Karthik Sankaranarayanan

A Privacy-Preserving Vehicular Crowdsensing-Based Road Surface Condition Monitoring System Using Fog Computing

In the recent past, great attention has been directed toward road surface condition monitoring. As a matter of fact, this activity is of critical importance in transportation infrastructure management. In response, multiple solutions have been proposed which make use of mobile sensing, more specifically contemporary applications and architectures that are used in both crowdsensing and vehicle-based sensing. This has allowed for automated control as well as analysis of road surface quality. These innovations have thus encouraged and showed the importance of cloud to provide reliable transport services to clients. Nonetheless, these initiatives have not been without challenges that range from mobility support, locational awareness, low latency, as well as geo-distribution. As a result, a new term has been coined for this novel paradigm, called, fog computing. In this paper, we propose a privacy-preserving protocol for enhancing security in vehicular crowdsensing-based road surface condition monitoring system using fog computing. At the onset, this paper proposes a certificateless aggregate signcryption scheme that is highly efficient. On the basis of the proposed scheme, a data transmission protocol for monitoring road surface conditions is designed with security aspects such as information confidentiality, mutual authenticity, integrity, privacy, as well as anonymity. In analyzing the system, the ability of the proposed protocol to achieve the set objectives and exercise higher efficiency with respect to computational and communication abilities in comparison to existing systems is also considered.

The micro-dynamics of queuing: understanding the formation of queues

Most work in queuing theory is performed at an aggregate level, with linear models for which closed-form solutions can be derived. We are interested in creating a better understanding of how queues are formed by taking a bottom-up approach to their formation. We use a cellular automata framework to structure a set of agents who must choose which service facility to use. After using the facility, they update their expectations of sojourn time based on their own experience, and information received from their neighbours. On the basis of these updated expectations, they make their choice for the next period. We find that, after an initial transition period, customers mostly reach a quasi-stable situation, where the average sojourn time is close to the Nash equilibrium and social optimum, unless agents forget one of the facilities. We analyse different parameterizations of the agents’ decision rules, and consider homogeneous and heterogeneous agent populations.

Future Research in Humanitarian Operations: A Behavioral Operations Perspective

Behavioral operations have established itself as a mature field of research in operations and supply chain management. Since almost all operational contexts contain people (e.g., managers, employees and customers) who are prone to decision-making errors that can negatively impact performance, behavioral operations can inform models and frameworks in operations management to account for such shortcomings. In this chapter, we explore the application of different methodological tools used in behavioral operations to study behavior in humanitarian logistics and supply chain management (HLSCM). Specifically, the chapter will explore the use of behavioral experiments, system dynamics and agent-based modeling as methodologies to understand decision making in humanitarian operations.

Modelling Decisions Under Uncertainty In A Behavioural Queuing System.

In this paper we use an agent-based modelling and simulation approach to model a queuing system with autonomous customers who routinely choose a facility for service. We propose a Cellular Automata model to represent the customers’ interactions and study how customers use their own experience and that of their neighbours in order to update their memory and decide what facility to join the next period. We use exponential smoothing to update the customers’ expected sojourn time. We incorporate uncertainty regarding these expectations into the customers’ decision. We compare the resulting behaviour when customers take into account uncertainty to the case where they ignore uncertainty at both the individual and the system level.

Intelligent agents behavior in the queueing Process: Integrating cellular automata & Genetic Algorithms

Traditional queueing research has concentrated mainly on design, performance and running of the service facility with customers arriving following a stochastic process. In this paper we develop a behavioral model of a queueing system including repeat (returning) customers using a combination of Cellular Automata models (CA) and Genetic Algorithms (GA). An agent based evolutionary approach using simulation is adopted where agents structural properties are defined using a CA and evolutionary strategies are implemented with the help of a GA.

Behavioral analysis of agent based service channel design using neural networks

The integration of neural networks into agent based models can provide a better understanding of dynamic agent responses when modelling complex systems. Additionally, due to the nature of agent based models and the networks that exist in them, individual neural networks can be trained in a supervised learning environment and assigned to individual agents. The advantage of using this approach is that individual agents become more unique (Samuelson and Macal, 2006), and make decisions based on what the neural network has learned during the training phase. Also, in this work the neural networks are trained based on data collected from human-based simulations, due to this, individual strategies learned by the neural network can be translated to individual agents. Integrating neural networks into agent based models can provide more realistic simulations.

Genetic algorithm based optimization of an agent based queuing system

Queuing research and its applications have been studied extensively by concentrating mainly on design, performance and running of the service facility under study. In this paper we show how a simple behavioral queuing system can be modeled using a Cellular Automata; and then we show how a Genetic Algorithm can be used to optimize the behavioral properties of this agent based model.

Safety Does Not Happen by Accident, Can Gaming Help Improve Occupational Health and Safety in Organizations?

In 2015, the Association of Workers’ Compensation Boards of Canada recorded around quarter-million workplace injuries, a staggering figure which does not include incidents that go undocumented. A lack of health and safety training and/or lack of safety awareness can lead to workplace injuries and in the worst cases a workplace death. It is imperative that organizations make Occupational Health and Safety (OHS) one of their top priorities.

Video games to the rescue: Can game design make software based lab experiments engaging?

In this paper we report our efforts to explore the usefulness of game design principals in a software based social science lab experiment. We discuss a case example that shows how game design principals could make the experiment more engaging and informative which in turn helps researchers collect meaningful data i.e. reducing randomness in the collected data.

Behavioral Queueing: An Agent Based Modeling Approach

Queueing research has a plethora of applications and has been an area of study spanning from telecommunications to economics. Traditionally, studies on queueing has mainly concentrated on design, performance and running of the service facility with customers arriving following a stochastic process. In this paper we take an agent based modeling approach to develop a behavioral model of a queueing system using Cellular Automata (CA). We study how adaptive expectation along with a simple information network (as defined by the CA) affects decision-making behavior among agents (customers).