A nearly optimal sensor placement algorithm for boundary coverage

Locating visual sensors in 2D can be often modeled as an Art Gallery problem. Tasks such as surveillance require observing or “covering” the interior of a polygon with a minimum number of sensors or “guards”. For other tasks, such as inspection and image based rendering, observing the boundaries of the environment is sufficient. As Interior Covering (IC), also Edge Covering (EC) is NP-hard, and no finite algorithm is known for its exact solution. Approximate EC solutions are provided by many heuristic algorithms, but their performances with respect to optimality (minimum number of sensors) is unknown. In this paper, we propose a new EC sensors location technique. The algorithm is incremental, and converges toward the optimal solution. It refines an initial approximation provided by an integer covering algorithm (IEC) where each edge is observed entirely by at least one sensor. A lower bound for the number of sensors, specific of the polygon considered, is used at each step for evaluating the quality of the current solution, and a set of rules are provided for performing a local refinement to reduce the computational burden. The algorithm has been implemented, and tests over hundreds of random polygons show that it supplies solutions very close to and often coincident with the lower bound, and then suboptimal or optimal. In addition, the approximate starting solutions provided by the IEC algorithms are, on the average, close to optimum. The tight lower bound can also be used for testing other EC sensor location algorithms. Running times allow dealing with polygons with up to a few hundreds of edges, which appears adequate for many practical cases. An enhanced version of the algorithm, also taking into account range and incidence constraints, has also been implemented and tested.     

Efficient sensor network design for continuous monitoring of moving objects

We study the problem of localizing and tracking multiple moving targets in wireless sensor networks, from a network design perspective i.e. towards estimating the least possible number of sensors to be deployed, their positions and operation characteristics needed to perform the tracking task. To avoid an expensive massive deployment, we try to take advantage of possible coverage overlaps over space and time, by introducing a novel combinatorial model that captures such overlaps.     

Adaptive extended Kalman filtering for visual motion estimation of 3D objects

An algorithm for the real-time estimation of the position and orientation of a moving object of known geometry is presented in this paper. An estimation algorithm is adopted where a discrete-time extended Kalman filter computes the object pose on the basis of visual measurements of the object features. The scheme takes advantage of the prediction capability of the extended Kalman filter for the pre-selection of the features to be extracted from the image at each sample time. To enhance the robustness of the algorithm with respect to measurement noise and modelling error, an adaptive version of the extended Kalman filter, customized for visual applications, is proposed. Experimental results on a fixed single-camera visual system are presented to test the performance and the feasibility of the proposed approach.     

Accurate and energy-efficient boundary detection of continuous objects in duty-cycled wireless sensor networks

With the development and wide adoption of industrial wireless sensor networks to support various domain applications, the boundary detection of continuous objects has become an important research challenge, where improving the accuracy of boundary area while reducing the energy consumption are the first-class citizens to be considered. To address this research challenge, this article proposes a two-stage boundary face detection mechanism, where sensor nodes are duty-cycled and to be deployed in a dense fashion. When the occurrence of potential events are recognized using the initially activated sensor nodes, the boundary faces of continuous objects are constructed through adopting planarization algorithms. Thereafter, sensor nodes contained in certain boundary faces are examined, where their sensory data are estimated using spatial interpolation methods. Certain sensor nodes are selected to be woken up, only when their sensory data suggest that they should be more appropriate candidates of boundary sensor nodes. Consequently, the size of boundary faces is reduced, and this coarse-to-fine refinement procedure iterates, until all sensor nodes contained in the boundary faces have been examined. Experimental evaluation result shows that the boundary area can be refined significantly and be more precise, where the half of the initial boundary face area should be reduced in most situations.     

Knowledge-based duty cycle estimation in wireless sensor networks: Application for sound pressure monitoring

Wireless sensor networks comprise an important research area and a near future for industry and communications. Wireless sensor networks contain resource-constrained sensor nodes that are powered by small batteries, limited process and memory and wireless communication. These features give sensors their versatility and drawbacks, such as their limited operating lifetimes. To feasibly deploy wireless sensor networks with isolated motes, several approaches and solutions have been developed; the most common, apart from using alternative power sources such as solar panels, are those that put sensors to sleep for time periods established by the application. We thus propose a fuzzy rule-based system that estimates the next duty cycle, taking the magnitude being tested and battery charge as input. To show how it works, we compare an analytical delta system to our contribution. As an application to test both systems, a sound pressure monitoring application is presented. The results have shown that the fuzzy rule-based system better predicts the evolution of the magnitude by which errors committed by idle periods decrease. This work also shows that application-oriented duty cycle control can be an alternative for measuring systems, thus saving battery and improving sensor node lifetime, with a reasonable loss of precision.     

Adaptive texture and color segmentation for tracking moving objects

Color segmentation is a very popular technique for real-time object tracking. However, even with adaptive color segmentation schemes, under varying environmental conditions in video sequences, the tracking tends to be unreliable. To overcome this problem, many multiple cue fusion techniques have been suggested. One of the cues that complements color nicely is texture. However, texture segmentation has not been used for object tracking mainly because of the computational complexity of texture segmentation. This paper presents a formulation for fusing texture and color in a manner that makes the segmentation reliable while keeping the computational cost low, with the goal of real-time target tracking. An autobinomial Gibbs Markov random field is used for modeling the texture and a 2D Gaussian distribution is used for modeling the color. This allows a probabilistic fusion of the texture and color cues and for adapting both the texture and color over time for target tracking. Experiments with both static images and dynamic image sequences establish the feasibility of the proposed approach.     

Accurate constrained pose estimation for small objects

The paper proposes a novel method for accurate pose estimation of small objects. A range sensor is used to find object orientation that constraints a PnP solver. The method finds the global minimum of a cost function (as opposed to traditional PnP algorithms) in closed form. This approach was used to enable robust robotic operation with sub millimeter precision.     

A heuristic algorithm for optimal placement of rectangular objects

The problem of partitioning a two-dimensional area into pieces having certain sizes with a minimum of wasted space is very important, especially in packing components tightly in the manufacture of very large-scale integrated circuits. The purpose of this paper is to examine the problem of placing rectangular objects in a rectangular area so as to minimize the wasted space, from the viewpoint of establishing maximum empty rectangles rather than the standard linear-programming approach. A comparison of our results with those of the Steudel [5] is reported. Empirical comparisons of our results indicate that our algorithm is very simple and efficient.     

Optimal sensor placement and motion coordination for target tracking

This work studies optimal sensor placement and motion coordination strategies for mobile sensor networks. For a target-tracking application with range sensors, we investigate the determinant of the Fisher Information Matrix and compute it in the 2D and 3D cases, characterizing the global minima in the 2D case. We propose motion coordination algorithms that steer the mobile sensor network to an optimal deployment and that are amenable to a decentralized implementation. Finally, our numerical simulations illustrate how the proposed algorithms lead to improved performance of an extended Kalman filter in a target-tracking scenario.     

Moment computation for objects with spline curve boundary

A new approach is proposed for computation of area and geometric moments for a plane object with a spline curve boundary. The explicit formulae are obtained for area and low order moment calculation. The complexity of calculation depends on the moment order, spline degree, and the number of control points used in spline representation. The formulae proposed use the advantage that the sequence of spline control points is cyclic. It allowed us to reduce substantially the number of summands in them. The formulae might be useful in different applications where it is necessary to perform measurements for shapes with a smooth boundary.     

A dominance-based stepwise approach for sensor placement optimization

A Wireless Sensor Network (WSN) usually consists of numerous wireless devices deployed in a region of interest, each of which is capable of collecting and processing environmental information and communicating with neighboring devices. The problem of sensor placement becomes non trivial when we consider environmental factors such as terrain elevations. In this paper, we differentiate a stepwise optimization approach from a generic optimization approach, and show that the former is better suited for sensor placement optimization. Following a stepwise optimization approach, we propose a Crowd-Out Dominance Search (CODS), which makes use of terrain information and intersensor relationship information to facilitate the optimization. Finally, we investigate the effect of terrain irregularity on optimization algorithm performances, and show that the proposed method demonstrates better resistance to terrain complexity than other optimization methods.     

Automatic sensor placement for model-based robot vision.

This paper presents a method for automatic sensor placement for model-based robot vision. In such a vision system, the sensor often needs to be moved from one pose to another around the object to observe all features of interest. This allows multiple three-dimensional (3-D) images to be taken from different vantage viewpoints. The task involves determination of the optimal sensor placements and a shortest path through these viewpoints. During the sensor planning, object features are resampled as individual points attached with surface normals. The optimal sensor placement graph is achieved by a genetic algorithm in which a min-max criterion is used for the evaluation. A shortest path is determined by Christofides algorithm. A Viewpoint Planner is developed to generate the sensor placement plan. It includes many functions, such as 3-D animation of the object geometry, sensor specification, initialization of the viewpoint number and their distribution, viewpoint evolution, shortest path computation, scene simulation of a specific viewpoint, parameter amendment. Experiments are also carried out on a real robot vision system to demonstrate the effectiveness of the proposed method.     

Virus coevolution partheno-genetic algorithms for optimal sensor placement

A virus coevolutionary partheno-genetic algorithm (VEPGA), which combined a partheno-genetic algorithm (PGA) with virus evolutionary theory, is proposed to place sensors optimally on a large space structure for the purpose of modal identification. The VEPGA is composed of a host population of candidate solutions and a virus population of substrings of host individuals. The traditional crossover and mutation operators in genetic algorithm are repealed and their functions are implemented by particular partheno-genetic operators which are suitable to combinatorial optimization problems. Three different optimal sensor placement performance index, one aim on the maximization of linear independence, one aim on the maximization of modal energy and the last is a combination of the front two indices, have been investigated. The algorithm is applied to two examples: sensor placement for a portal frame and a concrete arc dam. Results show that the proposed VEPGA outperforms the sequential reduction procedure (SRP) and PGA. The combined performance index makes an excellent compromise between the linear independence aimed index and the modal energy aimed index.     

Pareto-based evolutionary computational approach for wireless sensor placement

Wireless sensor networks (WSNs) have become increasingly appealing in recent years for the purpose of data acquisition, surveillance, event monitoring, etc. Optimal positioning of wireless sensor nodes is an important issue for small networks of relatively expensive sensing devices. For such networks, the placement problem requires that multiple objectives be met. These objectives are usually conflicting, e.g. achieving maximum coverage and maximum connectivity while minimizing the network energy cost. A flexible algorithm for sensor placement (FLEX) is presented that uses an evolutionary computational approach to solve this multiobjective sensor placement optimization problem when the number of sensor nodes is not fixed and the maximum number of nodes is not known a priori. FLEX starts with an initial population of simple WSNs and complexifies their topologies over generations. It keeps track of new genes through historical markings, which are used in later generations to assess two networks’ compatibility and also to align genes during crossover. It uses Pareto-dominance to approach Pareto-optimal layouts with respect to the objectives. Speciation is employed to aid the survival of gene innovations and facilitate networks to compete with similar networks. Elitism ensures that the best solutions are carried over to the next generation. The flexibility of the algorithm is illustrated by solving the device/node placement problem for different applications like facility surveillance, coverage with and without obstacles, preferential surveillance, and forming a clustering hierarchy.     

Adaptive supervision of moving objects for mobile robotics applications

One of the main tasks of mobile robotics is vision. Lighting independence, adaptivity and automated learning are still the main issues when it comes to applications. In this article, we present an image understanding system and its methods targeting automatic, lighting-independent and reliable color-based object recognition under real time conditions. Its application test bed is global vision robot soccer (i.e. FIRA MiroSot und RoboCup Small Size leagues) but it has many other applications in color-based supervision of moving objects. Under typical conditions, it learns the objects of recognition automatically, has zero setup time and tolerates environmental changes during run-time.     

Energy-efficient area monitoring for sensor networks

The nodes in sensor networks must self-organize to monitor the target area as long as possible. Researchers at the Fundamental Computer Science Laboratory of Lille are developing strategies for selecting and updating an energy-efficient connected active sensor set that extends the network lifetime. We report on their work to optimize energy consumption in three separate problems: area coverage, request spreading, and data aggregation.     

Gas sensor network for air-pollution monitoring

This paper describes the development of a gas sensor system to be used as a sensing node to form a dense real-time environmental monitoring network. Moreover, a new auto-calibration method is proposed to achieve the maintenance-free operation of the sensor network. The network connectivity can be used not only for data collection but also for the calibration and diagnosis of the sensors since the measured pollutant concentrations can be easily compared through the network with nearby sensors and governmental monitoring stations. Different pollutant concentrations are usually monitored at different sites. However, a case study on local NO₂ distribution has shown that there exists a special condition under which pollutant concentrations become low and uniform in a certain local area. The baseline of the gas sensor response can be adjusted in this special occasion using the pollutant concentration values reported from the neighboring environmental monitoring stations. The experimental result has shown that NO₂ concentration can be measured with sufficient accuracy by incorporating appropriate temperature and humidity compensation into calibration curves. Moreover, a case study on auto-calibration demonstrates its effectiveness in keeping the measurement accuracy of the sensor system in long-term operation.     

Region digitization and boundary estimation

Digital complexes most often arise in practice as binary images, digitized by computer vision systems from real objects. In this paper, schemes for region digitization and boundary estimation are discussed in relation to the actual physical process of binary image acquisition. The region digitization scheme motivated naturally by such a discussion is also appropriate for use in theoretical studies of digital complexes. The same discussion suggests a method for obtaining an estimate of the boundary of an object from its binary image.