Research on energy efficient fusiondriven routing in wireless multimedia sensor networks
 Kai Lin^{1}Email author and
 Min Chen^{2}
https://doi.org/10.1186/168714992011142
© Lin and Chen; licensee Springer. 2011
Received: 28 June 2011
Accepted: 27 October 2011
Published: 27 October 2011
Abstract
The limitation of energy supply is a crucial problem in wireless multimedia sensor networks. In this article, we research and optimize the energy utilization during data collection by adopting mobile agent nodes. First, we demonstrate that the energy consumption of the whole network is affected by the data correlation coefficient for a multimedia sensor network with a random node deployment. Then, we give the method to find the optimized position of mobile agent node for maximizing the energysaving efficiency by fusion process. Finally, we propose an energy efficient fusiondriven routing (EEFR) based on cluster hierarchy. To obtain a better performance, the cluster structure is divided based on square grid topology. Extensive simulation experiments have been made to evaluate our proposed EEFR with several performance criteria. The results show that EEFR can effectively save energy consumption of network, and the consumption is relatively balanced.
Keywords
wireless multimedia sensor networks energy efficiency data fusion cluster hierarchy fusiondriven routing1. Introduction
Wireless multimedia sensor networks (WMSNs) have been the target of active research with a particular emphasis on highcaliber information collection. Recent rapid development in the sensor, wireless network, multimedia and embedded computing areas now make it possible to deploy a large number of multifunctional and inexpensive multimedia sensor nodes to achieve high quality data acquisition [1]. In general, the sensor nodes of WMSNs are equipped with CMOS camera, microphone, and other kinds of sensors for achieve the finegrained, accurate information in a comprehensive environmental monitoring. Compared with traditional WMSN, WMSNs can capture the surrounding environment in a variety of media information and has outstanding performance in multimedia signal acquisition and processing. It not only can enhance existing multimedia sensor network applications, but also enable several new applications, such as multimedia surveillance multimedia sensor networks, advanced health care delivery, industrial process control, mobile multimedia sensor networks [2] and so on [3, 4].
These sensor nodes are typically lightweight with limited battery capacity, processing power and communication bandwidth. In order to ensure the normal operation of WMSNs, the energy consumption of communication must be maintained in minimum level. During the network operation, data fusion should be considered to eliminate unnecessary communications since the massively deployed sensor nodes generate a huge amount of redundant data, which makes it critical to collect only the valuable data.
In traditional fusion architecture of WMSNs, all the sensory data have the same structure and need to be fused by the routing nodes before sent to the sink node. However, for some complicate phenomena, it is a trend for WMSNs to be transferred from homogeneous to heterogeneous, wherein nodes are equipped with various sensors in the heterogeneous network to monitor multitargets separately or cooperatively. The heterogeneous network increases the complexity of fusion process but decrease the data correlation coefficient that is used to examine the relationship strength between sensory data, both of them will consume the energy resources additionally [5].
In this article, we study the data fusion with the consideration of data attribute difference in heterogeneous network with relatively high node density. Thus, cluster hierarchical structure is exploited in the network because of its effectiveness of distributed management in a largescale network. In addition to the use of data fusion to alleviate the strong correlativeness of sensory data among the sensor nodes located in one cluster. Different with the sensor node hosting agent (soft agent) [6], the mobile agent node in our research has unique physical structure, it can freely move and is not limited by energy supply. Hence, the mobile agent node is introduced to act as the cluster head because of its much heavier tasks than the ordinary sensor nodes. Thus, a kind of energy efficient cluster routing protocol is proposed by the use of the mobility of agent node. The main contributions of this article are summarized as follows:

Compared to the traditional cluster routing schemes with the intrinsic feature of scalar data collection, the proposed algorithm emphasizes the interaction among different multimedia data attributions. Thus, we systemically analyze the data collecting characterization in a heterogeneous environment with cluster hierarchy. Then, the effect of multiattributebased data fusion regarding energy consumption is analyzed.

Based on our analysis of the data collecting characterization, an energy efficient fusiondriven routing (EEFR) scheme is proposed to maximize the efficiency of fusion by the mobility of agent node. Moreover, the remaining energy of node is also considered during data collection to balance energy consumption.
The rest of this article is organized as follows: Section 2 states related works. Section 3 gives the system models and problems. Section 4 analyzes energy efficiency with fusion process. Section 5 presents EEFR hierarchy. Simulation results are provided in Section 6. Finally, Section 7 concludes the article.
2. Related work
With the development of WMSNs, more and more attentions are focused on improving the network efficiency and saving energy consumption by data fusion process combined with different applications. Currently, the relative research includes data fusion mechanism, cluster routing, and mobility of agent.
Rickenbach et al. proposed an optimal algorithm MEGA for foreigncoding and an approximating algorithm LEGA for selfcoding in [7]. In MEGA, each node sent raw data to its encoding point using directed minimum spanning tree, and encoded data were then transmitted to the sink through SPT. Krishnamachari et al. investigated the impact of data aggregation on these networking metrics by surveying the existing data aggregation protocols in [8]. Goel et al. proposed LEGA using shallow light tree as the data gathering topology in [9]. Lin et al. investigated the process and performance of multiattribute fusion in data gathering, and then proposed a selfadaptive threshold method to balance the different change rates of each attributive data. They presented a method to measure the energyconservation efficiency of multiattribute fusion and designed a novel energy equilibrium routing method, multiattribute fusion tree [5]. Luo et al. developed an online algorithm capable of dynamically adjusting the route structure when sensor nodes joined or left the network in [10]. Furthermore, by only performing such reconstructions locally and maximally preserving existing routing structure, the online algorithm could be readily implemented in real networks in a distributed manner and promised extremely small performance deviation from the offline version and outperformed other routing schemes with static aggregation decision. Motivated by the limitation of minimum fusion Steiner tree, they designed a novel routing algorithm, called adaptive fusion Steiner tree for energy efficient data gathering in multimedia sensor networks. Anandkumar et al. presented a novel formulation for optimal sensor selection and innetwork fusion for distributed inference known as the prizecollecting data fusion in terms of optimal tradeoff between the costs of aggregating the selected set of sensor measurements and the resulting inference performance at the fusion center in [11]. Wun et al. presented a novel system for decoupling the process of semantic data fusion from application logic based on semantic Contentbased Publish/Subscribe techniques [12].
Many cluster routing have been developed to optimize the energy consumption of network. Heinzelman et al. proposed a low energy adaptive clustering hierarchy (LEACH) in [13]. Since then, the clustering routing plays an important and essential role in the routing. However, LEACH cannot guarantee either the position or the number of clusters in the network. Besides, it does not fully consider the energy of sensor nodes during the selection of cluster head nodes. Wang et al. proposed SoRCA to implement selfhealing, 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 [14]. Xu et al. proposed GAF to divide the coverage area into squares and considers the nodes in a square to be equivalent for routing in [15]. Lin et al. proposed an adaptive reliable routing based on clustering hierarchy named ARCH, which included energy prediction and power allocation mechanism. To obtain a better performance, the cluster structure was formed based on cellular topology. The introduced prediction mechanism makes the sensor nodes predicted the remaining energy of other nodes, which dramatically reduced the overall information needed for energy balancing [16]. The research work only was focused on the reliable transmission while the data attribute and fusion process were not considered. Liu and Chang et al. proposed GAFh 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 in [17, 18]. These four cluster routings only consider the position of sensor nodes, while ignore the energy level of the candidate cluster head node. Besides, each sensor node has to know its accurate position to form the cellular structure. It cannot meet the requirement of low cost. Lin et al. proposed a clustering hierarchy based on cellular topology (CHCT), 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 locating device [19]. Wu et al. explore the theoretical aspects of the nonuniform node distribution strategy that addresses the energy hole problem. They propose a distributed shortest path routing algorithm tailored for the proposed nonuniform node distribution strategy [20].
Many moving or QoS provisioning strategies for sink node or agent are presented to optimize the energy consumption; however, they can only support the data collection based on inquiry but not reduce the energy load [21–24]. Wang et al. changed the station mobility model to linear plan for the optimized station mobility and special stop point [25]. Shah et al. presented a data mules, which can complete the data transmission by mules mobility [26]. The load in this method is small but the real time of 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 [27]. This method cannot solve the node load balance over two hops. Gandham et al. analyzed this problem and presented a combining model with data routing and station mobility to reach a load balancing [28].
3. System model and problem statement
A. System model
Network model: In this article, we adopt a WMSN formed by n randomly deployed sensor nodes, denoted by S= {s_{1}, s_{2}, ..., s_{ n } } and only one static sink node acts as the destination of the whole network. All the sensor nodes are used for data collection in the monitoring area and keep stable after the deployment. In addition, for improving the performance of WMSNs, we also introduce m mobile agent nodes, denoted as G = {g_{1}, g_{2}, ..., g_{ m } }, where m is far less than n due to their higher cost. These agent nodes can control their moving traces and are responsible for collecting the sensory data generated by the surrounded sensor nodes, then relay to the sink node. The main distinguished features of the system are as the followings:

The sensor nodes are not equipped with the same kinds of sensors.

All the agent nodes are movable and not limited by energy. They have a much better ability of communication and computation than sensor nodes.

The sink node is not limited by energy and has highest ability of communication and computation.

Sensor nodes can adjust the transmission power to save energy and the communication links are symmetrical, where the distance from the receiver to the transmitter can be calculated by the intensity of the received signal.

All the sensor nodes have the same initial energy and the capacity of computing and communication.
We focus only on the communications among the sensor nodes, the mobile agents, and the sink node, whereas the communications between the sink node and devices outside the network are out of the scope of this article.
where D(s_{ i } , p) and D(s_{ j } , p) represent the data amount of attribute p generated by node s_{ i } and s_{ j } , respectively. σ represents the data correlation coefficient between node s_{ i } and s_{ j } . From this equation, it can be seen that the higher σ can generate less data amount.
Energy model: We assume that all the nodes have the same initial energy while only the sink node and mobile agent node are not limited by energy supply. Similar to [19], the energy spent by transmitting 1 bit data over distance d is e_{ t } (d) = ε_{elec} + ε_{amp} · d^{ k } , where ε_{elec} is the energy spent by transmitter electronics, ε_{amp} is the transmitting amplifier, and k(k ≥ 2) is the propagation loss exponent. ε_{elec} and ε_{amp} are both system parameters. The corresponding energy dissipation in data reception is e_{ r } = ε_{elec}. In addition, although data fusion can reduce the energy consumption, it still introduces the extra energy consumption, where each fusion for 1 bit data is denoted by e_{ f } .
B. Problem statement
Data collection is divided into two phases, one is intracluster collection and the other one is intercluster collection. For the intracluster collection, the sensor nodes are the source and the agent node is the destination. For the intercluster collection, the agent node is the source and the sink node is the destinations. As the communication capacity of sensor node is limited, most of them cannot directly transmit the data to the mobile agent node; hence multihop transmissions are necessary to solve this problem. For avoiding the data loss, each sensor node needs to establish at least one path to the mobile agent in its cluster.
Definition 3.1 Data collection space: Let D_{ p } be the data set that complete the p kind of task for data collection. Each kind of task corresponds to one kind of data set, and we assume there are k kinds of tasks. Then, the generated k kinds of data sets compose the data collection space, denoted as $\hat{D}={D}_{1}\times {D}_{2}\times \cdots \times {D}_{k}$.
Definition 3.2 Efficient data fusion: Only those fusion processes that can reduce the energy consumption of network are regarded as efficient data fusion.
Definition 3.3 Energy efficiency: Energy efficiency refers to complete the task with the least energy while the energy consumption is more balanced.
where x(s_{ i } , s_{ j } ) represents whether a connection exists between node s_{ i } and s_{ j } . If node s_{ j } is the forwarding node of node s_{ i } , then x(s_{ i } , s_{ j } ) = 1, otherwise x(s_{ i } , s_{ j } ) = 0. e(s_{ i } , s_{ j } ) represents the energy consumption on the edge from node s_{ i } to s_{ j } consists of three components: node s_{ i } transmitting data, node s_{ i } receiving data and fusing data. In Equation 2, the constraint specifies that the node s_{ i } has only one forwarding node.
4. Energy efficient data fusion during data collection
In this section, we will discuss the performance of data fusion in cluster hierarchy and make a priority analysis on energy consumption of network, where the cost of transmitting and receiving data are also both considered.
A. Network topology
The square grid that we adopted has strong regularity and is easy to be analyzed. When the sensor nodes are deployed in uniform, the number of sensor node in each grid is same. Without loss of generality, we suppose that all nodes are random deployed, which make the number of sensor nodes in each cluster and grid are different. Then, we will analyze the energy efficiency of intragrid and intergrid.
B. Energy efficient data fusion of intragrid
where D_{ ut } (x,y), D_{ ur } (x,y), D_{ uf } (x,y) represent the total data amount of sensor nodes of grid g_{ u } (x,y) in time T for transmitting, receiving, and fusing.
From Equation 4, it can be seen that the energy consumption is determined by the data amount of the above three operations. Aiming for energy efficiency, we need to judge whether sensor nodes should complete data fusion process during intragrid data collection. D(s_{ i } ) and D(s_{ j } ) represent the data amount generated by node s_{ i } and s_{ j } in grid g_{ u } (x,y). The decision of whether proceeding data fusion of intragrid is based on the following theorem.
Theorem 1: ∀s_{ i } , s_{ j } ∈ g_{ u } (x,y), the condition of intragrid data fusion that can save energy is: $\sigma >\frac{D\left({s}_{i}\right)\left[{e}_{t}\left(a\right)+{e}_{r}+{e}_{f}\right]+D\left({s}_{j}\right){e}_{f}}{min\left(D\left({s}_{i}\right),D\left({s}_{j}\right)\right){e}_{t}\left(\sqrt{2}a\right)}$.
Hence, Theorem 1 is proved.
It can be seen from Theorem 1 that the energysaving degree of fusion process is determined by the relativity of the processed data when the data amount is known.
C. Energy efficient data fusion of intergrid
Theorem 2: ∀s_{ i } ∈ g_{ u } (x,y), s_{ j } ∈ g_{ u } (x', y'), the condition of intergrid data fusion that can save energy is $\sigma >\frac{\left[D\left({s}_{i}\right)+D\left({s}_{j}\right)\right]{e}_{f}}{min\left(D\left({s}_{i}\right),D\left({s}_{j}\right)\right)\left({e}_{t}\left(\sqrt{2}a\right)+{e}_{r}\right)\left(\mid {x}_{0}\mid +\mid {y}_{0}\mid \right)}$
In the other situation, the routes of s_{ i } and s_{ j } to mobile agent node g_{ u } meet at grid g_{ u } (x_{0}, y_{0}), then their data are fused.
Hence, Theorem 2 is proved.
We can use Theorem 2 to direct the routing establishment and find the optimization strategy for saving energy. According to this point, the nodes in the same grid may establish different routes to the mobile agent node.
5. Energy efficient fusiondriven routing with mobile agent
In this section, we design an EEFR based on the above analysis. Using the mobility of agent node, our proposed fusiondriven routing aims to attain the energy efficient data collection with random node deployment.
A. Cluster and grid division
The topology division of network includes the following two steps. The first is to divide the network into a series of larger equal square area and each square represents one cluster. There is only one mobile agent node in each cluster which can move freely. The second one is to further divide the cluster into many smaller equal square, and each square represents one grid. For the sake of avoiding data loss during transmission, the size of grid is not too large but should guarantee the communication of two random nodes in the neighbor grids. When the sensor node knows which grid it belongs to, it needs to broadcast the information with identification and receive the information from other nodes in the neighbor grid.
For completing more complicated tasks, sensor nodes are equipped with many different sensors so that the traditional data collection model is not valid. The network needs to complete more than one task, that one task is respond to one data set as described in Definition 3.1, then all the data sets compose of the data space. The mobile agent node needs to arrange the suitable nodes to complete data collection according to different tasks.
B. Movement of mobile agent node
When the mobile agent node moves to a new position, the coordinate of the grid will also change. Only the sensor nodes in the grids with the coordinate of (1, 1), (1, 1), (1, 1), and (1, 1) can directly send data to mobile agent node while all the other sensor nodes need to send their data to the mobile agent node by multihop method. In EEFR, multihop routes are formed between neighbor grids from source node to the mobile agent node, which can guarantee the sensory data generated by the farthest node not be lost. The data collection of intercluster starts from the outermost grid and ends when all data are transmitted to the mobile agent node. Onetoone or manytoone mappings are formed among sensor nodes of neighbor grid. If a sensor node in grid g_{ u } (x, y) has sensory data to be transmitted, it needs to select the corresponding relay node from its neighbor grids which is much closer to the mobile agent node. For example, when x, y > 0, the selected relay node should be in the grid g_{ u } (x1, y) or g_{ u } (x, y1). In this case, g_{ u } (x, y) acts as a source grid, while g_{ u } (x1, y) or g_{ u } (x, y1) acts as the destination grid.
The data collection involves in many kinds of sensors to complete different tasks in EEFR. According to the Definition 1 in Section 3, the data collection space $\hat{D}$ consists of k data sets and denoted as $\hat{D}={D}_{1}\times {D}_{2}\times \cdots \times {D}_{k}$. Each data set includes sensory data in different attributes.
where d and d_{ s } represent the maximum correlation distance between nodes and the space distance between two sensor nodes, f represents the effect of the fusion algorithm on the data correlations, and p is the data attribute.
The mobile agent node is responsible to find the efficient data fusion which can reduce the energy consumption in its cluster and achieve the optimization target to direct the establishment of routing. Based on Theorems 1 and 2, we can judge whether the data fusion is effective and take use of the mobility of agent node to maximum the performance of saving energy by data fusion. s_{ i } and s_{ j } are any two nodes in different grid of the same cluster. The following Lemma 1 gives the calculation method of finding the mobile agent position which can realize the efficient data collection using the efficient data fusion in maximum.
Lemma 1: The energy efficiency can be realized if the location of a mobile agent node can meet the requirement of $max{\sum}_{{s}_{j}\in {g}_{u}\left({x}^{\prime},{y}^{\prime}\right){s}_{i}\in {g}_{u}\left(x,y\right)}\left[D\left({s}_{i}\right)+D\left({s}_{j}\right)\right]{e}_{f}\left({e}_{t}\left(\sqrt{2}a\right)+{e}_{r}\right)\left(\mid {x}_{0}\mid +\mid {y}_{0}\mid \right)\sigma \left({s}_{i},{s}_{j}\right)min\left(D\left({s}_{i}\right),D\left({s}_{j}\right)\right)$
Hence, Lemma 1 is proved.
According to different tasks, the mobile agent node can find the optimized position by Lemma 1, then complete the establishment of intracluster routing.
C. Routing establishment
Although the minimizing energy consumption is the prior target during the routing establishment, EEFR also try to balance the energy consumption among different sensor nodes. As the sensor nodes are deployed in high density, more than one node might meet the requirement of minimizing energy consumption during routing establishment. For the sake of balancing the energy consumption, the node with more remaining energy should be selected to undertake the routing task. The routing selection includes three steps.
The second step: the mobile agent node determines the sensor nodes that participate the intragrid data fusion by Theorem 1, and point the nodes with more remaining energy to complete the fusion by Theorem 1. As shown in Figure 5b, the nodes with short distance in the same grid participate intragrid fusion process since they can meet the requirement of Theorem 1, while the nodes in long distance are forbidden in such operations.
For saving energy consumption, it is a general method to make the sensor nodes turn to sleep when they do not have any task. EEFR routing also adopts timeslot allocation to reduce the probability of communication collision and save energy. Let t_{intra} and t_{inter} represent the consumed time of intragrid and intergrid data fusion process. n_{maxf}represents the maximum number of each grid to complete intragrid fusion process. n_{ t } (x, y) is the number of sending data from grid g_{ u } (x, y) to other grids, n_{ r } (x, y) is the number of receiving data of grid g_{ u } (x, y) from other grids. T_{ u } is the running time of a round. The following Lemma 2 gives the allocated operating time of a random grid g_{ u } (x, y):
Lemma 2: For ∀g_{ u } (x, y), the allocated operating time is $\frac{\left\{{n}_{maxf}{t}_{\mathsf{\text{intra}}}+\left[{n}_{t}\left(x,y\right)+{n}_{r}\left(x,y\right)\right]{t}_{\mathsf{\text{inter}}}\right\}{T}_{u}}{{n}_{maxf}{t}_{\mathsf{\text{intra}}}+{\sum}_{{g}_{u}\left(x,y\right)\in {C}_{u}}\left[{n}_{t}\left(x,y\right)+{n}_{r}\left(x,y\right)\right]{t}_{\mathsf{\text{inter}}}}$.
Hence, the allocated operation time of each grid is $T\left(x,y\right)={T}_{u}\left[{T}_{\mathsf{\text{inter}}}\left(x,y\right)+{T}_{\mathsf{\text{intra}}}\left(x,y\right)\right]\u2215{T}_{\mathsf{\text{total}}}=\frac{\left\{{n}_{max\mathsf{\text{}}f}{t}_{\mathsf{\text{intra}}}+\left[{n}_{t}\left(x,y\right)+{n}_{r}\left(x,y\right)\right]{t}_{\mathsf{\text{inter}}}\right\}{T}_{u}}{{n}_{max\mathsf{\text{}}f}{t}_{\mathsf{\text{intra}}}+{\sum}_{{g}_{u}\left(x,y\right)\in {C}_{u}}\left[{n}_{t}\left(x,y\right)+{n}_{r}\left(x,y\right)\right]{t}_{\mathsf{\text{inter}}}}.$
Hence, Lemma 2 is proved.
Lemma 2 shows that the allocated time of grid is determined by the task amount undertaken by its nodes, where the longer time will be allocated because of more tasks.
6. Simulation and result analysis
In this section, we evaluate the performance of the proposed EEFR routing by experiments. Our experiments demonstrate the energy consumption of network and node survival condition by adopting EEFR with different parameters. We further evaluate the performance of EEFR by comparing with a clustering hierarchy based on cellular topology named CHCT [19] and LEACHMT which modified based on classic LEACH algorithm [13].
A. Simulation environment
Parameters in simulation
Parameter  Value 

Initial energy  20 J 
Distribution density  0.003/m^{2} 
Energy consumption/circuit  50 nJ/bit 
Energy consumption of amplifier  d < 87 m10 pJ/bit m^{2} 
Network area  9 × 10^{4} m^{2} 
Bandwidth  1 Mbps 
Of data fusion  15 nJ/bit 
Of amplifier  d ≥ 87 m10 pJ/bit m^{2} 
We use network lifetime and the number of alive nodes to evaluate the performance in our simulations. Generally, the network lifetime can be measured by three methods. One is the time when the first node exhausts its energy, the second is the time when the dead nodes reach a certain degree, and third is the time of all nodes dead. Here, we choose the third one as the network lifetime. The number of alive sensor node means the node still can normally work, which decreases with the network operation.
B. Network lifetime and node survival condition
In this simulation, we focus on evaluating the performance of EEFR by network lifetime and remaining energy. The network lifetime is recorded as the time when all the sensor nodes are energy exhausted. The longer network lifetime means the higher efficiency of saving energy. When the first dead node appears, the less remaining energy of other nodes means the better energy balancing performance.
C. Comparison with other routings
7. Conclusion
For the energy limited WMSNs, the most challenge problem is how to effectively use the energy of network during data collection. In this article, we theoretically analyze the energy consumption of sensor nodes during data collection when the nodes are random deployed. We find that fusion process can reduce the energy consumption, and the efficiency of saving energy is determined by data correlation coefficient. Then, we study the mobility of mobile agent node on maximizing the saving energy efficiency of data fusion. Finally, we design an EEFR based on cluster hierarchy for WMSNs, where the cluster structure is formed based on square grid topology. Extensive simulations are performed to validate our proposed EEFR. Simulation results show that EEFR shows high performance in decreasing energy consumption with node random deployment.
Declarations
Acknowledgements
This study was partially supported by the National Science Foundation of China (NSFC) under Grant No. 61103234.
Authors’ Affiliations
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