Transportation Network Data Requirements for Assessing Criticality for Resiliency and Adaptation Planning

This report is one of two NCST Research Report documents produced as part of a project to advance the technical modeling tools for resiliency and adaptation planning, especially those used for criticality rankings. The official final technical report, Climate Adaptation and Resiliency Planning: Agency Roles and Workforce Development Needs, summarizes a climate adaptation framework and describes current planning practices and workforce needs of Departments of Transportation and other planning agencies. This additional technical documentation report looks specifically at the network data challenges of objectively assessing asset criticality, one step in the larger adaptation planning framework and a prerequisite for efficient allocation of limited adaptation resources. Specifically, this report explores the modeling resolution (in terms of the completeness of the road network and the spatial disaggregation of origin and destination matrices) needed to produce accurate criticality ratings. Original modeling work using a well-establish criticality measure, the Network Robustness Index (NRI), on both a small hypothetical network and the road network for Chittenden County, Vermont, demonstrated a need for higher resolution networks for criticality modeling. Since this part of the work was published in the Transportation Research Record it is only summarized here. A conceptual discussion of methods explored for creating networks for larger real-world areas that are sufficiently complete for criticality assessment is also included based on exploratory work using the travel demand model for the Greater Sacramento California area.View the NCST Project Webpag

Human resource allocation to multiple projects based on members’ expertise, group heterogeneity and social cohesion

Project managers regularly allocate human resources to construction projects. This critical task is usually executed by fulfilling the minimum project staffing requirements normally based around the quantity and competence of project members. However, research has shown that team performance can increase by up to 10% and 18%, respectively, as a consequence of the group members’ heterogeneity and social cohesion. Also, there is currently no practical quantitative tool which incorporates these aspects to allow project managers to achieve this task efficiently and objectively. A new quantitative model for the effective allocation of human resources to multiple projects, which takes into account group heterogeneity and social cohesion is proposed. This model is easy to build, update and use in real project environments with the use of a spreadsheet and a basic optimization engine (e.g. Excel Solver). A case study is proposed and solved with a Genetic Algorithm to illustrate the model implementation. Finally, a validation example is provided to exemplify how group heterogeneity and social cohesion condition academic achievement in an academic setting

Optimal Virtualized Inter-Tenant Resource Sharing for Device-to-Device Communications in 5G Networks

Device-to-Device (D2D) communication is expected to enable a number of new services and applications in future mobile networks and has attracted significant research interest over the last few years. Remarkably, little attention has been placed on the issue of D2D communication for users belonging to different operators. In this paper, we focus on this aspect for D2D users that belong to different tenants (virtual network operators), assuming virtualized and programmable future 5G wireless networks. Under the assumption of a cross-tenant orchestrator, we show that significant gains can be achieved in terms of network performance by optimizing resource sharing from the different tenants, i.e., slices of the substrate physical network topology. To this end, a sum-rate optimization framework is proposed for optimal sharing of the virtualized resources. Via a wide site of numerical investigations, we prove the efficacy of the proposed solution and the achievable gains compared to legacy approaches.Comment: 10 pages, 7 figure

Future RAN architecture: SD-RAN through a general-purpose processing platform

In this article, we identify and study the potential of an integrated deployment solution for energy-efficient cellular networks combining the strengths of two very active current research themes: 1) software-defined radio access networks (SD-RANs) and 2) decoupled signaling and data transmissions, or beyond cellular green generation (BCG2) architecture, for enhanced energy efficiency. While SD-RAN envisions a decoupled centralized control plane and data-forwarding plane for flexible control, the BCG2 architecture calls for decoupling coverage from the capacity and coverage provided through an always-on low-power signaling node for a larger geographical area; the capacity is catered by various on-demand data nodes for maximum energy efficiency. In this article, we show that a combined approach that brings both specifications together can not only achieve greater benefits but also facilitate faster realization of both technologies. We propose the idea and design of a signaling controller that acts as a signaling node to provide always-on coverage, consuming low power, and at the same time host the control plane functions for the SDRAN through a general-purpose processing platform. The phantom cell concept is also a similar idea where a normal macrocell provides interference control to densely deployed small cells, although our initial results show that the integrated architecture has a much greater potential for energy savings than phantom cells

Optimal association of mobile users to multi-access edge computing resources

Multi-access edge computing (MEC) plays a key role in fifth-generation (5G) networks in bringing cloud functionalities at the edge of the radio access network, in close proximity to mobile users. In this paper we focus on mobile-edge computation offloading, a way to transfer heavy demanding, and latency-critical applications from mobile handsets to close-located MEC servers, in order to reduce latency and/or energy consumption. Our goal is to provide an optimal strategy to associate mobile users to access points (AP) and MEC hosts, while contextually optimizing the allocation of radio and computational resources to each user, with the objective of minimizing the overall user transmit power under latency constraints incorporating both communication and computation times. The overall problem is a mixed-binary problem. To overcome its inherent computational complexity, we propose two alternative strategies: i) a method based on successive convex approximation (SCA) techniques, proven to converge to local optimal solutions; ii) an approach hinging on matching theory, based on formulating the assignment problem as a matching game