723 research outputs found
Geographic Centroid Routing for Vehicular Networks
A number of geolocation-based Delay Tolerant Networking (DTN) routing
protocols have been shown to perform well in selected simulation and mobility
scenarios. However, the suitability of these mechanisms for vehicular networks
utilizing widely-available inexpensive Global Positioning System (GPS) hardware
has not been evaluated. We propose a novel geolocation-based routing primitive
(Centroid Routing) that is resilient to the measurement errors commonly present
in low-cost GPS devices. Using this notion of Centroids, we construct two novel
routing protocols and evaluate their performance with respect to positional
errors as well as traditional DTN routing metrics. We show that they outperform
existing approaches by a significant margin.Comment: 6 page
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
LOCATION AWARE CLUSTER BASED ROUTING IN WIRELESS SENSOR NETWORKS
Wireless sensor nodes are usually embedded in the physical environment and report sensed data to a central base station. Clustering is one of the most challenging issues in wireless sensor networks. This paper proposes a new cluster scheme for wireless sensor network by modified the K means clustering algorithm. Sensor nodes are deployed in a harsh environment and randomly scattered in the region of interest and are deployed in a flat architecture. The transmission of packet will reduce the network lifetime. Thus, clustering scheme is required to avoid network traffic and increase overall network lifetime. In order to cluster the sensor nodes that are deployed in the sensor network, the location information of each sensor node should be known. By knowing the location of the each sensor node in the wireless sensor network, clustering is formed based on the highest residual energy and minimum distance from the base station. Among the group of nodes, one node is elected as a cluster head using centroid method. The minimum distance between the cluster node’s and the centroid point is elected as a cluster head. Clustering of nodes can minimize the residual energy and maximize the network performance. This improves the overall network lifetime and reduces network traffic
Effects of Data Resolution and Human Behavior on Large Scale Evacuation Simulations
Traffic Analysis Zones (TAZ) based macroscopic simulation studies are mostly
applied in evacuation planning and operation areas. The large size in TAZ and
aggregated information of macroscopic simulation underestimate the real
evacuation performance. To take advantage of the high resolution demographic
data LandScan USA (the zone size is much smaller than TAZ) and agent-based
microscopic traffic simulation models, many new problems appeared and novel
solutions are needed. A series of studies are conducted using LandScan USA
Population Cells (LPC) data for evacuation assignments with different network
configurations, travel demand models, and travelers compliance behavior.
First, a new Multiple Source Nearest Destination Shortest Path (MSNDSP)
problem is defined for generating Origin Destination matrix in evacuation
assignments when using LandScan dataset. Second, a new agent-based traffic
assignment framework using LandScan and TRANSIMS modules is proposed for
evacuation planning and operation study. Impact analysis on traffic analysis
area resolutions (TAZ vs LPC), evacuation start times (daytime vs nighttime),
and departure time choice models (normal S shape model vs location based model)
are studied. Third, based on the proposed framework, multi-scale network
configurations (two levels of road networks and two scales of zone sizes) and
three routing schemes (shortest network distance, highway biased, and shortest
straight-line distance routes) are implemented for the evacuation performance
comparison studies. Fourth, to study the impact of human behavior under
evacuation operations, travelers compliance behavior with compliance levels
from total complied to total non-complied are analyzed.Comment: PhD dissertation. UT Knoxville. 130 pages, 37 figures, 8 tables.
University of Tennessee, 2013. http://trace.tennessee.edu/utk_graddiss/259
A novel k-means powered algorithm for an efficient clustering in vehicular ad-hoc networks
Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
K-means online-learning routing protocol (K-MORP) for unmanned aerial vehicles (UAV) adhoc networks
Unmanned Aerial Vehicles (UAVs) have become a hot topic due to their flexible architecture adopted in many wireless technologies. In UAV ad hoc networks, traditional routing protocols with a fixed topology are ineffective due to dynamic mobility and unstable paths. Therefore, the mobility patterns of UAVs challenge efficient and reliable routing in UAV networks. Traditional routing algorithms are often based on assumptions of static nodes and predetermined network topologies. Which are not suitable for the dynamic and unpredictable nature of UAV mobility patterns. To address this problem, this paper introduces a K-means online learning routing protocol (KMORP) scheme employing a Markov mobility model for UAV ad hoc networks. Initially, the proposed method utilizes a 3D Gauss Markov mobility model to accurately estimate UAV positions, while K-means online learning is adopted for dynamic clustering and load balancing. Designed for real-time data processing, KMORP is well suited for UAV ad hoc networks, quickly adapting to network environmental changes such as UAV mobility, interference, and signal degradation to ensure efficient data transmission and communication. This is achieved while reducing the overall communication overhead and increasing the packet delivery ratio(PDR%). In the routing phase, the proposed scheme employs inter-cluster forwarding nodes to transmit messages among different clusters. Extensive simulations demonstrate the performance of the proposed KMORP, showing a 38% better PDR compared to OLSR and over 50% less end-to-end(E2E) delay compared to typical K-Means. Furthermore, the proposed KMORP exhibited an average throughput of 955 kbps, showing a substantial improvement in network performance. The results underscore that the proposed KMORP outperforms existing techniques in terms of PDR, E2E delay, and throughput.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed
Achieving Small World Properties using Bio-Inspired Techniques in Wireless Networks
It is highly desirable and challenging for a wireless ad hoc network to have
self-organization properties in order to achieve network wide characteristics.
Studies have shown that Small World properties, primarily low average path
length and high clustering coefficient, are desired properties for networks in
general. However, due to the spatial nature of the wireless networks, achieving
small world properties remains highly challenging. Studies also show that,
wireless ad hoc networks with small world properties show a degree distribution
that lies between geometric and power law. In this paper, we show that in a
wireless ad hoc network with non-uniform node density with only local
information, we can significantly reduce the average path length and retain the
clustering coefficient. To achieve our goal, our algorithm first identifies
logical regions using Lateral Inhibition technique, then identifies the nodes
that beamform and finally the beam properties using Flocking. We use Lateral
Inhibition and Flocking because they enable us to use local state information
as opposed to other techniques. We support our work with simulation results and
analysis, which show that a reduction of up to 40% can be achieved for a
high-density network. We also show the effect of hopcount used to create
regions on average path length, clustering coefficient and connectivity.Comment: Accepted for publication: Special Issue on Security and Performance
of Networks and Clouds (The Computer Journal
- …