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.
@cite_3
2099719044
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. We propose and investigate a compressive architecture for estimation and tracking of sparse spatial channels in millimeter (mm) wave picocellular networks. The base stations are equipped with antenna arrays with a large number of elements (which can fit within compact form factors because of the small carrier wavelength) and employ radio frequency (RF) beamforming, so that standard least squares adaptation techniques (which require access to individual antenna elements) are not applicable. We focus on the downlink, and show that “compressive beacons,” transmitted using pseudorandom phase settings at the base station array, and compressively processed using pseudorandom phase settings at the mobile array, provide information sufficient for accurate estimation of the two-dimensional (2D) spatial frequencies associated with the directions of departure of the dominant rays from the base station, and the associated complex gains. This compressive approach is compatible with coarse phase-only control, and is based on a near-optimal sequential algorithm for frequency estimation which approaches the Cramer Rao Lower Bound. The algorithm exploits the geometric continuity of the channel across successive beaconing intervals to reduce the overhead to less than 1 even for very large ( @math ) arrays. Compressive beaconing is essentially omnidirectional, and hence does not enjoy the SNR and spatial reuse benefits of beamforming obtained during data transmission. We therefore discuss system level design considerations for ensuring that the beacon SNR is sufficient for accurate channel estimation, and that inter-cell beacon interference is controlled by an appropriate reuse scheme. In this paper, we present a robust, decentralized approach to RF-based location tracking. Our system, called MoteTrack, is based on low-power radio transceivers coupled with a modest amount of computation and storage capabilities. MoteTrack does not rely upon any back-end server or network infrastructure: the location of each mobile node is computed using a received radio signal strength signature from numerous beacon nodes to a database of signatures that is replicated across the beacon nodes themselves. This design allows the system to function despite significant failures of the radio beacon infrastructure. In our deployment of MoteTrack, consisting of 20 beacon nodes distributed across our Computer Science building, we achieve a 50th percentile and 80th percentile location-tracking accuracy of 2 meters and 3 meters respectively. In addition, MoteTrack can tolerate the failure of up to 60 of the beacon nodes without severely degrading accuracy, making the system suitable for deployment in highly volatile conditions. We present a detailed analysis of MoteTrack's performance under a wide range of conditions, including variance in the number of obstructions, beacon node failure, radio signature perturbations, receiver sensitivity, and beacon node density. This paper presents and evaluates two principles for wireless routing protocols. The first is datapath validation: data traffic quickly discovers and fixes routing inconsistencies. The second is adaptive beaconing: extending the Trickle algorithm to routing control traffic reduces route repair latency and sends fewer beacons. We evaluate datapath validation and adaptive beaconing in CTP Noe, a sensor network tree collection protocol. We use 12 different testbeds ranging in size from 20--310 nodes, comprising seven platforms, and six different link layers, on both interference-free and interference-prone channels. In all cases, CTP Noe delivers > 90 of packets. Many experiments achieve 99.9 . Compared to standard beaconing, CTP Noe sends 73 fewer beacons while reducing topology repair latency by 99.8 . Finally, when using low-power link layers, CTP Noe has duty cycles of 3 while supporting aggregate loads of 30 packets minute. This study investigated the impact of Bluetooth Low Energy devices in advertising beaconing mode on fingerprint-based indoor positioning schemes. Early experimentation demonstrated that the low bandwidth of BLE signals compared to WiFi is the cause of significant measurement error when coupled with the use of three BLE advertising channels. The physics underlying this behaviour is verified in simulation. A multipath mitigation scheme is proposed and tested. It is determined that the optimal positioning performance is provided by 10Hz beaconing and a 1 second multipath mitigation processing window size. It is determined that a steady increase in positioning performance with fingerprint size occurs up to 7 ±1, above this there is no clear benefit to extra beacon coverage. The application of the extended Kaman filter to the problem of mobile robot navigation in a known environment is presented. An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed. The algorithm is based on an extended Kalman filter that utilizes matches between observed geometric beacons and an a priori map of beacon locations. Two implementations of this navigation algorithm, both of which use sonar, are described. The first implementation uses a simple vehicle with point kinematics equipped with a single rotating sonar. The second implementation uses a 'Robuter' mobile robot and six static sonar transducers to provide localization information while the vehicle moves at typical speeds of 30 cm s. > The human genetics community needs robust protocols that enable secure sharing of genomic data from participants in genetic research. Beacons are web servers that answer allele-presence queries—such as “Do you have a genome that has a specific nucleotide (e.g., A) at a specific genomic position (e.g., position 11,272 on chromosome 1)?”—with either “yes” or “no.” Here, we show that individuals in a beacon are susceptible to re-identification even if the only data shared include presence or absence information about alleles in a beacon. Specifically, we propose a likelihood-ratio test of whether a given individual is present in a given genetic beacon. Our test is not dependent on allele frequencies and is the most powerful test for a specified false-positive rate. Through simulations, we showed that in a beacon with 1,000 individuals, re-identification is possible with just 5,000 queries. Relatives can also be identified in the beacon. Re-identification is possible even in the presence of sequencing errors and variant-calling differences. In a beacon constructed with 65 European individuals from the 1000 Genomes Project, we demonstrated that it is possible to detect membership in the beacon with just 250 SNPs. With just 1,000 SNP queries, we were able to detect the presence of an individual genome from the Personal Genome Project in an existing beacon. Our results show that beacons can disclose membership and implied phenotypic information about participants and do not protect privacy a priori. We discuss risk mitigation through policies and standards such as not allowing anonymous pings of genetic beacons and requiring minimum beacon sizes. In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors. This paper investigates the issue of interference avoidance in body area networks (BANs). IEEE 802.15 Task Group 6 presented several schemes to reduce such interference, but these schemes are still not proper solutions for BANs. We present a novel distributed TDMA-based beacon interval shifting scheme that reduces interference in the BANs. A design goal of the scheme is to avoid the wakeup period of each BAN coinciding with other networks by employing carrier sensing before a beacon transmission. We analyze the beacon interval shifting scheme and investigate the proper back-off length when the channel is busy. We compare the performance of the proposed scheme with the schemes presented in IEEE 802.15 Task Group 6 using an OMNeT++ simulation. The simulation results show that the proposed scheme has a lower packet loss, energy consumption, and delivery-latency than the schemes of IEEE 802.15 Task Group 6. This paper presents the design, implementation, and evaluation of Cricket , a location-support system for in-building, mobile, location-dependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze information from beacons spread throughout the building. Cricket is the result of several design goals, including user privacy, decentralized administration, network heterogeneity, and low cost. Rather than explicitly tracking user location, Cricket helps devices learn where they are and lets them decide whom to advertise this information to; it does not rely on any centralized management or control and there is no explicit coordination between beacons; it provides information to devices regardless of their type of network connectivity; and each Cricket device is made from off-the-shelf components and costs less than U.S. $10. We describe the randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy. Our experience with Cricket shows that several location-dependent applications such as in-building active maps and device control can be developed with little effort or manual configuration. Geographic routing has emerged as one of the most efficient and scalable routing solutions for wireless sensor networks. In traditional geographic routing protocols, each node exchanges periodic one-hop beacons to determine the position of its neighbors. Recent studies proved that these beacons can create severe problems in real deployments due to the highly dynamic and error-prone nature of wireless links. To avoid these problems, new variants of geographic routing protocols that do not require beacons are being proposed. In this article we review some of the latest proposals in the field of beacon-less geographic routing and introduce the main design challenges and alternatives. In addition, we perform an empirical study to assess the performance of beacon-based and beacon-less routing protocols using a real WSN deployment. Most autonomous underwater vehicle (AUV) systems rely on prior knowledge of beacon locations for localization. We present a system capable of navigating without prior beacon locations. Noise and outliers are major issues; we present a powerful outlier rejection method that imposes geometric constraints on measurements. We have successfully applied our algorithm to real-world data and have demonstrated navigation performance comparable to that of systems that assume known beacon locations. Genomic datasets are often associated with sensitive phenotypes. Therefore, the leak of membership information is a major privacy risk. Genomic beacons aim to provide a secure, easy to implement, and standardized interface for data sharing by only allowing yes no queries on the presence of specific alleles in the dataset. Previously deemed secure against re-identification attacks, beacons were shown to be vulnerable despite their stringent policy. Recent studies have demonstrated that it is possible to determine whether the victim is in the dataset, by repeatedly querying the beacon for his her single nucleotide polymorphisms (SNPs). In this work, we propose a novel re-identification attack and show that the privacy risk is more serious than previously thought. Using the proposed attack, even if the victim systematically hides informative SNPs (i.e., SNPs with very low minor allele frequency -MAF-), it is possible to infer the alleles at positions of interest as well as the beacon query results with very high confidence. Our method is based on the fact that alleles at different loci are not necessarily independent. We use the linkage disequilibrium and a high-order Markov chain-based algorithm for the inference. We show that in a simulated beacon with 65 individuals from the CEU population, we can infer membership of individuals with 95 confidence with only 5 queries, even when SNPs with MAF less than 0.05 are hidden. This means, we need less than 0.5 of the number of queries that existing works require, to determine beacon membership under the same conditions. We further show that countermeasures such as hiding certain parts of the genome or setting a query budget for the user would fail to protect the privacy of the participants under our adversary model. Localization schemes using a mobile beacon have similar effects as the use of many static beacons in terms of improving localization accuracy. Specifically, the localization scheme with mobile beacons proposed by has finegrained accuracy, scalability, and power efficiency without requiring measured distance or angle information. However, this scheme often has large location errors in ill-conditioned cases. To improve the localization accuracy in Ssu's scheme, this letter proposes a localization scheme that estimates sensor location from possible areas by using geometric constraints. During simulations, the proposed scheme was shown to provide higher localization accuracy than Ssu's scheme and other schemes using a mobile beacon. Direct radio-based vehicle-to-vehicle communication can help prevent accidents by providing accurate and up-to-date local status and hazard information to the driver. In this paper, we assume that two types of messages are used for traffic safety-related communication: 1) Periodic messages (ldquobeaconsrdquo) that are sent by all vehicles to inform their neighbors about their current status (i.e., position) and 2) event-driven messages that are sent whenever a hazard has been detected. In IEEE 802.11 distributed-coordination-function-based vehicular networks, interferences and packet collisions can lead to the failure of the reception of safety-critical information, in particular when the beaconing load leads to an almost-saturated channel, as it could easily happen in many critical vehicular traffic conditions. In this paper, we demonstrate the importance of transmit power control to avoid saturated channel conditions and ensure the best use of the channel for safety-related purposes. We propose a distributed transmit power control method based on a strict fairness criterion, i.e., distributed fair power adjustment for vehicular environments (D-FPAV), to control the load of periodic messages on the channel. The benefits are twofold: 1) The bandwidth is made available for higher priority data like dissemination of warnings, and 2) beacons from different vehicles are treated with ldquoequal rights,rdquo and therefore, the best possible reception under the available bandwidth constraints is ensured. We formally prove the fairness of the proposed approach. Then, we make use of the ns-2 simulator that was significantly enhanced by realistic highway mobility patterns, improved radio propagation, receiver models, and the IEEE 802.11p specifications to show the beneficial impact of D-FPAV for safety-related communications. We finally put forward a method, i.e., emergency message dissemination for vehicular environments (EMDV), for fast and effective multihop information dissemination of event-driven messages and show that EMDV benefits of the beaconing load control provided by D-FPAV with respect to both probability of reception and latency. During the last decades, location based services have become very popular and the developed indoor positioning systems have achieved an impressive accuracy. The problem though is that even if the only requirement is room-level localization, those systems are most of the times not cost-efficient and not easy to set-up, since they often require time-consuming calibration procedures. This paper presents a low-cost, threshold-based approach and introduces an algorithm that takes into account both the Received Signal Strength Indication (RSSI) of the Bluetooth Low Energy (BLE) beacons and the geometry of the rooms the beacons are placed in. Performance evaluation was done via measurements in an office environment composed of three rooms and in a house environment composed of six rooms. The experimental results show an improved accuracy in room detection when using the proposed algorithm, compared to when only considering the RSSI readings. This method was developed to provide context awareness to the international research project named SmartHeat. The projects aims to provide a system that efficiently heats a house, room by room, based on the habitants' habits and preferences. Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential.