Spectrum sensing: A distributed approach for cognitive terminals

Cognitive radios is emerging in research laboratories as a promising wireless paradigm, which will integrate benefits of software defined radio with a complete aware communication behavior. To reach this goal many issues remain still open, such as powerful algorithms for sensing the external environment. This paper presents a further step in the direction of allowing cooperative spectrum sensing in peer-to-peer cognitive networks by using distributed detection theory. The approach aims at improving the radio awareness with respect to stand alone scenario as it is shown with theoretical and experimental results     

A distributed compressed sensing approach for speech signal denoising

Compressed sensing, a new area of signal processing rising in recent years, seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction. The precondition of compressed sensing theory is the sparsity of signals. In this paper, two methods to estimate the sparsity level of the signal are formulated. And then an approach to estimate the sparsity level directly from the noisy signal is presented. Moreover, a scheme based on distributed compressed sensing for speech signal denoising is described in this work which exploits multiple measurements of the noisy speech signal to construct the block-sparse data and then reconstruct the original speech signal using block-sparse model-based Compressive Sampling Matching Pursuit (CoSaMP) algorithm. Several simulation results demonstrate the accuracy of the estimated sparsity level and that this denoising system for noisy speech signals can achieve favorable performance especially when speech signals suffer severe noise.     

A distributed and adaptive signal processing approach to exploiting correlation in sensor networks

We propose a novel approach to reducing energy consumption in sensor networks using a distributed adaptive signal processing framework and efficient algorithm.¹ While the topic of energy-aware routing to alleviate energy consumption in sensor networks has received attention recently [C. Toh, IEEE Commun. Mag. June (2001) 138; R. Shah, J. Rabaey, Proc. IEEE WCNC, March 2002], in this paper, we propose an orthogonal approach to complement previous methods. Specifically, we propose a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles. Our approach enables sensor nodes to blindly compress their readings with respect to one another without the need for explicit and energy-expensive inter-sensor communication to effect this compression. Furthermore, the distributed algorithm used by each sensor node is extremely low in complexity and easy to implement (i.e., one modulo operation), while an adaptive filtering framework is used at the data gathering unit to continuously learn the relevant correlation structures in the sensor data. Applying the algorithm to testbed data resulted in energy savings of 10–65% for a multitude of sensor modalities.     

A control-theoretic approach to the design of an explicit ratecontroller for ABR service

We present a control-theoretic approach to the design of closed-loop rate-based flow control in high-speed networks. The proposed control uses a dual proportional-plus-derivative controller, where the control parameters can be designed to ensure the stability of the traffic patterns and propagation delays. We show how the control mechanism can be used to design a controller to support ABR service based on feedback of explicit rates (ERs). We demonstrate the excellent transient and steady-state performance of the controller through a number of examples. We also show experimental results that have been obtained from our asynchronous transfer mode (ATM) testbed, which consists of two interconnected ATM LANs, one located in Princeton, NJ, and the other in Berlin, Germany, with an all-software ER-controller implementation     

Packet-centric approach to distributed sparse-graph coding in wireless ad hoc networks

In this paper we present a packet-centric approach for distributed coding in decentralized wireless ad hoc networks, for applications in distributed data storage, data persistence and efficient data gathering. We study the setting where each of N network nodes generates an information packet and the goal is to efficiently encode information packets and disseminate produced encoded packets across the network in such fashion that gathering of any subset of slightly more than N encoded packets allows for retrieval of the original information. The process of distributed encoding is performed using packets that randomly walk over the network and sample information packets from network nodes, producing the encoded packets in a simple, elegant, fully decentralized and stateless way. The proposed scheme maintains properties of centralized codes in terms of performance parameters, offering at the same time advantage of robustness to node failures and changes in network topology. We specialize the proposed scheme for several important classes of low-complexity encodable/decodable sparse-graph codes – LDGM, LDPC (IRA), LT, and Raptor codes, evaluating its performance via simulation for various data-gathering scenarios.     

A new distributed route selection approach for channelestablishment in real-time networks

We propose a new distributed route selection approach, called parallel probing, for real-time channel establishment in a point-to-point network. The existing distributed routing algorithms fall into two major categories: preferred neighbor based or flooding based. The preferred neighbor approach offers a better call acceptance rate, whereas the flooding approach is better in terms of call setup time and routing distance. The proposed approach attempts to combine the benefits of both preferred neighbor and flooding approaches in a way to improve all the three performance metrics simultaneously. This is achieved by probing k different paths in parallel, for a channel, by employing different heuristics on each path. Also, the proposed approach uses a notion called intermediate destinations (IDs), which are subset of nodes along the least-cost path between source and destination of a call, in order to reduce the excessive resource reservations while probing for a channel by releasing unused resources between IDs and initiating parallel probes at every ID. Further, it has the flexibility of adapting to different load conditions by its nature of using different heuristics in parallel, and hence, a path found for a channel would have different segments (a segment is a path between two successive IDs), and each of these segments would very well be selected by different heuristics. The effectiveness of the proposed approach has been studied through simulation for well-known network topologies for a wide range of quality-of-service and traffic parameters. The simulation results reveal that the average call acceptance rate offered by the proposed route-selection approach is better than that of both the flooding and preferred neighbor approaches, and the average call setup time and routing distance offered by it are very close to that of the flooding approach     

DARYN-a distributed decision-making algorithm for railway networks:modeling and simulation

The state of the art in scheduling “point-to-point” trains in a railway network utilizes the principles of centralized decision-making. The major difficulty of this approach is that the execution time and the memory requirements increase nonlinearly as the system grows in size. The present paper introduces a new approach, “DARYN”, wherein the overall decision process is analyzed and distributed onto every natural entity of the system. In DARYN, the decision process for every train is executed by an on-board processor that negotiates, dynamically and progressively, for temporary ownership of the tracks with the respective station controlling the tracks, through explicit processor to processor communication primitives. This processor then computes its own route utilizing the results of its negotiation, its knowledge of the track layout of the entire system, and its evaluation of the cost function. Every station's decision process is also executed by a dedicated processor that, in addition, maintains absolute control over a given set of tracks and participates in the negotiation with the trains. Presently, DARYN utilizes a simple cost function. However, if one chooses to increase the complexity of the cost function, DARYN's advantage over the traditional approach increases due to its enormous available computational power. Given that the current microprocessors such as MC68030, MC88000, Intel 486, and Intel 860 are powerful yet relatively inexpensive, a network of concurrently executing processors may offer superior price-performance quotient over a single high performance computer     

Channel estimation based on distributed compressed sensing in amplify-and-forward relay networks

In orthogonal frequency-division multiplexing(OFDM)amplify-and-forward(AF)relay networks,in order to exploit diversity gains over frequency-selective fading channels,the receiver needs to acquire the knowledge of channel state information(CSI).In this article,based on the recent methodology of distributed compressed sensing(DCS),a novel channel estimation scheme is proposed.The joint sparsity model 2(JSM-2)in DCS theory and simultaneous orthogonal matching pursuit(SOMP)are both introduced to improve the estimation performance and increase the spectral efficiency.Simulation results show that compared with current compressed sensing(CS)methods,the estimation error of our scheme is reduced dramatically in high SNR region while the pilot number is still kept small.     

A control-theoretic approach to adapting VBR compressed video fortransport over a CBR communications channel

Future broad-band communications networks are expected to be dominated by video and image traffic. Variable bit-rate (VBR) video compression is generally preferred to constant bit-rate (CBR) compression because constant image quality can be provided. In contrast, CBR transport is preferred to VBR transport from the networking standpoint because of its simplicity. This paper studies the important issue of adapting VBR compressed video for transport over a CBR channel. We focus on temporal traffic smoothing using an elastic buffer. The target image quality and the output rate of the video encoder is controlled by feedback based on the buffer-occupancy level. Previous adaptation schemes are not readily analyzable. An analyzable control-theoretic adaptation framework is proposed. It allows systematic and quantitative investigation of issues such as stability, robustness against scene changes, robustness against image-quality oscillations due to coding-mode switching, and tradeoffs between image-quality and buffer-occupancy (delay) fluctuations. Perhaps more importantly, the framework opens up many new possibilities for further research     

认知自组网分布式频谱感知最优协作算法

认知无线电技术使得自组织网络节点能够充分利用空闲频谱资源,提高了传输性能。通过协作频谱感知,可有效解决由于无线信道存在阴影、噪声和衰落等情况导致的单节点感知准确性偏低。为了解决梯度算法随着协作节点数量增大后计算复杂度变高,文中提出部分梯度算法ψ-GBCS,该模型通过基于SNR的动态阈值保证了感知准确性,同时通过最佳协作节点数提高了感知效率。仿真结果表明,该模型下,综合评估系统效率和性能的J函数值提高37%,能耗降低50%,有效保证大规模认知自组网频谱感知的鲁棒性,降低了对主用户的干扰及设备功耗。     

An overlay and distributed approach to node mobility in multi-access wireless networks

Nowadays many manufacturers are building mobile devices with multiple interfaces. Thus, users have access to different types of wireless access networks, which often, as for WLAN and cellular systems, coexists independently. The challenge is to make such multiple access networks to cooperate to have ubiquitous access and enhanced user quality of service. In this paper we present a scheme to allow inter-technology mobility by the introduction of an overlay network, which works on top of current (and future) networks. The proposed architecture controls all the aspect related to the mobility management: mobile node localization, handover decision and execution. The approach is distributed: it is the mobile node that decides which network to use, based on the offered service quality and the cost of the communication of the available networks, and triggers the handover execution directly to the corresponding host, using optimized SIP-based procedures. The overlay network copes with the mobile node localization. We implemented our solution in the laboratory to prove its validity and to test performance using real equipment. We also simulated the scheme using ns-3 to extend the evaluation to large scale deployments. In both test environments, our solution demonstrates high accurateness in selecting the network with the best quality as well as in supporting seamless vertical handover.     

A QoE centric distributed caching approach for vehicular video streaming in cellular networks

Distributed caching‐empowered wireless networks can greatly improve the efficiency of data storage and transmission and thereby the users' quality of experience (QoE). However, how this technology can alleviate the network access pressure while ensuring the consistency of content delivery is still an open question, especially in the case where the users are in fast motion. Therefore, in this paper, we investigate the caching issue emerging from a forthcoming scenario where vehicular video streaming is performed under cellular networks. Specifically, a QoE centric distributed caching approach is proposed to fulfill as many users' requests as possible, considering the limited caching space of base stations and basic user experience guarantee. Firstly, a QoE evaluation model is established using verified empirical data. Also, the mathematic relationship between the streaming bit rate and actual storage space is developed. Then, the distributed caching management for vehicular video streaming is formulated as a constrained optimization problem and solved with the generalized–reduced gradient method. Simulation results indicate that our approach can improve the users' satisfaction ratio by up to 40%. Copyright © 2015 John Wiley & Sons, Ltd.     

A novel distributed scheduling approach powered by central databases in optical burst networks

In terms of the growth rate, network capacity is slower than network traffic. To solve the mismatch between them, utilization of available capacity can be maximized to provide “best-fit” service for emerging applications (e.g., eBanking, Peer-to-Peer (P2P) file exchanges, etc.). To satisfy such a requirement, we propose a three-dimensional label scheduling algorithm (TD-LSA), which is a distributed scheduling approach powered by central databases. In the proposal, all connections are provisioned in three dimensions (i.e., space, time and wavelength) in a distributed way and uniquely identified by three-dimensional labels. And in this way a three-dimensional connection is more robust than a single-dimensional connection because the three-dimensional approach can avoid potential scheduling conflicts as much as possible by using central databases in a network. Thus, the central databases can reduce bandwidth consumption and offer “best-fit” service for three-dimensional connections. Furthermore, in order to further reduce bandwidth consumption and to match with a priority of traffic, K-least hop first path (K-LHPF) and a rescheduling mechanism are applied to our proposal. Simulation experiments demonstrate that our proposal achieves the expected performance gain against existing alternates.

A new approach for asynchronous distributed rate control of elasticsessions in integrated packet networks

We develop a new class of asynchronous distributed algorithms for the explicit rate control of elastic sessions in an integrated packet network. Sessions can request for minimum guaranteed rate allocations (e.g., minimum cell rates in the ATM context), and, under this constraint, we seek to allocate the max-min fair rates to the sessions. We capture the integrated network context by permitting the link bandwidths available to elastic sessions to be stochastically time varying. The available capacity of each link is viewed as some statistic of this stochastic process [e.g., a fraction of the mean, or a large deviations-based equivalent service capacity (ESC)]. The ESC is obtained so as to satisfy an overflow probability constraint on the buffer length. For fixed available capacity at each link, we show that the vector of max-min fair rates can be computed from the root of a certain vector equation. A distributed asynchronous stochastic approximation technique is then used to develop a provably convergent distributed algorithm for obtaining the root of the equation, even when the link flows and the available capacities are obtained from on-line measurements. The switch algorithm does not require per connection monitoring, nor does it require per connection marking of control packets. A virtual buffer based approach for on-line estimation of the ESC is utilized. We also propose techniques for handling large variations in the available capacity owing to the arrivals or departures of CBR/VBR sessions. Finally, simulation results are provided to demonstrate the performance of this class of algorithms in the local and wide area network context     

分布式信源编码的压缩感知技术在无线传感器网络中的应用

文章提出了分布式压缩感知理论,利用传感器节点的数据相关性,把单个信号的压缩采样扩展到信号群的压缩采样,可以实现无线传感器网络的数据重构,减少节点的通信开销,降低整个网络的能耗。     

Toward secure distributed spectrum sensing in cognitive radio networks [cognitive radio communications and networks]

Cognitive radio is a revolutionary technology that promises to alleviate the spectrum shortage problem and to bring about remarkable improvement in spectrum utilization. Spectrum sensing is one of the essential mechanisms of CR and is an active area of research. Although the operational aspects of spectrum sensing are being studied actively, its security aspects have attracted very little attention. In this paper, we discuss security issues that may pose a serious threat to spectrum sensing. Specifically, we focus on two security threats - incumbent emulation and spectrum sensing data falsification - that may wreak havoc in distributed spectrum sensing. We also discuss methods for countering these threats and the technical hurdles that must be overcome to implement such countermeasures.     

An approach for near-optimal distributed data fusion in wireless sensor networks

In wireless sensor networks (WSNs), a lot of sensory traffic with redundancy is produced due to massive node density and their diverse placement. This causes the decline of scarce network resources such as bandwidth and energy, thus decreasing the lifetime of sensor network. Recently, the mobile agent (MA) paradigm has been proposed as a solution to overcome these problems. The MA approach accounts for performing data processing and making data aggregation decisions at nodes rather than bring data back to a central processor (sink). Using this approach, redundant sensory data is eliminated. In this article, we consider the problem of calculating near-optimal routes for MAs that incrementally fuse the data as they visit the nodes in a WSN. The order of visited nodes (the agent’s itinerary) affects not only the quality but also the overall cost of data fusion. Our proposed heuristic algorithm adapts methods usually applied in network design problems in the specific requirements of sensor networks. It computes an approximate solution to the problem by suggesting an appropriate number of MAs that minimizes the overall data fusion cost and constructs near-optimal itineraries for each of them. The performance gain of our algorithm over alternative approaches both in terms of cost and task completion latency is demonstrated by a quantitative evaluation and also in simulated environments through a Java-based tool.     

A neural-network approach to radiometric sensing of land-surfaceparameters

A biophysically-based land-surface process/radiobrightness (LSP/R) model is integrated with a dynamic learning neural network (DLNN) to retrieve the land-surface parameters from its radiometric signatures. Predictions from the LSP/R model are used to train the DLNN and serve as the reference for evaluation of the DLNN retrievals. Both horizontally polarized and vertically polarized brightnesses at 1.4 GHz, 19 GHz, and 37 GHz for an incidence angle of 53° make up the input nodes of the DLNN. The corresponding output nodes are composed of land-surface parameters, canopy temperature and water content, and soil temperature and moisture (uppermost 5 mm). Under no-noise conditions, the maximum of the root mean-square (RMS) errors between the retrieved parameters of interest and their corresponding reference from the LSP/R model is smaller than 28 for a four-channel case with 19 GHz and 37 GHz brightnesses as the inputs of the DLNN. The maximum RMS error is reduced to within 0.5% if additional 1.4 GHz brightnesses are used (a six-channel case). This indicates that the DLNN produces negligible errors onto its retrievals. For the realization of the problem, two different levels of noises are added to the input nodes. The noises are assumed to be Gaussian distributed with standard deviations of 1 K and 2 K. The maximum RMS errors are increased to 9.3% and 10.3% for the 1 K-noise and 2 K-noise cases, respectively, for the four-channel ease. They are reduced to 6.0% and 9.1% for the 1 K-noise and 2 K-noise cases, respectively, for the six-channel case. This is an implication that 1.4 GHz is a better frequency in probing soil parameters than 19 GHz and 37 GHz     

A distributed self-healing approach to Bluetooth scatternet formation

This paper proposes a distributed self-healing technique for topology formation in dynamic Bluetooth wireless personal area networks (BT-WPANs) and analyzes three new algorithms for scatternet formation. The three algorithms employ distributed procedures for the insertion of one or more nodes in a BT-WPAN, and are able to effectively compromise between the need for system efficiency and the desire to promptly adapt to topology changes. Depending on which algorithm is employed, the proposed approach generates BT-WPANs with different connectivity properties as well as topology structures.