BSS: A Distributed Top-k Processing in Mobile ... - Semantic Scholar

BSS: A Distributed Top-k Processing in Mobile BusNet for Security Surveillance Xu Li*, Jiajun Hu†, Hongyu Huang^, Jialiang Lu†,Wei Shu♦, Minglu Li† and Min-You Wu† *

Department of Computer Science and Engineering, State University of New York at Buffalo, US Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China ^ College of Computer Science, Chongqing University, Chongqing, China ♦ Department of Electrical and Computer Engineering, The University of New Mexico, USA

†

Abstract—We consider distributed top-k processing problem in a mobile scenario. Specially, we focus on a real application of a bus network (N nodes), where buses are equipped with cameras for real-time security surveillance. Due to the limited number of screens (k, k pn j ,t ∧ pn j ,t ∈ PNTi ,t }

(4)

Here, r is the threshold for sampling. Last, we find that top-k node selection cannot terminate because some of nodes are not able to communicate with others for a long time due to the intermittent connectivity of mobile network. Putting some redundant tokens in the network can overcome this problem:

k ' = (1 + α ) × k; α = f (cr) (5) where k' is actual token number in the network, α is the redundancy ratio of token and it is the function of communication range (cr). How to choose α is based on the network condition, which will be discussed in the performance evaluation part. Accordingly, at traffic management center side, the administrator only accepts the images from the first k nodes and discards images from others. Based on the discussion, we propose our distributed top-k processing scheme BSS, as shown in Figure 1. V. PERFORMANCE EVALUATION A. Overview of BusNet simulator Currently, over 4,000 taxies and 2000 buses in Shanghai, equipped with GPS devices, report their locations in real time. We map these data onto a digital map to obtain their real traces, with which we developed a real-trace driven simulator to carry out our testing. Specially, in our work, we focus on a BusNet including 45 bus lines and nearly 700 buses. The total covering area of all bus lines is approximately 150 km², as represented in Figure 2. Table I lists the parameters used for the experiments in our simulation. The default values of experimental parameters are selected based on field experience. Initially, we random choose a passenger number ranging from [0, 80] for each bus. Meantime, the passenger number will change with time. The time duration of each simulation is two hours and the warm-up time is about 500 seconds. After that, the top-k node selections will be executed every Γ time interval (As mentioned in Section III.B, GPS devices can be used for time synchronization). How to set Γ is based on the network

condition and average time cost of each top-k node selection, which will be discussed in the next section. In addition, each data point in the following figures is averaged over 30 runs.

shown in 5(a) and (b), with increase of cr, we can see a slow increase in ADA and considerable decrease in ATC. In other words, increasing cr is more beneficial for improving ATC. The ATC is less than 20s and ADA is over 90% when cr=400m and we believe that such a good performance can be expected in the real deployment (The maximum communication range in DSRC standard is about 1000m). Overall, BSS scheme has high ADA and low ATC in the most of testing cases which demonstrates the effectiveness and scalability of BSS.

Figure 2. A BusNet for security surveillance in Shanghai TABLE 1: THE EXPERIMENT PARAMETERS Focused Area Number of bus lines Number of bus (N) The number of screen (k) Threshold for sampling (r) Communication Range (cr) Bandwidth Packet Size

150 km2 45 694 50 150 300 m 1 M/bps 10 KB

B. Testing results As shown in Section IV.B, the redundancy ratio of token α is a function of communication range cr. To obtain best performance of BSS scheme, we first need to choose the empirical values of α under different communication ranges. In Figure 3, the red points will be selected as the default values of α because they have maximum marginal utility under different communication ranges, respectively. In other words, the red point has maximum gain in both decreasing the average time cost for top-k node selection (ATC) and increasing the average degree of accuracy (ADA). Meanwhile, we found that with different parameter settings, most of the top-k node selection will terminate in a short time, ranging from 50 to 200 seconds. Thus, in our simulation, we simply set the update interval =300s, which is acceptable for security surveillance application. Figure 4 uses Cumulative Distribution Function (CDF) curve to present the progress of top-k node selection with BSS. We can see that under the default setting, 70% top-k members has been elected in 50s and 90% top-k members has been elected in 100s, which shows the effectiveness of BSS to support real-time security surveillance. The BASELINE scheme, however, does not provide good results as expected. In most of simulations, the top-k node selection cannot terminate in 1000s. The main reason is that BASELINE is designed to execute exact top-k node selection so it will take much more time. Especially, it is more difficult to use BASELINE with small communication range and sparse node density because of intermittent connectivity and partitioned network. Due to the poor performance of BASELINE, we will mainly focus on BSS scheme in the remainder of this section. Figures 5(a) and (b) show the ADA and ATC as offered communication range cr increases from 250m to 400m. As

Figure 3. Redundancy ratio selection of token α with different communication ranges (ADA: Average Degree of Accuracy)

VI. CONCLUSIONS We present a new distributed scheme BSS in a bus network, which is designed to support top-k node selection for the realtime security surveillance application. Compared with previous works, we studied top-k processing problem in a mobile scenario. Especially, BSS utilizes various strategies (such as partial sampling, redundant tokes, approximation, etc) to speed up top-k node selection facing poor network condition and application requirements (low time cost and high accuracy). We carried out our testing on a real-trace driven simulator, which utilizes about 700 buses equipped with GPS-based mobile sensors in Shanghai. Our results show that BSS has a good performance in terms of time cost and

high average degree of accuracy, which demonstrated the feasibility of our BSS scheme.

Figure 4. The progress of top-k node selection with BSS

(a)

(b) Figure 5. BSS with different communication ranges

ACKNOWLEDGEMENT We thank anonymous reviewers for their helpful comments. This research was partially supported by Natural Science Foundation of China grant No.60573138 and 60773091, the National Grand Fundamental Research 973 Program of China under Grant No.2006CB303000, the National 863 Program grant No.2006AA01Z247, and National Science Foundation of USA grant CNR-0626380.

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