1,945 research outputs found
Exploiting Semi-Directional Transceivers for Localization in Communication Systems
Localization is the process of determining relative, as well as absolute, positions of communicating devices. Traditionally, the process is conducted using range or directional estimates. In contrast, this research uses weak information to form relatively tight bounds on possible locations of communicating devices. Under certain conditions, achieved location estimation results are strong. However, these results are highly sensitive to the operating conditions of the proposed networks. More significant results were obtained from specialized cases and that the application yields somewhat limited information for a general randomized network topology. Feasible localization results were found to be attainable but not necessarily practical for multiple experiments. This is due to the brute force nature of the implemented localization algorithm which experiences an exponential increase in runtime as the number of nodes increases
Recurrent Neural Networks For Accurate RSSI Indoor Localization
This paper proposes recurrent neuron networks (RNNs) for a fingerprinting
indoor localization using WiFi. Instead of locating user's position one at a
time as in the cases of conventional algorithms, our RNN solution aims at
trajectory positioning and takes into account the relation among the received
signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a
weighted average filter is proposed for both input RSSI data and sequential
output locations to enhance the accuracy among the temporal fluctuations of
RSSI. The results using different types of RNN including vanilla RNN, long
short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM
(BiLSTM) are presented. On-site experiments demonstrate that the proposed
structure achieves an average localization error of m with of the
errors under m, which outperforms the conventional KNN algorithms and
probabilistic algorithms by approximately under the same test
environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization,
recurrent neuron network (RNN), long shortterm memory (LSTM),
fingerprint-based localizatio
Implicit Cooperative Positioning in Vehicular Networks
Absolute positioning of vehicles is based on Global Navigation Satellite
Systems (GNSS) combined with on-board sensors and high-resolution maps. In
Cooperative Intelligent Transportation Systems (C-ITS), the positioning
performance can be augmented by means of vehicular networks that enable
vehicles to share location-related information. This paper presents an Implicit
Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle
(V2V) connectivity in an innovative manner, avoiding the use of explicit V2V
measurements such as ranging. In the ICP approach, vehicles jointly localize
non-cooperative physical features (such as people, traffic lights or inactive
cars) in the surrounding areas, and use them as common noisy reference points
to refine their location estimates. Information on sensed features are fused
through V2V links by a consensus procedure, nested within a message passing
algorithm, to enhance the vehicle localization accuracy. As positioning does
not rely on explicit ranging information between vehicles, the proposed ICP
method is amenable to implementation with off-the-shelf vehicular communication
hardware. The localization algorithm is validated in different traffic
scenarios, including a crossroad area with heterogeneous conditions in terms of
feature density and V2V connectivity, as well as a real urban area by using
Simulation of Urban MObility (SUMO) for traffic data generation. Performance
results show that the proposed ICP method can significantly improve the vehicle
location accuracy compared to the stand-alone GNSS, especially in harsh
environments, such as in urban canyons, where the GNSS signal is highly
degraded or denied.Comment: 15 pages, 10 figures, in review, 201
Socionomic modelling in wireless sensor networks
The performance and efficiency of a Wireless Sensor Network (WSN) is typically subject to techniques used in data routing, clustering, and localization. Being primarily driven by resource constraints, a Socionomic model has been formulated to optimize resource usage and boost collaboration among sensor nodes. In this paper, we present several experimental results to ascertain the underlying philosophy of the Socionomic model for improving network lifetime of resource constrained devices - such as, sensor nodes
Local heuristic for the refinement of multi-path routing in wireless mesh networks
We consider wireless mesh networks and the problem of routing end-to-end
traffic over multiple paths for the same origin-destination pair with minimal
interference. We introduce a heuristic for path determination with two
distinguishing characteristics. First, it works by refining an extant set of
paths, determined previously by a single- or multi-path routing algorithm.
Second, it is totally local, in the sense that it can be run by each of the
origins on information that is available no farther than the node's immediate
neighborhood. We have conducted extensive computational experiments with the
new heuristic, using AODV and OLSR, as well as their multi-path variants, as
underlying routing methods. For two different CSMA settings (as implemented by
802.11) and one TDMA setting running a path-oriented link scheduling algorithm,
we have demonstrated that the new heuristic is capable of improving the average
throughput network-wide. When working from the paths generated by the
multi-path routing algorithms, the heuristic is also capable to provide a more
evenly distributed traffic pattern
- …