Scheduling for Multi-Camera Surveillance in LTE Networks

Wireless surveillance in cellular networks has become increasingly important, while commercial LTE surveillance cameras are also available nowadays. Nevertheless, most scheduling algorithms in the literature are throughput, fairness, or profit-based approaches, which are not suitable for wireless surveillance. In this paper, therefore, we explore the resource allocation problem for a multi-camera surveillance system in 3GPP Long Term Evolution (LTE) uplink (UL) networks. We minimize the number of allocated resource blocks (RBs) while guaranteeing the coverage requirement for surveillance systems in LTE UL networks. Specifically, we formulate the Camera Set Resource Allocation Problem (CSRAP) and prove that the problem is NP-Hard. We then propose an Integer Linear Programming formulation for general cases to find the optimal solution. Moreover, we present a baseline algorithm and devise an approximation algorithm to solve the problem. Simulation results based on a real surveillance map and synthetic datasets manifest that the number of allocated RBs can be effectively reduced compared to the existing approach for LTE networks.Comment: 9 pages, 10 figure

Investigating the Resilience of Accessibility to Emergency and Lifesaving Facilities under Natural Hazards

Studying accessibility, including the resilience of city transportation networks, is critical to understand how these networks influence individuals’ mobility and lives. This study developed an analytical research framework to examine the resilience of accessibility to emergency and lifesaving facilities under the threats of natural hazards such as earthquakes and wildfires. With a cumulative-opportunity approach, the authors measured accessibility by counting emergency and lifesaving facilities (including parks, schools, hospitals, roads, and fire stations) that can be reached by driving at the census tract level in San Fernando Valley, CA. With the calculated accessibility, the authors run simulations to collect data showing what would happen if an area were affected by a selected disaster. They then used statistical analysis to identify those areas where accessibility is significantly reduced compared to the original status. A normalized difference accessibility index (NDAI) was further created to suggest plans and strategies to help those vulnerable areas through adding facilities/services or improving transportation infrastructure

Do Multi-Use-Path Accessibility and the Clustering Effect Play a Role in Residents\u27 Choice of Walking and Cycling?

The transportation studies literature recognizes the relationship between accessibility and active travel. However, there is limited research on the specific impact of walking and cycling accessibility to multi-use paths on active travel behavior. Combined with the culture of automobile dependency in the US, this knowledge gap has been making it difficult for policy-makers to encourage walking and cycling mode choices, highlighting the need to promote a walking and cycling culture in cities. In this case, a clustering effect (“you bike, I bike”) can be used as leverage to initiate such a trend. This project contributes to the literature as one of the few published research projects that considers all typical categories of explanatory variables (individual and household socioeconomics, local built environment features, and travel and residential choice attitudes) as well as two new variables (accessibility to multi-use paths calculated by ArcGIS and a clustering effect represented by spatial autocorrelation) at two levels (level 1: binary choice of cycling/waking; level 2: cycling/walking time if yes at level 1) to better understand active travel demand. We use data from the 2012 Utah Travel Survey. At the first level, we use a spatial probit model to identify whether and why Salt Lake City residents walked or cycled. The second level is the development of a spatial autoregressive model for walkers and cyclists to examine what factors affect their travel time when using walking or cycling modes. The results from both levels, obtained while controlling for individual, attitudinal, and built-environment variables, show that accessibility to multi-use paths and a clustering effect (spatial autocorrelation) influence active travel behavior in different ways. Specifically, a cyclist is likely to cycle more when seeing more cyclists around. These findings provide analytical evidence to decision-makers for efficiently evaluating and deciding between plans and policies to enhance active transportation based on the two modeling approaches to assessing travel behavior described above

A Gravity Model Integrating Land-Use and Transportation Policies for Sustainable Development: Case Study of Fresno, California

The idea of urban compaction has been long proposed and promoted to address the problem of urban sprawl in many American cities. However, there are still rare successful cases of such implementation in the United States. This study uses a classic gravity model, TELEM (Transpiration, Economic, and Land-Use Model) to examine to what extent a land-use or transportation policy must be regulated to make the urban compaction occur in a typical auto-dependent city—Fresno, California. Five scenarios are considered (BL, L1, L2, T1, and T2), in which the baseline (BL) is a natural growth scenario. Without any policy interventions, the city will inevitably expand outward. The L1 (high-intensity zoning) and L2 (growth boundary) results suggest that high-density zoning and growth boundary policies could make the compaction occur. The T1 (location impedance) and T2 (carbon tax) results reveal that transportation interventions would create barriers among regions/areas and therefore should be carefully used for compaction. This study not only adds to the literature on urban modeling but also contributes to the practice of smart growth or new urbanism policies for sustainability

Maximizing Friend-Making Likelihood for Social Activity Organization

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), by modeling both existing friendships and the likelihood of new friend making. To find a set of attendees for socialization activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships and the objective function on the likelihood of friend making. We prove that HMGF is NP-Hard, and no approximation algorithm exists unless P = NP. We then propose an error-bounded approximation algorithm to efficiently obtain the solutions very close to the optimal solutions. We conduct a user study to validate our problem formulation and per- form extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm