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Energy Efficiency QoS Assurance Routing in Wireless Multimedia Sensor Networks Kai Lin, Joel J. P. C. Rodrigues, Senior Member, IEEE, Hongwei Ge, Naixue Xiong, and Xuedong Liang
Abstract—As a new multimedia information acquisition and processing method, wireless multimedia sensor network (WMSN) has great application potential and attracts more and more attentions. Compared to traditional wireless sensor networks, the routing design of WMSN has higher requirements on the quality of service (QoS). This paper systemically analyzes the similarity between social network and WMSN, then designs a QoS trust estimation model based on social network analysis, which enables each sensor node measuring the service quality by monitoring the behaviors of neighbor nodes. An energy efficient QoS assurance routing based on cluster hierarchy (EEQAR) is proposed to achieve energy efficiency and meet the requirement of QoS. To obtain a better performance, EEQAR routing adopts cellular topology to form the cluster structure and balance the energy consumption by structure movement. The simulation results show the high performance of EEQAR routing in lifetime and quality of service. Index Terms—Cellular topology, data correlation, energy efficiency, social network analysis, wireless multimedia sensor networks (WMSNs).
I. Introduction
W
IRELESS multimedia sensor networks (WMSNs) as a novel derivative network have recently emerged as an important technology and drawn the attention of the researchers in the past few years. With rapid improvements and miniaturization in hardware, the sensor nodes of WMSNs are equipped with CMOS camera, microphone, and other kinds of sensors to ubiquitously capture the fine-grained, accurate information in a comprehensive environmental monitoring. They can capture the surrounding environment in a variety of media information and have outstanding performance in multimedia signal acquisition and processing [1]–[5]. Such a single sensor device can be equipped with audio and visual Manuscript received October 30, 2010; revised March 1, 2011; accepted April 5, 2011. Date of publication October 10, 2011; date of current version November 23, 2011. This work was supported in part by the National Science Foundation of China, under Grant 61103234, by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group, Portugal, and by the National Funding from FCT—Fundacao para a Ciencia e a Tecnologia through the PEst-OE/EEI/LA0008/2011 Project. K. Lin and H. Ge are with the Dalian University of Technology, Dalian 116024, China (e-mail:
[email protected];
[email protected]). J. J. P. C. Rodrigues is with the University of Beira Interior, Covilh¨a 6201-001, Portugal (e-mail:
[email protected]). N. Xiong is with the Department of Computer Science, Georgia State University, Atlanta, GA 30303 USA (e-mail:
[email protected]). X. Liang is with the University of British Columbia, Vancouver, BC V6T 1Z4, Canada (e-mail:
[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/JSYST.2011.2165599
information collection models. It cannot only enhance existing sensor network applications, but also enable several new applications, such as multimedia surveillance sensor networks, advanced health care delivery, industrial process control, and so on [6]–[12]. Since it is energy sensitive and without fixed infrastructure, the design of WMSNs routing mechanism with high energy efficiency is still very important and faces more challenges than WSNs, which concerns energy constraints, limited computing power, and memory availability of the sensor nodes. Moreover, the quality of service (QoS) is also an important criterion to measure the performance of network. For the sake of developing the performance of WMSNs, the critical issue is to solve a tradeoff problem for QoS assurance and energy efficiency in routing design, which is impossible to achieve the two targets simultaneously. Xiong et al. investigated and developed a power-efficient node placement scheme in linear WMSNs, which can decrease the average energy consumption of sensor node [13]. Cobo et al. presented ant-based multiQoS routing metric for WMSNs [14]. The AntSensNet protocol built a hierarchical structure on the network before choosing suitable paths to meet various QoS requirements. Cui et al. presented a power control game-theoretic approach for WMSNs by studying the effect of transmission power on QoS and energy efficiency [15]. The game approach determines the transmission strategy using utility optimization according to the fluctuation of channel states. Felemban et al. proposed MMSPEED routing protocol [16]. By localization algorithm and multirouting mechanism, MMSPEED has good properties of QoS and expendability and can well support media data. All the above works have made effective contributions, however, they did not consider the energy efficiency and QoS. In this paper, we focus on how to build an energy efficient QoS assurance routing for WMSNs, where cluster hierarchy is adopted on account of the good flexibility and high communication efficiency [17]. The obvious advantages of hierarchical architecture in WMSNs are as follows. First, for a real WMSN contains hundreds or thousands of multimedia sensor nodes, hierarchical architecture is efficient to divide and manage the application of distributed computation and communication. Second, the sensory data are in high relativity in one cluster because the sensor nodes are unavoidable to be distributed in redundancy. The unnecessary data transmission can be reduced by data fusion process of cluster head. Third, most of sensor nodes can turn off radio model to reduce energy consumption and avoid the communication conflicts in a quite long period which can significantly prolong the
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lifetime and improve the QoS of the whole network. For these reasons, the cluster hierarchy is suitable for WMSNs, specially in a large scaled deployment network. Moreover, for the sake of reducing the node burden and improving the whole performance of network, some agent nodes are introduced into network acting as cluster head to manage and collect the data sent from the nodes in their clusters. To optimize the network performance, we introduce a social network analysis similar to wireless multimedia sensor network. In this analysis, social structures are formed, where the nodes are usually represented by individuals or organizations and the links are represented by the relationships that exist between these entities. Sensor and social networks can interface both ways due to the similarities between them. For example, WMSNs can sense and provide information to personalized social applications while social networks analysis can supply algorithms and techniques which can lead to energy saving and efficient storage in WMSNs depending each time on the application being executed. The rest of this paper is organized as follows. Section II surveys related works. Section III gives the system models and presents the problem statement. Section IV analyzes how to use social networks to supply the routing design in WMSN and present the QoS trust estimation model. An energy efficient QoS assurance routing based on cellular topology is proposed in Section V. Simulation results and analysis are provided in Section VI. Finally, Section VII concludes this paper. II. Related Works In wireless multimedia sensor network, the routing design needs to guarantee delivering multimedia content with high performance in energy efficiency and meet the requirement of the QoS, such as communication reliability, real time, and so on. Our design for energy efficient QoS routing in wireless multimedia sensor networks consists of four kinds of current researches: QoS routing, energy efficient routing, cluster or grid topology, and social network. Although providing QoS guarantees in WMSN during data gathering is a very challenging problem, some approaches have been proposed in the literature for QoS support. The typical ones are SAR [18], RAP [19], SPEED [20]. In SAR, sensor nodes can send the information met the needs of tree to the sink node based on the path source, additional QoS measure, and package priority rank. RAP uses a velocity monotonic scheduler to prioritize packets and schedules them, which does not consider energy issues and the number of hops executed by the packets. SPEED is the first real time routing protocol for WMSN, which introduces a soft real-time endto-end to support all non-realtime MAC protocols and provide the management of controlling network traffic. Weighted fair queuing is used in every node to provide the required share of bandwidth for both traffic classes. The scalability of this approach will be limited if the algorithm is lack of completed knowledge of the network topology at the base station to calculate multiple routes. Some researchers have explored approaches to improve the energy efficiency among sensor nodes. Many moving strategies for sink node or agent are presented to balance the energy
consumption. Wang et al. changed the station mobility model to a linear plan for the optimized station mobility and special stop point [21]. Shah et al. presented a data mule, which can complete the data transmission by mule mobility [22]. The load in this method is small but the real-time data cannot be guaranteed. Wang et al. presented a data gathering model by agent mobility, where the station is stable and the agents distributed among the station are moving around the circle [23]. This method cannot solve the node load balance over two hops. Gandham et al. analyzed this problem and presented a model that combines data routing with station mobility to enable load balancing [24]. This strategy can only support the data collection based on inquiry and not reduce the energy load. Heinzelman et al. proposed a low energy adaptive clustering hierarchy (LEACH) [25]. From then on, the clustering routing plays an important and essential role. However, the position and the number of clusters in LEACH are randomly generate and it does not fully consider the remaining energy of sensor nodes during the selection of cluster heads. Wang et al. proposed SoRCA to implement self-healing, but it partitions the working area into fixed hexagons, and considers each hexagon to be fully covered if there is one active node within the cell in [26]. Xu et al. proposed GAF to divide the coverage area into squares and considered the nodes in a square to be equivalent for routing in [27]. Liu and Chang proposed GAF-h and ZBP to take the advantage of hexagon-like cellular in stead of square, but they are not suitable in random deployment of nodes in practice [28], [29]. These cellular clustering routing approaches only consider the position of sensor nodes but ignore the energy level of the candidate cluster head. Besides, each sensor node has to know its accurate position to form the cellular structure. It cannot meet the low cost requirement of WSNs. Lin et al. proposed a clustering hierarchy based on cellular topology for WSNs, in which the remaining energy and position of sensor nodes are simultaneously considered during the cluster structure construction, and the desired cluster structure is generated even in the case of nodes without a locating device [30]. Cambridge University and Intel Institute proposed a pocket switched network that is composed of wireless communication equipments carried by people [31]. This kind of network can make people communicate under the environment without fundamental equipments. Cerf et al. made a deep study on the social center, and proposed the SimBet routing algorithm based on the estimation of individual network on the center of social network and the similarity of local society [32]. Hui et al. proposed three kinds of algorithm for the distributed social network [33]. However, the shortage of this algorithm is the measurement lack of society at different phase and variability. Watts and Strogatz pointed that a small random shortcuts can make the topology network of network in a large clustering and a small average distance [34]. III. System Model and Problem Statement A. System Model 1) Network Model: In our research, the wireless multimedia sensor network is organized by a set of multimedia
LIN et al.: ENERGY EFFICIENCY QOS ASSURANCE ROUTING IN WIRELESS MULTIMEDIA SENSOR NETWORKS
Fig. 2.
Fig. 1.
Network architecture of WMSN.
sensor nodes, denoted as s = {s1 , s2 , . . . , sn }, and only one sink node. Additionally, for improving the whole performance of network, we also introduce some agent node, denoted as G = {g1 , g2 , . . . , gm }, where m is much less then n due to the high cost of such agent node. All the multimedia sensor nodes are isomorphic with the same initial energy and the same capacity of sensing, computation and communication, which can adjust the transmission power to save energy and the links are symmetrical. The agent nodes are movable and not limited by energy. They have a much better ability of communication and computation than multimedia sensor nodes. The network architecture is depicted in Fig. 1. The sensor nodes are grouped into clusters based on many criteria such as communication range, number and type of sensors, and geographical location. Each cluster has a agent node that manages all the sensor nodes in its cluster, which are significantly less energy-constrained than the ordinary sensor nodes. The agent node will take charge of sensor organization and network management. 2) Fusion Model: Data fusion can be used to reduce the redundant data during data collection. Similar to [35], a data fusion model is employed in this paper. In this model, when the node si receives the data sent from node sj . The total data amount after fused with the data generated by node si is expressed as follows: i) D(s
= max(D(si ), D(sj )) +min(D(si ), D(sj ))(1 − σ)
(1)
where D(si ) and D(sj ) represent the data amount generated by nodes si and sj , respectively. ρ represents the data correlation coefficient between node si and sj . 3) Energy Model: We assume that all the nodes have the same initial energy while only the sink node is not limited by energy. Similar to [25], the energy spent by transmitting 1 bit data over distance d is et (d) = εelec + εamp , where elec is the energy spent by transmitter electronics, amp is the transmitting amplifier and k (k ≥ 2) is the propagation loss exponent. elec and amp are both system parameters. The corresponding energy dissipation in data reception is er = εelec . Data fusion can introduce the extra energy consumption, which are denoted by ef . B. Problem Statement As shown in Fig. 1, such a wireless multimedia sensor network consists of a series of multimedia sensor nodes, one
497
Routes from the source node to the agent node.
sink node, and some agent nodes. Multimedia sensor nodes complete monitoring task and send their data to the agent nodes, while the agent nodes act as cluster heads to manage and collect the data sent from all the sensor nodes in their clusters. Therefore, the sensor nodes are the source nodes and the agent node is the destination node in each cluster. Due to the different communication ability from agent node to sensor node, most of the senor nodes cannot send their data to agent node directly. To avoid the loss of data, all the sensor nodes need to find at least one route which can reach to the agent node in their cluster. Sometimes, there will only be one route that can be found. However, the densely deployed nodes make it find more possible paths. The crucial of our research is how to select one routing with high energy efficiency and service of quality. For example, in Fig 2, the yellow dot represents a source sensor node si and there are three different routes from it to the agent node. We use Q(si ) represents the service quality of path between node si and agent node, where Q(si ) = {Q1 (si ), Q2 (si ), · · · , Qk (si )}. Here k represents the kth quality factor, such as transmission rate and isochronous. We use R(si , p, h) represents the pth routing from node u to agent node with path number as h. Hence, if we want to improve the service quality of the whole path or any path, it needs to meet the equation Q(si ) ≥ 0, where Q represents the minimum requirement of QoS for data collection. Except for QoS, the energy efficiency is also important, which includes saving and balancing energy. As the diversity of multimedia sensor, the difference of routing will influence the correlation among nodes, which determine the data amount that can be reduced. Hence, the selection of routing should benefit the fusion efficiency. Moreover, the intermediate nodes deplete their energy faster because they take more tasks, such as forwarding the received data or completing the fusion process to curtail the network load, leading to the existence of energy hole. We must consider how to balance the energy difference among nodes. Above all, our ideal design target can be formulated as follows: ⎧ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎩
¯ 2 min (si )∈S (E(si ) − E) h h ρj (si , p) ≥ ρj (si , p ) i=1
(2)
i=1
s.t. for ∀ si ∈ s, Q(si ) = Q
¯ where Esi represents the remaining energy of node si and E represents the average remaining energy of all nodes. ρj (si , p) represents the data correlation coefficient between node si and the jth node in route p. The first formula is to minimize the energy difference of nodes, and the second formula is to
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maximize the correlation of nodes. The constraint specifies that it should guarantee the requirement of end-to-end QoS from each source node to the agent node. It needs mention that this optimized objective is hard to achieve because the two targets of the above are tradeoff problems. For example, the routing with higher data correlation coefficient might contain some nodes with less remaining energy, which can intensify the unbalanced energy consumption. We need to find approximate solutions for these combinatorial optimization problems. Fig. 3.
IV. QoS Trust Estimation Model Based on Social Network Analysis In this section, we systemically analyze the similarity between social network and wireless multimedia sensor network. Then we show how to use social network analysis to supply routing design in WMSN. We design a QoS trust estimation model based on social network analysis, which enables each sensor node to measure the service quality by monitoring their neighbor nodes. A. Social Network Analysis in WMSNs With the development of wireless multimedia sensor network, the QoS is hard to be guaranteed due to the property of network self-organization, limited resource, and open working environment. The efficient data collecting is influenced, which directly affects the data-centered network performance. To solve this problem, an urgent method is necessary to find to judge the service level of different nodes and select the most trustable node. Trust in general existed in human society. In social network, trust is the core of interpersonal relationship. In WMSN, trust is also an important conception which can reflect the QoS among nodes. Trust relationship is easy to be established. However, WMSN is a kind of total open distributed network, where the data are distributed in each node. The crucial of trust mechanism is how to quantify the trust value and proceed the trust management without central control for improving the service quality. Social network analysis is a branch of network, which mainly studies the social network, understands the social structure, behavior and relationship. The analysis based on social network is helpful to treat the management, coordination, cooperation, and trust problem in network. The element of social network needs to communicate, coordinate, cooperate, and collaborate. The design of inter language, group thinking mode, and group analysis expands to study the efficiency of group, social reason, power, standard, and social network organization, social mechanism, social model and application. The society allows the members to pursue their own targets and coordinate to obtain the overall target, which must employ some mechanisms to guarantee the normal social order. The nodes in the formed social structure are usually represented by individuals or organizations and the links between these nodes are represented by the relationships that exist between these entities. Social network analysis views social relationships in terms of nodes and ties. Nodes are the individual actors within the networks, and ties are the relationships between the
Trust relationship in social network.
actors. There can be many kinds of ties between the entities. One tie is the trust tie between the nodes. In social network, trust relationship is the core of interpersonal relationship. The trust relationship consists of a so-called trust network. The dynamic, openness, and autonomy of WMSN are similar to the trust relationship in society, which provides the possibility as a reference for society study. The similarities exhibit in: 1) autonomous choice of object among nodes; 2) sharing the mutual information; 3) searching and analyzing the past mutual information, then determining whether to establish the trust relationship; 4) transmit the trust by recommendation; 5) not the absolute reliability and service quality, which means nodes can endure the loss as small mistakes; and 6) obligation of recommending information for other nodes Table I gives the property comparison between human society and WMSN. These characteristics determine the trust relationship to be established by simulating the trust mechanism of social network to guarantee the service quality of nodes. Social network analysis can supply algorithms and techniques which can lead to energy saving and trust evaluation in WSMNs depending each time on the application being executed. During the routing establishment of WMSN, forward node that can supply the service with high quality is crucial of successfully transmitting data packet. Similarly, in social networks, it is important to determine whom to trust and on what topic before making a decision. For example, as Fig. 3 shows, Alice trusts Bob and Bob trusts Dick to be a trustworthy person because of Bob’s past interactive information with Dick. On the basis of this, Alice may infer trust in Dick to be a faithful person upon recommendation from Bob. Then we consider one trust tie has been built between Alice and Dick by Bob‘s recommendation. However, the trust relationship is slowly varying. If Bob is fraudulent, Alice may be cheated by Bob and mistrust Dick though Dick is still faithful. Similarly, in WMSNs one node may consider its “old friend” that still can supply the service with high quality. So we must build an efficient trust detection mechanism for finding the sensor node that can meet the requirement of QoS. B. QoS Trust Estimation Model In WMSN, nodes transport various kinds of traffic. For instance, real-time audio/video data, packet losses, and nontime-critical snapshot multimedia content are delay-tolerant and loss-tolerant kinds of data with low or moderate bandwidth demands. Finally, each type of traffic has its own requisites for QoS metrics. The established QoS trust model should first
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TABLE I Comparison of Social Network and WMSN Content Unit Ability property Trust Reputation
Social network Individual Personal ability Confidence on other people Honor from other people
clearly estimate the contents of QoS requirement. Based on the description of Section III-A, we estimate the trust value of QoS in our research from the several parts: transmission delay is the time interval elapsed between the departures of data from the source to its final arrival at the destination. Packet lost mainly denotes the amount of data that did not make it to the destination in a specified time period. It is directly proportional to the QoS applicable. Different methods can be used to reduce the chances of loss; either by providing individual channels/guaranteed bandwidth for specific data transmissions, or by retransmission of data for loss recovery. Reliability reflects the ratio of the data correctly transmitted from the source node to the destination. Most multimedia applications are error tolerant to a certain extent. However, few multimedia applications, like distance learning examination or tele-surgery are sensitive to packet loss. The successful delivery of all packets of the multimedia content in such applications is vital. Frame rate defines the number of frames sent to network per unit time. A higher frame rate requires higher bandwidth. If QoS adaptation is required, the frame rate can be modified to lessen the bandwidth requirements at the cost of video quality. Image clarity refers to the perceptual quality of the image which a user perceives for a certain delivery of multimedia content. Human eyes are less sensitive to high frequency components of the image, so the high frequency components can be suppressed without any noticeable loss in image quality. Audio quality. It is defined in terms of sampling rate per second. A higher sampling rate renders better audio quality. For example, audio encoded in 128 kb/s is higher in quality than that is encoded in 16 kb/s. Usually, for audio conversation, 64 kb/s audio quality is adequate, however, 128 kb/s or higher is required for stereo music contents. Above all, we introduce distributed metrics to build the trust relationship between the sensor nodes in our scheme. Each node needs to judge the service level of its neighbor nodes supply. Behavior monitoring is the fund of this model, which can judge the behavior of neighbor node during data collection and give the trust evaluating on its service level. Besides direct monitoring the target node, the indirect information is also the important component to calculate the trust value. They are not obtained from the direct evaluating the nodes but from the monitoring of neighbor nodes on other nodes. The introducing the indirect information causes from the following reasons. 1) The sensor nodes cannot directly monitor the target node, so they have to refer to other monitoring results on the target node. Then, they can make an accurate comparison and obtain the real trust estimation. 2) The monitoring mechanism is an energy cost operation. As the energy of WMSN is non-renewable, the energy
WMSN Node Computing, storage, communication Service level Estimation from other nodes
should be saved as much as possible. By inquiring on other nodes, more monitoring results can be obtained. 3) The convergence of QoS trust value can be fasted. As the communication ability of sensor nodes is limited, the indirect monitoring can obtain more information to fast the trust value computing than direct information. To facilitate the present, we set N(si ) as neighbors of node si and define ql (si ) as the lth kinds of service. The QoS trust evaluation of sensor node si on sj is obtained from the comparison of the service from node si and its neighbor node. For the lth kind of service, the direct trust value can be calculated as follows: ql (sj ) − μl (si ) | (3) σl (si ) where γl denotes weight factor of the l kind of service and k l=1 γl = 1. μl (si ) and σl (si ), respectively, sample average and standard deviation of the l kind of service of N(si ). Then the whole direct trust value of QoS for node si on sj can be calculated as follows. For ∀st ∈ N(si ) dl (sj ) = γl |
k
Td (si , sj ) =
γl dl (sj )
l=1
max
k
.
(4)
γl dl (st )
l=1
Each sensor node needs to save the trust values of all neighbor nodes and update the monitoring results. After node si computed the direct trust value Td (si , sj ) of its neighbor node sj , it broadcast the trust value within the neighbor set N(si ). If node st is in N(si ) and node j is in sj , node si will receive the trust value from node st about node sj and make it the indirect value Tr (st , sj ) of node si . The final trust value of node sj computed by node si is calculated as follows: T (si , sj ) = λd Td (si , sj ) +
st ∈N(si )
λr Tr (st , sj )
(5)
(sj )
where λd ∈ (0, 1) denotes the weight of direct trust value and λr = 1 − λd denotes the weight of indirect trust value. Compared to global management, the trust management can greatly reduce the energy consumption for exchanging information, which is more benefit for WMSN. V. Energy Efficient QoS Assurance Routing In this section, we propose an energy efficient QoS assurance routing based on cluster hierarchy, which named EEQAR.
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The above mentioned QoS trust estimation model is adopted in EEQAR to meet the QoS requirement. To obtain a better performance, the cluster structure is formed based on cellular topology. The design objective of EEQAR is to improve the energy efficiency in the condition of assurance of QoS. A. Cellular Topology in EEQAR As mentioned above, clustering hierarchy is adopted in EEQAR. Although clustering hierarchy has better performance than the flat structure for WMSN, there are still two unsolved problems which can be generalized as follows. 1) Cluster structure: for most existing designs, the cluster topology is not regular and the number of sensor nodes in each cluster is different. Although the cluster distribution can be improved by a centralized control method, the network will have a higher requirement for the fundamental conditions (the nodes with accurate location device). Additionally, the extra communication and calculation cost are unavoidable to be caused by a huge information exchange. 2) Cluster head selection: even all the sensor nodes have the same original energy but they are responsible for different tasks. As a result, the remaining energy of each sensor node is different after a certain period. Hence, the method for the selection of cluster head should fully consider the remaining energy of sensor node during the selection. With the introduction of agent node in WMSN, we can easily establish the cluster structure for our purpose. To obtain a better performance, the cluster structure of EEQAR is formed based on cellular topology. In such topology, the monitoring area is divided into cellular virtual unit cells with same size. At the center of each unit cell, an agent node plays as cluster head and the rest ordinary sensor nodes as member nodes belong to the cluster. The cellular topology is formed on the basis of geographical position of agent and multimedia sensor nodes. For this reason, each agent node should be deployed in the center of each hexagon. After the deployment, it sends an advertisement message including its ID. Then the multimedia sensor nodes receive these advertisement messages and select one cluster to join in. Some multimedia sensor nodes can receive more than one advertisement message. At this time, they need to compare the signal strength of the received messages and select the agent node with strongest signal. Only the agent node needs to maintain in activity, while the multimedia sensor nodes can turn to sleep when they have no task. Two kinds of transmission power are adopted in EEQAR: the higher one is used by agent node to guarantee the intercluster communication, while the lower one is used for the communication between the neighbor sensor nodes in the same cluster. Considering to reduce the network cost, the number of agent nodes should be as less as possible. Therefore, EEQAR needs these agent nodes to manage as large as possible area. Theorem 1 gives the maximum area of each cluster. 1: The maximum area of each cellular cluster is √ Theorem 6R2 . 2
Fig. 4.
Example of intracluster multihop.
Proof: The intercluster communication is completed by the agent node as cluster head, the distance of two neighbor nodes in two clusters should not be over the maximum communication range. Since each agent node is at the center of cellular cluster, the length of hexagon is L, then √
3L R = =⇒ L = 2 2
√ 3R . 3
(6)
Therefore, the area of cellular cluster can be calculate as follows:
Sc =
6·
1 2
·
√
3R 3
2
·
√
2R
=
√ 2 6R . 2
(7)
Theorem 1 is proofed. As the communication ability of ordinary sensor nodes is far weaker than that of agent node, most of them cannot directly communicate with agent node. The multihop method is necessary for the intracluster communication. To guarantee all the sensory data can be successively collection, at least one route is necessary to be established from each source node to the agent node. According to the communication ability of multimedia sensor nodes, each cluster is divided into many concentric coronas with agent node as the center, denoted as C1 , C2 , . . . , CN , the width of each coronas equaling to the communication distance of multimedia sensor node. The nodes in corona Ci−1 send advertisement messages with their node ID to corona Ci . According to the received advertisement message, each node in corona Ci can find its forward node from the corona Ci−1 . All the nodes in the corona C1 can communicate with the agent node directly. Fig. 4 shows the intracluster multihop routing. After each node finds its forward node, the establishment of intra cluster routing is finished. Agent node is responsible for collecting and processing all the sensory data in its cluster, then send all of the data to the sink node. If the network scale is large, even though agent nodes need using multihop method to transmit their data to the sink node. We can form robust intercluster multihop routing according to the property of cellular structure.
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TABLE II Optimization Factor Neighbor s1 s2 .. . sj
Trust Value T (si , s1 ) T (si , s2 )
Energy Level E(si , s1 ) E(si , s2 )
Correlation C(si , sj ) C(si , s2 )
... T (si , sj )
... E(si , sj )
... C(si , sj )
B. Routing Establishment The process of routing establishment means to each node selecting its forward node. In order to realize the optimization target, an optimization factor table needs to be built to store relative information for routing probe from source node to the agent node. This structure is organized as shown in Table II, where each column reflects the different requirements of the application. Each row corresponds to a neighbor node. There are three kinds of values in the table and each value is a optimization factor used by EEQAR protocol: 1) T (si , sj ): the QoS trust value of node si to node sj ; 2) E(si , sj ): energy level of node sj ; 3) C(si , sj ): correlation between node si and sj . These values can be exchanged by two neighbor nodes without the overall information. As we described above, T (si , sj ) is determined by the direct and indirect information. It can be defined as trust value and calculated by (5). It should be noticed that the acquirement of indirect information will increase the communication cost. So the updating of the indirect information has to be updated after a certain period. The energy level from link between nodes si and sj is determined by the remaining energy of node sj and can be described as follows: E(si , sj ) =
Er (sj ) Eint
(8)
where Er (sj ) represents the remaining energy of node sj , Eint represents the initial energy of sensor node. As each node only knows its own remaining energy, it will increase the communication consumption of neighbor nodes to exchange these information, which is not suitable for updating the remaining energy in high frequency. Here, the energy information will be sent together with the data transmission. In WMSN, the multimedia sensor node often needs to be equipped with more than one kind of sensors, which can collect different kinds of monitoring data. We assume there are gth kinds of data in node si and h kinds of data in node sj . Then, the common property between the two nodes still have correlation. It can be calculated as follows: C(si , sj ) =
g=h
ρg (si , sj )
n(g) + n(h) − n(g, h)
(9)
where ρg (si , sj ) represents the correlation of the gth common property in nodes si and sj . n(g) and n(h) represent the type number of data in nodes si and sj , respectively. n(g, h) represents the number of the same common property.
Fig. 5.
Example of cellular topology movement.
Based on the optimization factor table each node finds its forward node according to (8). For H(sj ) < H(si ) [T (si , sj )]α [E(si , sj )]β [C(si , sj )]γ P(si , sj ) = [T (si , sj )]α [E(si , sj )]β [C(si , sj )]γ
(10)
Sj ∈N(si )
where P(si , sj ) is the value of the path from node si to node sj . It combines energy, QoS and data correlation into a single quantity with a comparable magnitude. α, β, and γ are the parameters that control the relative weight of different components. H(si ) and H(sj ) represent the hop number of nodes si and sj to agent node. The node with the highest value of P will be selected as the forward node and this process is looped and ended by all routes established. During data collection, the forward nodes need to forward relay the received data. Additionally, although data fusion often has better performance, the cost may not be negligible for certain applications. Therefore, the appearance of energy hole is avoidable which results in the phenomena that the remote data cannot be sent to agent node any more. To balance the energy consumption of WMSNs, the cluster structure needs to reconstruct after each round. In EEQAR, the cellular topology will take a whole movement after each round. The position of agent node will change and the movement distance can be set as a fix value according to the node density and the cluster area. It can also be generated randomly before each movement. As shown in Fig. 5, when the positions of the agent nodes migrate in group, cellular topology is then reestablished. For example, the green node in Fig. 5, the hop to agent node changes from the original two hops to one hop. Some of the ordinary nodes join in their clusters managed by the agent node. The task of sensor nodes changes with the status of sensor node. C. Round Allocation and Time-Slot Assignment The operation procedure of EEQAR is divided into a number of rounds and there are three phases in each round, namely cluster building phase and routing probe phase and steady state phase as shown in Fig. 6, which is similar to most of the existing clustering routings. In the cluster building phase, the cellular cluster topology construction is formed. In the routing-probe phase, intercluster and intracluster routes are
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A. Simulation Environment
Fig. 6.
Round allocation of EEQAR.
Fig. 7.
Example of time-slot assignment of EEQAR.
built. In the steady state phase, the data collection will be completed. The duration time of steady state phase is much longer than other two phases are introduced. Like other cluster schemes, to avoid the conflicts during data transmission, the agent nodes also need to manage a table of time division multiple access (TDMA) to assign the time-slot for sensor nodes belonging to its cluster. The ordinary sensor nodes can turn off their radio model in the sleep period to reduce their energy consumption. For multihop communication, the simple one variable linear table is not suitable in EEQAR any longer. Here, the agent node does not need to distribute time-slot for all the member sensor nodes, but only for those sensor nodes with one hop. If a member node does not contain any son node, its time-slot is allocated as 1, then notify to the upper sensor node. If the member node contains only one son sensor node, they will set the time-slot assignment as same as the son sensor node. If the member sensor node contains more than one son node, the time-slot of this member node is distributed as sum of all its son nodes. Fig. 7 shows an example of intracluster the time-slot distribution. For nodes A, B, and C do not have any son nodes, they are assigned one time-slot for transmitting their data. Node D reserves as a forward node for node C, so it is assigned two time-slots. Node E serves as a forward node of node A, B, and D, so it is assigned five time-slots. In this example, the agent node is response for informing the member node the determined TDMA table. VI. Simulation and Numerical Results In this section, we evaluate the performance of the proposed EEQAR routing via simulation experiments. Our experiments are organized as follows. First, we demonstrate the relationship between the service quality of network and energy consumption. Second, we investigate the relationship between remaining energy ratio and network lifetime. Third, we evaluate the performance of EEQAR by comparing with an adaptive reliable routing based on cluster hierarchy (ARCH) for WMSN.
In all simulations, there are 500 multimedia sensor nodes and 10 agent nodes uniformly deployed into a circle with diameter of 300 m, where a sink node is at the center of circle. The sink node and all agent nodes are not energy limited, while the multimedia sensor nodes are all stationary and have the same initial energy 50 J. All the sensor nodes are the source of information and can be the forward nodes. All simulations are based on a collision-free MAC protocol without data loss. The running time of each round during data gathering is 200 s and each sensor node generates 500 bit data in one round. For ease of reading, the related system parameters are listed in Table III. The performance metrics we used in our simulations are the quality of service, the network lifetime, and remaining energy ratio of sensor nodes. Here, the quality of service reflects the supplied service quality of network. The network lifetime can be measured by the time when the first node exhausts its energy or the network can be declared dead when a certain fraction of nodes die, or even when all nodes die. Here, we define it as the time until the first node dies due to energy depletion for the sake simplicity. The remaining energy ratio is the ratio of energy remained to the total initial energy when the running of network is end. B. Simulation Result 1) Energy Consumption of Network: In this simulation, the energy consumption is illustrated with different QoS requirements. We deployed the sensor nodes and agent nodes uniformly in a circular area. Here, we adopt the reliability and real-time of data transmission as the reference factor of QoS requirement. According to the experiment environment, the total energy of network is 2.5104 J. When all the nodes run off their energies, the survive lifetime of the network is ended. At this time, the total energy consumption is fixed at 2.5104 J without changing any longer. Fig. 8 shows the energy consumption of EEQAR with different reliability of data transmission. Here, the reliability of data transmission from source to destination is set as 60%, 70%, 80%, and 90%. At the same time, the real-time requirement of data transmission is fixed at 20 s and the data correlation is fixed at 0.2. It can be seen that the network lifetime of EEQAR decreased obviously with increasing the reliability of data transmission. More network energy consumption is necessary to guarantee the data transmission in high quality. Fig. 9 shows the energy consumption of EEQAR with different real-time request of data transmission. Here, the realtime of data transmission is separately set at 15 s, 20 s, 25 s, and 30 s and the reliability is fixed at 80%. The data correlation is also fixed at 0.2. It can be seen that the network lifetime of EEQAR decreased obviously with increasing the reliability of data transmission. The more energy consumption is needed when the requirement of real-time is high. 2) Impact of Correlation Coefficient: Data fusion can curtail network load and the effect of fusion process is dependent on the correlation coefficient of sensory data. We set the correlation coefficient changes from 0 to 0.4. Fig. 10 illustrates the
LIN et al.: ENERGY EFFICIENCY QOS ASSURANCE ROUTING IN WIRELESS MULTIMEDIA SENSOR NETWORKS
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TABLE III Parameters in Simulation Parameter Initial energy Number of nodes Energy consumption/circuit Energy consumption of amplifier
Fig. 8.
Value 50 J 500 50 nJ/bit d < 87m10 pJ/bit · m2
Parameter Network area Communication bandwidth Correlation coefficient of amplifier
Energy consumption with different reliability requirements. Fig. 11.
Fig. 9.
Value 9π × 104 m2 1 Mb/s 0.2 d ≥ 87m 10 pJ/bit · m2
Network lifetime with different correlation coefficients.
Energy consumption with different real-time requirements. Fig. 12. Comparison of network lifetime between EEQAR and ARCH with different reliability.
Fig. 10.
Remaining energy ratio with different correlation coefficients.
simulation results of the remaining energy ratio with different correlation coefficients. It can be seen that the remaining energy ratio under different parameters in the simulations is all quite low, which exhibits the well energy equilibrium performance of EEQAR. Additionally, the remaining energy ratio decreases with increasing correlation coefficient. This is
also caused by the work reduction due to fusion process, which can further balance the energy consumption. Fig. 11 shows the simulation results of the network lifetime with correlation coefficient changes from 0 to 0.4. It can be seen that the lifetime increases with increasing correlation coefficient. The main reason is the fusion process reducing the data amount, which can reduce the network consumption. 3) Comparison with ARCH: We also focus on evaluating the performance of EEQAR in terms of network lifetime extension by comparing with adaptive reliable routing based on cluster hierarchy, which named ARCH. The main idea of ARCH is to balance the energy consumption with meeting the need of reliability between the source and destination. Here, the reliability of data transmission from end to end is set as 70%, 80%, and 90%. Fig. 12 shows the lifetime ratio of EEQAR to ARCH. It can be seen that the lifetime of EEQAR is obviously longer than that of ARCH. The increasing of difference raises with increasing correlation coefficient. This
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Fig. 13. Comparison of network lifetime between EEQAR and ARCH with different real-time.
is because that the data fusion can improve the quality of data transmission. Next, we add real-time as a parameter for routing selection in ARCH. Then, we compare the lifetime of ARCH and EEQAR under different real-time parameters. The real-time of data transmission is separately set at 15 s, 20 s, 25 s and the reliability is fixed at 80%. The correlation coefficient changes from 0 to 0.4. Fig. 13 shows that the lifetime of EEQAR is obviously longer than that of ARCH at the same parameter. Specially, the difference under the real-time parameter is obviously large than that of reliability. VII. Conclusion As a resource-constrained network, wireless multimedia sensor network should try to reduce the unnecessary energy consumption and guarantee the quality of data transmission. In this paper, we studied how to select the routing with high energy efficiency and quality of service. We proposed an energy efficient QoS assurance routing based on cluster hierarchy for WMSN (EEQAR). The EEQAR routing can efficiently balance the energy consumption and meet the requirement of QoS between the source and destination. To achieve better performance, we formed the cluster structure by cellular topology. Moreover, we designed the QoS trust estimation model based on social network analysis, which enables sensor nodes measuring the supplied service quality of neighbor nodes. We performed extensive simulation experiments to evaluate EEQAR by several performance indexes. The results show that EEQAR performs high efficiency on network lifetime and QoS in wireless multimedia sensor network. References [1] L. Zhou, X. Wang, W. Tu, G. Mutean, and B. Geller, “Distributed scheduling scheme for video streaming over multichannel multiradio multihop wireless networks,” IEEE J. Sel. Areas Commun., vol. 28, no. 3, pp. 409–419, Apr. 2010. [2] Y. S. Yen, R. S. Chang, and H. C. Chao, “Controlled deployments for wireless sensor networks,” IET Commun., vol. 3, no. 5, pp. 820–829, 2009. [3] C. Y. Lin, Y. C. Tseng, W. C. Peng, T. H. Lai, and H. W. Fang, “TPGF: Message-efficient in-network location management in a multisink wireless sensor network,” Int. J. Ad Hoc Ubiquitous Comput., vol. 3, no. 1, pp. 1108–1117, 2008.
[4] L. Zhou, N. Xiong, L. Shu, A. Vasilakos, and S.-S. Yeo, “Context-aware multimedia service in heterogeneous networks,” IEEE Intell. Syst., vol. 25, no. 2, pp. 40–47, Mar.–Apr. 2010. [5] J. Wang, L. Shu, X. Wu, and S. Lee, “A load balancing and energy aware clustering algorithm in wireless ad hoc networks,” J. Multimedia Technol., vol. 5, no. 1, pp. 1108–1117, 2005. [6] Q. J. Tian, S. Bandyopadhyay, and E. J. Coyle, “Designing directional antennas to maximize spatio-temporal sampling rates in multihop clustered sensor networks,” J. Internet Technol., vol. 8, no. 1, pp. 1–9, 2007. [7] L. Zhou and Y. Zhang, “Distributed media-service scheme for P2P-based vehicular networks,” IEEE Trans. Vehicular Technol., vol. 60, no. 2, pp. 692–703, Feb. 2011. [8] M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, and V. Leung, “Body area networks: A survey,” ACM/Springer Mobile Netw. Applicat. (MONET), vol. 16, no. 2, pp. 171–193, Apr. 2010. [9] M. Chen, V. Leung, S. Mao, and T. Kwon, “RLRR: Receiver-oriented load-balancing and reliable routing in wireless sensor networks,” Wirel. Commun. Mobile Comput., vol. 9, no. 3, pp. 405–416, 2009. [10] K. Lin, M. Chen, and X. Ge, “Adaptive reliable routing based on cluster hierarchy for wireless multimedia sensor networks,” EURASIP J. Wirel. Commun. Netw., vol. 2010, pp. 1–11, Jan. 2010. [11] L. Zhou, X. Wang, W. Tu, G. Mutean, and B. Geller, “Distributed scheduling scheme for video streaming over multichannel multiradio multihop wireless networks,” IEEE J. Sel. Areas Commun., vol. 28, no. 3, pp. 409–419, Apr. 2010. [12] L. Shu, M. Hauswirth, Y. Zhang, J. Ma, G. Min, and Y. Wang, “Crosslayer optimization on data gathering in wireless multimedia sensor networks within expected network lifetime,” Proc. Comput. Sci. Eng., Aug. 2009, pp. 961–966. [13] N. Xiong, M. Cao, A. V. Vasilakos, L. T. Yang, and F. Yang, “An energyefficient scheme in next-generation sensor networks,” Int. J. Commun. Syst., vol. 23, nos. 9–10, pp. 1189–1200, Sep. 2010. [14] L. Cobo, A. Quintero, and S. Pierre, “Ant-based routing for wireless multimedia sensor networks using multiple QoS metrics,” Comput. Netw., vol. 54, no. 17, pp. 2991–3010, Dec. 2010. [15] H. Cui, G. Wei, Q. Huang, and Y. Yu, “A game theoretic approach for power allocation with QoS constraints in wireless multimedia sensor networks,” Multimed Tools Appl., vol. 51, no. 3, pp. 983–996, Feb. 2011. [16] E. Felemban, L. Chang-Gun, and E. Ekici, “MMSPEED: Multipath multispeed protocol for QoS guarantee of reliability and timeliness in wireless sensor networks,” IEEE Trans. Mobile Comput., vol. 5, no. 6, pp. 738–754, Jun. 2006. [17] L. Shu, Y. Zhang, Z. Zhou, M. Hauswirth, Z. Yu, and G. Hynes, “Transmitting and gathering streaming data in wireless multimedia sensor networks within expected network lifetime,” ACM/Springer Mobile Netw. Applicat. (MONET), vol. 13, nos. 3–4, pp. 306–322, 2008. [18] K. Sohrabi, J. Gao, V. Ailawadhi, and G. J. Pottie, “Protocols for selforganization of a wireless sensor network,” IEEE Pers. Commun., vol. 7, no. 5, pp. 16–27, Oct. 2000. [19] L. Chenyang, B. M. Blum, T. F. Abdelzaher, J. A. Stankovic, and H. Tian, “RAP: A real-time communication architecture for large-scale wireless sensor networks,” in Proc. 8th IEEE Real-Time Embedded Technol. Applicat. Symp., Sep. 2002, pp. 55–66. [20] T. He, J. A. Stankovic, C. Lu, and T. Abdelzaher, “SPEED: A stateless protocol for real-time communication in sensor networks,” in Proc. 23rd IEEE ICDCS, May 2003, pp. 46–55. [21] Z. M. Wang, S. Basagni, E. Melachrinoudis, and C. Petrioli, “Exploiting sink mobility for maximizing sensor networks lifetime,” in Proc. 38th Hawaii Int. Conf., 2005, pp. 287–295. [22] R. Shah, S. Roy, S. Jain, and W. Brunette, “Data MULEs: Modeling a three-tier architecture for sparse sensor networks,” in Proc. IEEE Workshop Sensor Netw. Protocols Applicat., May 2003, pp. 30–41. [23] F. Wang, D. Wang, and J. C. Liu, “Traffic-aware relay node deployment for data collection in wireless sensor networks,” in Proc. 6th Annu. IEEE Commun. Soc. Conf. Sensor Mesh Ad-HoC Commun. Netw., Jun. 2009, pp. 351–359. [24] S. Gandham, M. Dawande, and R. Prakash, “Energy-efficient schemes for wireless sensor networks with multiple mobile base stations,” in Proc. IEEE GLOBECOM, Dec. 2003, pp. 377–381. [25] W. R. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Trans. Wirel. Commun., vol. 1, no. 4, pp. 660–670, Oct. 2002. [26] X. Wang and T. Berger, “Self-organizing redundancy-cellular architecture for wireless sensor networks,” in Proc. IEEE WCNC Broadband Wirel. Masses Ready Take-Off, Mar. 2005, pp. 1945–1951.
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[27] Y. Xu, J. Heide, and D. Estrin, “Geography-informed energy conservation for ad hoc routing,” in Proc. 7th Annu. Int. Conf. Mobile Comput. Netw., 2001, pp. 70–84. [28] R. Liu, G. Rogers, and S. Zhou, “Honeycomb architecture for energy conversation in wireless sensor networks,” in Proc. IEEE Global Telecommun. Conf., Nov.–Dec. 2006, pp. 1–5. [29] C. Y. Chang, K. P. Shih, and S. C. Lee, “ZBP: A zone-based broadcasting protocol for wireless sensor networks,” in Proc. 18th IEEE Int. Conf. Adv. Inform. Netw. Applicat., Aug. 2004, pp. 84–89. [30] K. Lin, K. Q. Li, W. L. Xue, and Y. G. Bi, “A clustering hierarchy based on cellular topology for wireless sensor networks,” Int. J. Comput. Sci. Eng., vol. 5, no. 5, pp. 51–59, May 2009. [31] P. Hui, A. Chaintreau, J. Scott, R. Gass, J. Crowcroft, and C. Diot, “Pocket switched networks and human mobility in conference environments,” in Proc. ACM SIGCOMM Workshop Delaytolerant, 2005, pp. 244–251. [32] V. Cerf, R. Durst, K. Scott, E. Travis, and H. Weiss, Interplanetary Internet (IPN): Architectural Definition, Internet Draft, IPN Research Group, May 2001. [33] P. Hui and E. Yoneki, “Distributed community detection in delay tolerant networks,” in Proc. SIGCOMM Workshop MobiArch, Aug. 2007, pp. 1–8. [34] D. J. Watts and S. H. Strogatz, “Collective dynamics of ‘small-world’ networks,” Nature, vol. 393, pp. 440–442, Jun. 1998. [35] H. Luo, Y. Liu, and S. K. Das, “Routing correlated data with fusion cost in wireless sensor networks,” IEEE Trans. Mobile Comput., vol. 5, no. 11, pp. 1620–1632, Nov. 2006.
Kai Lin received the B.S. degree from the School of Electronic and Information Engineering, Dalian University of Technology, Dalian, China, in 2001, and the M.S. and Ph.D. degrees from the College of Information Science and Engineering, Northeastern University, Shenyang, China, in 2005 and 2008, respectively. He is currently an Assistant Professor with the School of Computer Science and Technology, Dalian University of Technology. His current research interests include wireless networks, ubiquitous computing, and embedded technology.
Joel J. P. C. Rodrigues (S’01–M’06–SM’06) is currently a Professor with the Department of Informatics, University of Beira Interior, Covilh¨a, Portugal, and a Researcher with the Instituto de Telecomunicac¸o˜ es, Porto, Portugal. He is the Editor-in-Chief of the International Journal on E-Health and Medical Communications and the General Chair and the TPC Chair of many international conferences. He is a member of many international TPCs and several editorial review boards. He is a Licensed Professional Engineer, a member of ACM SIGCOMM, a member of the Internet Society, and an IARIA fellow.
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Hongwei Ge received the B.S. and M.S. degrees in mathematics from Jilin University, Changchun, China, and the Ph.D. degree in computer application technology from Jilin University in 2006. He is currently a Vice Professor with the College of Electronic and Information Engineering, Dalian University of Technology, Dalian, China. His current research interests include computational intelligence, optimization and modeling, system control, social networks, and bioinformatics. He has published more than 40 papers in these areas. His research was featured in the IEEE Transactions on Systems, Man, and Cybernetics, the Computers and Structures, the Nonlinear Analysis: Real World Applications, and the Advances in Soft Computing and the Nero-Computing.
Naixue Xiong is currently a Research Scientist with the Department of Computer Science, Georgia State University, Atlanta. Until now, he has published over 90 research articles (including over 30 international journal articles). His current research interests include communication protocols, network architecture and design, network technologies, and distributed and parallel systems. He has been a Program Chair, General Chair, Publicity Chair, PC Member, and OC Member of over 60 international conferences, and was invited to serve as a reviewer for over 20 international journals. Currently, he is an Associate Editor, an Editorial Board Member, and a Guest Editor for four international journals.
Xuedong Liang received the Ph.D. degree from the Department of Informatics, University of Oslo, Oslo, Norway, in 2009. He is currently a Post-Doctoral Research Fellow with the Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada. His current research interests include wireless communication protocols, cooperative communications, game theory and its applications, resource allocation and optimization, quality of service provisioning, formal modeling, and validation of wireless networks.
