Data fusionoriented cluster routing protocol for multimedia sensor networks based on the degree of image difference
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Abstract
In recent years, wireless multimedia sensor networks (WMSN) have attracted considerable attentions because of the extensive applications of providing audiovisual information conveniently. However, the traditional routing protocols are not directly applicable to WMSN due to the disadvantages on realtime or energy cost. The routing based on combination of clusters and data fusion provides an effective approach to improve the efficiency of data transmission. Consequently, this paper proposes a novel data fusionoriented routing protocol, which takes the degree of image difference into consideration when clustering. And meanwhile, the clusterhead is rotated periodically within a cluster, for the purpose of the establishment of data fusion tree which is responsible for the data transmission and redundancy elimination. The simulation results show that the proposal achieves a better performance on energy efficiency, network delay and loading balance, and simultaneously prolongs the network lifetime, compared with other typical protocols.
Keywords
Wireless multimedia sensor networks Clustering Degree of image difference Routing protocol Data fusion Network lifetime1 Introduction
Wireless multimedia sensor networks (WMSN) are composed of a large number of sensor nodes that can capture multimedia content, such as image, video and audio. Compared with traditional wireless sensor networks (WSN), WMSN can acquire various data types to complete more complex tasks such as wildlife tracking, elderly antifall monitoring and shared vision in field environment (Hasan et al. 2017; He et al. 2018a, b). However, the processing of multimedia information also brings difficulties and challenges, especially in the aspects of QoSguarantee and energy consumption (Bhandary et al. 2016; Wang et al. 2016a). The demands on higher bandwidth, more desirable realtime performance and more powerful processing capacity are indispensable in WMSN owing to huge amounts of multimedia data. Unfortunately, resourceconstrain is exactly one of the most typical characteristics of sensor networks due to limited computation, communication and storage capacity. If every node transmits data to the sink without data processing, the data redundancy will cause a great waste of bandwidth and energy.
To solve these issues, researchers find the combination of data fusion algorithm and clustering based routing protocol is an effective method to reduce the amount of data transmission, improve network reliability and prolong the network lifetime (Alanazi and Elleithy 2015; Wang et al. 2016b).
However, existing clustering methods of WSN can not be directly applied to WMSN due to the uniqueness of WMSN. Some current studies on clustering consider the field of view of multimedia sensors but are with heavy computation overheads or require accurate location information. Moreover, most WMSN clustering methods haven’t comprehensively consider energy consumption and QoS of the network.
Therefore, the challenges of realworld applications based on WMSN and limitations of existing studies have motivated to propose a clustering algorithm for WMSN which considers both the unique properties of WMSN and low computation overheads. Furthermore, the selection of cluster heads and the structure of the data fusion tree have great influence on the network lifetime and QoS of WMSN, which are also worth being further studied.
We also consider that the function of data aggregation for WMSN is not only to simply discard or merge duplicate data, but also to pick the most important media information from specific sensors to further reduce data redundancy. Furthermore, clustering enables the above functions to be executed parallelly, and meanwhile the proposed routing algorithm based on clustering can select optimal paths for those specific nodes to achieve QoSguarantee multimedia data transmission. So the data fusionoriented routing protocol becomes the focus of this paper.

The definition of image difference in WMSN is introduced.

A clustering algorithm for WMSN based on image difference is proposed.

The selection of cluster heads and the construction of cluster head tree are studied to achieve the satisfactory QoS performance and balanced network lifetime.
The rest of this paper is organized as follows. Section 2 reviews the related work briefly and summarizes our contributions. In Sect. 3, it introduces the definition of the degree of image difference for clustering. Section 4 describes our proposed data fusionoriented routing protocol in detail. Simulation evaluation and performance analysis will be presented in Sect. 5. Finally, in Sect. 6, we conclude this paper and outline our future work.
2 Related works
LEACH (Heinzelman et al. 2000) protocol is the earliest sensor network routing algorithm based on data fusion, which employs clustering to aggregate data, however LEACH cannot achieve satisfactory efficiency and availability. The directed diffusion routing protocol (ab. DD) (Intanagonwiwat et al. 2000) uses caching mechanism to achieve data transmission in path establishment phase and packet routing phase, whose disadvantage is not all the optimal path can be obtained for each selection. PEGASIS (Lindsey and Raghavendra 2002) is a chainbased routing algorithm which collects data on the constructed intermediate nodes, however this causes the fast energy consumption of these intermediate nodes. Besides these three classic routing protocols, Necchi et al. (2007) also point out that clustering network structure is suitable for data fusion; and Mehrabi et al. (2015) adopt a data fusion scheme which employs mobile sink to discover the transmission path simultaneously taking the energy cost into account. However, most research on data fusion algorithm and routing protocol is based on the traditional wireless sensor networks, such as AFST (Luo et al. 2006), GRMax (Attoungble and Okada 2012), EAR (De et al. 2012), DRINA (Villas et al. 2013), HCCR (Nayak et al. 2012), NCOF (Alaei and BarceloOrdinas 2010), EEGRA (Chen et al. 2016), NEECRP (Lee et al. 2014) and DGFP (Sha et al. 2016) and so on. In WMSN, each multimedia sensor node has a certain field of view (Fov), Soro et al. (2005) indicate that the classic algorithms are not directly applicable to WMSN not only because of their potential limitations such as infeasibility or expensive energy cost, but also because the Fov of multimedia sensor is different from that of the traditional sensor. The Fov of traditional sensor node is a circle that generally takes itself as its center, sensing distance as its radius, while the multimedia sensor’s Fov is a sector. Therefore, Obraczka et al. (2002) suggest that Fov is an important factor to be considered when studying clustering in WMSN.
Alaei and BarceloOrdinas (2010) put forward a clustering algorithm based on overlapping Fovs for wireless multimedia sensor networks. It calculates overlapping Fovs by geometric method and takes the result as an important basis of clustering. Nevertheless, the calculation process is complicated and not suitable for practical applications of WMSN. Similar with Alaei and BarceloOrdinas (2010), Li and Chuang (2014) propose a multipath scheme for the achievement of effective transmission for multimedia data; however, the use of accurate geographic information brings extra expense. Aiming at different service demands, Demir et al. (2013) suggest the image transmission routing algorithm based on source coding, which is only suitable for image sensor networks. Furthermore, a scheme of tasks schedule and data transmission for video sensor networks is proposed in Huang et al. (2009), where the convergence between simulated annealing algorithm and ant colony algorithm obtains the optimal solution to tasks schedule and meanwhile reduces the end to end delay. Spachos et al. (2015) propose an anglebased QoS and energyaware dynamic routing scheme designed for wireless multimedia sensor networks which extends network lifetime by optimizing the selection of the forwarding candidate set. In Magaia et al. (2015), the routing problems in WMSN are studied as multiobjective optimization problems, and it puts forward an improved genetic algorithm which shows significant improvement on QoS metrics. Ahmed (2017) evolves a novel algorithm for accomplishing adaptive traffic shaping of multimedia streaming and utilizes the multipath forwarding with dynamic cost calculation for selecting next hop in WMSN.
In this paper, based on the model in Ma and Liu (2005), we propose a Data Fusionoriented Routing Protocol for WMSN (ab. DFRP), whose contributions can be described as follows. First it can select those nodes with little degree of image difference into the same cluster with low computation complexity. Second, it can ensure the energy consumption balance while the selection of clusterhead occurs in the network, which indicates an energyefficient data fusion process can be carried out in clusterheads. Finally, a data fusion tree will be established, which can further save energy and prolong the lifetime of WMSN.
3 The degree of image difference
In this section, we assume that multimedia sensors are intentionally deployed in monitor area, providing detailed visual information from disparate viewpoints. A multimedia sensor network with n sensors is represented by \( S = \{ S_{1} ,S_{2} , \ldots ,S_{n} \} . \)
In which, \( P_{i} \) is the coordinates of point \( P \) on the capturing planar of sensor \( S_{i} \). \( R \) is the rotation matrix, which represents the rotation degree relative to standard coordinate system. \( O_{i} \) is the coordinates of origin with respect to \( S_{i} \).
Take \( S_{1} \) as an example, where \( R = \left[ {\begin{array}{*{20}c} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \\ \end{array} } \right] \), \( O_{i} = (d,0,0)^{T} \). So, the coordinates of points in Fig. 2 on the capturing planar of sensor \( S_{1} \) are \( O_{1} = (d,0,0)^{T} ,A_{1} = (d + 1,0,0)^{T} ,B_{1} = (d  1,0,0)^{T} ,C_{1} = (d,1,0)^{T} ,D_{1} = (d,  1,0)^{T} \), \( E_{1} = (d,0,1)^{T} ,F_{1} = (d,0,  1)^{T} \).
Coordinates of points after mapping
Point  Coordinates on imaging planar  

S _{1}  S _{2}  S _{3}  
O  \( (0,0)^{T} \)  \( (0,0)^{T} \)  \( \left( {\frac{r}{d}f,0} \right)^{T} \) 
A  \( (0,0)^{T} \)  \( \left( {\frac{  \sin \theta }{d + \cos \theta }f,\,0} \right)^{T} \)  \( \left( {\frac{r  \sin \theta }{d + \cos \theta }f,\,0} \right)^{T} \) 
B  \( (0,0)^{T} \)  \( \left( {\frac{\sin \theta }{d  \cos \theta }f,\,0} \right)^{T} \)  \( \left( {\frac{r + \sin \theta }{d  \cos \theta }f,\,0} \right)^{T} \) 
C  \( \left( {\frac{f}{d},0} \right)^{T} \)  \( \left( {\frac{\cos \theta }{d + \sin \theta }f,\,0} \right)^{T} \)  \( \left( {\frac{r + \cos \theta }{d + \sin \theta }f,\,0} \right)^{T} \) 
D  \( \left( {  \frac{f}{d},0} \right)^{T} \)  \( \left( {\frac{  \cos \theta }{d  \sin \theta }f,\,0} \right)^{T} \)  \( \left( {\frac{r  \cos \theta }{d  \sin \theta }f,\,0} \right)^{T} \) 
E  \( \left( {0,\frac{f}{d}} \right)^{T} \)  \( \left( {0,\frac{f}{d}} \right)^{T} \)  \( \left( {\frac{r}{d}f,\frac{f}{d}} \right)^{T} \) 
F  \( \left( {0,  \frac{f}{d}} \right)^{T} \)  \( \left( {0,  \frac{f}{d}} \right)^{T} \)  \( \left( {\frac{r}{d}f,  \frac{f}{d}} \right)^{T} \) 
Unit vector after mapping onto imaging planar
Unit vector  Unit vector on imaging planar  

S _{1}  S _{2}  S _{3}  
\( \overrightarrow {OA} \)  \( (0,0)^{T} \)  \( \left( {\frac{  \sin \theta }{d + \cos \theta }f,0} \right)^{T} \)  \( \left( {\frac{r  \sin \theta }{d + \cos \theta }f  r\frac{f}{d},0} \right)^{T} \) 
\( \overrightarrow {OB} \)  \( (0,0)^{T} \)  \( \left( {\frac{\sin \theta }{d  \cos \theta }f,0} \right)^{T} \)  \( \left( {\frac{r + \sin \theta }{d  \cos \theta }f  r\frac{f}{d},0} \right)^{T} \) 
\( \overrightarrow {OC} \)  \( \left( {\frac{f}{d},0} \right)^{T} \)  \( \left( {\frac{\cos \theta }{d + \sin \theta }f,0} \right)^{T} \)  \( \left( {\frac{r + \cos \theta }{d + \sin \theta }f  r\frac{f}{d},0} \right)^{T} \) 
\( \overrightarrow {OD} \)  \( \left( {  \frac{f}{d},0} \right)^{T} \)  \( \left( {\frac{  \cos \theta }{d  \sin \theta }f,0} \right)^{T} \)  \( \left( {\frac{r  \cos \theta }{d  \sin \theta }f  r\frac{f}{d},0} \right)^{T} \) 
\( \overrightarrow {OE} \)  \( \left( {0,\frac{f}{d}} \right)^{T} \)  \( \left( {0,\frac{f}{d}} \right)^{T} \)  \( \left( {0,\frac{f}{d}} \right)^{T} \) 
\( \overrightarrow {OF} \)  \( \left( {0,  \frac{f}{d}} \right)^{T} \)  \( \left( {0,  \frac{f}{d}} \right)^{T} \)  \( \left( {0,  \frac{f}{d}} \right)^{T} \) 
As can be seen from Table 2, the lengths of vectors vary with the imaging planar. As O, E and F have the same depth of focus and focal length for different nodes, \( \frac{{\overrightarrow {OE} }}{{\overrightarrow {OF} }} \) is a constant. We use the projection factor \( s = \frac{f}{d} \) to represent the respective length of \( \overrightarrow {OE} \) and \( \overrightarrow {OF} \) after mapping.
 Step 1

Obtain the location and Fov information of \( S_{i} \) and \( S_{j} \) via any lightweight localization method for wireless sensor networks;
 Step 2

Calculate the distances for unit vectors \( \overrightarrow {OA} ,\overrightarrow {OB} ,\overrightarrow {OC} ,\overrightarrow {OD} \). For example, assume that the mapping of \( \overrightarrow {OA} \) on \( S_{i} \) is \( \overrightarrow {OA}_{i} = (u_{i} ,v_{i} )^{T} \) and \( \overrightarrow {OA}_{j} = (u_{j} ,v_{j} )^{T} \) is its mapping on \( S_{j} \), and the distance between the two mappings is \( d_{OA} = \sqrt {(u_{i}  u_{j} )^{2} + (v_{i}  v_{j} )^{2} } \);
 Step 3

Take the average distances of the 4 vectors as the degree of image difference \( \delta_{i,j} \) for \( S_{i} \) and \( S_{j} \):
If \( S_{i} \) and \( S_{j} \) are deployed as \( S_{1} \) and \( S_{2} \) in Fig. 3, according to Table 2, we can figure out \( \delta_{1,2} = \frac{1}{4}(\left {\frac{d\sin \theta }{d + \cos \theta }} \right + \left {\frac{d\sin \theta }{d  \cos \theta }} \right + \left {\frac{d\cos \theta }{d + \sin \theta }  1} \right + \left {\frac{  d\cos \theta }{d  \sin \theta } + 1} \right) \);
The larger \( \delta_{i,j} \) is, the images captured by the two nodes will have a smaller correlation, which means less redundant information. If two nodes have the same sensing orientation and their locations are close, the value of \( \delta_{i,j} \) can be 0; when their sensing orientations are mutually perpendicular, \( \delta_{i,j} \) comes to 1.
4 Data fusionoriented routing protocol
The capturinglayer consists of member nodes within clusters and will transmit data gathered to cluster heads in the fusionlayer. The cluster heads will then transmit data along the paths in the constructed fusion tree to the upper layer until to the sink node. The multilayer structure illustrated in Fig. 4 can effectively reduce the data redundancy and energy consumption in data transmission.
In the clustering phase, nodes exchange image difference information (which comes from location and Fov information) and perform clustering based on image difference.
In the cluster head election phase, a cluster head is generated according to the weighted average of its remaining energy and its average distance from other nodes in the same cluster. The election result is then broadcasted within the cluster.
In the final phase, the structure of the data fusion tree is decided according to the remaining energy of all cluster heads as well as the distances from the sink node. The link message of the data fusion tree will be broadcasted for further routing selection.
4.1 Clustering
\( \delta_{n \times n} \) is a symmetric matrix whose main diagonal is composed of 0. If \( \delta_{i,j} \le \rho \) (\( \rho \) is a given threshold), it considers images captured by the two nodes have the high correlation, which means they can be assigned into the same cluster.
Initially there are n clusters and each cluster has only one member node \( S_{i} \). The maximum capacity of each cluster is n_{max}. Unlike traditional clustering algorithms which considers Euclidean distance between nodes, the clustering result is mainly influenced by the correlation of the images captured between different nodes. The algorithm tries to find all nodes \( S_{j} (j \ne i) \) that satisfies \( \delta_{i,j} \le \rho \), and merges \( cluster\_j \) with \( cluster\_i \) into one set \( cluster\_i^{'} \). The algorithm terminates when m clusters are constructed or no more nodes which satisfies \( \delta_{i,j} \le \rho \) can be found.
4.2 Clusterhead selection
Selecting the clusterhead is the next step after the clustering phase. Every node first broadcasts a HELLO_MSG with the form \( Sensor(ID_{i} ,E_{i} ,x_{i} ,y_{i} ) \) to indicate its unique ID, rest energy and located coordinate. Every node maintains and updates a member table which contains the information of the nodes in the same cluster.
4.3 Construction of clusterhead fusion tree
The construction of clusterhead fusion tree can effectively avoid every clusterhead communicating with the sink which may cause more energy consumption. This construction of fusion tree will consider both the rest energy of nodes and the distance between clusterhead to the sink, which means the clusterhead with more energy and closer to the sink will connect to the sink in the first place, forming the primary trunk. Then, it finds out the next suitable node among the remainder clusterheads, and connects it to the fusion tree to form the next trunk, and so forth, until all the clusterheads are included in the fusion tree.
According to the WEIGHT_MSG, the sink chooses the clusterhead who has the largest \( W_{i} \) as the first trunk node. The next suitable one will be chosen as the next trunk node, and so forth. The sink sends the message LINK_MEG with the form \( Link(ID_{i} ,ID_{j} ) \), where \( ID_{i} \) and \( ID_{j} \) represent the parent node and the child one separately. After receiving the LINK_MEG, the child node will proactively send the message CHILD_MEG with the form \( Child(ID_{i} ) \) to its parent in order to request connection, and consequently a fusion tree will be established by this way.
5 Simulation and analysis
Simulation parameters
Parameter  Value 

Network size  100 m × 100 m 
Sink location  (50, 50) m 
Number of multimedia nodes  \( 50 \) 
Number of video frames transmitted  50 
Communication radius of multimedia nodes  10 m 
Transmission speed of video stream  2 frame/s 
Initial energy  2 J 
Energy consumption of transmitting one data packet  0.01 J 
\( \rho \)  0.26, 0.38, 0.45, 0.6, 0.8 respectively 
\( \alpha \)  \( 0.33 \) 
\( \beta \)  \( 0.67 \) 
\( \chi \)  \( 0.4 \) 
The values of α as well as χ are weighted parameters which reflects the importance of different metric when clustering (or constructing data fusion trees). Greater α value indicates that the remaining energy of sensor node is considered in first priority in the clustering process. The sensor node with more remaining energy is more likely to be selected as cluster head. Here α is set as 0.33 and β as 0.67, which means that network delay and communication energy cost (influenced by \( d_{i,s} \)) are considered to be more important than energy balance when clustering. The choice of value of χ is similar to that of α, which values the remaining energy more when generating a data fusion tree. The values of α and χ should be chosen carefully in realworld applications according to the requirements.
5.1 Clustering results
From Fig. 6, we can find out that the average size of a cluster increases as the value of \( \rho \) increases, while the number of clusters decreases as \( \rho \) increases. The figures show when the number of video sensor nodes is 50, 0.4 < \( \rho \) < 0.6, it can achieve the reasonable clustering result. In actually, the efficiency of clustering is related with the area size, the counts of the nodes and the value of ρ. Under the given area size and the number of nodes, how to choose the appropriate ρ value is significant. The too small value of ρ may cause the overfull levels of data fusion tree while too large one may lead to the runty tree, and both of them will result in low efficiency of data fusion.
The simulation evaluations of Sect. 5.3, 5.4 and 5.5 are conducted in the scenario based on the clustering result of Fig. 6c (\( \rho \) = 0.45).
5.2 The degree of image difference
In Fig. 7, the degree of image difference between \( S_{1} \) and \( S_{2} \) increases along with the angle \( \theta \). The larger the degree of image difference is, the smaller the correlation is, and there will be less redundancy in the images captured by \( S_{1} \) and \( S_{2} \).
5.3 The mean square error of energy
5.4 The network lifetime and average rest energy
From Figs. 9 and 10, we can observe that DFRP has a better performance compared with AFST and NCOF in both experiment settings. In DFRP, nodes capturing high correlation images may be classified into the same cluster based on the degree of image difference, which enables a lot of redundant data eliminated, and simultaneously enables the data fusion phase carried out by clusterhead with a higher efficiency. In addition, as the clusterhead is rotated periodically, the energy consumption of each node is approximately average, which prolongs the network lifetime.
5.5 The average delay and delivery success rate
6 Conclusions
Both energy efficiency and loading balance are two important metrics in routing protocols in wireless multimedia sensor networks. In DFRP, clustering process based on the degree of image difference makes sure that nodes with similar images will be divided into the same cluster, which enables the data fusion process will consume less energy. And meanwhile, the clusterhead selection considers both the rest energy and the average communication distance to other nodes, which means more suitable nodes will be chosen as the clusterhead and it will be rotated periodically within a cluster. In addition, the construction of clusterhead fusion tree further decreases the energy consumption. So our proposal i.e. DFRP has better applicability and efficiency in the scenarios of wireless multimedia sensor networks, which has been verified through simulation comparisons with other routing protocols. However, improved fusion method should be designed needs to more discussion in order to further enhance the data fusion efficiency by special techniques such as compressive sensing and noise recognition, and accurate selection algorithm of value ρ is another future work will be concerned.
Notes
Acknowledgements
The authors would like to thank the anonymous reviewers of this paper for his/her objective comments and helpful suggestions while at the same time helping us to improve the English spelling and grammar throughout the manuscript. And meanwhile, the subject was sponsored by the National Natural Science Foundation of People’s Republic of China (No. 61672297), the Key Research and Development Program of Jiangsu Province (Social Development Program, No. BE2017742), Jiangsu Natural Science Foundation for Excellent Young Scholar (No. BK20160089), and the Sixth Talent Peaks Project of Jiangsu Province (No. DZXX017).
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
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