Chapter 3 Design Approach
3.5 Mobility
In [24] it showed mobile nodes can bridge gaps between nodes where the accurate location information cannot be obtained. In addition, It also showed that mobility is helpful for the improvement of localization accuracy. When a mobile node arrives at a new location, it sends a seed information request to other neighbors. If the neighbors have the seeds’ RGB values, they transmit packets that include the RGB values of seeds and the hop counts to seeds to the node. After receiving the packets from neighbors, the node i compares and calculates the D to kik th seed and get the smallest D . With the RGB values and ik D to all seeds, the node i can calculate its ik RGB values using equation (1), (2), (3), and (4). The RGB values are then transmitted to the server and the position of node i will be calculated by looking up the location database. Fig. 3 is the flowchart of the CDL algorithm.
Fig. 3. The CDL algorithm. node by looking up the location database
Chapter 4
Simulation Results and Discussion
To evaluate our method, the location accuracy is the main issue to be investigated. Location accuracy can be improved by several ways, including increasing the number of seeds and density of nodes or seeds. However, the tradeoff of cost and location accuracy needs to be considered carefully. In this section, we evaluate the proposed approach CDL by measuring its estimated location errors with various parameters and compared with an existing method MCL [17]. The simulation was conducted using Matlab, which is a tool for numerical computations and is powerful for its computing with matrices and vectors, and is easy for representing numerical data in graphs.
4.1 Simulation Model
Our simulation models a network where all sensor nodes are placed randomly in a 500 m × 500 m area [17]. We first introduce the random waypoint models [25], which is a popular mobility model for mobile sensor networks. In this model, a node selects its destination and velocity randomly. After it reaches the destination, it pauses for a period of time (pause time) selected randomly. In [19], the random way point model was shown to fail to provide a steady state because of the decreasing of the average node speed over time. [20] provided a proof for the phenomenon mentioned above and came up with a methodology which guarantees the constancy of the average node speed distribution. For comprising with MCL[17], we adopted the
modified random waypoint model from [17], in which nodes randomly choose their speed during each movement instead of selecting a certain speed for each destination.
In addition, we assume the radio range is a perfect circle [17]. The node speed is uniformly distributed within [Vmin, Vmax]. Table 2 lists the simulation parameters, which are defined as follows:
¾ Seed density (s ): the average number of seeds in one hop transmission range d [17] .
¾ t ：Time slot length between location announcements [17] . u
¾ Estimate error: expressed in terms of the node transmission range R .
¾ Node density (n ): the average number of sensor nodes in one hop transmission d range [17] .
Table 2: Simulation parameters [17]
Parameter Value
Area size 500×500 m 2
Node speed Randomly choose from [Vmin,Vmax]
Node transmission range (R) 50 m
Pause time 0
Number of samples maintained
(for MCL) 50
Measurement period 50 t u
Time slot length (time unit) t u
Fig. 4 shows the estimate error measured in terms of the node transmission range R, for different node density (n ). The more the number of sensor nodes, the better d the location accuracy of sensor nodes. The estimate error can be archived below 0.3 R when the node density is more than 6.
Fig. 4. Impact of node density.
Fig. 5 shows the location estimate error in R with different number of seeds, with Vmax= R in terms of meters per time slot for n = 10. A seed is a node aware of its d location. It can assist other sensor nodes to estimate their locations. That is, with the help of information given by seeds, sensor nodes can localize their locations. With s d
= 0.4, it is sufficient for CDL to perform as accurately as MCL.
Fig. 5. Impact of seed density.
4.2 Comparison with MCL [17]
We compare the CDL with MCL, which is also a range-free approach for mobile WSNs. Fig. 6 compares the localization estimate error between MCL and CDL over time with s = 0.5, Vd min = R, and n = 10. The MCL can exploit past information, so d its accuracy improves over time. Although the CDL does not use the past information, it performs quite well and stable over time.
Fig. 6. Accuracy comparison.
The node density plays an important role in localization. Since the location information is provided by neighbors, the more the node density, and the more accurate the localization. Besides, the estimate error caused by the uneven distribution of nodes topology can be reduced significantly by increasing the node density. Fig. 7 shows the impact of node density on location accuracy, withVmax= R and s = 0.5. d The accuracy of each scheme can be improved quickly by increasing the node density.
However, in any case, CDL always performs better than MCL regardless of node density.
Fig. 7. Impact of node density.
Fig. 8 shows the accuracy of MCL and CDL improve with the increasing of seed density, with Vmax= R and nd = 10. MCL performs localization by collecting information from seeds or sensor nodes which is one hop away from seeds. As a result, seed density is a critical factor for MCL. The location accuracy improves significantly as the seed density soars. For CDL, its location accuracy does not rely on sufficient numbers of seed nodes, because CDL collects location information from all seeds. It performs good with few seeds, compared to MCL.
Fig. 8. Impact of seed density.
Chapter 5
Conclusions and Future Work
5.1 Concluding Remarks
Localization is a critical issue in wireless sensor network. With the aid of locations of sensor nodes, for example, the efficiency of routing can be improved significantly. We have proposed an efficient Color-theory based Dynamic Localization (CDL) for mobile wireless sensor networks. The basic idea of CDL is based on color theory, which exploits the changes of colors with distances to localize sensor nodes. CDL is suitable in the scenarios that need a centralized server to collect and monitor all users, such as in health-care systems and hospital monitor systems.
Our simulation results reveal the location estimate error can be reduced up to 0.2 R when the seed density = 0.8 and node density = 0.5. In addition, the location accuracy of CDL is 40% - 50% better than that of MCL [17]
5.2 Future Work
To enhance location accuracy, the uniqueness of mapping RGB values to coordinates for building up the location database can be studied for developing a better mapping algorithm. In addition, we will implement the CDL in real mobile wireless sensor networks, such as a Berkeley Mote platform, to further evaluate its localization effectiveness.
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