Perspective Chapter: Edge-Cloud Collaboration for Industrial IoT – An Online Approach

*You Shi, Yuye Yang, Changyan Yi, Bing Chen and Jun Cai*

## **Abstract**

In this chapter, we take the Industrial Internet of Things (IIoT) as the background for studying the energy-saving resource management framework to control the cloud center (CC), edge server (ES), and terminal equipment in a closed loop. In this framework, industrial sensors collect data and transmit it to the ES for aggregation. These data form computing tasks for data analysis. Our goal is to minimize the energy consumption of the whole system while ensuring satisfied data processing accuracy and service delay of all IIoT tasks. We formulate the ES preprocessing mode selection, sensor sampling rate adaptation, and edge cloud computing and communication resource allocation as a joint optimization problem. Due to the random arrival of data and time-varying channel conditions, we introduce an online dynamic algorithm with low complexity, which efficiently solves the problem.

**Keywords:** edge-cloud collaboration, industrial IoT, preprocessing method selection, sampling rate adaption, computing and communication resource allocation

#### **1. Introduction**

Due to the rapid development of 5G and Industry 4.0, IIoT-sensing devices, such as smart manufacturing, smart plants, and smart industrial services, have generated a abundant of data. For traditional cloud computing, it is considerably challenging to process such massive data efficiently. Fortunately, the edge computing can significantly reduce the cloud computing's computing load, and thus has been proposed as supplementary paradigm recently [1]. In industrial area, the edge server (ES) close to the data source can be enabled to process some computing tasks, so as to provide more effective data processing services and lead to less communication overhead.

Although some researchers have proposed edge cloud collaboration to increase the industrial systems' operational and energy efficiencies, there are still some inherent while unaddressed limitations. Particularly, computing and communication resources of the edge-cloud collaboration are relatively limited [2]. Hence, if closed-loop optimization is not considered in the management of cloud, ES, and terminal devices, edge cloud collaboration cannot fully make use of its advantages. Some relevant researchers have studied the resource allocation problem of IIoT's edge cloud

collaboration [3, 4], including delay awareness, price-based service scheduling [2, 3], and energy-aware resource allocation [4, 5].

However, data collection and data analysis have some special requirements which will be affected by the complex industrial environment, which has been ignored by most studies: (*i*) In IIoT system, industrial equipment needs high-precision adjustment. Any small error may cause industrial equipment to make wrong behavior and cause serious troubles. [5]. Therefore, ensuring the accuracy of data processing in IIoT service is very important. This motivates the investigation and optimization of edge cloud management variables such as processing mode and sampling rate. (*ii*) There are commonly a variety of industrial noises in practical applications, such as electromagnetic noise [6]. Because of these, we cannot analyze the data collected by the sensor directly [7]. Hence, enabling data preprocessing at ESs is necessary (for example, data cleaning [7] and data denoising [6]) before conveying data to the cloud. This necessitates a balance of optimal resource allocations between the cloud and ESs. (*iii*) Since the IIoT system environment is always complex and there are random data arrival and time-varying channels, we are required to carefully manage the computing and communication resources with the guarantee of a long-term performance. Otherwise, the system will soon run out of limited CPU resources and network capacity [8]. As a result, the system efficiency will be seriously affected.

However, solving the aforementioned issues to achieve the closed-loop management is very challenging: (*i*) It is intuitive that the processing accuracy is increasing with the sampling rate. However, increasing the rate is equivalent to the increase of the computing load, and thus will also increase transmission delay and computing energy consumption, leading to the degradation of the system performance [5]. This implies that sampling rate must be carefully chosen for balancing different performance indicators. (*ii*) In practical applications, different preprocessing methods have different computing resource requirements and corresponding processing performance. In addition, data's edge preprocessing will bring extra computing delay and energy consumption. It is difficult to optimize service delay, processing accuracy, and power consumption with mutual trade-offs. (*iii*) In response to the random data's arrival and the time-varying channel, we need to jointly optimize and dynamically adjust the selection of preprocessing methods, sampling rate, and resource allocation. However, it is hard if not impossible to obtain random information of dynamic network in time, which is a necessary condition for long-term optimization of system performance. This will obviously lead to incomplete decision information of IIoT system. Lyapunov optimization method is often used to solve such problems. However, in IIoT applications, decision variables (such as preprocessing method and sampling rate) are often integers, and constraints like processing accuracy and service delay are sometimes nonlinear. This makes the problem much more complex than traditional ones.

#### **2. Chapter contributions and organization**

In this book chapter, we study an IIoT energy resource management framework. This framework is constructed on the basis of edge cloud collaboration, and aims to conduct a closed-loop management on the cloud center (CC), ESs, and terminal devices. To be more specific, in this chapter, we consider to optimize the selection of ESs' preprocessing mode, terminal devices'sampling rates, edge cloud computing and communication resource allocation for jointly to minimize the system's energy consumption. Meanwhile, we ensure service delay and accuracy of data processing in the

long term. In addition, considering the random arrival of data and time-varying channel conditions, we introduce a dynamic online algorithm with a low complexity to solve this problem.

In particular, based on the network state of the current time slot, we decompose the long-term optimization problem into a sequence of deterministic instantaneous subproblems. After that, we define a continuous probability model and take into account the future influences, and by such we use the Markov approximation algorithm to solve these subproblems to near optimal. Finally, we theoretically analyze the system performance in terms of its asymptotic upper bound.

This chapter's main contributions are listed as follows.


This chapter's rest contents are listed below. Section 3 models the IIoT edge-cloud collaboration's system. Section 4 formulates the corresponding joint online optimization problem for the closed-loop resource management. Section 5 introduces a novel algorithm with a low complexity based on the Markov approximation and Lyapunov optimization. Section 6 analyzes theoretical performance. Section 7 demonstrates the simulation outcomes and Section 8 concludes the chapter.
