A User-Customizable Urban Traffic Information ... - IEEE Xplore

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A User-Customizable Urban Traffic Information Collection Method Based on Wireless Sensor Networks Jin Zhou, Student Member, IEEE, C. L. Philip Chen, Fellow, IEEE, Long Chen, Member, IEEE, and Wei Zhao, Fellow, IEEE

Abstract—Traffic monitoring can efficiently promote urban planning and encourage better use of public transport. Efficient traffic information collection is one important part of traffic monitoring systems. Based on a technique using wireless sensor networks (WSNs), this paper provides a flexible framework for regional traffic information collection in accordance with user request. This framework serves as a basis for future research in designing and implementing traffic monitoring applications. A two-layer network architecture is established for traffic information acquisition in the context of a WSN environment. In addition, a user-customizable data-centric routing scheme is proposed for traffic information delivery, in which multiple routing-related information is considered for decision-making to meet different user requirements. Simulations have shown good performance of the proposed routing scheme compared with other traditional routing schemes on a real-world urban traffic network. Index Terms—Multiattribute decision-making (MADM), traffic information collection, wireless sensor networks (WSNs).

I. I NTRODUCTION

O

NE of the most important aspects of research in intelligent transportation systems (ITS) [1], [2] is the automatic detection of the information of local traffic and road conditions and traffic-related pollution, as well as the convenient transmission of this information to the user as a query response. Investment in monitoring, collecting, and processing traffic information can promote better urban planning and encourage better use of public transport, both of which would help to reduce congestion and pollution [3]. Over the last few years, research in traffic information acquisition and propagation based on vehicular ad hoc networks (VANETs) [3]–[9] has been developed to improve vehicle and road safety and traffic efficiency, as well as to reduce the negative impact Manuscript received November 12, 2012; revised February 1, 2013; accepted March 5, 2013. This work was supported in part by the National 973 Basic Research Program of China under Grant 2011CB302801 and in part by the Macau Science and Technology Development Fund under Grant 008/2010/A1. The Associate Editor for this paper was Q. Kong. J. Zhou is with the Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau, and also with the School of Information Science and Engineering, University of Jinan, Jinan 250022, China (e-mail: [email protected]). C. L. P. Chen, L. Chen, and W. Zhao are with the Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau (e-mail: [email protected]; [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TITS.2013.2252902

of transportation on the environment. However, most VANETbased methods assume that each vehicle is equipped with a Global Positioning System device and preloaded digital maps to provide location information [3], [7]–[9]. These requirements, including the equipment, computation, and storage capacity, will greatly increase the cost of the traffic information collection. Moreover, the extra endeavors for data routing in a sparsely connected network will increase the complexity of the algorithm, thereby reducing the practicality of the algorithm [3], [9]. In particular, the burst traffic accidents will block the road, resulting in the failure of the data transfer. Recently, with the development of microelectronic technologies, low-energy-consumption, low-cost, and easily widely disposed wireless sensor networks (WSNs) have brought innovative techniques in the ITS [10], [11]. These also boost the recent developments in the field of traffic information collection. However, constraints of energy supply and bandwidth in sensor nodes make the traffic information acquisition and delivery in WSNs much different from those that are applied in VANETs [12], [13]. The new traffic information collection methods should be considered. On one hand, based on WSN techniques, a great deal of real-time solutions for traffic data sensing and processing in ITS has been discussed. In [14] and [15], three types of sensor nodes are employed in the traffic control system for traffic information acquisition. Li et al., in [16], discussed two estimation algorithms named LBA and VBA to obtain the real traffic status data by utilizing the vehicle-based sensors, while Wang et al., in [17], designed and implemented a pervasive traffic information acquisition system named EasiTia. Recently, Pascale et al. [18] have illustrated the basics of WSN-based traffic monitoring and summarize the benefits of using these methods for ITS applications. However, one general problem of these studies is that they mainly focus on effective traffic information acquisition and processing but pay less attention to traffic information delivery and are rarely able to give the detailed and integrated framework of traffic information collection. On the other hand, for the traffic information delivery, a large number of data-routing protocols based on WSNs have been proposed to optimize the performance of the entire network in terms of energy conservation, network lifetime (NL), and delay time [19]. Considering the characteristics of traffic information delivery in a WSN environment, the data-centric-type routing methods are more suitable for traffic information delivery [20].

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In this type of data routing, according to the user requirement, the city’s traffic information center/sink sends a request to a certain region and waits for traffic information from the sensors in the selected region. Upon receiving the request, sensor nodes reply to the sink with monitoring data through the optimal routes. Directed diffusion [21] is an important milestone in data-centric routing research. It aims to combine the data coming from different sources by eliminating redundancy and minimizing the number of transmissions, thus saving network energy and prolonging NL. Other variants of this algorithm are further discussed and popularized in [22]–[28], which some methods focus on the minimum delay time in data delivery. For example, Hong et al. [25] modeled the sensor network into levels according to the hop distance from the node to the sink. Each node can only forward the data to the nodes with lower level. Some other methods aim to pursue the minimum energy consumption of sensors. From the point of view of NL and long-term connectivity, Shah and Rabaey [26] used a set of suboptimal paths instead of the lowest energy path. Gan et al. [27] determined the optimal energy-efficient route by combining routing cost with the remaining energy of nodes. Because they are only paying attention to the energy saving, these methods consider fewer real-time requirements and may not be suited for time-aware applications. Recently, to achieve a balance of energy consumption and delay time, Heo et al. [28] presented a real-time energy-efficient routing scheme by setting a delivery deadline, i.e., only the path that may relay the data packets to the sink before deadline is selected. However, an appropriate deadline is difficult to be selected. In summary, most of these data-centric routing methods usually aim to pursue either the minimum energy consumption of sensors (the maximum NL) or the minimum delay time in data delivery. It is difficult to achieve a good tradeoff. The limited/narrow consideration of routing information and the simple mathematical model used for routing decision-making are not enough to ensure a good routing performance. More importantly, because the variations of user requirements and the conditions of sensors nodes, the fixed routing scheme is not very suitable. Providing the user the capability to shift quickly from one preference to another in the routing decision is of a great value. All of these show that much space is available for the improvement in the traffic data delivery. Considering the problems mentioned previously, we hope to design a flexible framework of WSN-based traffic information collection in accordance with the user requests that can minimize energy consumption and bandwidth utilization while adhering to user-required traffic information freshness. The contributions are listed as follows. 1) We design the network architecture for WSN-based traffic information collection in which three types of sensor nodes are deployed for data acquisition and data delivery. 2) We specify three phases of the traffic information collection in the context of WSN environment and illustrate the details of each phase. 3) We propose a novel user-customizable data-centric routing scheme for traffic information delivery, which considers multiple routing-related information for

Fig. 1. Distribution of vehicle sensor nodes, roadside sensor nodes, and intersection sensor nodes on an urban traffic network.

decision-making. A good balance between the energy consumption in sensor nodes and the delay time in data delivery can be achieved. Moreover, different attribute combinations can provide different routing paths to meet diverse user routing requirements. The quick transition from one routing preference to another is realizable. 4) We evaluate the performance of our routing scheme on a real-world city traffic network. The rest of the paper is organized as follows. The framework of the traffic information collection based on WSNs is presented in Section II. The new traffic information routing scheme is discussed in Section III. Section IV shows the simulations. Finally, conclusions are given in Section V. II. T RAFFIC I NFORMATION C OLLECTION BASED ON W IRELESS S ENSOR N ETWORKS A. Network Architecture Without loss of generality, wireless sensor nodes are widely distributed in an urban traffic network. Given a sensor network that is capable of transmitting and receiving signals, as shown in Fig. 1, there are three types of sensor nodes in our traffic information collection network, including the vehicle sensor nodes in the vehicles, the roadside sensor nodes along both sides of the road, and the intersection sensor nodes deployed in the intersection of the road. Each vehicle sensor node measures the host vehicle’s position, travel direction, velocity, and exhaust data (obtained from different sensors installed in the vehicle [29]) and transmits them to the roadside sensor node. The roadside sensor node collects the information coming from nearby vehicle sensor nodes and transfers the data to the intersection sensor node. The intersection sensor node is responsible for aggregating all traffic information of the road and for computing the average vehicle velocity, the average vehicle density, and the average vehicle exhaust density of the road. Therefore, a two-layer hierarchical communication channel is established among the three types of sensor nodes to detect and process the real-time traffic information, as shown in Fig. 2. Among these sensors, vehicle sensors are only used for traffic information acquisition, and utilize the vehicle power as their energy sources. The other two kinds of sensors, i.e., the roadside sensors and the intersection sensors, are used not only for traffic information acquisition but for traffic information

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Fig. 4. “Interest” propagation from the sink to all routing sensor nodes.

Fig. 2. Two-level hierarchical network structure for traffic information collection.

Fig. 3.

Phases of traffic information acquisition and delivery.

delivery as well. We unify them as routing sensor nodes. In this paper, these two kinds of sensors are randomly deployed along both sides of the road and the intersection of the road. They are considered using battery power and are assumed nonrecyclable. It is worth pointing out that the network architecture applied in this paper only provides a relative simple solution for regional traffic information acquisition. This approach does not invalidate other traffic information acquisition methods. For example, combining with some statistical estimation and data mining techniques such as data clustering, the proposed sensor network is still possible to estimate the complex traffic conditions by collected information [3], [16], [17]. The alternative and advanced traffic acquisition technologies can also be adopted in our framework by combining with the comprehensive routing method proposed in the framework to realize accurate traffic information collection. B. Traffic Information Acquisition and Delivery The process of traffic information acquisition and delivery is divided into three phases, including the “interest” propagation/ routing table setup phase, the traffic information acquisition phase, and the traffic information delivery/routing phase, as shown in Fig. 3. In the “interest” propagation phase, upon receiving the user request of traffic information in a selected region, the resourcerich sink broadcast the “interest” throughout the WSN. All kinds of routing-related information are collected along with the “interest” flooding. The routing table of each routing sensor

node will be set up. In the traffic information acquisition phase, when matching the “interest,” vehicle sensor nodes in the selected region measure the traffic information and send them to the intersection sensor node through roadside sensor nodes for further processing. Then, the intersection sensor node will forward data packets to the sink via multihop routes, which is referred to as the traffic information delivery phase. The traffic information acquisition phase and delivery phase will be repeated until the end of user demand. To achieve an efficient data-centric routing operation, five kinds of routing-related information are considered for routing decision-making. They are classified into two categories as follows: the benefit-type attributes (i.e., the larger, the better) and the cost-type attributes (i.e., the smaller, the better). • Residual energy: It indicates the current energy of a sensor node. Routing schemes aim to economize residual energy of nodes to maximize the NL. This is a benefit-type attribute. • Transmission energy: It means the energy consumed in forwarding a data packet from a sensor node to its neighbor sensors. It is expressed as the distance-related power loss [30]. This is a cost-type attribute. • Routing level: It stands for the closeness degree from a sensor node to the sink according to the number of hops apart from the sink. This is a cost-type attribute. • Routing cost: The routing cost of a sensor node is the expected energy cost to forward a data packet from this sensor node to the sink along the optimal path. It is a costtype attribute. • Routing cost trend: The routing cost trend of a sensor node indicates the average routing cost of its neighbor sensor nodes. It reflects the energy consumption trend when considering one of its neighbor sensors as an intermediate routing node. This is a cost-type attribute. Among the five kinds of routing-related information, the routing level is related to the transmission delay time, and the others are energy-aware information. All this routing-related information can be obtained in the “interest” propagation phase through the straightforward flooding described as follows. 1) “Interest” propagation/routing table setup phase. In this phase, the user “interest” for obtaining the traffic information in a selected region, e.g., average vehicle velocity or average vehicle exhaust of the road, is first generated by the sink. Then, the sink floods the



Fig. 5.

Calculating routing cost and routing level for each routing sensor node. Fig. 6.

“interest” throughout the network, as shown in Fig. 4. All kinds of routing-related information are collected in a straightforward way along with the “interest” propagation, and the routing table of each sensor is set up. The details are as follows. The sink broadcasts an advertisement packet announcing a routing cost of 0 (Cost(sink) = 0) and a routing level of 0 (Level(sink) = 0). The rest of the routing sensor nodes initially have a routing cost of ∞ (Cost(Ni ) = ∞) and a routing level of ∞ (Level(Ni ) = ∞). Referring to Fig. 5, upon hearing an advertisement packet containing the routing cost and the routing level from sender Ni , receiver Nj calculates temporary routing cost Cost(Ni ) + LinkCost(Ni , Nj ), where LinkCost(Ni , Nj ) is the transmission energy between node Ni and node Nj , and calculates temporary routing level Level(Ni ) + 1. Node Nj compares its current routing cost Cost(Nj ) with temporary cost Cost(Ni ) + LinkCost(Ni , Nj ) and chooses the smaller one as its new routing cost. Similarly, node Nj compares its current routing level Level(Nj ) with temporary level Level(Ni ) + 1, and chooses the smaller one as its new routing level. Then, node Nj continues to broadcast the advertisement packet with the new routing cost and the new routing level. Eventually, each routing sensor node may obtain the optimal routing cost and level to the sink through flooding. After that, a simple “hello” protocol [31] is utilized for each routing sensor node to exchange all routing-related information with its neighbor routing sensor nodes. Then, the routing table of each sensor node is set up. 2) Traffic information acquisition phase The real-time traffic information is measured and processed in this phase. When receiving the user “interest” for the traffic information in a selected region from the sink, the roadside sensor node in this region periodically broadcasts messages, including its ID and position. Normally, the vehicle sensor nodes are in the listening state. When a vehicle sensor node comes into the broadcast range of a roadside sensor node and receives its message, the vehicle sensor node will switch to the active state. Then, it senses the host vehicle’s location, travel direction, velocity, and exhaust data and sends them to the roadside sensor nodes nearby. The roadside sensor collects the real-time traffic information and transfers the data to the adjacent intersection sensor node. Then, the

Diagram of data-routing algorithm based on MADM method.

intersection sensor node will gather all data and calculate the average vehicle velocity, vehicle density, and exhaust density of the adjacent roads. 3) Traffic information delivery/routing phase After processing the real-time traffic information, the intersection sensor node will forward data packets to the sink via multiple intermediate routing sensor nodes. Upon receiving the data packets, a routing sensor node will select one of the neighbor routing sensors as its next-hop node and forward the data packets to it. Then, the simple “hello” protocol [31] is applied here to exchange the updated residual energy information between the sender and the receiver. This process will be reiterated until the data packets are forwarded to the sink. It is mentioned that, in the traffic information delivery/routing phase, the energy efficiency of the sensor node and information freshness in data delivery are two important issues. However, it is difficult to trade them off. Therefore, we concentrate more on this phase and propose a user-customizable datarouting scheme, in which different attribute combinations can provide different routing paths to meet diverse user routing requirements. The quick transition from one routing preference to another is realizable. The details of this phase are described in the following. III. DATA -ROUTING A LGORITHM BASED ON A M ULTIATTRIBUTE D ECISION -M AKING M ETHOD In the traffic information delivery/routing phase aforementioned, after measuring and processing the real-time traffic information, the intersection sensor node will send data packets to the sink through multihop routes. When a sensor node receives the data packets, it will select one of the neighbor sensor nodes as the direction of data forwarding. Five kinds of routing-related information as multiple attributes are used to make a routing decision. It can be considered as a multiattribute decision-making (MADM) problem [32], which aims to implement decent selections among alternatives associated with multiple attributes. In this paper, the MADM-based routing scheme (MADM-R) is proposed to realize the traffic information delivery. The diagram of the MADM-R scheme is shown in Fig. 6. In the MADM-R scheme, five kinds of routing-related information, i.e., residual energy, transmission energy, routing level, routing cost, and routing cost trend, are considered as


routing attributes, with which the next-hop sensor nodes’ rank is measured. The next-hop sensors with the five kinds of routingrelated information can be expressed in a decision matrix format A = [aij ]n×m as ⎤ ⎡ a11 a1 ⎢ a2 ⎥ ⎢ a21 ⎥ ⎢ A=⎢ ⎣ ... ⎦ = ⎣ ... ⎡

an

an1

a12 a22 .. .

··· ··· .. .

⎤ a1m a2m ⎥ .. ⎥ . ⎦

an2

···

anm

(1)

Step 1: Normalize decision matrix A = [aij ]n×m to get normalized decision matrix R = [rij ]n×m . The normalization seeks to obtain comparable scales, which is a linear transformation, as defined in the following:

rij =

j ∈ S1

⎪ ⎩

j ∈ S2

aj −aj a∗ j −aij

a∗ −a− j j

,

method [33] as follows: n n

wj =

(rij − rkj )2

i=1 k=1 m n n

, (rij − rkj )2

for 1 ≤ j ≤ m

(3)

j=1 i=1 k=1

where a1 , . . . , an are routing attribute vectors for the n possible next-hop sensor nodes, among which the decision-maker should choose. Each sensor node has m attributes or the m considered routing attributes. aij (1 ≤ i ≤ n, 1 ≤ j ≤ m) is the jth routing attribute value of the ith sensor node. Not all attributes in the decision matrix should be considered equally important. The weights serve to indicate the importance of each attribute relative to the others, which are defined as w = [w1 , . . . , wj , . . . , wm ], where wj is the weight assigned to mthe jth attribute. All weights are normalized, such that j=1 wj = 1. Hence, the assignment of weights plays a crucial role in the MADM process. In the proposed MADM-R scheme, the maximizing deviation method [33] is applied to calculate the attribute weights. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [34] has been proven to be a valuable method to deal with MADM problems in many applications. This method can be also fuzzified to deal with fuzzy data [11], which may be derived from domain experts or information granulation methods [35], [36]. TOPSIS is inspired by the concept that the chosen alternative should have the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution. In the MADM-R scheme, the TOPSIS method, collaborating with the maximizing deviation method, is utilized to rank alternatives and select finally the most desirable one. The details of routing decision-making are shown as follows.

⎧ a −a− ⎪ ⎨ ij∗ −j ,

5

(2)

where a∗j = maxi {aij }, a− j = mini {aij }, S1 is the set of benefit-type attributes (the larger, the better), S2 is the set of cost-type attributes (the smaller, the better), 1 ≤ i ≤ n, and 1 ≤ j ≤ m. Step 2: Calculate the weight vector w = [w1 , w2 , . . . , wm ] for each attribute based on the maximizing deviation

The proof is shown in the Appendix. Step 3: Construct the weighted normalized decision matrix Z = [zij ]n×m , and identify the positive ideal (z∗ ) and negative ideal (z− ) solutions as follows: zij = wj rij

(4)

∗ z∗ = [z1∗ , z2∗ , . . . , zm ]

− − − − z = z1 , z2 , . . . , z m

(5)

where zj∗ = maxi {zij }, zj− = mini {zij }, 1 ≤ i ≤ n, and 1 ≤ j ≤ m. Step 4: Obtain the m-dimensional Euclidean-based distance of each next-hop sensor node to z∗ and z− as s∗ and s− , i.e., s∗i =

m

zij − zj∗

2

,

for 1 ≤ i ≤ n

(6)

,

for 1 ≤ i ≤ n

(7)

j=1

s− i =

m

zij − zj−

2

j=1

Step 5: Calculate similarity ci for each next-hop sensor node according to the following: ci =

s− i , ∗ s− i + si

for 1 ≤ i ≤ n.

(8)

Then, we choose the sensor node with the maximum similarity ci as the best one for routing. In decision matrix A = [aij ]n×m , n is the number of nexthop sensor nodes. Usually, its value is smaller than ten. m is the number of routing-related attributes involved in decisionmaking. In this paper, it is set to five. As a result, the decision matrix A just needs a very small amount of storage. Moreover, the operations involved in routing decision-making are all basic arithmetic operations. Therefore, the low computational complexity of routing decision-making can be guaranteed. It is worth to point out that the proposed MADM-R scheme can be used for reaching different routing goals. Different attribute combinations in the decision matrix can provide different routing paths to meet diverse user routing requirements. For example, if we choose a routing level as the only routing feature in the decision-making scheme, the goal of minimum delay time can be achieved. Similarly, taking residual energy, transmission energy, and routing cost as the routing features will lead to the low-energy-consumption routes. Furthermore, the compromised routing path can be obtained by using all routing attributes for decision-making. Such flexibility provided by the proposed routing algorithm offers the users with customizability in the traffic information delivery. In addition, the convenience of transition from one routing scheme to another



(by routing-related information selection) also suggests the great potential of the proposed routing method in adapting to the quick-changing traffic environment and sensor conditions. IV. S IMULATIONS In the following simulations, Simulation of Urban Mobility (SUMO) [37], which is a suite of microscopic and continuous road traffic simulation software tools developed by German Aerospace Center, is used to model the movement and behavior of vehicles in traffic networks. SUMO helps us to obtain dynamic traffic data that are similar to those in the real world. For the traffic information delivery, to demonstrate the suitability and efficiency of the proposed routing method, four typical routing decision schemes (level-based routing scheme (L-R) [25], cost-based routing scheme (C-R) [26], cost-and-residualenergy-combined routing scheme (CRE-R) [27], and cost-anddelay-time-combined routing scheme (CDT-R) [28]) are chosen for comparison analysis. Six metrics are used to evaluate the performance of all routing schemes. A real-world urban road network from Macau is employed for simulation.

Fig. 7.

Urban road map of Macau.

Fig. 8.

Distribution of sensor nodes on road network of Macau.

A. Performance Metrics Six performance metrics are listed as follows. • NL: It is usually defined as the time elapsed until the first sensor node in WSNs depletes its energy. In the following simulations, traffic data packets are generated by source sensor nodes every 500 ms; we define this as one round. In this paper, we use the number of rounds before the first sensor node die as NL. • Average residual energy (ARE) of nodes: It indicates the average energy level of all sensor nodes in WSNs and gives an indication of the network state in terms of energy consumption. • Minimum residual energy (MRE) of nodes: It denotes the minimum energy level of all sensor nodes in WSNs and gives a good measure of the NL. • Standard deviation of residual energy (SDRE): It indicates the SDRE of all sensor nodes and provides a measure of the dispersion of residual energy of all sensor nodes from the average. • Average delay time (ADT) of all data packets: It is defined as the average time of relaying data packets from the source sensor nodes to the sink. • Average transmission energy (ATE) consumed of all data packets: It is defined as the ATE consumed to relay data packets from the source sensor nodes to the sink. B. Simulation in the Macau Road Network A real-world road network from Macau shown in Fig. 7 is employed for simulation. It is obtained from http://www. Openstreetmap.org/. Fig. 8 shows the corresponding distribution of sensor nodes. In this figure, a roadside sensor node is represented by a blue circle, and an intersection sensor node is represented by a blue triangle. The source sensor nodes are denoted by blue solid triangles, and the sink is denoted by a

red solid five-pointed star. The coordinate of each intersection node and the length of each link in the road network are in accordance with real map data. The initial battery energy of each sensor node is set as 2.5 J, and the communication range R of each sensor node is set as 80 m. The traffic data packet (assume 1024 bits) is generated by each source sensor node every 500 ms. Each sensor node needs to spend 5 ms on making routing decision and forwarding the data. Many practical factors in the wireless sensor communication will affect the energy consumption of sensors [38]; in this paper, we apply the energy model introduced in [30]. If a data packet with k-bit message is transmitted at distance d(m) from node Ni to node Nj , then for the node Ni , the energy consumed is ENi (k, d) = Eelec · k + Eamp · k · d2

(9)

and for the node Nj , the energy consumed is ENj (k) = Eelec · k

(10)

where Eelec = 50 nJ/bit, and Eamp = 100 pJ/bit/m2 Tables I and II present the simulation results of different routing schemes on the road network of Macau. The proposed


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TABLE I S TATISTICS OF D IFFERENT ROUTING S CHEMES A FTER 1000 ROUNDS ON THE ROAD N ETWORK OF M ACAU

TABLE II S TATISTICS OF D IFFERENT ROUTING S CHEMES W HEN THE F IRST S ENSOR N ODE D EPLETES I TS E NERGY ON THE ROAD N ETWORK OF M ACAU

MADM-R scheme can outperform other schemes in most performance metrics. The only exception is that the L-R scheme achieves the least ADT (35.0 ms per round), as highlighted in Table II. This is because of L-R scheme’s emphasis on choosing the routing path with the minimum hops to the sink. However, this relative less ADT is obtained with the cost of the excessive energy consumption of the sensors and the great reduction of NL (only 2492 rounds). By contrast, our scheme can achieve much longer NL (5423 rounds) with a small exchange of ADT (46.1 ms per round). This also shows the advantage of our scheme as a compromised one that considers all routing-related information for decision-making. The individual performance index analyses for different routing schemes on the road network of Macau are shown in Fig. 9. It is shown in the subplots in Fig. 9(a), (b), and (d) that the MADM-R scheme can achieve more residual energy saving in sensor nodes, lower energy consumption in data transmission, and longer NL. The only performance index that needs more attention is the ADT plotted in Fig. 9(c). We can see that the L-R scheme can obtain the least latency, but it excessively shortens the NL. Another interesting finding is that the CRE-R scheme can achieve lower ADT at the beginning. However, with the round increasing, the latency shows an ascending trend. It reveals the instability of this scheme. In contrast, our scheme shows stable performance throughout the routing process. Fig. 10 shows the snapshots of all routing paths of different routing schemes after 1000 rounds on the road network of Macau. In this paper, we use “routing width” to describe the distribution of the routing paths. The larger “routing width” means that more paths are used for routing and that the energy consumption is more evenly distributed over all the sensor nodes in the network. However, if the “routing width” is too large, more long distance paths will be selected. This will bring about longer data transmission delay time and heavier data

Fig. 9. Individual performance index analyses for different routing schemes on the road network of Macau. (a) MRE. (b) ARE. (c) ADT. (d) ATE.



Fig. 10. Snapshots of the routing paths of different routing schemes after 1000 rounds on the road network of Macau. (a) L-R. (b) CRE-R. (c) C-R. (d) CDT-R. (e) MADM-R.

transmission energy consumption. A proper “routing width” needs to be considered in the routing schemes. In Fig. 10(a), we can see that the L-R scheme has the narrow selection of routing paths, which leads to the excessive energy consumption in nodes on these paths and the reduction of NL. The CRE-R scheme holds a relatively reasonable “routing width,” as shown in Fig. 10(b). However, because of only considering the routing cost and residual energy in the routing decision-making, this scheme focuses more on energy efficient in routing and cares less about the delay time of data transmission. Therefore, with

the round increasing, the latency shows ascending trend, as shown in Fig. 9(c). The C-R scheme and the CDT-R scheme have large “routing width,” as shown in Fig. 10(c) and (d). Due to the probabilistic selection mechanism, these schemes spread the traffic of a route throughout almost the entire network. However, the unnecessary selection of longer routing paths brings about longer latency and more energy consumption. Compared with other routing schemes, the MADM-R scheme possesses a compromised “routing width,” as shown in Fig. 10(e), and obtains a better tradeoff performance.


V. C ONCLUSION

From (13) and (14), we derive that

In this paper, we have focused on a WSN-based framework for traffic information collection according to the user requests. In addition to the basic network architecture, two key processes associated with traffic information acquisition and delivery have been specified. For the data acquisition, three types of sensor nodes are deployed, and a two-layer hierarchical communication channel is established among these sensors. For the data delivery, a user-customizable data-centric routing scheme is proposed, in which the MADM method is adopted for the routing decision-making. Five kinds of routing-related information are utilized as routing attributes, and the maximizing deviation method is used for calculating the attribute weights. As a result, the proposed routing scheme can provide various routing decisions according to different attribute choices to meet diverse user routing requirements. At the same time, this scheme can achieve the good balance between the energy consumption of sensors and the delay time of data delivery by using all routing attributes for decision-making. Simulations have demonstrated the good performance of the proposed routing scheme compared with other schemes on the city traffic network of Macau. A PPENDIX Theorem: The weight vector w = [w1 , w2 , . . . , wm ] for each attribute can be calculated by (3) based on the maximizing deviation method [33]. Proof: The maximizing deviation method is applied here to calculate the differences of the performance values of each alternative. In the actual implementation, we first need to ] for compute the initial weight vector w = [w1 , w2 , . . . , wm the attributes and establish the nonlinear optimization model as follows:

max F (w ) = subject to

m

m n n

wj (rij − rkj )2

j=1 i=1 k=1

wj2 = 1, 0 ≤ wj ≤ 1

(11)

j=1 where w = [w1 , w2 , . . . , wm ] is the initial weight. To solve this model, let ⎛ ⎞ m m n n 1 G(w , ξ) = wj (rij − rkj )2 − ξ ⎝ wj2 − 1⎠ 2 j=1 i=1 j=1 k=1

(12) denotes the Lagrange function of the constrained optimization problem, where ξ is a Lagrange multiplier. Then, the partial derivatives of G are computed as n n ∂G(w ) = (rij − rkj )2 + ξwj = 0 ∂wj i=1 k=1 for 1 ≤ j ≤ m

⎛ ⎞ m 1 ∂G(w ) =− ⎝ w2 − 1⎠ = 0. ∂ξ 2 j=1 j

9

(13) (14)

n n

wj =

(rij −rkj )2

i=1 k=1 m

j=1

n n

2 , for 1 ≤ j ≤ m. (rij −rkj

(15)

)2

i=1 k=1

The final attribute weight w is derived from the normalization of w

wj =

wj m j=1

wj

n n

=

(rij −rkj )2

i=1 k=1 m n n

(rij −rkj )2

, for 1 ≤ j ≤ m. (16)

j=1 i=1 k=1

This completes the proof.

R EFERENCES [1] F. Y. Wang, “Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 630–638, Sep. 2010. [2] F. Y. Wang, “Agent-based control for networked traffic management systems,” IEEE Intell. Syst., vol. 20, no. 5, pp. 92–96, Sep./Oct. 2005. [3] A. Skordylis and N. Trigoni, “Efficient data propagation in trafficmonitoring vehicular networks,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 3, pp. 680–694, Sep. 2011. [4] J. J. Blum, A. Eskandarian, and L. J. Hoffman, “Challenges of intervehicle ad hoc networks,” IEEE Trans. Intell. Transp. Syst., vol. 5, no. 4, pp. 347– 351, Dec. 2004. [5] H. Hartenstein and K. P. Laberteaux, “A tutorial survey on vehicular ad hoc networks,” IEEE Commun. Mag., vol. 46, no. 6, pp. 164–171, Jun. 2008. [6] I. Broustis and M. Faloutsos, “Routing in vehicular networks: Feasibility, security, and modeling issues,” Int. J. Veh. Technol., vol. 2008, pp. 267513-1–267513-8, Dec. 2008. [7] A. Vahdat and D. Becker, “Epidemic routing for partially connected ad hoc networks,” Duke Univ., Durham, NC, USA, Tech. Rep. CS-200006, Apr. 2000. [8] M. Jerbi, S. M. Senouci, T. Rasheed, and Y. Ghamri-Doudane, “Towards efficient geographic routing in urban vehicular networks,” IEEE Trans. Veh. Technol., vol. 58, no. 9, pp. 5048–5059, Nov. 2009. [9] J. Zhao and G. Cao, “VADD: Vehicle-assisted data delivery in vehicular ad hoc networks,” IEEE Trans. Veh. Technol., vol. 57, no. 3, pp. 1910– 1922, May 2008. [10] M. Tubaishat, P. Zhuang, Q. Qi, and Y. Shang, “Wireless sensor networks in intelligent transportation systems,” Wireless Commun. Mobile Comput., vol. 9, no. 3, pp. 287–302, Mar. 2009. [11] C. L. P. Chen, J. Zhou, and W. Zhao, “A real-time vehicle navigation algorithm in sensor network environments,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 4, pp. 1657–1666, Dec. 2012. [12] D. Culler, D. Estrin, and M. Srivastava, “Overview of sensor networks,” IEEE Comput., vol. 37, no. 8, pp. 41–49, Aug. 2004. [13] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Comput. Netw., vol. 52, no. 12, pp. 2292–2330, Aug. 2008. [14] W. J. Chen, L. F. Chen, Z. L. Chen, and S. L. Tu, “A realtime dynamic traffic control system based on wireless sensor network,” in Proc. Int. Conf. Parallel Process. Workshops, Jun. 2005, pp. 258–264. [15] W. J. Chen, L. F. Chen, Z. L. Chen, and S. L. Tu, “WITS: A wireless sensor network for intelligent transportation system,” in Proc. 1st Int. Conf. Multi-Symp. Comput. Comput. Sci., Jun. 2006, pp. 635–641. [16] X. Li, W. Shu, M. L. Li, H. Y. Huang, P. E. Luo, and M. Y. Wu, “Performance evaluation of vehicle-based mobile sensor networks for traffic monitoring,” IEEE Trans. Veh. Technol., vol. 58, no. 4, pp. 1647–1653, May 2009. [17] R. Wang, L. Zhang, R. L. Sun, J.B. Gong, and L. Cui, “EasiTia: A pervasive traffic information acquisition system based on wireless sensor networks,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 2, pp. 615–621, Jun. 2011. [18] A. Pascale, M. Nicoli, F. Deflorio, B. Dalla Chiara, and U. Spagnolini, “Wireless sensor networks for traffic management and road safety,” IET Intell. Transp. Syst., vol. 6, no. 1, pp. 67–77, Mar. 2012.



[19] J. N. Al-Karaki and A. E. Kamal, “Routing techniques in wireless sensor networks: A survey,” IEEE Wireless Commun., vol. 11, no. 6, pp. 6–28, Dec. 2004. [20] K. Akkaya and M. Younis, “A survey on routing protocols for wireless sensor networks,” Ad Hoc Netw., vol. 3, no. 3, pp. 325–349, May 2005. [21] C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed diffusion: A scalable and robust communication paradigm for sensor networks,” in Proc. 6th Annu. ACM/IEEE Int. Conf. Mobile Comput. Netw., Boston, MA, USA, Aug. 2000, pp. 56–67. [22] C. Schurgers and M. B. Srivastava, “Energy efficient routing in wireless sensor networks,” in Proc. MILCOM, McLean, VA, USA, 2001, pp. 357–361. [23] D. Braginsky and D. Estrin, “Rumor routing algorithm for sensor networks,” in Proc. 1st WSNA, Atlanta, GA, USA, Oct. 2002, pp. 22–31. [24] L. Liu, Z. Wang, and M. Zhou, “Energy-efficient cooperative routing for wireless sensor networks using space time block code,” in Proc. IEEE Conf. Syst., Man, Cybern., Montreal, QC, Canada, Oct. 2007, pp. 2837–2842. [25] X. Hong, M. Gerla, H. Wang, and L. Clare, “Load balanced, energy-aware communications for Mars sensor networks,” in Proc. IEEE Conf. Aerosp., 2002, vol. 3, pp. 3-1109–3-1115. [26] R. Shah and J. Rabaey, “Energy aware routing for low energy ad hoc sensor networks,” in Proc. IEEE Conf. Wireless Commun. Netw., 2002, vol. 1, pp. 350–355. [27] L. Gan, J. M. Liu, and X. L. Jin, “Agent-based energy efficient routing in sensor networks,” in Proc. 3rd Int. Joint Conf. Auton. Agents Mutli-Agent Syst., 2004, pp. 472–479. [28] J. Heo, J. Hong, and Y. Cho, “EARQ: Energy aware routing for real-time and reliable communication in wireless industrial sensor networks,” IEEE Trans Ind. Informat., vol. 5, no. 1, pp. 3–11, Feb. 2009. [29] J. Georgy, A. Noureldin, M. J. Korenberg, and M. M. Bayoumi, “Modeling the stochastic drift of a MEMS-based gyroscope in gyro/ odometer/GPS integrated navigation,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 4, pp. 856–872, Dec. 2010. [30] W. R. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Trans. Wireless Commun., vol. 1, no. 4, pp. 660–670, Oct. 2002. [31] Y. Yu, R. Govindan, and D. Estrin, “Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks,” Comput. Sci. Dept., Univ. Calif., Los Angeles, CA, USA, Tech. Rep. UCLA-CSD TR-01-0023, May 2001. [32] M. Zeeny, Multiple Criteria Decision Making. New York, NY, USA: McGraw-Hill, 1982. [33] Z. B. Wu and Y. H. Chen, “The maximizing deviation method for group multiple attribute decision making under linguistic environment,” Fuzzy Sets Syst., vol. 158, no. 14, pp. 1608–1617, Jul. 2007. [34] C. L. Hwang and K. P. Yoon, Multiple Attribute Decision Making: Methods and Applications. New York, NY, USA: Springer-Verlag, 1981. [35] L. Chen, C. L. P. Chen, and W. Pedrycz, “A gradient-descent-based approach for transparent linguistic interface generation in fuzzy models,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, no. 5, pp. 1219– 1230, Oct. 2010. [36] C. L. P. Chen and Y. Lu, “FUZZ: A fuzzy-based concept formation system that integrates human categorization and numerical clustering,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 27, no. 1, pp. 79–94, Feb. 1997. [37] Simulation of Urban MObility, German Aerosp. Center, Cologne, Germany, May 2010. [38] W. Guo, W. M. Healy, and M. Zhou, “Battery discharge characteristics of wireless sensors in building applications,” in Proc. IEEE Conf. Netw. Sens. Control, Apr. 2012, pp. 133–138.

Jin Zhou (S’11) received the B.S. degree in computer science and technology and the M.S. degree in software engineering from Shandong University, Jinan, China, in 1998 and 2001, respectively. He is currently working toward the Ph.D. degree with the Department of Computer and Information Science, University of Macau, Taipa, Macau. His current research interests include intelligent transportation systems, computational intelligence, and other machine learning techniques and their applications.

C. L. Philip Chen (S’88–M’88–SM’94–F’07) received the M.S. degree in electrical engineering from the University of Michigan, Ann Arbor, MI, USA, in 1985 and the Ph.D. degree in electrical engineering from Purdue University, West Lafayette, IN, USA, in 1988. From 1989 to 2002, he was with the Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA, first as an Assistant Professor, then as an Associate Professor, and finally as a Full Professor. Then, he joined the University of Texas at San Antonio, TX, USA, where he was a Professor and the Chair of the Department of Electrical and Computer Engineering and the Associate Dean for Research and Graduate Studies of the College of Engineering. He is currently a Chair Professor with the Department of Computer and Information Science and the Dean of the Faculty of Science and Technology with the University of Macau, Taipa, Macau. Dr. Chen is the Fellow of the IEEE & AAAS. He is currently the President of the IEEE Systems, Man, and Cybernetics Society. In addition, he is an Accreditation Board of Engineering and Technology Education Program Evaluator for computer engineering, electrical engineering, and software engineering programs.

Long Chen (M’11) received the M.S. degree in computer engineering from the University of Alberta, Edmonton, AB, Canada, in 2005 and the Ph.D. degree in electrical engineering from the University of Texas at San Antonio, TX, USA, in 2010. He is currently an Assistant Professor with the Department of Computer and Information Science, University of Macau, Taipa, Macau. His current research interests include computational intelligence, Bayesian methods, and other machine learning techniques and their applications. Dr. Chen has been involved with publication matters for many IEEE conferences. He was the Publications Cochair for the IEEE International Conference on Systems, Man, and Cybernetics in 2009.

Wei Zhao (F’01) received the B.S. degree in physics from Shaanxi Normal University, Xi’an, China, in 1977 and the M.Sc. and Ph.D. degrees in computer and information science from the University of Massachusetts Amherst, MA, USA, in 1983 and 1986, respectively. He was the Dean of the School of Science, Rensselaer Polytechnic Institute, Troy, NY, USA; the Director of the Division of Computer and Network Systems, U.S. National Science Foundation; and the Senior Associate Vice President for Research with Texas A&M University, College Station, TX, USA. He is currently the Rector of the University of Macau, Taipa, Macau. At the same time as assuming the new rectorship, he has also been named the first Chair Professor of the University of Macau. He has made significant contributions in distributed computing, real-time systems, computer networks, and cyberspace security.