Abstract Two detectors making independent observations must decide which one of two hypotheses is true. The decisions are coupled through a common cost function. It is shown that the detectors' optimal decisions are characterized by thresholds which are coupled and whose computation requires the solution of two coupled sets of dynamic programming equations. An approximate computation of the thresholds is proposed and numerical results are presented.

Decentralized wald problem.

Spectrum sensing is essential to identify the presence of primary users (PUs) in cognitive radio networks. Cooperative spectrum sensing (CSS) obtained from the diversity of different sensors improves the reliability of decisions made about the presence of PU. Although CSS offers significant advantages, it may introduce some shortcomings including the energy consumption and overhead in the sensing phase. Also, due to the limited life span of batteries, the energy consumption process for performing CSS should be balanced among sensors so that some sensors do not encounter rapid battery drain. Therefore, the management of sensors for the participation in CSS is of great importance. In this paper, a new energy-based sensor selection algorithm is proposed to provide approximately the same lifetimes for sensors via the appropriate design of CSS. Furthermore, a mathematical model is proposed to analyze the process of energy consumption for sensors. The system model we consider in this paper focuses on different sensors consuming various amounts of energy for CSS. The analysis and simulation results show that the proposed algorithm achieves approximate fairness among sensors in terms of lifetime. In addition, comparison between the proposed algorithm and the existing algorithms reveals that our proposed algorithm can balance the process of energy consumption among sensors and significantly increase the average number of live sensors before the network lifetime finishes.

Analysis of a New Energy-Based Sensor Selection Method for Cooperative Spectrum Sensing in Cognitive Radio Networks

This paper proposes an energy-efficient counting rule for distributed detection by ordering sensor transmissions in wireless sensor networks. In the counting rule-based detection in an $N$ -sensor network, the local sensors transmit binary decisions to the fusion center, where the number of all $N$ local-sensor detections are counted and compared to a threshold. In the ordering scheme, sensors transmit their unquantized statistics to the fusion center in a sequential manner; highly informative sensors enjoy higher priority for transmission. When sufficient evidence is collected at the fusion center for decision making, the transmissions from the sensors are stopped. The ordering scheme achieves the same error probability as the optimum unconstrained energy approach (which requires observations from all the $N$ sensors) with far fewer sensor transmissions. The scheme proposed in this paper improves the energy efficiency of the counting rule detector by ordering the sensor transmissions: each sensor transmits at a time inversely proportional to a function of its observation. The resulting scheme combines the advantages offered by the counting rule (efficient utilization of the network's communication bandwidth, since the local decisions are transmitted in binary form to the fusion center) and ordering sensor transmissions (bandwidth efficiency, since the fusion center need not wait for all the $N$ sensors to transmit their local decisions), thereby leading to significant energy savings. As a concrete example, the problem of target detection in large-scale wireless sensor networks is considered. Under certain conditions the ordering-based counting rule scheme achieves the same detection performance as that of the original counting rule detector with fewer than $N/2$ sensor transmissions; in some cases, the savings in transmission approaches $(N-1)$ .

Energy-Efficient Decision Fusion for Distributed Detection in Wireless Sensor Networks

We consider distributed detection applications for a wireless sensor network (WSN) that has a limited time to collect and process local decisions to produce a global decision. When this time is not sufficient to collect decisions from all nodes in the network, a strategy is needed for collecting those with the highest reliability. This can be accomplished by incorporating a reliability-based splitting algorithm into the random access protocol of the WSN: the collection time is divided into frames and only nodes with a specified range of reliabilities compete for the channel using slotted ALOHA within each frame-nodes with the most reliable decisions attempt transmission in the first frame, nodes with the next most reliable set of decisions attempt in the next frame, etc. The detection error probability (DEP) of the proposed scheme is minimized, and the efficacy is maximized by determining the reliability intervals that define which nodes attempt to transmit in each frame. Intervals that maximize the channel throughput do not always minimize the DEP or maximize the efficacy. Because the scheme orders transmissions of the local decisions in approximately descending order of reliability but suffers collisions, it will offer better performance than a collision-free scheme with no reliability ordering when the time constraint prevents transmission of all local decisions. The transition point between the two schemes is found by deriving the asymptotic relative efficiency (ARE) of the proposed scheme relative to a TDMA-based scheme.

Reliability-Based Splitting Algorithms for Time-Constrained Distributed Detection in Random-Access WSNs

Cognitive radio networks being a widespread disruptive technology where idle spectrum are being analysed and made available to the unlicensed secondary users (SU) from licensed primary users (PU). During this battle of allocation of spectrum to the SU, the attackers can masquerade the network and disrupt the channels. The vulnerability attacks are more often happened based on the nature of usage of the spectrum in PU and they collect those information from secondary agent cluster, one among them is the Spectrum Sensing data falsification attack. In heterogeneous network, the vulnerabilities affect the utility of the channel and changes the information in fusion centre, where the sensing reports are might be corrupted during transmission. The paper focus on assigning the idle spectrum to be allocated based on binary stage honest based winner determination using greedy approach and the broadcasted information is secured using two phase cryptography. Besides, a rational player approach has been proposed to make the network to be susceptible to the idle environment using stochastic game modeling to prevent the attack, where a optimal solution is being estimated with nash equilibrium to identify the best response for the user to exploit the attack. The proposed mechanism is much more significantly improved duly compared with traditional mechanism and it highly improves the network performance in monitoring the induced attack and allocating the spectrum with error free transmission.

Honest Auction Based Spectrum Assignment and Exploiting Spectrum Sensing Data Falsification Attack Using Stochastic Game Theory in Wireless Cognitive Radio Network

Cooperative spectrum sensing is a robust strategy that enhances the detection probability of primary licensed users. However, a large number of detectors reporting to a fusion center for a final decision causes significant delay and also presumes the availability of unreasonable communication resources at the disposal of a network searching for spectral opportunities. In this paper, we employ the idea of sequential detection to obtain a quick, yet reliable, decision regarding primary activity. Local detectors take measurements, and only a few of them transmit the log likelihood ratios (LLR) to a fusion center in descending order of LLR magnitude. The fusion center runs a sequential test with a maximum imposed on the number of sensors that can report their LLR measurements. We calculate the detection thresholds using two methods. The first achieves the same probability of error as the optimal block detector. In the second, an objective function is constructed and decision thresholds are obtained via backward induction to optimize this function. The objective function is related directly to the primary and secondary throughputs with possible privilege for primary operation. Simulation results demonstrate the enhanced performance of the approaches proposed in this paper. We also investigate the case of fading/shadowing channels between the local sensors and the fusion center, and the situation in which the sensing cost is negligible.

Distributed Spectrum Sensing With Sequential Ordered Transmissions to a Cognitive Fusion Center

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Distributed Spectrum Sensing With Sequential Ordered Transmissions to a Cognitive Fusion Center