Source: http://www.google.com/patents/US7890568?ie=ISO-8859-1
Timestamp: 2014-03-08 13:19:13
Document Index: 493112028

Matched Legal Cases: ['Application No. 07010671', 'Application No. 07010652', 'Application No. 07010654', 'Application No. 08017795', 'Application No. 07008409', 'Application No. 07008409', 'Application No. 07008409', 'Application No. 07008409', 'Application No. 07008409']

Patent US7890568 - Service-to-device mapping for smart items using a genetic algorithm - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsSystems and methods allow service-to-device mapping using genetic algorithm logic by which services are mapped onto best-suited smart items selected from a plurality of smart items of a sensor network. As part of the mapping, and/or subsequent thereto, a deployment of the service(s) onto the smart items...http://www.google.com/patents/US7890568?utm_source=gb-gplus-sharePatent US7890568 - Service-to-device mapping for smart items using a genetic algorithmAdvanced Patent SearchPublication numberUS7890568 B2Publication typeGrantApplication numberUS 11/413,230Publication dateFeb 15, 2011Filing dateApr 28, 2006Priority dateApr 28, 2006Also published asCN101064652A, CN101064652B, EP1850277A1, EP1850277B1, US20070251998Publication number11413230, 413230, US 7890568 B2, US 7890568B2, US-B2-7890568, US7890568 B2, US7890568B2InventorsMikhail BelenkiOriginal AssigneeSap AgExport CitationBiBTeX, EndNote, RefManPatent Citations (83), Non-Patent Citations (207), Referenced by (12), Classifications (13), Legal Events (1) External Links: USPTO, USPTO Assignment, EspacenetService-to-device mapping for smart items using a genetic algorithmUS 7890568 B2Abstract Systems and methods allow service-to-device mapping using genetic algorithm logic by which services are mapped onto best-suited smart items selected from a plurality of smart items of a sensor network. As part of the mapping, and/or subsequent thereto, a deployment of the service(s) onto the smart items is initiated. A sorted repair structure indicating an ordering of services in accordance with a hierarchy of atomic services and composite services based on a composition of services for one or more devices of a sensor network is determined. One or more derived generation structures are determined based on genetic algorithm logic that is operable on the sorted repair structure. A service is mapped onto at least one device of the sensor network, based on one of the derived generation structures. As a result, users may achieve a desired result in a fast, convenient, and reliable manner, even when executing the deployment remotely or automatically.
BACKGROUND Software systems exist that provide various services for enterprises or other organizations. Such software systems may rely on decentralized, manual, and potentially error-prone data collection, while storing collected data in a centralized back-end system where business logic execution also occurs. These and other software systems may be extended through the use of smart item (also referred to as smart device) technologies, in which physical items (e.g., goods, tools, rooms, vehicles, persons, or shelves) are augmented or enhanced by the addition or inclusion of locally-provided or embedded technology.
SUMMARY According to one general aspect, a sorted repair structure indicating an ordering of services in accordance with a hierarchy of atomic services and composite services based on a composition of services for one or more devices of a sensor network is determined. One or more derived generation structures based on genetic algorithm logic that is operable on the sorted repair structure is determined. A service is mapped onto at least one device of the sensor network, based on one of the derived generation structures.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram of an example system for service-to-device mapping for smart items using a genetic algorithm.
DETAILED DESCRIPTION FIG. 1 is a block diagram of a system 100 for a service-to-device mapping for smart items using a genetic algorithm. As described in more detail below, the system 100 may be configured to perform a mapping function, using a genetic algorithm, by which services may be mapped onto suitable smart items selected from a plurality of smart items of a sensor network 102. As part of the mapping, and/or subsequent thereto, a deployment of the service(s) onto the smart items may be initiated. Genetic algorithm techniques may be used as part of the mapping process to attempt to produce an optimal mapping. As a result, operators and users of the system 100 may achieve a desired result in a fast, convenient, and reliable manner, even when executing the deployment remotely or automatically, and may thus more conveniently use the system 100 in a desired fashion.
In the example of FIG. 1, the sensor network 102 may include various smart items or smart devices 104, 106, 108, and 110. That is, it should be understood that the terms �smart items,� �smart devices,� �smart item devices,� and similar terms, may be used similarly or interchangeably in various contexts. For example, the term �smart item� or �smart device� may refer to a device having local processing, storage, and communications capability, as referenced above, or may refer to a combination of such a device and an object to which the device is affixed (e.g., a pallet containing merchandise for sale). As part of the sensor network 102, such devices and/or device/object combinations also may be referred to as �nodes,� or �network nodes� in some contexts. In the present description, the term �device� is used for brevity and consistency to refer to the described devices having the described features within the sensor network 102.
In the example of FIG. 1, then, it should be understood that the terms �deployment condition� and �allowable deployment condition,� or similar terms, may refer at least in part to condition(s) of physical conditions existing (or desired/required to be existing) with respect to the sensor network 102, such as a number or type of services deployed on the sensor network 102, an amount of the memory 114 existing or available for deployment of services, or a deployment constraint. In other words, such deployment conditions may overlap or encompass, in the context of a particular deployment at a particular time, some or all of the service metadata 124, the device metadata 130, the network metadata 132, or the deployment constraint(s) 134. Thus, such deployment conditions may represent a matching or fulfillment (or lack thereof) between the service metadata 124, the device metadata 130, the network metadata 132, or the deployment constraint(s) 134, for (in this case) deployment of the service C 138 onto the sensor network 102. As such, such deployment conditions may be represented, in whole or in part, as, for example, XML documents that describe the associated physical conditions for use by the mapping system 140 (as represented by the allowable deployment condition 142), where parameters of such XML documents at a given time may be determined from the service repository 120, the network monitor 128, and the deployment constraints 134.
For example, if a network includes 3 nodes and a service composition, for example, indicating service composition of 5 requested services, and the 5 services are requested to be installed onto the network, an example chromosome may include 15 binary values, determined as the number of network nodes multiplied by the number of requested services. Each example chromosome may include 5 parts, each part corresponding to one of the 5 services, and each part may include 3 genes, each gene corresponding to one of the 3 network nodes. For example, if a gene has a value of 1, the corresponding service may be deployed to its corresponding node. One possible chromosome may then have a value of �001 101 011 111 000�. For this example, parts of the chromosome may reference services in an order Y, Z, W, X, and V. Thus, according to the example chromosome, the service Y, represented by the first three bits, may be installed on the third node, the service Z, represented by the second three bits, may be installed on the first and third nodes, the service W may be installed on the second and third nodes; the service X may be installed on all nodes, and the service V may be not installed at all. The sorted repair structures 144 may include a structure similar to the base generation structures 146 and the derived generation structures 148.
Techniques for communication between the computer 162 (or other just-described examples or variations of the computer 162) with devices of the sensor network 102 may vary, for example, on the types of devices. For example, the devices and/or embedded systems may represent anything from small-scale, one-chip microcomputers, all the way to full-blown PC hardware. Thus, for example, for devices that have the capabilities of a mobile phone or more (e.g., are able to run a Java Virtual Machine� or other middleware platform(s)), implementation may be performed in Java� and/or based on the Open Services Gateway initiative (OSGi), which represents a known framework for implementing a component model for remote installation and management of applications and/or application components.
Based on the above-described input data (202-210), and as understood from the description of FIG. 1, the service mapper 126 may execute at least the genetic algorithm logic 150 to determine a base generation (212) and to determine a derived generation (214) based on a result of the determined base generation (212). The service mapper 126, based on the determination of the derived generation (214), may then perform a mapping (216) of requested services to devices of the sensor network 102. Then, an output of the service mapping may include a set of instructions for performing code injection (218). Specifically, after successful mapping of service(s), the service injector 136 may be used to transfer executable service components to the respective selected devices. Depending on an available network infrastructure associated with the service network 102, the code injection may be performed, for instance, in a device-by-device fashion, in which the service injector 136 may send a message to the target node(s) containing appropriate commands, e.g., �remove the service B 116 and install the service C 138.� In this case, the appropriate service executable may be transmitted to the device 104 from the service repository 120.
The example sorted repair structure 500 may be based on the service metadata 124, device metadata 130, network metadata 132, and deployment constraints 134 of FIG. 1, the example sorted services list 400 of FIG. 4, and the example service composition hierarchy 300 of FIG. 3, for a sensor network including three devices such as the devices 104, 108, and 110 of FIG. 1, in an ordering 104, 110, 108. Thus, a request to map service A 160 to the sensor network 102 may result in the service mapper 126 initializing the sorted repair structure 500 to have a value �111 101 110 111 010� which indicates that service D 518 may be installed on all nodes (e.g., devices 104, 108, and 110), service E 558 may be installed on the first and third nodes (e.g., devices 104 and 108), service B 516 may be installed on the first and second node (e.g., devices 104 and 108), service C 538 may be installed on all nodes (e.g., devices 104, 108, and 110), and service A 560 may be installed on the second node (e.g., device 110).
The derived generation structures 148 may be determined (612) based on a genetic algorithm selection technique which may be used to select the base generation structures 146 or chromosomes from which the next generation may be created, as discussed further with regard to FIG. 9. Chromosomes that may be determined to be �good� chromosomes, for example, chromosomes having a lower unfitness value according to an unfitness function, may be given a higher weight, and thus may be assigned a larger section of a roulette wheel selection scheme, thus leading to a higher probability that �good� chromosomes may be selected for the next generation, e.g., the derived generation structures 148. The roulette wheel analogy may correspond, for example, to a roulette wheel in which each candidate solution, or chromosome, represents a pocket on the wheel. The size of a pocket is proportionate to the probability of selection of the solution. Thus, selecting N chromosomes from a population, or generation, may be considered as equivalent to playing N games on the roulette wheel, as each candidate is drawn independently. For the example genetic algorithm that may be utilized by the genetic algorithm logic 150, the number of chromosomes in each of the base generation structures 146 and the derived generation structures 148 may be constant, although it is generally possible to vary the size of populations. Chromosomes or structures whose unfitness is smaller than an average unfitness of the population, or generation, may be ignored.
The old base generation structures 146 may be replaced by the new derived generation structures 148, and steps 614-618 may be repeated until a predetermined maximum number of populations is generated. As each new generation is generated, a �best� structure or chromosome (e.g., having a smallest unfitness value) of that particular population may be stored so that when the predetermined maximum number of populations has been generated, the best chromosome, e.g., the chromosome having the smallest unfitness value may be determined resulting from all of the derived generation structures. If there are no chromosomes with an unfitness value of 0, which may represent a valid and complete solution of the mapping problem, the genetic algorithm logic 150 may be configured to attempt to complete the �best� chromosomes. Thus, an attempt may be made to install missing services on the network nodes via operations on the chromosomes, so that the deployment coverage constraints, for example, as included in the deployment constraints 134, may be satisfied. As a result, the �best� chromosome may be returned as a solution to the service mapping request.
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