Anna N. Kim

When HART goes wireless: Understanding and implementing the WirelessHART standard

As a newly released industrial communication standard, WirelessHART complements the ever so successful HART field devices by providing the possible means for communicating via wireless channels. The WirelessHART standard is designed to offer simple configuration, flexible installation and easy access of instrument data, and at the same time, ensure robust and reliable communications. In this paper, we first look closely into the specifications and present a comprehensive overview of the standard by summarizing the main functions of the various protocol layers. We then survey the literature and identify amongst the existing methods and algorithms, which ones can be effectively adopted in implementing the standard. More specifically, we set our focus on issues relating to realization of the medium access layer and the network manager, which are essential in creating a successful WirelessHART network for specific applications.

Zero-Delay Joint Source-Channel Coding for a Bivariate Gaussian on a Gaussian MAC

In this paper, delay-free, low complexity, joint source-channel coding (JSCC) for transmission of two correlated Gaussian memoryless sources over a Gaussian Multiple Access Channel (GMAC) is considered. The main contributions of the paper are two distributed JSCC schemes: one discrete scheme based on nested scalar quantization, and one hybrid discrete-analog scheme based on a scalar quantizer and a linear continuous mapping. The proposed schemes show promising performance which improves with increasing correlation and are robust against variations in noise level. Both schemes also exhibit a constant gap to the performance upper bound when the channel signal-to-noise ratio gets large.

Maximum Utility Peer Selection for P2P Streaming in Wireless Ad Hoc Networks

In the recent years, the peer-to-peer (P2P) overlay network has been a promising architecture for multimedia streaming services besides its common use for efficient file sharing. By simply increasing the number of peers, the P2P overlay network can meet the high bit rate requirements of multimedia applications. Optimal peer selection for newly joining peers is one of the important problems, especially in wireless networks which have limited resources and capacity, since the peer selection process has a direct impact on the throughput of the underlay network and the co-existing unicast traffic. In this paper we tackle the problem of peer selection for streaming applications over wireless ad hoc networks. We devise a novel peer selection algorithm which maximizes the throughput of the underlay network, and at the same time makes P2P streaming friendly towards the co-existing data traffic. The proposed receiver based rate allocation and peer selection (RPS) algorithm is derived using the network utility maximization (NUM) framework. The algorithm solves the peer selection and rate allocation problem distributedly while optimally adapting the medium access control (MAC) layer parameters and is easily extensible to large P2P networks. Simulation results show that by using the proper price exchange mechanism, the peer receivers can effectively maximize the throughput of the underlay network by intelligently selecting its source peers.

Low complexity bandwidth compression mappings for sensor networks

Compressive (2 ∶ 1) joint source-channel coding using direct mappings from source to channel symbol space is considered. To enable the use of prior information due to e.g. correlated samples at the receiver, or statistical knowledge of the source, minimum mean square error decoding is considered. The prior information is incorporated in the form of the a-priori distribution in the decoding. Four mapping methods are presented and evaluated using the generic Bayesian minimum mean square error estimator. The schemes are evaluated for transmitting a memoryless Gaussian source over additive white Gaussian noise channel with a quadratic distortion measure. The simplicity of implementation and applicability to a wider variety of sources is discussed.

Quality Incentive Assisted Congestion Control for Receiver-Driven Multicast

The potential problem of oversubscribing receivers in receiver-driven multicast is addressed. We present a framework based on harmonizing the erasure-resilience properties of video with existing congestion control algorithms. The result offers subscription alternatives for receivers in which a penalty in terms of visual quality will be experienced by oversubscribing receivers. Thus, we provide an incentive for performing proper congestion control. The presented framework is independent of specific congestion control algorithms. Simulation results show the intended performance.

Optimal joint power-rate adaptation for error resilient video coding

In recent years digital imaging devices become an integral part of our daily lives due to the advancements in imaging, storage and wireless communication technologies. Power-Rate-Distortion efficiency is the key factor common to all resource constrained portable devices. In addition, especially in real-time wireless multimedia applications, channel adaptive and error resilient source coding techniques should be considered in conjunction with the P-R-D efficiency, since most of the time Automatic Repeat-reQuest (ARQ) and Forward Error Correction (FEC) are either not feasible or costly in terms of bandwidth efficiency delay. In this work, we focus on the scenarios of real-time video communication for resource constrained devices over bandwidth limited and lossy channels, and propose an analytic Power-channel Error-Rate-Distortion (P-E-R-D) model. In particular, probabilities of macroblocks coding modes are intelligently controlled through an optimization process according to their distinct rate-distortion-complexity performance for a given channel error rate. The framework provides theoretical guidelines for the joint analysis of error resilient source coding and resource allocation. Experimental results show that our optimal framework provides consistent rate-distortion performance gain under different power constraints.

Improving the Rate-Distortion Performance of DPCM Using Multirate Processing With Application in Low-Rate Image Coding

Differential pulse code modulation (DPCM) is able to code highly correlated sources efficiently at high bit rates but not at low bit-rate regions. Motivated by the rate-distortion theory, a simple modified DPCM codec using multirate processing is proposed. A low-pass filter is used to limit the source signal spectrum according to the optimal mapping scheme. Bit rate reduction is achieved by reducing the number of transmitted samples through downsampling. A Wiener filter is appended at the decoder for further noise reduction. Simulation results agree with theoretical analysis and show that for a first-order autoregressive [AR(1)] process, the proposed codec makes substantial improvements over the classic DPCM and performs close to the rate-distortion bound. The proposed system is then implemented as a low-rate predictive image coder. In addition to apply two-dimensional DPCM, adaptive entropy coding is used to exploit variation in the local statistics of the prediction error image. Simulation results show that the system is able to outperform JPEG at low rates and has subjective quality comparable to that of JPEG2000.

Dimension Reduction and Expansion: Distributed Source Coding in a Noisy Environment

We studied the problem of distributed coding and transmission of inter-correlated sources with memory. Different from the conventional distributed source coding structure which relies on design of effective channel codes to model the inter-correlation and quantizer, the proposed system utilizes distributed compressed sensing [1] for signal dimension reduction through linear matrix operations and dimension expansion for protection against channel noise through a hybrid scalar quantizer linear coder [2]. The proposed system is optimized for minimum end-to-end distortion under a transmission energy constraint. Its performance is verified through simulation and can serve as a good starting point for designing similar analogue based dimension reduction- expansion schemes for applications in sensor networks.

Utility optimal real-time multimedia communication in wireless mesh networks

Supporting real-time multimedia communication over multi-hop wireless mesh networks is a challenging problem, considering the necessity of intelligent allocation of shared wireless medium amongst different nodes within the network while ensuring the desired level of quality of service (QoS). Many existing cross-layer design approaches that are aiming for an integrated operation of the different protocol layers often either not consider the network functions as a whole or rely on mechanisms that provide global network information. There is certainly a need for developing distributed and scalable algorithms for optimal utilization of system resources and providing the end-to-end QoS. In this paper, we utilize the general network utility maximization (GNUM) framework that tackles the cross-layer design problem in a systematic manner. More specifically by incorporating contention based IEEE 802.11 MAC protocol, we developed an optimal joint congestion-contention control scheme that maximize network throughput while at the same time providing end-to-end QoS for multimedia traffic. The proposed algorithm distributedly calculates the optimal solution by means of exchanging prices between the source and the network links, and hence fully scalable to large networks. Our results reveal the tradeoffs between QoS requirement and rate allocation, and ultimately provide guidelines for how the medium access and transport layer parameters should be selected in order to guarantee QoS for the application. The proposed algorithm can be easily generalized into other multi-hop wireless ad hoc networks.

Delay-Free Joint Source-Channel Coding for Gaussian Network of Multiple Sensors

We study the communication problem in a sensor network which consists of multiple sensor nodes that observe memoryless Gaussian sources which are inter-correlated. The observations are transmitted over orthogonal additive white Gaussian noise channels, and all source symbols are to be recovered at the receiver. We focus on communication schemes which utilize direct source to channel mappings that operate on a symbol-by-symbol basis to ensure zero coding delay. The distortion lower bound for the network with more than two sensors case is derived. Optimal linear schemes, both distributed and cooperative, are presented. Results show that the gap to the performance upper bound is large when there is high correlation and it increases significantly when the network size is large. We then present nonlinear mappings which can be implemented distributedly and show that they can provide substantial gain when the correlation is close to one. Examples are given for networks with two and three nodes.