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Abstract

Biologically inspired modeling techniques have received considerable attention for the realization of robustness, scalability, and adaptability with simple local interactions and limited information. Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and development of biological organisms from cells. In this paper, we employ GRN principles and propose a new GRN model for self-organizing control of Wireless Sensor Networks (WSNs) with dual aims of energy saving and delay guarantee. Through this scheme, each sensor node schedules its state autonomously according to the controlled gene expression and protein concentration of the proposed GRN model. Simulation results also indicate that the proposed scheme achieves good performance in meeting delay requirements and conserving energy in WSN systems.

1. Introduction

Biologically inspired systems have attracted extensive attention for solving scalability and adaptability issues while guaranteeing system robustness with individual simplicity [1–3]. Due to the properties of biological systems, much attention has been paid to employing genetic mechanisms for designing Wireless Sensor Networks (WSNs), in particular for self-organizing network. Concepts and principles derived from biological systems, such as immune system [4, 5], insect colonies [6, 7], activator-inhibitor systems [8, 9], and cellular signaling systems [10–13], have been applied to WSN system design.

Among them, Gene Regulatory Networks (GRNs), a representation of genes/proteins and the interactions between them, have been proposed for robust network design [14, 15]. GRN systems possess some attractive properties, like robustness and resilience to failures in WSNs. GRN topology has been used to determine node locations to achieve robustness and resilience to node failures [16]. A GRN model was proposed to solve the issue of optimal coverage in WSNs [17]. In specific, the nonlinear differential equation model of GRN was used to identify the minimum number of sensors required for maximum coverage in WSNs. The nonlinear differential equation based model was proposed to emulate the evolution process of genes in GRNs for self-network configuration [18]. In [19], the authors proposed an embedded controller that offers every sensor the ability to regulate its sampling rate based on its data and its neighbors’ behavior and shared information. In [20], the authors showed how a GRN-inspired controller can be used to configure submarines robots. In [21], the authors proposed the use of the attractor theory in GRN to achieve a fault-tolerant WSN routing. However, these GRN-inspired approaches have difficulty in predicting a desired behavior from local interactions of individual nodes. Rules for local interactions have not been designed to generate a specific desired behavior for mission-critical applications [22]. Thus, the individual behavior with local interaction does not always guarantee application-specific global requirements.

To address this issue, we propose a GRN model for self-organizing control of WSNs, which aims to automatically schedule node states according to the environmental changes and the application-specific requirements. We develop a theoretical model for robust node scheduling design using GRN framework. Dynamics of the proposed model automatically drive system performance to meet a specific application requirement while minimizing energy consumption among nodes under dynamically changing network condition.

2. Fundamentals of GRNs

In recent years, much research work has been reported on computational modeling of signal transduction and developmental genetic networks. These networks have been modeled in a variety of ways. For a single cell, the expression of a gene with autoregulation can be described by the following differential equations [19]:

\begin{matrix} \frac{d g_{i}}{d t} = - γ_{g} g_{i} + α_{g} f (p_{i}), \\ \frac{d p_{i}}{d t} = - γ_{p} p_{i} + α_{p} g_{i}, \end{matrix}

(1)

where

g_{i}

is the expression level of gene i (measured by the concentration of its mRNA product) and

p_{i}

is the concentration of protein i.

γ_{g}

and

γ_{p}

are the decay rates of mRNA and protein concentration, respectively.

α_{g}

and

α_{p}

are the synthesis rates of mRNA and protein concentration, respectively.

f (x)

is a sigmoid function.

For a multicell organism, it is necessary to consider external factors, transcription factors diffused from other cells, and interactions between cells into the GRN model. In [20], the authors suggested a generalized GRN model that considered diffusion of transcription factors among the cells:

\begin{matrix} \frac{d x_{i j}}{d t} = - γ_{i} x_{i j} + ϕ [\sum_{l = 1}^{n_{g}} W_{j l} x_{i l} + θ_{j}] + D_{j} \nabla^{2} x_{j i}, \end{matrix}

(2)

where

x_{i j}

denotes the concentration of jth gene product in the ith cell. The first term on the right hand side represents the degradation of the protein at a rate of

γ_{i}

, the second term specifies the production of protein

x_{i j}

, and the last term describes the diffusion component at diffusion rate

D_{j}

. ϕ is an activation function for the protein production, which is usually defined as a sigmoid function. The interaction between the genes is described with an interaction matrix

W_{j l}

, the element of which can be either active or repressive.

θ_{j}

is a threshold for activation of gene expression and

n_{g}

is the number of gene products.

3. GRN-Based Scheme for Self-Organizing Control

We denote the expression level of g as an internal state of sensor node and that of p as the probability of being active state:

\begin{matrix} \frac{d g_{i}}{d t} = - a g_{i} + η f_{g}^{h} (\bar{E_{i}} p_{i}), \\ \frac{d p_{i}}{d t} = - c p_{i} + κ f_{p}^{a} (\bar{D_{i}} g_{i}) + b \sum_{\forall j \in N_{i}} (g_{i} - g_{j}), \end{matrix}

(3)

where

g_{i}

is the expression level of the gene (mRNA) of sensor node i and

p_{i}

is the concentration of sensor node i's proteins produced by the gene, respectively. a and c are the decay rates of mRNA and protein concentration, respectively. η and κ are the synthesis rates of mRNA and protein concentration, respectively.

N_{i}

is the set of neighbor nodes of node i and

N_{i}

is the cardinality of

N_{i}

In order to consider consumed energy level and application-specific requirement, we introduce $\bar{E_{i}}$ and $\bar{D_{i}}$ , where $\bar{E_{i}}$ is the ratio of consumed energy level to the neighbors’ average and $\bar{D_{i}}$ is the difference between the measured delay and delay requirement:

\begin{matrix} \bar{E_{i}} = \frac{E_{i}}{\sum_{\forall j \in N_{i}} E_{j} / N_{i}}, \\ \bar{D_{i}} = d_{i} - d_{r}, \end{matrix}

(4)

where

E_{i}

is the consumed energy level, defined by the ratio of consumed energy to the initial energy of node i.

d_{i}

and

d_{r}

are the measured delay and delay requirement of an application, respectively. Functions

f_{g}^{h}

and

f_{p}^{a}

are defined as follows:

\begin{matrix} f_{g}^{h} (x) = e^{- x}, \\ f_{p}^{a} (x) = \frac{1 - e^{- x}}{1 + e^{- x}} . \end{matrix}

(5)

The last term of

d p_{i} / d t

specifies the sum of the concentration of g diffused from neighboring nodes, which aims to keep the energy consumption of nodes in balance. The diffusion term in the regulatory model simulates the intercellular signaling in multicellular system.

For WSN system, each sensor node stores its own gene expression and protein concentration. A sensor node exchanges the level of g with its neighboring nodes and a sink node piggybacks the difference between the measured delay and the delay requirement in an ACK (acknowledgement) message. When a sensor node detects its neighbor, it will receive the protein emitted from that neighbor and reevaluate p and g.

According to the proposed GRN model, the gene expression g represents the local environmental state of a node in WSNs. In specific, the expression level of the gene of a node is determined by its consumed energy level and protein concentration. As the consumed energy level of a node is larger than the averaged value across its neighbors or the protein concentration increases, the level of gene becomes smaller and vice versa. The concentration of protein is determined by the gene expression level and external factors, that is, delay requirement of an application and transcription factors diffused from other nodes. As the measured delay is larger than delay requirement or the expression level of gene is larger than the average across all the neighbors, the protein concentration increases and vice versa.

Figure 1 shows plots of inhibition function $f_{g}^{h} (x)$ and activation function $f_{p}^{a} (x)$ . Inhibition function $f_{g}^{h}$ decays with the protein concentration and the decay rate of $f_{g}^{h}$ increases with E. In other words, as the value of protein concentration increases or the consumed energy level is rather large compared to that of its neighbor, the local condition represented by gene expression g gets worse. Activation function $f_{p}^{a}$ has symmetrical shapes around g-axis. When $D > 0$ , as the local condition becomes better (larger g), the value of $f_{p}^{a}$ increases, which leads to higher probability of being active. Also, $f_{p}^{a}$ increases rapidly as the measured delay is larger than the desired delay requirement. In other words, a positive D means the measured delay exceeds the delay bound; thus the protein concentration increases, resulting in increase of active sensor nodes. On the contrary, when $D < 0$ , the protein concentration decreases due to the negative value of $f_{p}^{a}$ . This leads more active nodes to go to sleep and energy saving.

Figure 1

(a) Inhibition function $f_{g}^{h}$ and (b) activation function $f_{p}^{a}$ .

In our proposed scheme, each sensor node balances two objectives: one objective is to reduce the energy consumption as provided inside the gene expression, and the other one is to guarantee delay requirement and balance the gene expression with each other via diffusion. According to the proposed model, gene expression $g_{i}$ and protein concentration $p_{i}$ regulate each other via positive or negative feedback; when the measured delay exceeds the delay requirement or the gene expression level is larger than the averaged one among neighbors, the protein concentration increases, which leads to a higher probability of being active. This leads to delay reduction with more frequent packet transmission. Meanwhile, the increased protein concentration represses the expression of gene due to the degraded local environment quality, caused by increases in energy consumption. The repressed gene regulates the protein concentration again, which leads to an increase of sleep time and energy saving.

Consequently, the decreased protein concentration and lower $E_{i}$ promote the gene expression, and this gene expression also promotes the protein concentration. The increased protein concentration and higher $E_{i}$ repress the gene expression, resulting in energy saving with an increase of sleep time. Through these interactions with each other, our proposed scheme governs the state of each node by regulating the gene expression and protein concentration according to the desired delay requirement and local environmental state of the node.

4. Simulation Results

In order to evaluate the performance of our proposed algorithm, we develop a simulation environment using the MATLAB simulator. The simulation area is 100 m × 100 m and the transmission range is 40 m. We use a uniform grid topology, where the entire network is divided into equally shaped grids, and the sensor nodes are deployed uniformly. All source nodes generate packets in a Poisson distribution with an average packet arrival rate of one packet per second. Each packet is 100 bytes and the controller time slot duration is one second. We set the channel capacity to 200 kbps. The transmitting power and sleeping power are set to 24.75 mW and 15 μW, respectively. According to the stability conditions, we set $a = η = c = 0.1$ and $κ = 0.05$ .

Figure 2 shows the trajectories of protein concentration, delay and the averaged active nodes ratio, and consumed energy ratio for two different delay requirements, $d_{r} = 10$ s and 50 s, respectively. As shown in Figure 2, the protein concentration with $d_{r} = 10$ s is higher than that with $d_{r} = 50$ s.

Figure 2

Time-series plots of protein concentration for different delay requirements.

Also, we observe that the average delay successfully converges to each delay requirement as shown in Figure 3. The stricter delay requirement brings the higher protein concentration and probability to be active state, which leads to more frequent packet transmission in order to meet the delay requirement. This causes the different active node ratio and consumed energy ratio for different delay requirements.

Figure 3

Average delay for different delay requirements.

Figure 4 validates the point mentioned above that when the delay requirement becomes looser, the active node ratio slightly decreases and power consumption ratio becomes lower, resulting in energy saving.

Figure 4

Averaged protein concentration, active node ratio, and consumed energy ratio.

Figure 5 shows the averaged behavior of the proposed scheme varying delay requirements. Figure 5(a) illustrates the average delay and we observe that our proposed scheme successfully controls the average delay according to the varying requirements.

Figure 5

Average performance varying delay requirements: (a) delay, (b) active node ratio, and (c) consumed energy ratio.

Figure 5(b) shows the active node ratio for different delay requirements. As the requirement becomes looser, the number of nodes to be active decreases, which leads to energy saving. Figure 5(c) verifies the point mentioned above that when the delay requirement becomes looser, the energy consumption ratio becomes lower.

We investigate the effects of control parameters on the convergence speed. We measure the convergence time of our proposed scheme varying control parameters η, a, κ, and c. The convergence time is determined by the total number of iterations until reaching an equilibrium point.

Figure 6(a) illustrates the convergence iteration varying control parameters, η and a. We set $η = 0.1$ and $a = 0.1$ , 0.1/2, 0.1/3, 0.1/4, 0.1/5, 0.1/6, 0.1/7, 0.1/8, and 0.1/9. According to the proposed scheme, the protein concentration increases with $η / a$ , which makes the convergence speed faster. However, the large value of $η / a$ may destabilize a system by incurring oscillatory behavior.

Figure 6

Convergence performance varying control parameters: (a) convergence iteration with $η = 0.1$ and $a = 0.1 / [1 : 9]$ and (b) convergence iteration with $κ = 0.1$ and $c = 0.1 / [1 : 9]$ .

Figure 6(b) shows the convergence iteration and protein concentration varying control parameters, κ and c. We set $κ = 0.1$ and $c = 0.1$ , 0.1/2, 0.1/3, 0.1/4, 0.1/5, 0.1/6, 0.1/7, 0.1/8, and 0.1/9. A larger value of $κ / c$ speeds up the GRN algorithm convergence and raises the protein concentration. However, it leads to unnecessary energy waste caused by a higher probability of being active. Therefore, the choice of the control parameters should be studied.

5. Conclusions

In this paper, we have presented a self-organizing control scheme for WSNs based on the GRN model. We represent the coordination problem of on-off cycles of wireless sensor nodes by modifying gene regulation dynamics of multicellular mechanisms.

Considering the measured delay and energy consumption, the proposed scheme controls the expression level of gene and the concentration of protein in order to guarantee delay requirement while achieving energy balancing among sensor nodes.

In the future, we will provide performance analysis for the large-scale networks. Also, we plan to extend our research to the identification of optimal control parameters.

Footnotes

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

This research was supported by the GRRC program of Gyeonggi Province (GRRC SUWON 2014-B4).

References

Charalambous

Cui

A biologically inspired networking model for wireless sensor networks

IEEE Network 2010 24 3 6 13

10.1109/MNET.2010.5464221

2-s2.0-77952524829

Leibnitz

Murata

Attractor selection and perturbation for robust networks in fluctuating environments

IEEE Network 2010 24 3 14 18

10.1109/MNET.2010.5464222

2-s2.0-77952522478

Cheng

C. T.

Tse

C. K.

Lau

F. C. M.

A bio-inspired scheduling scheme for wireless sensor networks

Proceedings of the IEEE 67th Vehicular Technology Conference-Spring (VTC ′08)

May 2008

223 227

10.1109/vetecs.2008.58

2-s2.0-47749135942

Bokareva

Bulusu

Jha

SASHA: toward a self-healing hybrid sensor network architecture

Proceedings of the 2nd IEEE Workshop on Embedded Networked Sensors (EmNetS-II ′05)

May 2005

IEEE

71 78

10.1109/emnets.2005.1469101

2-s2.0-33745142579

Atakan

Akan

Ö. B.

Immune system based distributed node and rate selection in wireless sensor networks

Proceedings of the 1st IEEE/ACM International Conference on Bio-Inspired Models of Network, Information and Computing Systems

December 2006

1 8

10.1109/bimnics.2006.361806

2-s2.0-50249167096

Degesys

Rose

Patel

Nagpal

DESYNC: self-organizing desynchronization and TDMA on wireless sensor networks

Proceedings of the 6th International Symposium on Information Processing in Sensor Networks (IPSN ′07)

April 2007

11 20

10.1145/1236360.1236363

2-s2.0-35348850729

Barbarossa

Scutari

Bio-inspired sensor network design

IEEE Signal Processing Magazine 2007 24 3 26 35

10.1109/msp.2007.361599

2-s2.0-34249715159

Dressler

Self-organized event detection in sensor networks using bio-inspired promoters and inhibitors

Proceedings of the ACM/ICST International Conference on Bio-Inspired Models of Network, Information and Computing Systems (BIONETICS ′08)

November 2008

Neglia

Reina

Evaluating activator-inhibitor mechanisms for sensors coordination

Proceedings of the IEEE/ACM International Conference on Bio-Inspired Models of Network, Information and Computing Systems (BIONETICS ′07)

December 2007

129 133

10.1109/bimnics.2007.4610097

2-s2.0-53149116733

10.

Monk

N. A.

Sherratt

J. A.

Owen

M. R.

Spatiotemporal patterning in models of juxtacrine intercellular signaling with feedback

Institute for Mathematics and Its Applications 2001 121 165 193

11.

Collier

J. R.

Monk

N. A. M.

Maini

P. K.

Lewis

J. H.

Pattern formation by lateral inhibition with feedback: a mathematical model of delta-notch intercellular signalling

Journal of Theoretical Biology 1996 183 4 429 446

10.1006/jtbi.1996.0233

2-s2.0-0030597511

12.

Webb

S. D.

Owen

M. R.

Oscillations and patterns in spatially discrete models for developmental intercellular signalling

Journal of Mathematical Biology 2004 48 4 444 476

10.1007/s00285-003-0247-1

MR2065532

2-s2.0-3242691289

13.

Charalambous

Cui

A bio-inspired clustering algorithm for wireless sensor networks

Proceedings of the 4th Annual International Conference on Wireless Internet

November 2008

10.4108/icst.wicon2008.4846

14.

MacNeil

L. T.

Walhout

A. J. M.

Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression

Genome Research 2011 21 5 645 657

10.1101/gr.097378.109

2-s2.0-79953886457

15.

Prill

R. J.

Iglesias

P. A.

Levchenko

Dynamic properties of network motifs contribute to biological network organization

PLoS Biology 2005 3 11, article e343

10.1371/journal.pbio.0030343

2-s2.0-33744797673

16.

Nazi

Raj

Francesco

M. D.

Preetan

Das

S. K.

Robust deployment of wireless sensor networks using gene regulatory networks

Distributed Computing and Networking 2013 7730

Berlin, Germany

Springer

192 207 Lecture Notes in Computer Science

10.1007/978-3-642-35668-1_14

17.

Das

Koduru

Cai

Welch

Sarangan

The gene regulatory network: an application to optimal coverage in sensor networks

Proceedings of the 10th Annual Genetic and Evolutionary Computation Conference (GECCO ′08)

July 2008

1461 1467

2-s2.0-57549094849

18.

Markham

Trigoni

Discrete gene regulatory networks (DGRNS): a novel approach to configuring sensor networks

Proceedings of the 29th Conference on Computer Communications (INFOCOM ′10)

2010

1 9

19.

Markham

Trigoni

The automatic evolution of distributed controllers to configure sensor network operation

The Computer Journal 2011 54 3 421 438

10.1093/comjnl/bxq016

2-s2.0-79952152952

20.

Taylor

A genetic regulatory network-inspired real-time controller for a group of underwater robots

Proceedings of the 8th Conference on Intelligent Autonomous System

2014

21.

Ghosh

Mayo

Chaitankar

Habib

Das

Principles of genomic robustness inspire fault-tolerant WSN topologies: a network science based case study

Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops '11)

March 2011

Seattle, Wash, USA

160 165

10.1109/PERCOMW.2011.5766861

22.

Nolfi

Floreano

Evolutionary Robotics: The Biology, Intelligence and Technology of Self-Organizing Machine 2000

MIT Press

A Gene Regulatory Network-Inspired Self-Organizing Control for Wireless Sensor Networks

Abstract

1. Introduction

2. Fundamentals of GRNs

3. GRN-Based Scheme for Self-Organizing Control

4. Simulation Results

5. Conclusions

Footnotes

Conflict of Interests

Acknowledgment

References