Nadia Erdogan

Naval Mine Countermeasure Missions

Undersea operations using autonomous underwater vehicles (AUVs) provide a different and in some ways a more challenging problem than tasks for unmanned aerial vehicles and unmanned ground vehicles. In particular, in undersea operations, communication windows are restricted, and bandwidth is limited. Consequently, coordination among agents is correspondingly more difficult. In traditional approaches, a central planner initially assigns subtasks to a set of AUVs to achieve the team goal. However, those initial task assignments may become inefficient during real-time execution because of the real-world issues such as failures. Therefore, initial task allocations are usually subject to change if efficiency is a high concern. Reallocations are needed and should be performed in a distributed manner. To provide such flexibility, we propose a distributed auction-based cooperation framework, distributed and efficient multirobot-cooperation framework (DEMiR-CF), which is an online dynamic task allocation (reallocation) system that aims to achieve a team goal while using resources effectively. DEMiR-CF, with integrated task scheduling and execution capabilities, can also respond to and recover from real-time contingencies such as communication failures, delays, range limitations, and robot failures.

Multiple Traveling Robot Problem: A Solution Based on Dynamic Task Selection and Robust Execution

The multiple traveling robot problem (MTRP), the real-world version of the well-known NP-hard multiple traveling salesman problem (MTSP), asks for finding routes of robots to visit a set of targets. Various objectives may be defined for this problem (e.g., minimization of total path length, time, etc.). The overall solution quality is dependent on both the quality of the solution constructed by the paths of robots and the efficient allocation of the targets to robots. Unpredictability of the exact processing times of tasks, unstable cost values during execution, and inconsistencies due to uncertain information further complicate MTRP. This paper presents a multirobot cooperation framework employing a dynamic task selection scheme to solve MTRP. The proposed framework carries out an incremental task allocation method that dynamically adapts to current conditions of the environment, thus handling diverse contingencies. Globally efficient solutions are obtained through mechanisms that result in the allocation of the most suitable tasks from dynamically generated priority-based rough schedules. Since the presented approach is for real-world task execution, computational requirements are kept at a minimum, and the framework is designed to be applicable on real robots even with limited capabilities. The efficiency and the robustness of the proposed scheme is evaluated through experiments both in simulations and on real robots.

Incremental multi-robot task selection for resource constrained and interrelated tasks

When the tasks of a mission are interrelated and subject to several resource constraints, more efforts are needed to coordinate robots towards achieving the mission than independent tasks. In this work, we formulate the Coordinated Task Selection Problem (CTSP) to form the basis of an efficient dynamic task selection scheme for allocation of interrelated tasks of a complex mission to the members of a multi-robot team. Since processing times of tasks are not exactly known in advance, the incremental task selection scheme for the eligible tasks prevents redundant efforts as, instead of scheduling all of the tasks, they are allocated to robots as needed. This approach results in globally efficient solutions through mechanisms that form priority based rough schedules and select the most suitable tasks from these schedules. Since our method is targeted at real world task execution, communication requirements are kept limited. Empirical evaluations of the proposed approach are performed on the Webots simulator and the real robots. The results validate that the proposed approach is scalable, efficient and suitable to the real world safe mission achievement.

A Generic Framework for Distributed Multirobot Cooperation

DEMiR-CF is a generic framework designed for a multirobot team to efficiently allocate tasks among themselves and achieve an overall mission. In the design of DEMiR-CF, the following issues were particularly investigated as the design criteria: efficient and realistic representation of missions, efficient allocation of tasks to cooperatively achieve a global goal, maintenance of the system coherence and consistency by the team members, detection of the contingencies and recover from various failures that may arise during runtime, efficient reallocation of tasks (if necessary) and reorganization of team members (if necessary). DEMiR-CF is designed to address different types of missions from the simplest to more complex ones, including missions with interrelated tasks and multi-resource (robot) requirements. Efficiency of the framework is validated through experiments in three different types of domains.

Influential user weighted sentiment analysis on topic based microblogging community

Abstract Nowadays, social microblogging services have become a popular expression platform of what people think. People use these platforms to produce content on different topics from finance, politics and sports to sociological fields in real-time. With the proliferation of social microblogging sites, the massive amount of opinion texts have become available in digital forms, thus enabling research on sentiment analysis to both deepen and broaden in different sociological fields. Previous sentiment analysis research on microblogging services generally focused on text as the unique source of information, and did not consider the social microblogging service network information. Inspired by the social network analysis research and sentiment analysis studies, we find that people’s trust in a community have an important place in determining the community’s sentiment polarity about a topic. When studies in the literature are examined, it is seen that trusted users in a community are actually influential users. Hence, we propose a novel sentiment analysis approach that takes into account the social network information as well. We concentrate on the effect of influential users on the sentiment polarity of a topic based microblogging community. Our approach extends the classical sentiment analysis methods, which only consider text content, by adding a novel PageRank-based influential user finding algorithm. We have carried out a comprehensive empirical study of two real-world Twitter datasets to analyze the correlation between the mood of the financial social community and the behavior of the stock exchange of Turkey, namely BIST100, using Pearson correlation coefficient method. Experimental results validate our assumptions and show that the proposed sentiment analysis method is more effective in finding topic based microblogging community’s sentiment polarity.

SECMAP: a secure mobile agent platform

This paper describes a mobile agent platform, Secure Mobile Agent Platform (SECMAP), and its security infrastructure. Unlike other agent systems, SECMAP proposes a new agent model, the shielded agent model, to meet security requirements and provides functionalities which ensure the implementation of the the shielded agent model. It provides secure agent communication and migration facilities, and maintains security policy information to examine agent actions and to prevent undesired/unauthorized activity, while employing cryptographic techniques to meet security constraints.

A Two-Leveled Mobile Agent System for E-commerce with Constraint-Based Filtering

This paper presents a two-leveled mobile agent system for electronic commerce. It is based on mobile agents as mediators and uses the publish/subscribe paradigm for registration and transaction processing. In the system we present, suppliers can connect, register or unregister to the system at any time, thus preserving the dynamic nature of the system. To reduce network load and the flow of unnecessary information in the product brokering part of the buying process, a rule based subscription methodology is used for necessary filtering operations.

RUBDES: A Rule Based Distributed Event System

In the last years, event-based communication paradigm has been largely studied and considered as a promising approach to develop the communication infrastructure of distributed systems. RUBDES (Rule Based Distributed Event System) is a distributed event system that allows the use of composite events in publish/subscribe computational model. In this system, an event is represented as an object and a rule is represented as an expression or a function that is evaluated or executed depending on the occurrence of events. This paper presents the design details of RUBDES and usage of Rule Definition Language (RDL) to describe composite events and event filters.

A distributed execution environment for shared java objects

Abstract This paper discusses the implementation of a distributed execution environment, DJO, which supports the use of shared Java objects for parallel and distributed applications and provides the Java programmer with the illusion of a network-wide shared object space on loosely coupled distributed systems. DJO supports shared objects through an implementation of multiple reader/single writer write-invalidate DSM protocol in software, providing the shared memory abstraction at object granularity. Object distribution and sharing are implemented through the replication mechanism, transparently to application. The system enforces mutual consistency among replicas of an object. The main benefits of DJO are enhanced availability and performance due to the replicated object model and easier application design, as the underlying software takes care of distribution and memory consistency issues.

Parallel Genetic Algorithm to Solve Traveling Salesman Problem on MapReduce Framework using Hadoop Cluster.

Traveling Salesman Problem (TSP) is one of the most common studied problems in combinatorial optimization. Given the list of cities and distances between them, the problem is to find the shortest tour possible which visits all the cities in list exactly once and ends in the city where it starts. Despite the Traveling Salesman Problem is NP-Hard, a lot of methods and solutions are proposed to the problem. One of them is Genetic Algorithm (GA). GA is a simple but an efficient heuristic method that can be used to solve Traveling Salesman Problem. In this paper, we will show a parallel genetic algorithm implementation on MapReduce framework in order to solve Traveling Salesman Problem. MapReduce is a framework used to support distributed computation on clusters of computers. We used free licensed Hadoop implementation as MapReduce framework.