REVOLUTIONIZING MANUFACTURING SYSTEMS WITH PERVASIVE COMPUTING: P-ROMS Mohan Kumar, John Priest, Behrooz Shirazi, Brian Huff, and Mary Johnson University of Texas at Arlington P.O. Box 19015, Arlington, TX 76019-0015, USA

{kumar}@cse.uta.edu Abstract We propose to apply novel pervasive computing technologies to revolutionize manufacturing of extremely complex products such as commercial and defense aerospace systems. Such manufacturing systems consist of a collection of extensive manual and automated data, participation of many specialized personnel and use of distributed product data repositories. These complex, low volume products have tens of thousands of components produced by hundreds of suppliers, requiring component and process traceability. These manufacturing systems are subject to dynamically changing environments and use cutting edge processes. Pervasive computing has the potential to revolutionize current manufacturing systems by drastically improving their ability to handle dynamically changing situations in a seamless and cost-effective fashion. To address these significant problems, we propose to use automated, continual, and unobtrusive software services and proactive real-time collaborations among physical devices, software agents, and personnel in heterogeneous manufacturing environments. Keywords: something, something

1.

Introduction

Pervasive computing technologies and associated software are being employed to facilitate such applications as telemedicine, education, space endeavors, marketing, crisis management, transportation, and military for all the time and everywhere use. These applications demand automated, continual, and unobtrusive services and proactive real-time collaborations among devices, software agents and geographically distributed personnel in dynamic heterogeneous environments. We propose to apply pervasive computing technologies to solve problems in dy-

2 namic and complex environments such as those found in many areas of manufacturing. In P-RoMS1 (Revolutionizing Manufacturing Systems through Pervasive computing) computing and communication capabilities are distributed throughout both higher and lower levels of the manufacturing process resulting in a fundamental and revolutionary change in current models of information systems for production control and management. The novelty of our approach lies in the ability to exploit pervasive computing and communications at all manufacturing levels through the use of a middleware framework called PICO (Pervasive Information Community Organization), being developed by us with support from an earlier NSF grant2 . PICO creates mission-oriented dynamic computing communities that perform tasks on behalf of users and devices autonomously, and provides a framework for collaboration among seemingly disparate entities [1,2]. The production of today’s complex products, such as commercial and defense aerospace systems pose huge challenges to manufacturing systems. These products are extremely complex, often made up of millions of components. These highly customized products have dynamic specifications and are produced in very low volumes. Aerospace and defense products also require component and process traceability. This places a massive burden on manual and automated data collection systems, product data repositories, and personnel. The facilities required to manufacture these systems are extremely large and represent hundreds of millions of dollars of capital investment. The products themselves represent large investments in money and time that are placed in jeopardy by the potential for a non-conformant event at each subsequent step in the manufacturing sequence. Dynamic and complex manufacturing systems still require a high degree of urgent contingency planning when a non-conformant event arises. Real-time handling of exceptions in a manufacturing system poses several complex challenges: autonomous and continual monitoring of processes, components and parts; sustained collaborative interactions among the manufacturing processes, design engineers, personnel involved and communication with various parts and components; swift discovery of resources and services to aid in interactive decision making. The question is: how can we meet such demands with the existing or near future information technologies? The advent of pervasive computing with its associated hardware and software solutions has the potential to revolutionize manufacturing systems for the production of highly complex, low volume products. Pervasive computing would allow remote querying of intelligent subassemblies in real time with responses that include pertinent data such as location, status, history, missing parts/fixtures, schedule, and process plan. In

Revolutionizing Manufacturing Systems with Pervasive Computing: P-RoMS 3

addition, subassemblies on their own could request fixtures and components or directly contact management as problems occur. Higher levels of intelligence would allow a part on its own to change its process plan or change priority to meet schedule, call a vendor, request supplies, identify where the next part is located, inform the next higher assembly that it is ready, etc. Managers could find real time information and send control signals directly to parts or fixtures. New methods and technologies are required to use the potential power of pervasive computing in manufacturing. The key to this success is the ability to distribute computing and communication capabilities to lower level devices through the use of low cost technologies. The idea behind our proposed approach is rather simple and elegant. Using the enabling pervasive computing technology, we propose to make manufacturing environments and processes smarter in two steps: We propose to add simple, cost effective computing and communication capabilities to low level manufacturing parts, fixtures, machinery, and components. This is carried out using sensors (such as the UC Berkeley Network Sensor Platform) and Radio Frequency ID (RFID) tags. Through this process we provide intelligent situation awareness about the manufacturing environment. We propose to use intelligent software agents which continually i) monitor the manufacturing process by collecting data from low level sensors; ii) detect exceptions and problems; iii) form collaborative software communities with each other to find solutions to the problems either automatically or with assistance from human personnel; and, iv) participate in or facilitate the execution of the solutions to the problems. Our existing PICO framework is ideally suited to the problems in dynamic complex environments due to its methods of timely and automated creation of mission-oriented communities of collaborative software agents with a goal to better utilize resources and provide justin-time services to users. PICO consists of software entities, called delegents (intelligent delegates) and hardware entities, called camileuns (connected, adaptive, mobile, intelligent, learned, efficient, ubiquitous nodes). Any physical device that possesses a CPU, memory, communication ability and a subset of the above attributes can serve as a camileun. A delegent is a logical entity created by the programmer, user, application or another delegent. Delegents work diligently on behalf of their creator and may also work in communities sharing information and resources with other sibling delegents. The community computing concept

4 of PICO provides the necessary platform to enable effective communication and collaboration among heterogeneous hardware and software entities. P-RoMS inherits the community computing concept from PICO and will allow real time, pervasive, autonomous and continual management of dynamic and complex environments such as manufacturing systems. Current methods such as Enterprise Resource Planning (ERP) systems try to integrate the business processes and functions to present a holistic view of the enterprise [3..5]. “ERP is the umbrella for integrating sets of business applications that allow a company to manage almost all aspects of operations.” [6]. An enterprise data warehouse provides ERP’s business information [7]. Aerospace companies are implementing ERP systems on major aircraft programs. Like homegrown, CIM, and MRP II systems are today, ERPs will be the future legacy systems in many aerospace plants [4, 5]. Current and future IT solutions addressing flexibility and quick adaptation to customer demands will require the addition of adaptable architectures (such as P-RoMS) allowing encapsulation of heterogeneous software, modeling human enterprise structure, and flexible interconnection inside and outside the enterprise [8]. The scope and outcomes of P-RoMS are far wider and are expected to revolutionize the information management, control and decision making systems for dynamic complex environments, as well as provide novel solutions for challenges in pervasive computing research. The major challenges to improve manufacturing systems using the P-RoMS architecture include: 1 abstraction of product components, parts, process equipment, and fixtures 2 provisioning situation-aware services 3 swift response to exceptions through the dynamic creation of communities of software agents 4 architectures and protocols for service discovery in P-RoMS 5 architectures for dynamic, complex environments using contingency theory

2.

An overview of PICO architecture

Camileuns and delegents are the basic building blocks of PICO. In this section, we first describe camileun and delegent architectures and then present a layered architecture for PICO.

Revolutionizing Manufacturing Systems with Pervasive Computing: P-RoMS 5

2.1

Camileuns

In the foreseeable future, the environment will be replete with adaptable smart devices, called camileuns. Typically, a camileun possesses one or more of the functionalities such as: see, hear, adapt, compute, communicate, learn, and process information. A camileun can be described by two tuples: C = < H, F >, where H is the set of hardware the camileun is equipped with and F is the set of functions it is expected to perform. For example, a heart monitor camileun, can be described by C1 =

with the hardware set, H1 = and the functionality set F1 =. Camileuns can be of different types and complexities –a temperature sensing device, an active network node, and a state-of-the-art workstation. They can communicate in broadband, wired/wireless, or optical networks. Camileuns are self-adaptive in the sense that they can change their behavior and functionalities either to suit the environment they are in, or the application tasks they are required to perform. A camileun, C2 =

mounted on a streetlamp can provide several functionalities such as F2 = The definition of camileuns is broad, almost all existing computing hardware devices can be considered as camileuns. The degree of granularity of a camileun’s functionality is an issue that requires further research. In this project we plan to define a formal set of requirements for a device in order to be classified as a camileun. The experimental work of this project includes use of network processors (e.g., Intel’s IXP 1200) as active elements within the network. In this context the network processor (an active network element) is a camileun residing in the network, monitoring and processing packets, and assessing traffic situations and taking proactive decisions. For example, the BATs in the sentient computing environment [9] and the H21 in the Oxygen project [10] are good examples of camileuns with multiple functionalities. In the rest of this proposal, we refer to computing hardware devices in general as camileuns.

2.2

Delegents

A delegent is an intelligent delegate that works diligently on behalf of a camileun or a user. For example, a delegent can gather information locally or remotely, collaborate with other delegents to form a computing community. A delegent is represented by the tuple Delegent = where Ad is the delegent’s identity and D is its functional descrip-

6 tion. The identity of a delegent is given by the two tuple: Ad =, where, C is the camileun where it was created, and P is the community it belongs to. Functionally, a delegent is described by a three tuple: D = < M,R,L>, where M is the set of program modules, R is the set of rules for building a delegent, and L is the set of low level functions. For example, a delegent associated with the streetlamp camileun, D(streetlamp, camera) can be defined as, D1 = , where, M1 =, R1 =, and L1 = Delegents represent camileuns in various communities, and it is this attribute that makes camileuns adapt to different situations much like the real chameleons. An example camileun and associated delegents are shown in Figure 2. The streetlamp equipped with a camera, a CPU, a network processor, RAM, hard disk space and wireless transceivers is the camileun. The streetlamp can function as a video camera for traffic surveillance and control, an active network element for IP packet processing and forwarding, and as an information kiosk capable of exchanging information with pocket PCs carried by humans and mobile devices in passing vehicles. Typically, a camileun has a delegent manager (DM) responsible for coordinating with the camileun hardware and creation and coordination of other delegents. The DM, represented by D(streetlamp), activates three delegents to represent in three communities: i) traffic surveillance and control, ii) network routing, and iii) distributed information kiosk. The first community comprises of delegents from several surveillance cameras, motor vehicles, police, and other relevant computers (for example weather bureau), and ensures collisionfree, congestion-free flow of traffic by gathering real-time context-aware and location-aware information. For example, in the telemedicine scenario, the traffic surveillance and control community combines with the ambulance community to provide an uninterrupted fast path to the ambulance. Similarly delegents on the streetlamp exchange information with the ambulance community, assess the situation, anticipate transfer of time critical data, and therefore reserve communication paths between the ambulance, the hospital and other associated communities. The network routing community ensures maintenance of required QoS for IP packets flowing between the ambulance and hospital communities. A model architecture for delegents is shown in Figure 3. A delegent receives inputs from many sources: external events, internal events, intentions, and others. A delegent can be in one of three states: dormant, active, or mobile as shown in Figure 4. A dormant delegent is a piece of code in the hard disk (of a camileun). A DM activates a dormant

Revolutionizing Manufacturing Systems with Pervasive Computing: P-RoMS 7

delegent by transporting it to memory and supplying necessary condition codes and data. When a delegent migrates from one camileun to another, it is in the mobile state. Specifically, in the PICO concept a repository of general purpose and special purpose delegents is maintained as shown in Figure 5. For example, a delegent which can perform signal filtering operations is a general-purpose delegent. However, when the delegent is modified to filter particular frequency components, it transforms into a special purpose delegent.

2.3

Communities

A community is defined by P = < D,φ >, where D is the set of delegents, and φ is the community’s mission or goal. As an example, PICO for the accident victim in Figure 1, can be defined as P1 =, where D1 =, D(x), is a delegent representing camileun x in this context and φ1 =. A given camileun may have more than one delegent, for simplicity, D(x) represents the DM. For example, D(car) is the DM in the car’s computer, the delegent responsible for sensing motion feeds into D(car). A sequence of events lead to the creation of communities. In the telemedicine scenario of Figure 1, the occurrence of the accident triggers several events: D(pocket PC) detects unusual activity such as a crash, D(streetlamp) detects an image of a crashed motor vehicle, D(car) senses a sudden deceleration and halt. One or more of these corresponding delegents attempt to contact each other, assess the situation and contact D(cellphone). Community P1 , whosecurrent goal is to get help is formed. The victim’s community at the top left of Figure 1 depicts P1 . Suppose, the cell phone is not functioning, and/or no acknowledgement is received after a certain period of time, the delegents of P1 attempt to get help via streetlamp’s connection to the traffic management network. These communities which are created autonomously upon the occurrence of a set of events can be classified as dynamic communities. On the other hand, there are static PICO communities whose mission is predefined. Delegents facilitate communication and coordination within communities and with other communities. Delegents reside in camileuns or roam around in communities, performing information processing tasks on behalf of camileuns and users. The paradigm for delegent-to-delegent communication will determine the performance of the PICO framework in many applications. Shared space, message passing, and combination of both will be investigated. Basically, there can be three types of delegentto-delegent communications: a) intra-camileun: when both the delegents

8 reside in the same camileun (or computer); b) inter-camileun: when two delegents reside on different camileuns, and c) multiple delegent interaction. The subtasks associated include the following: a) protocol design for communication, b) routing and multicasting algorithms, c) models for coordination among delegents: client-server, meeting-oriented, blackboard-oriented, or Linda-like. Community formation, integration, and disintegration are very important aspects of PICO. To achieve certain goals, two or more delegents may merge together to form a community. Similarly, two or more communities may integrate to form a larger community with a larger, more encompassing goal. Finally, once the goals of a community are achieved, the community may be disintegrated. Each community of delegents can be represented by a graph. We will investigate the use of graph theoretic techniques and develop algorithms for tasks associated with the formation, integration and disintegration of communities.

3.

The P-RoMS Architecture

The proposed P-RoMS architecture is illustrated using an example scenario. A common problem in a dynamic complex manufacturing environment is dealing with exceptions such as the detection of a nonconforming part. Terry knew it was going to be another bad Tuesday when Pat grimly handed over a pile of phone messages – from their own VPs and the customer. At last Friday’s meeting, there were no problems on the horizon. Now, there’s a major emergency with dozens of people scouring the airplane manufacturing plant for information to help solve a problem even though there are no problems yet identified on ERP(Enterprise Resource Planning). Terry has to solve the problem now – there is no time to feed new information into the ERP and no time to wait for its answer. Terry thinks, “If a vision system is smart enough to detect a bad part, why can’t it be smart enough to immediately notify key employees and take proactive measures? We need IT that is proactive and able to bring the right people together with the right information at the right time wherever they may be.” Here’s how a P-RoMS enabled manufacturing system would form dynamic communities in an aircraft manufacturing plant (Figure 1). In the wing assembly fixture, many parts are already loaded when the embedded processor in the fixture detects the arrival of a rib cap part (with RF capability). The delegent representing the fixture recognizes that the part must is on the critical path and must be loaded next. During loading, the smart fixture detects that the rib cap is not fitting

Revolutionizing Manufacturing Systems with Pervasive Computing: P-RoMS 9

properly, (violating assembly specs and halting production). This event causes the fixture delegent to initiate the creation of a tailored low-level mission-oriented community of agents. The delegent community tries to identify the problem and check the part’s manufacturing history and possible pre-approved work-around solutions. This will depend on the equipment’s level of intelligence and knowledge of this particular situation. If a satisfactory solution is possible, the part/fixture community solves the problem, lists work-around actions and work resumes along the assembly path. Upon satisfactory completion of the mission, the community is dismantled. If the problem cannot be resolved by the above fixture community, then a larger community is formed by inviting relevant human personnel to join. This enhanced community immediately contacts the geographically dispersed workers, supervisor, and quality engineer on their PDAs to perform the required collaborative tasks in real-time. Once the process is in the care of the response team, optimization can begin immediately. As needed, the delegents within the newly formed community will notify other appropriate pieces of equipment and parts, legacy software systems, and personnel who are in geographically distributed locations to participate in solving the problem at hand. In our example, if the quality engineer (and her delegent) decides to scrap the part, it is necessary re-order a new part immediately. This action can be carried out by contacting the delegents of manufacturing and material departments to determine if a replacement part is available. If no replacement is available, additional delegents from legacy software (ERP, MRP, Scheduler) try to resolve the problem on their own using actionable knowledge. Suppose in our example, a replacement part is available in time, but requires “stealing” the part allocated to another future assembly. It is a long-lead time purchased part, so major effort is required to change the schedule to get this part replaced for the assembly it was ‘stolen’ from, and to identify corrective actions to ensure that future parts are ready to fit in the fixture. In P-RoMS, this event causes the formation of a new community representing the distributed management team purchasing, vendor, manufacturing, quality, scheduling. Effective collaboration among software agents to optimize the current schedule and minimize cost takes place. Management staffs are alerted to the part’s effects on overall production and impending schedule changes. Critical manufacturing information is continually updated via a continuous feed from the software community around the monitoring equipment. If needed, communities create a multi-way videoconferencing session to allow management and specialists to visually assess the part’s effects.

10 The team uses communities of delegents in its investigation of the root cause(s) and takes action to prevent a recurrence. Delegents save information and problem resolutions paths, thereby adding to the knowledge framework.

4. 4.1

Research Challenges Abstraction and formal representation of low level devices, components and parts

The concepts of delegents and camileuns map quite naturally into the manufacturing domain. Products as well as production resources can be abstracted as camileuns. This notion of a camileun can be applied to both physical as well as conceptual entities. For example, the concept of a product (both class and instance) is required within the production scheduling system long before the product is in physical existence. In the same manner, aggregated resources, like automated machining centers, physically consist of a group of specific tools that when combined provide the capability to perform a specific task. In PICO, a camileun possesses one or more of the functionalities such as: see, hear, adapt, compute, communicate, learn, and process information. A camileun can be described by the hardware the camileun is equipped with and its functionality. Camileuns can be of different types and complexities –a temperature sensing device, an active network node, or a state-of-the-art workstation. In P-RoMS, entities such as component part, fixtures and product designs and plans are also considered as camileuns. These entities do not possess computing abilities, but parts and fixture can be equipped with RF tags for recognition and identification, and delegents created to represent them in the manufacturing environments. For convenience we refer to the two types of camileuns as active and passive camileuns respectively. In PICO, a delegent is an intelligent delegate that works diligently on behalf of a camileun or a user. In P-RoMS, delegents can also represent passive camileuns in computing communities. For example, a delegent representing an assembled part, carries information about the design plan, physical characteristics, details of when, where, and who assembled the part, and so on. The novelty of the community computing concept makes it possible for delegents representing different parts/components of a sub-system such as the wing of an aircraft to coordinate and collaborate with each other in a community and ensure design specs, time schedules, quality control, and safety requirements are met. The manufacturing system is replete with heterogeneous devices and software entities. Delegents representing passive camileuns are hosted on

Revolutionizing Manufacturing Systems with Pervasive Computing: P-RoMS11

active camileuns. Each passive camileun may have one or more delegents representing it in different communities. Resources may have a demand management delegent, a health monitoring delegent, and a state management delegent to name a few. Production orders, and the corresponding products that they represent, would also have a set of associated delegents. Products could further be decomposed into sub-components until the product is fully decomposed into its atomic level components that are either purchased or fabricated. Each product/sub-system/component may be associated with additional delegents and communities. Process Plan communities would outline the processes and corresponding resources needed to produce the product/sub-system/part. A Process Plan “object” delegents are capable of determining or accessing all the potential or practical process plans that would lead to the successful production of the product/sub-system/part. The “object” product traceability delegent would define the data that needs to be collected at each process step. The “instance” product plan delegent will define the actual process plan that product instance follows through the facility.

4.2

Situation-aware services

The problem of pervasive information acquisition and processing is a very important issue in the P-RoMS architecture. Delegents acquire information, within their community as well as in remote communities. Optimal caching and prefetching techniques [11. . . 14] will be employed to maintain up-to-date consistent data in communities. Our research on flexible caching for wireless Internet [15] will be augmented for adaptation to the community concept model of the P-RoMS architecture. Fast and reliable information storage and retrieval is crucial for PRoMS and database systems provide an attractive platform. In wireless computing environments frequent position updates would impose a serious performance and wireless-bandwidth overhead. We will develop methods to capture and maintain system states on databases that will be distributed among the static and moving hosts. We will investigate the concept of dynamic attributes (i.e., attributes that change continuously as a function of time) to represent the position of moving objects. Global state of the system is dynamic and its knowledge is crucial in making decisions. On-the-fly collection of global state is a difficult problem because computation and communication are asynchronous and there is no global clock [16]. Current global snapshot collection techniques that rely on message diffusion approach to record the system state [17] are unsuitable for P-RoMS. Delegents will collect the global state of the system and store in a distributed database. We will research appli-

12 cation of the methods evaluated in [18, 19] to manufacturing systems. We will investigate how to adapt protocols and solutions proposed in [20] to better manage the information acquisition and dissemination in a manufacturing system. We need to make sure the developed protocols are lightweight (in terms of memory and processing requirements) to be deployed in handheld and other capacity-constrained devices. Rapid advances in a wide range of wireless access technologies along with an industry-wide IP-convergence have set up the stage for contextaware computing. By using a variety of technologies to infer the current activity state of parts, components, fixtures and design plans, communities can intelligently manage both the content and the mode of delivery of information. The “location” information of the camileun or person plays a very important role in defining this context. We shall critically review some of the research prototypes for location-aware computing and evaluate the different ways and resolutions at which location information is managed. We shall then review the current trends towards integrated access technologies in wireless mobile environment and also outline the generic functions such as location update and paging that are an important part of most location management systems [20]. In this project, we propose to outline the design of a community computing based architecture for providing location support to complex dynamic environments such as manufacturing systems. This architecture will be based on the definition of open interfaces using the industry-standard Lightweight Directory Access Protocol (LDAP) [21] and will provide a flexible way to exchange and coordinate location information across multiple systems.

4.3

Proactive service discovery in P-RoMS

Service discovery is a critical issue in any pervasive computing system. In the proposed manufacturing system, discovering various services and resources will determine the efficiency of the P-RoMS architecture. In the P-RoMS environment, a service can be an active or passive camileun, a delegent, a database, a communication channel or network, a community based service or a person. For example, upon arrival of a part, it may be necessary to discover the product plan or the availability of a resource such as a special task machinery. On occurrence of an exceptional event, a delegent may have to discover a community, consult with other delegents, or personnel. Given the dynamic, rapidly developing nature of a crisis due to an exception, improved methods for the dynamic discovery of information relative to an exception and for the fusion of information from multiple sources are important. Information

Revolutionizing Manufacturing Systems with Pervasive Computing: P-RoMS13

processing and management technologies are essential to support service discovery. We will investigate various service discovery mechanisms – directory based or peer-to-peer, address-based or intentional [22], and local area or wide area network. Several discovery protocols such as JINI [23], SLP [24] and Salutation [25] will be investigated as candidate protocols for service discovery in P-RoMS. Due to the heterogeneous nature of the complex environment, it will be necessary investigate the use of service bridges in P-RoMS. We anticipate to develop a new service discovery mechanism for the P-RoMS architecture to provide scalable, secure, and pervasive services. This will be achieved by considering the service discovery process itself as a service that can be provided by a community of delegents.

4.4

Dynamic community creation in response to exceptions

In the PICO concept, we have defined two types of communities – service provider and dynamic. In the ongoing project, we are developing service provider communities for provisioning services in various applications. Development of dynamic communities is a challenging task that lies ahead of us in this project. The manufacturing system comprises both types of communities. The mission or goal of service provider communities and the rules for creation of such communities are pre-defined. For example, community for the wing assembly consists of delegents that represent different components. However, when an exceptional event occurs, a dynamic community may be created. In the example of Figure 2, the delegent representing the fixture initiates the formation of an enhanced community of delegents if and only if the problem is unresolved at the lower level. Creation and dismantling of dynamic communities is a challenging research issue in this project. The problems to be addressed include: i) what events trigger the creation of dynamic communities, ii) when and how to form communities; iii) when and how to dismantle communities; and iv)membership of a community. Our experience in developing methodologies for the creation of service provider communities will be useful in this research [2].

4.5

Knowledge architecture using contingency theory and information processes

Organizational theory (OT) examines an organization’s structure, constituencies, processes, and operational results in an effort to understand the relationships involved in creating effective and efficient systems. A

14 major division of OT, Contingency Theory (CT), postulates that no organization operates without constraints from personnel, technology and informational influences. If any object such as an agent is to produce its intended results, then any facets of the objects structure that provide inherent capability and limitations must be recognized. Dynamic and complex environments too often coerce organizations into highly centralized, hierarchical structures with strictly enforced standards-based rules of operation. This results in organizations wherein only a few experienced individuals have the knowledge and experience to cope with frequent change, exceptions, and their complex inter-relationships. The rest of the organization is thus poorly informed and error-prone when employees must make decisions. This highly centralized, hierarchical structure is the wrong approach for a knowledge-based organization. The organizational goal is to get the right knowledge to the right person at the right time so better decisions and fewer mistakes will be made. The more heterogeneous, unpredictable, and dependent upon other environmental resources a task is, the greater the information processing that the organization must be able to do in order to successfully accomplish it. IPT shows that as diversity and unpredictability increase, uncertainty increases due to incomplete information. As diversity of processes, or outputs increase, inter-process coordination requirements and system complexity increase. As uncertainty increases, informationprocessing requirements increase because of management’s inability to predict every situation. Thus, the basic premise of IPT for this project is that the greater the uncertainty in the overall tasks in an organizational system, the greater is the amount of information that the system must process. Organizational structures such as P-RoMS agent communities must attempt to deal with uncertainty, complexity and dynamicism.

5.

Discussion

PICO technologies are ideally suited for dynamic and complex environments whereas traditional static information infrastructures become brittle due to the rapid change in application requirements and dramatic shifts in the information processing technologies used to support their implementation. These systems are primarily designed to support the information processing requirements of a system that is “operating as planned.” Conventional ERP systems, shop floor scheduling systems, and process planning systems to name a few, are ideally suited to the planning and control of static and deterministic production environments. In reality, the manufacturing environment is anything but static and deterministic. The high degree of variance and disruptions within

Revolutionizing Manufacturing Systems with Pervasive Computing: P-RoMS15

the manufacturing environment limits the effectiveness of conventional manufacturing information processing technologies and sometimes the IT systems themselves are the cause for system instability. Many characteristics and requirements for the P-RoMS infrastructure are application domain independent. The P-RoMS technologies will be designed to support the construction of a Dynamically Reconfigurable Information Infrastructure for Complex Manufacturing Environments. The basic components are camileuns, delegents and communities. Active camileuns are characterized by CPU, memory, communication channels and sensory function (if any). Passive camileuns characterized by a simple data structure such a record or an object. Delegents are software entities whose main components are program modules, rules for their behavior, functions and a mission or goal. There are three fundamental questions that need to be answered by this activity: 1 Is the proposed P-RoMS architecture and technology physically capable of providing a Dynamically Reconfigurable Information Infrastructure? 2 Can P-RoMS support Contingency Decision – Making, Automated Error Detection & Mitigation, as well as Embedded & Distributed Process Monitoring and Control? 3 Does P-RoMS improve quantifiable financial and system performance benefits? if deployed in dynamic and complex environments such as like Manufacturing Systems. To answer these questions, we propose to develop a series of related system simulations. Finally, a prototype for the P-RoMS architecture will be developed. Current and emerging commercially available communications and computing platforms will be evaluated for their suitability for inclusion into a P-ROMS environment. There will be three aspects of this evaluation: a) the technology’s applicability for inclusion in a generalized P-ROMS environment, b) the technology’s applicability to support the targeted manufacturing systems domain, c) a technology gap analysis aimed to identify what is currently available and determine how they currently fail to support the computing platform requirements of the P-ROMS architecture. Several classes of systems will be considered ranging from: personal computing devices and PCs, embedded microcontrollers, wireless sensor platforms such the Berkeley sensor platform (http://xbow.com), wireless and conventional networking, asynchronous messaging services, and information storage and retrieval systems. This will intentionally

16 be a heterogeneous mix of computing and communications technologies. Three dimensions of performance will be assessed: a) capability, b) extensibility, and c) affordability. We plan to create a software toolkit to simplify the creation, dissolution, adjustment, and inspection of missionoriented communities. This development effort is also expected to provide the proposed research with insights that will lead to better understanding of practical limitations as well as identification of new research challenges.

Notes 1.

P-RoMS - pronounced as ‘promise’

2.

NSF Award STI-0129682

References

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