AI-Based Decision Support System Optimizing Wireless Sensor Networks for Consumer Electronics in E-Commerce

Abstract

The purpose of this study is to investigate the potential of AI technology in developing a decision support system that can improve the effectiveness of wireless sensor networks (WSNs) in e-commerce, specifically in enhancing the features of consumer electronics. This research project is focused on optimizing wireless sensor networks for e-commerce consumer electronics by incorporating AI-based decision support systems. The primary objective of this study is to enhance energy efficiency and performance in online shopping platforms. Various algorithms and methodologies are proposed and assessed, including Adaptive Clustering, the Path Selection Algorithm, Fuzzy Logic-Controlled Energy Management, the Genetic Algorithm for Resource Allocation, and Deep Sleep Scheduling. These techniques improve network efficiency and reduce power consumption in e-commerce applications. The study demonstrates that integrating AI in consumer electronics can result in a remarkable 40% increase in energy efficiency. Comparative analyses conducted through simulations and real-world assessments indicate that the proposed methodology outperforms traditional techniques by 35%. This research underscores the vital role of AI in enhancing network performance and energy efficiency in e-commerce. The results suggest that implementing AI-driven strategies in wireless sensor networks for consumer electronics can significantly improve online shopping experiences. AI-based decision support systems can optimize wireless sensor networks for consumer electronics, improving energy efficiency and network performance on online shopping platforms.

1. Introduction

The rapid growth of e-commerce has fundamentally altered consumer behaviors and business operations, creating a massive demand for reliable data transmission and processing. This shift has highlighted the role of consumer electronics not just as devices for personal use but as integral components in more extensive, interconnected systems like online shopping platforms.

Wireless sensor networks (WSNs) can be described as a wirelessly connected network of sensors gathering information to be used by a central controller. These networks feature many sensor nodes and consumer electronics, and WSNs must balance power and utility [1]. Online stores rely on Wireless sensor networks to collect and distribute data over a wireless connection, which is essential for processing orders. Wireless sensor networks with AI integration are intriguing and more viable than simple WSNs [2]. Using artificial intelligence (AI), WSNs can optimize energy usage by learning, adapting, and making decisions. As a result of this upgrade, both network speed and data transmission efficiency will improve. Consumer electronics have progressed from simple devices to complex systems with self-monitoring and self-control capabilities. Due to this integration, procedures have become faster and more efficient [3]. The research mentioned offers a significant addition to online shopping in the context of electronic commerce by establishing algorithms specially adapted to the needs of WSNs. These algorithms are based on artificial intelligence concepts. These algorithms seek to reduce power consumption while maintaining data transmission speed. These protocols are crucial for e-commerce applications involving consumer devices as they handle challenges like balancing energy saving with operational efficiency, which is critical for the effective operation of certain programs [3]. This study seeks to not only enhance current technology but also reconsider the function of WSNs in the context of electronic commerce. The objective is to considerably reduce energy consumption while ensuring error-free and efficient data transfer. The AI-powered strategy aims to accomplish energy utilization that is well-suited to the shifting needs of online shopping by distributing resources in WSNs as efficiently as feasible [4]. These solutions are centered on developing and implementing sophisticated frameworks for regulating power consumption in WSNs linked to consumer devices. Techniques like machine learning, optimization, and predictive modeling are employed to create dynamic protocols that can adjust to the ever-evolving needs of the online shopping industry [5]. WSNs are in high demand in the ever-changing consumer electronics sector, notably e-commerce. This project will propose a novel technique using AI, Intelligent Resource Management (IRM), and dynamic adaptation to enhance consumer electronics WSN performance and energy efficiency. This study considers each node’s energy restrictions to determine the optimum network path for real-time data.

WSNs can be applied to various tasks in the field of agriculture. WSNs allow farmers to monitor and control their farms from anywhere in the world through the usage of the zigbee wireless sensor network, reducing the risks faced by the farmers and putting them much more at ease [6]. WSNs provide essential services to e-commerce retailers, especially in the field of logistics. Energy-efficient WSNs can ensure quick and efficient delivery service even in the face of obstacles such as traffic congestion [7]. Additionally, they also provide essential services in the field of cold food chains in the food industry. Wireless sensor networks can be used to identify Critical Control Points (CCPs) based on the temperature data, allowing the companies to more effectively chose between frozen and cold storage, enabling them to obtain a greater degree of efficiency in their operations [8].

Internet shopping has expanded tremendously throughout the digital revolution and is essential to any economy. Consumer technology advancements have fueled this trend by making online markets more accessible. These advances have had significant results. Wireless Sensor Networks (WSNs), which enable real-time data transmission and resource management, are critical to this integration. Wireless sensor networks (WSNs) are most suitable for this purpose and can potentially improve the efficacy of e-commerce systems [7]. These networks differ from others in that they can gather, analyze, and disseminate data across several nodes.

It is difficult to achieve energy efficiency with wireless sensor networks, as well as performance. The performance of the sensor network is based on its data transmission: both the quality of the data transmitted and the quality of the connections by which the data is transferred. However, continuous data transmission can result in increased energy consumption. As a result, sensors require replacement with greater frequency. In order to reduce the energy consumption of the network, WSNs will often switch off data transmission during periods of low activity, enabling them to preserve battery power. However, disruptions to the sensor network can result in the quality of the transmission being affected, as previous paths to the cluster head are no longer available. Wireless sensor networks have been negatively affected by an inability to balance these two conflicting goals [9].

One method to balance the two conflicting goals is through the usage of Machine Learning (ML)-based algorithms. There have been applications of such algorithms in similarly complex systems, such as fault detection in gearboxes [10]. By allowing the network to adjust which sensors are switched off and which routes are selected, such an algorithm could enable the reconciliation of these seemingly conflicting goals.

AI and machine learning have provided a new perspective on optimizing wireless sensor networks (WSNs). AI-powered systems may improve wireless sensor networks (WSNs) by alerting management to changing network conditions, calculating energy usage, and reallocating resources [4]. Resource management is critical because consumer gadgets are significant in e-commerce, characterized by frequent and quick trading.

Advanced WSN optimization approaches use various AI techniques, resulting in low-power, self-regulating intelligent networks. This course focuses on fuzzy logic, genetic algorithms, and reinforcement learning. Algorithms such as GARA (Genetic Algorithm for Resource Allocation) and ACRL (Adaptive Clustering for Reinforcement Learning) have significantly increased network lifespan and data transmission efficiency. These findings have been reported. Fuzzy logic-controlled energy management (FLEMA) and deep sleep scheduling (DSS) have improved WSN energy management. Modern consumer gadgets optimize these technologies to improve energy efficiency while maintaining functionality.

This study’s AI-based decision support system, which employs sophisticated machine learning models and optimization methodologies, can potentially enhance wireless sensor networks (WSNs), notably in e-commerce consumer electronics. We achieve this by using an optimization approach. This technology improves energy efficiency and wireless sensor network performance by combining cutting-edge computational algorithms with thorough data analysis. As consumer gadgets become more reliable and efficient, growing eco-friendly e-commerce will be possible. This innovative approach addresses WSN energy management challenges using AI to govern network resources. In the authors’ opinion, this is ideal for internet and consumer electronics purchases.

This technology for consumer electronics-linked WSNs uses the Path Selection Algorithm for efficient routing. The network parameters for each node are specified, and a route effectiveness statistic is created [11]. This and the following iterations continue until the operation is almost complete. We can select the option that transfers data effectively by comparing each option according to this statistic. This method ensures that the network operates efficiently while consuming little power by adjusting the routing table in response to changes in the network via dynamic optimization. Consumer electronics researchers will uncover fresh information in the study’s conclusions, especially about online shopping. AI-driven strategies may help internet service providers (ISPs) boost network speed and control energy use. WSNs require resource management and adaptive clustering as consumer electronics grow more complex [12]. Ultrafast e-commerce requires quick responses to energy and traffic fluctuations. Due to these variables, the suggested technique reduces power consumption and improves data transfer. WSNs in consumer electronics must balance data transmission speeds and power consumption. The proposed solution uses FLEMA and GARA to maximize energy utilization and allocation. These algorithms prioritize energy efficiency and resource allocation. Machine Learning Deep Sleep Scheduling (ML-DSS) may adjust node sleep schedules to network conditions to improve energy efficiency. This theoretical article on AI in wireless sensor networks advances past studies. According to this paper, AI may enhance network efficiency and flexibility. It guides consumer electronics makers in implementing AI-driven approaches in WSNs, which might boost energy efficiency and network performance [13]. The report provides more significant research on AI in consumer electronics, particularly online purchasing. This allows for deeper study into AI algorithms’ practical applications, which might lead to greener and more efficient consumer gadgets. E-commerce has advanced using an AI-optimized consumer electronics WSN [4]. AI, IRM, and dynamic adaptation may improve WSNs and reduce energy usage. As a digital product, WSNs will enhance consumer electronics.

Consumer electronics use this technology to convey data effectively while utilizing little power. ACRL uses reinforcement learning to prolong the lifespan of consumer electronics networks and minimize energy consumption. ACRL modifies cluster design dynamically for efficient data flow. Nodes may learn new talents and behave better [14]. SIEO controls consumer electronics WSN power utilization via swarm intelligence algorithms. Like natural swarming, this technology utilizes less energy while maintaining network performance, showing how biological notions may be applied to consumer electronics to allow decentralized decision making and self-organization. GARA optimizes WSN resource allocation using evolutionary methods. Consumer gadgets must adapt since they use the network [15]. GARA simulates natural selection to allocate resources and extend network life. FLEMA improves consumer electronics by using fuzzy logic control for adaptive energy management in WSNs. The severe but balanced approach to increasing efficiency and lowering emissions by limiting power usage addresses changeable network circumstances. The Particle Swarm Optimization Protocol (PSOP) improves power consumption in WSNs used in consumer electronics [10,16]. Imitating the cooperative tendencies of swarming particles improves data flow and resource allocation while decreasing the network power burden. Deep sleep scheduling uses machine learning to enhance the sleep schedules of WSN nodes in consumer electronics. Automatic sleep cycles have been established for historical and present network data structures [17]. These provide for power savings without interfering with node activation. These systems use algorithms to enhance consumer electronics by lowering power consumption or increasing network dependability. Consumer electronics are assisting e-commerce operations in becoming more effective and sustainable by incorporating cutting-edge technologies into WSNs [18].

Table 1. Comparative analysis of methods for energy-efficient WSNs in e-commerce.

Method	Energy Consumption	Data Transmission	Network Overhead	Scalability	Adaptability
Dynamic Energy-Aware Routing	Low	Efficient	Minimal	High	High
Adaptive Clustering RL	Low	Optimal	Moderate	High	High
Swarm Intelligence Optimization	Moderate	Moderate	Low	High	Moderate
Genetic Algorithm Resource Alloc.	Low	Efficient	Minimal	High	High
Fuzzy Logic Energy Management	Moderate	Adaptive	Low	Moderate	High
Particle Swarm Optimization	Low	Efficient	Minimal	High	High
ML-driven Dynamic Sleep Schedule	Low	Adaptive	Low	High	High
Ant Colony Optimization Routing	Low	Optimal	Minimal	High	High
Q-Learning Data Transmission	Low	Efficient	Low	High	High
Adaptive Network Traffic Pred.	Moderate	Adaptive	Low	High	High

shows a comparative analysis of the different methods of producing energy-efficient WSNs in e-commerce. The methods were compared on their level of energy consumption, the quality of data transmissions, the amount of excessive network resources used or the network overhead, the extent to which the method can be applied to different scales without affecting overall quality of the model, and the extent to which the model can adapt to different scenarios. The model’s performance in each of these categories was broadly subdivided into a series of subcategories of Low, Minimal, Moderate, and High (in order of least to greatest values), while the data transmission quality was ranked as being Moderate, Efficient, Adaptive, or Optimal, in order of least to greatest. Of the various methods listed in Table 1, Ant Colony Optimization Routing demonstrates the most optimal properties, with low energy consumption, optimal data transmission, minimal network overhead, and high scalability and adaptability. Meanwhile, Swarm Intelligence Optimization has the least optimal properties, with moderate energy consumption, moderate data transmission, low network overhead, high scalability, and moderate adaptability.

The strategies listed in Table 1 will aid in the energy efficiency of WSNs used for online shopping. The complexity of the algorithms is measured using the values of their energy consumption, network overhead, scalability, adaptability (Low, Minimal, Moderate, High), and data transmission (Moderate, Efficient, Adaptive, or Optimal). The study aims to evaluate network resilience, energy efficiency, scalability, flexibility, and durability. When applied to networks’ primary nodes, all techniques achieve respectable standards for energy consumption.

Setting up energy-efficient wireless sensor networks for online shopping requires parameter input, technique selection, implementation, and performance evaluation, as shown in Figure 1. Figure 1 showcases the loop by which an optimal method is selected. The optimal method is selected based upon a variety of operating characteristics, including the energy efficiency, network lifetime, and data transmission rates. Optimization is achieved by comparing the results of the current method against the optimum method. If the current method contains similar or greater degrees of efficacy as the previous method, then it is selected as the optimum method and compared against the next method. In this manner, an optimal energy-efficient wireless sensor network is formulated.

The goal of this research study is to improve the usage of wireless sensor networks for consumer electronics in e-commerce through the introduction of AI-based decision support systems. This research introduces pre-existing innovative techniques to improve the efficiency and performance of wireless sensor networks employed in online shopping platforms, including the Path Selection Algorithm, Adaptive Clustering, the Genetic Algorithm for Resource Allocation, Fuzzy Logic-Controlled Energy Management, and Deep Sleep Scheduling. After comparing these novel methods against the traditional approaches, this research establishes that the usage of AI-based techniques results in significant improvements in energy efficiency. It highlights AI’s essential role in transforming wireless sensor networks for consumer electronics in e-commerce and enhancing online shopping experiences.

The research is broken down as follows. In Section 2, the Materials and Methods section, we outline the methodology employed and the algorithms that are applied; Section 3 includes the results of the research conducted, comparing different methods of optimizing wireless sensor networks; Section 4 contains a detailed discussion of the implications of the research and its potential applications; finally, Section 5 contains some concluding remarks.

2. Materials and Methods

An innovative technique for optimizing WSNs, critical to the consumer electronics sector, mainly e-commerce, blends AI, IRM (Inverse Rank Mean), and MSN (Maximum Signal-to-noise Ratio). This technique saves electricity and improves network efficiency, making it vital. This method is incredibly complicated, from designing unique network settings for each node to thoroughly evaluating the route’s usefulness, including energy consumption, data transmission speed, path stability, and other factors. Our method relies on the Path Selection Algorithm [19]. This dynamic algorithm varies with network history. It updates the routing database and optimizes routes for speed and efficiency. Controlling WSN energy and data utilization requires adaptive clustering. This process builds and optimizes node clusters for network architecture and data flow. Depending on data speed and energy use, dynamically adding and removing clusters increases network flexibility. A genetic algorithm for resource allocation will boost system capacity. Evolutionary techniques for adaptive resource allocation boost network efficiency and reduce power use. Effective resource allocation may improve network performance and sustainability [19,20]. GARA’s resource identification, allocation, and real-time network data-driven modification guarantee this. FLEMA dynamically controls WSN energy usage. Combining FLEMA and GARA provides optimal results. This algorithm excels at responding swiftly to changing network traffic, making real-time network dynamics choices, and minimizing energy usage to balance efficiency and conservation. Energy management flexibility is essential for network efficiency in the fast-changing consumer electronics industry [21]. Furthermore, machine learning deep sleep scheduling (ML-DSS) is a novel wireless sensor network energy management method. ML-DSS adjusts network node sleep times using machine learning to preserve energy and maintain responsiveness. To implement this technique, energy resource management must be proactive. It controls sleep cycles by monitoring network performance and data. This reduces power usage by only activating nodes when required. The suggested approach uses cutting-edge algorithms and methodologies due to the increasing complexity and interconnection of digital devices and networks. Featuring consumer electronics technology is a credit to the industry. It redefines e-commerce network WSN optimization and delivers continuous performance [22]. It uses resource allocation, dynamic flexibility, and intelligent decision making.

The proposed technique improves key WSN capabilities over prior attempts to optimize them for consumer devices, such as for online shopping applications. It highlights a device’s capacity to operate in complex networks beyond its present technological use. This tech innovation is better than others because multidimensional WSN technology solves WSN problems in the fast-paced e-commerce business [23]. The system includes resource allocation, adaptive clustering, sleep scheduling, and efficient routing. ML supports these statements. This comprehensive approach makes consumer electronics part of complex and intelligent networks. The real-time optimization of these networks signals a watershed moment in using AI in consumer electronics and e-commerce. The three key elements driving the proposed technique for decreasing power consumption and improving network performance are artificial intelligence, intelligent resource management, and dynamic adaptability. The algorithm continues, as depicted in Figure 2, to arrive at and compute a route efficiency measure that considers energy consumption, data transmission speed, and path stability. This measure evaluates the practicality of each alternative route to facilitate real-time information flow throughout the network while accounting for the nodes’ energy restrictions.

This comprehensive and rigorous WSN routing solution benefits consumer electronics online companies by regulating energy costs and network performance. Figure 2 demonstrates how consumer electronics are becoming more complex and sophisticated. Algorithm 1 optimizes the path selection by utilizing path performance data (Performance_Data) gathered based on the efficiency of the routes. A cut-off threshold of routes is devised based upon the performance data, and the routes are optimized based on the gathered data. The performance of the routes is assessed (“performance router ← Assess_Route(Selected_Route):”) based upon the calculated energy metrics, with low energy routes being prioritized.

Based on the performance data of the routes that have been calculated, a feedback loop is generated, whereby the routes are updated to improve their efficiency, their efficiency results in improved metrics, and these metrics are used to further optimize the routes, until further changes to the routes result in an increase in energy consumption.

The node network parameters used are the coordinates of the node in the network and the energy consumption of the node.

The algorithms are numbered for clarity. Each bracketed number represents a step in the algorithm.

A.

The Algorithm of Path Selection:

Algorithm 1 Route Optimization in WSN for Consumer Electronics

Input: Network Nodes, Data Transmission Speed, Path Stability (path stability is the likelihood that a particular path between any two nodes exists and continues to exist for the foreseeable future) [24].

Output: Optimized Routing for Energy Efficiency and Performance

Begin

// 1. Initialize Network Parameters

For each node i in WSN, Do

Pi ← (xi, yi, Ei) (1)

// where (xi, yi) are coordinates, Ei is the initial energy

End For

// 2. Define Route Efficiency Metric

Function Route Efficiency (Energy Consumption,

Data_Transmission_Speed, Path_Stability) (2)

Return Energy_Consumption/(Data_Transmission_Speed × Path_Stability) (3)

End Function

// 3. Calculate Efficiency for All Possible Routes

For each route k in WSN, Do

Efficiency[routek] ← RouteEfficiency(route) (4)

End For

// 4. Select Route

Selected_Route ← argmax (Efficiency[routek]) (5)

// 5. Monitor Energy Consumption

For each node i in WSN, Do

Current_Energy[i] ← Initial_Energy[i] − Energy_Used[i] (6)

End For

// 6. Update Route Efficiency

For each route k in WSN, Do

Updated_Efficiency[routek]←Σ(Energy_Consumed[routek])/Σ(Data_

Transmitted[routek]) (7)

End For

// 7. Dynamic Optimization

For each route k in WSN, Do

New_Energy_Ratio ← New_Energy_Consumed/Data_Transmitted (8)

Delta_Energy ← Old_Energy_Ratio − New_Energy_Ratio (9)

If Delta_Energy > Threshold Then (10)

Update the Routing Table for the route

End If

End For

// 8. Check for Changes in Network Conditions

Network_Change ← Check_Conditions (Current_State) (11)

// 9. Re-evaluate Routes

If Network_Change Then

For each route k in WSN, Do

Re_Evaluated_Efficiency[routek]←RouteEfficiency (route) (12)

End For

End If

// 10. Repeat Optimization Process

Repeat Optimization for Current_Network_State (13)

// 11. Implement Routing Decision

Implement_Route (Selected_Route) (14)

// 12. Monitor Route Performance

For each route k in WSN, Do

performance router ← Assess_Route (Selected_Route) (15)

End For

// 13. Adjust Threshold Values

Adjusted_Threshold ← f (Performance_Data) (16)

// 14. Energy Management

Manage_Energy (Route_Set) (17)

// 15. Feedback Loop

Feedback ← Collect_Data (performance router) (18)

// 16. Refine Efficiency Metric

Refined_Route_Efficiency←Refine_Metric (Feedback) (19)

End

The most effective method for improving data transmission efficiency and lowering power consumption in consumer devices is to dynamically redirect packets depending on the traffic patterns and energy levels shown in Figure 3. This strategy is critical in consumer electronics, where preserving energy efficiency and ensuring fast data transmission rates are critical. The reconfiguration and grouping of network nodes are crucial to the system’s operation.

This comprehensive and adaptive approach is crucial in consumer electronics, particularly for applications like e-commerce, which rely heavily on WSNs, as shown in Figure 4. It ensures that the network remains efficient and responsive, capable of adjusting to varying energy and data demands, thus enhancing overall network performance and energy efficiency. This method reflects the growing sophistication of consumer electronics, no longer just passive devices but active components in complex, adaptive networks.

Network Topology for the purposes of the algorithm is taken to be the schematic description of the layout of the nodes and their connections [25], while Node Data refers to the data generated by the specific node. Here, Network Traffic refers to the amount of data being transmitted between nodes in the network. These are taken as the input variables for the algorithms.

Algorithm 2 provides the algorithm for the optimal clustering of sensor nodes. The optimal cluster is one that produces the highest level of performance, measured as a function of the amount of data produced by the node cluster divided by its energy consumption. The performance over time of the node cluster is used to identify the performance trends, which are then used to update the targets for rewards. Iterative processes like this one give rise to network clusters, which expand and evolve, as seen in Figure 5.

B.

The Algorithm for Node Cluster Management

Algorithm 2 Algorithm Node Cluster Management in WSN for Consumer Electronics

Input: Network Topology, Node Data, Network Traffic

Output: Optimally Managed Node Clusters

Begin

//Initialize Node Clusters

Apply Clustering Algorithm based on Network Topology

//1. Define Cluster Efficiency Metrics

Function ClusterEfficiency (Energy_Consumption, Data_Throughput) (20)

Return Energy_Consumption/Data_Throughput (21)

End Function

//2. Monitor Node Energy Levels

For each node i in WSN, Do

Node_Energy[i] ← Current_Energy_Level (22)

End For

//3. Setup Q-learning Parameters

α, γ ← Initialize Learning Rate, Discount Factor

//4. Calculate the Reward for Each Cluster

For each cluster c in WSN, Do

Reward[c] ← Data_Transmission_Efficiency[c] × Energy_Efficiency[c] (23)

End For

//5. Adjust Clusters based on Energy and Data Flow

Adjust_Clusters(Data_Flow, Energy_Levels) (24)

//6. Update Q-values for Reinforcement Learning

Q(s, a) ← (1 − α) * Q(s, a) + α * [r + γ * max(Q(s’, a’))] (25)

//7. Evaluate Cluster Performance

Performance ← Evaluate (ClusterEfficiency) (26)

//8. Dynamic Cluster Reformation

Reformation ← Function (Q-values, Rewards) (27)

//9. Monitor Network Traffic

Network_Traffic ← Monitor_Current_Traffic() (28)

//10. Energy Management within Clusters

Implement_Energy_Saving_Measures(Clusters) (29)

//11. Feedback from Cluster Performance

Adjust_Parameters←Feedback (Cluster_Performance) (30)

//12. Real-time Adaptation

Adaptation ← Continuous_Monitoring_And_Adjustment (31)

//13. Balance Load Across Clusters

Load_Balance(Clusters)

//14. Periodic Re-evaluation of Clusters

Reevaluate_Clusters(Long_Term_Data) (32)

//15. Update Reward Mechanism

Updated_Reward ← Update_Based_On_Performance_Trends (33)

//16. Refine Clustering Algorithm

Refined_Algorithm ← Refine_Using_Historical_Data (34)

End

C.

The Algorithm for Allocation of Resources in the Network

Algorithm 3 explains genetic algorithm for resource allocation in WSN. This method moves beyond traditional, static network management methods, embracing dynamic, adaptable, and intelligent systems. Such strategies are becoming increasingly vital in consumer electronics, where devices are not just standalone units but are part of interconnected and intelligent systems, requiring sophisticated management techniques to operate optimally.

Algorithm 3 Algorithm: Genetic Algorithm for Resource Allocation in WSN

Input: Network Resources, Node Data

Output: Optimized Resource Allocation Strategy

Begin

//1. Define Network Resources

R ← {bandwidth, energy, computational_power} (35)

//2. Initialize Population with Strategies

Population ← {Strategy1, Strategy2, …, Strategyn} (36)

//Each Strategy = (RI,1, ri,2, …, RI,m) (37)

//3. Define Fitness Function

Function Fitness (Strategy)

Return Energy_Consumedi/Data_Transmittedi (38)

End Function

//4. Evaluate Initial Population

For each strategy in Population, Do

Fitness_Scores[i] ← Fitness (Strategy) (39)

End For

//5. Selection Process

Selected ← Select_Top_Performers(Fitness_Scores, n) (40)

//6. Crossover

For each pair in Selected Do

Offspring ← Crossover (pair. Parent1, pair. Parent2)

New_Population ← New_Population ∪ Offspring (41)

End For

//7. Mutation

For each Offspring in New_Population, Do

Mutated_Offspring ← Mutate (Offspring, Mutation_Rate) (42)

End For

//8. Evaluate New Generation

For each Strategy in New_Population, Do

New_Fitness_Scores[Strategy] ← Fitness(Strategy) (43)

End For

Ranked_New_Population←Sort(New_Population, New_Fitness_Scores) (44)

//9. Update Allocation Strategies

Best_Strategy ← Ranked_New_Population[0]

Implement_Strategy(Best_Strategy) (45)

//10. Monitor Network Performance

Performance ← Monitor (Network, Best_Strategy) (46)

//11. Adapt to Changing Network Conditions

Modified_Strategy ← Modify (Best_Strategy, Real_Time_Data) (47)

Adjusted_Population ← Adjust (Population, Modified_Strategy) (48)

//12. Repeat Genetic Algorithm Cycle

Repeat_Cycle (Adjusted_Population) (49)

End

The dynamic distribution of node resources is shown in Figure 6. It uses evolutionary algorithms for adaptive resource allocation depending on energy needs to maximize network efficiency while minimizing power consumption. GARA is a technique that answers the resource management problem in wireless sensor networks. It is built using genetic algorithms. It also describes the network’s resources and populates a population with various resource distribution strategies. FLEMA uses fuzzy logic to regulate network energy usage. It makes judgments in real time based on imperfect and changing information, controlling energy consumption while keeping the network running smoothly. High traffic levels indicate high energy consumption, whereas low energy levels indicate fewer active nodes.

Figure 7 displays the adaptive regulation of energy use. FLEMA uses fuzzy logic to dynamically alter energy consumption to match efficiency and conservation goals in response to changing network circumstances.

Figure 8 depicts how machine learning determines node sleep durations. Using data and network information, the ML-DSS approach adjusts sleep cycles in real time. The system may be able to save energy while being responsive.

FLEMA is a novel energy operations system management approach that combines fuzzy logic-controlled energy management with DSS. A real-time data-reactive energy management system may consider node power use and traffic volume, allowing it to function optimally. This method enables flexible energy control and ensures network operation under all circumstances. ML-DSS uses machine learning and a dynamic sleep scheduling algorithm to plan network node sleep patterns.

Input_Features = Historical_Data + Current_Network_Condtions

(50)

This equation combines historical data and current network conditions to determine the optimal sleep timing for network nodes.

The regression model for predicting sleep duration is

Regression: Output = Sleep Duration

(51)

This equation indicates that the regression model’s output is the predicted sleep duration for a network node. This information optimizes energy consumption by adjusting the node’s active and sleep states.

Efficient Energy Metric for Routing (ERM):

Reinforcement Method for Energy Use and Data Transmission Learning Model for Clusters

D.

The Algorithm to Optimize Energy Use

Algorithm 4 explains unified WSN optimization for consumer electronics. These equations and algorithms constitute the heart of the proposed technique, carefully intended to enhance energy efficiency inside e-commerce-focused wireless sensor networks via dynamic adaptation, resource allocation, and intelligent decision making, ensuring continuous network performance. The mutually beneficial interaction of the recommended strategy’s components makes it appealing. The Path Selection Algorithm is responsible for generating the best possible data routing; ACRL is accountable for cluster formation and energy consumption optimization; GARA is responsible for resource management; FLEMA is responsible for dynamic energy consumption regulation; and ML-DSS is accountable for node activity cycles. In consumer electronics, particularly for e-commerce applications, the proposed technique integrates various advanced algorithms and approaches to maximize network efficiency while minimizing power consumption. This method is highly relevant in today’s consumer electronics, where devices and networks are increasingly interconnected and intelligent. GARA utilizes genetic algorithms for adaptive resource allocation based on energy needs. GARA manages WSN resources by exploring the network for available resources and distributing them using different techniques. FLEMA controls network power consumption. It regulates power use and makes real-time choices based on erroneous and changing data to ensure network efficiency. FLEMA tailors its energy usage to the demands of each network to save money and enhance efficiency. ML-DSS uses machine learning to plan node sleep cycles. This strategy lowers power usage while maintaining system responsiveness. It modifies sleep cycles depending on network conditions and data. An efficient energy metric for routing models may improve energy consumption and data transmission efficiency. It improves cluster data processing and routing in response to network changes, increasing productivity and reducing energy consumption. This is an example of an inheritance algorithm. Fitness is critical to the energy efficiency of consumer electronics networks. Based on the statistics, this function minimizes power consumption. An algorithm in a fuzzy logic-based power management system determines the best power use under heavy network traffic to meet demand. For better rest period scheduling, the model recommends that each node sleep for a certain number of hours daily to achieve optimum energy savings and network performance.

Algorithm 4 Unified WSN Optimization for Consumer Electronics

Input: Network states (S), Actions (A), Node data

Output: Optimized network performance and energy efficiency

Begin

//Initialize variables and parameters

α, γ, NetworkTraffic, NodeData, PreviousDemand, NewDemand

TotalResources, AvailableResources, InformationTransferred, PowerConsumption

//Main Optimization Loop

While True Do

//1. Q-Learning for Network Adaptation

For each (s, a) in (S, A), Do (52)

Q(s, a) ← (1 − α) * Q(s, a) + α * [Reward + γ * max(Q(s’, a’))] (53)

End For

//2. Inheritance Algorithm Fitness Function

Fitness ← InformationTransferred/PowerConsumption

//3. Fuzzy Logic for Power Regulation

If NetworkTraffic is High, Then

EnergyConsumption ← High

Else

EnergyConsumption ← Low

End If

//4. Sleep Scheduling Optimization

SleepTime ← ModelBasedLearningOutcome(NodeData)

//5. WSN Energy Use Model

For each node i in WSN, Do

ECi ← Σ(Pi * Ti * Vi) (54)

End For

//6. Cluster Formation

For each node i in WSN, Do

Similarity_Index ← 1 + Σ(xi − yi)^2 (55)

End For

FormClusters(Similarity_Index) (56)

//7. Resource Allocation Heuristic

Allocation_of_Resources ← (TotalResources/AvailableResources) * 100 (57)

//8. Dynamic Adaptation Mechanism

AdaptationRate ← (PreviousDemand/NewDemand) * 100% (58)

//9. Apply the optimized parameters

UpdateNetworkParameters(Fitness, EnergyConsumption,

SleepTime, ECi, Allocation_of_Resources, AdaptationRate) (59)

//10. Monitor and update network conditions

NetworkTraffic, NodeData, PreviousDemand, NewDemand ← MonitorNetwork (60)

End While

End

3. Results

Estimating WSN energy consumption entails calculating node power requirements based on data volume, power consumption rate, and other variables. Data clustering utilizing similarity indices and resource allocation is critical for network smoothness. The dynamic adaptation mechanism must adapt to new requirements to keep the network running in the face of unforeseen changes. Path Selection Algorithms, ACRL, GARA, FLEMA, and ML-DSS are examples of complex technological interactions in modern consumer electronics. This occurs while mixing algorithms. In addition to the device’s capabilities, this entails managing complex computer networks. This involvement extends beyond the functioning of the item. In the next section, we discuss how e-commerce platforms have altered corporate procedures and customer purchasing behaviors. This circumstance necessitates using WSNs for reliable data transmission and network administration. The usefulness of AI algorithms in optimizing energy consumption depends mainly on the type and relevance of the datasets employed. Datasets are used that are typical of those found in e-commerce applications; these datasets include things like traffic and energy statistics. These datasets mimic the complex dynamics of an online retail system by including real-time transaction information, a wide variety of user interactions, and traffic surges during peak hours.

Figure 9, Figure 10, Figure 11, Figure 12 and Figure 13 were generated based on the performance of the algorithms in optimizing the energy consumption using the aforementioned datasets.

In

Table 2. Comparative performance analysis of different network methods across multiple metrics.

Method	Network Stability Score (1–5)	Energy Efficiency Ratio	Operational Robustness (1–100)	Adaptability Index (1–5)	Overall Performance (1–10)
Proposed Method	4.8	0.95	95	4.9	9.7
AI-Optimized Routing	4.2	0.85	85	4.5	8.5
Legacy Centralized System	3.5	0.70	75	3.2	6.5
Hierarchical Clustering	2.8	0.65	60	2.5	4.5
Reactive Protocol	3.0	0.70	70	3.0	5.5
Grid-Based Topology	2.9	0.65	65	3.1	5.0
Load-Balancing Algorithm	3.1	0.70	72	3.0	6.0

, a comparison between various network methods for optimizing wireless sensor networks in e-commerce is presented. The methods evaluated are scored on four categories—Network Stability Score (measured from 1–5, with 1 as the lowest), Energy Efficiency Ratio (0–1, with 0 as the lowest and 1 representing 100% energy efficiency), Operational Robustness (scored from 0–100, with 0 the lowest and 100 the highest), and Adaptability Index (scored 1–5, with 1 the lowest and 5 the highest)—based on which the overall performance of the method is scored from 1–10, with 1 as the lowest and 10 as the highest. The methods tested are commonly used methods for WSN optimization, including AI-Optimized Routing, the Legacy Centralized System, Hierarchical Clustering, the Reactive Protocol, Grid-Based Topology, and the Load-Balancing Algorithm. Based on the analysis, the AI-powered decision support system outperforms other methods in different metrics. It achieves a network stability score of 4.8, an energy efficiency ratio of 0.95, an operational robustness of 95, and an adaptability index of 4.9, resulting in an overall performance rating of 9.7. These findings highlight the superiority of the proposed methodology over previous centralized systems, hierarchical clustering, and other methods, indicating its potential to revolutionize consumer electronics in e-commerce by efficiently optimizing wireless sensor networks. Optimizing wireless sensor networks would reduce both the time needed to maintain the system and the direct costs of the network for the business.

Network lifespan refers to the length of time for which the network is able to maintain full functionality [26]. According to Figure 9 of the network lifespan study, the proposed AI-based solution achieves the highest value of around 88 out of all the techniques that were evaluated. Thus, the suggested approach greatly improves energy efficiency, increasing the network’s lifetime. With a 35% performance gain and a decrease of 40% in energy usage, these outcomes stand out when compared to previous approaches. Based on these results, fuzzy logic-controlled energy management, adaptive clustering, and genetic algorithms for resource allocation might transform e-commerce wireless sensor networks, which would improve consumer electronics platforms. Overall, the suggested approach improves the network’s performance and efficiency, as demonstrated by the higher network life cycle value. This demonstrates a practical approach to deploying this algorithm in existing WSNs, enabling these theoretical techniques to be practically implemented while conserving energy and optimizing network resources.

Figure 10 illustrates the patterns of network lifetime within performance cycles of different approaches. A performance cycle is measured from the moment one task ends and another task begins. The AI-based approach is represented by the blue line. Regarding energy economy and long-term performance, the results reveal that the AI algorithm exceeds rival approaches when considering the network’s lifetime. Findings show that AI methods, including Adaptive Clustering, the Genetic Algorithm for Resource Allocation, and Fuzzy Logic-controlled Energy Management, can increase the system’s effectiveness and durability. Since the blue line signifies “legend AI,” one can assume that this AI approach is well-known for how it enhances the effectiveness and reliability of wireless sensor networks used in online consumer electronics purchases. Applications of WSNs, such as in optimal route selection for delivery, will offer more reliability and improved performance, encouraging people to switch to these algorithms.

The Energy Utilization Index was formulated by summing together the energy consumption of the network at different levels of activity, measured as the bits sent per joule of energy. Figure 11 compares several different methods of displaying the EUI during performance cycles. The EUI trends downward, indicating that the AI-optimized routing strategy consumes less energy than its rivals. The AI method’s ever-improving ability to achieve optimum energy utilization throughout performance cycles will ultimately decrease the energy consumed for data transmission. This could allow for more frequent transmission of recorded data, improving the efficacy of the models deployed by e-commerce retailers.

Figure 10 shows the MTBF for different techniques across several performance cycles, measured as the average of the time taken before the network fails per performance cycle. The AI-optimized routing technique is becoming more dependable as its MTBF increases. This graph shows the method’s improved capacity to perform network operations without difficulties for a long time. The upwards trend of the reliability metrics shows that AI-optimised routing improves at a comparable rate to the other methods while also having a longer initial MTBF than any other tested method.

This trend implies that the technique is improving at executing network tasks over extended periods. Figure 11 depicts the application of the energy index. The suggested method delivers data faster than alternatives since it requires less energy.

4. Discussion

In consumer electronics, improved artificial intelligence algorithms for WSN management in e-commerce applications are a significant advancement. The results of this study support the findings of other studies, such as [27,28,29], which suggest that an artificial intelligence algorithm has the potential to increase the efficacy of wireless sensor networks.

This technique improves WSN effectiveness, adaptability, and performance for consumer products, focusing on e-commerce applications. The network dynamically transfers data and regulates energy use using AI algorithms, enabling data mobility and energy management. Dynamic data loads in consumer electronics need simple node mobility, improving network efficiency and flexibility. ACRL uses less power and processes data better than static clustering systems. Data and energy management are necessary for ACRL, a practical suggestion for consumer electronics. ACRL finds its applications in routing—specifically, in identifying the ideal paths for communications between a node and the sink node it is connected to [27]. GARA dynamically assigning resources to maintain network performance is an improvement over earlier methods. Consumer electronics devices and network performance depend on resource management. Genetic algorithms are useful to fine-tune a sensor network, allowing the network to maintain the required properties for the purpose it is designed for while reducing the energy costs of the sensor network, thus extending the lifetime of the network [28]. Additionally, Genetic Algorithms can be used to maintain the minimum energy costs of the system as the size of the system is scaled up, particularly in the case of cloud computing [30]. FLEMA increases WSN energy management flexibility [5]. It makes real-time judgments using fuzzy logic. This strategy may enhance home equipment energy efficiency compared to rule-based alternatives. Additionally, algorithms based on fuzzy logic also improve the overall performance of the individual network nodes [29]. This makes fuzzy logic extremely useful in event-detection problems where a simple rule-based scenario may not be as useful, such as in the detection of fires in mines based on environmental data [31,32]. DSS adjusts network node sleep time based on data trends, eliminating reactive sleep scheduling. Thus, reactive sleep scheduling is unnecessary. The power economy makes power consumption reduction a priority for consumer gadgets [22]. DSS allows this without affecting network responsiveness. As one of the biggest limitations of WSNs is their limited battery time, the usage of the Deep Sleep Scheduling algorithm can extend the operational lifetime of wireless sensor nodes, thus reducing the need for replacement of the nodes over time, as well as allowing for reduced general maintenance of the network. As a result, the overall efficiency of the network can be greatly increased through adoption of this algorithm [27]. Other models can predict and alter in real time. The suggested technique differs from consumer electronics items in real-time adaptation and predictive modeling. This is a significant advance over manual operations and rudimentary network management. Considering e-commerce platforms during design, Internet trade WSNs can manage substantial data quantities due to their processing power and rapid response times [7,9]. However, e-commerce retailers that utilize these networks find that their networks will often face sudden surges in data traffic, whether expected during peak holiday seasons such as Christmas or unexpected due to unforeseeable events. This can result in congestion at the node level [33]. The methods outlined in this paper have the potential to mitigate this issue, enabling greater effectiveness of the networks in use. Therefore, they are the finest data management choice. Wireless sensor networks may improve consumer electronics energy efficiency and business alignment. These technologies and strategies transform consumer electronics from static systems to dynamic, intelligent networks. They emphasize the rising relevance of AI and sophisticated computational approaches in improving consumer devices’ usefulness, efficiency, and sustainability, especially in data-intensive e-commerce.

5. Conclusions

This article will discuss a novel e-commerce wireless sensor network optimization strategy. The algorithms and techniques outlined in this research have the potential to improve the energy efficiency and performance of the wireless sensor networks deployed in e-commerce websites. AI in consumer gadgets is responsible for this. It manages consumer device energy well. The network can adapt to the ever-changing online consumer products supply chain while saving energy. This system balances power consumption and data transmission efficiency using adaptive clustering, resource allocation, and energy management algorithms. AI might boost energy efficiency and network resilience by 40% in consumer electronics and e-commerce WSNs. Research shows it outperforms consumer item network management solutions and is 35% more versatile. This suggests a massive edge over such solutions. Predictive modelling allows it to react instantly to consumer device network traffic changes, which is its biggest strength. This discovery has advanced our efforts to construct energy-efficient WSNs that connect consumer items to e-commerce.

Upon analyzing the utilization of decision support systems based on AI in wireless sensor networks for consumer electronics in e-commerce, it is apparent that it can enhance network optimization and energy efficiency. However, it is vital to consider specific restrictions. For example, the proposed methods and algorithms must be thoroughly scrutinized to ensure they are effective in more extensive network environments. Furthermore, there could be challenges in implementing these AI-driven approaches in the real world, such as compatibility issues with the existing infrastructure and the acceptance of new technologies by consumers and businesses.

This research is limited by the lack of extensive field testing and available case studies. Future research should look into more extensive field testing and case studies to confirm the feasibility and scalability of AI-based strategies, with an eye towards keeping algorithms and methodologies in line with the ever-changing trends of e-commerce and advancements in technology. It is essential for researchers to collaborate with industry stakeholders and policy-makers in order to ensure AI-based solutions in wireless sensor networks for consumer electronics in e-commerce are widely adopted. By overcoming these limitations and promoting collaboration, there is a significant potential for enhancing online shopping experiences.

The optimization of wireless sensor networks for consumer electronics can be significantly enhanced by integrating AI-based decision support systems. According to one study, the inclusion of AI algorithms such as Adaptive Clustering, Fuzzy Logic-Controlled Energy Management, and the Genetic Algorithm for Resource Allocation can lead to a 40% increase in energy efficiency and a 35% improvement in network performance on online shopping platforms.

These findings hold significant implications for e-commerce businesses that aim to enhance user experiences, reduce operational costs, and streamline online shopping platforms. By incorporating AI into consumer electronics, companies can achieve faster transactions, improved customer satisfaction, and reduced energy consumption. The proposed methodology sets a precedent for future research, highlighting the potential of AI-driven solutions to optimize wireless sensor networks and drive innovation in e-commerce technologies.