9,685 research outputs found

    A component-based middleware framework for configurable and reconfigurable Grid computing

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    Significant progress has been made in the design and development of Grid middleware which, in its present form, is founded on Web services technologies. However, we argue that present-day Grid middleware is severely limited in supporting projected next-generation applications which will involve pervasive and heterogeneous networked infrastructures, and advanced services such as collaborative distributed visualization. In this paper we discuss a new Grid middleware framework that features (i) support for advanced network services based on the novel concept of pluggable overlay networks, (ii) an architectural framework for constructing bespoke Grid middleware platforms in terms of 'middleware domains' such as extensible interaction types and resource discovery. We believe that such features will become increasingly essential with the emergence of next-generation e-Science applications. Copyright (c) 2005 John Wiley & Sons, Ltd

    Discovering the Dynamics of Smart Business Networks

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    In an earlier paper ,was discussed the necessary evolution from smart business networks, as based on process need satisfaction and governance, into business genetics [1] based on strategic bonds or decay and opportunistic complementarities. This paper will describe an approach and diffusion algorithms whereby to discover the dynamics of emergent smart business network structures and their performance in view of collaboration patterns over time. Some real life early analyses of dynamics are discussed based on cases and date from the high tech sector. Lessons learnt from such cases are also given on overall smart network dynamics with respect to local interaction strategies, as modelled like in business genetics by individual partner profiles, goals and constraints. It shows the weakness of static "business operating systems", as well as the possibly destabilizing clustering effects amongst nodes linked to filtering, evaluation and own preferences.dynamics;network performance;smart business networks;SBN;business genetics

    Discovering the dynamics of smart business networks

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    Earlier research discussed the necessary evolution from smart business networks, as based on process need satisfaction and governance, into business genetics [1] based on strategic bonds or decay and opportunistic complementarities. This paper will describe an approach and diffusion algorithms whereby to discover the dynamics of emergent smart business network structures and their performance in view of collaboration patterns over time. Some real life early analyses of dynamics are discussed based on cases and date from the high tech sector. Lessons learnt from such cases are also given on overall smart network dynamics with respect to local interaction strategies, as modelled like in business genetics by individual partner profiles, goals and constraints. It shows the weakness of static โ€œbusiness operating systemsโ€, as well as the possibly destabilizing clustering effects amongst nodes linked to filtering, evaluation and own preferences.smart business networks; business genetics; network performance; SBN; dynamics

    ํ˜‘์—… ๋กœ๋ด‡์„ ์œ„ํ•œ ์„œ๋น„์Šค ๊ธฐ๋ฐ˜๊ณผ ๋ชจ๋ธ ๊ธฐ๋ฐ˜์˜ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ๋ฐฉ๋ฒ•๋ก 

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ํ•˜์ˆœํšŒ.๊ฐ€๊นŒ์šด ๋ฏธ๋ž˜์—๋Š” ๋‹ค์–‘ํ•œ ๋กœ๋ด‡์ด ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ํ•˜๋‚˜์˜ ์ž„๋ฌด๋ฅผ ํ˜‘๋ ฅํ•˜์—ฌ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ชจ์Šต์€ ํ”ํžˆ ๋ณผ ์ˆ˜ ์žˆ๊ฒŒ ๋  ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ๋กœ ์ด๋Ÿฌํ•œ ๋ชจ์Šต์ด ์‹คํ˜„๋˜๊ธฐ์—๋Š” ๋‘ ๊ฐ€์ง€์˜ ์–ด๋ ค์›€์ด ์žˆ๋‹ค. ๋จผ์ € ๋กœ๋ด‡์„ ์šด์šฉํ•˜๊ธฐ ์œ„ํ•œ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๋ช…์„ธํ•˜๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ๋Œ€๋ถ€๋ถ„ ๊ฐœ๋ฐœ์ž๊ฐ€ ๋กœ๋ด‡์˜ ํ•˜๋“œ์›จ์–ด์™€ ์†Œํ”„ํŠธ์›จ์–ด์— ๋Œ€ํ•œ ์ง€์‹์„ ์•Œ๊ณ  ์žˆ๋Š” ๊ฒƒ์„ ๊ฐ€์ •ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋กœ๋ด‡์ด๋‚˜ ์ปดํ“จํ„ฐ์— ๋Œ€ํ•œ ์ง€์‹์ด ์—†๋Š” ์‚ฌ์šฉ์ž๋“ค์ด ์—ฌ๋Ÿฌ ๋Œ€์˜ ๋กœ๋ด‡์ด ํ˜‘๋ ฅํ•˜๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์ž‘์„ฑํ•˜๊ธฐ๋Š” ์‰ฝ์ง€ ์•Š๋‹ค. ๋˜ํ•œ, ๋กœ๋ด‡์˜ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๊ฐœ๋ฐœํ•  ๋•Œ ๋กœ๋ด‡์˜ ํ•˜๋“œ์›จ์–ด์˜ ํŠน์„ฑ๊ณผ ๊ด€๋ จ์ด ๊นŠ์–ด์„œ, ๋‹ค์–‘ํ•œ ๋กœ๋ด‡์˜ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ๋„ ๊ฐ„๋‹จํ•˜์ง€ ์•Š๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ƒ์œ„ ์ˆ˜์ค€์˜ ๋ฏธ์…˜ ๋ช…์„ธ์™€ ๋กœ๋ด‡์˜ ํ–‰์œ„ ํ”„๋กœ๊ทธ๋ž˜๋ฐ์œผ๋กœ ๋‚˜๋ˆ„์–ด ์ƒˆ๋กœ์šด ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, ๋ณธ ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ํฌ๊ธฐ๊ฐ€ ์ž‘์€ ๋กœ๋ด‡๋ถ€ํ„ฐ ๊ณ„์‚ฐ ๋Šฅ๋ ฅ์ด ์ถฉ๋ถ„ํ•œ ๋กœ๋ด‡๋“ค์ด ์„œ๋กœ ๊ตฐ์ง‘์„ ์ด๋ฃจ์–ด ๋ฏธ์…˜์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ง€์›ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋กœ๋ด‡์˜ ํ•˜๋“œ์›จ์–ด๋‚˜ ์†Œํ”„ํŠธ์›จ์–ด์— ๋Œ€ํ•œ ์ง€์‹์ด ๋ถ€์กฑํ•œ ์‚ฌ์šฉ์ž๋„ ๋กœ๋ด‡์˜ ๋™์ž‘์„ ์ƒ์œ„ ์ˆ˜์ค€์—์„œ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋Š” ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์–ธ์–ด๋Š” ๊ธฐ์กด์˜ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด์—์„œ๋Š” ์ง€์›ํ•˜์ง€ ์•Š๋Š” ๋„ค ๊ฐ€์ง€์˜ ๊ธฐ๋Šฅ์ธ ํŒ€์˜ ๊ตฌ์„ฑ, ๊ฐ ํŒ€์˜ ์„œ๋น„์Šค ๊ธฐ๋ฐ˜ ํ”„๋กœ๊ทธ๋ž˜๋ฐ, ๋™์ ์œผ๋กœ ๋ชจ๋“œ ๋ณ€๊ฒฝ, ๋‹ค์ค‘ ์ž‘์—…(๋ฉ€ํ‹ฐ ํƒœ์Šคํ‚น)์„ ์ง€์›ํ•œ๋‹ค. ์šฐ์„  ๋กœ๋ด‡์€ ํŒ€์œผ๋กœ ๊ทธ๋ฃน ์ง€์„ ์ˆ˜ ์žˆ๊ณ , ๋กœ๋ด‡์ด ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋Šฅ์„ ์„œ๋น„์Šค ๋‹จ์œ„๋กœ ์ถ”์ƒํ™”ํ•˜์—ฌ ์ƒˆ๋กœ์šด ๋ณตํ•ฉ ์„œ๋น„์Šค๋ฅผ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๋กœ๋ด‡์˜ ๋ฉ€ํ‹ฐ ํƒœ์Šคํ‚น์„ ์œ„ํ•ด 'ํ”Œ๋žœ' ์ด๋ผ๋Š” ๊ฐœ๋…์„ ๋„์ž…ํ•˜์˜€๊ณ , ๋ณตํ•ฉ ์„œ๋น„์Šค ๋‚ด์—์„œ ์ด๋ฒคํŠธ๋ฅผ ๋ฐœ์ƒ์‹œ์ผœ์„œ ๋™์ ์œผ๋กœ ๋ชจ๋“œ๊ฐ€ ๋ณ€ํ™˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋‚˜์•„๊ฐ€ ์—ฌ๋Ÿฌ ๋กœ๋ด‡์˜ ํ˜‘๋ ฅ์ด ๋”์šฑ ๊ฒฌ๊ณ ํ•˜๊ณ , ์œ ์—ฐํ•˜๊ณ , ํ™•์žฅ์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด, ๊ตฐ์ง‘ ๋กœ๋ด‡์„ ์šด์šฉํ•  ๋•Œ ๋กœ๋ด‡์ด ์ž„๋ฌด๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๋„์ค‘์— ๋ฌธ์ œ๊ฐ€ ์ƒ๊ธธ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ƒํ™ฉ์— ๋”ฐ๋ผ ๋กœ๋ด‡์„ ๋™์ ์œผ๋กœ ๋‹ค๋ฅธ ํ–‰์œ„๋ฅผ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ๊ฐ€์ •ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋™์ ์œผ๋กœ๋„ ํŒ€์„ ๊ตฌ์„ฑํ•  ์ˆ˜ ์žˆ๊ณ , ์—ฌ๋Ÿฌ ๋Œ€์˜ ๋กœ๋ด‡์ด ํ•˜๋‚˜์˜ ์„œ๋น„์Šค๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ทธ๋ฃน ์„œ๋น„์Šค๋ฅผ ์ง€์›ํ•˜๊ณ , ์ผ๋Œ€ ๋‹ค ํ†ต์‹ ๊ณผ ๊ฐ™์€ ์ƒˆ๋กœ์šด ๊ธฐ๋Šฅ์„ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด์— ๋ฐ˜์˜ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ํ™•์žฅ๋œ ์ƒ์œ„ ์ˆ˜์ค€์˜ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด๋Š” ๋น„์ „๋ฌธ๊ฐ€๋„ ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ํ˜‘๋ ฅ ์ž„๋ฌด๋ฅผ ์‰ฝ๊ฒŒ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋‹ค. ๋กœ๋ด‡์˜ ํ–‰์œ„๋ฅผ ํ”„๋กœ๊ทธ๋ž˜๋ฐํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ํ”„๋ ˆ์ž„์›Œํฌ๊ฐ€ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์žฌ์‚ฌ์šฉ์„ฑ๊ณผ ํ™•์žฅ์„ฑ์„ ์ค‘์ ์œผ๋กœ ๋‘” ์—ฐ๊ตฌ๋“ค์ด ์ตœ๊ทผ ๋งŽ์ด ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„์˜ ์ด๋“ค ์—ฐ๊ตฌ๋Š” ๋ฆฌ๋ˆ…์Šค ์šด์˜์ฒด์ œ์™€ ๊ฐ™์ด ๋งŽ์€ ํ•˜๋“œ์›จ์–ด ์ž์›์„ ํ•„์š”๋กœ ํ•˜๋Š” ์šด์˜์ฒด์ œ๋ฅผ ๊ฐ€์ •ํ•˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ, ํ”„๋กœ๊ทธ๋žจ์˜ ๋ถ„์„ ๋ฐ ์„ฑ๋Šฅ ์˜ˆ์ธก ๋“ฑ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์—, ์ž์› ์ œ์•ฝ์ด ์‹ฌํ•œ ํฌ๊ธฐ๊ฐ€ ์ž‘์€ ๋กœ๋ด‡์˜ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๊ฐœ๋ฐœํ•˜๊ธฐ์—๋Š” ์–ด๋ ต๋‹ค. ๊ทธ๋ž˜์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž„๋ฒ ๋””๋“œ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ์„ค๊ณ„ํ•  ๋•Œ ์“ฐ์ด๋Š” ์ •ํ˜•์ ์ธ ๋ชจ๋ธ์„ ์ด์šฉํ•œ๋‹ค. ์ด ๋ชจ๋ธ์€ ์ •์  ๋ถ„์„๊ณผ ์„ฑ๋Šฅ ์˜ˆ์ธก์ด ๊ฐ€๋Šฅํ•˜์ง€๋งŒ, ๋กœ๋ด‡์˜ ํ–‰์œ„๋ฅผ ํ‘œํ˜„ํ•˜๊ธฐ์—๋Š” ์ œ์•ฝ์ด ์žˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ ์™ธ๋ถ€์˜ ์ด๋ฒคํŠธ์— ์˜ํ•ด ์ˆ˜ํ–‰ ์ค‘๊ฐ„์— ํ–‰์œ„๋ฅผ ๋ณ€๊ฒฝํ•˜๋Š” ๋กœ๋ด‡์„ ์œ„ํ•ด ์œ ํ•œ ์ƒํƒœ ๋จธ์‹  ๋ชจ๋ธ๊ณผ ๋ฐ์ดํ„ฐ ํ”Œ๋กœ์šฐ ๋ชจ๋ธ์ด ๊ฒฐํ•ฉํ•˜์—ฌ ๋™์  ํ–‰์œ„๋ฅผ ๋ช…์„ธํ•  ์ˆ˜ ์žˆ๋Š” ํ™•์žฅ๋œ ๋ชจ๋ธ์„ ์ ์šฉํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋”ฅ๋Ÿฌ๋‹๊ณผ ๊ฐ™์ด ๊ณ„์‚ฐ๋Ÿ‰์„ ๋งŽ์ด ํ•„์š”๋กœ ํ•˜๋Š” ์‘์šฉ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด, ๋ฃจํ”„ ๊ตฌ์กฐ๋ฅผ ๋ช…์‹œ์ ์œผ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์—ฌ๋Ÿฌ ๋กœ๋ด‡์˜ ํ˜‘์—… ์šด์šฉ์„ ์œ„ํ•ด ๋กœ๋ด‡ ์‚ฌ์ด์— ๊ณต์œ ๋˜๋Š” ์ •๋ณด๋ฅผ ๋‚˜ํƒ€๋‚ด๊ธฐ ์œ„ํ•ด ๋‘ ๊ฐ€์ง€ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•œ๋‹ค. ๋จผ์ € ์ค‘์•™์—์„œ ๊ณต์œ  ์ •๋ณด๋ฅผ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ํƒœ์Šคํฌ๋ผ๋Š” ํŠน๋ณ„ํ•œ ํƒœ์Šคํฌ๋ฅผ ํ†ตํ•ด ๊ณต์œ  ์ •๋ณด๋ฅผ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๋˜ํ•œ, ๋กœ๋ด‡์ด ์ž์‹ ์˜ ์ •๋ณด๋ฅผ ๊ฐ€๊นŒ์šด ๋กœ๋ด‡๋“ค๊ณผ ๊ณต์œ ํ•˜๊ธฐ ์œ„ํ•ด ๋ฉ€ํ‹ฐ์บ์ŠคํŒ…์„ ์œ„ํ•œ ์ƒˆ๋กœ์šด ํฌํŠธ๋ฅผ ์ถ”๊ฐ€ํ•œ๋‹ค. ์ด๋ ‡๊ฒŒ ํ™•์žฅ๋œ ์ •ํ˜•์ ์ธ ๋ชจ๋ธ์€ ์‹ค์ œ ๋กœ๋ด‡ ์ฝ”๋“œ๋กœ ์ž๋™ ์ƒ์„ฑ๋˜์–ด, ์†Œํ”„ํŠธ์›จ์–ด ์„ค๊ณ„ ์ƒ์‚ฐ์„ฑ ๋ฐ ๊ฐœ๋ฐœ ํšจ์œจ์„ฑ์— ์ด์ ์„ ๊ฐ€์ง„๋‹ค. ๋น„์ „๋ฌธ๊ฐ€๊ฐ€ ๋ช…์„ธํ•œ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด๋Š” ์ •ํ˜•์ ์ธ ํƒœ์Šคํฌ ๋ชจ๋ธ๋กœ ๋ณ€ํ™˜ํ•˜๊ธฐ ์œ„ํ•ด ์ค‘๊ฐ„ ๋‹จ๊ณ„์ธ ์ „๋žต ๋‹จ๊ณ„๋ฅผ ์ถ”๊ฐ€ํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด, ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์—ฌ๋Ÿฌ ๋Œ€์˜ ์‹ค์ œ ๋กœ๋ด‡์„ ์ด์šฉํ•œ ํ˜‘์—…ํ•˜๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋Œ€ํ•ด ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค.In the near future, it will be common that a variety of robots are cooperating to perform a mission in various fields. There are two software challenges when deploying collaborative robots: how to specify a cooperative mission and how to program each robot to accomplish its mission. In this paper, we propose a novel software development framework that separates mission specification and robot behavior programming, which is called service-oriented and model-based (SeMo) framework. Also, it can support distributed robot systems, swarm robots, and their hybrid. For mission specification, a novel scripting language is proposed with the expression capability. It involves team composition and service-oriented behavior specification of each team, allowing dynamic mode change of operation and multi-tasking. Robots are grouped into teams, and the behavior of each team is defined with a composite service. The internal behavior of a composite service is defined by a sequence of services that the robots will perform. The notion of plan is applied to express multi-tasking. And the robot may have various operating modes, so mode change is triggered by events generated in a composite service. Moreover, to improve the robustness, scalability, and flexibility of robot collaboration, the high-level mission scripting language is extended with new features such as team hierarchy, group service, one-to-many communication. We assume that any robot fails during the execution of scenarios, and the grouping of robots can be made at run-time dynamically. Therefore, the extended mission specification enables a casual user to specify various types of cooperative missions easily. For robot behavior programming, an extended dataflow model is used for task-level behavior specification that does not depend on the robot hardware platform. To specify the dynamic behavior of the robot, we apply an extended task model that supports a hybrid specification of dataflow and finite state machine models. Furthermore, we propose a novel extension to allow the explicit specification of loop structures. This extension helps the compute-intensive application, which contains a lot of loop structures, to specify explicitly and analyze at compile time. Two types of information sharing, global information sharing and local knowledge sharing, are supported for robot collaboration in the dataflow graph. For global information, we use the library task, which supports shared resource management and server-client interaction. On the other hand, to share information locally with near robots, we add another type of port for multicasting and use the knowledge sharing technique. The actual robot code per robot is automatically generated from the associated task graph, which minimizes the human efforts in low-level robot programming and improves the software design productivity significantly. By abstracting the tasks or algorithms as services and adding the strategy description layer in the design flow, the mission specification is refined into task-graph specification automatically. The viability of the proposed methodology is verified with preliminary experiments with three cooperative mission scenarios with heterogeneous robot platforms and robot simulator.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Contribution 7 1.3 Dissertation Organization 9 Chapter 2. Background and Existing Research 11 2.1 Terminologies 11 2.2 Robot Software Development Frameworks 25 2.3 Parallel Embedded Software Development Framework 31 Chapter 3. Overview of the SeMo Framework 41 3.1 Motivational Examples 45 Chapter 4. Robot Behavior Programming 47 4.1 Related works 48 4.2 Model-based Task Graph Specification for Individual Robots 56 4.3 Model-based Task Graph Specification for Cooperating Robots 70 4.4 Automatic Code Generation 74 4.5 Experiments 78 Chapter 5. High-level Mission Specification 81 5.1 Service-oriented Mission Specification 82 5.2 Strategy Description 93 5.3 Automatic Task Graph Generation 96 5.4 Related works 99 5.5 Experiments 104 Chapter 6. Conclusion 114 6.1 Future Research 116 Bibliography 118 Appendices 133 ์š”์•ฝ 158Docto

    SPAD: a distributed middleware architecture for QoS enhanced alternate path discovery

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    In the next generation Internet, the network will evolve from a plain communication medium into one that provides endless services to the users. These services will be composed of multiple cooperative distributed application elements. We name these services overlay applications. The cooperative application elements within an overlay application will build a dynamic communication mesh, namely an overlay association. The Quality of Service (QoS) perceived by the users of an overlay application greatly depends on the QoS experienced on the communication paths of the corresponding overlay association. In this paper, we present SPAD (Super-Peer Alternate path Discovery), a distributed middleware architecture that aims at providing enhanced QoS between end-points within an overlay association. To achieve this goal, SPAD provides a complete scheme to discover and utilize composite alternate end-to end paths with better QoS than the path given by the default IP routing mechanisms

    Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

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    The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent โ€œdevicesโ€, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew โ€œcognitive devicesโ€ are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
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