28,955 research outputs found
A Classification Framework for Beacon Applications
Beacons have received considerable attention in recent years, which is partially due to the fact that they serve as a flexible and versatile replacement for RFIDs in many applications. However, beacons are mostly considered from a purely technical perspective. This paper provides a conceptual view on application scenarios for beacons and introduces a novel framework for characterizing these. The framework consists of four dimensions: device movement, action trigger, purpose type, and connectivity requirements. Based on these, three archetypical scenarios are described. Finally, event-condition-action rules and online algorithms are used to formalize the backend of a beacon architecture
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
A Taxonomy for Congestion Control Algorithms in Vehicular Ad Hoc Networks
One of the main criteria in Vehicular Ad hoc Networks (VANETs) that has
attracted the researchers' consideration is congestion control. Accordingly,
many algorithms have been proposed to alleviate the congestion problem,
although it is hard to find an appropriate algorithm for applications and
safety messages among them. Safety messages encompass beacons and event-driven
messages. Delay and reliability are essential requirements for event-driven
messages. In crowded networks where beacon messages are broadcasted at a high
number of frequencies by many vehicles, the Control Channel (CCH), which used
for beacons sending, will be easily congested. On the other hand, to guarantee
the reliability and timely delivery of event-driven messages, having a
congestion free control channel is a necessity. Thus, consideration of this
study is given to find a solution for the congestion problem in VANETs by
taking a comprehensive look at the existent congestion control algorithms. In
addition, the taxonomy for congestion control algorithms in VANETs is presented
based on three classes, namely, proactive, reactive and hybrid. Finally, we
have found the criteria in which fulfill prerequisite of a good congestion
control algorithm
LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance System
Collision avoidance is a critical task in many applications, such as ADAS
(advanced driver-assistance systems), industrial automation and robotics. In an
industrial automation setting, certain areas should be off limits to an
automated vehicle for protection of people and high-valued assets. These areas
can be quarantined by mapping (e.g., GPS) or via beacons that delineate a
no-entry area. We propose a delineation method where the industrial vehicle
utilizes a LiDAR {(Light Detection and Ranging)} and a single color camera to
detect passive beacons and model-predictive control to stop the vehicle from
entering a restricted space. The beacons are standard orange traffic cones with
a highly reflective vertical pole attached. The LiDAR can readily detect these
beacons, but suffers from false positives due to other reflective surfaces such
as worker safety vests. Herein, we put forth a method for reducing false
positive detection from the LiDAR by projecting the beacons in the camera
imagery via a deep learning method and validating the detection using a neural
network-learned projection from the camera to the LiDAR space. Experimental
data collected at Mississippi State University's Center for Advanced Vehicular
Systems (CAVS) shows the effectiveness of the proposed system in keeping the
true detection while mitigating false positives.Comment: 34 page
DDH-MAC: a novel dynamic de-centralized hybrid MAC protocol for cognitive radio networks
The radio spectrum (3kHz - 300GHz) has become saturated and proven to be insufficient to address the proliferation of new wireless applications. Cognitive Radio Technology which is an opportunistic network and is equipped with fully programmable wireless devices that empowers the network by OODA cycle and then make intelligent decisions by adapting their MAC and physical layer characteristics such as waveform, has appeared to be the only solution for current low spectrum availability and under utilization problem. In this paper a novel Dynamic De-Centralized Hybrid “DDH-MAC” protocol for Cognitive Radio Networks has been presented which lies between Global Common Control Channel (GCCC) and non-GCCC categories of cognitive radio MAC protocols. DDH-MAC is equipped with the best features of GCCC MAC protocols but also overcomes the saturation and security issues in GCCC. To the best of authors' knowledge, DDH-MAC is the first protocol which is hybrid between GCCC and non-GCCC family of protocols. DDH-MAC provides multiple levels of security and partially use GCCC to transmit beacon which sets and announces local control channel for exchange of free channel list (FCL) sensed by the co-operatively communicating cognitive radio nodes, subsequently providing secure transactions among participating nodes over the decided local control channel. This paper describes the framework of the DDH-MAC protocol in addition to its pseudo code for implementation; it is shown that the pre-transmission time for DDH-MAC is on average 20% better while compared to other cognitive radio MAC protocols
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