Agent mobility architecture based on IEEE-FIPA standards

Mobile agents are autonomous software entities driven by a set of goals and tasks. Reactivity, social ability, autonomy, the ability to move to different network locations, and the weak agent notion of proactiveness, allow for autonomous processing of distributed information according to their environment (context awareness).Although agent mobility has been devised for homogeneous environments, deployment of agent mobility in heterogeneous environments has been hindered by the absence of a common set of interoperation rules and ontologies for different agent middlewares.In this article, an agent migration model based on the communication standards of the IEEE-FIPA organisation is proposed. The approach described encompasses the definition of several specifications to achieve interoperability in the migration process in heterogeneous environments.The model provides a basic and extensible common migration process, which is flexible enough to support different kinds of migration methods and future upgrades. It is completely independent of any specific middleware implementation.     

An 802.11e HCCA scheduler with an end-to-end quality aware territory method

In this paper we present a solution for the IEEE 802.11e HCCA (Hybrid coordination function Controlled Channel Access) mechanism which aims both at supporting strict real-time traffic requirements and, simultaneously, at handling TCP applications efficiently. Our proposal combines a packet scheduler and a dynamic resource allocation algorithm. The scheduling discipline is based on the Monolithic Shaper-Scheduler, which is a modification of a General Processor Sharing (GPS) related scheduler. It supports minimum-bandwidth and delay guarantees and, at the same time, it achieves the optimal latency of all the GPS-related schedulers. In addition, our innovative resource allocation procedure, called the territory method, aims at prioritizing real time services and at improving the performance of TCP applications. For this purpose, it splits the wireless channel capacity (in terms of transmission opportunities) into different territories for the different types of traffic, taking into account the end-to-end network dynamics. In order to give support to the desired applications, we consider the following traffic classes: conversational, streaming, interactive and best-effort. The so called territories shrink or expand depending on the current quality experienced by the corresponding traffic class. We evaluated the performance of our solution through extensive simulations in a heterogeneous wired-cum-wireless scenario under different traffic conditions. Additionally, we compare our proposal to other HCCA scheduling algorithms, the HCCA reference scheduler and Fair Hybrid Coordination Function (FHCF). The results show that the combination of the MSS and the territory method obtains higher system capacity for VoIP traffic (up to 32 users) in the simulated scenario, compared to FHCF and the HCCA reference scheduler (13 users). In addition, the MSS with the territory method also improves the throughput of TCP sources (one FTP application achieves between 6.1 Mbps without VoIP traffic and 2.1 Mbps with 20 VoIP users) compared to the reference scheduler (at most 388 kbps) and FHCF (with a maximum FTP throughput of 4.8 Mbps).     

MHS: A distributed metadata management strategy

This paper proposes a novel distributed metadata management strategy to efficiently handle different metadata workloads. It can deliver high performance and scalable metadata service through four techniques, including directory conversion metadata, mimic hierarchical directory structure, flexible partition methods targeted different kinds of metadata of diverse characteristics, and the application of database to metadata backend. Using micro-benchmarks and a prototype system, we firstly demonstrate the performance superiority of our strategy compared to Lazy Hybrid, and then present the detailed performance results and analysis of our strategy on different MDS scales.     

Learning Fast Emulators of Binary Decision Processes

Computation time is an important performance characteristic of computer vision algorithms. The paper shows how existing (slow) binary decision algorithms can be approximated by a (fast) trained WaldBoost classifier. WaldBoost learning minimises the decision time of the classifier while guaranteeing predefined precision. We show that the WaldBoost algorithm together with bootstrapping is able to efficiently handle an effectively unlimited number of training examples provided by the implementation of the approximated algorithm. Two interest point detectors, the Hessian-Laplace and the Kadir-Brady saliency detectors, are emulated to demonstrate the approach. Experiments show that while the repeatability and matching scores are similar for the original and emulated algorithms, a 9-fold speed-up for the Hessian-Laplace detector and a 142-fold speed-up for the Kadir-Brady detector is achieved. For the Hessian-Laplace detector, the achieved speed is similar to SURF, a popular and very fast handcrafted modification of Hessian-Laplace; the WaldBoost emulator approximates the output of the Hessian-Laplace detector more precisely.     

Broadband system to increase bitrate in train communication networks

MVB (Multifunction Vehicle Bus), defined in IEC 61375, has been broadly adopted as the communication standard between embedded control systems on-board modern trains. In this work a new method to take advantage of the full bandwidth of the channel using an OFDM technique is described. With this new method it is possible to share the physical medium between standard MVB traffic and new OFDM traffic. A 90 Mbps theoretical bitrate can be achieved. The results of this work have been validated in a test bench including standard MVB nodes transmitting on a line similar to a real vehicle bus.     

A formal analysis of database replication protocols with SI replicas and crash failures

This paper provides a formal specification and proof of correctness of a basic Generalized Snapshot Isolation certification-based data replication protocol for database middleware architectures. It has been modeled using a state transition system, as well as the main system components, allowing a perfect match with the usual deployment in a middleware system. The proof encompasses both safety and liveness properties, as it is commonly done for a distributed algorithm. Furthermore, a crash failure model has been assumed for the correctness proof, although recovery analysis is not the aim of this paper. This allows an easy extension toward a crash-recovery model support in future works. The liveness proof focuses in the uniform commit: if a site has committed a transaction, the rest of sites will either commit it or it would have crashed.     

UAVs Integration in the SWIM Based Architecture for ATM

The System Wide Information Management (SWIM) approach has been conceived to overcome the capacity and flexibility limitations of the current ATM systems. On the other hand the commercial applications of Unmanned Aerial Vehicles (UAVs) require the integration of these vehicles in the ATM. From this perspective, the unavoidable modernization of the ATM is seen as an opportunity to integrate the UAVs with the rest of the air traffic. This paper is devoted to study the feasibility and impact of the aggregation of UAVs on the future ATM supported by a SWIM inspired architecture. Departing from the existing technical documents that describe the fundamentals of SWIM we have explored the compatibility with a potential UAVs integration and also explored how the UAVs could help to improve the future ATM system. We will use the weather application as an example in both cases.     

Reinforcement learning of pedagogical policies in adaptive and intelligent educational systems

In an adaptive and intelligent educational system (AIES), the process of learning pedagogical policies according the students needs fits as a Reinforcement Learning (RL) problem. Previous works have demonstrated that a great amount of experience is needed in order for the system to learn to teach properly, so applying RL to the AIES from scratch is unfeasible. Other works have previously demonstrated in a theoretical way that seeding the AIES with an initial value function learned with simulated students reduce the experience required to learn an accurate pedagogical policy. In this paper we present empirical results demonstrating that a value function learned with simulated students can provide the AIES with a very accurate initial pedagogical policy. The evaluation is based on the interaction of more than 70 Computer Science undergraduate students, and demonstrates that an efficient and useful guide through the contents of the educational system is obtained.