Connectivity Properties of Free Diffusion-Based Molecular Nanoscale Communication Networks

The connectivity properties of nanonetworks that employ free diffusion-based molecular communication are analyzed by constructing a model based on Gilbert’s disk graph. In free diffusion-based molecular communication, “path loss” has a different functional form compared with wireless propagation and the scenarios under consideration are not generally limited to 2-D environments as in the case of wireless networks. Hence, the constructed model takes into account the peculiarities of diffusion-based communication in 1-D, 2-D, and 3-D scenarios. The model incorporates particle-counting noise and is independent of the signaling mechanism employed. Analytical formulas are derived for the critical values of the signal strength-to-detection threshold ratio and the number of nodes that are required to achieve a connected or an almost-connected network. Monte Carlo simulations are used to validate model predictions and to suggest design guidelines for deploying nanonetworks. An empirical study is also conducted to evaluate the time required to broadcast an alarm in a connected nanonetwork of various sizes. The time to broadcast as a function of the number of nodes in a fixed region is observed to follow a power law, where the exponent depends on the dimensionality of the medium.

[1]  Özgür B. Akan,et al.  On Molecular Multiple-Access, Broadcast, and Relay Channels in Nanonetworks , 2008, BIONETICS.

[2]  Abdelhakim Hafid,et al.  Channel Impulse Responses in Diffusive Molecular Communication with Spherical Transmitters , 2016, ArXiv.

[3]  Adam Noel,et al.  3D Stochastic Geometry Model for Large-Scale Molecular Communication Systems , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[4]  Massimo Franceschetti,et al.  Random networks for communication : from statistical physics to information systems , 2008 .

[5]  Ian F. Akyildiz,et al.  Nanonetworks: A new communication paradigm , 2008, Comput. Networks.

[6]  Christian Bettstetter,et al.  On the minimum node degree and connectivity of a wireless multihop network , 2002, MobiHoc '02.

[7]  Peter J. Diggle,et al.  Statistical Analysis of Spatial and Spatio-Temporal Point Patterns , 2013 .

[8]  Murat Kuscu,et al.  On the Physical Design of Molecular Communication Receiver Based on Nanoscale Biosensors , 2015, IEEE Sensors Journal.

[9]  Chan-Byoung Chae,et al.  Simulation study of molecular communication systems with an absorbing receiver: Modulation and ISI mitigation techniques , 2014, Simul. Model. Pract. Theory.

[10]  Özgür B. Akan,et al.  Receiver Design for Molecular Communication , 2013, IEEE Journal on Selected Areas in Communications.

[11]  Eitan Altman,et al.  Coverage and connectivity of ad hoc networks presence of channel randomness , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[12]  Tuna Tugcu,et al.  Energy model for communication via diffusion in nanonetworks , 2010, Nano Commun. Networks.

[13]  Robert G. Endres,et al.  Physical Principles in Sensing and Signaling: With an Introduction to Modeling in Biology , 2013 .

[14]  Massimiliano Pierobon,et al.  Diffusion-Based Noise Analysis for Molecular Communication in Nanonetworks , 2011, IEEE Transactions on Signal Processing.

[15]  Mathew D. Penrose,et al.  On k-connectivity for a geometric random graph , 1999, Random Struct. Algorithms.

[16]  Arash Fereidouni Analysis of Connectivity in Diffusion-Based Molecular Nano Communication Networks , 2013 .

[17]  A. Vasilakos,et al.  Molecular Communication and Networking: Opportunities and Challenges , 2012, IEEE Transactions on NanoBioscience.

[18]  J. Rospars,et al.  Perireceptor and receptor events in olfaction. Comparison of concentration and flux detectors: a modeling study. , 2000, Chemical senses.

[19]  Massimiliano Pierobon,et al.  Capacity of a Diffusion-Based Molecular Communication System With Channel Memory and Molecular Noise , 2013, IEEE Transactions on Information Theory.

[20]  M. Penrose The longest edge of the random minimal spanning tree , 1997 .

[21]  Andrew W. Eckford,et al.  A Comprehensive Survey of Recent Advancements in Molecular Communication , 2014, IEEE Communications Surveys & Tutorials.

[22]  H. Berg,et al.  Physics of chemoreception. , 1977, Biophysical journal.

[23]  Qiang Liu,et al.  Multiple-access channel capacity of diffusion and ligand-based molecular communication , 2013, MSWiM.

[24]  Ian F. Akyildiz,et al.  On Receiver Design for Diffusion-Based Molecular Communication , 2014, IEEE Transactions on Signal Processing.

[25]  Dogu Arifler,et al.  Deterministic Model for Pulse Amplification in Diffusion-Based Molecular Communication , 2014, IEEE Communications Letters.

[26]  H. Berg Random Walks in Biology , 2018 .

[27]  Tuna Tugcu,et al.  A tunnel-based approach for signal shaping in molecular communication , 2013, 2013 IEEE International Conference on Communications Workshops (ICC).

[28]  Robert Schober,et al.  Using dimensional analysis to assess scalability and accuracy in molecular communication , 2013, 2013 IEEE International Conference on Communications Workshops (ICC).

[29]  Ian F. Akyildiz,et al.  Nanonetworks: A new frontier in communications , 2012, 2010 International Conference on Security and Cryptography (SECRYPT).

[30]  Sebastian Magierowski,et al.  Optimum receiver for molecule shift keying modulation in diffusion-based molecular communication channels , 2012, Nano Commun. Networks.

[31]  Özgür B. Akan,et al.  Single and Multiple-Access Channel Capacity in Molecular Nanonetworks , 2009, NanoNet.

[32]  Gaston H. Gonnet,et al.  On the LambertW function , 1996, Adv. Comput. Math..

[33]  D. Stoyan,et al.  Stochastic Geometry and Its Applications , 1989 .

[34]  Tuna Tugcu,et al.  Three-Dimensional Channel Characteristics for Molecular Communications With an Absorbing Receiver , 2014, IEEE Communications Letters.

[35]  Thomas G. Robertazzi,et al.  Critical connectivity phenomena in multihop radio models , 1989, IEEE Trans. Commun..

[36]  Robert Schober,et al.  Improving Receiver Performance of Diffusive Molecular Communication With Enzymes , 2013, IEEE Transactions on NanoBioscience.

[37]  Maryam Farahnak-Ghazani,et al.  On the Capacity of Point-to-Point and Multiple-Access Molecular Communications With Ligand-Receptors , 2015, IEEE Transactions on Molecular, Biological and Multi-Scale Communications.

[38]  Pietro Liò,et al.  Opportunistic routing through conjugation in bacteria communication nanonetwork , 2012, Nano Commun. Networks.

[39]  Dimitrios Makrakis,et al.  A Comprehensive Analysis of Strength-Based Optimum Signal Detection in Concentration-Encoded Molecular Communication With Spike Transmission , 2015, IEEE Transactions on NanoBioscience.

[40]  Mohsen Sardari,et al.  Networks of bacteria colonies: A new framework for reliable molecular communication networking , 2016, Nano Commun. Networks.

[41]  K. Kaissling,et al.  Flux detectors versus concentration detectors: two types of chemoreceptors. , 1998, Chemical senses.

[42]  Özgür B. Akan,et al.  Deterministic capacity of information flow in molecular nanonetworks , 2010, Nano Commun. Networks.

[43]  Robert Schober,et al.  Analysis and Design of Multi-Hop Diffusion-Based Molecular Communication Networks , 2014, IEEE Transactions on Molecular, Biological and Multi-Scale Communications.

[44]  Martin Haenggi,et al.  Stochastic Geometry for Wireless Networks , 2012 .

[45]  Ian F. Akyildiz,et al.  Molecular communication options for long range nanonetworks , 2009, Comput. Networks.

[46]  Tadashi Nakano,et al.  Molecular Communication , 2005 .