Gürkan Solmaz

A Mobility Model of Theme Park Visitors

Realistic human mobility modeling is critical for accurate performance evaluation of mobile wireless networks. Movements of visitors in theme parks affect the performance of systems which are designed for various purposes including urban sensing and crowd management. Previously proposed human mobility models are mostly generic while some of them focus on daily movements of people in urban areas. Theme parks, however, have unique characteristics in terms of very limited use of vehicles, crowds social behavior, and attractions. Human mobility is strongly tied to the locations of attractions and is synchronized with major entertainment events. Hence, realistic human mobility models must be developed with the specific scenario in mind. In this paper, we present a novel model for human mobility in theme parks. In our model, the nondeterminism of movement decisions of visitors is combined with deterministic behavior of attractions in a theme park. The attractions are categorized as rides, restaurants, and live shows. The time spent at these attractions are computed using queueing-theoretic models. The realism of the model is evaluated through extensive simulations and compared with the mobility models SLAW, RWP and the GPS traces of theme park visitors. The results show that our proposed model provides a better match to the real-world data compared to the existing models.

Optimizing event coverage in theme parks

AbstractTheme parks can be modeled as geographical areas where large crowds of people move among different attractions. The operators of a theme park are interested in quickly and efficiently handling events occurring at various locations in the park. We propose a model which deploys a wireless network with mobile sinks to facilitate event coverage. The event coverage problem can be divided into two sub-problems: the static problem of mobile sink positioning and the dynamic problem of event handling decisions of the mobile sinks. For the mobile sink positioning problem we propose two strategies: crowd density based probability estimation and hot-spot based probability estimation. For the event handling decision problem, we propose an approach which represents movement opportunities in the park as a graph with dynamically changing weights, and searches for the shortest path in this dynamic graph. The proposed approaches are simulated on scenarios which model the movement of the visitors using two sophisticated human mobility models.

Modeling visitor movement in theme parks

Realistic modeling of the movement of people in an environment is critical for evaluating the performance of mobile wireless systems such as urban sensing or mobile sensor networks. Existing human movement models are either fully synthetic or rely on traces of actual human movement. There are many situations where we cannot perform an accurate simulation without taking into account what the people are actually doing. For instance, in theme parks, the movement of people is strongly tied to the locations of the attractions and is synchronized with major external events. For these situations, we need to develop scenario specific models. In this paper, we present a model of the movement of visitors in a theme park. The nondeterministic behavior of the human walking pattern is combined with the deterministic behavior of attractions in the theme park. The attractions are divided into groups of rides, restaurants and live shows. The time spent by visitors at different attractions is calculated using specialized queuing-theoretic models. We compare the realism of the model by comparing its simulations to the statistics of the theme parks and to real-world GPS traces of visitor movement. We found that our model provides a better match to the real-world data compared to current state-of-the-art movement models.

Animal monitoring with unmanned aerial vehicle-aided wireless sensor networks

In this paper, we focus on an application of wireless sensor networks (WSNs) with unmanned aerial vehicle (UAV). The aim of the application is to detect the locations of endangered species in large-scale wildlife areas or monitor movement of animals without any attachment devices. We first define the mathematical model of the animal monitoring problem in terms of the value of information (VoI) and rewards. We design a network model including clusters of sensor nodes and a single UAV that acts as a mobile sink and visits the clusters. We propose a path planning approach based on a Markov decision process (MDP) model that maximizes the VoI while reducing message delays. We used real-world movement dataset of zebras. Simulation results show that our approach outperforms greedy and random heuristics as well as the path planning based on the solution of the traveling salesman problem.

Pedestrian mobility in theme park disasters

Realistic mobility simulation is critical for evaluating the performance of communication networks. Although various mobility models exist, they do not capture the changes in the mobility decisions of pedestrians in specific environments. For instance, in the case of a natural or manmade disaster, the main goal is the safe evacuation of the area, creating unique pedestrian mobility patterns. In this article we focus on the scenario of evacuating a theme park in response to a disaster. We discuss the characteristics of theme parks, modeling the environment, and the mobility decisions of pedestrians. Real theme park maps are used for modeling the environment with roads, physical obstacles, and simulating disaster events. The mobility decisions of the pedestrians are based on the evacuation goal, the limited knowledge of the area, and the obstacles. The impact of the interactions between the crowd flows is modeled based on the concept of social force. The model is evaluated by comparison with the existing mobility models and the GPS traces of theme park visitors.

Event coverage in theme parks using wireless sensor networks with mobile sinks

Theme parks are large crowded areas with unique characteristics in terms of movement behavior of visitors, attractions in different locations and walking paths connecting the attractions. Wireless sensor networks (WSNs) with mobile sinks can be used for various purposes including security and emergency issues as major challenges in such environments. Modeling of human mobility in theme parks allows us to consider scenario-specific applications of WSNs in these entertainment areas for event coverage purposes. In this paper, we propose a WSN model with mobile sinks and provide a novel approach to cover the events occurring in the environment. Furthermore, we propose new strategies for mobile sink positioning and event handling decision problems. We evaluate the benefits of our approach through extensive simulations using two sophisticated human mobility models for visitor movement.

Theme park mobility in disaster scenarios

In this paper, we propose a scenario-specific human mobility model (TP-D) in theme parks. We focus on the disaster scenarios which have significant differences compared to the ordinary mobility behavior of the theme park visitors. The main goal of the theme park operation in disaster scenarios is the evacuation of the visitors from the disaster areas. We first model theme park as a combination of roads, obstacles, lands, and disaster events. We use real theme park maps for generating the theme park models. We incorporate the macro and micro mobility behaviors of the visitors in theme park models. We use the social force concept to model the impact of social interactions on the micro mobility of the visitors. Macro mobility decisions are based on the local knowledge of the visitors, the waypoints, and the disaster events. We analyze and compare the results of the simulation of our model with simulations of currently used models and real-world GPS traces of visitor movement.

Actor positioning based on molecular geometry in aerial sensor networks

Advances in unmanned aerial vehicle (UAV) technology and wireless sensor and actor networks (WSAN) made it possible to equip small UAVs with sensors and deploy aerial sensor and actor networks. Aerial sensor networks enable high quality observation of events while reducing the number of requirements. Positioning of UAVs with actor nodes is critical in these systems for effective data collection. In this paper we propose an actor positioning strategy for aerial WSANs considering the scenario of toxic plume observation after a volcanic eruption. The positioning algorithm utilizes the Valence Shell Electron Pair Repulsion (VSEPR) theory of chemistry, which is based on the correlation between molecular geometry and the number of atoms in a molecule. The limitations of the basic VSEPR theory are eliminated by extending the approach for multiple central data collectors. The simulations show that the proposed system provides high connectivity and coverage for the aerial sensor network.

Tracking pedestrians and emergent events in disaster areas

Most of the existing research on emergency evacuation strategies focus on city evacuation planning that highly depends on the use of vehicles or evacuation from buildings. However, for large areas with limited use of vehicles such as theme parks, evacuation of pedestrians and emergent events must be tracked for safety reasons. As hazards may cause certain damages to services, networks with disaster resilience are needed to achieve mission-critical operations such as search and rescue. In this paper, we develop a method for tracking pedestrians and emergent events during disasters by opportunistic ad hoc communication. In our network model, smart-phones of pedestrians store and carry messages to a limited number of mobile sinks. Mobile sinks are responsible for communicating with smart-phones and reaching the emergent events effectively. Since the positioning of the mobile sinks has a direct impact to the network performance, we propose physical force based (PF), grid allocation based (GA) and road allocation based (RA) approaches for sink placement and mobility. The proposed approaches are analyzed through extensive network simulations using real theme park maps and a human mobility model for disaster scenarios. The simulation results show that the proposed approaches achieve significantly better network coverage and higher rescue success without producing increased communication overhead compared to two random mobile sink movement models.

FogFlow : Easy Programming of IoT Services Over Cloud and Edges for Smart Cities

Smart city infrastructure is forming a large scale Internet of Things (IoT) system with widely deployed IoT devices, such as sensors and actuators that generate a huge volume of data. Given this large scale and geo-distributed nature of such IoT systems, fog computing has been considered as an affordable and sustainable computing paradigm to enable smart city IoT services. However, it is still a major challenge for developers to program their services to leverage benefits of fog computing. Developers have to figure out many details, such as how to dynamically configure and manage data processing tasks over cloud and edges and how to optimize task allocation for minimal latency and bandwidth consumption. In addition, most of the existing fog computing frameworks either lack service programming models or define a programming model only based on their own private data model and interfaces; therefore, as a smart city platform, they are quite limited in terms of openness and interoperability. To tackle these problems, we propose a standard-based approach to design and implement a new fog computing-based framework, namely FogFlow, for IoT smart city platforms. FogFlow’s programming model allows IoT service developers to program elastic IoT services easily over cloud and edges. Moreover, it supports standard interfaces to share and reuse contextual data across services. To showcase how smart city use cases can be realized with FogFlow, we describe three use cases and implement an example application for anomaly detection of energy consumption in smart cities. We also analyze FogFlow’s performance based on microbenchmarking results for message propagation latency, throughput, and scalability.