An Integrated Data Management Framework of Wireess Sensor Network for Agricutura Appications 1,2 Zhao Liang, 2 He Liyuan, 1 Zheng Fang, 1 Jin Xing 1 Coege of Science, Huazhong Agricutura University, Wuhan 430070, Peope s Repubic of China,zhaoiang323@mai.hzau.edu.cn *2 Resources and Environmenta Science, Huazhong Agricutura University, Wuhan 430070, Peope s Repubic of China,heiyuan@mai.hzau.edu.cn Abstract The framework proposed in this paper is to provide integrated services of sensor data management, which processes data coected from agricutura environment appying WSN (Wireess Sensor Network) technoogy. The main functions are: anayzing sensor data, standardizing different data forms, providing inteigent diagnosis service and event service based on sensor data ibrary and crop knowedge database, which wi guide agricuture production process, such as irrigation contro or disease prevention. In addition, a fuzzy inference-based inteigent diagnosis method is deveoped to provide more precise decision-makings for agricutura producers, and a web-based remote ogin is provided to a users to interact with the integrated service system. Different from other patforms, the advantage of the integrated framework is to provide transparent services to users rather than to dispay sensor data ony, which means itte use. Keywords: WSN, Agricutura Appication, Inteigent Diagnosis, Fuzzy Inference 1. Introduction WSN technoogy is increasingy common in a sectors because of its sma size node and ow cost advantages. Typica appications incude agricutura production process management, precision agricuture, optimization of pant growth, farmand monitoring and so on [1]. In these appications, the acquisition of farmand environmenta parameters, such as air temperature, humidity, ight intensity, wind speed, soi moisture information, are important foundation of the practice of agricuture and farmand information management. But there are some difficuties to get these parameters continuousy and quicky, the same to farmand managers and agricutura decision-makers to make accurate decisions timey. With the maturity and popuarity of WSN technoogy, a arge number of heterogeneous wireess sensor nodes depoyed in the fieds, which can be organized into a muti-hop inteigent network to reaize the distributed farmand environmenta information acquisition continuousy and timey. Whie WSN can sove the probem of data acquisition, but there are sti severa contradictions in most appications: the first, the data coection and storage of heterogeneous sensor node; the second, how to convert these raw data into meaningfu information and decision-makings; and the third, system maintenance, depoyment and deveopment of WSN appications subject to specific constraints, there is no universa soution mode [2]. To sove these probems, finding effective methods of data coection, processing, management and appication is the key probem. The research probems based on sensor data are mainy concentrated on the foowing areas: sensor data missing vaue imputation, faut management and outier detection, as we as effectivey sensing data coection. In the iterature [3] [4], the missing vaues estimation agorithms based on the mutipe regression mode and the spatio-tempora correation are introduced, separatey for the sensing data changing smoothy or changing non-smoothy. In [5], a ow compexity, effective recursive impementation, and good performance faut detection method for WSNs based on principa component anaysis is introduced. Two new robust subspace tracking agorithms, the robust orthonorma projection approximation subspace tracking (OPAST) with rank-1 modification and the robust OPAST with defation are deveoped to reduce the compexity of the computation of eigendecomposition (ED) or singuar vaue decomposition. Furthermore, new robust T 2 score and SPE detection criteria with recursive update formuas are deveoped to improve the robustness over their conventiona counterparts Internationa Journa of Digita Content Technoogy and its Appications(JDCTA) Voume7,Number6,March 2013 doi:10.4156/jdcta.vo7.issue6.116 1021
and to faciitate onine impementation for the proposed robust subspace ED and tracking agorithms. In [6], a faut detection method for greenhouse WSN is introduced, the spatio-tempora correation of the sampe data is anayzed to estabish the faut detection mathematica mode, and a comprehensive agorithm is given to anayze the working status of the sensor nodes. In [7], a faut detection strategy based on modeing a sensor node by Takagi Sugeno Kang (TSK) fuzzy inference system (FIS) and recurrent TSK-FIS (RFIS), where the sensor measurement of the node is approximated as function of rea measurements of the neighboring nodes and the previousy approximated vaue of the node itsef. But the data the proposed method used is generated by mathematica formua, so the performance of the agorithm needs to be verified on measured data set. Same as [7], a fuzzy ogic based faut detection and management scheme proposed in [8] is to anayze the possibiity of sensor node faiure from the hardware point of view of battery condition, sensor condition and receiver condition. In addition, a distributed faut detection agorithm is presented in the iterature [9] for wireess sensor networks based on comparisons between neighboring nodes and dissemination of the decision made at each node. A siding window is empoyed to eiminate deay invoved in time redundancy scheme. Effective coection agorithms of sensing data based on the spatia correation and spatio-tempora correation are discussed in the iterature [10] [11], at the same time, the probems of deay and the energy of sensor nodes are concerned. In order to deay the sensor networks ifetime, many data compression methods are proposed, such as in [12], a new distributed data aggregation technique hybrid compression technique (HCT) based on voronoi diagram is proposed considering the characteristics and ocation information of nodes in sensor networks. In the iterature [13], an approximate data gathering technique, caed EDGES, is presented that utiizes tempora and spatia correations. The mutipe mode Kaman fiter as an approximation approach is utiized to efficienty obtain the sensor reading within a certain error bound. What s more, the data storage is a hot point in Heterogeneous WSN area. Some data storage technoogies focus on the efficient data storage and access ways, and some focus on other fieds, but the security probems of data storage in WSN are often ignored. In [14], a new security data storage technoogy for Heterogeneous WSN by appying the muti-key mechanism into the data storage is proposed based on the efficient network hierarchy. 2. Architecture design of the proposed system The architecture of the data management middeware is divided into five functions, namey, data coection, data preprocessing, data storage, service deivery, and Web service. The system hierarchy is shown in Fig.1. Web service interface ayer Service deivery ayer Data storage ayer Data preprocessing ayer Network interface ayer Figure 1. Hierarchy Chart of the Proposed Framework The specific data fow chart and structure is shown in Fig. 2. 1022
Environment monitoring Web service interface Feedback contro Inteigent diagnosis Data query/visuaization Rea-time aerting Diagnosis Forecast Service deivery Query Parse Sensor data DB Database controer Crop knowedge DB Aggregation Estimation Sensor Sensor Sensor Data Preprocessing Gateway WSN interface Figure 2. Data Fow Chart and Structure of the Proposed System The main function of data coection is to interact with the heterogeneous WSN, continuousy access to sensor data, and to manage functions for the states of various sensor networks. There are two main functions of data preprocessing, data estimation and data aggregation. The received sensor data may be not continuous or even missing because of nodes fauty or interference in the transmission process which wi impact on the data anaysis. In order to reduce the impacts by the missing data, the corresponding missing vaue estimation agorithms are used to predict the missing data if it is found when the sensor data is deivered from the data acquisition engine. The data aggregation is another function, which is to cacuate sensor data, such as average vaue, the maximum vaue and the minimum vaue during per aggregation cyce. The data storage s main task is to store the heterogeneous sensor data preprocessed in data preprocessing ayer and then be deposited in the sensor DB. In addition, crop modes are stored in crop knowedge DB. Each crop may have more than one crop mode, and each mode may have one or more rues. A data access controer is paced in the data storage ayer to support various forms of queries. The service deivery pays the roe of providing various query processing functions for sensor data and crop mode data. In this ayer, there are two functions, incuding inteigent diagnosis and rea-time forecasting. An aggregation engine is caed to make inteigent diagnosis possibe by comparing with the rues, in advance to notify the inteigent service management modue if it is identica or exceeds some threshod vaue. The Web service is an open interface, which is the top ayer of the system, supports connections with the outside users through browse and query processing. 1023
3. Impementation of the proposed system 3.1. Data requirement The main data repository of the proposed middeware is RDBM and is buit on MySQL. Fig.3 depicts the entity reationships of data management subsystem, composed by 9 tabes [15], which is part of the entity reationship diagram. Figure 3. Entity Reationship Diagram of Sensor Data Management System The sensor tabe is the main tabe and it is the most basic device of generating data in the network. In the tabe, sensor type, data vaue, and other information such as date are stored. Each sensor beongs to a node, and each node beongs to a user specific management zone, that is a certain gateway, in which the coordinate stores a reevant ocation within a zone. At the same time, there are different sensor types in one zone, a the sensor type information is stored in sensortype tabe. Operation rues are described in the rue tabe, which contains trip point vaues that are used by the crop mode. In addition, diagnosis resuts are stored in the diagnosis tabe. Z shows zero or mutipe reationship, P expresses one or mutipe reationship, and FK is foreign key. 3.2. Inteigent Diagnosis Agorithm based on fuzzy inference Generay, if temperature, humidity or iumination is in a certain range, the crop growth state is the best, or vunerabe to some kind of pant diseases or insect pests, etc. For exampe, paddy is easiy to have rice bast under the existing conditions of optimum temperature, humidity, rain, and fog. The suitabe hypha growth temperature is 8 ~ 37, and the optimum temperature is 26 ~ 28. Spore 1024
forming temperature is 10 ~ 35, optimum temperature is 25 to 28, reative humidity is above 90%, and the spore wi germinate in the condition of water for 6 ~ 8 hours. How to accuratey contro and adjust environment parameters so as to contro the growth in the best state, or to make effective prediction, which is a key question for crop growth management. To sove the above probems, the foowing severa aspects must be considered: the first is how to use the sensor data to mining hidden knowedge, the second is how to use and express crop mode and expert knowedge, and the third is which methods are used to predict. In the proposed system, the fuzzy ogic based system is used to diagnose and predict crop growth states in time. The process is as foows: (1) Processing sensor data a. Receive sensor data D from gateway; b. Send D to Data Queue; c. Receive data D from Data Queue and decode the data; (2) Parse Crop mode a. Query ModeID, CropID according to GWID and ModeType; b. Get information of a rues according to ModeID, such as RueType and other vaues; (3) Aggregate sensor data a. Compute the MaxVaue, MinVaue, and AvrVaue of each type sensor in a time duration according to user s requirement; (4) Ca fuzzy inference system a. Determine fuzzy parameters, membership functions and fuzzy rues; b. Input the data obtained in the third step to the fuzzy inference system; (5) Return resuts to the proposed system 3.3. Impementation of the proposed system A simpe disease probabiity monitoring prediction subsystem is deveoped using Java and Matab. The exampe is based on rice bast prediction mode. The input variabes of the fuzzy system are parameters of each rue, such as hypha growth temperature, spore forming temperature, humidity and so on, and the output of the fuzzy system is occurrence probabiity of rice bast, cassified into three types, 0~50% is ow, 50%~80% is midde, and 80%~100% is high. The abes of input variabes are as foows: Hypha growth temperature = {Low, Optimum, High} Spore growth temperature = {Low, Optimum, High} Humidity = {Low, High} Time duration = {Short, Optimum, Long} The output abes are as foows: Rice bast disease possibiity = {Low, Medium, High} The trianguar and trapezoida membership functions are seected to mode the environmenta parameters, the membership functions of hypha growth temperature, spore growth temperature and time duration are represented as foows[8]: 0, x a 1, x a x a, a x b 0, x a x a b a L, a x b x a b 1, a M b x c H, a x b b a 0, x b d x, c x d 1, x b d c 0, x d The membership functions of humidity are presented as: 1, x a 0, x a x a x a L, a x b H, a x b b a b a 0, x b 1, x b 1025
And there are 54 fuzzy rues buit to mode different conditions. A fuzzy rue is written as the foowing statement [7]: R :IF x 1 is B and x 1 2 is B and 2 x is n B THEN y is n y where R (=1,2,,M) denotes the th impication, x j (j=1,2,,n) are input environmenta variabes of the fuzzy ogic system, y is a singeton, B is the fuzzy membership function which can represent j the uncertainty in the reasoning. Part of the rues is shown in Tabe1. Number Hypha growth temperature Tabe 1. Part of the Fuzzy Reasoning Rues Spore growth temperature Humidity Time duration Output 1 Low Low Low Short Low 2 Low Optimum High Optimum Medium 3 Low High High Long Low 4 Optimum Low High Optimum Medium 5 Optimum Optimum High Optimum High 6 Optimum High High Optimum Medium 7 High Optimum High Optimum Medium 8 High High Low Short Low 9 Low Low Low Short Low 10 Low Optimum High Optimum Medium The pot of membership functions of the variabe x (where i = 4) obtained through fuzzy too box i of Matab. A group of 10 aggregated sensor data is seected to the fuzzy system, the fuzzy reasoning resuts are shown in Tabe 2. Tabe 2. Part of the Fuzzy Reasoning Resuts Hypha growth temperature( ) Spore growth temperature( ) Humidity (%) Time duration (hour) Possibiity (%) 4 5 50 4 19.1842 15 20 50 4 21.1544 21 26 70 7 56.0277 27 26 95 7 93.6667 27 30 50 4 20.6033 35 30 95 7 55.7214 50 40 95 7 19.1842 50 40 50 4 19.1842 15 20 95 7 54.5700 4 5 99 7 19.1842 From the resuts in the tabe, duration of time has the minimum impact on the resuts. When hypha growth temperature and spore growth temperature are suitabe, the probabiities of bast occurrence is higher than in other conditions, but in the optimum range, the probabiity is significanty higher than the other conditions. Thus a reference wi be provided for agricutura manager to adjust the environmenta parameters and to contro the crop growth in the optima state. At the same time, because there is no consideration of correation coefficient of the four factors to the possibiity resut, 1026
so there are four same resuts such as 19.1842. It s difficut to distinguish which is the key factor and the secondary. The web service of this system is impemented with JSP(Java Server Pages), users can remotey ogin to the system, browse and query a the information, incuding historica sensor data, expert knowedge, inteigent diagnosis and other services. Fig. 4 shows the data statistics interface, and Fig.5 shows the functions UI of the integrated system, which dispays rea-time sensor data fuctuations such as iumination, temperature, humidity, etc. coected from the environment, at the same time, if an inteigent diagnosis produces, a warning information is dispayed in the interface. Figure 4. the Data Statistics Functions UI of the Integrated System Figure 5. Inteigent Diagnosis Functions UI of the Integrated System 1027
4. Concusions and Future Work In this paper, an agricutura appication-oriented sensor data management middeware is deveoped, which can efficienty process sensor data coected from the environment and impement combined services through Web. Different from other patforms, the advantage of the integrated framework is to provide transparent services to users rather than to dispay sensor data ony, which means itte use. The system runs a data anaysis engine and a mode parse engine. An inteigent diagnosis is deveoped based on the fuzzy inference to provide more precise information for agricuture management. The simpe sensor data set is changed into meaningfu knowedge. In future studies, an expansion of different modes is needed to enrich the mode DB, which wi make the diagnosis more versatie, and simutaneousy expression of crop modes and associated expert knowedge need to be improved, more effective and performance agorithms wi be deveoped for sensor data estimation and inteigent diagnosis. In addition, in the fuzzy inference system, correation coefficient of different factors can be considered to improve the accuracy of the resuts. And more than that is, sensor data can not simpy as discrete data, better methods for sensor data stream processing must be expored in future work. 5. References [1] W.S. Lee, V.Achanatis, C.Yang, M.Hirafuji, D.Moshou, C.Li, Sensing Technoogies for Precision Speciaty Crop Production, Computers and Eectronics in Agricuture, vo.74, pp.2-33, 2010. [2] Jeonghwang, H., Hyun, Y., Study on the Context-Aware Middeware for Ubiquitous Greenhouses Using Wireess Sensor Networks, Sensors, vo.11, pp. 4539-4561, 2011. [3] Pan Liqiang, Li Jianzhong, A Mutipe-Regression-Mode-Based Missing Vaues Imputation Agorithm in Wi reess Sensor Network, Journa of Computer Research and Deveopment, pp. 2101-2110, 2009.(in Chinese) [4] PAN Li Qiang, LI Jian-Zhong, LUO Ji Zhou, A Tempora and Spatia Correation Based Missing Vaues Imputation Agorithm in Wireess Sensor Networks, CHINESE JOURNAL OF COMPUTERS, pp 1-11, 2010.(in Chinese) [5] S. C. Chan, H. C. Wu, K. M. Tsui, Robust Recursive Eigendecomposition and Subspace-Based Agorithms with Appication to Faut Detection in Wireess Sensor Networks, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, pp 1703-1718, 2012. [6] Zhang Rongbiao, Bai Bin, Li Kewei, et a, Faut Diagnosis of the Greenhouse WSN Based on the Time Seris and Space Series Anaysis, Transactions of the Chinese Society for Agricutura Machinery, pp. 155-179, 2009 [7] S.A.Khan, B.Daachi, K.Djouani, Appication of fuzzy inference systems to detection of fauts in wireess sensor networks, Neurocomputing, pp. 111-120, 2012. [8] P. Chanak, I. Banerjee, T. Samanta, et a, FFMS: Fuzzy Based Faut Management Scheme in Wireess Sensor Networks, Proc.of ICECCS 2012,CCIS, pp. 30-38. [9] Myeong-Hyeon Lee, Yoon-Hwa Choi, Faut detection of wireess sensor networks,computer Communications, pp. 3469-3475, 2008. [10] Leandro A.Vias, Azzedine Boukerche, Danie L. Guidoni,et a, An energy-aware spatio-tempora correation mechanism to perform efficient data coection in wireess sensor networks, Computer Communications, pp. 1-13, 2012. [11] Leandro A. Vias, Azzedine Boukerche, Horacio A.B.F. de Oiveira,et a, A spatia correation aware agorithm to perform efficient data coection in wireess sensor networks. Ad Hoc Networks, pp. 1-17, 2011. [12] Zaid A. Ai A-Marhabi, LiRen Fa, FanZi Zeng and et.a, The Design and Evauation of a Hybrid Compression Technique (HCT) for Wireess Sensor Network. Internationa Journa of Digita Content Technoogy and its Appications (JDCTA), pp.201-207, 2011. [13] Jun-Ki Min, Chin-Wan Chung, EDGES: Efficient data gathering in sensor networks using temporaand spatia correations. The Journa of Systems and Software, pp. 271-282, 2010. 1028
[14] Fang Rui, The Study of Security Data Storage Technoogy in Heterogeneous Wireess Sensors Network. Internationa Journa of Digita Content Technoogy and its Appications(JDCTA), pp.21-27, 2012. [15] Emanue, P., Migue, A. F., Rau, M., et a, An Autonomous Inteigent Gateway Infrastructure for in-fied Processing in Precision Viticuture, Computers and Eectronics in Agricuture, pp. 176-187, 2011. 1029