12,713 research outputs found
Modeling the Internet of Things: a simulation perspective
This paper deals with the problem of properly simulating the Internet of
Things (IoT). Simulating an IoT allows evaluating strategies that can be
employed to deploy smart services over different kinds of territories. However,
the heterogeneity of scenarios seriously complicates this task. This imposes
the use of sophisticated modeling and simulation techniques. We discuss novel
approaches for the provision of scalable simulation scenarios, that enable the
real-time execution of massively populated IoT environments. Attention is given
to novel hybrid and multi-level simulation techniques that, when combined with
agent-based, adaptive Parallel and Distributed Simulation (PADS) approaches,
can provide means to perform highly detailed simulations on demand. To support
this claim, we detail a use case concerned with the simulation of vehicular
transportation systems.Comment: Proceedings of the IEEE 2017 International Conference on High
Performance Computing and Simulation (HPCS 2017
A customizable multi-agent system for distributed data mining
We present a general Multi-Agent System framework for
distributed data mining based on a Peer-to-Peer model. Agent
protocols are implemented through message-based asynchronous
communication. The framework adopts a dynamic load balancing
policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances
A group learning management method for intelligent tutoring systems
In this paper we propose a group management specification and execution method that seeks a compromise between simple course design and complex adaptive group interaction. This is achieved through an authoring method that proposes predefined scenarios to the author. These scenarios already include complex learning interaction protocols in which student and group models use and update are automatically included. The method adopts ontologies to represent domain and student models, and object Petri nets to specify the group interaction protocols. During execution, the method is supported by a multi-agent architecture
Cost Adaptation for Robust Decentralized Swarm Behaviour
Decentralized receding horizon control (D-RHC) provides a mechanism for
coordination in multi-agent settings without a centralized command center.
However, combining a set of different goals, costs, and constraints to form an
efficient optimization objective for D-RHC can be difficult. To allay this
problem, we use a meta-learning process -- cost adaptation -- which generates
the optimization objective for D-RHC to solve based on a set of human-generated
priors (cost and constraint functions) and an auxiliary heuristic. We use this
adaptive D-RHC method for control of mesh-networked swarm agents. This
formulation allows a wide range of tasks to be encoded and can account for
network delays, heterogeneous capabilities, and increasingly large swarms
through the adaptation mechanism. We leverage the Unity3D game engine to build
a simulator capable of introducing artificial networking failures and delays in
the swarm. Using the simulator we validate our method on an example coordinated
exploration task. We demonstrate that cost adaptation allows for more efficient
and safer task completion under varying environment conditions and increasingly
large swarm sizes. We release our simulator and code to the community for
future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
Distributed and Centralized Task Allocation: When and Where to Use Them
Self-organisation is frequently advocated as the solution for managing large, dynamic systems. Distributed algorithms are implicitly designed for infinitely large problems, while small systems are regarded as being controllable using traditional, centralised approaches. Many real-world systems, however, do not fit conveniently into these "small" or "large" categories, resulting in a range of cases where the optimal solution is ambiguous. This difficulty is exacerbated by enthusiasts of either approach constructing problems that suit their preferred control architecture. We address this ambiguity by building an abstract model of task allocation in a community of specialised agents. We are inspired by the problem of work distribution in distributed satellite systems, but the model is also relevant to the resource allocation problems in distributed robotics, autonomic computing and wireless sensor networks. We compare the behaviour of a self-organising, market-based task allocation strategy to a classical approach that uses a central controller with global knowledge. The objective is not to prove one mechanism inherently superior to the other; instead we are interested in the regions of problem space where each of them dominates. Simulation is used to explore the trade-off between energy consumption and robustness in a system of intermediate size, with fixed communication costs and varying rates of component failure. We identify boundaries between regions in the parameter space where one or the other architecture will be favoured. This allows us to derive guidelines for system designers, thus contributing to the development of a disciplined approach to controlling distributed systems using self-organising mechanisms
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