In mobile wireless sensor networks, priori-trail planning for the mobile sink is a commonly used solution to data collection from the whole network, for its low protocol overhead. However, these trail-based approaches lack efficient load balance mechanism to handle burst WSN traffic, which needs to be sent to the base station correctly with low delay. This paper proposed a dynamic path planning for mobile sink to balance load and avoid traffic bottleneck. It contains grid partition of the network, priori-trail creation, burst-traffic awareness and estimation, resources collaborative strategy, and dynamic routing adjustment. Experiments on NS-2 platform show that the proposed algorithm can efficiently balance the regular and burst data traffic with a low-delay and low loss rate performance of the network.
In recent years, Wireless Sensor Networks (WSN) have been used in various fields, such as ocean exploration, battlefield target location, physiological data collection, and intelligent transportation systems [
The main contributions of this paper are three aspects: Dynamic path planning for mobile sink node, which could quickly respond to emergent data collection. Reducing packets loss rate by waking up neighboring nodes to cache data when burst traffic occurs. Prolonging network’s lifetime by re-electing cluster heads based on the balance between residual energy and center location of nodes.
The rest of the paper is organized as follows. In Section
In regular data collecting, a mobile sink needs to move back and forth along some predesigned paths across the whole WSN network. Hot spot strategy has been proved to be an effective method for data collection so far, and it needs grouped nodes gather their information on hot spots for mobile sinks’ visit in sequence [
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Based on studies mentioned above, we saw that periodic collection of data on the network and burst traffic were usually done separately, and we tried to integrate these two works by changing mobile sink’s path dynamically to collect burst data in one of our previous study [
In this paper we will focus on the after-mentioned situation. In a dense wireless sensor networks, we need to collect the whole network data regularly, and in a round of data collection process only one area may produce burst traffic. In order to solve this problem, this paper proposes a dynamic node movement strategy. Firstly, the monitoring area is divided into independent virtual grids. Each node selects a node as a cluster head, and each cluster head position is an alternate position for mobile sink node movement. Then we can transform the moving position of the mobile sink node into a classic traveler problem (Traveling Salesman Problem, TSP). If an area has an unexpected event, it is necessary to ensure that burst traffic data is collected in the shortest possible time and that the delay of collecting other nodes is reduced as much as possible, also, saving node energy at the same time.
Communication between nodes is set over a single shared channel to avoid multichannels inter-coordination, which would lead to nodes’ end-to-end time delay. In order to cover the bandwidth limitation due to mutual interference between neighboring nodes, we introduce a neighbor interference degree aware algorithm for local links allocation [
In a regular round, mobile sink node moves along with predesigned path to reach each cluster head node to collect data.
The following assumptions are given:
(1) The nodes in the network are evenly distributed, with unique ID and no longer moved after deployment.
(2) All nodes in the network have the same initial energy and communication radius and transmit and receive power.
(3) Sink nodes, whose energy is not restricted, move at a constant speed
(4) Each sensor node can obtain its own location information through GPS or other positioning algorithm.
(5) The mobile sink node can access the existing network wirelessly, in addition to being able to communicate freely with the sensor node in the detection area.
In this paper, a simple energy consumption model is used, assuming that all nodes in the network use fixed transmit power and receive power. A round of data collection is completed when the mobile sink node moves back to the original position.
When no burst traffic occurs, a shortest route
(1) If yes, nodes in this region are rather idle and we can forward burst traffic data through these nodes to the mobile sink node.
(2) If no, the mobile sink node will temporarily change its way for quick response to the emergency. This will interrupt originally planned routine work, and we readjust the original plan and route, whose detailed description is shown in Figure
DPPMSBT algorithm flow chart.
In the initial planning of the mobile sink node path, we refer to the MREEMRP algorithm [
In each cluster, only cluster head node remains active to collect data from its cluster and send data to the mobile sink node, while the others stay in the sleeping state.
Before the start of the data collection, the mobile sink node can obtain the shortest travelling path
where
After that, the mobile sink node can calculate the total time required to reach each cluster head for data collection as Original planned path time. The mobile sink node collects the data of each cluster head within one hop, with a combination of static collection and dynamic movement alternative mode. Suppose the mobile sink stays
When no burst traffic occurs, the mobile sink node moves according to the Original Planned Path
Sink Data Collection Path Planned Model Based on Grid.
Suppose, at a moment, a node labelled with The mobile sink node has already visited the cluster which node
In this case, node The mobile sink node has not yet visited the cluster which node
Routing Table Format.
Firstly, node
Fourthly, we subdivide the unvisited clusters into two sets, where set
Fifthly, clusters in set
After collecting the burst traffic data of node
An example of this burst traffic data transmission is shown in Figure
An Example of Data Collection with Burst Traffic.
At the same time, the time
After all, the mobile sink re-establishes its remaining path
In this paper, we simulate the DPPMSBT algorithm and analyze the results by using the object-oriented network simulation platform NS2 (Network Simulator version 2), which must run on UNIX/LINUX platform.
Running environment of NS2 is described as follows:
(1) On windows 7 operating system, virtual machine software VMware workstation with version 12.5.9 is installed.
(2) On virtual machine, ubuntu operating system with version 14.04LTS is installed.
(3) On ubuntu14.04 platform, NS2 application with version ns-allinone-2.35 is downloaded and unzipped to directory /usr /ns-allinone-2.35, and then NS2 environment is setup by using installation procedure one by one.
Simulation is setup according to the following: The network size is set to 100m The number of nodes is 27 The nodes’ location is randomly and evenly distributed The start position of mobile sink node is at the left top of the monitoring area, which is marked with number ‘0’ in Figure
Network Topology for Simulations.
The simulation environment parameters are shown in Table
Simulation environment parameters setting.
Parameter name/value | Remarks |
---|---|
S/100m | Size of the monitored area |
N/27 | Number of nodes |
| MAC layer protocol |
| Energy of the sink node |
Ei/1J | Ordinary node energy |
Tmax/200s | Maximum simulation time |
ts/5s | cluster head data collection time |
vs/10m/s | sink node moving speed |
R/25m | node communication radius |
A round of data collection is completed when the mobile sink node returns to the first cluster head node, and then we re-elect cluster head and plan the next travelling path according to the node's survival energy. At this point, if the first cluster head node has run out of energy, mobile sink node needs to move to the newly elected cluster head node.
We conduct simulation experiments with 3 different methods, which are Original Path Planning, Queen-Bee routing algorithm, and DPPMSBT algorithm presented in this manuscript. Topology of the simulation network is shown in Figure
Here end-to-end delay refers to a network latency metric, which is the time difference between expected arriving and actual arriving of packets. Figure
Delivery curve of the data packet.
In Figure
Path length refers to the total length of route that mobile sink node travels along with. Network lifetime refers to the survival time of the entire network, especially in this manuscript, we define it as the time from starting until network is 20% disconnected due to nodes’ death. In Figure
Path length comparison.
From Figure
It also can be seen that “DPPMSBT_length” curve holds on until the end of 200s simulation time, while other two methods last not more than 120s, meaning that network lifetime in our DPPMSBT algorithm is much longer than that in other two methods. The reason is because energy on several related nodes rapidly reduce when burst traffic occurs, and their early death leads to network’s breakdown. However, in our proposed DPPMSBT algorithm, we wake up other nodes to help forwarding the burst traffic data, balancing the energy consumption of nodes, and gain longer lifetime for network.
Packet loss rate is the percentage of dropped packets during transmission from cluster heads to the mobile sink node. This paper takes simple Bernoulli loss model to simulate network losing packets and leverages DE- (Direct-Estimation-) MLE algorithm to estimate packets loss rate from terminal node data. In Figure
Packet loss rate comparison.
From Figure
In this paper, a dynamic mobile sink node moving path planning algorithm (DPPMSBT) is proposed for the intensive wireless sensor networks that collect periodic data of the whole network and a single area may generate burst data. From the analysis of the end-to-end delay, the path length and the network lifetime, and the packet loss rate, the proposed algorithm DPPMSBT has a greater advantage; it can guarantee the periodic collection of the whole network data and make dynamic sink path planning to collect accurate burst traffic data when there is an incident. And it can also reduce the packet loss rate and extends the network lifetime.
The data used to support the findings of this study are included within the article.
The absence of author Chang Jie in this paper is due to work transfer.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This paper is funded by Scientific Project of Guangdong Provincial Transport Department (No. Sci & Tec-2016-02-30), Surface Project of Natural Science Foundation of Guangdong Province (No. 2016A030313703), and Surface Project of Natural Science Foundation of Guangdong Province (No. 2016A030313713).