An Energy-Efficient Target-Tracking Strategy for Mobile Sensor Networks.

In this paper, an energy-efficient strategy is proposed for tracking a moving target in an environment with obstacles, using a network of mobile sensors. Typically, the most dominant sources of energy consumption in a mobile sensor network are sensing, communication, and movement. The proposed algorithm first divides the field into a grid of sufficiently small cells. The grid is then represented by a graph whose edges are properly weighted to reflect the energy consumption of sensors. The proposed technique searches for near-optimal locations for the sensors in different time instants to route information from the target to destination, using a shortest path algorithm. Simulations confirm the efficacy of the proposed algorithm.

Distributed Sensor Coordination Algorithms for Efficient Coverage in a Network of Heterogeneous Mobile Sensors.

The main focus of this work is directed towards distributed coordination algorithms for coverage in a mobile sensor network. The sensors are assumed to have nonidentical sensing ranges, and it is desired to move them in such a way that the total sensing coverage increases as much as possible. To this end, the field is partitioned using the multiplicatively weighted Voronoi cells, and then different geometric methods are developed to find new locations for the sensors such that the coverage is improved. The proposed algorithms are iterative, and use the available local information to place the sensors properly, aiming to reduce the size of the coverage holes in the network. Simulations demonstrate the good performance of the proposed algorithms.

Maximum Lifetime Strategy for Target Monitoring with Controlled Node Mobility in Sensor Networks with Obstacles.

In this paper, an efficient technique is proposed for a mobile sensor network used to monitor a moving target in a field with obstacles while the network lifetime is maximized. The main sources of energy consumption of the sensors in the network are sensing, communication, and movement. A graph is constructed and its edges are weighted properly based on the remaining energy of each sensor. This graph is subsequently employed to address the lifetime maximization problem by solving a sequence of shortest path problems, which can be solved using existing methods. The proposed technique determines a near-optimal relocation strategy for the sensors as well as an energy-efficient route to transfer information from the target to destination. This near-optimal solution is calculated in every time instant, using the information of the previous time step. It is shown that by choosing appropriate parameters, sensors' locations and the communication route from target to destination obtained by the proposed algorithm can be arbitrarily close to the optimal locations and route at each time instant. Simulation results confirm the effectiveness of the proposed technique.

Toward Autonomous Mobile Sensor Networks Technology.

Mobile sensor networking technology has attracted considerable attention in various research communities in recent years due to their widespread applications in civilian and military environments. One objective when using mobile sensors is to obtain maximum field coverage by properly deploying sensor nodes. In many real-world applications a priori knowledge about the best deployment position for the sensors is not available. However, the motion capability of the sensors could allow each node to adjust its position (i.e. relocate) so that a better (and ultimately maximal) coverage is achieved. In this paper, a novel autonomous joint sensing range and relocation control algorithm is presented that achieves improved coverage and network lifetime at the same time. In the proposed algorithm, the sensing range of each sensor is adjusted iteratively based on its residual energy. At the same time, the sensor is directed to move within its corresponding multiplicatively weighted Voronoi (MW-Voronoi) region to ultimately increase sensing coverage in the field. Simulation results demonstrate the efficacy of the technique.

Edge-preserving ultrasonic strain imaging with uniform precision.

Ultrasound elastography involves measuring the mechanical properties of tissue, and has many applications in diagnostics and intervention. A common step in different elastography methods is imaging the tissue while it undergoes deformation and estimating the displacement field from the images. A popular next step is to estimate tissue strain, which gives clues into the underlying tissue elasticity modulus. To estimate the strain, one should compute the gradient of the displacement image, which amplifies the noise. The noise is commonly minimized by least square estimation of the gradient from multiple displacement measurements, which reduces the noise by sacrificing image resolution. In this work, we adaptively adjust the level and orientation of the smoothing using two different mechanisms. First, the precision of the displacement field decreases significantly in the regions with high signal decorrelation, which requires increasing the smoothness. Second, smoothing the strain field at the boundaries between different tissue types blurs the edges, which can render small targets invisible. To minimize blurring and noise, we perform anisotropic smoothing parallel to the direction of edges. The first mechanism ensures that textures/variations in the strain image reflect underlying tissue properties and are not caused by errors in the displacement estimation. The second mechanism keeps the edges between different tissue structures sharp while minimizing the noise. We validate the proposed method using phantom and in-vivo clinical data.