Data dissemination based on mobile agent in wireless sensor networks

Recently, mobile agents have been proposed for efficient data dissemination in sensor networks [1-6]. In the traditional client/server-based computing architecture, data at multiple sources is transferred to a destination; whereas, a task-specific executable code traverses the relevant sources to gather data in the mobile-agent based computing paradigm. As described in [1], many inherent advantages (e.g. scalability, extensibility, energy awareness, reliability) of the mobile agent architecture make it more suitable for sensor networks than the client/server architecture. In this paper, a mobile agent is exploited in three levels (e.g. node level, task level, and combined task level) to reduce the information redundancy and communication overhead. 1

Analysis of Context Dependence in Social Interaction Networks of a Massively Multiplayer Online Role-Playing Game

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots

Robust Software Tokens: Towards Securing a Digital Identity

This paper presents a new method called the robust software token for providing users with a stable and portable container in which a private key is stored and kept from adversaries, by simple software-only techniques. The proposed scheme is comparable with the related noble work such as a cryptographic camouflage scheme and a networked cryptographic device, but equipped with several advantages; (1) it uniquely supports both closed and open domains on public key infrastructures, (2) it supports more protocol setup, (3) and it is more efficient than the others. This paper handles the new RSA-based scheme only. The DSA-based scheme sharing the basic idea can be found in our previous work

Authentication and Key Agreement via Memorable Password

This paper presents a new password authentication and key agreement protocol called AMP in a provable manner. The intrinsic problem with password authentication is a password, associated with each user, has low entropy so that (1) the password is hard to transmit securely over an insecure channel and (2) the password file is hard to protect. Our solution to this complex problem is the amplified password proof idea along with the amplified password file. A party commits the high entropy information and amplifies her password with that information in the amplified password proof. She never shows any information except that she knows it for her proof. Our amplified password proof idea is similar to the zero-knowledge proof in that sense. A server stores amplified verifiers in the amplified password file that is secure against a server file compromise and a dictionary attack. AMP mainly provides the passwordverifier based authentication and the Diffie-Hellman based key agreement, securely and efficiently. AMP is simple and actually the most efficient protocol among the related protocols. 1