Min-cost Problem Research Articles

SDN-Enabled Energy-Efficient Routing Optimization Framework for Industrial Internet of Things

The traditional Internet architecture relies on the best-effort principle, which is not suitable for critical industrial Internet of Things (IIoT) applications such as healthcare systems with stringent quality-of-service (QoS) requirements. In this article, a software-defined network (SDN) based on an analytical parallel routing framework is proposed by using the massive processing power of a graphics processing unit (GPU) for dynamically optimizing multiconstrained QoS parameters in the IIoT. The framework considers three types of QoS applications for smart healthcare traffic: loss-sensitive, delay-sensitive, and jitter-sensitive. A QoS-enabled routing optimization problem is formulated as a max-flow min-cost problem, while a greedy heuristic that dispatches the path calculation task concurrently to the GPU for calculating optimal forwarding paths considering the QoS requirement of each flow is proposed. The results show that the proposed scheme efficiently utilizes the limited bandwidth cost in terms of energy and bandwidth while satisfying the QoS requirement of each flow with maximizing the network resources for future IIoT traffic flows. Comparative analysis of simulation results with shortest path delay, Lagrangian relaxation-based aggregated cost, and Sway schemes indicate a reduced violation in the service-level agreement by 17%, 19%, and 4%, respectively, by using the AttMpls topology, while it is 48%, 44%, and 7% when the Goodnet topology is used. Moreover, SEQOS is seen to be energy efficient and eight times faster than the benchmark algorithms in large IIoT networks.

Restricted Constraints in a Max-Flow and Min-Cost problem

The present paper defines a combination of two most important and old flow problems such as maximum flow and minimum cost flow. In the first one a flow with the maximum value from source node to sink node is sought. The second one seeks a flow with the minimum total transferring costs from source nodes to demand nodes. Here an attempt is made to obtain an optimal route of a more realistic situation as to scheduling some restricted constraint, in which its transferring cost is minimized and its value is maximized. The proposed algorithm is formulated and solved by the lexicographic search approach. It is seen that the time required for the search of the optimal solution is fairly less. Also the algorithm is applied to different order matrices with number of stations = 5, 8, 10, …, 50 and in the dimensions of 6 to exhibit its effectiveness.

Tri-track: Free Software for Large-Scale Particle Tracking

The ability to correctly track objects in time-lapse sequences is important in many applications of microscopy. Individual object motions typically display a level of dynamic regularity reflecting the existence of an underlying physics or biology. Best results are obtained when this local information is exploited. Additionally, if the particle number is known to be approximately constant, a large number of tracking scenarios may be rejected on the basis that they are not compatible with a known maximum particle velocity. This represents information of a global nature, which should ideally be exploited too. Some time ago, we devised an efficient algorithm that exploited both types of information. The tracking task was reduced to a max-flow min-cost problem instance through a novel graph structure that comprised vertices representing objects from three consecutive image frames. The algorithm is explained here for the first time. A user-friendly implementation is provided, and the specific relaxation mechanism responsible for the method's effectiveness is uncovered. The software is particularly competitive for complex dynamics such as dense antiparallel flows, or in situations where object displacements are considerable. As an application, we characterize a remarkable vortex structure formed by bacteria engaged in interstitial motility.

Bus surveillance: how many and where cameras should be placed

Public transport safety is an important issue that has recently gained substantial attention, especially with the increasing number of violent incidents occurring abroad. To avoid such incidents and to perform post-incident investigations, many buses today are equipped with surveillance cameras. These cameras are usually installed in key places such as doors, the front and the middle of the bus. This camera placement is often performed manually based on human intuition and knowledge; however, there is no scientific basis to justify: (1) how many cameras would be sufficient, and (2) where (location) and how (with what orientation) they should be placed, to increase the area of coverage at the minimum cost. This paper addresses this issue by breaking it down into two separate problems: MaxGain and MinCost. The MaxGain problem is aimed to maximize the overall coverage with a specific number of cameras; while the MinCost problem attempts to minimize the number of cameras to cover a specified area in the bus. The solutions to these two problems are presented. The proposed method computes the approximate coverage of a camera inside the 3D bus model. Furthermore, in order to improve the efficiency of the solution, an algorithm called "SmartMax" is proposed. The proposed solution advises precise locations and orientations (pan and tilt angles) of required cameras and can be used to validate the current camera installations in various types of public transit buses.

On-Line Resource Management with Application to Routing and Scheduling

We propose a framework to model on-line resource management problems based on an on-line version of positive linear programming. We consider both min cost problems and max benefit problems and propose logarithmic competitive algorithms that are optimal up to a constant factor. The proposed framework provides a general methodology that applies to a wide class of on-line problems: shop scheduling, packet routing, and in general a class of packing and assignment problems. Previously studied problems as on-line multiprocessor scheduling and on-line virtual circuit routing can also be modeled within this framework.