bio-inspired and nano-scale communication and...
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DLT Lecture on Bio-inspired and Nano Communications 1
Computer and Communication Systems (Lehrstuhl für Technische Informatik)
Biologically-inspired and Nano-scale Communication and Networking
Falko Dressler University of Innsbruck, Austria
DLT Lecture on Bio-inspired and Nano Communications 2
This tutorial is mainly based on survey papers:
Falko Dressler, Ozgur B. Akan, "Bio-inspired Networking: From Theory to Practice," IEEE Communications Magazine, vol. 48, no. 11, pp. 176-183, November 2010.
Falko Dressler and Ozgur B. Akan, "A Survey on Bio-inspired Networking,“ Computer Networks Journal (Elsevier), vol. 54 (6), pp. 881-900, April 2010.
DLT Lecture on Bio-inspired and Nano Communications 3
Outline
(R)Evolution in Information Network Architectures Biological Models for Communication Network Design Approaches to Bio-inspired Networking Nano-scale and Molecular Communication Future Research Avenues Conclusions
DLT Lecture on Bio-inspired and Nano Communications 4
Communication Network
(A set of) equipment (hardware & software) and facilities that provide basic communication services (among computing entities) Virtually invisible to the user; usually represented by a cloud Equipment
Routers, servers, switches, multiplexers, hubs, modems, WLAN cards, cellular phones, etc.
Facilities Copper wires, coaxial cables, optical fiber, air, etc.
Communication Network
No biology here !?
DLT Lecture on Bio-inspired and Nano Communications 5
Evolution in Information Networks
Internet
Packet switching & computer applications Extremely large-scale autonomic behavior
Internet2 – Next-Generation Internet
Dedicated optical fiber backbone Dense Wavelength-division multiplexing capability Much greater bandwidth for IP communications
DLT Lecture on Bio-inspired and Nano Communications 6
Evolution in Information Networks
Next Generation Wireless Internet Convergence of heterogeneous wireless systems
WLAN, 2G/3G Systems, Satellite Networks
IP-based Infrastructure Anytime/anywhere high data rate & multimedia Very large-scale networking Energy-constraint networks, e.g., sensor networks
InterPlaNetary Internet
InterPlaNetary Backbone Network InterPlaNetary External Network PlaNetary Network Extreme channel conditions
DLT Lecture on Bio-inspired and Nano Communications 7
Evolution…
Novel paradigms are needed for designing, engineering, and managing NGN that provide
Anywhere anytime connectivity High service quality level expectations Seamless convergence of heterogeneous systems
Evolution in couple millenniums From Cursus Publicus (30BC) to Quantum Communication Networks (2000)
DLT Lecture on Bio-inspired and Nano Communications 8
Evolution…
Artifacts of Evolution in Nature Over billions years As a result of genetic diversity and natural selection
Elegant and efficient SOLUTIONS !!
DLT Lecture on Bio-inspired and Nano Communications 9
Characteristics of Biological Systems
Adaptive to the varying environmental circumstances Resilient to failures by internal or external factors Complex behaviors on basis of limited set of basic rules Able to learn and evolve itself under new conditions Effective management of constrained resources with a globally amplified intelligence Able to self-organize in a fully distributed fashion Collaboratively achieving efficient equilibrium Survivable to harsh conditions with inherent and sufficient redundancy
Inspiring features towards addressing networking challenges posed by current and NGN architectures !!
DLT Lecture on Bio-inspired and Nano Communications 10
Outline
(R)Evolution in Information Network Architectures Biological Models for Communication Network Design Approaches to Bio-inspired Networking Nano-scale and Molecular Communication Future Research Avenues Conclusions
DLT Lecture on Bio-inspired and Nano Communications 11
Biological Models: Modeling Approaches
First modeling approaches date back to early 1970ies [1, 2] Many other approaches mimicking bio systems followed
Bio-networking architecture with complete modeling [3, 4]
Catalyzer for many other investigations in the last decade
Many (engineering) technical solution attempts with similarities to biological counterparts without investigating their key advantages
Mimicking alone is not designing “Bio-inspired” solutions !
[1] M. Eigen, P. Schuster, “The Hypercycle: A Principle of Natural Self Organization”, Springer, 1979. [2] W. R. Ashby, “Principles of the Self-Organizing System”, in: H. von Foerster, G. W. Zopf (Eds.), Principles of Self-Organization, Pergamon Press, pp. 255–278, 1962. [3] M. Wang, T. Suda, “The Bio-Networking Architecture: A Biologically Inspired Approach to the Design of Scalable, Adaptive, and Survivable /Available Network Applications”, in: 1st IEEE Symposium on Applications and the Internet (SAINT), San Diego, CA, 2001. [4] J. Suzuki, T. Suda, “Adaptive Behavior Selection of Autonomous Objects in the Bio-Networking Architecture”, in: 1st Annual Symposium on Autonomous Intelligent Networks and Systems, Los Angeles, CA, 2002.
DLT Lecture on Bio-inspired and Nano Communications 12
Biological Models: Modeling Approaches
Three steps in developing bio-inspired techniques:
Identifying analogies – which structures and methods seem to be similar Understanding – detailed modeling of realistic biological behavior Engineering – model simplification and tuning for technical applications
Identification of analogies between
biology and ICT
Modeling of realistic biological behavior
Model simplification and tuning for ICT
applications
Understanding Engineering
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Examples of Biological Models
Biological principles with application to networking: Ant Colony Optimization (ACO) Firefly Synchronization Activator-Inhibitor Systems Artificial Immune System (AIS) Epidemic Spreading Cellular Signaling Networks
DLT Lecture on Bio-inspired and Nano Communications 14
Outline
(R)Evolution in Information Network Architectures Biological Models for Communication Network Design Approaches to Bio-inspired Networking Nano-scale and Molecular Communication Future Research Avenues Conclusions
DLT Lecture on Bio-inspired and Nano Communications 15
Ant Colony Optimization (ACO)
Mostly analyzed branch of swarm intelligence-based mechanisms
Swarm intelligence – based on the observation of collective behavior of decentralized and self-organized systems such as ant colonies, schools of fish, or swarms of bees or birds [1]
Ant colonies Solve complex tasks by simple local means Indirectly interact via modifying environment (stigmergic communication and collaboration e.g., pheromone trails for foraging) Perform random walk for food search while depositing pheromone When successful, turn back nest following and amplifying the trail
[1] E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, 1999.
Nest Food
(a)
Nest Food
(b)
Nest Food
(c)
DLT Lecture on Bio-inspired and Nano Communications 16
Ant Colony Optimization (ACO)
Ant colony optimization (ACO) [1,2] Mimics the foraging behavior of ants A path searching strategy that relies on
Random search to explore the search space to find candidate solutions Pheromone level to characterize the quality of previous search operations Further ants use the shortest (and reinforced) paths
Based on transition probability for ant k to move from location i to j, pij
Jik : list of not yet visited nodes, ant k avoids visiting node j
more than once using Jik (tabu list)
ηij : visibility of node j when ant k is in node i (inverse of distance) τij : pheromone level at edge (i,j) (learned desirability of choosing j α and β : adjustable parameters (control the relative weight of trail intensity and visibility)
[1] M. Dorigo, V. Maniezzo, A. Colorni, “The Ant System: Optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics, 1996. [2] M. Dorigo, G. Di Caro, L. M. Gambardella, ”Ant Algorithms for Discrete Optimization”, Artificial Life, vol. 5 no.2 pp. 137-172, 1999.
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DLT Lecture on Bio-inspired and Nano Communications 17
Ant Colony Optimization (ACO)
After completing a tour, each ant k lays a quantity of pheromone Δτk
ij (t) on each edge (i, j) according to following rule Pheromone slowly evaporates Pheromone update rule is also needed If ant not successful, trail dissolves and random search continues Too many ants quickly reinforce suboptimal tracks → early convergence to bad solutions Too few ants would not produce enough pheromone → fail to achieve desired cooperative behavior
Tk(t) : the tour done by ant k at iteration t Lk(t ) : the length of the tour done by ant k at iteration t
Q : is a parameter which only influences the final result
m : total number of ants ρ : decay coefficient
DLT Lecture on Bio-inspired and Nano Communications 18
ACO: Routing
Most popular ACO routing algorithms AntNet and AntHocNet [1,2] Agents (ants) concurrently explore network and exchange collected info Stigmergic communication among agents mediated by network
AntNet : proactive routing
Periodically launches mobile agents (forward ants) to random destination nodes
to find a minimum cost path to destination with a greedy stochastic policy
Upon locating the destination, agents become backward ants and return home
update routing tables on the way back home for each destination d and neighbor n, routing tables stores a probabilistic value Pnd
expressing the quality of choosing n as a next hop towards destination d
[1] G. Di Caro, M. Dorigo, “AntNet: Distributed Stigmergetic Control for Communication Networks,” Journal of Artificial Intelligence Research, vol. 9, pp. 317–365, 1998. [2] G. Di Caro, F. Ducatelle, L. M. Gambardella, “AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks,” European Transactions on Telecommunications, Special Issue on Self-organization in Mobile Networking, vol. 16, pp. 443–455, 2005.
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DLT Lecture on Bio-inspired and Nano Communications 19
Cellular Signaling Pathways
Signaling describes the interactions between molecules Cellular interactions process in two steps [1]:
Extracellular molecule binds to a specific receptor on a target cell, converting the dormant receptor to an active state Subsequently, the receptor stimulates intracellular biochemical pathways leading to a cellular response
Two cellular signaling techniques:
Intracellular signaling – signal from extracellular source transferred through the cell membrane. Inside the target cell, complex signaling cascades result in gene expression or an alteration in enzyme activity defining the cellular response Intercellular signaling – Cells can communicate via cell surface molecules: A surface molecule of one cell binds to a specific receptor molecule on another cell
[1] T. Pawson, “Protein modules and signalling networks,” Nature 373 (6515) (1995) 573–80.
DLT Lecture on Bio-inspired and Nano Communications 20
Cellular Signaling Networks
A key challenge is to understand the structure and the dynamics of the complex web of interactions that contribute to the structure and function of a cell [1]
Target: Programming schemes for massively distributed systems such as sensor networks Two of the most successful approaches:
Rule-based Sensor Network (RSN) [2] Fraglets [3]
[1] F. Dressler, I. Dietrich, R. German, B. Kruger, “Effcient Operation in Sensor and Actor Networks Inspired by Cellular Signaling Cascades”, in: 1st ACM/ICST International Conference on Autonomic Computing andCommunication Systems (Autonomics 2007), ACM, Rome, Italy, 2007.
[2] F. Dressler, I. Dietrich, R. German, B. Kruger, “A Rule-based System for Programming Self-Organized Sensor and Actor Networks”, Elsevier Computer Networks, vol. 53 (10), pp. 1737-1750, July 2009.
[3] C. Tschudin, “Fraglets - a Metabolistic Execution Model for Communication Protocols”, in: 2nd Symposium on Autonomous Intelligent Networksand Systems (AINS), Menlo Park, CA, 2003.
Init measurement
Input Outcome
Computational expensive process
If value > threshold
Enforce action A
Enforce action B
Enforce action C
If Va > Ta If Vb > Tb
Aggregate Va + Vb Enforce actuation
Feed forward motifs Single input motifs Multi-input motifs
DLT Lecture on Bio-inspired and Nano Communications 21
Rule-based Sensor Networks (RSN)
Light-weight programming for Sensor-Actor Networks (SANET) [1] Based on data-centric message forwarding, aggregation, and processing, i.e., using self-describing messages instead of network-wide unique addresses
Received message stored in a message buffer Rule interpreter started either periodically or after message reception
Outperforms other SANET protocols for distributed sensing and network-centric data pre-processing in two dimensions:
Reactivity of the network – reduced response times for network-controlled actuation Communication overhead – improved bandwidth utilization on wireless channels
[1] F. Dressler, I. Dietrich, R. German, B. Krüger, “A Rule-based System for Programming Self-Organized Sensor and Actor Networks”, Elsevier Computer , vol. 53 (10), pp. 1737-1750, July 2009.
DLT Lecture on Bio-inspired and Nano Communications 22
Rule-based Sensor Networks (RSN)
[1] F. Dressler, I. Dietrich, R. German, B. Krüger, “A Rule-based System for Programming Self-Organized Sensor and Actor Networks”, Elsevier Computer , vol. 53 (10), pp. 1737-1750, July 2009.
Communication with othercells via cell junctions
Nucleus
DNA Gene transcription
mRNA translation into proteins
Intracellular signaling molecules
Reception of signaling molecules
Secretion of hormones etc.
Nucleus
DNA
Reception of signaling molecules (ligands such as hormones, ions, small molecules)
Different cellular answer
(1-a)
(1-b)
(2)
(3-a)
(3-b)
Submission of signaling molecules
Neighboring cell
cells nodes receptors conditions transcription rule execution signaling broadcast
Message buf fer
Sourceset
Workingset 1
Workingset 2
Workingset nΔt
Actionset
return
drop
Incoming messages
modifymodify
actuate
send
DLT Lecture on Bio-inspired and Nano Communications 23
Outline
(R)Evolution in Information Network Architectures Biological Models for Communication Network Design Approaches to Bio-inspired Networking Nano-scale and Molecular Communication Future Research Avenues Conclusions
DLT Lecture on Bio-inspired and Nano Communications 24
Nanomachines and Nanonetworks
Nanomachines designed based on mimicing Artificial machines (e.g., nanoradio, nanomotors) Natural nanomachines (e.g., molecular motors receptors, bacteria, DNA-based systems)
Sophisticated applications new challenges Nanonetwork – a set of communicating nanomachines to realize a common task
Molecular communication Neuro-spike-based communication Nano-EM radio communication
I. F. Akyildiz, F. Brunetti, C. Blazquez, “Nanonetworks: A New Communication Paradigm”, Computer Networks (Elsevier), vol. 52, pp. 2260-2279, 2008.
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Nanonetwork Applications
Biomedical Bio-hybrid implants Monitoring glucose levels Heart Monitoring Brain pathologies, Alzheimer's, epilepsy and depression Medication carrying smart nanoshells that detect and destroy tumors
Military Nuclear, biological, chemical defense
Industrial Food and water quality control Functionalized materials and fabric
Environmental Biodegradation Animals and biodiversity control Air pollution control
(A) the injected nanocarrier landing on the inner wall of a tumor-associated blood vessel, (B) the release of nanoparticles that penetrate both the blood vessel wall and the tumor cell membrane and, (C) the delivery to the tumor of doses of a cancer killing medication. http://www.nanotech-now.com/news.cgi?story_id=27008
DLT Lecture on Bio-inspired and Nano Communications 26
Nanonetworks with Molecular Communication
Transmission and reception of information encoded in molecules
New interdisciplinary field spanning nano, ece, cs, bio, physics, chemistry, medicine, and ICT domains
Molecular paths are severely prone to errors due to
Temperature, pH, density of the aqueous medium, and the denaturalizing enzymes or inhibitors moving around the carried molecule
Short range (nm-mm), medium range (mm-mm), long range (mm-m)
DLT Lecture on Bio-inspired and Nano Communications 27
Short-range Communication with Ca2+ Signaling
Gap Junctions: Gates that allow different molecules and ions to pass freely between cells (membranes)
Direct Access – Ca2+signal travel through gates
Indirect Access – TX nanomachine release information molecules to the medium. Generate a Ca2+ at the RX nanomachine
DLT Lecture on Bio-inspired and Nano Communications 28
Medium-range Communication with Flagellated Bacteria
Bacteria are microorganisms composed only by one prokaryotic cell. Flagellum allows them to convert chemical energy into motion.
Escherichia coli (E. coli) has between 4 and 10 flagella, which are moved by rotary motors, fuelled by chemical compounds.
Au/Ni/Au/Ni/Pt striped nanorods (catalytic nanomotors), 1.3 μm long, 400 nm on diameter, and can be directed by applying magnetic fields
Propel themselves with self-generated gradients produced by catalyzing the free chemical energy present in the environment
[1] M. Gregori, I. F. Akyildiz, "A New NanoNetwork Architecture using Flagellated Bacteria and Catalytic Nanomotors,” IEEE JSAC, 2010.
DLT Lecture on Bio-inspired and Nano Communications 29
Long-range Communication with Pheromones
Pheromones – chemical compounds released by insects, plants and animals that trigger different behaviors in the receptor member of the same species Encoding and Emitting
Nano-sensor releases a specific type of pheromones, into either an aqueous or a gaseous medium
Receiving and Decoding Pheromones may or may not bind to the receptors and with different affinities Reaction of a cell will depend on the type of molecule received and different stimulus
[1] L. P. Gine, I. F. Akyildiz, “Molecular Communication Options for Long Range Nanonetworks”, Computer Networks (Elsevier), Fall 2009,
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Physical Model for Molecular Diffusion Channel
Molecule Diffusion Communication: Exchange of information encoded in the concentration variations of molecules Transmitter/receiver models based on RC-circuit analogy Propagation modeling with particle diffusion process (Fick’s law)
Modulated concentration Delay and attenuation as a function of transmission range
Diffusion process
Reception process
Emission process
TN RN
M. Pierobon, I. F. Akyildiz, ``A Physical Channel Model for Molecular Communication in Nanonetworks,’’ IEEE J. Selected Areas Comm. (JSAC), vol. 28, pp. 602-611, 2010.
DLT Lecture on Bio-inspired and Nano Communications 31
Single and Multiple Access Channels
We adopt the natural ligand-receptor binding mechanism to enable the molecular communication between nanomachines called Transmitter Nanomachine (TN) and Receiver Nanomachine (RN) Preliminary capacity bounds
Ligand (Molecule)
Ligand-receptor binding
Receptor
TN1 RN
TN2
TNiMolecular multiple-access channel
[1] B. Atakan, O. B. Akan, “An Information Theoretical Approach for Molecular Communication”, in: 2nd IEEE/ACM International Conference on Bio-Inspired Models of Network, Information and Computing Systems (IEEE/ACM BIONETICS 2007), Budapest, Hungary, 2007.
[2] B. Atakan, O. B. Akan, “On Channel Capacity and Error Compensation in Molecular Communication”, Springer Transactions on Computational Systems Biology (TCSB) LNBI 5410, February 2009.
[3] B. Atakan, O. B. Akan, “On Molecular Multiple-Access, Broadcast, and Relay Channel in Nanonetworks”, in: 3rd ACM/ICST International Conference on Bio-Inspired Models of Network, Information and Computing Systems (Bionetics 2008), ACM, Hyogo, Japan, 2008.
[4] B. Atakan, O. B. Akan, “Single and Multiple Access Channel Capacity in Molecular Nanonetworks”, ICST/ACM NANO-NET 2009, December 2009.
DLT Lecture on Bio-inspired and Nano Communications 32
Nanonetworks with CNT Radio
Antenna, tuner, demodulator, amplifier of a radio with a single nanotube Signal reception, tuning, amplification, demodulation electromechanical processes
CNT resonance frequency must match carrier ωc (affected by nanotube length) Nanotube length degrades with field emission current as well
FM modulation (transmitter) by applying info signal to external electrode (Vtension)
K. Jensen, J. Weldon, H. Garcia, A. Zettl, “Nanotube Radio”, Nano Letters, vol. 7, pp. 3508-3511, 2007.
DLT Lecture on Bio-inspired and Nano Communications 33
CNT-based Nanoscale Ad Hoc Networks (CANET)
Extremely challenged wireless ad hoc communication domain Medium’s molecular composition affects communication rate and range
Low signal power of nanotransmitter very limited radio range Severely prone to thermal noise and fading
High power required by nanotube radio: crucial challenge for realization
CANET Node Hardware
[1] K. Jensen, J. Weldon, H. Garcia, A. Zettl, “Nanotube Radio”, Nano Letters, vol. 7, pp. 3508-3511, 2007. [2] B. Atakan, O. B. Akan, “Carbon Nanotube-based Nanoscale Ad Hoc Networks”, IEEE Communications Magazine, vol. 48, pp. 129-135, June 2010.
A CANET Application: Nanoscale Bio-Sensor Network
DLT Lecture on Bio-inspired and Nano Communications 34
NanoNS: NS-2 based Molecular Nanonetwork Simulator
Developed on top of open-source event-based network simulator ns-2 Environment is assumed to be split into lattices
Three lattices types, i.e., cytosol, membrane, vitro Molecules propagate by means of diffusion process
Validated against theoretical results Available at http://nwcl.ku.edu.tr
E. Gul, B. Atakan, O. B. Akan, "NanoNS: A Nanoscale Network Simulator Framework for Molecular Communications,“ Nano Communication Networks Journal (Elsevier), vol. 1, no. 2, pp. 138-156, 2010.
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Carbon Nanotube Sensor Node
Many nanodevices at early stages of their development To harness their unique features, the objective is to combine them in unified a system architecture CNT sensor node hardware includes many fundamental components:
Nano-transceiver Nano-power unit Nano-processor and memory Nano-sensing units
Extremely limited power, data storage, computation, and synchronization capabilities severely restrict CNT sensor communication
B. Atakan, O. B. Akan, “Carbon Nanotube Sensor Networks”, in Proc. IEEE NanoCom 2009, San Francisco, CA, August 2009.
I. F. Akyildiz, J.M. Jornet, “Electromagnetic Wireless Nanosensor Networks,” Nano Communication Networks Journal, vol. 1, pp.3-19, March 2010.
DLT Lecture on Bio-inspired and Nano Communications 36
Nano-battery
Current technologies may support nano-scale power source for the development of CNT sensor nodes Overall power budget analysis can be critical to assess whether the existing nano-battery technologies allows the CNT sensor communication or not Nano-battery may not sufficiently feed the nano-transceiver circuitry to amplify the received and transmitted signal
Hinder reliable transmission and reception of the information signal
[1] F. Albano, Y. S. Lin, D. Blaauw, D. M. Sylvester, K. D. Wise, A. M. Sastry, “A fully integrated microbattery for an implantable microelectromechanical system”, Journal of Power Sources, vol. 185, no. 2, pp. 1524-1532, 2008.
DLT Lecture on Bio-inspired and Nano Communications 37
Nano-memory and Nano-processor
Some nano-scale memory systems have been previously designed for the development of memorized nanodevices
Nano-wire crossbar circuit is used to design a nano-scale memory system that operates with 0.5V-0.3V. By switching and setting the resistance of the crossbars, each cross point is used as an active memory cell Adressable nano-memory is also designed using aligned carbon nanotubes with cross geometry
Some nano-processor designs can be found in the current literature and involved in the hardware structure of CNT sensor node
Nanowire junction arrays are configured to build OR, AND, and NOR logic gates and to enable simple computations A bottom up design approach for Programmable Logic Arrays (PLAs) is introduced using molecular-scale nano-wires Sequential nano-memory and processor with clocked operations are devised
Extremely limited data storage, computations, and synchronization capabilities severely restrict CNT sensor communication in terms of power consumption and computational complexity
[1] Y. Chen, G. Y. Jung, D. A. A. Ohlberg, X. Li, D. R. Stewart, J. O. Jeppesen, K. A. Nielsen, J. F. Stoddart, R. S. Williams, “Nanoscale molecular-switch Crossbar Circuits”, Nanotechnology, vol. 14, pp. 462-468, 2003.
[2] G. S. Rose, M. R. Stan, “Memory arrays based on molecular RTD devices”, in Proc. of IEEE NANO, pp. 453-456, CA, USA, 2003.
DLT Lecture on Bio-inspired and Nano Communications 38
Nano-sensing Unit
Due to their fast response capabilities and substantially higher sensitivities, carbon nanotubes are promising to enable future nano-scale sensor nodes
Using single-walled carbon nanotubes, the fabrication and characterization of pressure sensors with circular radius range of 50-100 mm are introduced For infrared (IR) applications, carbon nanotube arrays provide significant response toward IR signals having very broad wavelength range (1-15 mm)
[1] C. Stampfer, T. Helbling, D. Obergfell, B. Sc¨oberle, M. K. Tripp, A. Jungen, S. Roth, V. M. Bright, C. Hierold, “Fabrication of Single-Walled Carbon-Nanotube-Based Pressure Sensors”, Nano Letters, vol. 6, pp. 233-237, 2006.
[2] J. Kong, N. R. Franklin, C. Zhou, M. G. Chapline, S. Peng, K. Cho, H. Dai, “Nanotube Molecular Wires as Chemical Sensors”, Science, vol. 287, pp. 622-625, 2000.
DLT Lecture on Bio-inspired and Nano Communications 39
Outline
(R)Evolution in Information Network Architectures Challenges in Networking Biological Models for Communication Network Design Approaches to Bio-inspired Networking Nano-scale and Molecular Communication Future Research Avenues Conclusions
DLT Lecture on Bio-inspired and Nano Communications 40
Recent Research Projects Project Funding Research Area URL
NanoComNet NSF Molecular communications, nanonetworks, nano-EM communications, bacteria-based nanonetworks
http://www.ece.gatech.edu/research/labs/bwn/nanos/index.html
ANA EU FET Autonomic network architecture and principles http://www.ana-project.org/
NanoNet IBM Molecular nanonetworks, theory and algorithms http://nwcl.ku.edu.tr
BioNet NSF, DARPA
Bio-networking architecture for implementation of scalable, adaptive, survivable/network applications
http://netresearch.ics.uci.edu/bionet/
BIONETS EU FET Bio-inspired service evolution for the pervasive age http://www.bionets.eu/
CASCADAS EU FET Autonomic and situation-aware communications, and dynamically adaptable services
http://www.cascadas-project.org/
ECAgents EU FET Embodied and communicating agents interacting directly with the physical world
http://ecagents.istc.cnr.it/
Haggle EU FET Situated and autonomic communications http://www.haggleproject.org/
MC NSF, DARPA
Molecular communication as a solution for communication between nanomachines
http://netresearch.ics.uci.edu/mc/
Swarmoid EU FET Design, implementation and control of a novel distributed robotic system
http://www.swarmoid.org/
Swam-bots EU FET Design and implementation of self-organizing and self-assembling artifacts
http://www.swarm-bots.org/
WASP EU IP Self-organization of nodes and services in WSNs http://www.wasp-project.org/
DLT Lecture on Bio-inspired and Nano Communications 41
Related Venues
Name of the event URL
Bionetics International Conference on Bio-inspired Models of Network, Information and Computing Systems
http://www.bionetics.org/
Nano-Net International ICST Conference on Nano-Networks http://www.nanonets.org/
MoNaCom IEEE International Workshop on Molecular and Nano Scale Communication (MoNaCom)
http://monacom.tssg.org/
Biowire Workshop on Bio-inspired Design of Wireless Networks and Self-Organizing Networks
http://www.cl.cam.ac.uk/conference/biowire09/
EvoCOMNET European Workshop on Nature-inspired Techniques for Telecommunications and other Parallel and Distributed Systems
http://www.evostar.org/
SASO International Conference on Self-Adaptive and Self-Organizing Systems
http://www.inf.u-szeged.hu/saso10/
DLT Lecture on Bio-inspired and Nano Communications 42
Related Venues
Journals and special issues
Elsevier Nano Communication Networks
ICST Transactions on Bio-Engineering and Bio-inspired Systems
IEEE Transactions on Nanobioscience
Journal of Bio-inspired Computation Research (JBICR)
International Journal of Bio-inspired Computation (IJBIC)
IEEE Journal on Selected Areas in Communications (JSAC)
Special Issue on Bio-inspired Networking
Ad Hoc Networks Journal Special Issue on Bio-inspired Communications and Networking
Springer Transactions on Computational Systems Biology (TCSB)
Special Issue on Biosciences and Bio-inspired Information Technologies
Springer Swarm Intelligence Special Issue on Swarm Intelligence for Telecommunication Networks
International Journal of Autonomous and Adaptive Communication Systems (IJAACS)
Special Issue on Bio-inspired Wireless Networks
DLT Lecture on Bio-inspired and Nano Communications 43
Outline
(R)Evolution in Information Network Architectures Challenges in Networking Biological Models for Communication Network Design Approaches to Bio-inspired Networking Nano-scale and Molecular Communication Future Research Avenues Conclusions
DLT Lecture on Bio-inspired and Nano Communications 44
Conclusions
NG networks vision poses significant challenges Biological systems intrinsically have matching capabilities Hence, bio-inspired communications/networking solutions for
Wireless networks Sensor networks Underwater acoustic communications Space communications Large-scale complex networks Nanonetworks …
Difficult interdisciplinary domain with still young community Many ICT challenges and a vast space of biological systems still unexplored