A Pervasive Computational Intelligence based Cognitive Security Co-design Framework for Hype-connected Embedded Industrial IoT

The amplified connectivity of routine IoT entities can expose various security trajectories for cybercriminals to execute malevolent attacks. These dangers are even amplified by the source limitations and heterogeneity of low-budget IoT/IIoT nodes, which create existing multitude-centered and fixed perimeter-oriented security tools inappropriate for vibrant IoT settings. The offered emulation assessment exemplifies the remunerations of implementing context aware co-design oriented cognitive security method in assimilated IIoT settings and delivers exciting understandings in the strategy execution to drive forthcoming study. The innovative features of our system is in its capability to get by with irregular system connectivity as well as node limitations in terms of scares computational ability, limited buffer (at edge node), and finite energy. Based on real-time analytical data, projected scheme select the paramount probable end-to-end security system possibility that ties with an agreed set of node constraints. The paper achieves its goals by recognizing some gaps in the security explicit to node subclass that is vital to our system’s operations

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. © 2013 IEEE

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

Abstract — 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. I

Decision-making for Vehicle Path Planning

This dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate world models and path planning behaviors. There are many different practical applications that map to this model. In this dissertation we propose algorithms for two applications that are very different in domain but share important formal similarities: the scheduling of taxi services in a large city and tracking wild animals with an unmanned aerial vehicle. The first application models a centralized taxi dispatch center in a big city. It is a multivariate optimization problem for taxi time scheduling and path planning. The first goal here is to balance the taxi service demand and supply ratio in the city. The second goal is to minimize passenger waiting time and taxi idle driving distance. We design different learning models that capture taxi demand and destination distribution patterns from historical taxi data. The predictions are evaluated with real-world taxi trip records. The predicted taxi demand and destination is used to build a taxi dispatch model. The taxi assignment and re-balance is optimized by solving a Mixed Integer Programming (MIP) problem. The second application concerns animal monitoring using an unmanned aerial vehicle (UAV) to search and track wild animals in a large geographic area. We propose two different path planing approaches for the UAV. The first one is based on the UAV controller solving Markov decision process (MDP). The second algorithms relies on the past recorded animal appearances. We designed a learning model that captures animal appearance patterns and predicts the distribution of future animal appearances. We compare the proposed path planning approaches with traditional methods and evaluated them in terms of collected value of information (VoI), message delay and percentage of events collected