3,044 research outputs found
A Novel Multiobjective Cell Switch-Off Framework for Cellular Networks
Cell Switch-Off (CSO) is recognized as a promising approach to reduce the
energy consumption in next-generation cellular networks. However, CSO poses
serious challenges not only from the resource allocation perspective but also
from the implementation point of view. Indeed, CSO represents a difficult
optimization problem due to its NP-complete nature. Moreover, there are a
number of important practical limitations in the implementation of CSO schemes,
such as the need for minimizing the real-time complexity and the number of
on-off/off-on transitions and CSO-induced handovers. This article introduces a
novel approach to CSO based on multiobjective optimization that makes use of
the statistical description of the service demand (known by operators). In
addition, downlink and uplink coverage criteria are included and a comparative
analysis between different models to characterize intercell interference is
also presented to shed light on their impact on CSO. The framework
distinguishes itself from other proposals in two ways: 1) The number of
on-off/off-on transitions as well as handovers are minimized, and 2) the
computationally-heavy part of the algorithm is executed offline, which makes
its implementation feasible. The results show that the proposed scheme achieves
substantial energy savings in small cell deployments where service demand is
not uniformly distributed, without compromising the Quality-of-Service (QoS) or
requiring heavy real-time processing
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Multiobjective auction-based switching-off scheme in heterogeneous networks: to bid or not to bid?
©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The emerging data traffic demand has caused a massive deployment of network infrastructure, including Base Stations (BSs) and Small Cells (SCs), leading to increased energy consumption and expenditures. However, the network underutilization during low traffic periods enables the Mobile Network Operators (MNOs) to save energy by having their traffic served by third party SCs, thus being able to switch off their BSs. In this paper, we propose a novel market approach to foster the opportunistic utilization of the unexploited SCs capacity, where the MNOs, instead of requesting the maximum capacity to meet their highest traffic expectations, offer a set of bids requesting different resources from the third party SCs at lower costs. Motivated by the conflicting financial interests of the MNOs and the third party, the restricted capacity of the SCs that is not adequate to carry the whole traffic in multi-operator scenarios, and the necessity for energy efficient solutions, we introduce a combinatorial auction framework, which includes i) a bidding strategy, ii) a resource allocation scheme, and iii) a pricing rule. We propose a multiobjective framework as an energy and cost efficient solution for the resource allocation problem, and we provide extensive analytical and experimental results to estimate the potential energy and cost savings that can be achieved. In addition, we investigate the conditions under which the MNOs and the third party companies should take part in the proposed auction.Peer ReviewedPostprint (author's final draft
Multi-objective road pricing: a cooperative and competitive bilevel optimization approach
Costs associated with traffic externalities such as congestion, air pollution, noise, safety, etcetera are becoming unbearable. The Braess paradox shows that combating congestion by adding infrastructure may not improve traffic conditions, and geographical and/or financial constraints may not allow infrastructure expansion. Road pricing presents an alternative to combat traffic externalities. The traditional way of road pricing, namely congestion charging, may create negative benefits for society. In this effect, we develop a flexible pricing scheme internalizing costs arising from all externalities. Using a game theoretical approach, we extend the single authority road pricing scheme to a pricing scheme with multiple authorities/regions (with likely contradicting objectives)
Design of evacuation plans for densely urbanised city centres
The high population density and tightly packed nature of some city centres make emergency planning for these urban spaces especially important, given the potential for human loss in case of disaster. Historic and recent events have made emergency service planners particularly conscious of the need for preparing evacuation plans in advance. This paper discusses a methodological approach for assisting decision-makers in designing urban evacuation plans. The approach aims at quickly and safely moving the population away from the danger zone into shelters. The plans include determining the number and location of rescue facilities, as well as the paths that people should take from their building to their assigned shelter in case of an occurrence requiring evacuation. The approach is thus of the location–allocation–routing type, through the existing streets network, and takes into account the trade-offs among different aspects of evacuation actions that inevitably come up during the planning stage. All the steps of the procedure are discussed and systematised, along with computational and practical implementation issues, in the context of a case study – the design of evacuation plans for the historical centre of an old European city
Simulation Framework for Cooperative Adaptive Cruise Control with Empirical DSRC Module
Wireless communication plays a vital role in the promising performance of
connected and automated vehicle (CAV) technology. This paper proposes a
Vissim-based microscopic traffic simulation framework with an analytical
dedicated short-range communication (DSRC) module for packet reception. Being
derived from ns-2, a packet-level network simulator, the DSRC probability
module takes into account the imperfect wireless communication that occurs in
real-world deployment. Four managed lane deployment strategies are evaluated
using the proposed framework. While the average packet reception rate is above
93\% among all tested scenarios, the results reveal that the reliability of the
vehicle-to-vehicle (V2V) communication can be influenced by the deployment
strategies. Additionally, the proposed framework exhibits desirable scalability
for traffic simulation and it is able to evaluate transportation-network-level
deployment strategies in the near future for CAV technologies.Comment: 6 pages, 6 figure, 44th Annual Conference of the IEEE Industrial
Electronics Societ
Building accurate radio environment maps from multi-fidelity spectrum sensing data
In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g., Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated
Bi-objective modeling approach for repairing multiple feature infrastructure systems
A bi-objective decision aid model for planning long-term maintenance of infrastructure systems is presented, oriented to interventions on their constituent elements, with two upgrade levels possible for each element (partial/full repairs). The model aims at maximizing benefits and minimizing costs, and its novelty is taking into consideration, and combining, the system/element structure, volume discounts, and socioeconomic factors. The model is tested with field data from 229 sidewalks (systems) and compared to two simpler repair policies, of allowing only partial or full repairs. Results show that the efficiency gains are greater in the lower mid-range budget region. The proposed modeling approach is an innovative tool to optimize cost/benefits for the various repair options and analyze the respective trade-offs.info:eu-repo/semantics/publishedVersio
Modeling Profit of Sliced 5G Networks for Advanced Network Resource Management and Slice Implementation
The core innovation in future 5G cellular networksnetwork slicing, aims at
providing a flexible and efficient framework of network organization and
resource management. The revolutionary network architecture based on slices,
makes most of the current network cost models obsolete, as they estimate the
expenditures in a static manner. In this paper, a novel methodology is
proposed, in which a value chain in sliced networks is presented. Based on the
proposed value chain, the profits generated by different slices are analyzed,
and the task of network resource management is modeled as a multiobjective
optimization problem. Setting strong assumptions, this optimization problem is
analyzed starting from a simple ideal scenario. By removing the assumptions
step-by-step, realistic but complex use cases are approached. Through this
progressive analysis, technical challenges in slice implementation and network
optimization are investigated under different scenarios. For each challenge,
some potentially available solutions are suggested, and likely applications are
also discussed
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