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BMAMOD - Remote Cluster Grenade
Control of Public Distribution System Using GPS, GIS, Remote Sensing With Data Mining & RFID
I.Introduction
Public distribution system (PDS), a major instrument of the Indian government's economic policy, procures and distributes major commodities such as wheat, rice, sugar and kerosene to a large number of people living in India through Fair Price Shops (FPS). It also ensures the availability of food to the general public at affordable price. It helps in eradicating poverty and intends to serve as a safety net for the 330 million Indian poor people, who are nutritionally at risk. PDS is one of the largest supply chain networks in the world with 4.99 lakh Fair Price Shops (FPS) and is operated under the joint responsibility of the central and the state government. PDS supply chain consists of several central and state government bodies, private industries, farmers, warehouses and transportation agencies. Central and state government is responsible for procurement, storage, transportation and distribution of products. Though the PDS supply chain provides food to vast majority of the Indian population, we should accept the fact that the whole chain is exploited by mismanagement, corruption and anti-social elements. Though enough produce are procured and distributed by PDS, the beneficiaries often find difficulties in getting what they are entitled to. This mismanagement and corruption is the indicator of inefficiency in PDS supply chain and distribution systems. In order to overcome these deficiency, in this paper we have suggested a model using GPS, GIS, Spatial data mining techniques & RFID [1, 7, 14]. The use of these technologies could help PDS to curtail. II.Technologies
A. Global Positioning System (GPS) GPS, short for Global Positioning System, is a means for locating any point on the earth[13]. It has many uses; navigation, surveying, vehicle tracking, hiking and outdoor recreation just to name a few. In the 1970s the Department of Defense (DoD) conceived the idea of GPS. It was born from a need to accurately determine the position of ballistic missile submarines prior to launching missiles. All the old methods of determining position had their flaws. Those methods were affected by atmospheric conditions, limited in range, subject to enemy jamming, or degraded by interference.The GPS system is made of 24 NAVSTAR satellites and five ground stations. The ground stations are responsible for keeping the satellites in precise orbit. The DoD placed each of the 24 satellites in a precise orbit at an altitude of 10,900 miles. Each satellite weighs two tons, is 18.5 feet long, and orbits the earth in a little less than 12 hours. FIG: 1 shows the working of GPS.
B. A Geographic Information System (GIS) It is a tool that uses the power of the computer to pose and answer geographic questions. The user guides the program to arrange and display data about places on the planet in a variety of ways - including maps, charts and tables. The hardware and software allows the users to see and interact with data in new ways by blending electronic maps and databases to generate color-coded displays. Users can zoom in and out of maps freely; add layers of new data, and study detail and relationships [12, 13].
C. Remote sensing It is the science and art of obtaining information about a phenomenon without being in contact with it[12]. Remote sensing deals with the detection and measurement of phenomena with devices sensitive to electromagnetic energy such as: Light (cameras and scanners) Heat (thermal scanners) Radio Waves (radar).
GIS and remote sensing both are correlated to each other. Without remote sensing, GIS is nothing because GIS used Remote sensing data. In GIS, there are two uses of use of remote sensing data; as classified data and as image data. Remote Sensing is defined as the science and technology by which the characteristics of objects of interest can be identified, measured or analyzed the characteristics without direct contact.
Electro-magnetic radiation, which is reflected or emitted from an object, is the usual source of remote sensing data. However any media such as gravity or magnetic fields can be utilized in remote sensing. A device to detect the electro-magnetic radiation reflected or emitted from an object is called a "remote sensor" or "sensor". Cameras or scanners are examples of remote sensors. A vehicle to carry the sensor is called a "platform". Aircraft or satellites are used as platforms.
D.Spatial data mining The remote sensing concept is illustrated in figure 2 where three different objects are measured by a sensor in a limited number of bands with respect to their, electro-magnetic Spatial data mining, i.e., extracting useful information from huge amounts of data, is highly relevant to applications in which the tremendous data volumes are involved [1], thus exceeding human analytical capabilities. Data mining is no longer restricted to the relational databases alone. Spatial data mining is proving to be a promising technique, and holds the key to solving several challenging issues concerning spatial databases [2]. Data Mining offers intelligent functionalities and works well with complicated algorithms, such as, neural networks, rule induction, decision trees, and genetic algorithms and constantly updates its models based on self-learning[10]. Some of the widely accepted and applied methodologies of spatial data mining include Association Rules, Characteristic Rules, Discriminated Rules, Classification, Clustering and Trend Detection [11].The following sentences briefly illustrate the aforesaid data mining methodologies. 1. Association Rules: The notion of association rules was put forward by Agrawal etal. [1993] in a study of mining large transaction databases. The structure of an association rule is: A B(c %), where A and B are predicate sets and c% is the confidence level. For instance, an example association rule is: If the city is large, the probability of its being near to a river is 80%. 2. Characteristic Rules: These are rules concerning the characteristics of the mined objects and these are rules formulated by specialists and may not be completely appropriate. An example of a characteristic rule is: A bridge is an object that is present at the location where a road crosses a river. 3. Discriminatory Rules: These are rules that aim to differentiate between groups of objects by finding features that are close to one cluster and far-off from another. Such a rule is applied to find the differences between cities with high and low unemployment rates. 4. Classification rules: These rules classify a pixel into one of the given set of classes, e. g. water, field, forest. IF population of city = high AND economic power of city = high THEN unemployment of city is classified as" low". 5. Clustering: Clustering involves grouping pixels into similarity classes based on spectral characteristics. An example is to find clusters of cities with similar levels of unemployment [3]. 6. Trend Detection: Trend detection refers to looking for similarity in prototype among the mined objects. An example of trend detection may be as follows: when moving away from Brno, the unemployment rate increases. 7. Sequential Pattern: These are used to detect sequences of events or values, such as stock and share values, business transactions etc. we can use Rule- and Motif-based Anomaly Detection in moving objects. Using this model object trajectories are expressed using discrete pattern fragments called motifs [15, 16]. Associated features are extracted to form a hierarchical feature space, which facilitates a multi-resolution view of the data. We can develop a general-purpose, rule based classifier which explores the structured feature space and learns effective rules at multiple levels of granularity. Compared to related work in classification or clustering of moving objects, Rule- and Motif-based Anomaly Detection in moving objects incorporates a fuller feature space and examines more than just trajectories [17, 18]. At a high level, Rule- and Motif-based Anomaly Detection in moving objects presents three novel features. 1. Motif-based feature space: Instead of modeling whole trajectories, we partition them into fragments (motifs) and construct a multi-dimensional feature space oriented on the motifs with associated attributes [15]. 2. Automated hierarchy extraction: By examining he patterns in the trajectories, we automatically derive hierarchies in the feature space. This yields a multi-resolution view of the data [15]. 3. Hierarchical rule-based classifier: We develop a rule-based classifier which explores the hierarchical feature space and finds the effective regions for classification [16]. The problem of anomaly detection in moving object data is defined as follows. The input data is a set of labeled trajectories: D = {(t1, c1), (t2, c2), . . .}, where ti is a trajectory and ci is the associated class label. A trajectory1 is a sequence of spatiotemporal records of a moving object, in GPS records. Each record has the geographic location as well as a timestamp, and records can be made at arbitrary time intervals. The set of possible class labels is C = {c1, c2, .}. In simple anomaly detection, there could just be two classes: cnormal and cabnormal.To learn a function f which maps trajectories to class labels: f(t) ! c 2 C. f should be consistent with D as well as future trajectories not in D[18]. In other words, we want to learn a model which can classify trajectories as being normal or abnormal. E. RFID System Most RFID systems consist of tags that are attached to the objects to be identified [8]. Each tag has its own "read-only" or "rewrite" internal memory depending on the type and application. Typical configuration of this memory is to store product information, such as an object's unique ID manufactured date, etc. The RFID reader generates magnetic fields that enable the RFID system to locate objects [4,5 ] (via the tags) that are within its range. The high-frequency electromagnetic energy and query signal generated by the reader triggers the tags to reply to the query; the query frequency could be up to 50 times per second. As a result communication between the main components of the system i.e. tags and reader is established. As a result large quantities of data are generated. The RFID system consists of various components which are integrated in a manner defined in the above section. This allows the RFID system to deduct the objects (tag) and perform various operations on it. The integration of RFID components enables the implementation of an RFID solution[ 9] . Fig:2 The RFID system consists of following five components Tag (attached with an object, unique identification).• Antenna (tag detector, creates magnetic field). • Reader (receiver of tag information, manipulator). • Communication infrastructure (enable reader/RFID to work through IT infrastructure). • Application software (user database/application/ interface).
III. PROPOSED MODEL Our proposed model is designed to satisfy the requirement of PDS using GPS, GIS, Remote Sensing Method with RFID & data mining approach.Fig:3 1. The data of the product is stored in the tag which will be attached to the product (eg: when the grain was harvested and procured, how long the grain was kept in warehouse, packaging and transportation details and point of sale information).2. GPS is used to track the product. RFID is used to Communication between tagged objects and GPS [6], to ascertain and communicate current position and status of the product.3. Data sent to servers on a regular basis, or when the object moves or upon request Satellite data coming from the networks captured at SDC. It is an interface capable of capturing information flowing from (such as network card on a machine) satellite.4. Rule- and Motif-based Anomaly Detection is used to track moving objects [15,16].The raw data storage store collected network data. Typically, it is a set of hard drives where an application dumps information passing through the SDC, usually according to some requirement.The Pre-processor handles the conversion of raw image or connection data image into a format that mining algorithms utilize and may store the result in the knowledge base. It can perform a range of duties, such as additional filtering, noise elimination, and include third party detection tool that recognize known disaster pattern of track.
The knowledge base stores rule produced by mining and any additional information used in the mining process [16]. It may also hold the information for the per-processor, such as patterns for recognizing attack and conversion templates.
The profiler is responsible for generating snapshot rule sets to be used for deviation analysis [2,3]. It can be triggered automatically based on time of day or the amount of pre-processed data available.
The deviation analyzer examines rule sets in the knowledge base and creates a description of difference by meta-learning [3]. The results are stored in the knowledge base for further reference. If necessary, it signals the alarm generator. A strategy for invoking the deviation analyzer could be periodic queries to the knowledge base for the availability of new profile. Alternatively, the profiler may signal the analyzer when new profile is deposited to the knowledge base.
The alarm generator is responsible for notifying the administrator when the deviation analyzer reports unusual pattern in the movement of product.
IV.CONCLUSION The effective use and implementation of RFID, GPS & data mining techniques in PDS can facilitate PDS supply chain and promise eradicating mismanagement, corruption, trafficking, theft and anti social elements. RFID provides highly accurate and detailed information by capturing the data and information at each stage of the supply chain, automatically. It also improves the safety and efficiency of the food supply chain. Locationing technology GPS can also be combined with RFID technology to automatically track and record the information regarding the field where the produce was picked, when and where it was transported and the current location of the produce. This also helps in reducing theft and trafficking. Data mining techniques based on the rule base classification model is used to identify the suspicious moving behavior of the objects.
V.REFERENCES
[1] Ester M., Kriegel H.-P., Sander J.: Spatial Data Mining: A Database Approach, Proc. 5th Int. Symposium on Large Spatial Databases (SSD'97), Berlin, Germany, 1997, pp. 47-66. [2] Ester M., Gundlach S., Kriegel H.-P., Sander J.: Database Primitives for Spatial Data Mining, Proc. 8. GI-Fachtagung Datenbanksysteme in Büro, Technik und Wissenschaft (BTW'99) (Int. Conf. on Databases in Office, Engineering and Science), Freiburg, Germany, 1999, pp. 137-150. [3] Ester M., Kriegel H.-P., Sander J.: Knowledge Discovery in Spatial Databases, invited paper at 23rd German Conf. on Artificial Intelligence (KI '99), Bonn, Germany, in: Lecture Notes in Computer Science, Vol. 1701, 1999, pp. 61-74. [4] J. Bohn, "Prototypical implementation of location-aware services based on a middleware architecture for super-distributed RFID tag infrastructures", Pers Ubiquit omputing, (2008) Journal 12:155-166. [5] J. Schwieren1, G. Vossen, "A Design and Development Methodology for Mobile RFID Applications based on the ID-Services Middleware Architecture", IEEE Computer Society, (2009), Tenth International Conference on Mobile Data Management: Systems, Service and Middleware. [6] K. Ahsan, H. Shah, P. Kingston, "Context Based Knowledge Management in Healthcare: An EA Approach", AMCIS 2009, Available at AIS library. [7] S. Garfinkel, B. Rosenberg, "RFID Application, Security, and Privacy", USA, (2005), ISBN: 0-321-29096-8. [8] L. Srivastava, RFID: Technology, Applications and Policy Implications, Presentation, International Telecommunication Union, Kenya, (2005). [9] Application Notes, "Introduction to RFID Technology" CAENRFID: The Art of Identification (2008). [10] Jiawei Han, Micheline Kamber, "Data Mining Concept and Techniques" Morgan Kaufmann Publisheres 2001 [11] Michael J.A.Berry, and Gordon S. Linoff, "Data Mining Techniques: for marketing, sales, and customer support". Taiwan: SuperPoll.net, Inc. January [12] He-Hai Wu, Jian-Ya Gong, "Geographic Information Systems Spatial Data Structure and Process Technology". China: Surveying and Drawing Publisheres , April 1997. ISBN 7-5030-0937-3 [13] Peng Hu, Xing-Yuan Huang, Yi-Xin Hua, "Geographic Information SystemsCourse". China: Wuhan University Publisheres , February 2002. ISBN [14] H. Cao and O. Wolfson. Nonmaterialized motion information in transport networks. In ICDT'05. [15] B. Chiu, E. Keogh, and S. Lonardi. Probabilistic discovery of time series motifs. In KDD'03. [16] Fran¸cois Denis. Pac learning from positive statistical queries. In ALT'98. [17] L. Forlizzi, R. H. G¨uting, E. Nardelli, and M. Schneider. A data model and data structures for moving objects databases. In SIGMOD'00. [18] S. Gaffney and P. Smyth. Trajectory clustering with mixtures of regression models. In KDD'99.
About the Author
Ms.S.Ranjithakumari & Kathiresan.V,
Assistant Professor,School of Computer Studies,
RVS CAS,SULUR-402,
TAMILNADU
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