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This paper proposes an efficient topology synthesis method for on-chip interconnection network based on crossbar switches. The efficiency of topology synthesis methods is often measured by two metrics—the quality of the synthesized topology and synthesis time. These two metrics are critically determined by the definition of the topology design space and the exploration method. Furthermore, an efficient representing method for the design space is required to tightly link the design space and the exploration method. Even though topology synthesis methods have actively been researched, most of the previous methods were not deep in thought for these factors. Unlike the previous methods, we propose a topology synthesis method with a careful consideration of these factors. Our method efficiently defines the design space by a technique called chained edge partitioning, in conjunction with a representing method for the points in the space, called enhanced restricted growth function. We also provide an exploration method which well incorporates with the aforementioned search space. To prove the effectiveness of our method, we compared our method with previous methods. The experimental results show that our method outperforms the compared methods by up to 49.8% and 104.6x in the quality of the synthesized topology and the synthesis time, respectively.
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In the following paper, we study the tradeoff between network utility and network lifetime for energy-constrained wireless sensor networks (WSNs). By introducing a weighted factor, we combine these two objectives into a single weighted objective, and we consider rate control and routing in this tradeoff framework simultaneously. First, by using a dual decomposition method, we decompose the tradeoff model into two subproblems: the congestion control/routing problem and the network lifetime problem, both of which interact through the dual variables for energy dissipation constraints. Based on the decomposition results, we propose a fully distributed algorithm to solve these two sub-problems and the dual problem by using gradient and sub-gradient projection methods. Second, we propose a fully distributed algorithm by approximating the network lifetime maximization problem by using the network utility maximization (NUM) framework. Third, we extend our distributed algorithm to deal with reliable communication and the real-time requirement. Rigorous analysis and simulations are presented to validate our algorithms.
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This paper proposes and evaluates a framework for cooperative control of a multi-agent system. The framework is evaluated on a target-tracking application where a distributed sensor network is tasked to autonomously observe targets within the environment. The problem of cooperative control is defined using two distinct levels of cooperation: implicit and explicit. Implicit cooperation is defined as cooperation through only the exchange of environmental data to compile a common picture over which to reason locally. For example, in this paper, decentralized data fusion algorithms are used to build and update a common picture of the target positions and velocities. Explicit cooperation, which is the main focus of this paper, negotiates the agents explicitly on a joint set of actions to perform. In this paper, the problem of explicit cooperation is formulated as a distributed optimization, and a framework to find the joint set of actions is proposed. The framework utilizes two algorithms, the Max-Sum algorithm, to globally solve a factorizable utility function, and Probability Collectives (PC), to solve the individual factors of the utility function. The paper presents experimental results of the two algorithms using a simulated distributed sensor network when the tracking problem is and is not factorizable. The results show that the proposed framework can efficiently and effectively enable cooperation in a distributed sensor network. The Max-Sum algorithm provides a distributed and flexible approach to solve a factorizable utility function, where the PC algorithm was shown to efficiently solve the individual factors when more than four sensors are required to cooperate.
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Sensor networks (SNs) have arisen as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical systems ease the development and proliferation of this kind of networks in a wide range of actual-world applications. Multiagent systems (MAS) have been identified as one of the most suitable technologies to contribute to the deployment of SNs that exhibit flexibility, robustness and autonomy. The purpose of this survey is 2-fold. On the one hand, we review the most relevant contributions of agent technologies to this emerging application domain. On the other hand, we identify the challenges that researchers must address to establish MAS as the key enabling technology for SNs.
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Tuning the parameters that control the operation of a wireless sensor network, such as sampling rate, is not a simple task. This is partly due to the distributed nature of the problem, but is also a result of the time-varying dynamics that a network experiences. Inspired by the way in which cells alter their behaviour in response to diffused protein concentrations, an abstract representation, termed a discrete gene regulatory network (dGRN), is introduced. Each node runs an identical dGRN controller which controls node activity and interaction. The controllers are authored automatically using an evolutionary algorithm. The communication that occurs between nodes is neither specified nor designed, but emerges naturally. As a particular example, we illustrate that our approach can generate effective strategies for nodes to cooperatively track a moving target. The obtained strategies vary according to the user’s accuracy requirements and the speed of the target, and are similar to those which would be expected from a network engineer. We also present results from our proof-of-concept dGRN implementation on T-Mote Sky nodes. Our approach takes high-level user application requirements and from these, automatically generates distributed parameter tuning algorithms. The dGRN framework thus greatly reduces the amount of effort involved in adjusting a sensor network’s operation.
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Many organizations depend on critical sensory information to achieve their tasks. As the number of those tasks increases, efficient determination and allocation of required resources in sensor networks become crucial. In this paper, we propose means to describe tasks semantically with their requirements and constraints so that software agents can reason about those tasks and determine what type of sensor resources they may need. Based on the semantic description and reasoning mechanisms, we propose a distributed agent-based approach to efficiently allocate sensor resources to tasks. Our evaluation of the proposed approach shows that not only it enables fully automated determination and allocation of resources for tasks, but also the resulting allocation is efficient and close to optimum.
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Wireless sensor networks (WSNs) are emerging as powerful platforms for distributed embedded computing supporting a variety of high-impact applications. However, programming WSN applications is a complex task that requires suitable paradigms and technologies capable of supporting the specific characteristics of such networks which uniquely integrate distributed sensing, computation and communication. Mobile agents are a distributed computing paradigm based on code mobility that has already demonstrated high effectiveness and efficiency in IP-based highly dynamic distributed environments. Due to their intrinsic characteristics, mobile agents may provide more benefits in the context of WSNs than in conventional distributed environments. In this paper we present the design, implementation and experimentation of MAPS (Mobile Agent Platform for Sun SPOT), an innovative Java-based framework for wireless sensor networks based on Sun SPOT technology which enables agent-oriented programming of WSN applications. The MAPS architecture is based on components that interact through events. Each component offers a minimal set of services to mobile agents that are modeled as multi-plane state machines driven by ECA rules. In particular, the offered services include message transmission, agent creation, agent cloning, agent migration, timer handling and easy access to the sensor node resources (sensors, actuators, input switches, flash memory and battery). Agent programming with MAPS is presented through both a simple example related to mobile agent-based monitoring of a sensor node and a more complex case study for real-time human activity monitoring based on wireless body sensor networks. Moreover, a performance evaluation of MAPS carried out by computing micro-benchmarks, related to agent communication, creation and migration, is illustrated.
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Wireless sensor networks (WSNs) are starting to mature into the next generation where they can be used for adaptive filtering and signal processing, breaking away from the current generation of microcontroller applications. The tasks involved, however, are computationally intensive and strain the energy resources of any single computational sensor node. Moreover, most sensor nodes do not have the computational resources to complete many of these tasks repeatedly. Hence, exploring distributed processing on WSNs becomes a necessity to enable such computational load to be processed in real-time. In this work, a new distributed QR decomposition algorithm, on WSNs, is developed and implemented. QR decomposition has prominent applications in adaptive filtering which is essential for many WSN applications, such as target tracking and beamforming. The contributions of this work can be summarized as follows: (i) developing a new scalable tile-based distributed QR decomposition algorithm, (ii) distributing the least-squares problem based on the proposed distribution of the QR decomposition, (iii) developing resource-aware task allocation and mapping and (iv) developing a simple decentralized transmission scheduling scheme to guarantee efficient operation. This work demonstrates that distributed processing on WSNs paves the way for larger computations beyond the capabilities of a single node. This is accomplished while decreasing the energy per node and increasing the speed of the computation versus the implementation on a single node. The experiments, on a test bed of Telosb sensor nodes, prove that the proposed distributed algorithm enables higher computational capabilities while reducing the energy per node by up to 91.93% and speeding up the computation by up to 79.29% compared with running the QR decomposition on a single node, thus laying the foundation for energy-feasible real-time in-network processing.
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