IFRAT: An IoT Field Recognition Algorithm based on Time-series Data
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1 IFRAT: An IoT Field Algorithm based on Time-series Data Shuai Guo, Zhongwen Guo, Zhijin Qiu, Yingjian Liu and Yu Wang Ocean University of China, Qingdao, Shandong, China University of North Carolina at Charlotte, Charlotte, North Carolina, USA Taiyuan University of Technology, Taiyuan, Shanxi, China Abstract The increasing interest in integration of Internet of Things (IoT) heterogeneous data has resulted in the introduction of a variety of systems designs and schema matching algorithms However, numerous algorithms for schema matching fail to process automatically and efficiently because of the unknown of data sources schema In this paper, we attempt to solve this problem by introducing a new algorithm that could recognize the field of data source through its large collections of timeseries data By knowing the field forward, we could get the basic schema, which makes great contribution to schema matching afterwards Our algorithm has a good advantage at extracting characteristics of time series data and cluster them by using the self-organizing map (SOM) Then we apply clustering results to recognize IoT fields and devices when a new unknown dataset is coming We demonstrate the utility and efficiency of our algorithm with a set of comprehensive experiments on real datasets from several fields The results show that our algorithm has good performance and efficiency Index Terms IoT, Heterogeneous data, Time-series data, Selforganizing map, Schema matching I INTRODUCTION With the rapid development of Internet of Things (IoT) technology and wide acceptance of the concept of Internet Plus, IoT [1] is transforming from fragmentation and isolation to integration and interconnection Everything in the world is going to interconnecting via IoT and Internet IoT technologies have been widely utilized in many fields, such as smart home, intelligent transportation, smart grid, and health care [2] [6], which generate massive heterogeneous data However, most of heterogeneous data of IoT are stored independently In order to take the most advantage of these data, more and more researchers devote to the study of data integration, which is defined as a problem of combining data from different sources [7] One of the main tasks in the design of data integration systems is to establish the mapping between the sources and the global schema There are a few approaches to deal with data integration such as both-as-view(bav), global-as-view(gav), local-as-view(lav) and global-local-as-view(glav) [8] Database schema matching plays an important role in the process of data integration, drawing much researchers attention Although we can integrate IoT heterogeneous data by standardizing data interface, it is hard to implement in practice because of the high cost of reforming database schema In a word, it is inevitable to deal with IoT heterogeneous data, whose characteristics are as follows: (1) wide data sources and diversified structures, (2) high growth, (3) time series, (4) noisy data The techniques of database schema matching provide a good solution to solve the problems More specifically, the approach based on time-series data is suitable to the characteristics of IoT heterogeneous data Many researchers use artificial neural network to analyze time-series data due to its good performance The self-organizing map (SOM) [9] is an unsupervised learning algorithm which is good at clustering and producing a low-dimensional representation of the input space of the training samples It differs from other artificial neural networks as they apply competitive learning as opposed to error-correction learning and use a neighborhood function to preserve the topological properties of the input space Based on the techniques mentioned above and the characteristics of IoT heterogeneous data, in our previous work [10] we have built an IoT-oriented heterogeneous data conversion model, which could make the integration process faster and more efficient However, in our model, the schema matcher has high artificial participation Therefore, we need to find an automatic approach to realize schema matching The first obstacle we have to get through is how to get the characteristics of schema of an unknown data source Every data source from different IoT fields has its own basic characteristics of schema and all the information is hidden in the time-series data In this paper, we design an algorithm to analyze time-series data of IoT, predict what kinds of devices are utilized and which field those data most likely come from Once we know the field, we could get the basic characteristics of schema, which play an important role in schema matching afterwards We make the following contributions in this paper: We analyze the characteristics of IoT heterogeneous data and design a data preprocessor for time-series data By analyzing preprocessed data, we find the correlation among different sensors in one device and turn the correlation into a mathematical model Based on numerical distribution characteristics of preprocessed data, we extract feature vectors as the input vectors of SOM By leveraging SOM, we improve the efficiency of data clustering From clustering results, we can calculate the similarity between incoming dataset and clustering results In the end, we fulfill the recognition of IoT fields and devices
2 The remaining of this paper is organized as follows Section II introduces some related work about data mining and time-series data analysis Section III details the design of proposed algorithm, which could recognize IoT field (here the IoT field may be one specific part of a big IoT field) by mining time-series data Section IV demonstrates the utility and efficiency of our algorithm with a set of comprehensive experiments on real datasets from several fields Finally, Section V gives a brief conclusion and possible direction of future work II RELATED WORK The huge heterogeneous data generated by the Internet of Things (IoT) are considered of high value, and a lot of researchers are dedicated to improving data mining algorithms for IoT, with a goal to obtain the information hidden in the data of IoT For example, He et al [11] present an intelligent parking and maintenance cloud service by mining vehicle data Qiu et al [12] utilize PCA-ELM classifier to analyze data and solve problems from fault identification of Tennessee Eastman process In this paper, we are trying to realize field recognition of IoT by analyzing time-series data Time-series analysis methods and techniques for analyzing time-series data are utilized to extract meaningful characteristics of data, such as finding similar time-series [13], subsequence searching in time-series [14], dimensionality reduction [15] and so on To recognize IoT field, we first need to recognize features of data of IoT devices and sensors Ding et al [16] focus on the discovery of semantic correspondence among multiple attributes of sensors data, deem every attribute as vector and cluster them to find similarity However, in most cases we are not able to get the attributes of IoT sensors data Even if we can, its not efficient because we have to do too much preprocess work to analyze all attributes manually Li and Liu [17] automatically fulfill sensor matching based on BP neural network, by analyzing the characteristics of data value distribution But it can only be applied to one-on-one sensor matching To fulfill IoT fields recognition, we have to extract the characteristics of IoT devices, which contain many sensors III IFRAT ALGORITHM The overview of IFRAT architecture is shown in Fig 1 IFRAT is capable of recognizing the IoT field by analyzing the time-series data Firstly, we preprocess training data and replace the original value of time-series data with the rate of data change Next, we extract the characteristics of numerical distribution from the preprocessed data and set them as feature vectors Then we utilize SOM to cluster these feature vectors In the end, we calculate similarity to recognize the IoT fields and devices A Data Preprocessing For every sensor of one IoT device, they have their own different units and value ranges Therefore, it is difficult to find the correlation among different sensors by comparing the value Training Dataset Unknown Dataset Data Preprocessor Feature Extractor SOM Network Similarity Compare Fig 1: Architecture overview of IFRAT Result of data In this paper, we use data change rate to replace raw data, taking sampling time into account, which makes it much easier to establish certain correlation of every sensor within one IoT device Our data preprocessor works as following steps At first, we make several definitions for the symbols used throughout this paper: F represents a certain IoT field D represents a set of all the devices used in F, and D = {d 1, d 2, d 3, } For any device d in D, we denote S as a set of all the sensors of d, and S = {s 1, s 2, s 3, } For a time-series data A from IoT field F, its time-series set is T = {t 1, t 2, t 3,, t m } A can be made into a two-dimensional matrix, which could be separated into many small matrixes A d by device d in D We assume A d has n sensors a i represents time-series data of sensor s i during T a 11 a 12, a 1n A d = (a 1, a 2, a 3,, a n ) = a 21 a m,1 a m,n (1) We definite a (m 1) n matrix B d, for each b ij in B d, b ij = (a i+1,j a ij )/(t i+1,j t ij ), thus every entry in B d is the rate of data change We specify B d as data change matrix, which reflects the features of A d For each device d in D, we implement the same math above and get every matrix B d of corresponding devices in D b 11 b 12 b 1n B d b = (b 1, b 2, b 3,, b n ) = 21 b m 1,1 b m 1,n (2) B Extracting Feature Vectors We extract feature vectors x of B d so that we could use x to represent the features of the entries in B d Our approach is relied on a notion of numerical distribution characteristics For all the entries in each row of B d, we calculate C 2 n to get all the combinations The combination result has six cases by looking at the positive and negative status of the data in B d : (negative, negative), (negative, 0), (negative, positive),
3 Input layer Computational layer Fig 2: Architecture of SOM neural network Algorithm 1: M ax/m in Algorithm Input: Matrix X Output: Max/Min features of the input matrix 1 Calculate Euclidean distance (ED) of every two vectors in this matrix 2 Choose Max(ED) and Min(ED), assume their pairs of vectors are (v 1, v 2 ) and (v 3, v 4 ) 3 Return {(ED v1 v 2, cos θ v 1 v 2), (ED v 3 v 4, cos θ v 3 v4)} as the features of the input matrix Here ED vi v j and θ v i v j are the ED and angle between vectors v i and v j (0, 0), (0, positive), (positive, positive) We go through all the combinations, count every time when any case happens and calculate the frequency of each case Then we put the results into vector P = {p 1, p 2, p 3, p 4, p 5, p 6 } as part of the features of B d Meanwhile, we get the maximum and minimum values of all the data in B d, dividing the region (min, max) into k parts and the length of each k: k = (max min)/k We go through all the data in B d, count every time when it falls in corresponding part and calculate the frequency at the end of counting each corresponding part We put the result into vector Q = {q 1, q 2, q 3,, q k } Finally we get our feature vector x of B d by combing P and Q After getting all the feature vectors of every B d for time-series data A, we can get feature vector matrix X = {x 1, x 2, x 3,, x r }, where r is the number of devices in D F C Training Algorithm In terms of feature vector matrix X, we cluster all the vectors in it by using SOM algorithm [9] Compared with k-means algorithm, SOM algorithm do not need to set the number of classes in clustering results, thus can be more slightly affected by using a wrong initial value The basic training steps of SOM algorithm are as follows Step 1: We use feature vector x i in matrix X = {x i : i = 1, 2,, r} as our input vector and build a two-dimensional computational layer with m 2 neurons The connection weights between the input units i and the neurons j in the computation layer can be written as w j = {w ji : i = 1, 2,, r; j = 1, 2,, m 2 } Initially, every weight vectors w j are set with random number in (0, 1) Then set winning region N(0) j and learning rate ρ [9] Step 2: Randomly choose input vector x i from matrix X = {x i : i = 1, 2,, r} where r is the number of neurons in input layer (also the number of devices in D F ) Step 3: Find the winning neuron I(x) We calculate the Euclidean distance between x i and w j, and choose the smallest distance as winning neuron r Min( (x i, w ji ) 2 ) I(x) (3) i=1 Step 4: Definite winning region N(t) j around the winning neuron [9] Step 5: Update the weight as follows: w ji = ρ(t)w j,i(x)) (t)(x i w ji ) (4) w j (t + 1) = w j (t) + w j (5) Step 6: Keep returning to Step 2 until the feature map (ie, weights) stops changing D of IoT Device and Field After training, we have clustering center set C F = {c 1, c 2, c 3, } of IoT field F Eventually, we could get several clustering centers of IoT fields we want, which occupy a significant position in recognizing IoT devices or fields 1) of IoT Field: When a dataset is coming and unknown about its IoT field, we first extract its features to generate feature vector matrix X = {x i : i = 1, 2, } Then utilize Algorithm 1 to get Max/Min features of input matrix X Then, for every clustering center set C of IoT fields F, use the same approach to get its Max/Min features (target features) Finally calculate the similarity of those target features and the features of X by using Euclidean distance, and get the optimal matching, which is most similar to X Fig 3 illustrates the whole procedure 2) of IoT Device: After we know the field of dataset, we can use that information to recognize what kinds of devices they have When a dataset is coming and it is from IoT field F with clustering center C F = {c j : j = 1, 2, }, firstly extract its features to generate feature vector matrix X = {x i : i = 1, 2, } For each c j in C F, calculate the Euclidean distance between x i and c j, then choose the smallest distance We can recognize every device in the dataset and their basic type, which could be used to do the matching X C F1 C F2 C F3 C F Max/Min Algorithm Similarity Calculator Fig 3: of IoT field Result
4 Xr X2 X1 C F c 1 c 2 c 3 c j x1 Similarity Calculator x2 x3 Fig 4: of IoT device TABLE I: Characteristics of household appliances data IFRAT Statistics MX100 PV190 WT550 (negative,negative) 191% 068% 872% (negative,0) 1093% 1382% 2253% (negative,positive) 025% 116% 781% (0,0) 7265% 6984% 3282% (0,positive) 1175% 1373% 2054% (positive,positive) 251% 077% 758% Value Distribution Statistics MX100 PV190 WT550 (0,025) 64% % (025,05) 198% % (05,075) 4519% 6416% 046% (075,1) 2864% 3584% 3414% Max value Min value afterwards Fig 3 depicts that x 1 most likely comes from c 2, the same with x 2 to c 1 and so on IV EXPERIMENT AND EVALUATION To demonstrate the utility and efficiency of IFRAT algorithm, we have chosen to leverage real life datasets from the fields of household appliances testing and ocean observation Both datasets are obtained by our research group The first datasets are from the test of fridges freezing capacity during April and May in 2016, which includes several IoT devices such as MX100, PV190 and WT550 The second datasets are from the observation of ocean energy generating test sites during August in 2015, which includes several ocean observation devices such as CTD, Wave Buoy and Wave Rider We utilize 80% data as training data and 20% data as testing data Table I and Table II are the results of characteristics of data preprocessed by IFRAT It is obvious from the results of IFRAT statistics that MX100 is similar to PV190 and there is a higher similarity between Wave Buoy and Wave Rider, which really match the facts Compare to value distribution method, IFRAT method is better at extracting the characteristics of data from IoT devices A Performance of Clustering After preprocessing data by IFRAT, we set the results of IFRAT statistics as input feature vectors of SOM algorithm As shown in Fig 5, SOM algorithm has better performances at clustering IoT data than K-Means algorithm x2 x1 xi x3 TABLE II: Characteristics of ocean observation data IFRAT Statistics CTD Wave Buoy Wave Rider (negative,negative) 1786% 1192% 1523% (negative,0) 913% 2130% 1945% (negative,positive) 3294% 2503% 2682% (0,0) 622% 556% 3890% (0,positive) 1217% 2352% 1822% (positive,positive) 2168% 1267% 1638% Value Distribution Statistics CTD Wave Buoy Wave Rider (0,025) 66% 8626% 382% (025,05) 2107% 605% 2833% (05,075) 994% 0 0 (075,1) 299% 769% 3347% Max value Min value TABLE III: Evaluation results on data recognition IFRAT(SOM) DVD(SOM) IFRAT(K-Means) Precision 8012% 7035% 7722% Recall 7033% 6339% 6853% B Evaluation on IoT Data We utilize 20% of all the datasets as test data, which contains 20 datasets from the two fields above To evaluate the quality of data recognition, we use the following two indexes: 1) Precision ratio: P recision = T/P = T/(T + F ) 2) Recall ratio: Recall = T/R Here, T, P, R, F are defined as follows: T : right recognition of matching result P : matching result from IFRAT algorithm R: manual matching result F : wrong matching result As shown in Table III and Fig 6, IFRAT has better precision and recall ratio compared by only data value distribution (DVD) method with SOM and IFRAT with K-Means V CONCLUSION In this paper, we analyze the characteristics of IoT heterogeneous data and design a new IoT field recognition method, IFRAT, for time-series data In IFRAT, by analyzing preprocessed data, we find the correlation among different sensors in one device Based on numerical distribution characteristics of preprocessed data, IFRAT extracts feature vectors as the input vectors of SOM, which improves the efficiency of data clustering Via calculating the similarity between incoming dataset and clustering results, IFRAT fulfill the recognition of IoT field and devices IFRAT shows good performances of extracting features from time-series data and clustering them by using SOM algorithm The next step, after knowing the field of coming data source, we will focus on automatic schema matching ACKNOWLEDGMENT Shuai Guo and Zhijin Qiu are supported by the fellowship from the China Scholarship Council (CSC) under Nos
5 (a) Household appliance + IFRAT (b) Ocean observation + IFRAT (c) Household appliance + K-means (d) Ocean observation + K-means Fig 5: Clustering results of household appliance and ocean observation testing data trained by K-Means and SOM Fig 6: Comparison of different methods by precision and recall ratio on data recognition and This work is also partially supported by the National Natural Science Foundation of China under Nos , and REFERENCES [11] W He, G Yan, and L Da Xu, Developing vehicular data cloud services in the IoT environment, IEEE Transactions on Industrial Informatics, vol 10, no 2, pp , 2014 [12] R Qiu, K Liu, H Tan, and L Jun, Classification algorithm based on extreme learning machine and its application in fault identification of Tennessee Eastman process, Journal of ZheJiang University, vol 50, no 10, pp , 2016 [13] R A K-l Lin and H S S K Shim, Fast similarity search in the presence of noise, scaling, and translation in time-series databases, in Proceeding of the 21th International Conference on Very Large Data Bases, 1995, pp [14] T Rakthanmanon, B Campana, A Mueen, G Batista, B Westover, Q Zhu, J Zakaria, and E Keogh, Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping, ACM Transactions on Knowledge Discovery from Data (TKDD), vol 7, no 3, p 10, 2013 [15] V Megalooikonomou, G Li, and Q Wang, A dimensionality reduction technique for efficient similarity analysis of time series databases, in Proceedings of the 13th ACM international conference on Information and knowledge management ACM, 2004, pp [16] G Ding, Y Xu, and J Guo, Multi-schema matching based on DBscan clustering algorithm, Computer Applications and Software, vol 33, no 2, 2016 [17] Y Li and D Liu, A method for automatic schema matching using characteristic of data distribution, Computer Science, vol 32, no 11, pp 85 86, 2005 [1] L Atzori, A Iera, and G Morabito, The internet of things: A survey, Computer networks, vol 54, no 15, pp , 2010 [2] D Miorandi, S Sicari, F De Pellegrini, and I Chlamtac, Internet of things: Vision, applications and research challenges, Ad Hoc Networks, vol 10, no 7, pp , 2012 [3] M&M Research Group and others, Internet of things (IoT) & M2M communication market-advanced technologies, future cities & adoption trends, roadmaps & worldwide forecasts , Electronics ca Publications, Tech Rep, 2012 [4] D Bandyopadhyay and J Sen, Internet of things: Applications and challenges in technology and standardization, Wireless Personal Communications, vol 58, no 1, pp 49 69, 2011 [5] J Zhao, T Jung, Y Wang, and X Li, Achieving differential privacy of data disclosure in the smart grid, in Proc of IEEE InfoCom 2014, 2014 [6] Y Wang, Y Ge, W Wang, and W Fan, Mobile big data meets cyberphysical system: Mobile crowdsensing based cyber-physical system for smart urban traffic control, in 2015 Workshop for Big Data Analytics in CPS: Enabling the Move From IoT to Real-Time Control, 2015 [7] X L Dong and D Srivastava, Big data integration, in Proc of 2013 IEEE 29th International Conference on Data Engineering (ICDE) IEEE, 2013, pp [8] M Lenzerini, Data integration: A theoretical perspective, in Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems ACM, 2002, pp [9] J Vesanto and E Alhoniemi, Clustering of the self-organizing map, IEEE Transactions on neural networks, vol 11, no 3, pp , 2000 [10] S Guo, Z Guo, N Hu, and Z Qiu, IoT-oriented heterogeneous data conversion model, Journal of Ocean University of China, to appear
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