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
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,
https://doi.org/10.1007/978-981-33-6757-9_42
332 J. Yu et al.
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 isN. The nodej, which is only within the perceived radiusRof nodei, 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 nodej,
N
j=1,j=ipjgijαijrepresents the sum of interference of nodeiby 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, setαij as the interference weighting factor, namely.
αij=exp −
D R
D= (xi−xj)
2
+(yi−yj)
2
(2) whereDrepresents the Euclidean distance of nodeiand nodej. It can be seen that when the node distance increases, the influence weight and interference value of nodej on
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)
whereWrepresents the propagation bandwidth,giis the link gain from nodeito 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.
maxRi(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 rateRiof nodei is a function of transmitting power pi and interferedIi, and the effective interference Ii of nodeiis a function ofpj, so t he following rate model can be obtained:
Ri(pi,Ii)=ln
⎛
⎜
⎝1+
pigi N j=1 j=i
pjgijαij+η2
⎞
⎟
⎠ (5)
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 nodei, andp−iis the strategy selection of the remaining nodes.
(2) Utility function:f ={f(Ri(pi,Ii),Ei)} denotes the network benefit when nodei performs data communication with transmission powerpiafter algorithm iteration, andEiis 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) ed(i)
334 J. Yu et al.
=c1ln
⎛
⎜
⎝1+
pigi N j=1 j=i
pjgijαij+η2
⎞
⎟
⎠−c2pigi
e0(i) ed(i)
(6)
wherec1andc2are utility weighting factors,e0(i) is the initial energy of nodeI,ed(i) is the residual energy of nodei. It can be seen from the second term that when the residual energyed(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 nodeidynamically 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 participantibelongs to a non-empty, closed, bounded convex set; (2) the utility functionf ={f(Ri(pi,Ii),Ei)} is a continuous function ofpi,pi∈[pimin,pimax] is quasi-concave, it satisfies:
∂
2
f(Ri(pi,Ii),Ei)
∂p
2 i
<0 (8)
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 powerpifor the utility functionf:
∂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=1 j=i
pjgijαij+η
2
+pigi 2
(10 )
It can be seen from Eq. (10) that the utility function is pseudo-concave onpi, 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
⎞
⎠ (11)
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 valueIiof the nodeiaccording 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 calculateEi;
Step 5 Calculating the optimal transmit powerpiof a single node from Eq. (11);
Step 6 Returning thepiresult 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 toM×M m
2
(M =100, 200, 300), and the number of nodes is set toN(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 atc1=0.84 andc2=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 ofc1is set to 0.72, andc2is 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 living survival nodes than the other criteria, and the convergence rounds of the proposed
336 J. Yu et al.
Table 1 Simulation initial parameters setting
Parameter name Value
Initial transmit power/W 0.1 Packet size/byte 10
Link gain 0.1
Initial transmit power/W 0.1 Packet size/byte 10
Link gain 0.1
Background noise/W 0.05
Bandwidth/Hz 100 K
Communication radius/m 20 Initial energy/J 60
M 100, 200, 300
N 50, 100, 200
(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
different weighting factors (d) Network utility under various weighting factors
Fig. 1 The sum of the space optimization of the power balance control strategy of WSN with a heuristic. subfigures.aInformation transmission rate with optimal as normalized average rate;
bthe variance of theoptimal transmit power obtained;cthe highest signal-to-noise ratio;and dthe 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
AliveNodes!
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
Bestscoreobtainedso far
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; andbbalance 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 information 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).
References
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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
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 gravitational 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 segmentation 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,
https://doi.org/10.1007/978-981-33-6757-9_43
340 T.-K. Dao et al.
[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 thetth particle in the d dimension at thetth iteration beX
d
i (t) andV
d
i (t), whered =1,2, . . . ,D,andDare the search space dimensions. In each iteration, the optimal solution of the individual particle ispbest
d i, and the optimal solution of the group isgbest
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· gbest
d
−X
d i (t)
(1) X
d
i (t+1)=X
d i (t)+V
d
i (t+1) (2)
In the formula:ωis the inertia weight of the particle;c1andc2are the acceleration factors; rand1and rand2are random numbers of [0,1] respectively. The first partωV
t i in Formula (1) reflects the mining ability of the particle swarm optimization algorithm, and the second and third partsc1·rand1· pbest
d
i −X
d
i (t) ,c2·rand2· gbest
d
−X
d i (t) , respectively, reflect the particle Ability to think independently and communicate with groups.
2.2 Standard Gravity Search Algorithm(GSA)
The particles in GSA [3] are attracted to each other by gravity, and the particle’s motion follows Newton’s law of motion. Gravitational force causes particles to move toward larger mass particles, while the largest mass particles occupy the optimal position.
Accordingtothis principle, the optimalsolution ofthe optimizationproblem canbe obtained.
The speed and position of particles in GSA are updated according to Eqs. (3)–(5) during each iteration:
V
d
i (t+1)=rand·V
d i (t)+a
d
i(t) (3)
X
d
i (t+1)=X
d i (t)+V
d
i (t+1) (4)
a
d
i(t+1)=F
d i (t)/M
d
i (t) (5)
where:X
d i (t),V
d i (t),a
d i(t),F
d i (t),M
d
i (t), respectively, represent the position, velocity, acceleration, and position of thetth particle in thed−dimension during thetth iteration.
The magnitude of the resultant force and the mass of inertia.
The calculation of the resultant force is shown in Eqs. (6) and (7):
F
d ij(t)=
G(t)·Mi(t)·Mj(t)
Rij(t)+ε
· X
d j (t)−X
d
i (t) (6)
F
d i (t)=
N
j=1,j=i
randj·F
d
ij(t) (7)
In the formula:N is the total number of particles;F
d
ij(t)represents the gravitational force of particlejto particlei; randjis a random number of [0, 1];Rij(t)is the Euclidean distance between particleiand particlej;εis a A constant with a small value;G(t)is the gravitational constant. The calculation formula is shown in Eq. (8):
G(t)=G0·exp(−α·t/maxt) (8)
Among them:G0andαare constants;tis the current number of iterations; maxtis the maximum number of iterations. The inertial mass of the particles in Eq. (6) can be obtained from Eqs. (9) and (10):
mi(t)=
fiti(t)−worst(t) best(t)−worst(t)
(9)
Mi(t)=mi(t)/
N
j=1
mj(t) (10)
where: fiti(t)represents the fitness value of thetth particle at the tth iteration. For the image multi-threshold segmentation problem for which the maximum value is obtained, best(t)and worst(t)are obtained from equations as follows.
best(t)=max fitj(t),j∈ {1,2, . . . ,N} (11)
