TY - GEN
T1 - Reaching Consensus in Ad-Hoc Diffusion Networks
AU - Kowalski, Dariusz R.
AU - Mirek, Jarosław
N1 - Funding Information:
by Polish National Science Center (NCN) grant UMO-2017/25/B/ST6/
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - We consider an algorithmic model of diffusion networks, in which n nodes are distributed in a 2D Euclidean space and communicate by diffusing and sensing molecules. Such a model is interesting on its own right, although from the distributed computing point of view it may be seen as a generalization or even a framework for other wireless communication models, such as the SINR model, radio networks or the beeping model. Additionally, the diffusion networks model formalizes and generalizes recent case studies of simple processes in environment where nodes, often understood as biological cells, communicate by diffusing and sensing simple chemical molecules. To demonstrate the algorithmic nature of our model, we consider a fundamental problem of reaching consensus by nodes: in the beginning each node has some initial value, e.g., the reading from its sensor, and the goal is that each node outputs the same value. Our deterministic distributed algorithm runs at every node and outputs the consensus value equal to the sum of inputs divided by the sum of the channel coefficients of each cells. For a node v consensus is reached in (formula presented) communication rounds, where dv is the sum of molecule reachability ratios to node v in the medium, dmin=minidi, dmax=maxidi and b is the sum of the initial values. p represents the second largest eigenvalue of a matrix of normalised molecule reachability ratios, that we analyze together with an associated Markov Chain.
AB - We consider an algorithmic model of diffusion networks, in which n nodes are distributed in a 2D Euclidean space and communicate by diffusing and sensing molecules. Such a model is interesting on its own right, although from the distributed computing point of view it may be seen as a generalization or even a framework for other wireless communication models, such as the SINR model, radio networks or the beeping model. Additionally, the diffusion networks model formalizes and generalizes recent case studies of simple processes in environment where nodes, often understood as biological cells, communicate by diffusing and sensing simple chemical molecules. To demonstrate the algorithmic nature of our model, we consider a fundamental problem of reaching consensus by nodes: in the beginning each node has some initial value, e.g., the reading from its sensor, and the goal is that each node outputs the same value. Our deterministic distributed algorithm runs at every node and outputs the consensus value equal to the sum of inputs divided by the sum of the channel coefficients of each cells. For a node v consensus is reached in (formula presented) communication rounds, where dv is the sum of molecule reachability ratios to node v in the medium, dmin=minidi, dmax=maxidi and b is the sum of the initial values. p represents the second largest eigenvalue of a matrix of normalised molecule reachability ratios, that we analyze together with an associated Markov Chain.
KW - Ad hoc networks
KW - Cells
KW - Consensus
KW - Diffusion networks
KW - Molecules
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U2 - 10.1007/978-3-030-14094-6_12
DO - 10.1007/978-3-030-14094-6_12
M3 - Conference contribution
AN - SCOPUS:85063420037
SN - 9783030140939
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 180
EP - 192
BT - Algorithms for Sensor Systems - 14th International Symposium on Algorithms and Experiments for Wireless Sensor Networks, ALGOSENSORS 2018, Revised Selected Papers
A2 - Gilbert, Seth
A2 - Hughes, Danny
A2 - Krishnamachari, Bhaskar
PB - Springer Verlag
T2 - 14th International Symposium on Algorithms and Experiments for Wireless Sensor Networks, ALGOSENSORS 2018
Y2 - 23 August 2018 through 24 August 2018
ER -