Are You in the Line? RSSI-based Queue Detection in Crowds

Crowd behaviour analytics focuses on behavioural characteristics of groups of people instead of individuals' activities. This work considers human queuing behaviour which is a specific crowd behavior of groups. We design a plug-and-play system solution to the queue detection problem based on Wi-Fi/Bluetooth Low Energy (BLE) received signal strength indicators (RSSIs) captured by multiple signal sniffers. The goal of this work is to determine if a device is in the queue based on only RSSIs. The key idea is to extract features not only from individual device's data but also mobility similarity between data from multiple devices and mobility correlation observed by multiple sniffers. Thus, we propose single-device feature extraction, cross-device feature extraction, and cross-sniffer feature extraction for model training and classification. We systematically conduct experiments with simulated queue movements to study the detection accuracy. Finally, we compare our signal-based approach against camera-based face detection approach in a real-world social event with a real human queue. The experimental results indicate that our approach can reach minimum accuracy of 77% and it significantly outperforms the camera-based face detection because people block each other's visibility whereas wireless signals can be detected without blocking.Comment: This work has been partially funded by the European Union's Horizon 2020 research and innovation programme within the project "Worldwide Interoperability for SEmantics IoT" under grant agreement Number 72315

Functional properties of throughput in tandem lines with unreliable servers and finite buffers

optimization;queueing theory;production

Proactive controller assignment schemes in SDN for fast recovery

​© 2020 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.A sizeable software defined network with a single controller responsible for all forwarding elements is potentially failure-prone and inadequate for dynamic network loads. To this end, having multiple controllers improves resilience and distributes network control overhead. However, when there is a disruption in the control plane, a rapid and performant controller-switch assignment is critical, which is a challenging technical question. In this work, we propose a proactive switch assignment approach in case of controller failures using a genetic algorithm based heuristic that considers controller load distribution, reassignment cost and probability of failure. Moreover, we compare the performance of our scheme with random and greedy algorithms. Experiment results show that our proposed PREFCP framework has better performance in terms of probability of failure and controller load distributio

eXtended hybridizable discontinuous Galerkin for incompressible flow problems with unfitted meshes and interfaces

The eXtended hybridizable discontinuous Galerkin (X-HDG) method is developed for the solution of Stokes problems with void or material interfaces. X-HDG is a novel method that combines the hybridizable discontinuous Galerkin (HDG) method with an eXtended finite element strategy, resulting in a high-order, unfitted, superconvergent method, with an explicit definition of the interface geometry by means of a level-set function. For elements not cut by the interface, the standard HDG formulation is applied, whereas a modified weak form for the local problem is proposed for cut elements. Heaviside enrichment is considered on cut faces and in cut elements in the case of bimaterial problems. Two-dimensional numerical examples demonstrate that the applicability, accuracy, and superconvergence properties of HDG are inherited in X-HDG, with the freedom of computational meshes that do not fit the interfacesPeer ReviewedPostprint (author's final draft