A power balance control strategy of wireless sensor network with collaborating heuristic

A distance between the weight factors and obtained nodes interference value is used to establish a useful interference model for enhancing thesignal-to-interference-noiseratioSINR.Theuti

Sensor Network with Collaborating Heuristic

Jie Yu

1

, Thi-Kien Dao

2 , Truong-Giang Ngo

3 (B) , and Trong-The Nguyen

2,4

1

College of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou

350118, China 2

Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of

Technology, Fuzhou, China 3

Faculty of Computer Science and Engineering, Thuyloi University, 175 Tay Son, Dong Da,

Hanoi, Vietnam giangnt@tlu.edu.vn 4

Department of Information Technology, Haiphong University of Management and

Technology, Haiphong, 18000, Vietnam

Abstract In order to effort a strong enough signal, nodes in wireless sensor

net-works (WSN) have to increase their transmission power that continues to maintain

the transmission power However, a vicious circle is iterated that causes a decline

in the overall network performance, low utility, and network life cycle shorten

This paper presents a solution to the power balance control strategy for WSN with

collaborating heuristic A distance between the weight factors and obtained nodes

interference value is used to establish a useful interference model for enhancing

thesignal-to-interference-noiseratio(SINR).Theutilityfunctionofthe nodes

residual energy and transmission rate is modeled by apply heuristic strategy The

optimaltransmissionpowerisobtained afterseveraliterationsofthe heuristic

algorithm Simulation results show that the proposed approach can prolong the

network life cycle and achieve higher network utility

Keywords:Wireless sensor network · Transmission power · Collaborating

heuristic

1 Introduction

Wireless sensor networks (WSN) composed of several nodes have integration, self-organization, and multi-hop, etc., [1,2] The nodes can perceive, collect, and transmit the surrounding information through cooperation to realize the monitoring of the treatment

of the test area [3,4] WSN has become an essential part of the internet industry with great convenience to people’s life and study, e.g., reflected in the smart home, intelligent transportation, and other aspects [5] However, nodes cannot be replaced with batteries

as the massive scale of sensor nodes, and premature death of the nodes will lead to the change of topology structure such as routing [6] The improper transmission power of the nodes will accelerate the paralysis of the network and reduce the network utility and

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021

J.-S Pan et al (eds.), Advances in Intelligent Information Hiding and Multimedia

Signal Processing, Smart Innovation, Systems and Technologies 212,

life cycle [7] It means improving the energy use efficiency of nodes and extending the network life cycle are the key technologies that need to be solved urgently in WSN [8] Effective power control is the precondition for WSNs and nodes to perform persistent work [9] Clustering WSNs have a lot of success in deserving energy effective sensor net-works [10] However, the problems of node interference have not been much considered comprehensively [5] In this paper, the short life cycle and low network utility caused

by improper transmit power are dealt with by establishing a useful interference model

to obtain active interference between channels and optimizes the game framework by using a utility function In this way, each node gets the corresponding optimal transmit power to reduce node energy consumption, extend the network life cycle, and achieve optimal system performance

2 Related Work

The heuristic is often used to deal with the selection of strategies in the process of mutual cooperation or competition by adjusting the behavior of participants to maximize the benefits at the minimum cost In order to simplify the modeling process, the following objects are set for the research object and the corresponding environment [2]

• Network nodes are randomly distributed and then remain stationary Sink nodes are located in the center of the whole region, and their energy is not limited

• The nodes can perceive the position and transmitting the power of each node within the communication radius

• Allnodeshave the sameinitial information, including energy,transmitting power, perceived radius, etc., and the transmitting power of nodes is controllable

Assume that the number of randomly distributed sensor nodes in the monitoring area

is N The node j, which is only within the perceived radius R of node i, will interfere with it, and the following useful interference model is established

Ii= N

j=1

j =i

pjgijαij+ η

2

(1)

where pj representsthe transmitpower of node j,gij isthe link gain of node i and node j,

N

j=1,j =ipjgijαijrepresents the sum of interference of node i by other working linksin one data receivingperiod, η

2

is channel noise.When thedistances of nodes aredifferent, the degree ofinterference is also different Toimprove the accuracy of practical interference calculation, set αij as the interference weighting factor, namely

αij= exp −

D R

D = (xi− xj)

2 + (yi− yj)

2

(2)

where D represents the Euclidean distance of node i and node j It can be seen that when

the link gradually decrease Based on the above analysis and the definition of SINR in literature, the improved SINR model is presented as follows

SINR =

W

Ri(pi, Ii)

pigi N j=1

j =i

pjgijαij+ η2

(3)

where W represents the propagation bandwidth, gi is the link gain from node i to the next-hop node, Ri(pi, Ii) is the information transmission rate obtained at the optimal power The transmission rate of information can be calculated using a power-interference model The optimization problem of the rate can be converted into optimization as follows

max Ri(pi, Ii) s.t.pi ≥ 0

(4)

Itindicates thatthe maximum transmissionrate supportedby the sensornode is related to the disturbance suffered by the node at this time, that is, the rate Riof node i

is a function of transmitting power pi and interfered Ii, and the effective interference Ii

of node i is a function of pj, so t he following rate model can be obtained:

Ri(pi, Ii) = ln

⎛

⎜

⎝1 +

pigi N j=1

j =i

pjgijαij+ η2

⎞

⎟

The node will increase the transmission power to make up for the expected SINR that leads to more mutual severe interference It is necessary to determine its own transmit power according to the characteristics of the surrounding nodes Therefore, it can sense the state information of neighbor nodes that is not a local optimization problem The transmission rate is a function of the transmission power and active interference The interaction between the three is independent of each other In this way, a collaborating heuristic model composed of relevant factors can be constructed In the collaborating heuristic, it is emphasized that the final equilibrium result tends to the overall optimal value, and the strategy adopted by each node is the optimal response under the premise The strategy heuristic model is chosen as = p, f , each element is respectively:

(1) Strategy space: P = {pi, p−i}(i = 1, …, n) is a strategy combination, piis the strategy selection of node i, and p−iis the strategy selection of the remaining nodes (2) Utility function: f = {f (Ri(pi, Ii), Ei)} denotes the network benefit when node i performs data communication with transmission power piafter algorithm iteration, and Eiis the ratio of initial energy to residual energy of node

f (Ri(pi, Ii), Ei) = c1Ri(pi, Ii) − c2pigiEi

= c1Ri(pi, Ii) − c2pigi

e0(i)

= c1ln

⎛

⎜

⎝1 +

pigi N j=1

j =i

pjgijαij+ η2

⎞

⎟

⎠ − c2pigi

e0(i)

ed(i)

(6)

where c1and c2are utility weighting factors, e0(i) is the initial energy of node I , ed(i) is the residual energy of node i It can be seen from the second term that when the residual energy ed(i) of the node is gradually reduced, the network utility shows a downward trend,

so the transmission power should be appropriately reduced to delay the falling speed of the remaining energy The node i dynamically adjusts its own strategy by considering the surrounding node states comprehensively, and the optimal power strategy set when generating the maximum benefit isp = {p1, …, pn}, and its element is expressed as fol lows

p = arg maxf (Ri(pi, Ii), Ei) (7)

3 WSN Energy Control Strategy with Collaborating Heuristic

Iteration in the heuristic means that the same heuristic form is constantly appearing, and all participants decide the strategy based on current earnings and possible future returns The strategic repeated heuristic stipulates that each stage is a standard strategy heuristic

In the actual judgment, the strategic repeated heuristic needs to satisfy: (1) The set of strategies of participant i belongs to a non-empty, closed, bounded convex set; (2) the utility function f = {f (Ri(pi, Ii), Ei)} is a continuous function of pi, pi∈[pimin, pimax] is quasi-concave, it satisfies:

∂ 2

f (Ri(pi, Ii), Ei)

∂ p 2 i

In the whole heuristic process, each node dynamically selects the appropriate power According to the definition of repeated heuristic, if the optimal response of all participants satisfies the above two conditions, there must be a Nash equilibrium point [11] The collaborating heuristic model is expressed as the utility function has

a Nash equilibrium point For the heuristic model = P , f there are: {p1, p−1}, {p2, p−2}, , {pn, p−n}; {f (R1, E1), f (R2, E2), , f (Rn, En)} , and

pmin≤ pi≤ pmaxand 0 ≤ Ii≤ Imax, which are in accordance with the constraints in the repeated heuristic Find the first-order partial derivative of the transmit power pifor the utility function f :

∂ f

∂ pi

= c1

gi N

j =1

j =i

pjgijαij+ η2+ pigi

− c2gi

e0(i)

ed(i)

(9)

The second-order partial derivative of the utility function is:

∂ 2 f

∂ p 2 i

= −c1

g 2 i

N j=1pjgijαij+ η

2 + pigi 2

(10 )

It can be seen from Eq (10) that the utility function is pseudo-concave on pi, which

is consistent with the decision condition, indicating that the heuristic model enables the node to generate an optimal power solution and a Nash equilibrium point According to the optimization theory, the optimal power for the differentiable functions is obtained:

pi= 1

gi

⎛

⎝

c1ed(i)

c2e0(i)

−

j =i

pjgijαij− η

2

⎞

The specific steps for implementing wireless sensor network power control under the collaborating heuristic proposed in this paper are:

Step 1 Set initial parameters, send their own location information list, energy and current transmit power between nodes;

Step 2 Calculating the sufficient total interference value Iiof the node i according to Eqs (1) and (2);

Step 3 Through Eqs (4) and (6), the transmission rate and network utility at this time can be obtained;

Step 4 Set node i to carry out packet transmission with a certain probability, and calculate Ei;

Step 5 Calculating the optimal transmit power piof a single node from Eq (11); Step 6 Returning the piresult of a single node to step1 and stepping iteratively; Step 7 Calculate the network revenue under the parameters of the above updated parameters

4 Experimental Result and Simulation Analysis

In the simulation experiment, the assumed network implementing area is set to M × M m

2

(M = 100, 200, 300), and the number of nodes is set to N (N = 50, 100, 200) to situational scenario tests as follows The initial parameters setting of the experiment [5]

is shown in Table1

The setting scheme is listed as follows: The values of the weighting factors c1 and c2 in the utility function should be determined Figure1shows the sum of the weight parameters c1 and c2 is set Subfigures (a) information transmission rate with optimal

as normalized average rate; (b) the variance of the optimal transmit power obtained; (c) the highest signal-to-noise ratio at c1= 0.84 and c2= 0.16; and (d) the network utility derived by the algorithm by selecting different weight factors It can be seen that the efficiency is maximized when the weights of c1is set to 0.72, and c2is set to 0.28 The obtained outputs results of the proposed scheme are compared with the other methods inthe literature,e.g., the PCOA[8], PLPC[3], OSPC [2], andLEACH[5] approaches to verify the effectiveness of the proposed approach performance

Figure2 shows the comparison of the proposed approach with the PCOA, PLPC, OSPC, andLEACHapproaches for the WSN powercontrol strategy Subfigures(a) comparison of survival nodes with different algorithms; (b) comparison of balance power variance of different algorithms It can see that the proposed approach produces longer

Table 1 Simulation initial parameters setting

Initial transmit power/W 0.1

Initial transmit power/W 0.1

Communication radius/m 20

(a) Normalized average rate under different

weighting factors (b) The transmission power variance value under various weighting factors

c) Signal to interference and noise ratio under

Fig 1 The sum of the space optimization of the power balance control strategy of WSN with a

heuristic subfigures.a Information transmission rate with optimal as normalized average rate;

b the variance of theoptimal transmit power obtained;cthe highest signal-to-noise ratio;and

d the network utility derived by the algorithm by selecting different weight factors

scheme alsoprovide the faster convergence of the other competitors The proposed method achieves convergence, which can effectively reduce node energy consumption and improve the network life cycle

100 20 0 300 400 500 600 700 800

Ro unds

140

150

160

170

180

190

200

Survival time of nodes

LEACH OPSPC PCOA PLPC Our Proposed

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Generations

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Avg Optimization Transmit Ennergy Errors

LEACH OPSPC PCOA PLPC Our Proposed

a) Survival nodes with different algorithms b) balance power variance of different algorithms Fig 2 Comparison of the proposed approach with the PCOA, PLPC, OSPC, and LEACH

approaches forthe WSN powercontrol strategy; subfigures.a Survivalnodes with different

algorithms; and b balance power variance of different algorithms

5 Conclusions

This paper introduced a solution to the power control strategy of wireless sensor net-works (WSN) utilizing collaborating heuristic concept, and the total interference and information transmission rate of nodes The energy factor was applied as a utility func-tion that used regularly updated network informafunc-tion to find the optimal power for each node.The network utilityvalue was improved by adjustingthe optimalpotential of the node.The power consumptionof nodeswas reduced inthe iterationprocess by using repeated heuristics to decline unnecessary energy consumption and decrease the complexity computation In simulation experiments, the output results obtained by the proposed approach was compared to the other same strategies in the literature Compared results show that the proposed method can adapt to balance the energy of the changing network and prolong the network life cycle

Acknowledgements Thiswork was supportedin part byFujian provincialbusesand special

vehicles R & D collaborative innovation center project (Grant Number: 2016BJC012)

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for Image Segmentation Based on Hybrid

Swarm Computation Optimization

Thi-Kien Dao

1 , Hong-Jiang Wang

1 , Jie Yu 2 , Huu-Quynh Nguyen

3 , Truong-Giang Ngo

3 (B) , and Trong-The Nguyen

4

1

Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of

Technology, Fuzhou, 350118, China 2

College of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou

350118, China 3

Faculty of Computer Science and Engineering, Thuyloi University, 175 Tay Son, Dong Da,

Hanoi, Vietnam giangnt@tlu.edu.vn 4

Department of Information Technology, Haiphong University of Management and

Technology, Haiphong, Vietnam

Abstract This paper suggests a solution for the image segmentation (IS) problem

with the multilevel thresholding based on one of the latest hybrid swarm

compu-tation optimization algorithms, particle swarms, and gravicompu-tational search (PSGA)

Theexperimentalresultsare comparablewithotherstate-of-the-artalgorithms

that show that the PSGA on selected images is better than the competitors

Keywords:Cross-entropy thresholding · Image segmentation · Particle warms ·

And gravitational search

1 Introduction

Image threshold segmentation is one of the most effective, and real-time methods that have received widespread attention in image processing [1] Multi-threshold image seg-mentation is considered as an extension of threshold segseg-mentation that can distinguish background and multiple goals, but the disadvantage is that the calculation is compli-cated and takes a long consumption time Many biological heuristics is the promising ways of applying successfully to deal with IS problems, e.g., genetic evolution, swarm behavior [2] For example, the gravity search algorithm (GSA) [3] was taken inspiration based on the theory of Newtonian physics as the gravity law and mass interactions; the

FA algorithm was taken inspiration from Firefly insect [4]; the CS algorithm was mim-icked from Cuckoo search [5] Some applications in image processing as segmentation issues have used these algorithms, e.g., a threshold selection criterion solved by GSA

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021

J.-S Pan et al (eds.), Advances in Intelligent Information Hiding and Multimedia

Signal Processing, Smart Innovation, Systems and Technologies 212,

[6], the multi-threshold calculated by FA [7], and the multi-threshold optimized by CS [8] Theseapplications show advantagesof computational time,butthey still havea drawback of local search capabilities that is easy to fall into the defect of local optimum This issue causes IS unexpectedly accurate

The hybrid swarm computation optimization algorithms is one of the proper ways

to deal with this issue of drop trap local optimal [9] The hybrid algorithm is the idea

of mixed algorithms by adding or combining the advantages of different algorithms for enhancing both of global exploration and local mining capabilities [10,11] The PSGA [12] is one of the latest metaheuristic algorithms that is a hybrid algorithm of particle swarm (PSO) [2] and gravitational search (GSA) [3] by combining the agents’ group to the optimization algorithm

In this paper, we consider a solution problem of the multi-threshold segmentation of color images with adjusting PSGA to avoid a single algorithm’s weak local search ability and easy local optimum for causing inaccurate segmentation Multi-threshold Otsu’s rule [13] is used as an IS evaluation function to perform multi-threshold segmentation on multi-target images

2 Hybrid Particle Swarm and Gravitational Search (PSGA)

The PSGA algorithm is a combined the global search ability of the PSO algorithm and the local mining ability of the GSA algorithm [12]

2.1 Standard Particle Swarm Optimization Algorithm

In PSO [2], each particle represents a feasible solution, and each moment has its own speed and position Let the position and velocity of the t th particle in the d dimension at the t th iteration be X

d

i (t ) and V

d

i (t ), where d = 1, 2, , D, and D are the search space dimensions In each iteration, the optimal solution of the individual particle is pbest

d

i, and the optimal solution of the group is g best

d Then the particle updates its speed and position according to Eqs (1) and (2) during each iteration:

V

d

i (t + 1) = ωV

d

i (t ) + c1· rand1· pbest

d

i − X d

i (t ) + c2· rand2· g best

d

− X d

i (t ) (1)

X d

i (t + 1) = X

d

i (t ) + V

d

In the formula: ω is the inertia weight of the particle; c1and c2are the acceleration factors; rand1and rand2are random numbers of [0, 1] respectively The first part ωV

t

i in Formula (1) reflects the mining ability of the particle swarm optimization algorithm, and the second and third parts c1· rand1· pbest

d

i − X d

i (t ) , c2· rand2· g best

d

− X d

i (t ) , respectively, reflect the particle Ability to think independently and communicate with groups