Typical mobile agent networks, such as multi-unmanned aerial vehicle (UAV) systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular, limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e., the computational and the sensing energy are small compared to the communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this paper, we aim at attaining an optimal tradeoff between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme.
Another approach is to have a mobile sensing node collect data from the nodes to reduce the communication overload @cite_5 @cite_38 @cite_15 @cite_13 . Since the UAVs are mobile, using another UAV to collect data from the surveying UAVs is not an ideal approach. However, similar to WSN data aggregation, the UAVs can perform computations on board to produce concise data and periodically transmit to the base station, as in @cite_28 for an image processing application. Data transmission to the base station can be performed either directly or through a UAV relay network @cite_45 @cite_48 . Therefore, in this work, we propose a self-organizing network topology that allows data aggregation as well as a multi-hop information routing pattern.
@cite_38 @cite_28 @cite_48 @cite_45 @cite_5 @cite_15 @cite_13
1832849348 2091685917 2157464905 2151287854 2103209505 1978442646 2024148187
Unlike traditional multihop forwarding among homogeneous static sensor nodes, use of mobile devices for data collection in wireless sensor networks has recently been gathering more attention. It is known that the use of mobility significantly reduces the energy consumption at each sensor, elongating the functional lifetime of the network, in exchange for increased data delivery latency. However, in previous work, mobility and communication capabilities are often underutilized, resulting in suboptimal solutions incurring unnecessarily large latency. In this paper, we focus on the problem of finding an optimal path of a mobile device, which we call "data mule," to achieve the smallest data delivery latency in the case of minimum energy consumption at each sensor, i.e., each sensor only sends its data directly to the data mule. We formally define the path selection problem and show the problem is @math -hard. Then we present an approximation algorithm and analyze its approximation factor. Numerical experiments demonstrate that our approximation algorithm successfully finds the paths that result in 10 -50 shorter latency compared to previously proposed methods, suggesting that controlled mobility can be exploited much more effectively. An image based system and process for rendering novel views of a real or synthesized 3D scene based on a series of concentric mosaics depicting the scene. In one embodiment, each concentric mosaic represents a collection of consecutive slit images of the surrounding 3D scene taken from a different viewpoint tangent to a circle on a plane within the scene. Novel views from viewpoints within circular regions of the aforementioned circle plane defined by the concentric mosaics are rendered using these concentric mosaics. Specifically, a slit image can be identified by a ray originating at its viewpoint on the circle plane and extending toward the longitudinal midline of the slit image. Each of the rays associated with the slit images needed to construct a novel view will either coincide with one of the rays associated with a previously captured slit image, or it will pass between two of the concentric circles on the circle plane. If it coincides, then the previously captured slit image associated with the coinciding ray can be used directly to construct part of the novel view. If the ray passes between two of the concentric circles of the plane, then the needed slit image is interpolated using the two previously captured slit images associated with the rays originating from the adjacent concentric circles that are parallel to the non-coinciding ray. If the objects in the 3D scene are close to the camera, depth correction is applied to reduce image distortion for pixels located above and below the circle plane. In another embodiment, a single camera is used to capture a sequence of images. Each image includes image data that has a ray direction associated therewith. To render an image at a novel viewpoint, multiple ray directions from the novel viewpoint are chosen. Image data is combined from the sequence of images by selecting image data that has a ray direction substantially aligning with the ray direction from the novel viewpoint. This paper presents a cooperative distributed planning algorithm that ensures network connectivity for a team of heterogeneous agents operating in dynamic and communication-limited environments. The algorithm, named CBBA with Relays, builds on the Consensus-Based Bundle Algorithm (CBBA), a distributed task allocation framework developed previously by the authors and their colleagues. Information available through existing consensus phases of CBBA is leveraged to predict the network topology and to propose relay tasks to repair connectivity violations. The algorithm ensures network connectivity during task execution while preserving the distributed and polynomial-time guarantees of CBBA. By employing under-utilized agents as communication relays, CBBA with Relays improves the range of the team without limiting the scope of the active agents, thus improving mission performance. The algorithm is validated through simulation trials and through experimental indoor and outdoor field tests, demonstrating the real-time applicability of the approach. Herein, we investigate the optimal deployment of an unmanned aerial vehicle (UAV) in a wireless relay communication system. The optimal UAV position is found by maximizing the average data rate, while at the same time keeping the symbol error rate (SER) below a certain threshold. We derive a closed-form expression for the average data rate in a fixed wireless link using adaptive modulation. By using the alternate definite integral form for the Gaussian Q-function, the symbol error rate (SER) of the system in the link level is evaluated. An upper bound on the SER is also derived using the improved exponential bounds for the Q-function. It is shown that the derived SER expression matches the simulation results very well and the derived upper bound is tight for a wide range of SNRs. Simulation results also show that the system data rate matches the derived closed-form expression. We explore synergies among mobile robots and wireless sensor networks in environmental monitoring through a system in which robotic data mules collect measurements gathered by sensing nodes. A proof-of-concept implementation demonstrates that this approach significantly increases the lifetime of the system by conserving energy that the sensing nodes otherwise would use for communication. Autonomous Vehicles (AV) are used to solve the problem of data gathering in large scale sensor deployments with disconnected clusters of sensors networks. Our take is that an efficient strategy for data collection with AVs should leverage i) cooperation amongst sensors in communication range of each other forming a sensor cluster, ii) advanced coding and data storage techniques for easing the cooperation process, and iii) AV route-planning that is both content and cooperation-aware. Our work formulates the problem of efficient data gathering as a cooperative route-optimization problem with communication constraints. We also analyze (network) coded data transmission and storage for simplifying cooperation amongst sensors as well as data collection by the AV. Given the complexity of the problem, we focus on heuristic techniques, such as particle swarm optimization, to calculate the AV's route and the times for communication with each sensor and or cluster of sensors. We analyze two extreme cases, i.e., networks with and without intra- cluster cooperation, and provide numerical results to illustrate that the performance gap between them increases with the number of nodes. We show that cooperation in a 100 sensor deployment can increase the amount of data collected by up to a factor of 3 with respect to path planning without cooperation. In conventional wireless sensor networks (WSNs) to conserve energy Low Energy Adaptive Clustering Heirarchy (LEACH) is commonly used. Energy conversation is far more challenging for large scale deployments. This paper deals with selection of sensor network communication topology and the use of Unmanned Aerial Vehicle (UAV) for data gathering. Particle Swarm Optimzation (PSO) is proposed as an optimization method to find the optimal clusters in order to reduce the energy consumption, bit error rate (BER), and UAV travel time. PSO is compared to LEACH using a simulation case and the results show that PSO outperforms LEACH in terms of energy consumption and BER while the UAV travel time is similar. The numerical results further illustrate that the performance gap between them increases with the number of cluster head nodes. The reduced energy consumption means that the network life time can be significantly extended and the lower BER will increase the amount of data received from the entrie network.