Engineering Agent Systems for Decision Support

This paper discusses how agent technology can be applied to the design of advanced Information Systems for Decision Support. In particular, it describes the different steps and models that are necessary to engineer Decision Support Systems based on a multiagent architecture. The approach is illustrated by a case study in the traffic management domain

Simple network management protocol co- existence with hydrocarbon process automation communication real-time network

Hydrocarbon Process Automation Applications (HPAA) utilizes Real-time network connecting process instrumentations, controllers, and real-time logic control applications. Conventional practice is to dedicate a real-time network for process automation applications and prevent other applications from utilizing the same infrastructure. An important application that can help optimize, improve network performance, and provide rapid response time in network diagnostics and mitigation is Simple Network Management Protocol (SNMP). This paper addresses the co-existence of SNMP traffic with real-time applications. The impacts of activating this protocol with the real-time HPAA utilizing high speed Ethernet network design will be examined. Empirical data for an implemented Hydrocarbon process automation system will be used to illustrate the interdependency of application performance, traffic mix, and potential areas of improvements. The outcomes of this effort demonstrate the co-existence of SNMP with HPPA, given special considerations (i.e., bandwidth, number of applications, etc.)

A forensically-enabled IASS cloud computing architecture

Current cloud architectures do not support digital forensic investigators, nor comply with today’s digital forensics procedures largely due to the dynamic nature of the cloud. Whilst much research has focused upon identifying the problems that are introduced with a cloud-based system, to date there is a significant lack of research on adapting current digital forensic tools and techniques to a cloud environment. Data acquisition is the first and most important process within digital forensics – to ensure data integrity and admissibility. However, access to data and the control of resources in the cloud is still very much provider-dependent and complicated by the very nature of the multi-tenanted operating environment. Thus, investigators have no option but to rely on cloud providers to acquire evidence, assuming they would be willing or are required to by law. Furthermore, the evidence collected by the Cloud Service Providers (CSPs) is still questionable as there is no way to verify the validity of this evidence and whether evidence has already been lost. This paper proposes a forensic acquisition and analysis model that fundamentally shifts responsibility of the data back to the data owner rather than relying upon a third party. In this manner, organisations are free to undertaken investigations at will requiring no intervention or cooperation from the cloud provider. The model aims to provide a richer and complete set of admissible evidence than what current CSPs are able to provide

Pricing, Investment, and Network Equilibrium

Despite rapidly emerging innovative road pricing and investment principles, the development of a long run network dynamics model for necessary policy evaluation is still lagging. This research endeavors to fill this gap and models the impacts of road financing policies throughout the network equilibration process. The manner in which pricing and investment jointly shape network equilibrium is particularly important and explored in this study. The interactions among travel demand, road supply, revenue mechanisms and investment rules are modeled at the link level in a network growth simulator. After assessing several measures of effectiveness, the proposed network growth model is able to evaluate the short- and long-run impacts of a broad spectrum of road pricing and investment policies on large-scale road networks, which can provide valuable information to decision-makers such as the implications of various policy scenarios on social welfare, financial situation of road authorities and potential implementation problems. Some issues hard to address in theoretical analysis can be examined in the agent-based simulation model. As a demonstration, we apply the network growth model to assess marginal and average pricing scenarios on a sample network. Even this relatively simple application provides new insights into issues around road pricing that have not previously been seriously considered. For instance, the results disclose a potential problem of over-investment when the marginal cost pricing scheme is adopted in conjunction with a myopic profit-neutral investment policy.Transportation network equilibrium; Road growth; Pricing; Congestion toll; Investment; Transport policy analysis.

A review of key planning and scheduling in the rail industry in Europe and UK

Planning and scheduling activities within the rail industry have benefited from developments in computer-based simulation and modelling techniques over the last 25 years. Increasingly, the use of computational intelligence in such tasks is featuring more heavily in research publications. This paper examines a number of common rail-based planning and scheduling activities and how they benefit from five broad technology approaches. Summary tables of papers are provided relating to rail planning and scheduling activities and to the use of expert and decision systems in the rail industry.EPSR

Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield