# Big data services drive mobile crowd embedded opportunistic control mechanism for biological engineering

- Hai-Chao Wang
^{1, 2}Email author and - Zeng Dong
^{1}

**2016**:13

https://doi.org/10.1186/s13639-016-0034-x

© Wang and Dong. 2016

**Received: **11 March 2016

**Accepted: **13 May 2016

**Published: **1 June 2016

## Abstract

Big data of biological engineering and mobile control increase the complexity of system control. In order to resolve the above problems and improve biological engineering system performance, this paper proposes a large data-driven and mobile crowd embedded opportunistic control mechanism. First of all, the measurement model of established big data-driven biological embedded engineering was proposed based on the research of biological engineering with non-linear and unpredictability. Based on the characteristics of the mobile crowd terminal perception outside interference, we proposed the mobile crowd biological engineering optimization opportunities control mechanism. The experimental results show that the established control mechanism in a long-term, large-scale performance still has high performance and high temperature. In addition, under different pressure, the rate of convergence of the scheme established is superior to the biological engineering control scheme based on coordination control.

## Keywords

## 1 Introduction

How to combine biological engineering with computer technology, software engineering, and a control engineering application has become a research hotspot in recent years [1]. At the same time, biological engineering multi-objective optimization [2] has gradually brought many challenges, such as the control strategy of biological reaction resistance to interference [3]. It has become the research key issue that how to improve the control precision of biological engineering [4] and provide a performance guarantee for large-scale biological engineering [5].

Regarding biological big data, Nounou et al. used Wavelet-based multiscale filtering to mine the important features in measured biological data [6]. Stegmayer presented a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation [7]. Chatziioannou et al. presented the web-based grid application that could exploit grid infrastructures for distributed data processing and management through a generic, consistent, computational analysis framework [8]. Nguyen et al. presented an alternative conception for local data integration based on a hybrid flat file, a generic data model, and configuration rules [9]. Carpendale et al. studied biological data visualization [10].

Mobile computing is closely associated with biological response. Atakan et al. proposed the biological foraging-inspired communication algorithm for the energy-efficient and spectrum-aware communication requirements [11]. Lin proposed the wireless power transfer scheme for cell phones or other mobile communication devices and biological implications [12]. Liu et al. studied the biological characteristic authentication and multimedia signal fast encoding over 5G for improving the security level of the Internet [13]. Lin designed the wireless power transfer for mobile applications and health effects [14]. Tsompanas proposed a CA model used as a virtual, easy-to-access, and bio-mimicking laboratory emulator, which would economize large time periods [15].

Regarding the relation of system control and biological engineering, Qian et al. analyzed and simulated an infinite-horizon optimal feedback control model, with linear plants, that contains both control-dependent and control-independent noise and optimizes steady-state accuracy and energetic costs per unit time [16]. Chowdhury et al. demonstrated the effectiveness of the approach by transporting a yeast cell using four different types of gripper formations along collision-free paths on our OT setup. We analyzed the performance of the proposed gripper formations with respect to their maximum transport speeds and the laser intensity experienced by the cell that depends on the laser power used [17]. Yao et al. proposed a newly developed all-solid-state nanosecond pulse generator based on the Marx generator concept for this application [18]. Chen et al. presented a novel approach for the automated transportation of multiple cells by using robotically controlled holographic optical tweezers [19]. Nakano et al. discussed the issues concerned with transmission rate control in molecular communication, an emerging communication paradigm for bio-nan machines in an aqueous environment [20].

Based on the results of the above research, we proposed the big data services drive mobile crowd embedded opportunistic control mechanism for biological engineering.

The rest of the paper is organized as follows. “Section 2” describes the big data-driven biological embedded engineering measurement model. In “Section 3,” we design the mobile crowd biological engineering opportunistic optimization control mechanism. Performance evaluations are given in “Section 4.” We conclude the paper in “Section 5.”

## 2 Big data-driven biological embedded engineering measurement model

Biological engineering is non-linear and unpredictable, and is composed of a series of large-scale physical reactions and the coupling reaction. Biological engineering of large data sources is an iterative cell. There are some problems in a survey of biological engineering, such as the non-linear objects, the coupling of data, and time-varying data source problems. In real-time measurements of biological engineering, the characteristics of the embedded outside objects would be obtained by measuring the object’s environment in the measurement model.

Therefore, we designed an embedded project measurement model. The model can obtain the bioengineering embedded external factors and the influence of the normal reaction of biological engineering. The impact model have to obtain the reverse metabolism energy consumption of embedded biological.

We have therefore designed an embedded biological engineering piece of equipment. The equipment outside of biological data should embed the required amount of energy. The device can obtain a biological engineering survey object scale. The equipment size and embedded objects of the reproduction life cycle would be compared by measuring the parameters of biological engineering. According to the measurement parameters, embedded devices for biological embedded engineering guarantee stability. There are some peripheral terminal interfaces in the embedded devices. These interfaces are used to establish the high coupling reaction environment for the intrinsic biological reproduction. The biological response module was designed in the embedded equipment.

_{b}is the embedded biological engineering response data. X denotes the iteration of the embedded data volume. X

_{max}denotes the largest amount of data. X

_{min}denotes the smallest amount of data. N is the embedded biological scale of biological engineering.

_{b}is the embedded biological engineering reaction cycle. α is the impedance coefficient of embedded devices. W is the intrinsic biological response module and embedded biological response module angle with the outside world. T is the iteration biological reaction cycle. T

_{i}is the biological iteration reaction cycle.

Here, i_{b} is embedded equipment ability of immunity.

Data buffer can effectively reduce the jitter of the data. For equations (1), (2), and (3) to establish the best matching principle can be a real-time perception biological engineering response to environmental changes. This principle can be applied to establish the effective matching between the data source and the data quantity.

_{M}vector, as shown in equation (4).

Here, k is the active biological response scale of sample points, u is embedded devices activated sample points, and v is the biological response data buffer factor for embedded devices.

_{W}, embedded weight E

_{W}, and buffer weight B

_{W}. The above big data characteristics have to meet the relationship shown in equation (5).

Here, B_{DC} is the business data catalog comprehensive weight vector. The empty L_{W} vector and E_{W} vector intersection is embedded in order to ensure the equipment does not affect the big data linear transformation. The change from R^{N} to R^{u} showed that N sample points of non-linear space would be mapping with the actual space biological response.

## 3 Mobile crowd biological engineering opportunistic optimization control mechanism

In the biological engineering optimization method, we mainly considered the random interference factors on biological engineering and presented a biological engineering control mechanism based on mobile crowd and its architecture. The architecture can solve the problem of the random interference and time variability of biological engineering by moving the crowd and big data.

Here, d_{input} is the input data parameters, d_{output} is the output data parameters, m is the data port number, n is the output port number, and R^{m}*^{n} is the port matrix space.

- (1)To keep the mobile crowd nonlinear characteristics, the function f (d
_{input}d_{output}) have to be the Multi - order Guide. The guide can be shown in equation (8).$$ \left\{\begin{array}{c}\hfill \frac{d(f)}{dt}\ge {y}_{avg}\hfill \\ {}\hfill \frac{df}{d\left(t-k\right)}\ge {y}_{avg}\hfill \\ {}\hfill {y}_{avg}=\frac{{\displaystyle {\sum}_{i=1}^k}{y}_i}{k}\hfill \end{array}\right. $$(8) - (2)The model of biological engineering in the matrix mapping relationship as shown in equations (9) and (10).$$ y(t)\left|{}_{t\to \infty}\right.={\displaystyle \sum_{i=1}^t}{y}_iT\left(\sqrt{\left({y}_{i-1},{y}_i\right)}\right) $$(9)$$ \left[\begin{array}{ccc}\hfill {y}_1\hfill & \hfill \cdots \hfill & \hfill {y}_m\hfill \\ {}\hfill {y}_{i-1}\hfill & \hfill \cdots \hfill & \hfill {y}_{m+i-1}\hfill \\ {}\hfill {y}_t\hfill & \hfill \cdots \hfill & \hfill {y}_{m+t-1}\hfill \end{array}\right]T=\left[\begin{array}{c}\hfill T\left({y}_1,{y}_2\right)\hfill \\ {}\hfill T\left({y}_i,{y}_{m+i-1}\right)\hfill \\ {}\hfill T\left({y}_t,{y}_{m+t-1}\right)\hfill \end{array}\right] $$(10)
Here, the matrix T denotes the measurement model.

- (3)Biological response of mobile crowd wisdom kernel function as shown in equation (11). The kernel function must be non-linear after dealing with equation (12) to achieve perception of biological engineering control.$$ \left\{\begin{array}{l}{K}_B=\left(\frac{{\left\Vert {y}_t-{y}_{t-k}\right\Vert}^2}{\delta}\sqrt{T_t}\right)\hfill \\ {}{T}_x=y(x)\left|{}_{x\to t}\right.\hfill \end{array}\right. $$(11)$$ \left\{\begin{array}{l}y(t)={\displaystyle \sum_{i=1}^t}T\left(\sqrt{\left({y}_{i-1},{y}_i\right)}\right){K}_B\hfill \\ {}{K}_B{T}_x\ge \sqrt{y(t)\left|{}_{t\to \infty}\right.}\hfill \end{array}\right. $$(12)

Here, the matrix G is the weighting factor matrix. O_{C} is the control matrix.

## 4 Performance evaluation

To analyze and validate the proposed scheme in the biological engineering control effect, we designed two sets of biological experiments. In order to verify the proposed scheme to the performance of the control mechanism and performance of embedded devices, there were two crowds of biological experiments in different experimental environments. The two test crowds adopted different experimental material.

Experiment settings

Parameters | Value | Parameters | Values |
---|---|---|---|

Tank capacity | 50 L | Glutamic acid dosage | 0.1–0.5*50 L |

Time | 28–50 h | Interference strains | Lysine |

Tank pressure | 0.01–0.1 Mpa | Experimental temperature | 20–40 °C |

Number of mobile embedded devices | 2–5 | Number of sample | 20 |

Sampling interval | 20 min | Interference frequency | 1–5 per min |

Opportunity control weight | 0.2–0.8 | Interference capacity | 0.2–0.8 L |

## 5 Conclusions

Large-scale biological engineering data increases the system control complexity. At the same time, there is a decrease in the mobile biological reaction control system performance. In order to solve the above problems, this paper puts forward a big data-driven and mobile crowd embedded chance control mechanism. On the one hand, the proposed data-driven larger embedded engineering measurement model can meet the demand of non-linear bioengineering and solve the problem of low accuracy. On the other hand, the embedded terminal actively eliminate random factors interference by sensing external interference factors. We proposed the mobile crowd biological engineering optimization opportunities control mechanism. Based on biological reaction time, experiment equipment size, and temperature of the experimental conditions, the established control mechanism can smooth the biological data jitter, reduce data error, and shorten the response delay. At the same time, the biological reaction scheme established by the convergence speed of pressure sensitivity is significantly lower than the biological engineering control scheme based on coordination control.

## Declarations

### Acknowledgements

This work is supported in part by General projects supported by the natural science in Anhui Province Department of Education (KJ2014B186) and Suzhou Institute of Characteristic Planting Seedling Production Engineering Technology Research Center Open Issues (2012YKF30).

### Competing interests

The authors declare that they have no competing interests.

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

## Authors’ Affiliations

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