Physical topology optimization of infrastructure health monitoring sensor network for high-speed rail

Measurement 79 (2016) 83–93

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Physical topology optimization of infrastructure health monitoring sensor network for high-speed rail Haijian Li a, Tingting Yao b, Moyu Ren c, Jian Rong a, Chengkun Liu d, Limin Jia b,⇑ a

Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China c Hatch Mott MacDonald, 10 Universal City Plaza, Suite 2100, 111 Universal Hollywood Drive, Los Angeles, CA 91608, USA d Qingdao Sifang Rolling Stock Research Institute Co., Ltd., Qingdao 266000, China b

a r t i c l e

i n f o

Article history: Received 15 May 2015 Received in revised form 15 October 2015 Accepted 22 October 2015 Available online 4 November 2015 Keywords: High-speed rail Infrastructure health monitoring sensor network Multi-knapsack problem Artificial intelligence algorithm Physical topology optimization

a b s t r a c t With the rapid development of high-speed rail (HSR) systems, the security and safety of these huge systems are becoming the primary concerns for passengers. HSR infrastructure plays an important role in HSR systems, making the maintenance of security and safety of the HSR infrastructure especially important. Meanwhile, sensor network technologies allow the realization of real-time and all-weather monitoring of HSR infrastructure. This paper analyzes the application requirements and characteristics of infrastructure health monitoring sensor network (IHMSN) through construction of a three-layer IHMSN which is composed of end devices, repeater points, and access points. The physical topology optimization goal of IHMSN is to set the optimal number of network nodes (namely, minimum cost) as well as the best physical connections. Given types and amount of the end devices, a multiple knapsack model is established which converts the physical topology optimization problems into multiple knapsack problems. Based on the different needs of practical application, three different cases (basis case, adding devices case and weight-based case) are proposed, and the corresponding models are built. Some artificial intelligence algorithms and a traditional dynamic programming algorithm are presented to solve the problems. In addition, a general algorithmic finite state machine is proposed to describe the solving process. After comparing these algorithms in execution time, memory, and optimal results, the genetic algorithm and particle swarm optimization algorithm stand out when used to solve the basic case as well as the extended cases. The numerical results show that these proposed models and algorithms can effectively solve the physical topology optimization problem of IHMSN for HSR systems. Moreover, these methods can effectively reduce network costs and provide a theoretical basis for network communication link optimization.
2015 Elsevier Ltd. All rights reserved.

1. Introduction High-speed rail has become the main arteries for economic development. It has been a primary problem to ensure the safety, sequence and efficiency of high-speed rail operation. As a result, infrastructure health monitoring

⇑ Corresponding author. Tel./fax: +86 10 51683824. E-mail address: [email protected] (L. Jia). http://dx.doi.org/10.1016/j.measurement.2015.10.035 0263-2241/
2015 Elsevier Ltd. All rights reserved.

in HSR systems has become a hot spot of studies. The current facilities of infrastructure health monitoring are conducted by manual polling, video supervision [1,2], diagnostic train [3], and vehicle detection system [4,5], etc. However, the drawbacks of current monitoring methods are obvious. Firstly, they cannot work in large scale and real time. Besides, the monitoring data obtained by these methods are not comprehensive enough for analyzing. In addition, most of the track traffic detection and processing systems developed for HSR, subways, and light

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rails are discrete, which contain no information exchange within the systems. Therefore, it is impossible to acquire monitoring information timely and automatically. Currently, there is still a lack of studies on effective infrastructure health monitoring in HSR systems. Nowadays, the sensor networks, especially wireless sensor networks which combine wireless communication, distributed processing, micro electromechanical systems, machine vision, embedded computing, and sensor technology has attracted a lot of attention [6]. Sensor network has been applied in health monitoring for infrastructure and equipment, such as structure health monitoring [7,8]. Sensor network technology for infrastructure health monitoring has become one of the research hotspots. Monitoring technology in HSR involves many aspects and lots of related researches have been done. Ho et al. [9] developed a new ground acceleration based method of evaluating HSR track bed integrity, which relies upon the use of an accelerometer array that can be installed in the substructure of the track. Chen et al. [10] proposed a turnout monitoring system for rail damage in HSR systems; he integrated three methods, i.e. fiber grating, optical imaging and Lamb guided wave, to realize the compound monitoring of rail damage. Piva et al. [11] proposed an algorithm which is suitable for real time processing, and it has been tested on a variety of color video-sequences taken from the point of view of trains moving along railways tracks. Kostryzhev et al. [12] carried out the acoustic emission (AE) monitoring of rail steel fatigue in a ‘noisy’ laboratory environment using different methods of signal analysis, in which signal parameters of AE for machine noise, sample deformation and crack growth were identified, and the crack growth related AE signature was found to be dependent on fracture mode. Asada et al. [13] developed a new approach for fault detection and diagnosis utilizing parameters collected from low-cost and accessible sensors, and it is found that these methods can detect and diagnose misalignment faults of electrical railway point machine to a high degree of accuracy. Papaelias et al. [14] put forward an alternating current field measurement micro-pencil probe, the results obtain through rail inspection experiments under simulated conditions suggest that this technique can be applied for the accurate and reliable detection of surface-breaking defects at high inspection speeds. Barke and Chiu [15] stated the parameters that define vehicle condition and their measurable effects, and also describe in detail a number of wayside detection methods of inspecting a vehicle for the same vehicle condition. Balestrino et al. [16] addressed active pantographs and advanced methods for measuring the quality of the current collection for high-speed trains, and also investigated the pantograph-overhead contact wire interaction by using a phototube sensor, wavelet transform applied to the line current and infrared camera. Liu et al. [1] designed a high-speed frame grabber system, in which the triggering rate of its line scan camera can be adjusted to match the vehicle speed of the train, so that the pixels per inch of images captured by this camera are fixed. With the development of HSR, wireless network and sensor network have been widely applied in HSR systems. As far as Fukuta is concerned in his paper [17], sensor

technologies play significant roles for train operations. The multi-hop transmitting function of sensor network can easily realize wireless networks. Implanting several sensor devices in a railroad crosstie and setting the crossties under rails, the sensor network will automatically form a communication path along the rail, where the resolution of sensing is around one meter and the path is flexible to reform. In terms of civil infrastructure, as Hoult et al. [18] proposed in his paper, managers and engineers require its performance data to ensure safe and efficient operation. So they report on the trial installations of wireless sensor networks in a suspension bridge, slab bridge, rail tunnel and water supply pipeline. Chen and Mao [19] designed a high-speed rail safety detecting system based on ZigBee wireless sensor network, and the corresponding communication protocols. Kim et al. [20] implemented an online monitoring system that checks power facility status by applying network based technology to urban transit substation power facilities, which is composed of a sensor part, a measurement part, a transceiver part, a host computer, and a power source part. As for monitoring sensor networks in HSR systems, many related researches in other fields have been done. As Shin and Park [21] pointed out in their paper that are assumed that the numbers of nodes of most wireless sensor networks are very large and they should operate with confined resources. Consequently, it is important to take a scalable and energy-efficient architecture. They presented railroad, a data collection and topology management architecture for large-scale wireless sensor networks. They exploited a virtual infrastructure called rail, which acts as a rendezvous area of the event data and queries. The communication cost and the hot area message complexity of railroad were evaluated. Similarly, Wesemann et al. [22] described a method for automatically obtaining the order and position of contactless connected network participants in a linear physical topology. The physical topology is based on a linear network topology using a backbone rail, which includes mechanisms for contactless energy and data transmission. Moreover, Wang et al. [23] established an on-line monitoring system, and presented a linear topology structure of WSNs and five linear routing protocols to obtain the desired minimum energy consumption of WSNs. In order to meet the health monitoring requirements of various infrastructures, such as rail longitudinal stress detection, rail displacement and crawling detection, rail integrity and railway embankment settlement detection, a three-layer IHMSN is established, which requires a large amount of and various types of communication nodes. By reducing the redundant nodes and improving the utilization efficiency, we can effectively streamline the network structure as well as reduce network construction and maintenance costs. Considering the factors of cost, node bandwidth and network complexity, this paper focuses on the key issue of physical topology optimization, which will enable IHMSN of HSR to be more economical and efficient. Based on the problems considered in this paper, the goals of this research are: (1) to find out the optimal architecture of IHMSN for HSR; (2) to establish appropriate models tailored to different needs of practical application of IHMSN

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for HSR, and (3) to find out suitable algorithms to solve these models with good performance in assessment indexes, such as execution time, memory, and optimization result. 2. Architecture of the IHMSN

a maximum-bandwidth-utilization-based MKM to deal with the basic case (denoted as case A) based on the types and amount of EDs. Moreover, focusing on the different needs of practical application, adding devices case (denoted as case B) and weight-based case (denoted as case C) are also proposed, and their corresponding models are extended.

A reasonable network architecture can conserve construction costs, reduce the communication jamming, extend the network lifetime and improve the communication efficiency. Based on the communication requirements and the detection objects, an IHMSN can be divided into three layers: sensor layer, repeater layer and access layer. Access layer and repeater layer are communication layers, which are composed of network nodes called access points (APs) and repeater points (RPs), respectively. RPs transmit detection information to APs by wireless communication. Sensor layer contains various types of end devices (EDs), which monitor the health status of the HSR infrastructures. Fig. 1 shows the architecture and the communication model of IHMSN proposed in this paper. In the network, AP is responsible for the information’s collection, access, integration and communication mode conversion, so as to send the detection information to a data center (DC) through a wired network (such as Ethernet, Field Bus) or wireless network (such as Wi-Fi, 3G, LTE); RP is responsible for transmitting and forwarding detection information. Some information come from the EDs and the others may be transmitted and forwarded from other RPs. The EDs in the sensor layer monitor the infrastructure status and transmit the information to the adjacent RPs or APs.

Considering a HSR infrastructure detection area, the number of types of ED is denoted as N, the number of each type is denoted as mi (1 6 i 6 N), data packet size of type i ED is denoted as pi, packet transmission frequency is P denoted as fi. Then the total number of ED is m ¼ Ni¼1 mi , and the bandwidth requirements of type i ED is si = pi  fi. The total bandwidth of each AP is denoted as RAP, and the utilization of an AP is a. The total bandwidth of each RP is denoted as RRP, and the utilization of a RP is b. So, the actual available bandwidth for each AP is R0 AP = RAP  a, and the actual available bandwidth for each RP is R0 RP = RRP  b. The ED bandwidth combination vector is defined as S = [s1, s2, . . ., si, . . ., sN] (1 6 i 6 N), and the quantities vector is M = [m1, m2, . . ., mi, . . ., mN] (1 6 i 6 N) (each element in the vector represents one type of ED). For all the EDs, the optimal ED combination of one AP to maximize the bandwidth utilization can be calculated. Then repeat this process in turn, until all the EDs are allocated for APs. The times of allocation are the optimal number of APs, which is also the number of sub-networks of the IHMSN given EDs. The MKM of one AP is as following:

3. Optimization model of the IHMSN

Model A-AP : max RðXÞ ¼

3.1. Case A: basic case

N X xi s i

ð1Þ

i¼1

The optimization goal of physical topology is to find out the optimal number of network nodes (namely, minimum cost) as well as the best physical connection of each node. Considering the similarity between the optimization problem and multiple knapsack model (MKM), we can establish

8 N > < Xx s 6 R0 i i AP s:t: i¼1 > : 0 6 xi 6 mi

Fig. 1. Architecture and communication model of IHMSN for HSR systems.

ð2Þ

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In the Model A-AP, R(X) is the effective bandwidth of one AP, and xi is the number of the type i ED. The solution vector X = [x1, x2, . . ., xi, . . ., xN] (1 6 i 6 N) will determine the optimal ED combination vector SAP accessing to the current AP. Then, notate mi = mi  xi (1 6 i 6 N) and repeat the above process until "mi = 0. The number of all the APs is the optimal number of APs. The solution vector X corresponds to the number and the type of EDs which belong to the current AP. Through the process above, we can get the optimal ED combination vector SAP of each AP. For 1 6 i 6 N, if si > R0 RP, the RP bandwidth cannot satisfy the requirement of the type i ED. Then the information of type i ED is transmitted to an AP by wired communication, at the same time, type i ED is removed from the optimal ED combination SAP. Finally, the new ED combination vector S0 AP can be obtained, in which si e S0 AP and si 6 R0 RP (1 6 i 6 N). Here, the process of deriving the optimal number of RPs accessing to each AP will be presented. Firstly, focusing on certain AP and its combination vector S0 AP, we can find out the optimal ED combination vector SRP accessing to certain RP, which maximizes bandwidth utilization; Secondly, repeat this process above until all the EDs accessing to the AP are allocated to certain RP between this AP and those EDs. The times of allocation are the optimal number of RP accessing to this AP. Therefore, the MKM of one RP accessing to one AP is as following:

Model A-RP : max ZðYÞ ¼

N X y i si

ð3Þ

i¼1

8 N > < Xy s 6 R0 i RP s:t: i¼1 i > : 0 6 yi 6 ni

ð4Þ

In the Model A-RP, Z(Y) is the effective bandwidth of one RP; ni is the total number of the type i ED accessing to current AP; yi is the selected number of the type i ED accessing to current AP; and yi is a non-negative integer. The solution vector Y = [y1, y2, . . ., yi, . . ., yN] (1 6 i 6 N) is the optimal ED combination vector SRP accessing to current AP. Similar with Model A-AP, notate ni = ni  yi(1 6 i 6 N), and repeat Model A-RP until "ni = 0. Then we can get the optimal number of RPs accessing to current AP, and the solution vector Y. In order to improve the applicability of the method, and expand the flexibility of IHMSN, more practical cases need to be studied further.

can only be transmitted through new RPs rather than the existing RPs to the existing APs or new APs. Denote the number of APs as h, and sort the available bandwidth of the existing APs in descending order: Re1 P Re2 P . . . P Rek P . . . P Reh, then the available bandwidth vector of existing APs can be represented as Re = [Re1, Re2, . . ., Rek, . . ., Reh] (1 6 k 6 h). The MKM of one AP when adding EDs is as following:

Model B-APðaÞ : Re is not empty; max RðXÞ ¼

N X xi s i i¼1

ð5Þ 8 N > < Xx s 6 R i i ek s:t: i¼1 > : 0 6 xi 6 mi

ð6Þ

Model B-APðbÞ : Re is empty; max RðXÞ ¼

N X xi si

ð7Þ

i¼1

8 N X > < xi si 6 R0AP s:t: i¼1 > : 0 6 xi 6 m0i

ð8Þ

In the model, m0 i is the remaining number of type i ED after allocating all the existing APs; k is the execution times for the Model B-AP when Re is not empty, and update Re as Re = Re  Rek, other parameters are the same as Model A-AP. This case requires a two-step model. The first step (Model B-AP(a)) is to allocate the added EDs to the existing APs. We first select an existing unallocated AP which has the maximum available bandwidth, and access the added EDs to this AP within the bandwidth restriction of the current AP, and repeat the process. If available bandwidth of the existing APs can satisfy the demand of all the added EDs, the program ends; if not, Model B-AP(a) will execute h times, and then enters the second step (Model B-AP(b)). At the same time, update the number of type i ED as m0 i, model solving will be same with Model A-AP. When executing Model B-AP(a) and Model B-AP(b) each time, the new ED combination vector accessing to current AP can be determined by corresponding solution vector X. The MKM of certain RP accessing to one AP when adding EDs is as following:

Model B-RP : max ZðYÞ ¼

N X yi si

ð9Þ

i¼1

3.2. Case B: adding devices case The case is set in a typical area where several subnetworks have been set up. When adding new EDs to the network, the increased number of APs and RPs need to be calculated. The existing number and the available bandwidth of APs and RPs are known. Since the bandwidth of RP is less than that of AP, in order to reduce the impacts on data transmission process in original network due to the added EDs, we formulate that information of added EDs

8 N X > < y si 6 R0RP s:t: i¼1 i > : 0 6 yi 6 n0i

ð10Þ

In which, n0 i is the number of type i ED accessing to one AP; other parameters are the same as Model A-RP. According to Model B-RP, the solution vector Y of RP accessing to one AP can be calculate. Then we can get the new ED combination vector accessing to each RP. When

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"mi = 0, the optimal number of APs and RPs for adding EDs can be obtained.

where T(Y) is the sum of weights of EDs accessing to current RP; and other parameters are the same as Model A-RP and Model C-AP.

3.3. Case C: weight-based case In practical application, depending on the data requirements and monitoring objects, EDs of different types may have different weights. The weight values can be determined according to the application requirements. In general, ED’s weight vector is denoted as W, i.e. W = [w1, w2, . . ., wi, . . ., wN] (1 6 i 6 N). The physical meaning of W is determined by the practical application, such as bandwidth requirements, price, power consumption, transmission cost or the combinations of them. Model A and Model B can be viewed as a special case whose weight is bandwidth. The weight-based MKM of one AP is as following:

Model C-AP : max FðXÞ ¼

N X xi wi

ð11Þ

i¼1

8 N > < Xx s 6 R0 i i AP s:t: i¼1 > : 0 6 xi 6 mi

ð12Þ

where F(X) is the sum of weights of EDs accessing to one AP; wi is the weight of type i ED; and other parameters are the same as Model A-AP. For certain AP, Maximum weight model of certain RP accessing to one AP is as following:

Model C-RP : max TðYÞ ¼

N X yi wi

ð13Þ

i¼1

8 N > < Xy s 6 R0 i RP s:t: i¼1 i > : 0 6 yi 6 ni

ð14Þ

Start

Knapsack problem is a typical NP-hard problem. The early researchers used many classical algorithms to solve this problem [24]. Traditional algorithms include: branch and bound (BB) [25], linear programming (LP) [26] and dynamic programming (DP) [27], etc. However, it is becoming more and more difficult to find the precise global optimal solution due to the growth of problem’s scale. With the development of artificial intelligence, there are many intelligent optimization algorithms, such as ant colony optimization algorithm (ACO) [28], genetic algorithm (GA) [29], simulated annealing (SA) [30], tabu search algorithm (TSA) [31], particle swarm optimization algorithm (PSO) [32], artificial neural networks (ANN) [33], and immune algorithm (IA) [34]. These algorithms are developed by explaining and simulating some natural phenomena and processes. Considering various characteristics of these algorithms, a traditional algorithm (DP) and some intelligent optimization algorithms (PSO, SA, and ACO) will be used to deal with this kind of problems for physical topology optimization of IHMSN. By comparing the execution time, memory, and optimal results of these algorithms, the appropriate solving methods or algorithms which are suitable for this problem can be found. Denoting the solution vector as X0 = [x1, x2, . . ., xj, . . ., xm] (1 6 j 6 m) instead of X = [x1, x2, . . ., xi, . . ., xN] (1 6 i 6 N), xj e {0, 1}. Where 1 indicates that the jth ED is selected, 0 means no selection, and m is the sum of all kinds of EDs. Similarly, the bandwidth combination vector and the weight vector are S0 = [s1, s2, . . ., sj, . . ., sm] (1 6 j 6 M), W0 = [w1, w2, . . ., wj, . . ., wm] (1 6 j 6 m), respectively. Each element of these vectors represents one ED. From the first

Initialization

ED Allocation under one AP

NAP=NAP+1, Update NED

4. Algorithms

ED(s) of current AP Allocation to RP(s)

End

Fig. 2. General state machine for solving case A (basic case).

NRP=NRP+1, Update NED of current AP

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Table 1 Basic information of EDs. Order

Type of ED

Application purpose

Bandwidth (kbps)

Number

1 2 3 4 5 6 7 8 9 10 11 12 13

Photoelectric switch Electrical sensor Optical grating transducer Water sensor Strain transducer Pressure sensor Humidity sensor Temperature Sensor Displacement sensor Video sensor Video sensor Video sensor Video sensor

Monitoring of overhead line system Safety monitoring of power supply system Monitoring of rapid slope Monitoring of track settlement deform Monitoring of track longitudinal stress Monitoring of track composite stiffness Safety monitoring of power supply system Monitoring of track longitudinal stress Monitoring of track displacement and crawling Monitoring of track integrity monitoring Monitoring of line foreign matter Pantograph image recognition Online monitoring of power systems

1 5 10 25 40 80 100 125 400 500 800 1024 2048

6 9 7 8 5 5 2 4 4 3 2 3 2

(f) Acquire the optimal number of RPs Update the ED combination vector S0 accessing to one AP, repeat step (a) to step (e) (NED is the element number of the new S0 ), then we can acquire the optimal number of RPs accessing to each AP, the total number of RPs and the ED combination vector of each RP.

Table 2 Basic information of AP and RP. Type of network node

Bandwidth (kbps)

Utilization (%)

Maximum available bandwidth (kbps)

AP RP

10,240 250

40 50

4096 125

0

0

A general finite state machine for case A is proposed (Fig. 2). The solving steps and finite state machines for the models of case B and case C are similar. This finite state machine recycles the algorithms with greedy strategy, and carries out a set of optimal results and ED allocation vectors of each RP and AP.

0

element of X , S , or W , continuous mi elements represent the same type of ED. Steps to solve Case A are as following: (a) Initialization Initialize the parameters. The number of all EDs: NED = m; the number of APs or RPs: NAP = 0 or NRP = 0; the available bandwidth of AP: R0 AP; the available bandwidth of RP: R0 RP. (b) EDs allocation to one AP Make use of MKM, allocate EDs to current AP according to the maximum bandwidth or maximum weight model. (c) Update NAP and NED Apply updating as NAP = NAP + 1, remove EDs which are allocated in step (b) from vector S0 , and then update NED. (d) Judge whether all the EDs are allocated or not If NED > 0, go back to step b, if NED = 0, go to step e. (e) Acquire the optimal number of APs Now NED = 0, EDs allocation has been completed. Thus, we can get the optimal number of APs and the ED combination vector of each AP.

5. Examples 5.1. Example 1: solving Case A There are 13 kinds of sensors in a typical application area to monitoring the infrastructure in HSR systems. The basic information of ED is shown in Table 1. It can be read from Table 1 that N = 13, m = 60, S = [1, 5, 10, 25, 40, 80, 100, 125, 400, 500, 800, 1024, 2048], M = [6, 9, 7, 8, 5, 5, 2, 4, 4, 3, 2, 3, 2]. The basic Information of AP and RP is shown in Table 2. It can be got from Table 2 that R0 AP = RAP  a = 10,240  40% = 4096 kbps, R0 RP = RRP  b = 250  50% = 125 kbps. Modeling Example 1 as Model A-AP and Model A-RP, we can figure out the execution time, memory, and optimal results with different solving algorithms. The results are shown in Table 3.

Table 3 Statistics comparison of different algorithms.

a

Evaluation indexesa

Intelligent optimization algorithms

Algorithms

PSO

ACO

SA

DP

Number of ED Number of AP Number of RP Execution time (s) Memory (kb)

60 EDs 4 14 3.978 227,164

60 EDs 4 14 2.272 212,588

60 EDs 4 14 3.226 226,360

21 EDs 4 7 761.392 223,324

The best one in ten times of solving results.

Traditional algorithm

22 EDs 4 8 1572.435 231,420

23 EDs 4 9 3201.992 239,832

89

33 139 22.922 234,668

RP14

SA

33 139 23.108 206,828

RP13

ACO

33 144 129.913 219,436

AP4 2

PSO

Number of AP Number of RP Execution time (s) Memory (kb)

80, 40, 1 40, 25, 25, 5, 1, 1, 1

RP11 RP10

80, 40, 5 125

RP9 RP8

80, 25, 5, 5, 5, 5 125

RP7 RP6 RP5

100, 10, 10, 5

RP3

40, 25, 10, 10, 10, 1, 1 125 80, 25, 10, 10

RP4 RP2 RP1

It can be seen from Table 3 that SA, PSO and ACO can achieve the optimal results (4 APs and 14 RPs) of the considered problem with 60 EDs with short time period. These three algorithms perform similarly in the terms of execution time and memory. However, DP cannot give the optimal result in the case of 60 EDs within a reasonable time period, since it is implemented with the implicit enumeration. Table 3 presents the optimization results when the number of EDs is 21, 22, and 23, respectively. As we can see, with the increase of the number of EDs, DP fails to solve the problem in acceptable time. Therefore, we just focus on the artificial intelligence algorithm. In order to evaluate these three algorithms (PSO, ACO, and SA) in a more general way, we use a tenfold expansion of the case in Table 1, i.e. to solve the topology optimization with the problem scale of 600 EDs. The corresponding results are shown in Table 4, from which we can draw the conclusion that there is little difference between the execution memories of these three algorithms. However, SA and ACO outperforms PSO with regard to the number of RPs and execution time. Their other evaluation indexes are either the same (number of APs) or of little difference (Memory). To sum up, SA and ACO carry out better results than PSO and DP, which lay the foundation for us to use SA or ACO to solve the problems in various situations and to obtain the optimal results and physical topology of each subset. The optimal results of Example 1 using SA are as following: the optimal numbers of APs is 4, i.e. we need to construct four subsets; and the number of RPs are 14, that is to say, at least 14 RPs are needed in order to transmit the information form EDs to the APs via wireless communication. Since the bandwidths of some EDs (type 9 to 13) are beyond the available bandwidth of RPs, they have to transmit directly to APs via wired communication. The allocation results are showed in Table 5. There are 7, 5, 1 and 1 RPs accessing to each AP, respectively, which are

AP2 7

1024, 800, 500, 500, 400, 400 2048, 800, 400 2048, 1024, 500, 400 1024

AP1 4

EDs transmitted by wired communication

AP1 AP2 AP3 AP4

Table 6 Allocation results of EDs transmitted by wireless communication accessing to each AP in Example 1 (ED is represented by its bandwidth).

AP

125

Table 5 Allocation results of EDs transmitted by wired communication in Example 1 (ED is represented by its bandwidth).

100, 25

RP12

The best one in ten times of solving results. AP3 1

a

Evaluation indexesa

80, 40, 5

Table 4 Statistics comparison of different intelligent optimization algorithms (600 EDs).

25, 25, 5

H. Li et al. / Measurement 79 (2016) 83–93

90

H. Li et al. / Measurement 79 (2016) 83–93

Fig. 3. Optimal physical topology structure of Example 1 based on SA.

Table 7 Allocation results of EDs transmitted by wired communication mode accessing to each AP in Example 2 (ED is represented by its bandwidth). AP

EDs transmitted by wired communication

e-AP1 e-AP2 e-AP3 a-AP4 a-AP5

2048, 2048 1024, 1024, 400 500 800, 500, 500, 500, 400, 400 1024, 800, 400

that based on these 3 existing APs (e-AP), in order to implement the transmission, we need to add two new APs (a-AP) and 14 RPs. The allocation results of EDs transmitted directly to AP via wired communication are shown in Table 7 and the allocation results of RPs and EDs accessing to each AP are shown in Table 8. Tables 7 and 8 can determine the optimal physical topology of IHMSN in Case B.

5.3. Example 3: solving Case C used to transmit the information from EDs in wired communication. Table 6 shows the experimental results, i.e. the ED distribution results under AP and RP (each ED is represented by its bandwidth). According to Tables 5 and 6, Fig. 3 gives the optimal physical topology of 60 EDs, which gives the minimal number of APs and RPs under the current bandwidth and cost. Besides, regardless of other factors, this optimal network achieves the lowest cost. 5.2. Example 2: solving Case B Case B is a more general situation, in which we add some new EDs to the existing monitoring area. Case B can be modeled based on Model B-AP and Model B-RP. The solutions of Case B are presented in this section. Assuming there are three existing APs, their remaining bandwidths are 4096 kbps, 2500 kbps and 500 kbps, respectively. The information of new adding EDs are shown in Table 1, and the information of APs and RPs are shown in Table 2. The results of Example 2 using SA are shown in Tables 7 and 8. It can be seen from the results

Case C is based on the weights of EDs. Due to the different monitoring duties of the EDs, the size of transmission, and real-time requirement, taking the weights of EDs into consideration is of higher value in practical application. By giving the weights for EDs, we can satisfy the practical demands, such as accessing certain EDs by priority, or EDs with small data size access to the same AP. For instance, we choose to access the EDs with small data by priority to the same AP. Based on Model C-AP and Model C-RP, the smaller the requirement of bandwidth, the larger the weight is. So we can realize the demand of accessing the EDs with small size by priory. The information of EDs and the intended RPs and APs are the same with example 1. The weights of EDs are shown in Table 9. The optimization results with SA are given in Tables 10 and 11, which can also determine the optimization physical topology of IHMSN. From the optimal results, it can be concluded that EDs with small bandwidth have priority access to AP with small index. In addition, RPs are allocated only in AP1, which verify that the priority access of small-bandwidth EDs.

91

40, 25, 5, 1, 1, 1 40, 25, 25, 25, 10 100, 25 80, 40, 5 125 40, 10, 1, 1 –

–

125

80, 25, 10, 10

80, 25, 10, 5, 5

80, 25, 10, 5, 1

100, 10, 5, 5, 5,

80, 40, 5

125

125

RP14 RP11 RP10 RP8 RP7 RP6 RP5 RP4 RP3 RP2 RP1 –

–

a-AP4 8 e-AP3 0 e-AP2 1 e-AP1 0

Table 8 Allocation results of EDs accessing to each AP in Example 2 based on PSO algorithm (ED is represented by its bandwidth).

RP9

a-AP5 5

RP12

RP13

H. Li et al. / Measurement 79 (2016) 83–93 Table 9 Weights of ED. Order 1 2 3 4 5 6 7 8 9 10 11 12 13

Type of ED

Bandwidth (kbps)

Photoelectric switch Electrical sensor Optical grating transducer Water sensor Strain transducer Pressure sensor Humidity sensor Temperature sensor Displacement sensor Video sensor Video sensor Video sensor Video sensor

Number

Weight

1 5 10

6 9 7

2048 409.6 204.8

25 40 80 100 125 400 500 800 1024 2048

8 5 5 2 4 4 3 2 3 2

81.92 51.2 25.6 20.48 16.384 5.12 4.096 2.56 2 1

Table 10 Allocation results of EDs transmitted by wired communication in Example 3 (ED is represented by its bandwidth). AP

EDs transmitted by wired communication

AP1 AP2 AP3 AP4

500, 500, 400, 400, 400 1024, 800, 800, 500, 400 2048, 1024, 1024 2048

5.4. Discussions The three examples above correspond to the three cases in reality. Through the comparison of the three different algorithms, we find that SA and ACO are more suitable to solve the problem considered in our paper. Especially for the increasing scale of the problem (in Table 4), the performances of these two algorithms are better than PSO and DP. With regard to different cases, the optimization models proposed in our paper describe the problems in a more accurate way, and the optimal results have been derived with SA, which determines the optimal physical topology of IHMSN in different situations. All the results above lay the foundation for conserving the construction cost as well as simplifying the network structure. In addition, due to the variance of EDs in IHMSN of high speed rail, and the hybrid network characteristics in IHMSN (both wired and wireless network need to be optimized), the solutions to the three practical cases with SA provide the theoretical foundation for the topology optimization in more complex hybrid networks. 6. Conclusion (1) This paper proposes a three-layer network architecture of IHMSN for HSR, which provides the foundation for establishing the physical topology optimization models. Focusing on the multiple knapsack problem-based physical topology optimization model (MKM), we carry out the optimal solution with artificial intelligence algorithms.

– –

– –

– 125

RP15 RP14

125 125

RP13 RP12

125 100

RP11 RP10

100 80 80 80 40, 25

RP9 RP8 RP7 RP6

40, 40, 25, 10, 10 40, 40, 25, 10, 5, 5 80, 10, 5, 5, 5, 5, 5, 5, 1 80, 25, 10, 5, 1, 1, 1, 1, 1

RP5 RP4 RP3 RP2 RP1

AP1 15

Table 11 Allocation results of EDs transmitted by wireless communication in Example 3 (ED is represented by its bandwidth).

25, 25, 25, 25, 10, 10

AP3 0 AP2 0

–

H. Li et al. / Measurement 79 (2016) 83–93

AP4 0

92

Besides, based on the different needs of practical application, three different cases (Basic case, Adding devices case and Weight-based case) are proposed, and the corresponding models are built, which expands the range of applications. (2) In order to choose the optimal algorithm, this paper puts forward both a traditional algorithm and some intelligent optimization algorithms to deal with different IHMSN topology optimization problems. Due to the performances in execution time, memory, and optimal results, SA outperforms the others. Algorithm selection and optimization improve applicability and effectiveness of the proposed methods in practical engineering problems. (3) The extension models enlarge the application area, and the optimal solutions with the suitable algorithms are given. Numerical examples and optimization results illustrate that this method can be applied to optimization problems of IHMSN in HSR systems. Besides, it contributes to build a low-cost sensor network which contains the optimal topology and simple structure. As a result, infrastructure monitoring system of HSR becomes more economic and efficient by saving construction costs and reducing unnecessary waste and redundancy. The future work can be done from two aspects: (1) Focusing on a model with regard to the spatial positions and constrains of EDs, which also considers the requirements of accessing to the same AP simultaneously by the groups of EDs or the EDs in the neighborhood. The solutions to this kind problem will be further extended in the applied field. (2) Make a further research into characteristics and topology optimization of the hybrid network. In addition to IHMSN, hybrid networks are widely used in many fields, such as sensing road traffic information, sensing meteorological data and structure state detection, hence it is necessary to research into the hybrid network based on information sensing technologies.

Acknowledgments This work was supported by the Beijing Postdoctoral Research Foundation – China and the Distinguished Young Scholar in Beijing Award – China. The support from the International Postdoctoral Exchange Fellowship Program (No. 20150037) is also gratefully acknowledged. References [1] Y.W. Liu, H.Y. Hsieh, N. Chen, High-speed frame grabber for track monitoring systems, in: ASME Proceedings of Joint Rail Conference on Railroad Infrastructure Engineering, 2011, pp. 159–164. [2] J. Yang, Q. Feng, A new method for measuring subgrade settlement in high-speed railway by using a linear CCD, Measurement 46 (5) (2013) 1751–1756. [3] M. Moretti, M. Triglia, G. Maffei, ARCHIMEDE-the first European diagnostic train for global monitoring of railway infrastructure, in: IEEE Intelligent Vehicles Symposium, 2004, pp. 522–526.

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