Biologically inspired distributed sensor networks: Collective signal amplification via ultra-low bandwidth spike-based communication

Wireless networks of biologically inspired distributed sensors (BIDS) are hypothesized to enable improved overall detection accuracy using ultra-low power and low bandwidth spike-based communication between nodes. Unlike traditional sensor networks, in which nodes communicate via digital protocols that require precise decoding of binary signal packets, BIDS nodes communicate by broadcasting generic radio frequency pulses, or spikes. Individual BIDS nodes are modeled after leaky integrate-and-fire (LIF) neurons, in which both filtered sensory signals and inputs from other BIDS nodes are accumulated as capacitive charge that decays with a characteristic time constant. A BIDS node itself broadcasts a spike whenever its internal state exceeds a threshold value. Here we present detailed simulations of a BIDS network designed to detect a moving target-modeled as a pure acoustic tone with a translating origin-against a background of 1/f noise. In the absence of a target, the average internal state is well below threshold and noise-induced spikes recruit little additional activity. In contrast, the presence of a target pushes the average internal state closer to threshold, such that each spike is now able to recruit additional spikes, leading to a chain reaction. Our results show that while individual BIDS nodes may be noisy and unreliable, a network of BIDS nodes is capable of highly reliable detection even when the signal-to-noise ratio (SNR) on individual nodes is low. We demonstrate that collective computation between nodes supports improved detection accuracy in a manner that is extremely robust to the damage or loss of individual nodes.

[1]  Ian Marshall,et al.  A weakly coupled adaptive gossip protocol for application level active networks , 2002, Proceedings Third International Workshop on Policies for Distributed Systems and Networks.

[2]  Deborah Estrin,et al.  Directed diffusion: a scalable and robust communication paradigm for sensor networks , 2000, MobiCom '00.

[3]  Junichi Suzuki,et al.  BiSNET: A Biologically-Inspired Architecture forWireless Sensor Networks , 2006, International Conference on Autonomic and Autonomous Systems (ICAS'06).

[4]  Wendi Heinzelman,et al.  Proceedings of the 33rd Hawaii International Conference on System Sciences- 2000 Energy-Efficient Communication Protocol for Wireless Microsensor Networks , 2022 .

[5]  Mohamed F. Younis,et al.  A survey on routing protocols for wireless sensor networks , 2005, Ad Hoc Networks.

[6]  B T Cox,et al.  k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields. , 2010, Journal of biomedical optics.

[7]  Lotfi Kamoun,et al.  Wireless sensors networks MAC protocols analysis , 2010, ArXiv.

[8]  Anthony N. Burkitt,et al.  A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input , 2006, Biological Cybernetics.

[9]  E. Bonabeau,et al.  Spatial patterns in ant colonies , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Gaurav S. Sukhatme,et al.  Mobile Sensor Network Deployment using Potential Fields : A Distributed , Scalable Solution to the Area Coverage Problem , 2002 .

[11]  Brian Hayes THE WORLD ACCORDING TO WOLFRAM , 2002 .

[12]  Satish Kumar,et al.  Next century challenges: scalable coordination in sensor networks , 1999, MobiCom.

[13]  William R. Softky,et al.  The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[14]  Jennifer C. Hou,et al.  Maintaining Sensing Coverage and Connectivity in Large Sensor Networks , 2005, Ad Hoc Sens. Wirel. Networks.

[15]  C. Mead,et al.  Neuromorphic analogue VLSI. , 1995, Annual review of neuroscience.

[16]  C. Koch,et al.  A detailed model of the primary visual pathway in the cat: comparison of afferent excitatory and intracortical inhibitory connection schemes for orientation selectivity , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[17]  Xin Wang,et al.  Retinal Oscillations Carry Visual Information to Cortex , 2008, Front. Syst. Neurosci..

[18]  Ralph K. Hillquist,et al.  Motor vehicle noise spectra, their characteristics and dependence upon operating parameters , 1973 .

[19]  Greg J. Stephens,et al.  See globally, spike locally: oscillations in a retinal model encode large visual features , 2006, Biological Cybernetics.