An adaptive scheduling scheme for fair bandwidth allocation

Class-based service differentiation is provided in DiffServ networks. However, this differentiation will be disordered under dynamic traffic loads due to the fixed weighted scheduling. An adaptive weighted scheduling scheme is proposed in this paper to achieve fair bandwidth allocation among different service classes. In this scheme, the number of active flows and the subscribed bandwidth are estimated based on the measurement of local queue metrics, then the scheduling weights of each service class are adjusted for the per-flow fairness of excess bandwidth allocation. This adaptive scheme can be combined with any weighted scheduling algorithm. Simulation results show that, comparing with fixed weighted scheduling, it effectively improve the fairness of excess bandwidth allocation.

Adaptive electrical signal post-processing in optical communications systems

Improving bit error rates in optical communication systems is a difficult and important problem. The error correction must take place at high speed and be extremely accurate. We show the feasibility of using hardware implementable machine learning techniques. This may enable some error correction at the speed required.

THz emission from quantum dot-based THz antennas pumped by a tunable quantum-dot laser diode

The THz optoelectronics field is now maturing and semiconductor-based THz antenna devices are becoming more widely implemented as analytical tools in spectroscopy and imaging. Photoconductive (PC) THz switches/antennas are driven optically typically using either an ultrashort-pulse laser or an optical signal composed of two simultaneous longitudinal wavelengths which are beat together in the PC material at a THz difference frequency. This allows the generation of (photo)carrier pairs which are then captured over ultrashort timescales usually by defects and trapping sites throughout the active material lattice. Defect-implanted PC materials with relatively high bandgap energy are typically used and many parameters such as carrier mobility and PC gain are greatly compromised. This paper demonstrates the implementation of low bandgap energy InAs quantum dots (QDs) embedded in standard crystalline GaAs as both the PC medium and the ultrafast capture mechanism in a PC THz antenna. This semiconductor structure is grown using standard MBE methods and allows the device to be optically driven efficiently at wavelengths up to ~1.3 µm, in this case by a single tunable dual-mode QD diode laser.

Design and evaluation of a framework for cooperative and adaptive QoS control of DSRC network for road safety applications

Dedicated short-range communications (DSRC) are a promising vehicle communication technique for collaborative road safety applications (CSA). However, road safety applications require highly reliable and timely wireless communications, which present big challenges to DSRC based vehicle networks on effective and robust quality of services (QoS) provisioning due to the random channel access method applied in the DSRC technique. In this paper we examine the QoS control problem for CSA in the DSRC based vehicle networks and presented an overview of the research work towards the QoS control problem. After an analysis of the system application requirements and the DSRC vehicle network features, we propose a framework for cooperative and adaptive QoS control, which is believed to be a key for the success of DSRC on supporting effective collaborative road safety applications. A core design in the proposed QoS control framework is that network feedback and cross-layer design are employed to collaboratively achieve targeted QoS. A design example of cooperative and adaptive rate control scheme is implemented and evaluated, with objective of illustrating the key ideas in the framework. Simulation results demonstrate the effectiveness of proposed rate control schemes in providing highly available and reliable channel for emergency safety messages. © 2013 Wenyang Guan et al.

Energy efficient transmission protocol for distributed source coding in sensor networks

Distributed source coding (DSC) has recently been considered as an efficient approach to data compression in wireless sensor networks (WSN). Using this coding method multiple sensor nodes compress their correlated observations without inter-node communications. Therefore energy and bandwidth can be efficiently saved. In this paper, we investigate a randombinning based DSC scheme for remote source estimation in WSN and its performance of estimated signal to distortion ratio (SDR). With the introduction of a detailed power consumption model for wireless sensor communications, we quantitatively analyze the overall network energy consumption of the DSC scheme. We further propose a novel energy-aware transmission protocol for the DSC scheme, which flexibly optimizes the DSC performance in terms of either SDR or energy consumption, by adapting the source coding and transmission parameters to the network conditions. Simulations validate the energy efficiency of the proposed adaptive transmission protocol. © 2007 IEEE.

In-Motes Bins:a real time application for environmental monitoring in wireless sensor networks

In-Motes Bins is an agent based real time In-Motes application developed for sensing light and temperature variations in an environment. In-Motes is a mobile agent middleware that facilitates the rapid deployment of adaptive applications in Wireless Sensor Networks (WSN's). In-Motes Bins is based on the injection of mobile agents into the WSN that can migrate or clone following specific rules and performing application specific tasks. Using In-Motes we were able to create and rapidly deploy our application on a WSN consisting of 10 MICA2 motes. Our application was tested in a wine store for a period of four months. In this paper we present the In-Motes Bins application and provide a detailed evaluation of its implementation. © 2007 IEEE.

High-gain frequency reconfigurable Vivaldi antenna

This article proposes a frequency agile antenna whose operating frequency band can be switched. The design is based on a Vivaldi antenna. High-performance radio-frequency microelectromechanical system (RF-MEMS) switches are used to realize the 2.7 GHz and 3.9 GHz band switching. The low band starts from 2.33 GHz and works until 3.02 GHz and the high band ranges from 3.29 GHz up to 4.58 GHz. The average gains of the antenna at the low and high bands are 10.9 and 12.5 dBi, respectively. This high-gain frequency reconfigurable antenna could replace several narrowband antennas for reducing costs and space to support multiple communication systems, while maintaining good performance.

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

Learning to be different:heterogeneity and efficiency in distributed smart camera networks

In this paper we study the self-organising behaviour of smart camera networks which use market-based handover of object tracking responsibilities to achieve an efficient allocation of objects to cameras. Specifically, we compare previously known homogeneous configurations, when all cameras use the same marketing strategy, with heterogeneous configurations, when each camera makes use of its own, possibly different marketing strategy. Our first contribution is to establish that such heterogeneity of marketing strategies can lead to system wide outcomes which are Pareto superior when compared to those possible in homogeneous configurations. However, since the particular configuration required to lead to Pareto efficiency in a given scenario will not be known in advance, our second contribution is to show how online learning of marketing strategies at the individual camera level can lead to high performing heterogeneous configurations from the system point of view, extending the Pareto front when compared to the homogeneous case. Our third contribution is to show that in many cases, the dynamic behaviour resulting from online learning leads to global outcomes which extend the Pareto front even when compared to static heterogeneous configurations. Our evaluation considers results obtained from an open source simulation package as well as data from a network of real cameras. © 2013 IEEE.

Fully probabilistic control design in an adaptive critic framework

Optimal stochastic controller pushes the closed-loop behavior as close as possible to the desired one. The fully probabilistic design (FPD) uses probabilistic description of the desired closed loop and minimizes Kullback-Leibler divergence of the closed-loop description to the desired one. Practical exploitation of the fully probabilistic design control theory continues to be hindered by the computational complexities involved in numerically solving the associated stochastic dynamic programming problem. In particular very hard multivariate integration and an approximate interpolation of the involved multivariate functions. This paper proposes a new fully probabilistic contro algorithm that uses the adaptive critic methods to circumvent the need for explicitly evaluating the optimal value function, thereby dramatically reducing computational requirements. This is a main contribution of this short paper.

Adaptive agents in changing environments, the role of modularity

We explored the role of modularity as a means to improve evolvability in populations of adaptive agents. We performed two sets of artificial life experiments. In the first, the adaptive agents were neural networks controlling the behavior of simulated garbage collecting robots, where modularity referred to the networks architectural organization and evolvability to the capacity of the population to adapt to environmental changes measured by the agents performance. In the second, the agents were programs that control the changes in network's synaptic weights (learning algorithms), the modules were emerged clusters of symbols with a well defined function and evolvability was measured through the level of symbol diversity across programs. We found that the presence of modularity (either imposed by construction or as an emergent property in a favorable environment) is strongly correlated to the presence of very fit agents adapting effectively to environmental changes. In the case of learning algorithms we also observed that character diversity and modularity are also strongly correlated quantities. © 2014 Springer Science+Business Media New York.

Adaptive Control and Multi-agent Interface for Infotelecommunication Systems of New Generation

Problems for intellectualisation for man-machine interface and methods of self-organization for network control in multi-agent infotelecommunication systems have been discussed. Architecture and principles for construction of network and neural agents for telecommunication systems of new generation have been suggested. Methods for adaptive and multi-agent routing for information flows by requests of external agents- users of global telecommunication systems and computer networks have been described.

General Aspects of Constructing an Autonomous Adaptive Agent

There are a great deal of approaches in artificial intelligence, some of them also coming from biology and neirophysiology. In this paper we are making a review, discussing many of them, and arranging our discussion around the autonomous agent research. We highlight three aspect in our classification: type of abstraction applied for representing agent knowledge, the implementation of hypothesis processing mechanism, allowed degree of freedom in behaviour and self-organizing. Using this classification many approaches in artificial intelligence are evaluated. Then we summarize all discussed ideas and propose a series of general principles for building an autonomous adaptive agent.

An adaptive IEEE 802.15.4 MAC layer transmission scheme for applications with heterogeneous traffic

IEEE 802.15.4 networks has the features of low data rate and low power consumption. It is a strong candidate technique for wireless sensor networks and can find many applications to smart grid. However, due to the low network and energy capacities it is critical to maximize the bandwidth and energy efficiencies of 802.15.4 networks. In this paper we propose an adaptive data transmission scheme with CSMA/CA access control, for applications which may have heavy traffic loads such as smart grids. The adaptive access control is simple to implement. Its compatibility with legacy 802.15.4 devices can be maintained. Simulation results demonstrate the effectiveness of the proposed scheme with largely improved bandwidth and power efficiency. © 2013 International Information Institute.

Wavelength-division and spatial multiplexing using tandem interferometers for Bragg grating sensor networks

We present a new method for the interrogation of large arrays of Bragg grating sensors. Eight gratings operating between the wavelengths of 1533 and 1555 nm have been demultiplexed. An unbalanced Mach—Zehnder interferometer illuminated by a single low-coherence source provides a high-phase-resolution output for each sensor, the outputs of which are sequentially selected in wavelength by a tunable Fabry-Perot interferometer. The minimum detectable strain measured was 90 ne-vHz at 7 Hz for a wavelength of 1535 nm.