Sensor Selection for Cooperative Spectrum Sensing

2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN)

2010

Abstract-In cognitive radio networks (CRNs), the design of an optimal spectrum sensing scheme is an important problem that has recently been drawing consideration attention. Various sensing-related performance tradeoffs have been studied as an efficient means to maximize the secondary network performance. Despite its importance, however, the sensing-access tradeoffbetween sensing overhead and the MAC-layer contention among secondary users in accessing the thus-discovered spectrum opportunities-has not yet been accounted for. In this paper, we show that the secondary network throughput can be improved significantly by incorporating the sensing-access tradeoff in the design of spectrum sensing. We first introduce a new concept of (α, β)-contention spectrum sharing and analyze the sensing requirement to meet a certain channel contention constraint by using the improper list-coloring in graph theory. Specifically, we derive the relationship among the sensing requirements, the secondary network density, and the transmission power of secondary users. To maximize the network throughput, we propose a distributed spectrum-sharing algorithm, called SmartShare, which exploits channel contention and heterogeneous channel conditions to maximize the secondary network throughput. We also describe how to realize SmartShare in an 802.11 MAC protocol for its practical use and evaluation. Our simulationbased evaluation shows that, sensing an optimal number of channels for given network density can improve the achievable throughput of SmartShare by up to 60 % over a single-channel sensing strategy.

Section: Introductionmentioning

confidence: 99%

On Sensing-Access Tradeoff in Cognitive Radio Networks

2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN)

2010

“…Various aspects of cooperative sensing have been studied, such as cooperation gain [3], [19], sensor selection [27], security [28], [29], [30], and performance-overhead tradeoffs [31], [32], [33]. The benefits of sensor collaboration have been reported to diminish as the degree of shadowing correlation among sensors increases [3], [19], [21], [34].…”

Section: Related Workmentioning

confidence: 99%

“…To minimize this detrimental effect of shadowing correlation on cooperative sensing, several sensor-selection algorithms have been introduced. For example, Selén et al [27] proposed heuristic algorithms for selecting an uncorrelated set of sensors, given different levels of information about sensor locations. In a similar vein, Kim and Shin [4] suggested selecting sensors based on their geographical separation, so as to make the sensors uncorrelated with each other.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Joint Optimal Sensor Selection and Scheduling in Dynamic Spectrum Access Networks

IEEE Trans. on Mobile Comput.

2013

Abstract-Spectrum sensing is key to the realization of dynamic spectrum access. To protect primary users' communications from the interference caused by secondary users, spectrum sensing must meet the strict detectability requirements set by regulatory bodies, such as the FCC. Such strict detection requirements, however, can hardly be achieved using PHY-layer sensing techniques alone with one-time sensing by only a single sensor. In this paper, we jointly exploit two MAC-layer sensing methods-cooperative sensing and sensing scheduling-to improve spectrum sensing performance, while incurring minimum sensing overhead. While these sensing methods have been studied individually, little has been done on their combinations and the resulting benefits. Specifically, we propose to construct a profile of the primary signal's RSSs and design a simple, yet near-optimal, incumbent detection rule. Based on this constructed RSS profile, we develop an algorithm to find 1) an optimal set of sensors; 2) an optimal point at which to stop scheduling additional sensing; and 3) an optimal sensing duration for one-time sensing, so as to make a tradeoff between detection performance and sensing overhead. Our evaluation results show that the proposed sensing algorithms reduce the sensing overhead by up to 65 percent, while meeting the requirements of both false-alarm and misdetection probabilities of less than 0.01.

“…In the absence of attack, cooperative sensing can significantly improve the incumbent detection performance by carefully choosing a suitable set of cooperative sensors [5], [8], [9]. However, sensors are often deployed in open hostile environments, and can be compromised by an attacker or exposed to external interferences that can distort the measurement results.…”

Section: Introductionmentioning

confidence: 99%

Robust cooperative sensing via state estimation in cognitive radio networks

Kim²,

2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

2011

Abstract-Cooperative sensing, a key enabling technology for dynamic spectrum access, is vulnerable to various sensingtargeted attacks, such as the primary user emulation or spectrum sensing data falsification. These attacks can easily disrupt the primary signal detection process, thus crippling the operation of dynamic spectrum access. While such sensing-targeted attacks can be easily launched by an attacker, it is very challenging to design a robust cooperative spectrum sensing scheme due mainly to the practical constraints inherent in spectrum sensing, particularly the shared/open nature of the wireless medium and the unpredictability of signal propagation. In this paper, we develop an efficient, yet simple attack detection framework, called IRIS (robust cooperatIve sensing via iteRatIve State estimation), that safeguards the incumbent detection process by checking the consistency among sensing reports via the estimation of system states, namely, the primary user's transmit-power and pathloss exponent. The key insight behind the design of IRIS is that the sensing results are governed by the network topology and the law of signal propagation, which cannot be easily compromised by an attacker. Consequently, the sensing reports must demonstrate consistency among themselves in estimating system states. Our analytical and simulation results show that, by performing consistency-checks, IRIS provides high attackdetection capability, and preserves satisfactory performance in estimating the system states even under very challenging attack scenarios. Based on these observations, we propose a new incumbent detection rule that can further improve the spectrum efficiency. IRIS can be readily deployed in infrastructure-based cognitive radio networks, such as IEEE 802.22 WRANs, with manageable processing and communication overheads.