobility data or other generated data, helping to simulate real-world environments. The ONE simulator generates reports on node movement, message passing, and overall pnetwork statistics that can be used to assess network performance.

In performance evaluation, energy consumption is one of the key metrics. The simulation evaluates the average energy consumption for receiving packets based on the energy consumption formulas provided in the research (Eqs. 2 and 3). Furthermore, it assesses the number of discovered events, including the influence of the number of nodes and data variations on the event discovery process. A comparison with other methods focuses on how factors such as the number of visitors and the number of packets sent affect the performance of the DGSD (Dynamic Group Scheduling and Distribution) algorithm.

Simulation parameters include simulation time, packet sizes, mobility models, and network size, which are detailed in the referenced tables (such as Table 1 of the paper). These parameters are critical for ensuring that the simulations accurately represent real-world conditions and are flexible enough to compare different scenarios. Table 1 displays the simulation parameters. To calculate the average energy consumption for receiving the packages, we use Eq. (2).

Table 1. Simulation parameters

Value	Parameter	Value	Parameter
			transmit speed of the
10,000 s	Simulation time	1Megabytes per second	interface
0.1 s	Update time slot	10 m	range of the interface
6	Number of groups	40 s	Packet production range
Shortest Path Map Based Movement	Movement model	500 kilobyte،1megabyte	Packet size
120 s	Stop time	500,500 square meters	Network size
50 min	Packet lifetime	5 megabyte	Buffer size
Bluetooth interface	Data transfer interface	4 km/h	Movement speed of groups
Simple Broadcast Interface	Type of bluetooth interface	200	Number of groups
15 min	Update time	339	Number of packages
1 J	Energy consumption for a receive	1000 J	Nodes Primary energy
10	Average difference of data	20	Number of visitors

4

Where, E_i refers to the energy of node i needed to receive m packages, and n represents the number of nodes in the network. The amount of E is calculated using Eq. (5). In this equation, e refers to the energy consumption to receive each package. The simulation settings are based on Table 1.

5

To address this, DGSD optimizes the scheduling process by extending collection intervals when data changes are minimal, thereby reducing redundant transmissions. Furthermore, the algorithm is designed to adapt to changes in node mobility, adjusting its scheduling strategy according to the movement speed of mobile data collectors. In high-mobility scenarios, DGSD shortens collection intervals to ensure timely data acquisition, while in low-mobility conditions, it extends the intervals to conserve energy. This adaptive mechanism enables DGSD to maintain an efficient balance between data accuracy, energy consumption, and network scalability, making it a robust solution for large-scale mobile social networks.

DGSD addresses specific limitations in related works such as PEGASIS by dynamically adjusting the data collection interval based on the difference in collected data, thus reducing redundant transmissions and optimizing energy efficiency. Table 2 shows Comparison of DGSD with related works.

Table 2. Comparison of DGSD with related works

Feature	DGSD	PEGASIS	MULE	MCPP	MoVe
Data Collection Mechanism	Based on the difference in collected data	Chain-based data gathering	Mobile agents (e.g., phones/vehicles) collect from static nodes	Path optimization for mobile data collectors	Uses regular vehicle movement to collect data opportunistically
Energy Consumption	Reduces energy via dynamic scheduling	More efficient than LEACH	Low sensor energy consumption, but may introduce delays	Energy-efficient through optimized collector paths	Moderate; utilizes existing mobility but lacks scheduling precision
Flexibility	Adaptive to environmental and data changes	Fixed, predefined paths	Flexible, mobility-aware	Highly flexible, based on adaptive path planning	Limited to available vehicle routes
Integration of Social Characteristics	Yes, considers user mobility and behavior	No	No	No	Indirectly, through public transport patterns
Scalability	High, due to dynamic and distributed design	Limited due to chain length	Moderate scalability, limited by mobile node coverage	High, with scalable collector coordination	High in urban areas with dense vehicle networks

Simulation and Results

We evaluate the performance of DGSD with PEGASIS [15], congestion-based [20], MCCP [29], MULE [30], and MoVe [31]. We implement these three algorithms on the ONE [34] simulator. The performance metrics are energy consumption and the number of discovered events, based on the number of nodes, packets, cell phones, and the average difference in data. The ONE Simulator is a sophisticated simulation tool designed to evaluate Delay-Tolerant Networks (DTNs) and mobile networks. It can simulate node movement through various mobility models and manage message routing between nodes.

Figure 3 illustrates the impact of the average difference between successive data values on overall energy consumption across various data collection strategies. As expected, when data variation increases, all methods show a rising trend in energy usage. However, the proposed DGSD method consistently demonstrates the lowest energy consumption across all levels of data variation. Compared to traditional methods like PEGASIS and congestion-based protocols, as well as mobile-sink-based strategies such as MULE, MCPP, and MoVe, the proposed method maintains superior energy efficiency. This superiority is due to its adaptive scheduling mechanism, which lengthens the collection interval when data variation is low and shortens it when variation is high, effectively minimizing redundant transmissions. Figure 4 illustrates the impact of the number of generated data packets on average energy consumption. As the number of packets increases, all protocols naturally experience a rise in energy usage. Nevertheless, the proposed method again outperforms all other approaches, including both static and mobile-sink-based algorithms. Notably, while protocols like PEGASIS and congestion-based schemes display a steep increase in energy consumption, the proposed method exhibits a more gradual slope, indicating better scalability. Mobile methods like MULE, MCPP, and MoVe also consume more energy under higher packet loads, whereas the DGSD algorithm effectively manages increased traffic with optimized scheduling and opportunistic data transfer, resulting in lower overall energy usage.

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Fig. 3

Average energy consumption VS average difference of data

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Fig. 4

Average energy consumption VS number of packets

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Fig. 5

Average energy consumption VS number of Nodes

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Fig. 6

Average energy consumption VS number of cellphones

Figure 5 illustrates how increasing the number of sensor nodes affects the average energy consumption across six different data collection methods. As expected, all methods show an upward trend as more nodes generate and transmit data. However, the proposed method exhibits the lowest and most gradual increase in energy consumption, reflecting its ability to scale efficiently in larger networks. In contrast, traditional protocols like PEGASIS and congestion-based approaches consume significantly more energy as node density rises. Mobile-sink-based methods (MULE, MCPP, MoVe) perform better than the static ones, but still fall behind the proposed method in terms of energy efficiency. These results validate the scalability and adaptability of the DGSD algorithm in dense deployment scenarios.

Figure 6 examines the influence of the number of mobile devices used as data mules on average energy consumption. As the number of participating smartphones increases, all protocols tend to consume more energy due to higher data exchange and coordination. However, the proposed method maintains the most efficient performance, benefiting from its adaptive scheduling and opportunistic collection strategies. While MoVe and MCPP perform relatively well by leveraging user mobility, they cannot match the energy-saving capability of the DGSD approach. The PEGASIS and congestion-based methods, which are not optimized for mobile environments, demonstrate the highest energy costs. This underscores the suitability of the proposed method for mobile social networks, where user participation can vary.

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Fig. 7

Discoveries vs. Data difference

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Fig. 8

Discoveries vs. Number of packets

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Fig. 9

Discoveries vs. Number of nodes

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Fig. 10

Discoveries vs. Number of cellphones

Figure 7 demonstrates how the variance between successive sensed data values influences the number of detected events. As the average data difference increases, all methods show improved event discovery rates. However, the proposed method clearly outperforms the others, detecting significantly more events at each level. This indicates that the DGSD algorithm effectively adapts its sampling intervals to data dynamics, ensuring that critical variations are captured in a timely manner. Mobile-sink-based methods like MCPP and MoVe perform better than static protocols, yet still lag behind the responsiveness of DGSD.

Figure 8 explores how the number of transmitted data packets impacts event detection. As the packet count increases, the opportunity to detect events also rises across all protocols. The proposed method maintains the highest discovery rate, demonstrating its ability to prioritize valuable data without overwhelming the network. Mobile approaches such as MCPP and MULE also perform well; however, the DGSD method’s adaptive and opportunistic nature enables it to detect events with fewer packets, thereby optimizing both discovery and energy efficiency.

Figure 9 illustrates the scalability of each method concerning the number of sensor nodes in the network. As node density increases, the proposed method continues to detect more events than any other technique. The gap between the proposed method and traditional protocols like PEGASIS and congestion-based schemes widens with scale, confirming the robustness of DGSD. While mobile methods show improvements over static ones, they still cannot match the consistent discovery performance of the proposed approach in dense network conditions.

Figure 10 illustrates how an increase in the number of mobile users impacts event detection. The proposed DGSD algorithm significantly benefits from additional mobile participants, leveraging their movement and contact opportunities to collect and relay event data more efficiently. MCPP and MoVe also capitalize on user mobility but are less optimized for adaptive scheduling. Unsurprisingly, static protocols perform the worst in mobile scenarios. The results highlight the effectiveness of DGSD in real-world mobile social networks where user availability and mobility fluctuate.

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Fig. 11

Comparison of proposed method with similar methods

A detailed comparative analysis in Fig. 11 was conducted to evaluate the performance of the proposed DGSD (Data Gathering Scheduler based on Data variance) against two representative and widely cited adaptive sampling strategies: ASAP (Adaptive Sampling Approach to Data Collection) proposed by Gedik et al. [2007], and the statistical monitoring method by Liu et al. [2015]. Each of these methods addresses adaptive sampling from a distinct perspective—ASAP focuses on clustering and local prediction to suppress redundant data transmission in static wireless sensor networks, while Liu et al. employ control charts (CUSUM/EWMA) to detect deviations in high-dimensional data streams for real-time monitoring in industrial settings. In contrast, DGSD is specifically designed for mobile social networks, where smartphones are used as opportunistic data collectors, and both user mobility and data variation are dynamically incorporated into the scheduling process.

As illustrated in Figure X, DGSD consistently outperforms both baseline methods in terms of energy efficiency. Across increasing numbers of data packets and varying degrees of data volatility, DGSD demonstrates significantly lower energy consumption, thanks to its lightweight, fully distributed design that avoids fixed infrastructure, centralized computation, or static routing structures. This allows DGSD to intelligently skip redundant data transmissions when data variation is low, and react quickly to changes when variation increases. In terms of detection performance, DGSD also shows a clear advantage by identifying more events with fewer packets, which reflects the effectiveness of its content-based scheduling mechanism. While ASAP achieves moderate event detection through local model predictions, its reliance on predefined clusters limits its responsiveness in dynamic environments. Liu’s approach, though statistically sensitive, incurs higher energy costs and lacks integration with spatial-temporal dynamics or mobility, making it less efficient in opportunistic and user-driven networks.

Further, DGSD demonstrates superior scalability and adaptability. When the network size increases or the number of participating mobile devices grows, DGSD maintains high performance levels with minimal degradation. This is in contrast to ASAP and Liu et al., which suffer from increased coordination overhead and centralized dependencies. Overall, DGSD combines the strengths of adaptive sampling with a novel, mobility-aware, and data-centric strategy, positioning it as a robust solution for energy-efficient data collection in modern, real-world mobile environments. Its ability to respond flexibly to both data behavior and human movement patterns enables it to outperform traditional methods that were not originally designed for dynamic, decentralized systems.

Conclusion

Data collection is one of the most important challenges in mobile social networks. During data collection, the data sensed by different sensors are integrated at intermediate nodes and ultimately transferred to a data sink for further processing. Utilizing smart mobile phones as data collection devices and leveraging human movement in urban areas is a novel approach to gathering data from mobile social networks. One crucial criterion in designing efficient data collection methods is energy constraints and maximizing the lifetime of the network. In previous data collection methods, the time interval was either considered too short, resulting in high energy consumption and repetitive data receipt, or too long, leading to data loss because this interval was fixed. In this article, we employ a time interval identification method for data collection based on the variance among the collected data in mobile social networks to reduce energy consumption in smart mobile phones. The proposed method determines the time range dynamically and comparatively based on the differences among the collected data. In this method, when the difference is small and the disparity between the collected data is significant, data collection occurs at shorter intervals. Our suggested method will reduce energy consumption in smart nodes while maintaining a high number of successfully delivered data instances. Results from the simulation environment indicate that our proposed method consumed less energy compared to the two previous methods based on overpopulation and the visitation of smart mobile phones. For further research, one could integrate the proposed method into the current project alongside related previous works. This implies that, in addition to considering the differences in the data, we can also factor in the influences of overpopulation and the number of visitors when identifying time intervals for data collection.

Acknowledgements

The authors are thankful to the reviewers and editor for providing valuable inputs to improve the quality and present format of this manuscript.

Author Contributions

All authors developed the whole work, discussed the results, and contributed to the e final manuscript. The authors read and approved the final manuscript.

Funding

No Funding.

Data Availability

Data will be made available on request.

Code Availability

Code availability available upon request.

Declarations

Ethical Approval

I have approved there is no conflict of interest for this study.

Consent to Participate

Not applicable.

Conflict of Interest

We certify that there is no actual or potential conflict of interest in relation to this article.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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