Appendix R
Publications and References
Publications 554
Lectures 557
Supervised Thesises 557
Bibliography 558
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
Chapter . Publications and References
R.1 Publications
To clearly distinguish self references providing the content of this work from
external references the reference label starts always with the initials of the
author of this work, regardless of first or second level authorship.
[BOS17A] S. Bosse, E. Pournaras, An Ubiquitous Multi-Agent Mobile Platform for Dis-
tributed Crowd Sensing and Social Mining, FiCloud 2017: The 5th Interna-
tional Conference on Future Internet of Things and Cloud,Aug 21, 2017 -
Aug 23, 2017, Prague, Czech Republic.
[[BOS17B] S. Bosse, D. Lehmhus, Towards Large-scale Material-integrated Computing:
Self-Adaptive Materials and Agents, The 3rd International Workshop on
Data-driven Self-regulating Systems (DSS 2017), 18-22 September 2017,
University of Arizona, Tucson, AZ
[BOS17C] S. Bosse, Incremental Distributed Learning with JavaScript Agents for Earth-
quake and Disaster Monitoring, International Journal of Distributed Sys-
tems and Technologies (IJDST), (2017), IGI-Global, Vol. 8, Issue 4, DOI:
[BOS16C] S. Bosse, Distributed Machine Learning with Self-organizing Mobile
Agents for Earthquake Monitoring, IEEE 1st International Workshops on
Foundations and Applications of Self Systems (FASW), SASO Conference,
DSS Workshop, 12 September 2016, Augsburg, Germany, 2016, 2016,
[BOS16B] S. Bosse. A. Lechleiter, Structural Monitoring with Distributed-Regional
and Event-based NN-Decision Tree Learning using Mobile Multi-Agent
Systems and common JavaScript platforms, Proc. of the SysInt 2016,
Proc. Technol., DOI: 10.1016/j.protcy.2016.08.063
[BOS16A] S. Bosse, Mobile Multi-Agent Systems for the Internet-of-Things and Clouds
using the JavaScript Agent Machine Platform and Machine Learning as a Ser-
vice, The IEEE 4th International Conference on Future Internet of Things
and Cloud, 22-24 August 2016, Vienna, Austria, 2016, 2016, DOI:10.1109/
[BOS15A] S. Bosse, From the Internet-of-Things to Sensor Clouds - Unified Distributed
Computing in Heterogeneous Environments with Smart and Mobile Multi-
Agent Systems, Proc. of the Smart Systems Integration Conference 2015,
11-12 March 2015, Copenhagen, Denmark
[BOS15B] S. Bosse, Design and Simulation of Material-integrated Distributed Sensor
Processing with a Code-based Agent Platform and mobile Multi-Agent Sys-
tems, MDPI Sensors 2015, 15(2), 4513-4549; doi:10.3390/s150204513, In-
vited Publication
[BOS15C] S. Bosse, A Unified Distributed Computing Framework with Mobile Multi-
Agent Systems and Virtual Machines For Large-Scale Applications: From the
Internet-of-Things to Sensor Clouds, 11th Workshop on Agent Based Com-
puting: from Model to Implementation (ABC:MI'15), Lodz, Poland, Sep-
tember 13-16, 2015, accepted as position paper
[BOS15D] S. Bosse, Unified Distributed Computing and Co-ordination in Pervasive/
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
R.1 Publications 555
Ubiquitous Networks with Mobile Multi-Agent Systems using a Modular and
Portable Agent Code Processing Platform, in The 6th International Confer-
ence on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN
2015), Procedia Computer Science, 2015
[BOS14A] S. Bosse, Distributed Agent-based Computing in Material-Embedded Sensor
Network Systems with the Agent-on-Chip Architecture, IEEE Sensors Journal,
Special Issue on Material-integrated Sensing, DOI 10.1109/
JSEN.2014.2301938, Top 25 Download Ranking IEEE Sensors May/June/
August 2014
[BOS14B] S. Bosse, Design of Material-integrated Distributed Data Processing Plat-
forms with Mobile Multi-Agent Systems in Heterogeneous Networks, Proc. of
the 6’th International Conference on Agents and Artificial Intelligence
ICAART 2014. DOI:10.5220/0004817500690080, Nominated for Best Pa-
per Award
[BOS14C] S. Bosse, A. Lechleiter, Structural Health and Load Monitoring with Materi-
al-embedded Sensor Networks and Self-organizing Multi-Agent Systems, Sys-
Int 2014, 2nd International Conference on System-Integrated
Intelligence, Procedia Technology, Elsevier, DOI: 10.1016/j.prot-
[BOS14D] S. Bosse, Processing of Mobile Multi-Agent Systems with a Code-based Agent
Platform in Material-Integrated Distributed Sensor Networks, 1st Interna-
tional e-conference on Sensors and Applications, Section D: Sensor Net-
works, 2014, 2014, DOI:10.3390/ecsa-1-d010, Best Presentation and
Paper Award in Sensor Networks Topic
[BOS14E] S. Bosse, Design and Simulation of a Low-Resource Processing Platform for
Mobile Multi-Agent Systems in Distributed Heterogeneous Networks, LNAI
2015, Springer, under publicaton, Invited Paper
[BOS14F] S. Bosse, A. Lechleiter, A Hybrid Approach for Structural Monitoring with
Self-organizing Multi-Agent Systems and Inverse Numerical Methods in Mate-
rial-embedded Sensor Networks, Elsevier Mechatronics Journal, Invited
Publication, under review
[BOS14G] D. Lehmhus, S. Bosse, W. Lang, P.C. Chao, F. Chang, Guest Editorial Special
Issue on Material-Integrated Sensing, Data Processing and Communication
(Article), IEEE Sensors, 14 (7), 2014, DOI:10.1109/JSEN.2014.2330133.
[BOS13A] S. Bosse, Intelligent Microchip Networks: An Agent-on-Chip Synthesis Frame-
work for the Design of Smart and Robust Sensor Networks, SPIE 2013, Mi-
crotechnologie Conference, Session EMT 102 VLSI Circuits and Systems,
24-26 April 2013, Alpexpo/Grenoble, France, DOI:10.1117/12.2017224
[BOS13B] D. Lehmhus, S. Bosse, M. Busse, Sensorial Materials, Chapter 17, Dirk Le-
hmhus, Matthias Busse, Axel S. Herrmann, Kambiz Kayvantash (Ed.):
Structural Materials and Processes in Transportation, pp. 517-548, Wiley-
VCH, 2013, ISBN: 9783527327874, DOI:10.1002/9783527649846
[BOS13C] T. Behrmann, C. Budelmann, S. Bosse, D. Lehmhus, M. C. Lemmel, Tool
chain for harvesting, simulation and management of energy in Sensorial Ma-
terials, Journal of Intelligent Material Systems and Structures, 2013,
DOI:10.1177/1045389X 13488248, Invited Publication.
[BOS13D] S. Bosse, F. Pantke, S. Edelkamp, Robot Manipulator with emergent Behav-
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
Chapter . Publications and References
iour supported by a Smart Sensorial Material and Agent Systems, Proceed-
ings of the Smart Systems Integration Conference SSI 2013, Topic 5,
Amsterdam NL, 13-14.3.2013, 2013, ISBN: 978-3-8007-3490-0
[BOS12A] S. Bosse, F. Pantke, Distributed computing and reliable communication in
sensor networks using multi-agent system, Production Engineering, Re-
search and Development, 2012, ISSN: 0944-6524, DOI:10.1007/s11740-
012-0420-8, Invited Publication.
[BOS12B] S. Bosse, F. Pantke, F. Kirchner, Distributed Computing in Sensor Networks
Using Multi-Agent Systems and Code Morphing, Proceedings of the 11th In-
ternational Conference on Artificial Intelligence and Soft Computing
Conference ICAISC 2012, 29.4. – 3.5.2012, Zakapone, Poland
[BOS12C] S. Bosse, F. Pantke, F. Kirchner, Data Processing and Communication in
Distributed Low-power Sensor Networks using Multi-agent Systems, 1st Joint
Symposium on System-integrated Intelligence: New Challenges for Prod-
uct and Production Engineering, Special Session Enabling Technologies
for Sensorial Materials – Taking sensor integration, June 27th – 29th
2012: Hannover, Germany
[BOS12D] K. Tracht, S. Hogreve, S. Bosse, Intelligent Interpretation of Multiaxial Grip-
per Force Sensors, Proceedings of CIRP Conference on Assembly Technol-
ogies, CATS 2012
[BOS12E] S. Bosse, F. Kirchner, Smart Energy Management and Energy Distribution in
Decentralized Self-Powered Sensor Networks Using Artificial Intelligence Con-
cepts, Proceedings of the Smart Systems Integration Conference 2012,
Session 4, Zürich, Schweiz, 22 - 23 Mar. 2012, ISBN 978-3-8007-3423-8
[BOS12F] F. Pantke, S. Bosse, D. Lehmhus, M. Lawo, M. Busse, Combining Simula-
tion and Machine-Learning for Real-Time Load Identification in Sensorial Ma-
terials, Proceedings of the International Conference SIMBIO-M-2011,
Simulations in BIO-Sciences and Multiphysics, 20-22.6.2011, Marseille,
France, 2011.
[BOS11A] S. Bosse, Hardware-Software-Co-Design of Parallel and Distributed Systems
Using a unique Behavioural Programming and Multi-Process Model with
High-Level Synthesis, Proceedings of the SPIE Microtechnologies 2011
Conference, 18.4.-20.4.2011, Prague, Session EMT 102 VLSI Circuits and
Systems, DOI 10.1117/12.888122
[BOS11B] S. Bosse, T. Behrmann, Smart Energy Management and Low-Power Design
of Sensor and Actuator Nodes on Algorithmic Level for Self-Powered Sensori-
al Materials and Robotics, Proceedings of the SPIE Microtechnologies
2011 Conference, 18.4.-20.4.2011, Prague, Session EMT 101 Smart Sen-
sors, Actuators and MEMS, 2011, DOI:10.1117/12.888124
[BOS11C] F. Pantke, S. Bosse, D. Lehmhus, M. Lawo, An Artificial Intelligence Ap-
proach Towards Sensorial Materials, Proceedings of the Future Comput-
ing 2011 Conference, DOI 10.13140/2.1.3124.0647, Best Paper Award
[BOS10A] S. Bosse, Hardware Synthesis of Complex System-on-Chip-Designs for Em-
bedded Systems Using a Behavioural Programming and Multi-Process Mod-
el, Proceedings of the 55th IWK – Internationales Wissenschaftliches
Kolloquium, Session C4, Ilmenau, 13 – 17 Sept. 2010, 2010.
[BOS10B] S. Bosse, System-On-Chip Design and Communication in Embedded Wired
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
R.2 Lectures 557
High-Density Sensor Networks: A Contribution from Behavioural High-Level
Synthesis and Functional Printing, E-MRS 2010 Spring Meeting, June 7-11,
2010, Congress Center, Strasbourg, France, 2010
[BOS10C] S. Bosse, D. Lehmhus, Smart Communication in a Wired Sensor- and Actu-
ator-Network of a Modular Robot Actuator System using a Hop-Protocol with
Delta-Routing, Proceedings of the Smart Systems Integration conference,
Como, Italy, 23-24.3.2010 (2010), 2010, ISBN: 978-3-8007-3208-1.
[BOS10D] T. Behrmann, C. Zschippig, M. Lemmel, S. Bosse, Toolbox for Energy Anal-
ysis and Simulation of self-powered Sensor Nodes, Proceedings of the 55th
IWK - Internationales Wissenschaftliches Kolloquium, Session A3, Il-
menau, 13 - 17 Sept. 2010
[BOS06A] S. Bosse, VAMNET: the Functional Approach to Distributed Program-
ming, SIGOPS Oper. Syst. Rev., 40, pp. 108-114, 2006, DOI:10.1145/
R.2 Lectures
(All lectures are held at the University of Bremen.)
[PDL] S. Bosse, Programmierbare (anwendungsspezifische) Digitallogik und VHDL-
Synthese, SWS: 4, ECTS: 6, VAK 03-ME-712.05
[PARSYS] S. Bosse, Hardware-Entwurf von parallelen und verteilten Systemen mit FP-
GAs und Logik- und Highlevel-Synthese, SWS: 4, ECTS: 6, VAK 03-ME-712.06
[MISS] S. Bosse, D. Lehmhus, Material-integrierte Sensorische Systeme, SWS: 4,
ECTS: 6, VAK 04-M10-2-PT08
[GDI] F. Kirchner, S. Bosse, Grundlagen der Informatik I+II, SWS: 3, ECTS: 4 , VAK
[SM] Ringvorlesung: Sensorische Materialien – Visionen, Technik, Grundlagen,
VAK 04-326-WP-01
R.3 Supervised Thesises
[BEN13] S. Bonucelli, Agent-Based Routing Algorithms for a Wired Sensor Network,
Master Thesis, University of Bremen and Università degli Studi di Geno-
va Scuola Politecnica, MsC Electrical Engineering, VDI Award, 2013
[TAS13] A. Tassi, Low-power design and energy management with agents at mi-
cro-chip level in autonomous distributed sensor networks, Master The-
sis, University of Bremen and and Università degli Studi di Genova
Scuola Politecnica, MsC Electrical Engineering, 2013
[WEI07] T. Weihmann, Genserver - An extrinsic and intrinsic Hardware Evolution
platform with an application to the nonlinear control problem of DC motors,
University of Bremen, MsC Computer Science, 2007
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
Chapter . Publications and References
R.4 Bibliography
[ALCAD] Alliance VLSI CAD Tools, ””
[AGR96] Agrawal, R., & Shafer, J. C. (1996). Parallel mining of association rules. Ieee
Trans. On Knowledge And Data Engineering, 8(6), 962–969
[ANG08] R. Angles and C. Gutierrez, Survey of graph database models, ACM Com-
puting Surveys (CSUR), 2008.
[ARV90] K. Arvind, R. S. Nikhil, Executing a Program on the MIT Tagged-Token Data-
flow Architecture, IEEE Transactions on Computers, vol. 39, no. 3, 1990.
[ATK08] A. Atkinson, “Tupleware: A Distributed Tuple Space for Cluster Comput-
ing,” in 2008 Ninth International Conference on Parallel and Distributed
Computing, Applications and Technologies, 2008.
[BAL90] H. E. Bal, A. S. Tanenbaum, and M. F. Kaashoek, Orca: a language for dis-
tributed programming, ACM SIGPLAN NOTICES, vol. 25, no. 4, pp. 17–24,
[BAR73] M. R. Barbacci, Automated exploration of the design space for register-
transfer (rt) systems, 1973, Thesis.
[BEL01] F. Bellifemine, A. Poggi, G. Rimassa, Developing multi-agent systems with a
FIPA-compliant agent framework, SOFTWARE—PRACTICE AND EXPERI-
ENCE, vol. 31, pp. 103–128, 2001.
[BEL15] J. Bell, Machine Learning - Hands-On for Developers and Technical Profes-
sionals, John Wiley & Sons, Ltd, 2015.
[BOL09] C. Boller, Structural Health Monitoring—An Introduction and Definition, in
Encyclopedia of Structural Health Monitoring, Wiley, 2009
[BRA05] M. Bravetti, R. Gorrieri, R. Lucchi, and G. Zavattaro, “Quantitative infor-
mation in the tuple space coordination model,” Theoretical Computer
Science, vol. 346, pp. 28–57, 2005.
[BUL08] V. Bulcke et al.:Process Technology for the Fabrication of a Chip-in Wire Style
Packaging. Proc. Electronic Comp. and Techn. Conf. 06/2008,
[BUR11] J. N. Burghartz, Ultra-thin Chip Technology and Applications. Springer
New York Dordrecht Heidelberg London, 2011.
[CAB95] F. G. McCabe, K. L. Clark, APRIL - Agent Process Interaction Language,
1995, (M. Wooldridge & N. R. Jennings, Eds.) Intelligent Agents Theories
Architectures and Languages LNAI volume 890. Springer-Verlag
[CAM12] I. del Campo, K. Basterretxea, J. Echanobe, G. Bosque, and F. Doctor, A
system-on-chip development of a neuro-fuzzy embedded agent for ambient-
intelligence environments., IEEE transactions on systems, man, and cyber-
netics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and
Cybernetics Society, vol. 42, no. 2, pp. 501-12, Apr. 2012
[CAN10] Cannata, G., Dahiya, R., Maggiali, M., Mastrogiovanni, F., Metta, G., &
Valle, M. (2010). Modular Skin for Humanoid Robot Systems. CogSys 2010
Conference Proceedings (Vol. 231500).
[CAR00A] L. Cardelli, A: Gordon, Mobile Ambients. Theoretical Computer Science, Spe-
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
R.4 Bibliography 559
cial Issue on Coordination 240(1), 177–213 (2000)
[CAR00B] M. Caridi and A. Sianesi, “Multi-agent systems in production planning
and control: An application to the scheduling of mixed-model assembly
lines,” Int. J. Production Economics, vol. 68, pp. 29–42, 2000
[CAR06] C. Carn, P. Trivailo, The inverse determination of aerodynamic loading
from structural response data using neural networks, Inverse Problems
in Science and Engineering, 14, 379-395, 2006
[CAS08] D. Gregg, K. Casey, M. A. Ertl, and Y. Shi,Virtual machine showdown,”
ACM Transactions on Architecture and Code Optimization, vol. 4, no. 4.
pp. 1–36, 2008.
[CAR13] G. Cardone et al., Fostering ParticipAction in Smart Cities: A Geo-Social
Crowdsensing Platform, IEEE Communications Magazine, no. 6, 2013.
[CER07] R. Cervenka and I. Trencansky, The Agent Modeling Language - AML A Com-
prehensive Approach to Modeling Multi-Agent Systems. Birkhäuser, 2007.
[CHO17] M. Choi, Y. Sui, I. H. Lee, R. Meredith, Y. Ma, G. Kim, D. Blaauw, Y. B. Gi-
anchandani, T. Li, Autonomous Microsystems for Downhole Applications:
Design Challenges, Current State, and Initial Test Results, doi:10.3390/
s17102190 (2017)
[CHU02] L. Chunlina, L. Zhengdinga, L. Layuanb, and Z. Shuzhia, A mobile agent
platform based on tuple space coordination, Advances in Engineering Soft-
ware, vol. 33, no. 4, pp. 215–225, 2002
[COU08] P. Coussy, A. Morawiec (Ed.), High-Level Synthesis - from Algorithm to Digi-
tal Circuit, Springer 2008
[COU09] P. Coussy, D. D. Gajski, M. Meredith, and A. Takach, An Introduction to
High-Level Synthesis, IEEE Design & Test of Computers, vol. 26, no. 4,
[DAH07] Dahiya, R., & Valle, M. (2007). Tactile sensing arrays for humanoid robots.
Research in Microelectronics and Electronics Conference, 2007. PRIME
2007 (pp. 201–204)
[DIE11] A. Dietzel, J. Brand, J. Vanfleteren, W. Christiaens, E. Bosman, J. De Baets ,
System-in-Foil Technology, in J. N. Burghartz, Ultra-thin Chip Technology
and Applications. Springer New York Dordrecht Heidelberg London,
[EBR11] M. Ebrahimi, M. Daneshtalab, P. Liljeberg, J. Plosila, H. Tenhunen, Agent-
based on-chip network using efficient selection method, 2011 IEEEIFIP 19th
International Conference on VLSI and SystemonChip (pp. 284-289). IEEE.
[ENG96] H. W. Engl, M. Hanke, A. Neubauer, Regularization of inverse problems,
Kluwer Acad. Publ., Dordrecht, Netherlands, 1996
[FER99] J. Ferber, Multi-Agent Systems: An Introduction to Distributed Artificial Intelli-
gence, Addison Wesley, 1999
[FIE07] Fiedrich, F., & Burghardt, P. (2007) Agent-based systems for disaster
management, Communications of the ACM - Emergency response infor-
mation systems: emerging trends and technologies CACM, vol. 50, no. 3,
pp. 41-42
[FRI01] M.I. Friswell, J.E. Mottershead, Inverse methods in structural health mon-
itoring, DAMAS 2001: 4th International Conference on Damage Assess-
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
Chapter . Publications and References
ment of Structures, Cardiff, June 2001, pp. 201-210
[FRI07] M. Friswell, Damage identification using inverse methods, Phil. Trans. R.
Soc. A, 365, 393–410, 2007
[GEL85] D. Gelernter, Generative communication in Linda, ACM Transactions on
Programming Languages and Systems (TOPLAS), vol. 7, no. 1, pp. 80–
112, 1985.
[GER07] C. Gershenson, Design and Control of Self-organizing Systems, Vrije Univer-
siteit Brussel, 2007.
[GHE10] F. Ghezzo, A. F. Starr, D. R. Smith, (2010). Integration of Networks of
Sensors and Electronics for Structural Health Monitoring of Composite
Materials. Advances in Civil Engineering, 2010, 1–13. doi:10.1155/2010/
[GRE92] A. Greiner, F. Pêcheux, ALLIANCE. A Complete Set of CAD Tools for Teaching
VLSI Design, in 3rd Eurochip Workshop on VLSI Design Training, 1992, pp.
[GUI11] M. Guijarro, R. Fuentes-fernández, and G. Pajares, A Multi-Agent System
Architecture for Sensor Networks, Multi-Agent Systems - Modeling, Con-
trol, Programming, Simulations and Applications, Faisal Alkhateeb (Ed.),
ISBN: 978-953-307-174-9, InTech, 2011
] S. Gupta, R.K. Gupta, N.D. Dutt, A. Nicolau, SPARK: A Parallelizing Approach
to the High-Level Synthesis of Digital Circuits, Kluwer Academic Publishers
[HEN07] M. Hennessy, A Distributed PI-Calculus, Cambridge University Press, 2007.
[HOA85] C. Hoare, Communicating Sequential Processes, Prentice Hall, 1985
[HUH11] A. Huhtala, S. Bossuyt, A Bayesian approach to vibration based structural
health monitoring with experimental verification, Rakenteiden Mekaniik-
ka (Journal of Structural Mechanics), 44, 330-344, 2011
[HUN01] S. R. Hunt, I. G. Hebden, Validation of the Eurofighter Typhoon structural
health and usage monitoring system, Smart Materials and Structures, Vol-
ume 10, 2001, pp. 497.
study of decision tree ID3 and C4.5, (IJACSA) International Journal of Ad-
vanced Computer Science and Applications, Special Issue on Advances in
Vehicular Ad Hoc Networking and Applications, 2014.
[ISE89] R. Isermann, Digital Control Systems, Springer, 1989
[IVI99] R. Ivimey-cook, Legacy of the transputer, in Architectures, Languages and
Techniques, 1999, pp. 1–15.
[JIA13] Jiang, F., Sui, Y. , & Cao, C. (2013) An incremental decision tree algorithm
based on rough sets and its application in intrusion detection, Artif. Intell.
Rev., vol. 40, pp. 517–530.
[JAY07] G. T. Jayaputera, A. Zaslavsky, and S. W. Loke, Enabling run-time composi-
tion and support for heterogeneous pervasive multi-agent systems, Journal
of Systems and Software, vol. 80, pp. 2039–2062, 2007.
[JUN12] R. Junges, F. Klügel, How to design agent-based simulation models using
agent learning, Proc. of the Simulation Conference (WSC) 2012.
] V. Kathail, S. Aditya, R. Schreiber, B. R. Rau, D. C. Cronquist, PICO: Auto-
matically Designing Custom Computers, IEEE Computer, 35 (9), pp 39-47,
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
R.4 Bibliography 561
[KED06] D. Kedar and S. Arnon, Optical wireless communication in distributed sen-
sor networks, SPIE Newsroom, 2006.
[KED12] D. J. Keddie, G. Moad, E. Rizzardo, S. H. Thang, RAFT agent design and syn-
thesis, Macromolecules (2012) Volume: 45, Issue: 13, Pages: 5321-5342
[KIR96] A. Kirsch, An introduction to the mathematical theory of inverse problems,
Springer, 1996
[KU92] D. C. Ku, G. Micheli, High Level Synthesis of ASICs Under Timing and Syn-
chronization Constraints, Kluwer, 1993
[KLA12] H. Klauk (Ed.), Organic Electronics II, More Materials and Applications.
Wiley-VCH, Germany, 2012.
[KLU09] F. Klügel, SeSAm: Visual Programming and Participatory Simulation for
Agent-Based Models, In: Multi-Agent Systems - Simulation and Applica-
tions, A. M. Uhrmacher, D. Weyns (ed.), CRC Press, 2009
[KON00] M. T Kone, A. Shimazu, , T. Nakajima, (2000), The State of the Art in Agent
Communication Languages. Knowledge and Information Systems, 2(3),
259–284. doi:10.1007/PL00013712
[KON16] Q. Kong, R. M. Allen, L. Schreier, and Y.-W. Kwon, My-Shake: A smart phone
seismic network for earthquake early warning and beyond, Sci. Adv., vol. 2,
[LAG10] J. Lagorse, D. Paire, and A. Miraoui, “A multi-agent system for energy
management of distributed power sources,” Renewable Energy, vol. 35,
pp. 174–182, 2010.
[LAN11] W. Lang, F. Jakobs, E. Tolstosheeva, H. Sturm, A. Ibragimov, A. Kesel, D.
Lehmhus, U. Dicke, From embedded sensors to sensorial materials—The
road to function scale integration., Sensors and Actuators A: Physical, Vol-
ume 171, Issue 1, 2011
[LAN12] W. Lang, D. Boll, E. Tolstosheeva, K. Schubert, C. Brauner, and C. Pille,
Embedding without disruption: The basic challenge of sensor integration, in
Proc. of the IEEE Sensors, 28-31 Oct. 2012, 2012.
[LAP09] Mark LaPedus, TSMC devises SRAM cell at 28-nm, EE Times, 17/6/2009,
original source: talk on Symposia on VLSI Technology and Circuits in Kyo-
to, Japan, 2009
[LEI15] P. Leitão and S. Karnouskos (ed.), in Industrial Agents Emerging Applica-
tions of Software Agents in Industry. Elsevier, 2015
[LEH13] D. Lehmhus, J. Brugger, P. Muralt, S. Pané, O. Ergeneman, M.-A. Dubois,
N. Gupta, M. Busse, When nothing is constant but change: Adaptive and
sensorial materials and their impact on product design, Journal of Intelli-
gent Material Systems and Structures, Volume 24, Issue 18, pp. 2172-
[LER90] X. Leroy, The Zinc experiment: an economical implementation of the ML lan-
guage, Rapport Technique 117, INRIA Rocquencourt, Le Chesnay, France,
[LI11] C. Li, H. Zhang, B. Hao, and J. Li, A survey on routing protocols for large-
scale wireless sensor networks., Sensors (Basel, Switzerland), vol. 11, no. 4,
pp. 3498–526, Jan. 2011.
[LIU01] J. Liu, Autonomous Agents and Multi-Agent Systems, World Scientific Pub-
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
Chapter . Publications and References
lishing, 2001 (ISBN 981-02-4282-4)
[MAR05] Marík, V., McFarlane, D.C., 2005. Industrial adoption of agent-based tech-
nologies. IEEE Intell. Syst. 20 (1), 27–35
[MAV97] Mavroidis, C., Dubowsky, S., & Thomas, K. (1997). Optimal sensor location
in motion control of flexibly supported long reach manipulators. Transac-
tions of the ASME, Journal of Dynamic Systems, Measurement and Con-
trol, 119
[MCC95] F. G. McCabe, K. L. Clark, APRIL - Agent Process Interaction Language,
1995, (M. Wooldridge & N. R. Jennings, Eds.) Intelligent Agents Theories
Architectures and Languages LNAI volume 890. Springer-Verlag
[MEN05] Y. Meng, An Agent-based Reconfigurable System-on-Chip Architecture
for Real-time Systems, in Proceeding ICESS ’05 Proceedings of the Sec-
ond International Conference on Embedded Software and Systems,
2005, pp. 166-173.
[MIL99] R. Milner, Communicating and mobile systems: the
-calculus, Cam-
bridge University Press, Cambridge (1999)
[MIL09] R. Milner, The space and motion of communicating agents. Cambridge Uni-
versity Press, 2009
[MIT97] T. M. Mitchell, Machine Learning, McGraw-Hill, 1997
[MOR04] D. Morley, K. Myers, The SPARK Agent Framework, in Proceedings of the
Third International Joint Conference on Autonomous Agents and Multi-
agent Systems, 2004. AAMAS, pp. 714 – 721.
[MUS16] F. Musciotto, S. Delpriori, P. Castagno, and E. Pournaras, Min- ing Social
Interactions in Privacy-preserving Temporal Net- works, in Advances in
Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM, 2016
[MUE07] R. Müller, G. Alonso, and D. Kossmann, A virtual machine for sensor net-
works, in Proceedings of the 2nd ACM SIGOPS/EuroSys European Confer-
ence on Computer Systems 2007, 2007, pp. 145–158
[MUL07] C. Muldoon, G. O. Hare, and J. F. Bradley, Towards reflective mobile agents
for resource constrained mobile devices, in AAMAS’07May 14–18 2007,
Honolulu, Hawai’i, USA., 2007.
[MUL08] C. Muldoon, G. M. P. O’Hare, M. J. O’Grady, and R. Tynan, Agent migration
and communication in WSNs, Ninth International Conference on Parallel
and Distributed Computing, Applications and Technologies PDCAT 2008
: proceedings, 2008.
[MUL90] S. J. Mullender and G. van Rossum, Amoeba: A Distributed Operating Sys-
tem for the 1990s, IEEE Computer, vol. 23, no. 5, pp. 44–53, 1990.
[NAJ04] H. Naji, Creating an adaptive embedded system by applying multi-agent
techniques to reconfigurable hardware, Future Generation Computer
Systems, vol. 20, no. 6, pp. 1055–1081, 2004
[NEW78] R. E. Newnham, D. P. Skinner, L. E. Cross: Connectivity and piezoelectric-
pyroelectric composites. Mat. Res. Bull. 13 (1978) 525-536.
[NIC07] R. De Nicola, D. Gorla, and R. Pugliese, “Global computing in a dynamic
network of tuple spaces,” Science of Computer Programming, vol. 64, pp.
187–204, 2007.
[NIC96] R. De Nicola and R. Pugliese, A Process Algebra Based on Linda, in Coordi-
nation Languages and Models Lecture Notes in Computer Science Vol-
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
R.4 Bibliography 563
ume 1061, 1996, pp. 160–178.
[NIC98] R. De Nicola, G. L. Ferrari, R. Pugliese, KLAIM: A Kernel language for agents
ING, vol. 24, no. 5, p. 315 -, 1998.
[OCC95] SGS Thomson Microelectronics, occam 2.1 reference manual, 1995.
[OH12] H.-S. Oh, B.-J. Kim, H.-K. Choi, and S.-M. Moon, “Evaluation of Android
Dalvik virtual machine,” in Proceedings of the 10th International Work-
shop on Java Technologies for Real-time and Embedded Systems - JTRES
’12, 2012.
[OCA03] The OCaml system release 3.06, 2003, INRIA;
[QIN10] Z. Qin, J. Xing, and J. Zhang, A Replication-Based Distribution Approach for
Tuple Space-Based Collaboration of Heterogeneous Agents, Research Jour-
nal of Information Technology, vol. 2, no. 4. pp. 201–214, 2010.
[PEC08] Pechoucek, M., Marík, V., 2008. Industrial deployment of multi-agent
technologies: review and selected case studies. Auton. Agent. Multi-
Agent Syst. 17 (3), 397–431
[POU15] E. Pournaras, I. Moise, D. Helbing, Privacy-preserving ubiquitous social
mining via modular and compositional virtual sensors. In 2015 IEEE 29th In-
ternational Conference on Advanced Information Networking and Appli-
cations (pp. 332- 338).
[PRI14] S. Priyabadini, T. Sterken, M.Cauwe, L. Van Hoorebeke, J. Vanfleteren,
High-Yield Fabrication Process for 3D-Stacked Ultrathin Chip Packages Using
Photo-Definable Polyimide and Symmetry in Packages, IEEE Transactions
on Components, Packaging, and Manufacturing Technology 4(2014) 158-
167, DOI: 10.1109/tcpmt.2013.2284068
[RAO95] A. S. Rao, M. P. Georgeff,“BDI Agents : From Theory to Practice, Practice,
vol. 95, no. Technical Note 56, pp. 312–319, 1995.
[RAN07] W. Rand, Machine Learning Meets Agent-based Modeling: When Not to Go to
a Bar, 2007.
[RAY13] M. Raynal, Concurrent Programming: Algorithms, Principles, and Foun-
dations. Springer, 2013.
[REM02] D. Rémy, Using, Understanding, and Unraveling the OCaml Language, Ap-
plied Semantics. Advanced Lectures. LNCS 2395. (2002), ISBN 3-540-
[ROK15] L. Rokach, O. Maimon, Data Mining with decision Trees; Theory and Applica-
tions. World Scientific Publishing, 2015.
[RUL13] R. P. Rulli, F. Dotta, P. A. da Silva, Flight Tests Performed by EMBRAER with
SHM Systems, Key Engineering Materials, Volume 558, 2013, pp. 305-313
[SAN08] C. Sansores, J. Pavón, An Adaptive Agent Model for Self-Organizing MAS, in
Proc. of 7th Int. Conf. on Autonomous Agents and Multiagent Systems
(AAMAS 2008), May, 12-16., 2008, Estoril, Portugal, 2008, pp. 1639–1642.
[SAN13] L. G. dos Santos, EMBRAER Perspective on the Challenges for the Introduc-
tion of Scheduled SHM (S-SHM) Applications into Commercial Aviation
Maintenance Programs, Key Engineering Materials, Volume 558, 2013,
pp. 323-330.
[SAM11] C. Sammut, G. I. Webb (Eds.), Encyclopdia of Machine Learning, Springer,
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
Chapter . Publications and References
[SHA98] Richard Sharp, Higher-Level Hardware Synthesis, Springer, 1998
[SHO91] Y. Shoham, AGENTO: A simple agent, in Proc. of the AAAI, 1991, pp. 704–
[SIB12] W. Sibanda and P. Pretorius, Artificial Neural Networks - A Review of Appli-
cations of Neural Networks in the Modeling of HIV Epidemic, International
Journal of Computer Applications, vol. 44, no. 6, 2012.
[STE12] T. Sterken, F. Vermeiren, F, P. Tremlett, W. Christiaens, J. Vanfleteren,
Embedding thinned chips in flexible PCBs, ESTC, 2012 4th , vol., no.,
pp.1,4, 17-20 Sept. 2012, doi: 10.1109/ESTC.2012.6542087
[SU06] J. Su and H. Zhang, A Fast Decision Tree Learning Algorithm, in AAAI’06 Pro-
ceedings of the 21st national conference on Artificial intelligence, Bos-
ton, Massachusetts — July 16 - 20, 2006, 2006, pp. 500-505.
[TIL10] S. Tilkov and S. Vinoski, Node.js: Using JavaScript to build high-performance
network programs, IEEE INTERNET COMPUTING, vol. 14, no. 6, pp. 80–83,
[TYN05] R. Tynan, D. Marsh, D. OKane, and G. M. P. O’Hare, Intelligent agents for
wireless sensor networks, in Proceedings of the fourth international joint
conference on Autonomous agents and multiagent systems - AAMAS
’05, 2005, p. 1179.
[VAN14] J. Vanfleteren, I. Chtioui, B. Plovie, Y. Yang, F. Bossuyt, T. Vervust, S.
Dunphy, B. Vandecasteele, Arbitrarily Shaped 2.5D Circuits Using Stretch-
able Interconnections and Embedding in Thermoplastic Polymers, Procedia
Technology Volume 15, 2014, DOI: 10.1016/j.protcy.2014.09.073
[VID11] Vidal-Verdú, F., Barquero, M. J., Castellanos-Ramos, J., Navas-González,
R., Sánchez, J. A., Serón, J., & García-Cerezo, A. (2011). A large area tactile
sensor patch based on commercial force sensors. Sensors (Basel, Switzer-
land), 11(5), 5489–507. doi:10.3390/s110505489
[VIL14] G. Villarrubia, J. F. De Paz, J. Bajo, and J. M. Corchado, Ambient Agents: Em-
bedded Agents for Remote Control and Monitoring Using the PANGEA Plat-
form, Sensors (Basel, Switzerland), vol. 14, pp. 13955–13979, 2014.
[WAN03] A.I. Wang, C.F. Sørensen, and E. Indal., A Mobile Agent Architecture for
Heterogeneous Devices, Wireless and Optical Communications, 2003
[WAR01] B. Warneke, M. Last, and B. Liebowitz, “Smart dust: Communicating with
a cubic-millimeter computer,” Computer, 2001
[WES05] N. H. E. Weste, D. Harris, CMOS VLSI Design, Circuits and Systems Per-
spective, Addison Wesley, 2005
[WIE13] P. Wierach et al.: Composites modified with Terfenol-D particles for stress
detection. Euromat 2013, Sept. 8th-13th, 2013, Sevilla, Spain) or conduc-
tive carbon fibres (D.-Y. Song et al. , Materials Science and Engineering: A
456 (2007) 286-291
[WOO99] M. Wooldrige, Intelligent Agents, in Multiagent Systems: A Modern Ap-
proach to Distributed Artificial Intelligence, G. Weiss (Ed), MIT Press,
[WU12] D. Wu, J. L. Thames, D. W. Rosen, and Dirk Schaefer, TOWARDS A CLOUD-
WARD, LOOKING FORWARD, in Proceedings of the ASME 2012 Interna-
tional Design Engineering Technical Conference & Computers and
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
R.4 Bibliography 565
Information in Engineering Conference, IDETC/CIE 2012 August 12-15,
2012, Chicago, Illinois, USA, 2012
[WUE16] T. Wuest, D. Weimer, C. Irgens, and K.-D. Thoben, Machine learning in
manufacturing: advantages, challenges, and applications, PRODUCTION &
MANUFACTURING RESEARCH, vol. 4, no. 1, pp. 23-45, 2016
[XIA02] Y. Xia, A.E.A. Almaini, Differential CMOS edge-triggered flip-flop with clock-
gating, ELECTRONICS LETTERS, Vol. 3rd January2002 Vol. 38 No. IJ
[ZHA08] X. Zhao, S. Yuan, Z. Yu, W. Ye, J. Cao. (2008), Designing strategy for multi-
agent system based large structural health monitoring, Expert Systems with
Applications, 34(2), 1154–1168. doi:10.1016/j.eswa.2006.12.022
[ZHO08] B. Zhou, H. Zhu, A Virtual Machine for Distributed Agent-oriented Program-
ming., in Proceedings of the Twentieth International Conference on Soft-
ware Engineering & Knowledge Engineering (SEKE’2008), San Francisco,
CA, USA, July 1-3, 2008.
[ZHU01] J. Zhu, MetaRTL: Raising the abstraction level of RTL Design, DATE ’01: Pro-
ceedings of the conference on Design, automation and test in Europe
(2001), pp. 71-76
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)
Chapter . Publications and References
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems
epubli, ISBN 9783746752228 (2018)