Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

Optimization of Mobility Parameters using Fuzzy Logic and Reinforcement Learning in Self-Organizing Networks

In this thesis, several optimization techniques for next-generation wireless networks are proposed to solve different problems in the field of Self-Organizing Networks and heterogeneous networks. The common basis of these problems is that network parameters are automatically tuned to deal with the specific problem. As the set of network parameters is extremely large, this work mainly focuses on parameters involved in mobility management. In addition, the proposed self-tuning schemes are based on Fuzzy Logic Controllers (FLC), whose potential lies in the capability to express the knowledge in a similar way to the human perception and reasoning. In addition, in those cases in which a mathematical approach has been required to optimize the behavior of the FLC, the selected solution has been Reinforcement Learning, since this methodology is especially appropriate for learning from interaction, which becomes essential in complex systems such as wireless networks. Taking this into account, firstly, a new Mobility Load Balancing (MLB) scheme is proposed to solve persistent congestion problems in next-generation wireless networks, in particular, due to an uneven spatial traffic distribution, which typically leads to an inefficient usage of resources. A key feature of the proposed algorithm is that not only the parameters are optimized, but also the parameter tuning strategy. Secondly, a novel MLB algorithm for enterprise femtocells scenarios is proposed. Such scenarios are characterized by the lack of a thorough deployment of these low-cost nodes, meaning that a more efficient use of radio resources can be achieved by applying effective MLB schemes. As in the previous problem, the optimization of the self-tuning process is also studied in this case. Thirdly, a new self-tuning algorithm for Mobility Robustness Optimization (MRO) is proposed. This study includes the impact of context factors such as the system load and user speed, as well as a proposal for coordination between the designed MLB and MRO functions. Fourthly, a novel self-tuning algorithm for Traffic Steering (TS) in heterogeneous networks is proposed. The main features of the proposed algorithm are the flexibility to support different operator policies and the adaptation capability to network variations. Finally, with the aim of validating the proposed techniques, a dynamic system-level simulator for Long-Term Evolution (LTE) networks has been designed

On Localization Issues of Mobile Devices

Mobile devices, such as sensor nodes, smartphones and smartwatches, are now widely used in many applications. Localization is a highly important topic in wireless networks as well as in many Internet of Things applications. In this thesis, four novel localization schemes of mobile devices are introduced to improve the localization performance in three different areas, like the outdoor, indoor and underwater environments. Firstly, in the outdoor environment, many current localization algorithms are based on the Sequential Monte MCL, the accuracy of which is bounded by the radio range. High computational complexity in the sampling step is another issue of these approaches. Tri-MCL is presented, which significantly improves on the accuracy of the Monte Carlo Localization algorithm. To do this, three different distance measurement algorithms based on range-free approaches are leveraged. Using these, the distances between unknown nodes and anchor nodes are estimated to perform more fine-grained filtering of the particles as well as for weighting the particles in the final estimation step of the algorithm. Simulation results illustrate that the proposed algorithm achieves better accuracy than the MCL and SA-MCL algorithms. Furthermore, it also exhibits high efficiency in the sampling step. Then, in the GPS-denied indoor environment, Twi-Adaboost is proposed, which is a collaborative indoor localization algorithm with the fusion of internal sensors such as the accelerometer, gyroscope and magnetometer from multiple devices. Specifically, the datasets are collected firstly by one person wearing two devices simultaneously: a smartphone and a smartwatch, each collecting multivariate data represented by their internal parameters in a real environment. Then, the datasets from these two devices are evaluated for their strengths and weaknesses in recognizing the indoor position. Based on that, the Twi-AdaBoost algorithm, an interactive ensemble learning method, is proposed to improve the indoor localization accuracy by fusing the co-occurrence information. The performance of the proposed algorithm is assessed on a real-world dataset. The experiment results demonstrate that Twi-AdaBoost achieves a localization error about 0.39 m on average with a low deployment cost, which outperforms the state-of-the-art indoor localization algorithms. Lastly, the characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, and sparse deployment, pose challenging issues for successful localization of sensor nodes. In addition, sensor nodes in UWSNs are usually powered by batteries whose replacements introduces high cost and complexity. Thus, the critical problem in UWSNs is to enable each sensor node to find enough anchor nodes in order to localize itself, with minimum energy costs. An Energy-Efficient Localization Algorithm (EELA) is proposed to analyze the decentralized interactions among sensor nodes and anchor nodes. A Single-Leader-Multi-Follower Stackelberg game is utilized to formulate the topology control problem of sensor nodes and anchor nodes by exploiting their available communication opportunities. In this game, the sensor node acts as a leader taking into account factors such as `two-hop' anchor nodes and energy consumption, while anchor nodes act as multiple followers, considering their ability to localize sensor nodes and their energy consumption. I prove that both players select best responses and reach a socially optimal Stackelberg Nash Equilibrium. Simulation results demonstrate that the proposed EELA improves the performance of localization in UWSNs significantly, and in particular the energy cost of sensor nodes. Compared to the baseline schemes, the energy consumption per node is about 48% lower in EELA, while providing a desirable localization coverage, under reasonable error and delay. Based on the EELA scheme, an Adaptive Energy Efficient Localization Algorithm using the Fuzzy game theoretic method (Adaptive EELA) is proposed to solve the environment adaptation problem of EELA. The adaptive neuro-fuzzy method is used as the utility function of the Single-Leader-Multi-Follower Stackelberg game to model the dynamical changes in UWSNs. The proposed Adaptive EELA scheme is able to automatically learn in the offline phase, which is required only once. Then, in the online phase, it can adapt to the environmental changes, such as the densities of nodes or topologies of nodes. Extensive numerical evaluations are conducted under different network topologies and different network node densities. The simulation results demonstrate that the proposed Adaptive EELA scheme achieves about 35% and 66% energy reduction per node on average comparing the state-of-the-art approaches, such as EELA and OLTC, while providing a desirable localization coverage, localization error and localization delay

Seismic risk of Open Spaces in Historic Built Environments: A matrix-based approach for emergency management and disaster response

Abstract Earthquakes affect the safety of the users hosted in both indoor and outdoor urban built environments, especially in Historic Built Environments (HBEs). Many full HBE-scale risk-assessment methods are defined, while methodologies oriented to local analysis of meso-scale elements, such as Open Spaces (OSs), are still limited. Nevertheless, OSs play a crucial role in the first emergency phases, like in the evacuation process, since they host emergency paths and gathering areas. The seismic risk of an OS mainly depends on the combination of the damage suffered from facing buildings and the exposure, which mainly refers to the quantification of human lives. Damage levels result from the combination of vulnerability and hazard-related issues, while exposure is essentially affected by the number of OS users, whose spatial distribution is strongly time-dependent. Methods to quickly combine these issues are needed, especially in view of the deeper insights for the implementation of risk-reduction strategies (i.e. according to simulation-based approaches). This work offers a novel methodology to quickly perform Seismic Risk Assessment and Management of an OS by correlating damage levels to exposure-related issues. The method is composed of two specific matrices, which are developed according to quick literature-based approaches prone to rapid meso-scale applications in HBEs, also by non-expert technicians. The "damage matrix" links the site hazard to the building vulnerability. The assessed damage levels are combined with the users' exposure into the "consequences matrix", to estimate the risk in emergency conditions for the OS users, thus supporting decision-makers in promoting robustness/preparedness strategies

Context-aware Self-Optimization in Small-Cell Networks

Most mobile communications take place at indoor environments, especially in commercial and corporate scenarios. These places normally present coverage and capacity issues due to the poor signal quality, which degrade the end-user Quality of Experience (QoE). In these cases, mobile operators are offering small cells to overcome the indoor issues, being femtocells the main deployed base stations. Femtocell networks provide significant benefits to mobile operators and their clients. However, the massive integration and the particularities of femtocells, make the maintenance of these infrastructures a challenge for engineers. In this sense, Self-Organizing Networks (SON) techniques play an important role. These techniques are a key feature to intelligently automate network operation, administration and management procedures. SON mechanisms are based on the analysis of the mobile network alarms, counters and indicators. In parallel, electronics, sensors and software applications evolve rapidly and are everywhere. Thanks to this, valuable context information can be gathered, which properly managed can improve SON techniques performance. Within possible context data, one of the most active topics is the indoor positioning due to the immediate interest on indoor location-based services (LBS). At indoor commercial and corporate environments, user densities and traffic vary in spatial and temporal domain. These situations lead to degrade cellular network performance, being temporary traffic fluctuations and focused congestions one of the most common issues. Load balancing techniques, which have been identified as a use case in self-optimization paradigm for Long Term Evolution (LTE), can alleviate these congestion problems. This use case has been widely studied in macrocellular networks and outdoor scenarios. However, the particularities of femtocells, the characteristics of indoor scenarios and the influence of users’ mobility pattern justify the development of new solutions. The goal of this PhD thesis is to design and develop novel and automatic solutions for temporary traffic fluctuations and focused network congestion issues in commercial and corporate femtocell environments. For that purpose, the implementation of an efficient management architecture to integrate context data into the mobile network and SON mechanisms is required. Afterwards, an accurate indoor positioning system is developed, as a possible inexpensive solution for context-aware SON. Finally, advanced self-optimization methods to shift users from overloaded cells to other cells with spare resources are designed. These methods tune femtocell configuration parameters based on network information, such as ratio of active users, and context information, such as users’ position. All these methods are evaluated in both a dynamic LTE system-level simulator and in a field-trial

Aerial social force model: a new framework to accompany people using autonomous flying robots

© 20xx 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.We proposed a novel Aerial Social Force Model (ASFM) that allows autonomous flying robots to accompany humans in urban environments in a safe and comfortable manner. To date, we are not aware of other state-of-the-art method that accomplish this task. The proposed approach is a 3D version of the Social Force Model (SFM) for the field of aerial robots which includes an interactive human-robot navigation scheme capable of predicting human motions and intentions so as to safely accompany them to their final destination. ASFM also introduces a new metric to fine-tune the parameters of the force model, and to evaluate the performance of the aerial robot companion based on comfort and distance between the robot and humans. The presented approach is extensively validated in diverse simulations and real experiments, and compared against other similar works in the literature. ASFM attains remarkable results and shows that it is a valuable framework for social robotics applications, such as guiding people or human-robot interaction.Peer ReviewedPostprint (author's final draft

A Novel Approach to Complex Human Activity Recognition

Human activity recognition is a technology that offers automatic recognition of what a person is doing with respect to body motion and function. The main goal is to recognize a person\u27s activity using different technologies such as cameras, motion sensors, location sensors, and time. Human activity recognition is important in many areas such as pervasive computing, artificial intelligence, human-computer interaction, health care, health outcomes, rehabilitation engineering, occupational science, and social sciences. There are numerous ubiquitous and pervasive computing systems where users\u27 activities play an important role. The human activity carries a lot of information about the context and helps systems to achieve context-awareness. In the rehabilitation area, it helps with functional diagnosis and assessing health outcomes. Human activity recognition is an important indicator of participation, quality of life and lifestyle. There are two classes of human activities based on body motion and function. The first class, simple human activity, involves human body motion and posture, such as walking, running, and sitting. The second class, complex human activity, includes function along with simple human activity, such as cooking, reading, and watching TV. Human activity recognition is an interdisciplinary research area that has been active for more than a decade. Substantial research has been conducted to recognize human activities, but, there are many major issues still need to be addressed. Addressing these issues would provide a significant improvement in different aspects of the applications of the human activity recognition in different areas. There has been considerable research conducted on simple human activity recognition, whereas, a little research has been carried out on complex human activity recognition. However, there are many key aspects (recognition accuracy, computational cost, energy consumption, mobility) that need to be addressed in both areas to improve their viability. This dissertation aims to address the key aspects in both areas of human activity recognition and eventually focuses on recognition of complex activity. It also addresses indoor and outdoor localization, an important parameter along with time in complex activity recognition. This work studies accelerometer sensor data to recognize simple human activity and time, location and simple activity to recognize complex activity

Coordinated multi-robot formation control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201