Game Theory-based Model Research Articles

A Driving Warning System for Explosive Transport Vehicles Based on Object Detection Algorithm.

Due to the flammable and explosive nature of explosives, there are significant potential hazards and risks during transportation. During the operation of explosive transport vehicles, there are often situations where the vehicles around them approach or change lanes abnormally, resulting in insufficient avoidance and collision, leading to serious consequences such as explosions and fires. Therefore, in response to the above issues, this article has developed an explosive transport vehicle driving warning system based on object detection algorithms. Consumer-level cameras are flexibly arranged around the vehicle body to monitor surrounding vehicles. Using the YOLOv4 object detection algorithm to identify and distance surrounding vehicles, using a game theory-based cellular automaton model to simulate the actual operation of vehicles, simulating the driver's decision-making behavior when encountering other vehicles approaching or changing lanes abnormally during actual driving. The cellular automaton model was used to simulate two scenarios of explosive transport vehicles equipped with and without warning systems. The results show that when explosive transport vehicles encounter the above-mentioned dangerous situations, the warning system can timely issue warnings, remind drivers to make decisions, avoid risks, ensure the safety of vehicle operation, and verify the effectiveness of the warning system.

Enhancing grid hosting capacity with coordinated non-firm connections in industrial energy communities

A significant increase in grid connection requests from industrial customers has lead to long connection queues. Combined with long lead times on grid construction, the result is significant socioeconomic losses due to lack of grid capacity. Distribution system operators (DSO) have therefore introduced non-firm connections as an alternative, where the new grid customer may connect on the condition that the DSO retains the right to disconnect the grid customer if necessary. An option to potential disconnection is for customers to leverage flexibility to stay below an agreed capacity level. Unfortunately, many existing grid customers possess flexibility potential but lack incentives to utilise it. To address this, we propose a “coordinated non-firm connection”, where the new grid customer forms an energy community with existing grid customers to coordinate flexibility and capacity utilisation. To demonstrate technical feasibility and incentive compatibility, we formulate a complementarity model. Applying this model to a case study involving three industrial grid customers, we showcase both technical potential and incentive compatibility. Results illustrate how energy community members engage in capacity trading within a local market, ensuring adherence to grid limitations. The game theory-based model confirms sufficient economic benefit for all members to participate.

Game theory-based mandatory lane change model in intelligent connected vehicles environment

In the environment of intelligent connected vehicles, drivers are capable of making wiser and safer decisions. However, the interaction between drivers and vehicle systems has undergone changes in the intelligent connected vehicles environment, leading to a decrease in the applicability of existing microscopic driving models, such as the mandatory lane change model, which requires reevaluation or improvement. Therefore, to investigate the influence of different intelligent connected vehicles environments on the microscopic mandatory lane-changing model, this study developed three interaction systems to characterize different intelligent connected vehicles environments: the baseline, warning group, and guidance group. The Baseline provides basic information, the warning group adds icons of preceding vehicles and real-time headway information, while the guidance group further includes speed and voice guidance functions. The baseline describes the traditional environment, while the other two groups describe the intelligent connected vehicles environment. Using a self-developed intelligent connected vehicle testing platform, we conducted driving simulation experiments with 43 participants at the interchange merging area of a highway. This study, grounded in game theory, establishes function models for participants, strategies, and payoff functions in the mandatory lane-changing process. Utilizing data from driving simulation experiments, the parameters of the dual-layered planning model are calibrated. Evaluation of the constructed model is conducted through confusion matrices and lane-changing spatiotemporal characteristic indicators. The results demonstrate satisfactory predictive performance of the baseline group model, warning group model, and guidance group model across different intelligent connected vehicles environments. Specifically, compared to existing literature, the baseline group model exhibits improvements of 7 % and 2 % respectively in overall lane-changing detection accuracy by drivers. The warning group model shows improvements of 2.9 % and 1.7 %, while the guidance group model exhibits improvements of 5.1 % and 4.3 %. Additionally, the baseline group model reduces the mean absolute error in predicting different game strategies by 16.7 % and 5.6 % respectively compared to existing literature. Concerning lane-changing position prediction, the warning and guidance group models demonstrate minimal errors, whereas the baseline group model exhibits good consistency in predicting lane-changing duration. Furthermore, both the warning and guidance group models show some delay in predicting lane-changing duration. While intelligent connected vehicles environments significantly influence the prediction of lane-changing positions, they do not significantly affect the prediction of lane-changing duration. However, game strategies significantly impact the prediction of lane-changing duration but do not significantly affect the prediction of lane-changing positions. The study findings offer valuable insights into micro lane-changing behaviors of drivers in intelligent connected vehicles environments, bearing crucial significance for the in-depth investigation of real-time control and guidance strategies for vehicles in merge areas of highways under intelligent connected vehicles conditions.

Game theory-based switch migration strategy for satellite networks

Software-Defined Networking (SDN) separates the control plane and data plane of the network, abstracting the underlying infrastructure of applications and network services and handing it over to the SDN controller for unified management. The introduction of SDN technology into satellite networks effectively addresses control challenges posed by dynamic topologies and intermittent inter-satellite links. Due to factors such as geography and usage patterns, the distribution of remote sensing data traffic is uneven in both space and time. Some remote sensing satellites may acquire large volumes of data within a short period and transmit them back to the ground through intermediate nodes, leading to a sudden surge in load on certain controllers. This situation adversely impacts the performance of remote sensing data transmission. To mitigate this, switch migration operations are required to alleviate the load on overloaded controllers and to achieve the load balance among all controllers. In this paper, a switch migration scheme based on game theory is proposed under a three-layer satellite network architecture, which is designed based on the management costs and ensures timely triggering of the migration operations. Moreover, a game theory-based matching optimization model is formulated in the scheme to address the matching problem between target controllers and migration candidate switches. We further propose a distributed algorithm to seek the Nash equilibrium within the game. Experimental results demonstrate that, compared to other relevant algorithms, this approach can find the near-optimal matching strategy for each target controller and reduce the average latency of these controllers.

Evaluation of the Development Vitality of New Quality Productive Forces in Beijing-Tianjin-Hebei Region Based on Game Theory-TOPSIS Method

This paper employs a Game Theory-based Weighting and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to comprehensively evaluate the development vitality of new quality productive forces in Beijing, Tianjin, and Hebei. By integrating the Analytic Hierarchy Process (AHP) and Entropy Method to determine weights, a scientific evaluation index system is constructed, incorporating both expert opinions and objective data. The evaluation results indicate that Beijing excels in technological innovation and efficiency, Tianjin performs well in green development, while Hebei requires enhancement in industrial transformation and upgrading. Encompassing multiple dimensions such as technological innovation, intellectual resources, green development, production efficiency, and industrial transformation and upgrading, the assessment further validates the effectiveness of the evaluation methodology through case study analysis. The Game Theory-based Weighting approach effectively combines subjective and objective weights, enhancing the scientific rigor and accuracy of the evaluation. The results provide a decision-making basis for optimizing industrial structures, promoting regional collaboration, advancing green and low-carbon development, and improving social well-being in the Beijing-Tianjin-Hebei region. In conclusion, the proposed Game Theory-based Weighting and TOPSIS evaluation model serves as a powerful tool for assessing the development vitality of new quality productive forces in Beijing, Tianjin, and Hebei. It holds significant implications for driving regional high-quality development, achieving economic growth with high quality, and fostering sustainable social development.

Strategic Analysis of Informatization of International Trade Enterprises’ Economic Management Models Based on Game Theory Models

Abstract Exploring the economic management models of international trade enterprises, this research highlights the significant role of informatization strategies informed by game theory. Through the prism of game theory, we analyze the interplay among various stakeholders, including risk takers, managers, and executors. Our study reveals that appropriate risk-taking can drastically lower operational risks. A case in point is Company A, which witnessed a notable improvement in financial stability, with its Z-score rising from 2.6785 in 2005 to 4.6314 in 2022. As theAnalysis shows, effective risk management plays a pivotal role in boosting economic efficiency, evident through enhanced total asset turnover and gross operating margins. This study underscores the utility of a game theory-based economic management model in improving risk management and financial efficiency, thereby advancing the implementation and refinement of informatization strategies within enterprises.

A game theory-based pricing model for ancillary services in electricity markets

Abstract Amidst the ongoing evolution and substantial reforms within the electric power market, the development of an auxiliary service pricing model grounded in game theory emerges as crucial. This study delineates the construction of an electricity auxiliary service pricing model, utilizing a dual mechanism approach: a cooperative game-based electricity price formation mechanism and a Stackelberg game-based time-sharing pricing mechanism. Furthermore, it incorporates demand response technology to conduct a detailed analysis of optimization results and the applicability of the proposed electricity service pricing model. The pricing scheme designated as Scheme 4, derived from the proposed model, demonstrates notable superiority in terms of economic efficiency and environmental sustainability when juxtaposed with three alternative schemes. Specifically, Scheme 4 yields a net profit of $13,267.6, achieves clean energy utilization amounting to 89.67 MWh, and minimizes wind abandonment to 17.35 MWh, outperforming all other considered scenarios in these metrics. Operational analysis reveals that the model's execution time varies between 15 and 50 seconds across different sample sizes, exhibiting minimal fluctuations. Additionally, the Monte Carlo simulations consistently produce values inferior to the objective function value of the developed model, with the discrepancy narrowing from 38 to 20, indicating the model's robust adaptability.

Domestic Regional Synergy in Achieving National Climate Goals—The Role of Comparative Advantage in Emission Reduction

Domestic regional synergistic emission reduction is important in achieving national climate goals. This study constructed a game theory-based model for regional synergistic emission reduction, modified the Basic Climate Game using the exact-hat algebra method, and expanded the game model using a general spatial equilibrium model to incorporate cross-regional economic impacts generated by emission reduction actions through factors and product flows. The formation of regional comparative advantages in emission reductions and their impact on synergistic emission reductions were revealed through regional characteristics such as emission elasticity, sectoral structure, regional trade shares, and green total factor productivity. A form of synergy was then proposed that utilizes the comparative advantages of different regions, allowing for synergistic emission reductions across different income regions and engagement with regions that are still at the carbon-peaking stage in cooperation. Moreover, the model was created to be as close to the economic reality as possible to provide a trade, industry, and economic growth policy that complements emission-reduction policies.

XTS: A Hybrid Framework to Detect DNS-Over-HTTPS Tunnels Based on XGBoost and Cooperative Game Theory

This paper proposes a hybrid approach called XTS that uses a combination of techniques to analyze highly imbalanced data with minimum features. XTS combines cost-sensitive XGBoost, a game theory-based model explainer called TreeSHAP, and a newly developed algorithm known as Sequential Forward Evaluation algorithm (SFE). The general aim of XTS is to reduce the number of features required to learn a particular dataset. It assumes that low-dimensional representation of data can improve computational efficiency and model interpretability whilst retaining a strong prediction performance. The efficiency of XTS was tested on a public dataset, and the results showed that by reducing the number of features from 33 to less than five, the proposed model achieved over 99.9% prediction efficiency. XTS was also found to outperform other benchmarked models and existing proof-of-concept solutions in the literature. The dataset contained data related to DNS-over-HTTPS (DoH) tunnels. The top predictors for DoH classification and characterization were identified using interactive SHAP plots, which included destination IP, packet length mode, and source IP. XTS offered a promising approach to improve the efficiency of the detection and analysis of DoH tunnels while maintaining accuracy, which can have important implications for behavioral network intrusion detection systems.

Abstract 4254: The ratio of key metabolic transcripts is a predictive biomarker of breast cancer metastasis to the lung

Abstract Understanding the rewired metabolism of organ-specific metastasis in breast cancer is an under-appreciated problem, with implications for the treatment and prevention of metastatic disease. Here, we used a systems biology approach and the MDA-MB 231 model to compare metabolic fluxes used by parental breast cancer cells and their brain- and lung-homing derivatives. We combined metabolomic profiling, flux measurements, spatial tissue mimetic systems, and mathematical modeling to dissect the cells’ metabolic rewiring, and identified changes specific for lung metastatic selection. We validated our results in other cell lines as well as patient data from project GENIE and the MBCP. Our results produced five biological insights: First, levels of mRNA and metabolic intermediates may anticorrelate with flux. Second, different lineages evolved from the same line can have distinct heritable metabolic fluxes. Metastatic lineages in our model display higher glycolytic flux and bioenergetics than parental cells, with lung-homing cells exhibiting by far the greatest glucose uptake and lactate production. Importantly, this occurs despite low levels of glycolytic intermediates in lung-homing cells. Third, this apparent paradox can be reconciled if feedback inhibition of the glycolysis pathway is prevented, a finding we modeled with flux-balance analysis and confirmed by 13C glucose tracing and measuring glycolytic enzymatic activities directly. Fourth, this distinct metabolic behavior in lung-homing cells is maintained by a high ratio of lactate dehydrogenase (LDH) to pyruvate dehydrogenase (PDH) gene expression, which also correlates with lung metastases in patients with breast cancer. Feature classification models trained on clinical characteristics alone were unable to predict tropism; however, the LDH/PDH ratio was a significant predictor for lung but not brain metastases, independent of other transcriptomic signatures, suggesting that this feature is a potential biomarker for lung metastasis. Fifth, this metabolic effect did not increase cellular growth rate, suggesting that lactate secretion may itself be a trait under selection in breast cancer lung metastasis. Follow up experiments and game theory-based models in a spatially structured tissue-mimetic system showed that lung-homing cells grow more favorably than brain-homing cells in nutrient-deprived gradients, demonstrating how divergent metabolisms of the lineages could lead to selection in different environments. Notably, lung metastases in a mouse model of pancreatic cancer had higher lactate production than other metastases, suggesting that this metabolic trait could be important for other cancer types. Together, our in vitro, in silico, and clinical data analyses highlight that metabolism—currency of all physiological processes—plays an essential role in the connection from gene to phenotype in metastatic disease. Citation Format: Deepti Mathur, Chen Liao, Wendy Lin, Alessandro La Ferlita, Salvatore Alaimo, Alfredo Ferro, Yi Zhong, Christine Iacobuzio-Donahue, Joao Xavier. The ratio of key metabolic transcripts is a predictive biomarker of breast cancer metastasis to the lung. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4254.

A Game Theory-Based Model for the Dissemination of Privacy Information in Online Social Networks

Online social networks (OSNs) have experienced rapid growth in recent years, and an increasing number of people now use OSNs, such as Facebook and Twitter, to share and spread information on a daily basis. As a special type of information, user personal information is also widely disseminated in such networks, posing threats to user privacy. The study on privacy information dissemination is thus useful for the development of mechanisms and tools for the effective protection of privacy information in OSNs. In this paper, we propose to apply the game theory to establish a sender–receiver game model and the Nash equilibrium to describe the behavioral strategies of users in disseminating privacy information. Factors that affect the dissemination of privacy information are also analyzed with two important aspects: intimacy and popularity of the privacy-concerning subject. Simulation experiments were conducted based on real data sets from scale-free networks and real social networks to compare and analyze the effectiveness of the model. Results show that the proposed game theory is applicable to the privacy information dissemination model, which implements intimacy and popularity in the modeling of the dissemination of privacy information in OSNs. Both the impact of the macro-level OSNs and the micro-relationships between users are evaluated on the dissemination of privacy information, which provides a new perspective for exploring the dissemination of privacy information and facilitates the development of effective mechanisms for privacy protection in OSNs.

A Game-Theory-Based Approach to Modeling Lane-Changing Interactions on Highway On-Ramps: Considering the Bounded Rationality of Drivers

In highway on-ramp sections, the conflictual interactions between a subject vehicle (merging vehicle) in the acceleration lane and a following vehicle (lagging vehicle) in the adjacent mainline can lead to traffic congestion, go–stop oscillations, and serious safety hazards. Human drivers combine their previous lane-changing experience and their perception of surrounding traffic conditions to decide whether to merge. However, the decisions that they make are not always optimal in specific traffic scenarios due to fuzzy perception and misjudgment. That is, they make lane-changing decisions in a bounded rational way. In this paper, a game-theory-based approach is used to model the interactive behavior of mandatory lane-changing in a highway on-ramp section. The model comprehensively considers vehicle interactions and the bounded rationality of drivers by modeling lane-changing behavior on on-ramps as a two-person non-zero-sum non-cooperative game with incomplete information. In addition, the Logit QRE is used to explain the bounded rationality of drivers. In order to estimate the parameters, a bi-level programming framework is built. Vehicle trajectory data from NGSIM and an unmanned aerial vehicle survey were used for model calibration and validation. The validation results were rigorously evaluated by using various performance indicators, such as the mean absolute error, root mean square error, detection rate, and false-alarm rate. It can be seen that the proposed game theory-based model was able to effectively predict merging and yielding interactions with a high degree of accuracy.

Optimal choice of power battery joint recycling strategy for electric vehicle manufacturers under a deposit-refund system

In recent years, worldwide electric vehicle (EV) sales have experienced rapid growth, and recycling an enormous quantity of spent power batteries has become a challenge. This study aims to explore the optimal joint recycling strategy from the EV manufacturer’s perspective by using game theory-based models. The model investigates four kinds of joint recycling strategies: 1) nonalliance (NA) mode, 2) alliance with the power battery manufacturer (APBM) mode, 3) alliance with the third-party recycler (ATPR) mode, and 4) integrated alliance (IA) mode. The results show that: 1) A deposit-refund system can effectively increase the recycling rate. However, an excessive upfront disposal fee will reduce overall profits. 2) For the closed-loop supply chain, the optimal alliance mode depends on the intensity of recycling competition. When recycling competition is fierce, the IA mode is the best alliance mode. When recycling competition is weak, the NA mode is the optimal choice from the recycling rate perspective, and the APBM mode is the optimal choice from the total profit perspective. 3) For the EV manufacturer, the alliance in which its proportion of the extra joint profit is highest tends to be the optimal choice.

Blockchain adoption in serial logistics service chain: value and challenge

ABSTRACT Inefficient practices in the logistics industry cause huge losses in social resources. Addressing the quality control issues is challenging in multi-stage transport. Due to the structural characteristics of the serial logistics service chains, the real delivery quality of each Third Party Logistics (3PL) is invisible to the Lead Logistics Provider (LLP), and the defects of a single 3PL’s delivery quality will be covered up. Thus, unobservable delivery quality triggers the moral hazard action of 3PL, and strengthens the inequities in a serial logistics service chain. Blockchain technology can be an effective tool to resolve the moral hazard problem. We employ game theory-based models to investigate the blockchain adoption issue in serial logistics service chains. Adopting blockchain technology might bring Pareto improvement in delivery quality and total profit of the service chain. However, 3PL’s profit may be hurt because of blockchain adoption. Therefore, successful blockchain implementation depends on whether the initiator of a blockchain programme can properly incentivise 3PLs.

Scheduling and Securing Drone Charging System Using Particle Swarm Optimization and Blockchain Technology

Unmanned aerial vehicles (UAVs) have emerged as a powerful technology for introducing untraditional solutions to many challenges in non-military fields and industrial applications in the next few years. However, the limitations of a drone’s battery and the available optimal charging techniques represent a significant challenge in using UAVs on a large scale. This problem means UAVs are unable to fly for a long time; hence, drones’ services fail dramatically. Due to this challenge, optimizing the scheduling of drone charging may be an unusual solution to drones’ battery problems. Moreover, authenticating drones and verifying their charging transactions with charging stations is an essential associated problem. This paper proposes a scheduling and secure drone charging system in response to these challenges. The proposed system was simulated on a generated dataset consisting of 300 drones and 50 charging station points to evaluate its performance. The optimization of the proposed scheduling methodology was based on the particle swarm optimization (PSO) algorithm and game theory-based auction model. In addition, authenticating and verifying drone charging transactions were executed using a proposed blockchain protocol. The optimization and scheduling results showed the PSO algorithm’s efficiency in optimizing drone routes and preventing drone collisions during charging flights with low error rates with an MAE = 0.0017 and an MSE = 0.0159. Moreover, the investigation to authenticate and verify the drone charging transactions showed the efficiency of the proposed blockchain protocol while simulating the proposed system on the Ethereum platform. The obtained results clarified the efficiency of the proposed blockchain protocol in executing drone charging transactions within a short time and low latency within an average of 0.34 s based on blockchain performance metrics. Moreover, the proposed scheduling methodology achieved a 96.8% success rate of drone charging cases, while only 3.2% of drones failed to charge after three scheduling rounds.

A Game Theory-Based Fractional Order Model for the Simulation of Human Responses in an Emerging Epidemic

A Game Theory-Based Fractional Order Model for the Simulation of Human Responses in an Emerging Epidemic

Sharing carbon permits in industrial symbiosis: A game theory-based optimisation model

The emerging effects of climate change caused by the rise of carbon dioxide (CO2) and equivalent greenhouse gas (GHG) emissions to the atmosphere have prompted policymakers to revise current emission reduction schemes. One of the schemes is a carbon permit scheme which places a restriction on the CO2 or equivalent GHG emissions to the atmosphere. However, limited literature explores the effectiveness of sharing carbon permits in an IS system to optimise and reduce waste emissions in a system. Therefore, this research presents an optimisation-based framework for plants in an industrial symbiosis (IS) system with a carbon permit scheme. The critical success factor for an IS system is the collaboration and synergistic possibilities between participating plants within geographical proximity. This paper takes a different approach from the typical physical resource sharing in IS. This research considers carbon permits as a shared resource between the plant operators. The sharing of carbon permits creates a cooperative environment between the plant operators in the IS system. Through this system, cooperative game theory (CGT) was used to determine the fair allocation of profit to each participating plant operator based on their marginal contribution in the IS system. A case study has been implemented for three process plants: palm oil mill (POM), palm oil-based biorefinery (POB) and combine heat power (CHP) plant that are operated under a carbon permit scheme. Results from the base case study illustrates that each plant operator receives an average of 12% higher profit when sharing their carbon permits relative to working individually under the carbon permit scheme. A further analysis has been conducted to investigate the framework′s economic feasibility at different emission cuts. With differing emission cuts, the results indicated plant operators obtained an increase in their profit margin by 25% when working in a coalition relative to working independently.

Efficient Asset Allocation: Application of Game Theory-Based Model for Superior Performance

In this paper, we compared the models for selecting the optimal portfolio based on different risk measures to identify the periods in which some of the risk measures dominated over others. For decades, the best known return-risk model has been Markowitz’s mean-variance model. Based on the criticism of the classical Markowitz model, a whole series of risk measures and models for selecting the optimal portfolio have been developed, which are divided into two groups: symmetrical and downside risk measures. Based on the tools provided by game theory, we presented a minimax model for selecting the optimal portfolio based on the maximum loss as a measure of risk. Recent research has shown the adequacy of the application of this risk measure and its dominance concerning variance in certain circumstances. Theoretically, the model based on maximum loss as a measure of risk relies on a much smaller number of assumptions that must be satisfied. In the empirical part of the paper, we analyzed the real return performance, structure, correlation, stability, and predictive efficiency of the model based on maximum loss return as a measure of risk and compared it with the other famous models to determine whether the maximum loss-based risk measure model is more suitable for use in certain circumstances than conventional return-risk models. We compared portfolios created based on different models over the period of 2000–2020 from a selected sample of stocks that are components of the STOXX Europe 600 index, which covers 90% of the free market capitalization in the European capital market. The observed period included 3 bear market periods, including the period of market decline during the COVID-19 crisis. Our analysis showed that there was no significant difference in portfolio returns depending on the selected model using the “buy-and-hold” strategy, but there were crisis periods. The results showed a significantly higher stability of portfolios selected on the criterion of minimizing the maximum loss than others. In periods of market decline, this portfolio achieved the best performance and had a shorter recovery period than others. This allowed superior use of the minimax model at least for investors with a pronounced risk aversion.

Calm or panic? A game-based method of emotion contagion for crowd evacuation

Panic in an emergency can be highly contagious; this can cause a situation to rapidly spin out of control, with serious consequences. Prior studies on emotion contagion have focused on panic and have ignored the rationality of pedestrians. Considering both panic and calm, this paper puts forward a new game theory-based crowd evacuation model incorporating emotion contagion. Transitions between three emotional states, i.e. infected, sensitive, and unchangeable, reveal the contagion of personalized emotions in the crowd. The influence of nearby pedestrians on an individual’s decision-making process is modeled using game theory. The proposed methodology is here applied to a real-world subway evacuation problem in Beijing. Simulation results show that emotion contagion has an important effect on pedestrian evacuation time. Moreover, calm pedestrians were found to be very effective at calming the crowd in an emergency. If management personnel can go deep into a crowd to calm pedestrians, then panic will be rapidly reduced.

Game theory-based energy efficiency optimization model for the Internet of Things

This paper conducts a game-theoretic based optimization study on the energy efficiency of the Internet of Things (IoT). For the problem that wireless sensor node information in the IoT requires the selection of a suitable backbone access point for energy efficiency optimization, this paper first establishes a mathematical model for the system-level energy optimization of the sensor node free-choice access point problem and then proposes a game model based on the concept of cooperation and the corresponding utility function, and after theoretical analysis. Among the new connections in the future, more than 30% are suitable for carrying by the cellular network, so the network challenges brought by mobile operators are huge. The paper then studied the current mainstream Internet of Things technology in the development of mobile networks—the​ application principles and key technologies of Narrow Band Internet of Things (NB-IoT), combined with the current wireless network optimization and maintenance work, and studied How does the Internet of Things become the focus of the industry and actively lay out and promote industrial development for operators, and how does NB-IoT move from a concept to small-scale commercial use, become the operator’s leading role in promoting the standard industry, and hope to become an industry leader It is proved that the best access point allocation scheme is the optimal equilibrium point of the proposed game. Then a non-correlated parallel learning algorithm is proposed, according to which the system can converge to the optimal equilibrium point with a very low probability after learning, which is the optimal solution of the proposed system energy efficiency optimization problem, and achieve the global optimal system energy efficiency. Compared with other models, our model has improved efficiency by about 12% and accuracy by about 8%, and it can be applied in practice.