30,921 research outputs found
Jointly Optimizing Placement and Inference for Beacon-based Localization
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.Comment: Appeared at 2017 International Conference on Intelligent Robots and
Systems (IROS
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Optical wireless communication (OWC) is a promising technology for future
wireless communications owing to its potentials for cost-effective network
deployment and high data rate. There are several implementation issues in the
OWC which have not been encountered in radio frequency wireless communications.
First, practical OWC transmitters need an illumination control on color,
intensity, and luminance, etc., which poses complicated modulation design
challenges. Furthermore, signal-dependent properties of optical channels raise
non-trivial challenges both in modulation and demodulation of the optical
signals. To tackle such difficulties, deep learning (DL) technologies can be
applied for optical wireless transceiver design. This article addresses recent
efforts on DL-based OWC system designs. A DL framework for emerging image
sensor communication is proposed and its feasibility is verified by simulation.
Finally, technical challenges and implementation issues for the DL-based
optical wireless technology are discussed.Comment: To appear in IEEE Communications Magazine, Special Issue on
Applications of Artificial Intelligence in Wireless Communication
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Energy-aware sink node localization algorithm for wireless sensor networks
Wireless sensor networks (WSNs) are a family of wireless networks that usually operate with irreplaceable batteries. The energy sources limitation raises the need for designing specific protocols to prolong the operational lifetime of such networks. These protocols are responsible for messages exchanging through the wireless communications medium from the sensors to the base station (sink node). Therefore, the determination of the optimal location of the sink node becomes crucial to assure both the prolongation of the network’s operation and the quality of the provided services. This paper proposes a novel algorithm based on a Particle Swarm Optimization (PSO) approach for designing an energy-aware topology control protocol. The deliverable of the algorithm is the optimal sink node location within a deployment area. The proposed objective function is based on a number of topology control protocol’s characteristics such as numbers of neighbors per node, the nodes’ residual energy, and how they are far from the center of the deployment area. The simulation results show that the proposed algorithm reveals significant effectiveness to both topology construction and maintenance phases of a topology control protocol in terms of the number of active nodes, the topology construction time, the number of topology reconstructions, and the operational network’s lifetime.Web of Scienceart. ID 81035
Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer
Radio frequency (RF) energy harvesting and transfer techniques have recently
become alternative methods to power the next generation of wireless networks.
As this emerging technology enables proactive replenishment of wireless
devices, it is advantageous in supporting applications with quality-of-service
(QoS) requirement. This article focuses on the resource allocation issues in
wireless networks with RF energy harvesting capability, referred to as RF
energy harvesting networks (RF-EHNs). First, we present an overview of the
RF-EHNs, followed by a review of a variety of issues regarding resource
allocation. Then, we present a case study of designing in the receiver
operation policy, which is of paramount importance in the RF-EHNs. We focus on
QoS support and service differentiation, which have not been addressed by
previous literatures. Furthermore, we outline some open research directions.Comment: To appear in IEEE Networ
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