IoT applications using cross neural network to improve performance - SkillBakery Studios


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Friday, October 30, 2020

IoT applications using cross neural network to improve performance

 Missing values are common in the Internet of Things (IoT) environment for various reasons, including regular maintenance or malfunction. In time-series prediction in the IoT, missing values may have a relationship with the target labels, and their missing patterns result in informative missingness. Thus, missing values can be a barrier to achieving high accuracy of prediction and analysis in data mining in the IoT. Although several methods have been proposed to estimate values that are missing, few studies have investigated the comparison of interpolation methods using conventional and Neural Network models. There has thus far been relatively little research into interpolation methods in the IoT environment. To address these problems, this research work presents the use of linear regression artificial neural networks, and long short-term memory to make time-series predictions for missing values. Finally, a full comparison and analysis of interpolation methods are presented. We believe that these findings can be of value to future work in IoT applications.

Time-series data are found in a wide range of real scenarios and various applications. Missing values have many causes, such as regular maintenance, malfunction, and lowering costs. Missing values might include variables related to target categories in machine learning tasks. Therefore, informative missingness will happen if the patterns of missing data are ignored [1]. Missing values can thus be a barrier to the achievement of high accuracy of prediction and analysis in data mining tasks. For example, air quality prediction is an IoT time-series application. To prevent harm caused by PM2.5, people can refer to the results of a prediction and then decide whether to go outside or stay indoors. However, air quality prediction is difficult because of several complex factors. One of the factors is that the existing sensors for missingness are not sufficiently accurate [2]. Therefore, we cannot ignore missing values arbitrarily, because each data point regarding air quality is valuable and the data sequence is important. In such situations, the missing patterns will lead to inaccurate predictions. Therefore, we must deal with missing values using approaches such as interpolation. However, there has thus far been relatively little research into the use of interpolation methods in the IoT environment. To analyze existing interpolation methods and identify the most competitive methods,  believe that these findings will benefit future IoT applications. The problems of missing values can be divided into three types as follows: Each type has a corresponding method of handling [4].

·       Missing Completely at Random (MCAR): In this type, the missing values are independent and might not be influenced by their own values or those of other attributes.

·       Missing at Random (MAR): This type of missing value might be influenced by the values of some other attributes instead of the attributes related to itself.

·       Missing Not at Random (MNAR): This type of missing value might be influenced by the values of attributes related to itself [5, 6]. To deal with missing values, a simple technique is to remove them from the data analysis or machine learning task. This way might be feasible in large, balanced datasets.

However, in most cases, data are imbalanced, and limited amounts of data are available. Missing values are hard to ignore in IoT applications because the most common applications are monitoring or time-series tasks under continuous conditions. Therefore, the most frequently used techniques are interpolation-based. Interpolation involves replacing each missing value with an appropriate one [6]. Interpolation methods often use the mean, median, or a predefined value of the data attribute which has the missing values, or predictive values calculated from the patterns of the missing data [7]. When the pattern of missing data is of the MAR or MCAR

type, such interpolation techniques can be used. They are valuable when each transaction or record is very important in the dataset, or if there is a single transaction without missing patterns across many data attributes [8]. Listwise deletion or maximum likelihood methods can be used to deal with MCAR patterns. In contrast, there are no general methods to deal with missing patterns of the MNAR type. In general, the air quality might belong to one of three types. The type of air quality might depend on the area because the generation of air pollutants is complex. Therefore, the previous works usually discuss the three types. The interpolation approaches can be divided into single interpolation and model-based interpolation as follows: 1. Single interpolation usually uses a constant value, such as a sample mean, to replace missing values. Although single interpolation is easy to do, it impairs estimates of variance and covariance, because it neglects the relationships between missing values and other attributes in the data. Therefore, the non-missing values or features can be used to train regression models for predicting the missing values because the regression models consider the relationships between data attributes.

The most powerful interpolation approaches are usually model-based. Model-based interpolation methods estimate the most probable value for a missing value by maximizing the probability using the non-missing values. Such methods aim to recover the real data as accurately as possible. The most well-known model is linear regression. For example,

 Proposed a recursive method to build and update a linear regression model without using previous transactions or records. Therefore, this study focused mainly on model-based interpolation approaches. The most well-known conventional regression models are linear regression and support vector regression, and these two models have previously been used to deal with missing values. Recent work has shown that deep learning models can achieve better performance in time-series prediction than other methods. This study aimed to analyze and compare interpolation performance in IoT applications using conventional learning and deep learning models.

IoT device identification

Device identification refers to a mechanism that predicts the type of an internet-of-thing (IoT) the device according to the device’s characteristics. Understanding the identifications of IoT devices is critical to service providers (e.g. mobile apps) for commercial purposes (e.g. advertising), and infrastructure (e.g. system/network) managers for security (e.g. finding vulnerable devices). Specifically, we define the IoT device identification problem as follows: the input is various data collected from a device, e.g. sensors’ data, network data, etc.; the output is a label for the IoT device indicating the type of the device. Figure 2 also shows the model for device identification. This problem receives extensive attention in recent years due to the proliferation of mobile computing, IoT deployment, and smart everything. Since this area is rapidly evolving due to fast wireless and mobile technology innovations, we review recent efforts on leveraging machine learning to identify IoT devices in the last five years. Table 2 presents a short summary of the reviewed works. It is worth noting that proactive approaches are based on IP address, MAC addresses, unique device numbers by manufacturer, or operating system are not stable; thus, researchers turned to machine learning approaches, which may also be passive identifications. In the following, we first review proposed approaches that tried to identify mobile phones, then we move to review works that aimed to identify general IoT devices

IoT fast and streaming

data Many research attempts suggested streaming data analytics that can be mainly deployed on high-performance computing systems or cloud platforms. The streaming data analytics on such frameworks is based on data parallelism and incremental processing [17]. By data parallelism, a large dataset is partitioned into several smaller datasets, on which parallel analytics are performed simultaneously. Incremental processing refers to fetching a small batch of data to be processed quickly in a pipeline of computation tasks. Although these techniques reduce time latency to return a response from the streaming data analytic framework, they are not the best possible solution for time-stringent IoT applications. By bringing streaming data analytics closer to the source of data (i.e., IoT devices or edge devices) the need for data parallelism and incremental processing is less sensible as the size of the data in the source allows it to be processed rapidly. However, bringing fast analytics on IoT devices introduces its own challenges such as limitations of computing, storage, and power resources at the source of data.


Recurrent Neural Networks (RNNs):

 In many tasks, the prediction is dependent on several previous samples such that, in addition to classifying individual samples, we also need to analyze the sequences of inputs. In such applications, a feed-forward neural network is not applicable since it assumes no dependency between input and output layers. RNNs have been developed to address this issue in sequential (e.g., speech or text) or time-series problems (sensor’s data) with various lengths. Detecting drivers’ behaviors in smart vehicles, identifying individual’s movement patterns, and estimating the energy consumption of a household are some examples where RNNs can be applied. The input to an RNN consists of both the current sample and the previously observed sample. In other words, the output of an RNN at time step t−1 affects the output at time step t. Each neuron is equipped with a feedback loop that returns the current output as an input for the next step. This structure can be expressed such that each neuron in an RNN has an internal memory that keeps the information of the computations from the previous input

Autoencoders (AEs): AEs consists of an input layer and an output layer that are connected through one or more hidden layers. AEs have the same number of input and output units. This network aims to reconstruct the input by transforming inputs into outputs in the simplest possible way, such that it does not distort the input very much. This kind of neural networks has been used mainly for solving unsupervised learning problems as well as transfer learning. Due to their behavior of constructing the input at the output layer, AEs are mainly used for diagnosis and fault detection tasks. This is of great interest for industrial IoT to serve many applications such as fault diagnosis in hardware devices and machines, and anomaly detection in the performance of assembly lines.


Application domain

IoT has already created a huge hype among the businesses. Not only big players, SMEs are also sensing lucrative potential in adopting IoT. It promises to bring value to all types of businesses by reinventing the business processes and operations that will eventually enhance the level and quality of products and services as well as the customer experience. collect any sort of data (e.g. contextual, locational, etc.) either related to business process or customer. IoT can contribute to improving business operations in several directions: As IoT has provided them the most important element of business—the data acquisition cog, organizations are emancipated to

Improved business process: The massive connected data from IoT makes business processes smarter. Analyzing the data collected from every division of the business will give new insights and knowledge

Increase business opportunities: IoT opens the door for new and innovative business opportunities and creates further revenue inlets. Exploiting acquired knowledge through IoT, corporates will be able to develop advanced and new business models, locate new markets to extend services and diversify their product line.

Uplifting business moment: Businesses can earn competitive velocity and agility by capitalizing and toning with the influx of dynamic and crucial business data generated through IoT devices across the domains.

Increase productivity: IoT helps to identify the need and lack of workforce expertise and also enables organizations to train the employees just-in-time. This improves workers’ efficiency and reduces mismatch of skills which in turn increases organizational productivity.

Improved operational efficiencies: The real-time sensor data from IoT devices enable organizations to monitor business operations observantly, minimizing human intervention. If IoT data collected from logistics network, factory floor and supply chain are utilized judiciously, inventory management can be optimized, and time to market as well as downtime due to maintenance can be curtailed significantly.

Enhanced asset utilization: Industrial IoT enables tracking of the production equipment, machinery, and tools. Examining the real-time status, better asset utilization can be achieved.

Faster decision-making: The real-time business process and operational knowledge will help organizations to make faster and smarter business decisions. The connected nature of IoT facilitates dispensing the intelligence and hence decision-makers are able to prioritize all business decisions

Machine learning has great potential to be the key technology for IoT. Machine learning trends to provide analytics for IoT applications. Despite the recent wave of success of machine learning for networking, there is a scarcity of machine learning literature about its applications for IoT services and systems, which this survey aims to address. This paper is different from the previously published survey papers in terms of focus, scope, and breadth; we have written this paper to emphasize the application of machine learning for IoT and the coverage of recent advances. Due to the versatility and evolving nature of IoT, it is impossible to cover each and every application. However, this paper has made an attempt to cover the major applications of machine learning for IoT and the relevant techniques, including traffic profiling, IoT device identification, security, edge computing infrastructure, network management based on SDN, and typical IoT applications. We have presented a thorough study on the recent researches about the application of machine learning for IoT, its technical progress, and application domains. We have also presented concise research challenges and open issues, which are critical to the application of machine learning for IoT.

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