Voting rule optimisation for double threshold energy detector-based cognitive radio networks

The method by which individual decisions are combined in cooperative cognitive radio networks is crucial to minimising the overall probabilities of false alarm and missed detection. In this paper, general expressions for these probabilities are derived for a double threshold energy detector-based network, and an analytical solution for the optimal value of voting rule is found so that the overall probability of error is minimised. Simulation results show that there are significant advantages to the use of double threshold energy detector-based networks as opposed to their single threshold-based counterparts; additional simulations verify that the analytical solution is optimal

Implementation issues for optimized hard decision energy detector-based cooperative spectrum sensing

Recent studies in cooperative energy detection have focused on the optimization of the threshold value and fusion center voting rule in an effort to minimize the sensing error probability. However, such studies operate under the assumption that the signal to noise ratio is equal at every node, which is rarely the case in practice. In this paper, generalized formulas for the optimal threshold value and optimal fusion center voting rule are derived for hard decision energy detector-based spectrum sensing networks where the signal to noise ratio is distinct at each node. It is shown that the implementation of this solution requires more data to be transmitted than the optimal soft decision scheme, which is known to have superior performance

Fast and accurate approximations for the analysis of energy detection in Nakagami-m channels

Previous research has identified several exact methods for the evaluation of the probability of detection for energy detectors operating on Nakagami-m faded channels. However, these methods rely on discrete summations of complicated functions, and so can take a prohibitively long time to evaluate. In this paper, three approximation for the probability of detection in Nakagami-m faded channels, having distinct regions of applicability, are derived. All have closed forms, and enable the fast and accurate computation of key performance metrics

On the convergence of the chi square and noncentral chi square distributions to the normal distribution

A simple and novel asymptotic bound for the maximum error resulting from the use of the central limit theorem to approximate the distribution of chi square and noncentral chi square random variables is derived. The bound enables the quick calculation of the number of degrees of freedom required to ensure a given approximation error, and is significantly tighter than bounds derived using the Berry-Esseen theorem. An application to widely-used approximations for the decision probabilities of energy detectors is also provided

Performance limits of cooperative energy detection in fading environments

In this paper, the performance of energy detector-based spectrum sensor networks is examined under the constraints of the IEEE 802.22 draft specification. Additive white Gaussian noise (AWGN) channels are first considered, and a closed form solution for sample complexity is derived for networks of any size. Rayleigh, Nakagami and Rice fading channel models are also examined, with numerical results demonstrating the effect of these models on the required sample complexity for varying numbers of cooperating nodes. Based on these results, the relationship between the sample complexity for AWGN, Rayleigh and Nakagami channels is examined. Through data fitting, an approximate model is derived, allowing the sample complexity for Rayleigh and Nakagami channels to be computed easily. The model is shown to be accurate across a range of practical values

Five year prognosis in patients with angina identified in primary care : incident cohort study

Peer reviewedPublisher PD

Spontaneous cognition in dysphoria: reduced positive bias in imagining the future.

Anomalies in future-oriented cognition are implicated in the maintenance of emotional disturbance within cognitive models of depression. Thinking about the future can involve mental imagery or verbal-linguistic mental representations. Research suggests that future thinking involving imagery representations may disproportionately impact on-going emotional experience in daily life relative to future thinking not involving imagery (verbal-linguistic representation only). However, while higher depression symptoms (dysphoria) are associated with impaired ability to deliberately generate positive relatively to negative imagery representations of the future (when instructed to do so), it is unclear whether dysphoria is associated with impairments in the tendency to do so spontaneously (when not instructed to deliberately generate task unrelated cognition of any kind). The present study investigated dysphoria-linked individual differences in the tendency to experience spontaneous future-oriented cognition as a function of emotional valence and representational format. Individuals varying in dysphoria level reported the occurrence of task unrelated thoughts (TUTs) in real time while completing a sustained attention go/no-go task, during exposure to auditory cues. Results indicate higher levels of dysphoria were associated with lower levels of positive bias in the number of imagery-based future TUTs reported, reflecting higher negative imagery-based future TUT generation (medium to large effect size), and lower positive imagery-based TUT generation (small to medium effect size). Further, this dysphoria-linked bias appeared to be specific in temporal orientation (future, not past) and representational format (imagery, not non-imagery). Reduced tendency to engage in positive relative to negative imagery-based future thinking appears to be implicated in dysphoria

Analyzing using software defined radios as wireless sensor network inspection and testing devices: An Internet of Things penetration testing perspective

Wireless sensor network (WSN) research and development is producing viable solutions for various innovative applications, including critical areas such as the Internet of Things (IoT), which is becoming a significant feature of modern technology. WSNs form an integral component of the IoT infrastructure by, frequently, implementing the communication links between sensors and the access point or central coordinator. This design and use in IoT applications intensifies the incentive to attack WSNs as sensitive data is available and transmitted in wireless links, which inherently contain security vulnerabilities, especially from external malicious interference. To ensure satisfactory performance, safety and privacy, communication links and WSN devices must be secure. Hence, penetration testing to identify security vulnerabilities and responses to external intrusions is a prerequisite to forming secure connections and an overall secure network. Derived from a prior study, this paper explores the benefits of using software-defined radios (SDRs) for WSN/IoT data analysis and penetration testing by concentrating on implementing various intrusions using signal processing block based software like Simulink or GNU Radio. A comparison with traditional WSN packet sniffing/debugging tools is provided and the main security vulnerabilities of existing WSNs are surveyed by adopting the ZigBee protocol. An extension to WSN security analysis and testing is established by utilizing low-cost SDRs and specifying the ease of implementing various analysis techniques even when certain equipment, such as anechoic chambers, are unavailable. Stemming from previous simulations, the benefits of obtaining the in-phase and quadrature-phase samples, both with and without external interference, is also discussed

Identifying distinct features based on received samples for interference detection in wireless sensor network edge devices

Wireless Sensor Network (WSN) technologies have developed considerably over the past decade or so and, now, feasible solutions exist for various applications, both critical and otherwise. Often these solutions are achieved by using commercial off the shelf components combined with standardized open-access protocols. As deployments diverge into safety-critical areas, attack incentives intensify, leading to persistent malicious intrusion challenges, which are ever-changing as interference techniques evolve and dynamic hardware becomes increasingly accessible. Unique WSN security vulnerabilities, a fluctuating radio frequency (RF) spectrum and physical environment and spectrum co-existence escalate the problem. Thus, securing WSNs is a critical and demanding requirement, heightened by the burden of protecting sensitive transmitted information. This paper, by utilizing ZigBee and Monte Carlo simulations, aims to develop an initial framework for interference detection in WSNs. Initially, bit error location analysis motivates a feature-based detection strategy, relating to both subtle and crude forms of interference. The work expands to analyze Matlab simulated error-free and erroneous transmissions to investigate whether feature useful differences exist. A feature set, including the measured probability density function of, and statistics on, the in-phase and quadrature-phase samples is demonstrated and initially validated/feasibility tested using a designed support vector machine