Bio-inspired and Nano-scale Communication and...

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

[email protected]

mailto:[email protected]


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.


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


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 !?


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


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


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)


Evolution…

Artifacts of Evolution in Nature Over billions years As a result of genetic diversity and natural selection

Elegant and efficient SOLUTIONS !!


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 !!


Outline



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.


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


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


Outline



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)



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.

[ ] [ ][ ] [ ]

∈

×

×

= ∑∈

otherwise0

if)(

)( ki

Jlilil

ijij

kij

Jjt

t

pki

βα

βα

ητητ



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


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.

∑∈

=)}({

1kneighbornndP


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.


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


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.


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


Outline



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.


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


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)


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


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.


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,


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.


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.


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.


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


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.


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.


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.


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.


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.


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


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/


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/


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


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


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

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