Alade O. Tokuta

Strengthening barrier-coverage of static sensor network with mobile sensor nodes

AbstractA wireless sensor network (WSN) provides a barrier-coverage over an area of interest if no intruder can enter the area without being detected by the WSN. Recently, barrier-coverage model has received lots of attentions. In reality, sensor nodes are subject to fail to detect objects within its sensing range due to many reasons, and thus such a barrier of sensors may have temporal loopholes. In case of the WSN for border surveillance applications, it is reasonable to assume that the intruders are smart enough to identify such loopholes of the barrier to penetrate. Once a loophole is found, the other intruders have a good chance to use it continuously until the known path turns out to be insecure due to the increased security. In this paper, we investigate the potential of mobile sensor nodes such as unmanned aerial vehicles and human patrols to fortify the barrier-coverage quality of a WSN of cheap and static sensor nodes. For this purpose, we first use a single variable first-order grey model, GM(1,1), based on the intruder detection history from the sensor nodes to determine which parts of the barrier is more vulnerable. Then, we relocate the available mobile sensor nodes to the identified vulnerable parts of the barrier in a timely manner, and prove this relocation strategy is optimal. Throughout the simulations, we evaluate the effectiveness of our algorithm.

Minimizing data collection latency in wireless sensor network with multiple mobile elements

This paper considers the problem of computing the optimal trajectories of multiple mobile elements (e.g. robots, vehicles, etc.) to minimize data collection latency in wireless sensor networks (WSNs). By relying on slightly different assumption, we define two interesting problems, the k-traveling salesperson problem with neighborhood (k-TSPN) and the k-rooted path cover problem with neighborhood (k-PCPN). Since both problems are NP-hard, we propose constant factor approximation algorithms for them. Our simulation results indicate our algorithms outperform their alternatives.

Minimum Latency Multiple Data MULETrajectory Planning in Wireless Sensor Networks

This paper investigates the problem of computing the optimal trajectories of multiple data MULEs (e.g., robots, vehicles, etc.) to minimize data collection latency in wireless sensor networks. By relying on a slightly different assumption, we define two interesting problems, the k-traveling salesperson problem with neighborhood ( k-TSPN) and the k-rooted path cover problem with neighborhood ( k-PCPN). Since both problems are NP-hard, we propose constant factor approximation algorithms for them along with two simpler heuristic algorithms. We also conduct simulations to compare the performance of the proposed approaches with the existing alternatives. Our simulation results indicate that the proposed algorithms outperform the competitors on average.

An experimental study of pupil constriction for liveness detection

As iris recognition systems have been deployed in many security areas, liveness detection that can distinguish between real iris patterns and fake ones becomes an important module. Most existing algorithms focus on the appearance difference between real and fake iris (for example, printed patterns, cosmetic contact lenses etc.) which is a very difficult problem. Instead of studying image properties of fake irises, we show that pupil constriction, the fundamental characteristic of real and live irises, can be very robust for liveness detection. In this experimental study, we first build an iris acquisition system that can acquire two eye images under two different illumination conditions in a less intrusive environment. Second, in order to model the process of pupil constriction, we propose a feature descriptor that consists of similarity measurement between iris patches and ratio of iris and pupil diameters. Third, the performance of liveness prediction is evaluated based on the training of a Support Vector Machine (SVM) classifier. The high success prediction rate shows that the classifier is effective without knowing any prior knowledge of fake irises.

Multiple heterogeneous data ferry trajectory planning in wireless sensor networks

This paper investigates two new groups of trajectory optimization problems which stem from networked multi-robotic systems. In particular, we study how to efficiently collect data from stationary sensor nodes using multiple robotic vehicles such as data ferries under different circumstance. The first group includes two new problems which aim to find the tours and the paths, respectively, of k robot vehicles with different mobilization conditions to collect data from ground sensor nodes with minimum latency. The second group consists of one new problem whose goal is to determine the quality tours of k robot vehicles with different speeds, where each of which follows its corresponding tour to repeatedly collect data from stationary sensors. We prove the three problems are NP-hard and propose constant factor approximation strategies for them. Through a simulation, an analytical study is conducted to evaluate the average performance of our core contribution.

Counting and Classification of Highway Vehicles by Regression Analysis

In this paper, we describe a novel algorithm that counts and classifies highway vehicles based on regression analysis. This algorithm requires no explicit segmentation or tracking of individual vehicles, which is usually an important part of many existing algorithms. Therefore, this algorithm is particularly useful when there are severe occlusions or vehicle resolution is low, in which extracted features are highly unreliable. There are mainly two contributions in our proposed algorithm. First, a warping method is developed to detect the foreground segments that contain unclassified vehicles. The common used modeling and tracking (e.g., Kalman filtering) of individual vehicles are not required. In order to reduce vehicle distortion caused by the foreshortening effect, a nonuniform mesh grid and a projective transformation are estimated and applied during the warping process. Second, we extract a set of low-level features for each foreground segment and develop a cascaded regression approach to count and classify vehicles directly, which has not been used in the area of intelligent transportation systems. Three different regressors are designed and evaluated. Experiments show that our regression-based algorithm is accurate and robust for poor quality videos, from which many existing algorithms could fail to extract reliable features.

Sweep-Coverage with Energy-Restricted Mobile Wireless Sensor Nodes

Most of the existing results in sweep-coverage focused on minimizing the number of the mobile sensor nodes by carefully planning their corresponding trajectories such that each target of interest can be periodically monitored (within every t time unit). However, the starting locations of the mobile sensors, at which the service depots (or equivalently base stations) of the nodes are usually located, are never considered in the trajectory planning. In order to provide sweep-coverage for a long period of time, each node also needs to periodically visit a base station to replace a battery or refueled (within every T time unit). Motivated by this observation, this paper introduces two new sweep-coverage problems, in which each mobile sensor node is required to visit a base station periodically, namely (t,T)-SCOPe-1 and (t,T)-SCOPe-M, each of which considers one single base station and M base stations for all of the nodes, respectively. We prove those problems are NP-hard and propose heuristic algorithms for them. In addition, we conduct simulations to evaluate the average performance of the proposed algorithms and study their average behavior characteristics.

On sleep-wakeup scheduling of non-penetrable barrier-coverage of wireless sensors

This paper identifies a new security problem of existing scheduling algorithms for barrier-coverage of sensors, which never considered before. A barrier-cover of wireless sensors is a subset of sensors seamlessly spanning between two opposite sides such that no intruder can move from one side to the other without being detected. The goal of the scheduling algorithms is to find a sleep-wakeup schedule of sensors such that the time to protect an area of interest using a series of alternating barrier-covers can be maximized. We introduce a new security problem which may exist when two barrier-covers, whose covered areas are not completely disjoint, alternate. We show how an intruder can utilize a set of points, namely “barrier-breaches”, to penetrate the alternating barrier-covers. We also propose two remedies for this problem for existing scheduling algorithms. Our analysis shows that depending on the input graph, one of our approaches works better than the other. Given that such scheduling algorithms only need to run during the initialization phase of a sensor network, we suggest to apply both approaches and pick the better schedule rather than relying solely on the approach which works well on average.

Maximum Lifetime Combined Barrier-Coverage of Weak Static Sensors and Strong Mobile Sensors

Recently, the concept of barrier-coverage of wireless sensor network has been introduced for various civilian and military defense applications. This paper studies the problem of how to organize hybrid sensor network, which consists of a number of energy-scarce ground sensors with homogenous initial battery level and energy-plentiful mobile sensors, to maximum the lifetime of barrier-coverage. Two key observations are (a) as the lifetime of each mobile sensor is much longer than that of the static ground sensors, each mobile sensor is capable of contributing multiple sensor barrier formations, and (b) no mobile sensor node can join two hybrid barriers which will be successively used to continuously protect the area of interest due to the moving delay. Based on these, we introduce a new maximum lifetime barrier-coverage problem in hybrid sensor network. We first propose a simple heuristic algorithm by combining existing ideas along with our own. Then, we design another efficient algorithm for the problem and prove that the lifetime of hybrid barrier constructed by this algorithm is at least three times greater than the existing one on average. Our simulation result shows that the second algorithm outperforms the first algorithm at least 33 percent and up to 100 percent.

Fortifying Barrier-Coverage of Wireless Sensor Network with Mobile Sensor Nodes

Recently, the barrier-coverage of wireless sensor network received huge attention thanks to the important applications such as border protection. In practice, sensor nodes are subject to intermittent failure to detect objects within its sensing range due to many reasons. Therefore, a barrier of sensor nodes may exhibit temporal loopholes. In this paper, we investigate the potential of mobile sensor nodes such as unmanned aerial vehicles and human patrols to fortify the barrier-coverage of static wireless sensors. We use a single variable first-order grey model, GM(1,1), based on the intruder detection history from the sensor nodes to determine which parts of the barrier is more vulnerable. Then, we relocate the available mobile sensor nodes to the identified vulnerable parts of the barrier in a timely manner. We show this relocation strategy is optimal in theory. By the simulations, we also evaluate the average performance of our algorithm.