Iowa Public Television’s Planning Targets 2011-2014

Agency Performance Plan, Iowa Public Televisio

Energy-Efficient selective activation in Femtocell Networks

Provisioning the capacity of wireless networks is difficult when peak load is significantly higher than average load, for example, in public spaces like airports or train stations. Service providers can use femtocells and small cells to increase local capacity, but deploying enough femtocells to serve peak loads requires a large number of femtocells that will remain idle most of the time, which wastes a significant amount of power. To reduce the energy consumption of over-provisioned femtocell networks, we formulate a femtocell selective activation problem, which we formalize as an integer nonlinear optimization problem. Then we introduce GREENFEMTO, a distributed femtocell selective activation algorithm that deactivates idle femtocells to save power and activates them on-the-fly as the number of users increases. We prove that GREENFEMTO converges to a locally Pareto optimal solution and demonstrate its performance using extensive simulations of an LTE wireless system. Overall, we find that GREENFEMTO requires up to 55% fewer femtocells to serve a given user load, relative to an existing femtocell power-saving procedure, and comes within 15% of a globally optimal solution

Assessing dynamic models for high priority waste collection in smart cities

Waste Management (WM) represents an important part of Smart Cities (SCs) with significant impact on modern societies. WM involves a set of processes ranging from waste collection to the recycling of the collected materials. The proliferation of sensors and actuators enable the new era of Internet of Things (IoT) that can be adopted in SCs and help in WM. Novel approaches that involve dynamic routing models combined with the IoT capabilities could provide solutions that outperform existing models. In this paper, we focus on a SC where a number of collection bins are located in different areas with sensors attached to them. We study a dynamic waste collection architecture, which is based on data retrieved by sensors. We pay special attention to the possibility of immediate WM service in high priority areas, e.g., schools or hospitals where, possibly, the presence of dangerous waste or the negative effects on human quality of living impose the need for immediate collection. This is very crucial when we focus on sensitive groups of citizens like pupils, elderly or people living close to areas where dangerous waste is rejected. We propose novel algorithms aiming at providing efficient and scalable solutions to the dynamic waste collection problem through the management of the trade-off between the immediate collection and its cost. We describe how the proposed system effectively responds to the demand as realized by sensor observations and alerts originated in high priority areas. Our aim is to minimize the time required for serving high priority areas while keeping the average expected performance at high level. Comprehensive simulations on top of the data retrieved by a SC validate the proposed algorithms on both quantitative and qualitative criteria which are adopted to analyze their strengths and weaknesses. We claim that, local authorities could choose the model that best matches their needs and resources of each city

Efficient Heuristic for Resource Allocation in Zero-forcing OFDMA-SDMA Systems with Minimum Rate Constraints

4G wireless access systems require high spectral efficiency to support the ever increasing number of users and data rates for real time applications. Multi-antenna OFDM-SDMA systems can provide the required high spectral efficiency and dynamic usage of the channel, but the resource allocation process becomes extremely complex because of the augmented degrees of freedom. In this paper, we propose two heuristics to solve the resource allocation problem that have very low computational complexity and give performances not far from the optimal. The proposed heuristics select a set of users for each subchannel, but contrary to the reported methods that solve the throughput maximization problem, our heuristics consider the set of real-time (RT) users to ensure that their minimum rate requirements are met. We compare the heuristics' performance against an upper bound and other methods proposed in the literature and find that they give a somewhat lower performance, but support a wider range of minimum rates while reducing the computational complexity. The gap between the objective achieved by the heuristics and the upper bound is not large. In our experiments this gap is 10.7% averaging over all performed numerical evaluations for all system configurations. The increase in the range of the supported minimum rates when compared with a method reported in the literature is 14.6% on average.Comment: 8 figure

The Influence of Multi-agent Cooperation on the Efficiency of Taxi Dispatching

The paper deals with the problem of the optimal collaboration scheme in taxi dispatching between customers, taxi drivers and the dispatcher. The authors propose three strategies that differ by the amount of information exchanged between agents and the intensity of cooperation between taxi drivers and the dispatcher. The strategies are evaluated by means of a microscopic multi-agent transport simulator (MATSim) coupled with a dynamic vehicle routing optimizer (DVRP Optimizer), which allows to realistically simulate dynamic taxi services as one of several different transport means, all embedded into a realistic environment. The evaluation is carried out on a scenario of the Polish city of Mielec. The results obtained prove that the cooperation between the dispatcher and taxi drivers is of the utmost importance, while the customer–dispatcher communication may be reduced to minimum and compensated by the use of more sophisticated dispatching strategies, thereby not affecting the quality of service

Submodular Function Maximization for Group Elevator Scheduling

We propose a novel approach for group elevator scheduling by formulating it as the maximization of submodular function under a matroid constraint. In particular, we propose to model the total waiting time of passengers using a quadratic Boolean function. The unary and pairwise terms in the function denote the waiting time for single and pairwise allocation of passengers to elevators, respectively. We show that this objective function is submodular. The matroid constraints ensure that every passenger is allocated to exactly one elevator. We use a greedy algorithm to maximize the submodular objective function, and derive provable guarantees on the optimality of the solution. We tested our algorithm using Elevate 8, a commercial-grade elevator simulator that allows simulation with a wide range of elevator settings. We achieve significant improvement over the existing algorithms.Comment: 10 pages; 2017 International Conference on Automated Planning and Scheduling (ICAPS