113 research outputs found
Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining
Service Function Chaining (SFC) allows the forwarding of a traffic flow along
a chain of Virtual Network Functions (VNFs, e.g., IDS, firewall, and NAT).
Software Defined Networking (SDN) solutions can be used to support SFC reducing
the management complexity and the operational costs. One of the most critical
issues for the service and network providers is the reduction of energy
consumption, which should be achieved without impact to the quality of
services. In this paper, we propose a novel resource (re)allocation
architecture which enables energy-aware SFC for SDN-based networks. To this
end, we model the problems of VNF placement, allocation of VNFs to flows, and
flow routing as optimization problems. Thereafter, heuristic algorithms are
proposed for the different optimization problems, in order find near-optimal
solutions in acceptable times. The performance of the proposed algorithms are
numerically evaluated over a real-world topology and various network traffic
patterns. The results confirm that the proposed heuristic algorithms provide
near optimal solutions while their execution time is applicable for real-life
networks.Comment: Extended version of submitted paper - v7 - July 201
Nonparametric Spatio-Temporal Joint Probabilistic Data Association Coupled Filter and Interfering Extended Target Tracking
Extended target tracking estimates the centroid and shape of the target in
space and time. In various situations where extended target tracking is
applicable, the presence of multiple targets can lead to interference,
particularly when they maneuver behind one another in a sensor like a camera.
Nonetheless, when dealing with multiple extended targets, there's a tendency
for them to share similar shapes within a group, which can enhance their
detectability. For instance, the coordinated movement of a cluster of aerial
vehicles might cause radar misdetections during their convergence or
divergence. Similarly, in the context of a self-driving car, lane markings
might split or converge, resulting in inaccurate lane tracking detections. A
well-known joint probabilistic data association coupled (JPDAC) filter can
address this problem in only a single-point target tracking. A variation of
JPDACF was developed by introducing a nonparametric Spatio-Temporal Joint
Probabilistic Data Association Coupled Filter (ST-JPDACF) to address the
problem for extended targets. Using different kernel functions, we manage the
dependency of measurements in space (inside a frame) and time (between frames).
Kernel functions are able to be learned using a limited number of training
data. This extension can be used for tracking the shape and dynamics of
nonparametric dependent extended targets in clutter when targets share
measurements. The proposed algorithm was compared with other well-known
supervised methods in the interfering case and achieved promising results.Comment: 12 pages, 8 figures, Journa
Optimized Path Planning for USVs under Ocean Currents
The proposed work focuses on the path planning for Unmanned Surface Vehicles
(USVs) in the ocean enviroment, taking into account various spatiotemporal
factors such as ocean currents and other energy consumption factors. The paper
proposes the use of Gaussian Process Motion Planning (GPMP2), a Bayesian
optimization method that has shown promising results in continuous and
nonlinear path planning algorithms. The proposed work improves GPMP2 by
incorporating a new spatiotemporal factor for tracking and predicting ocean
currents using a spatiotemporal Bayesian inference. The algorithm is applied to
the USV path planning and is shown to optimize for smoothness, obstacle
avoidance, and ocean currents in a challenging environment. The work is
relevant for practical applications in ocean scenarios where an optimal path
planning for USVs is essential for minimizing costs and optimizing performance.Comment: 9 pages and 7 figures, submitted for IEEE Transactions on Man,
systems ,and Cybernetic
Numerical simulations of oceanographic characteristics of the Persian Gulf and Sea of Oman using ROMS model
1978-1989In this paper, oceanographic characteristics of Oman Sea has been simulated using oceanographic model ROMS. The study area was limited from Arvand Roud in the north of Persian Gulf to RAAS AL HAD in the south east of Oman Sea. Oceanographic parameters on the study area were simulated by the ROMS model for 7 years period and the results were analyzed in comparison to satellite data in the study area in different seasons. Propagation pattern of Indian Ocean Surface Water (IOSW) in to the Persian Gulf and Oman Sea was considered for examination using the ROMS model. The model outputs for surface currents and temperature show the similar pattern as compared to remote sensing data and previous work
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