Event query processing is a very important issue in wireless sensor networks (WSNs). In order to detect event early and provide monitoring information and event query timely in WSNs, an efficient intelligent collaborative event query (ICEQ) algorithm is proposed, in which sensor nodes that are near to the boundary of events are selected to accomplish complex event monitoring and query processing through intelligent collaboration. ICEQ will select range-nearest neighbors as the basic components of surrounding nodes. Then it will identify the gaps between the surrounding nodes and try to select the nearest neighbor collaborative nodes for enclosing the event in the node selection phase, which can avoid redundant sensor nodes to join surrounding nodes via identifying a set of association surrounding nodes between the nearest sensor nodes and the query events. Detailed experimental results and comparisons with existed algorithm show that the proposed ICEQ algorithm can achieve better performance in terms of query-processing time, average number of selected collaborative nodes, and query message consumption.
The rapid development in computing, sensing, and wireless communication technologies has made the availability of wireless sensor networks (WSNs) [
In WSNs, an important task is to monitor dynamic and unpredictable events. Since the sensor network can be viewed as a distributed database [
Since sensor nodes have rigid energy constraints, it is hard to displace sensor nodes in the monitoring region [
In order to balance the inherent tradeoff between query reliability versus energy consumption in query-based wireless sensor systems, an adaptive fault-tolerant quality of service (QoS) control algorithms based on hop-by-hop data delivery utilizing “source” and “path” redundancy is proposed in [
In order to improve the query performance, range nearest-neighbor (RNN) query [
Although a number of event query schemes have been proposed to improve the query performance in WSNs, event query processing is still a very challenging task due to its complexity and ill-posed nature, and all of these works do not comprehensively consider the correlation between sensors and environment. And the most existing research work has focused on data aggregation to provide efficient data transmission. The overhead of query processing is generally ignored with the assumption that query transmission contributes to only a small portion of overall data transmission in the sensor network. However, there are many cases where this assumption does not hold any more. Therefore, the methods mentioned above all use statistical methods to differentiate whether sensor nodes are boundary nodes or not.
Another problem is that existed work always assumes that the monitoring nodes are often interested in obtaining either the actual readings or their aggregate values; from sensor nodes that detect interesting events, the detection of such events can often be identified by the readings of each sensor node. In such scenarios, each sensor node is not forced to include its measurements in the query output at each epoch, but rather such query participation is evaluated on a per epoch basis, depending on its readings and the definition of interesting events. However, in actual complex environment, due to the characteristics of WSNs, sensors are usually deployed in a noneasily accessible or harsh environment, and sensors are prone to failure, and these faulty sensors are likely to report arbitrary data very different from the true environmental phenomenon, and the faulty data of sensors are very common, which greatly influence the accuracy of data query. Hence, how to select appropriate nodes to accomplish complex event monitoring and query processing through intelligent collaboration is an important task.
Motivated by the above reasons, an efficient intelligent collaborative event query (ICEQ) algorithm is proposed, in which sensor nodes that are near to the boundary of events are selected to accomplish complex event monitoring and query processing through intelligent collaboration. ICEQ includes initial phase and node selection phase. In initial phase, ICEQ will select range nearest-neighbors as the basic components of surrounding nodes. Then, it will identify the gaps between surrounding nodes and try to select nearest neighbor collaborative nodes for enclosing the event in node selection phase, which can avoid redundant sensor nodes to join the surrounding nodes via identifying a set of association surrounding nodes between the nearest sensor nodes and query events. The main contributions of ICEQ may be summarized as follows. To retrieve a set of the nearest collaborative nodes of a specific event, ICEQ can identify a set of association surrounding nodes between the nearest sensor nodes and the query events that frequently appear in the system, which converts the demographic values and sensed data items presented in each query transaction into demographic types and event categories, respectively. Hence, ICEQ can select the nodes appropriately to decrease the number of selected nodes and prolong the lifetime of WSNs. ICEQ is able to identify where gaps exit between surrounding nodes by finding large or frequent demographic query itemsets of query, and then try to select proper collaborative nodes for enclosing the event with rule decision and computing confidence between rules. Hence, ICEQ can select the appropriately nodes according to the network topology and environment.
The rest of this paper is organized as follows. The proposed intelligent collaborative event query algorithm is given in Section
In a distributed WSN, assume that that each sensor node
In order to select the nodes appropriately, the proposed ICEQ algorithm will identify a set of association surrounding nodes between the nearest sensor nodes and the query events that frequently appear in the system, which will consider the spatial and the temporal correlation between sensors and environment. Suppose there are
Since the goal is to identify the associations between demographic types and event categories; the demographic values and sensed data items presented in each query transaction must be converted into demographic types and event categories, respectively, resulting in an extended query transaction. Here we include all demographic types of each demographic value and all sensed data categories of all item appeared in the sink node. Therefore, the
Similarly, we say that
The proposed ICEQ algorithm consists of two phases: initial phase and node selection phase. ICEQ will select range nearest neighbors as the basic components of surrounding nodes in initial phase. Node selection phase is to identify gaps between the rough surrounding nodes and then try to select proper surrounding nodes for monitoring the event by intelligent collaborative processing among nodes to decrease power consumption.
In the initial processing step, let
In order to differentiate different segments of corresponding query-line nearest-neighbor nodes, end nodes of subsegments of a specified query line
1: Initialization ( 2: 3: Dequeue node 4: 5: 6: 7: Add an end node 8: Update 9: Add an end node 10: 11: 12: 13: 14: Collaborative node selection; 15:
The goal of collaborative node selection phase is to select proper sensors to enclose the event. From Algorithm
If we sort nodes in the queue
In the phase, each sensor node
The proposed ICEQ algorithm will check whether gaps exit between this node and its adjacent neighbors in
A neighbor node
We also construct neighborhood relationship for
In order to select the appropriate node, we need to identify generalized profile association rules in the rough node set
Let
Considering that some of the strong generalized profile association rules could be related to each other in either the demographic itemset part or the sensor nodes, and, therefore, the existence of one such rule could makes some others not interesting. To overcome the problem, let
We call a rule
a D-ancestor of another rule
In context of collaborative nodes selection, we say a rule is valid if it can be used for making decision. Given a set of strong rules say
Let the immediate D-descendants of
Let the immediate B-descendants of
Suppose that we have obtained the confidences of both the D-deductive rule and the B-deductive rule of a given rule
Therefore, we define the estimated interestingness of
We approximate the confidence of a D-deductive rule by using the following theoretic results.
Let
Since
Similarly,
Therefore,
Without loss of generality, let
Let
By adding the denominators and numerators, respectively, from the left-hand sides of the two equations, we can obtain
Since
Now we discuss how to compute the confidence of a B-deductive rule
If
At last, we report all sensor nodes in
1: 2: Find out strong rules 3: Calculate sufficient confidence with ( 4: 5: 6: Choose Construct neighbourhood relationship for Insert 7: 8: Choose Construct neighbourhood relationship for Insert 9: 10: 11:
In order to evaluate the performance of the proposed ICEQ algorithm, we implemented the ICEQ in the well-known simulation tool NS-2 [
In the first scenario, we vary the size of the event with varying its radius from 5 m to 30 m. Figures
Processing time with varying event radius in grid topology.
Number of selected nodes with varying event radius in grid topology.
As shown in Figure
Figure
Figures
Processing time with varying event radius in random topology.
Number of selected nodes with varying event radius in random topology.
From Figure
The results of number of selected nodes of different algorithms with the varying event radius are shown in Figure
In this scenario, we vary query lines in random topology. And sensor nodes are deployed in random topology. The radius of the circular event is fixed at 15 m, and we assume that it occurs arbitrarily in the monitoring region so that we obtain different number of edges of the approximate polygonal boundary. Figures
Processing time with varying query lines in random topology.
Number of selected nodes with varying query lines in random topology.
The results of query-processing time of different algorithms with the varying number of query lines are shown in Figure
Figure
In this scenario, we investigate the total message consumption with varying network size from 100 to 10000 sensor nodes. Figures
Total messages with varying network size.
Standard deviation with varying network size.
In Figure
Figure
In this paper, we presented an efficient intelligent collaborative event query (ICEQ) algorithm to detect the event early and provide monitoring information and event query timely in WSNs. ICEQ can identify a set of association surrounding nodes between the nearest sensor nodes and the query events that frequently appear in the system, which converts the demographic values, and sensed data items presented in each query transaction into demographic types and event categories, respectively. Hence, ICEQ can select the nodes appropriately to decrease the number of selected nodes and prolong the lifetime of WSNs. ICEQ is able to identify where gaps exit between surrounding nodes by finding large or frequent demographic query itemsets of query, and then try to select proper collaborative nodes for enclosing the event with rule decision and computing confidence between rules. Hence, ICEQ can select the appropriately nodes according to the network topology and environment. Through ICEQ, we can select a set of surrounding nodes of the event instead of all the sensor nodes in the monitoring region to check if there is any event evolution. Therefore, sensor nodes which are not surrounding nodes can enter into sleep modes temporarily to save their battery energies and thus extend the lifetime of sensor networks. The future work will focus on the issues of query moving objects and track objects in WSNs.
This work was supported by the National Natural Science Foundation of China (no. 60902053), the Science and Technology Research Planning of Educational Commission of Hubei Province of China (no. B20110803), and the Natural Science Foundation of Hubei Province of China (no. 2008CDB339). The author also gratefully acknowledges the helpful comments and suggestions of the reviewers.