Neural Architectures for Control

The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs

A critical analysis of mobility management related issues of wireless sensor networks in cyber physical systems

Mobility management has been a long-standing issue in mobile wireless sensor networks and especially in the context of cyber physical systems; its implications are immense. This paper presents a critical analysis of the current approaches to mobility management by evaluating them against a set of criteria which are essentially inherent characteristics of such systems on which these approaches are expected to provide acceptable performance. We summarize these characteristics by using a quadruple set of metrics. Additionally, using this set we classify the various approaches to mobility management that are discussed in this paper. Finally, the paper concludes by reviewing the main findings and providing suggestions that will be helpful to guide future research efforts in the area

A critical analysis of mobility management related issues of wireless sensor networks in cyber physical systems

Mobility management has been a long-standing issue in mobile wireless sensor networks and especially in the context of cyber physical systems its implications are immense. This paper presents a critical analysis of the current approaches to mobility management by evaluating them against a set of criteria which are essentially inherent characteristics of such systems on which these approaches are expected to provide acceptable performance. We summarize these characteristics by using a quadruple set of metrics. Additionally, using this set we classify the various approaches to mobility management that are discussed in this paper. Finally, the paper concludes by reviewing the main findings and providing suggestions that will be helpful to guide future research efforts in the area. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record*

An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction

Learning-based approaches to modeling crowd motion have become increasingly successful but require training and evaluation on large datasets, coupled with complex model selection and parameter tuning. To circumvent this tremendously time-consuming process, we propose a novel scoring method, which characterizes generalization of models trained on source crowd scenarios and applied to target crowd scenarios using a training-free, model-agnostic Interaction + Diversity Quantification score, ISDQ. The Interaction component aims to characterize the difficulty of scenario domains, while the diversity of a scenario domain is captured in the Diversity score. Both scores can be computed in a computation tractable manner. Our experimental results validate the efficacy of the proposed method on several simulated and real-world (source,target) generalization tasks, demonstrating its potential to select optimal domain pairs before training and testing a model

Self Organized Multi Agent Swarms (SOMAS) for Network Security Control

Computer network security is a very serious concern in many commercial, industrial, and military environments. This paper proposes a new computer network security approach defined by self-organized agent swarms (SOMAS) which provides a novel computer network security management framework based upon desired overall system behaviors. The SOMAS structure evolves based upon the partially observable Markov decision process (POMDP) formal model and the more complex Interactive-POMDP and Decentralized-POMDP models, which are augmented with a new F(*-POMDP) model. Example swarm specific and network based behaviors are formalized and simulated. This paper illustrates through various statistical testing techniques, the significance of this proposed SOMAS architecture, and the effectiveness of self-organization and entangled hierarchies

Data-centric Misbehavior Detection in VANETs

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is very important problem with wide range of implications including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. Because of this (\emph{rational behavior}), it is more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can independently decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alert with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. Instead of revoking all the secret credentials of misbehaving nodes, as done in most schemes, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes.Comment: 12 page