Optimal Key Parameters Research Articles

Effective Identification and Authentication of Healthcare IoT Using Fog Computing with Hybrid Cryptographic Algorithm

Currently, Internet of Things (IoT)-based cloud systems face several problems such as privacy leakage, failure in centralized operation, managing IoT devices, and malicious attacks. The data transmission between the cloud and healthcare IoT needs trust and secure transmission of Electronic Health Records (EHRs). IoT-enabled healthcare equipment is seen in hospitals that have been implementing the technology for many years. Nonetheless, medical agencies fail to consider the security risk associated with healthcare IoT devices, which are readily compromised and cause potential threats to authentication and encryption procedures. Existing cloud computing methods like homomorphic encryption and the elliptic curve cryptography are unable to meet the security, identity, authentication, and security needs of healthcare IoT devices. The majority of conventional healthcare IoT algorithms lack secure data transmission. Therefore, fog computing is introduced to overcome the problems of IoT device verification, authentication, and identification for scalable and secure transmission of data. In this research manuscript, fog computing includes a hybrid mathematical model: Elliptic Curve Cryptography (ECC) and Proxy Re-encryption (PR) with Enhanced Salp Swarm Algorithm (ESSA) for IoT device verification, identification, and authentication of EHRs. ESSA is incorporated into the PR algorithm to determine the optimal key size and parameters of the PR algorithm. Specifically, in the ESSA, a Whale Optimization Algorithm (WOA) is integrated with the conventional Salp Swarm Algorithm (SSA) to enhance its global and local search processes. The primary objective of the proposed mathematical model is to further secure data sharing in the real time services. The extensive experimental analysis shows that the proposed model approximately reduced 60 Milliseconds (ms) to 18 milliseconds of processing time and improved 25% to 3% of reliability, compared to the traditional cryptographic algorithms. Additionally, the proposed model obtains a communication cost of 4260 bits with a memory usage of 680 bytes in the context of security analysis.

Applying the Taguchi Method to Improve Key Parameters of Extrusion Vacuum-Forming Quality.

This research investigates the control of thickness and weight in plastic extrusion vacuum-thermoforming products to identify optimal key parameters for cost reduction and energy savings. The initial step involves identifying crucial influencing factors. In this step, the Delphi technique was employed through a questionnaire administered to a panel of expert scholars to ensure minimal error and maximal reliability in determining key influencing factors. Consensus was sought to establish appropriateness and consistency. Subsequently, the Taguchi method was applied for quality design and planning of the extrusion vacuum-forming process. The experimental design parameters were selected using an L18 (21 × 37) orthogonal array, and the desired quality characteristics were determined. Comparative analysis of quantitative production data from two consecutive experiments was conducted, and based on F-values and contribution analysis, the combination of control factors maximizing the Signal-to-Noise (S/N) ratio was identified. The objective is to seek optimal parameters for improving the quality of the plastic polypropylene (PP cup lid) manufacturing process, reducing process variability, and identifying the most robust production conditions. Through multiple actual production prediction experiments, it was determined that five control factors, "polypropylene new material ratio," "T-die lips adjustment thickness", "mirror wheel temperature stability", "molding vacuum pressure time", and "forming mold area design", contribute to the maximization of the S/N ratio, i.e., minimizing variability. Statistical validation confirms a significant improvement in product quality and weight control. Noteworthily, the quality control model and experimental design parameters established in this study are also applicable to other plastic products and bio-based materials, such as PET, HIPS, and biodegradable PLA lids with added calcium carbonate. The results of the experimental production demonstrate its ability to consistently control product weight within the range of 3.4 ± 0.1 g, approaching the specified tolerance limits. This capability results in approximately 2.6% cost savings in product weight, contributing significantly to achieving a company's KPI goals for environmental conservation, energy efficiency, and operational cost reduction. Therefore, the findings of this study represent a substantial and tangible contribution.

Coordination control between vibration suppression and energy regeneration for vehicles driven by in-wheel motors under regenerative braking condition

In this paper, focusing on regenerative braking condition, a coordination control method is proposed to achieve the vibration suppression and braking energy recovery of vehicles driven by In-Wheel Switch Reluctance Motor (IWSRM). Firstly, a vehicle vertical dynamic model, including SRM drive system, vehicle braking system, and quarter SRM-suspension system, is established based on the analysis of the generating and vibration characteristics of IWSRM. Then, after analyzing the effects of eccentricity and electromagnetic excitation, the vertical unbalanced radial force is used as control signal in design of the control method. Moreover, for ensuring the recovered energy and reduce vertical vibration, the optimal key control parameters varying with the vehicle speed and braking strength are obtained by genetic algorithm. To achieve verification of the control method, the several vehicle performance indexes are selected as observation objects, which are analyzed and compared to other control methods, and the results indicate the control method can effectively weaken the vertical acceleration and improve the vehicle ride comfort without significantly decreasing regenerative braking energy. Finally, a hardware in the loop experiment is conducted, and the effectiveness and real-time ability of the proposed method can be verified.

Coal-rock drilling states recognition of drilling robot for rockburst prevention based on multi-sensor information fusion

Accurate recognition of coal-rock drilling sates is a prerequisite for achieving intelligent drilling pressure relief. In this paper, a novel coal-rock drilling states recognition method of drilling robot for rockburst prevention is proposed. Firstly, different coal-rock drilling signals are collected and processed by using improved antlion optimization (ALO) algorithm and variational mode decomposition (VMD). Meanwhile, the elite opposition-based learning (EOL) strategy is used to improve the global search ability and optimization performance of ALO, and the EOL-ALO is developed and employed to automatically search the optimal key parameters of VMD. Subsequently, the root mean square of frequency and kurtosis are used to extract the feature information from the decomposed signals and the singular value decomposition method is employed to reduce the dimensionality of high-dimensional feature vectors. Furthermore, an improved D-S evidence theory is developed to fuse the recognition results of support vector machine through a single sensor information and the fusion recognition framework of coal-rock drilling states is designed. Finally, a coal-rock drilling experimental platform is established and some experimental analysis is carried out. The experimental results indicate the feasibility and superiority of proposed coal-rock drilling states recognition method.

A Daily Air Pollutant Concentration Prediction Framework Combining Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Network

Precise and efficient air quality prediction plays a vital role in safeguarding public health and informing policy-making. Fine particulate matter, specifically PM2.5 and PM10, serves as a crucial indicator for assessing and managing air pollution levels. In this paper, a daily pollution concentration prediction model combining successive variational mode decomposition (SVMD) and a bidirectional long short-term memory (BiLSTM) neural network is proposed. Firstly, SVMD is used as an unsupervised feature-learning method to divide data into intrinsic mode functions (IMFs) and to extract frequency features and improve short-term trend prediction. Secondly, the BiLSTM network is introduced for supervised learning to capture small changes in the air pollutant sequence and perform prediction of the decomposed sequence. Furthermore, the Bayesian optimization (BO) algorithm is employed to identify the optimal key parameters of the BiLSTM model. Lastly, the predicted values are reconstructed to generate the final prediction results for the daily PM2.5 and PM10 datasets. The prediction performance of the proposed model is validated using the daily PM2.5 and PM10 datasets collected from the China Environmental Monitoring Center in Tianshui, Gansu, and Wuhan, Hubei. The results show that SVMD can smooth the original series more effectively than other decomposition methods, and that the BO-BiLSTM method is better than other LSTM-based models, thereby proving that the proposed model has excellent feasibility and accuracy.

Optimization of key parameters for continuous and precise nitrogen injection in goaf based on response surface methodology.

In order to solve the problems of coal spontaneous combustion, poor inerting effect of traditional nitrogen injection, and waste of resources in goaf, based on the response surface methodology and Box-Behnken combination test principle, the self-developed continuous and precise nitrogen injection and fire-fighting equipment was used to study the best possible combination of nitrogen injection position (20-90m), nitrogen injection amount (10-70 m3/min), and air supply volume (2100-2500 m3/min), aiming to minimize the width of the oxidation zone and CO concentration in goaf. The optimal key parameters of continuous precise nitrogen injection were determined as follows: nitrogen injection position 54.17m, nitrogen injection amount 31.04 m3/min, and air supply 2484.81 m3/min. Under this condition, the width of the oxidation zone was 29.21 ± 0.3m and the CO concentration was 28.1 ± 4.4ppm, which were similar to the predicted results of the model (the width of the oxidation zone was 29.41m; CO concentration was 27.28ppm). The reliability of the model was verified. These preliminary studies have achieved the purpose of rapid control of the fire in the whole region of the goaf and provided valuable lessons for similar nitrogen injection fire prevention and extinguishing technologies in goaf.

Adaptive and blind audio watermarking algorithm based on dither modulation and butterfly optimization algorithm.

How to improve the robustness to resist attacks and how to adaptively match the key parameters of the watermarking algorithm with the performance requirements to achieve the best performance in different applications are two hot issues in the research of audio watermarking algorithms. An adaptive and blind audio watermarking algorithm based on dither modulation and butterfly optimization algorithm (BOA) is proposed. Based on the convolution operation, a stable feature is designed to carry the watermark, which will improve the robustness by means of the stability of this feature to prevent the watermark loss. Blind extraction will be achieved only by comparing the feature value and the quantized value without the original audio. The BOA is used to optimize the key parameters of the algorithm which can be matched with the performance requirements by coding the population and constructing the fitness function. Experimental results confirm that this proposed algorithm can adaptively search for the optimal key parameters that match the performance requirements. Compared with other related algorithms in recent years, it exhibits strong robustness against various signal processing attacks and synchronization attacks.

Combined Optimization of Friction-Based Isolators in Liquid Storage Tanks

Large-scale tanks are widely used for storing chemicals and fuels. Their failure due to natural (e.g., earthquakes) and/or man-made hazards can lead to disastrous consequences. Nonetheless, they are often constructed in seismic-prone regions. For this reason, base isolation is often used for the seismic protection of large tanks, aiming to “decouple” the superstructure from the imposed ground motions. In this study, a combined optimization formulation is presented in order to further improve the seismic response of a base-isolated tank. The main aim is to optimize both the critical design parameters and the placement of the minimum number of isolators at the base of the tank. In particular, a Cuckoo Search (CS) optimizer is used to optimize the dynamic performance of liquid storage tanks, isolated either via single friction pendulum bearings (SFPB) or triple friction pendulum bearings (TFPB). The main objective is to minimize the eccentricity between the center of mass and the center of rigidity of the isolation system, while appropriate constraints are also imposed. Several cases are examined, while the results are compared with respect to isolator displacement fragility curves, as well as the reduced accelerations at the base of the tank. According to the findings of this study, the tank industry can significantly benefit from the proposed approach, as a more cost-efficient design of the base-isolation system of large-scale tanks can be achieved, i.e., using fewer isolators with optimal key parameters.

Brakerski‐Gentry‐Vaikuntanathan fully homomorphic encryption cryptography for privacy preserved data access in cloud assisted Internet of Things services using glow‐worm swarm optimization

AbstractHealth monitoring sensors are widely available to monitor patients' health remotely. The collected sensor data are sent to cloud storage and processed in a remote health monitoring system. The Internet of Things (IoT) with cloud support offers a promising answer to data exploding concerns for the ability to constrain specific gadgets. However, IoT faces many security issues when sharing data between two users because of the cloud's leverage nature. In the case of a private cloud, simple encryption techniques can ensure security. But, in the case of a public cloud, maintaining data privacy is a significant issue. To overcome this concern, an optimized Brakerski‐Gentry‐Vaikuntanathan fully homomorphic encryption (BGV‐FHE) encryption method for secured data mutuality is proposed in this article. In this method, IoT medical data are initially acquired and then encrypted using two key schemes, and the data are stored in cloud storage. The main objective of these key schemes is to select optimal key parameters. Key parameters are chosen using the glow‐worm swarm optimization and used for the encryption phase. Storing data in the cloud using a public key provides access to requested users. Meanwhile, sensitive data are encrypted using a private key and cannot be accessed unless authenticated. This proposed approach has two levels of encryption, and thus it is an efficient data secured protocol for cloud resources. Implementing the optimized BGV‐FHE scheme based encryption scheme in Python, two datasets result in an accuracy level of 77% and 91%, respectively.

Optimization of Power and Thermal Management System of Hypersonic Vehicle with Finite Heat Sink of Fuel

The scramjet of hypersonic vehicles faces severe high-temperature challenges, but the heat sink available for scramjet cooling is extremely finite. It is necessary to optimize its power and thermal management system (PTMS) with a finite heat sink of hydrocarbon fuel. This paper proposes a two-level optimization method for the PTMS of hypersonic vehicles at Mach 6. The PTMS is based on a supercritical carbon dioxide (SCO2) closed Brayton cycle, and its heat sink is airborne hydrocarbon fuel. System-level optimization aims to obtain the optimal system parameters for the PTMS. The minimum fuel weight penalty and the minimum heat sink consumption of fuel are the optimization objectives. The segmental (SEG) method is used to analyze the internal temperature distribution of fuel–SCO2 heat exchangers in the system-level optimal solution set. This ensures the selected optimal solutions meet the requirement of a pinch temperature difference greater than or equal to 10 °C. Further, the component-level optimization for the fuel–SCO2 heat exchanger is carried out based on the selected optimal solutions. The lightest weight of the heat exchanger and the minimum entropy production are the optimization objectives in this step. Finally, the optimal system parameters and the optimal key component parameters can be searched using this presented two-level optimization method.

Attribute-Based Adaptive Homomorphic Encryption for Big Data Security.

There is a drastic increase in Internet usage across the globe, thanks to mobile phone penetration. This extreme Internet usage generates huge volumes of data, in other terms, big data. Security and privacy are the main issues to be considered in big data management. Hence, in this article, Attribute-based Adaptive Homomorphic Encryption (AAHE) is developed to enhance the security of big data. In the proposed methodology, Oppositional Based Black Widow Optimization (OBWO) is introduced to select the optimal key parameters by following the AAHE method. By considering oppositional function, Black Widow Optimization (BWO) convergence analysis was enhanced. The proposed methodology has different processes, namely, process setup, encryption, and decryption processes. The researcher evaluated the proposed methodology with non-abelian rings and the homomorphism process in ciphertext format. Further, it is also utilized in improving one-way security related to the conjugacy examination issue. Afterward, homomorphic encryption is developed to secure the big data. The study considered two types of big data such as adult datasets and anonymous Microsoft web datasets to validate the proposed methodology. With the help of performance metrics such as encryption time, decryption time, key size, processing time, downloading, and uploading time, the proposed method was evaluated and compared against conventional cryptography techniques such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC). Further, the key generation process was also compared against conventional methods such as BWO, Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The results established that the proposed method is supreme than the compared methods and can be applied in real time in near future.

Volcanic lithology identification based on parameter-optimized GBDT algorithm: A case study in the Jilin Oilfield, Songliao Basin, NE China

The reservoir rocks in the volcanic strata of the Jilin oil field are characterized by great complexity and diversity in composition and structure of lithology. To enhance the rate of lithology identification in subsurface is very laborious. However, lithology identification is often ignored in quantitative studies, though it is the basis for reservoir characterization. In this paper, an ensemble learning algorithm named gradient boosting decision tree (GBDT) was used to establish the classification model for the volcanic lithology identification of the Lower Cretaceous Yingcheng Formation in the Songliao Basin, NE China. At the same time, support vector machine (SVM), logistic regression (LR) and decision tree (DT) classification models were also adopted in contrast with the classification accuracy of GBDT model. Subsequently, the optimal key parameters for each model were determined by employing validation curves and GridSearchCv. These results indicate that the GBDT model is superior to the single classifier and can accurately distinguish the lithologic interface of breccia tuff and rhyolite. Moreover, it also has better recognition ability for thin layer. It was concluded that the ensemble learning algorithm GBDT has significantly enhanced the accuracy of lithology identification and can be used as a lithologic identification technology.

A self-Adaptive CNN with PSO for bearing fault diagnosis

Convolutional neural network (CNN) is now widely applied in bearing fault diagnosis, but the design of network structure or parameter tuning is time-consuming. To solve this problem, a particle swarm optimization (PSO) algorithm is used to optimize the network structure and a self-adaptive CNN is proposed in this paper. In the proposed method, a theoretical method is used to automatically determine the window size of short-time Fourier transform (STFT). To reduce the computation time, PSO is only applied to obtain the optimal key parameters in CNN with a small number of training samples and a small epoch number. To simplify the CNN structure, a fitness function considering the numbers of kernels and neuron nodes is used in PSO. According to the verification experiments for two well-known public datasets, the proposed method can get higher accuracy than other state-of-art methods. Furthermore, the parameters that are required to be input only involve the bearing parameters, so the proposed method can be applied in industry readily.

Optimizations and comparison of three two-stage Rankine cycles under different heat source temperatures and NG distribution pressures

Two-stage Rankine cycles draw more and more attention with the increasing trade of LNG (liquefied natural gas) and the recovery of both the cold exergy of LNG and the thermal exergy of low temperature heat source. To figure out the best configuration of two-stage Rankine cycles, three mostly studied configuration, cascade two-stage ORC (organic Rankine cycle), parallel two-stage ORC and two-condensation ORC are optimized and compared under different heat source temperatures and different NG distribution pressures. Ten substances are selected as the working fluids. The evaporation, condensation and expander inlet temperatures of both ORCs and the LNG regasification pressure are optimized to obtain the maximum exergy efficiency. The results show that both the increase of heat source temperature and the decrease of NG distribution pressure can improve the exergy efficiencies greatly. The increase of heat source temperature not only increases the optimal key parameters but also affects the ranking of optimal fluids, while the increase of NG distribution pressure only affects the optimal parameters. Different from conventional Rankine cycles, there is also an optimal condensation temperature to match the heat absorption line of LNG and it increases with increase of the NG distribution pressure. The LNG regasification pressure is a key parameter which determines the proportion of power outputs between ORCs and direct expansion cycles. The thermodynamic performances of parallel two-stage ORC and two-condensation ORC are almost the same, which are obviously better than that of cascade two-stage ORC. Both parallel two-stage ORC and two-condensation ORC with ammonia as the working fluid achieve the maximum efficiency of 30.54% under the heat source temperature of 200 ℃ and the NG distribution pressure of 3 MPa. Two-condensation ORC is the optimum configuration with both good thermodynamic and economic performances. The lowest electricity production cost of two-condensation ORC achieved in this paper is 0.033$/kW·h. The environmental side effect caused by working fluid leakage is negligible compared with the saved CO2 emission via the electricity generation.

Recommendation of optimal design and operation parameters for constructed wetland for sludge treatment based on the effect of hydraulic retention time, sludge loading rate and vegetation

Industrial sludge is a by-product which is enormously generated in wastewater treatment plants. Constructed wetland for sludge treatment (CWST) is a low cost, effective technology. This study investigated the effect of various design and operation parameters on the efficiency of four pilot-scale CWSTs to determine the optimal parameters by using the Analytic Hierarchy Process (AHP) for Decision- Making. The wetland units were planted with Phragmites australis or Typha angustifolia, operated with four sludge loading rate (SLR) (50, 60, 70 and 80 L/m2) and monitored in six different hydraulic retention time (HRT) (2,5,7,9,12 and 14 days). AHP results provided the optimal key parameters (vegetation of P. australis, 14-day HRT, SLR of 60 L/m2) which gave the most effective sludge treatment, reducing 99.8%, 95.16% and 98.23% for COD, TKN and TP, respectively. The results also showed that HRT, SLR and vegetation remarkably affected to the efficiency of CWST. In addition, AHP is an effective method to determine the optimal design and operation parameters of CWST.

A Study on the CO2-Enhanced Water Recovery Efficiency and Reservoir Pressure Control Strategies

CO2 geological storage (CGS) proved to be an effective way to mitigate greenhouse gas emissions, and CO2-enhanced water recovery (CO2-EWR) technology may improve the efficiency of CO2 injection and saline water production with potential economic value as a means of storing CO2 and supplying cooling water to power plants. Moreover, the continuous injection of CO2 may cause a sharp increase for pressure in the reservoir system, so it is important to determine reasonable reservoir pressure control strategies to ensure the safety of the CGS project. Based upon the typical formation parameters of the China Geological Survey CO2-EWR test site in the eastern Junggar Basin, a series of three-dimensional (3D) injection-extraction models with fully coupled wellbores and reservoirs were established to evaluate the effect of the number of production wells and the well spacing on the enhanced efficiency of CO2 storage and saline production. The optimal key parameters that control reservoir pressure evolution over time are determined. The numerical results show that a smaller spacing between injection and production wells and a larger number of production wells can enhance not only the CO2 injection capacity but also the saline water production capacity. The effect of the number of production wells on the injection capacity and production capacity is more significant than that of well spacing, and the simulation scenario with 2 production wells, one injection well, and a well spacing of 2 km is more reasonable in the demonstration project of Junggar Basin. CO2-EWR technology can effectively control the evolution of the reservoir pressure and offset the sharp increase in reservoir pressure caused by CO2 injection and the sharp decrease of reservoir pressure caused by saline production. The main controlling factors of pressure evolution at a certain spatial point in a reservoir change with time. The monitoring pressure drops at the beginning and is controlled by the extraction of water. Subsequently, the injection of CO2 plays a dominant role in the increase of reservoir pressure. Overall, the results of analysis provide a guide and reference for the CO2-EWR site selection, as well as the practical placement of wells.

Numerical study of flocculation settling and thickening of whole-tailings in deep cone thickener using CFD approach

Deep cone thickener (DCT) is the key equipment in cemented paste backfill (CPB), so it is essential to study the flocculation settling and thickening characteristics of the whole-tailings in DCT. Coupled with population balance model (PBM), computational fluid dynamics (CFD) was used to study the characteristics, namely particle size distribution (PSD) and underflow concentration in DCT. Based on actual production, the effects of rake rotational speed, feed rate and tailings slurry concentration were simulated and analyzed in a certain range. The PSD varied with rake rational speed, feed rate and tailings slurry concentration almost in the same trend, but the influence of feed rate was less than that of rake rational speed and tailings slurry concentration. The underflow concentration increased at first and then declined with rake rational speed and feed rate, but it rose and fell with the tailings slurry concentration. Finally, the optimal key parameters on the flocculation settling and thickening of the whole-tailings in DCT were obtained: rake rotational speed of 17 r/min, feed rate of 3.25 m3/h and tailings slurry concentration of 20%, giving the reference values to the industrial production in Baishitamu Copper Mine.

Pricing Strategies of Logistics Distribution Services for Perishable Commodities

The problem of pricing distribution services is challenging due to the loss in value of product during its distribution process. Four logistics service pricing strategies are constructed in this study, including fixed pricing model, fixed pricing model with time constraints, dynamic pricing model, and dynamic pricing model with time constraints in combination with factors, such as the distribution time, customer satisfaction, optimal pricing, etc. By analyzing the relationship between optimal pricing and key parameters (such as the value of the decay index, the satisfaction of consumers, dispatch time, and the storage cost of the commodity), it is found that the larger the value of the attenuation coefficient, the easier the perishable goods become spoilage, which leads to lower distribution prices and impacts consumer satisfaction. Moreover, the analysis of the average profit of the logistics service providers in these four pricing models shows that the average profit in the dynamic pricing model with time constraints is better. Finally, a numerical experiment is given to support the findings.

Gust response and body freedom flutter of a flying-wing aircraft with a passive gust alleviation device

The effectiveness of a passive gust alleviation device (PGAD) mounted at the wingtip of aircraft in conventional and flying-wing configurations have been studied in previous research. However the PGAD influence on the aeroelastic stability in particular the body freedom flutter (BFF) of a flying-wing aircraft remains as a concern. This present investigation is focused on evaluating the beneficial effect of PGAD on both gust load alleviation and BFF of a small flying-wing aircraft of high aspect ratio wing made of composite. A small range of (1-cos) type of gust load has been considered to select a representative critical gust load case for the study. A parametric study indicates that there is a narrow band of optimal key parameters for the PGAD design. Subsequently a set of optimal parameters is selected to further the analysis of the PGAD mechanism. The case study results show that the PGAD can make the bending moment at the wing root due to gust reduced by 16%. In addition, the BFF speed of the flying-wing aircraft is increased by 4.2%. The investigation reveals that the PGAD mode and its interaction with the wing bending mode and short period oscillation of the aircraft can have beneficial aeroelastic effect on both gust alleviation and flutter suppression.

Key parameters decision for cloud computing: Insights from a multiple game model

SummaryIn service‐oriented cloud computing systems (CCSs), the aim of cloud service organizers (CSOs) is to achieve maximum profit by collecting metadata with low cost from big data reporters (BDRs) and to provide advanced services to customers at a high price. In these systems, BDRs receive payoffs by reporting metadata to CSOs and exchanging metadata with other BDRs, and customers expect to get high‐quality services at a low price. However, because the missing of a critical parameters decision model in such service‐oriented CCSs, it is difficult to measure key parameters in CCSs such as price and quality of services in the competitive market. In this paper, we propose a multiple game (MG) model to formulate the critical parameters decision process. In the MG model, there are multiple games: games among BDRs and games among CSOs under the rule of “survival of the fittest,” games between BDRs and CSOs under the rule of “the highest payoff first,” and games between customers and CSOs under the rule of “the lowest price and the highest quality of service (QoS) first.” With the proposed multiple game (MG) model, the optimal key parameters can be obtained and the Pareto‐optimal equilibrium point can be achieved. Extensive simulation results demonstrate the effectiveness and efficiency of the proposed MG model in dynamically deciding key parameters in CCSs.