Energy-Efficient Area Coverage for Intruder Detection in Sensor Networks
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There are two extreme cases in this deployed situation shown in Figure 10 and Figure Obviously, the sensor selecting in Figure 11 is better than it in Figure We denote them as line1 and line2. So the scheme should consider both the barrier construction and the work time of different line. In the Figure 10 , the number of overlap sensor which is 9, is much more than it in the Figure The work time of the barrier could be calculated according to the percentage of overlap sensors in the barrier.
Furthermore, there are some sensors in a barrier that are not overlap with other ones, but they also cover some area to be a segment in the barrier. So the expression of a barrier work time is related with both the number of overlap sensors PB i in a barrier and the coverage ratio RB i of a barrier. In this situation, there are no vertical directional break path. But there are still some break paths in the belt.
So the relationship between Euclidean distance of two sensors D and the sensing range R is the metric the measure the coverage quality. A typical deployment in a small region of the belt. In this deployment, a proper scheme to active the sensor could prolong the lifetime of the network. Case 1. Every sensor could get their exclusive serial number. This case the active sensor is No. It could reduce the risk that an intruder following a fixed crossing path to break the belt effectively.
So guaranteeing the coverage quality is not the primary task here. So the No. This means such couples have abundant energy than others.
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Thus the area they covered has less possibility to be the weak zone. Any movement or crossing could be detected by the barrier coverage model. But in some situations, it is not necessary for detecting both direction of crossing the belt. Such as the theater scene, it is free to leave the theater while it is illegal to enter the theater without tickets. Therefore, barrier coverage is not an suitable model since it may not differentiate the illegal intruders from the legal ones and detecting both legal and illegal intruders could cause the losses of energy shown in Figure Legal and illegal paths.
In some situation, only one direction is needed to be surveillance. Based on these consideration, we propose a new coverage, called one-direction barrier coverage, which has a great efficiency on directional detection. As shown in Figure 11 , the deployed sensor network in this rectangular belt can easily provide 1-barrier coverage. However, it cannot provide one-direction barrier coverage since the network cannot differentiate the illegal intruders from the legal ones. Therefore, the theory, protocols and algorithms that work with barrier coverage may not work with one-direction barrier coverage, and we have to study new measurement, design new protocols and algorithms for one-direction barrier coverage.
This problem is nontrivial since normally a sensor e. So, it requires sensors coordinate together to solve this problem. Before our work, Ma and Liu analyzed the probability of full area coverage in a directional sensor network where each sensor is fixed to one direction [ 32 ]. In [ 33 ], Ai and Abouzeid proposed centralized and distributed algorithms to find a minimal set of sensors that can cover the maximal number of targets in a sensor network, where a sensor is allowed to work in several directions.
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In [ 34 ], Cai et al. These papers are not very related to our topics. Plarre and Kumar studied the problem of how to track objects using scattering directional sensors in a sensor network [ 35 ]. Target tracking has long been an active topic in sensor network research. Some researchers e. Many researchers believe that simple sensor models, such as the binary sensor model, are more realistic in sensor networks. Recently, the use of Particle filters has become popular to handle more general observation models e. However, we are interested in the problem of how to identify the directions of targets by using much less sensors.
This paper is the first research on measuring the coverage quality and the efficiency of one-direction barrier coverage. As mentioned before, one barrier could not tell the direction of an intruder. But if there are two barriers in the belt region, then the network could provide one-direction barrier coverage under some assumptions. The assumptions are as follows: 1 There is no hole between two barriers.
So such two barriers region is shown as Figure Two barrier could provide the one direction coverage for one intruder. Let the two neighboring barriers be b 1 and b 2 , with b1 on the top. The network is in the Intruder In state if at least one sensor is sensing the intruder; otherwise, it is in the Intruder Out state.
When the network changes its state from Intruder Out to Intruder In, the network checks which barrier the intruder is entering. If the sensors that sense the intruder are in b1, then the intruder is crossing the alarm line from top to bottom and we let the network report an alarm. Otherwise, the network does not report any alarm. So, the network can provide one-way barrier coverage. Suppose there is more than one intruder.
Assume that two neighboring barriers are deployed and the top boundary l t 1 of the top barrier b1 is selected as the alarm line. If it is from the outside of the two barriers, it must cross l t 1 to enter the sensing region of u, which means it is illegal. In Figure 15 , the path A, happens in T 1 while leaves in T 2 , and path B, happens in T 3 while leaves in T 4 , are both the legal path.
That is to say, the sensor network will not report any alarm from T 1 to T 4. So in this condition, the ordinary barrier coverage may not detect an intruder who is crossing the belt in the illegal way. Two barrier could not provide the one direction coverage for two intruders. Two barriers could provide one direction coverage under some assumption only for one intruder. We also simulate the new model in Matlab.
When there are fifteen intruders across the belt region during the network lifetime, the new model could extend the lifetime. The result is shown in Figure Network Lifetime Performance. The new model could extend the lifetime from the network lifetime from The temporal series is very interesting and important in one direction barrier coverage.
We can say that if an illegal intruder could enter the belt after a legal one and leave before a legal one, then he will not reveal his track.
This is a very interesting event, cause the relationship of legal and illegal triggering series is a subtle issue. If the sensors are deployed according to the strategies described before. The detection rate of intrusion can be controlled at a high level. However, it is difficult to distinguish between intrusions and nuisance warnings caused by environment elements.
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In conventional wireless sensor networks, the false alarm rate can be lowered by the joint-detection of multiple adjacent sensors. Specifically, since the belt region in front of the border is k-barrier covered by the sensors according to the deployment strategy, an intruder may be detected by multiple sensors as the intruder passes through the belt region.
Meanwhile, not only the binary sensors but also camera sensors and mobile sensors are needed for intrusion detection. To minimize the uncovered time, a joint analysis of the coordination between adjacent camera sensors are required to determine the initial phase of the camera directions and camera rotating velocity. Therefore, the camera sensor and mobile sensor could be activated by binary sensor which are deployed in the belt region. The coordination among them should also be analyzed.
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In this paper, we introduce the border intrusion detection. An energy efficient manner for border patrol to reduce the intensive human involvement and to improve the detection accuracy. In order to guarantee the detection accuracy, we mainly discuss the coverage quality and the method to enhance the quality of the detection. We proposed a method to identify all weak zones that need to be repaired. Target tracking is another important property of border intrusion detection.
It is still a problem to illustrate which kind of coverage model is the most efficient one. We presented an one-directional coverage model for border intrusion detection. Specifically, we discussed both one intruder scene and multiple intruder scene. Furthermore we also proposed the possibility of heterogeneous sensors cooperative for intrusion detection. Energy efficiency is not only reflected in the barrier construction algorithm, but also the network coverage model. The experimental results shows that the proposed algorithm could be extend the lifetime of the wireless sensor network.
The one future research direction is to add some mobile sensors and consider their integrated movement strategy. Another future research direction is to realize the detection in reality, which will consider the network performance in 3D environment. The objective is not only to guarantee coverage quality but also to improve network lifetime, data report timeliness and reliability at the same time. It is easy to use and understand.
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