Prasanna Sridhar

A Discrete Event XML based Simulation Framework for System of Systems Architectures

System of systems (SoS) concept is essential in solving the issues in complex systems where there are heterogeneous independently operable systems. In order for these independent systems to establish an SoS, they need to understand each other through effective communication. Thus, there is a need for a common language which wraps the data that is sent and received from a system within an SoS. This paper presents the application of extensible markup language (XML) to represent data communicated among systems without underlying differences in hardware software of the individual systems. Moreover, since the operation of each system is asynchronous in nature, the entire SoS can be considered as an event driven system. This paper presents a simulation framework to simulate the structure of the systems in an event driven fashion and wraps the data with a common language. The DEVSJAVA software is used as the discrete event simulator. In addition, XML based encapsulation is created in the DEVSJAVA environment to in the SoS.

Towards optimization of a real-world Robotic-Sensor System of Systems

The problem of threat detection in an unstructured environment is considered. Three systems, comprising of robots and sensors, are proposed to form a system of systems (SoS) to find a solution to the problem. System interactions are defined to provide a framework for formulation as an SoS optimization problem. Different cost and objective functions are introduced for optimization of local criteria. Using different weights, a linear combination of the local cost and objective functions is obtained to propose a global objective function. An algorithm is suggested to find an optimum value for the global objective function leading towards optimization of the SoS.

System of systems approach to threat detection and integration of heterogeneous independently operable systems

This paper presents a system of systems approach to threat detection through integration of heterogeneous independently operable systems. The approach is presented on a realistic situation where a human-controlled base robot, swarm robot(s), and sensors work together to obtain a decision about a possible threat in the environment. The base robot is remotely operated by a human using a haptic control system. The swarm robot(s) are autonomous and can accept directives from the base robot. Finally, sensors directly communicate with (report to) the base robot. In this scenario, heterogeneous systems and human interact in a system of systems architecture. With the inclusion of human expert and sensor verification of swarm robots, the system can successfully perform the threat detection and reduce the false alarms. Finally, a system of systems simulation framework including a base robot, a swarm robot, and two sensors is presented in addition to an experimental evaluation of the proposed SoS architecture.

Multi-agent simulation using discrete event and soft-computing methodologies

With the emerging applications of multi-agent systems, there is always a need for simulation to verify the results before actual implementation. Multi-agent simulation provides a test bed for several soft-computing algorithms like fuzzy logic, learning automata, evolutionary algorithms, etc. In this paper we discuss the fusion of these soft-computing methodologies and existing tools for discrete event simulation (DEVS) for multi-agent simulation. We propose a methodology for combining the agent-based architecture, discrete event system and soft-computing methods in the simulation of multi-agent robotics and network security system. We also define a framework called Virtual Laboratory (V-Lab/spl reg/) for multi-agent simulation using intelligent tools.

Intelligent Object-Tracking using Sensor Networks

The idea of using sensor networks for monitoring events is to utilize the distributed nature provided by these tiny and low powered devices. Multiple sensors can be used collaboratively to monitor events or space more effectively than a single sensor. Several applications can be envisioned with sensor networks ranging from military to commercial applications to environment and earth sciences, such as, traffic monitoring of vehicles, military reconnaissance and surveillance, cross-border infiltration, habitat monitoring, to name a few. In this paper, we propose an algorithm for tracking a moving object in an environment with densely deployed sensing devices.

Hierarchical Data Aggregation in Spatially Correlated Distributed Sensor Networks

The central idea of using sensor networks for monitoring events and conditions is to exploit the distributed nature provided by tiny and low powered devices. Multiple sensors can be used collaboratively to monitor events or space more effectively than a single sensor. Several applications can be envisioned with sensor networks ranging from military and commercial applications to environment and earth sciences. Typical examples include: traffic monitoring of vehicles, military reconnaissance and surveillance, target tracking, cross-border infiltration, habitat monitoring and structural monitoring, to name a few. These sensors in general are prone to failure due to their inherent characteristics. In this paper, we propose a robust fault tolerant data aggregation scheme in sensor networks.

Discrete event modeling and simulation: V-Lab/spl reg/ - application to wireless sensor networks

The need for modeling and simulation (M&S) is seen in many diverse applications such as multi-agent systems, robotics, control systems, software engineering, complex adaptive systems, and homeland security. With the emerging applications of multi-agent systems, there is always a need for simulation to verify the results before the actual implementation. Multi-agent simulation provides a test bed for several soft computing algorithms like fuzzy logic, neural networks (NN), probabilistic reasoning (stochastic learning automata, reinforcement learning), and evolutionary algorithms (genetic algorithms). Fusion of soft computing methodology with existing simulation tools yields several advantages in simulating multi-agent systems. Such a fusion provides a novel and systematic way of handling tune-dependent parameters in the simulation without altering the essential functionality and problem solving capabilities of soft computing elements. The fusion here is the extension of the capabilities of simulation tools with intelligent tools from soft computing. This paper proposes a methodology for combining the agent-based architecture, discrete event system and the soft-computing methods in the simulation of multi-agent systems and defines a framework called virtual laboratory (V-Lab/spl reg/) for realizing such multi-agent system simulations. Detailed experimental results obtained from simulation of robotics agents and wireless sensor network is also discussed.

Dynamic power management of an embedded sensor network based on actor-critic reinforcement based learning

Wireless sensor networks (WSNs) have gained tremendous popularity in recent years due to the wide range of applications envisioned - ranging from aerospace and defense to industrial and commercial. Although limited by communication and energy constraints, the low cost, small sensor nodes lend themselves to be deployed in large numbers to form a network with high spatial distribution. The overall effectiveness of the sensor network depends on how well the mutually contradicting objectives of conserving the limited on-board battery power and keeping the sensors awake for stimuli, are managed. In this paper, we have proposed an actor-critic based reinforcement learning mechanism that can be practically implemented on an embedded sensor with limited memory and processing power. Specifically, the contribution of this paper is the development of the value function (or critic/reinforcement function) that is implemented on each sensor node which aids in dynamic power scheduling based on different situations. The effectiveness of the proposed method has been demonstrated with real world experiments.