Cooperative Sequential Compressed Spectrum Sensing over Wide Spectrum Band

Abstract-Cognitive radio (CR) techniques promise to significantly increase the available spectrum thus wireless bandwidth. With the increase of spectrum allowed for CR, it is critical and challenging to perform efficient wideband sensing. We propose an integrated sequential wideband sensing framework which concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and lower cost spectrum sensing. First, to ensure more timely spectrum detection while avoiding the high overhead involved in periodic recovery of CS signals, we design a CS-based sequential wideband detection scheme to effectively detect the PU activities in the wideband of interest. Second, to further identify the sub-channels occupied, we exploit joint sparsity of the signals among neighboring users to achieve efficient cooperative wideband sensing. Our performance evaluations demonstrate that our proposed scheme can outperform other peer schemes significantly in terms of the detection delay, detection accuracy, sensing overhead and sensing accuracy

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

1994 Science Information Management and Data Compression Workshop

This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on September 26-27, 1994, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival and retrieval of large quantities of data in future Earth and space science missions. It consisted of eleven presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center

Comprehensive survey on quality of service provisioning approaches in cognitive radio networks : part one

Much interest in Cognitive Radio Networks (CRNs) has been raised recently by enabling unlicensed (secondary) users to utilize the unused portions of the licensed spectrum. CRN utilization of residual spectrum bands of Primary (licensed) Networks (PNs) must avoid harmful interference to the users of PNs and other overlapping CRNs. The coexisting of CRNs depends on four components: Spectrum Sensing, Spectrum Decision, Spectrum Sharing, and Spectrum Mobility. Various approaches have been proposed to improve Quality of Service (QoS) provisioning in CRNs within fluctuating spectrum availability. However, CRN implementation poses many technical challenges due to a sporadic usage of licensed spectrum bands, which will be increased after deploying CRNs. Unlike traditional surveys of CRNs, this paper addresses QoS provisioning approaches of CRN components and provides an up-to-date comprehensive survey of the recent improvement in these approaches. Major features of the open research challenges of each approach are investigated. Due to the extensive nature of the topic, this paper is the first part of the survey which investigates QoS approaches on spectrum sensing and decision components respectively. The remaining approaches of spectrum sharing and mobility components will be investigated in the next part

Spectrum Sensing Scheduling in Cognitive Radio Networks

In cognitive radio (CR) networks, spectrum sensing has gained great importance for opportunistic spectrum access. There are many factors that affect the efficiency of spectrum sensing. High sensing accuracy can help reduce the chance of interference to primary user and improve the spectrum utility. However, high sensing accuracy requires a large amount of sensing resources including multiple collaborative CRs and the sensing duration. We propose a cost based framework for spectrum sensing scheduling, in which all these factors are modeled by certain cost or gain of the system. A sequential energy detector is used for accumulating all energy measurements for sensing. Depending on the decision made, the CRs decide whether to wait as the channel is occupied or to start data transmission as there is a spectral hole. The optimal number of CRs, the sensing accuracy levels and the waiting/transmission time are obtained such that the average gain per unit time including both sensing and wait/data transmission stages are maximized. We provide various experimental results to show the effectiveness of the proposed design and the effects of various parameters on the performance are analyzed. The idea is then extended to a multiple channel CR network. The channel profile generated from a single channel design is utilized for CR assignment to channels that request for sensing. Two approaches, viz., greedy approach and non-greedy approach are designed for scheduling. Then the two approaches are compared on the basis of total average gain obtained from each approaches. The non-greedy approach outperforms the greedy approach with respect to the total average gain.School of Electrical & Computer Engineerin

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs