The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.
Meanwhile, beacon allocation traditionally relies on coverage as a heuristic to guide placement in a specific environment. When the environment is polygonal and beacons have an unlimited field-of-view subject to occlusions by walls (e.g., cameras), coverage corresponds to the standard art-gallery visibility problem @cite_3 . Related, propose a greedy landmark-based method that solves for the placement of the minimum number of beacons (within a @math factor) necessary to cover all but a fraction of a given polygonal environment. However, a beacon's sensing geometry is often complex as a result of partial attenuation due to the environment, beacon noise, and signal interference. Interference varies according to the placement of other beacons and channel allocation, which most coverage algorithms do not reason over. These factors are particularly important for RF-based localization, which is sensitive to the effects of aliasing, further complicating the relationship between allocation and inference.
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Beacon placement strongly affects the quality of spatial localization, a critical service for context-aware applications in wireless sensor networks; yet this aspect of localization has received little attention. Fixed beacon placement approaches such as uniform and very dense placement are not always viable and will be inadequate in very noisy environments in which sensor networks may be expected to operate (with high terrain and propagation uncertainties). We motivate the need for empirically adaptive beacon placement and outline a general approach based on exploration and instrumentation of the terrain conditions by a mobile human or robot agent. We design, evaluate and analyze three novel adaptive beacon placement algorithms using this approach for localization based on RF-proximity. In our evaluation, we find that beacon density rather than noise level has a more significant impact on beacon placement algorithms. Our beacon placement algorithms are applicable to a low (beacon) density regime of operation. Noise makes moderate density regimes more improvable. Cooperative inter-vehicular applications rely on the exchange of broadcast single-hop status messages among vehicles, called beacons. The aggregated load on the wireless channel due to periodic beacons can prevent the transmission of other types of messages, what is called channel congestion due to beaconing activity. In this paper we approach the problem of controlling the beaconing rate on each vehicle by modeling it as a Network Utility Maximization (NUM) problem. This allows us to formally define the notion of fairness of a beaconing rate allocation in vehicular networks. The NUM model provides a rigorous framework to design a broad family of simple and decentralized algorithms, with proved convergence guarantees to a fair allocation solution. In this context, we propose the Fair Adaptive Beaconing Rate for Intervehicular Communications (FABRIC) algorithm, which uses a particular scaled gradient projection algorithm to solve the dual of the NUM problem. Simulation results validate our approach and show that, unlike recent proposals, FABRIC converges to fair rate allocations in multi-hop and dynamic scenarios. The localization of sensor nodes is a fundamental problem in sensor networks and can be implemented using powerful and expensive beacons. Beacons, the fewer the better, can acquire their position knowledge either from GPS devices or by virtue of being manually placed. In this paper, we propose a distributed method to localization of sensor nodes using a single moving beacon, where sensor nodes compute their position estimate based on the range-free technique. Two parameters are critical to the location accuracy of sensor nodes: the radio transmission range of the beacon and how often the beacon broadcasts its position. Theoretical analysis shows that these two parameters determine the upper bound of the estimation error when the traverse route of the beacon is a straight line. We extend the position estimate when the traverse route of the beacon is randomly chosen in a real-world situation, where the radio irregularity might cause a node to miss some crucial coordinate information from the beacon. We further point out that the movement pattern of the beacon plays a pivotal role in the localization task for sensors. To minimize estimation errors, sensor nodes can carry out a variety of algorithms in accordance with the movement of the beacon. Simulation results compare variants of the distributed method in a variety of testing environments. Real experiments show that the proposed method is feasible and can estimate the location of sensor nodes accurately, given a single moving beacon. Bluetooth beacons' signal is used to provide low-cost indoor localization.Sensor fusion approach with RGB-D camera is adopted to improve position accuracy.Customer positions allows to track shoppers movements and push context notifications.Customer paths are analyzed and clustered for customer behavior analysis.System can evaluate shop performances and support retailers' decision making process. The development of reliable and precise indoor localization systems would considerably improve the ability to investigate shopper movements and behavior inside retail environments. Previous approaches used either computer vision technologies or the analysis of signals emitted by communication devices (beacons). While computer vision approaches provide higher level of accuracy, beacons cover a wider operational area. In this paper, we propose a sensor fusion approach between active radio beacons and RGB-D cameras. This system, used in an intelligent retail environment where cameras are already installed for other purposes, allows an affordable environment set-up and a low operational costs for customer indoor localization and tracking. We adopted a Kalman filter to fuse localization data from radio signals emitted by beacons are used to track users' mobile devices and RGB-D cameras used to refine position estimations. By combing coarse localization datasets from active beacons and RGB-D data from sparse cameras, we demonstrate that the indoor position estimation is strongly enhanced. The aim of this general framework is to provide retailers with useful information by analyzing consumer activities inside the store. To prove the robustness of our approach, several tests were conducted into a real indoor showroom by analyzing real customers behavior with encouraging results.