Adaptive distributed detection with applications to cellular CDMA

Chair and Varshney have derived an optimal rule for fusing decisions based on the Bayesian criterion. To implement the rule, probabilities of detection PD and false alarm PF for each detector must be known, which is not readily available in practice. This dissertation presents an adaptive fusion model which estimates the PD and PF adaptively by a simple counting process. Since reference signals are not given, the decision of a local detector is arbitrated by the fused decision of all the other local detectors. Adaptive algorithms for both equal probable and unequal probable sources, for independent and correlated observations are developed and analyzed, respectively. The convergence and error analysis of the system are analytically proven and demonstrated by simulations. In addition, in this dissertation, the performance of four practical fusion rules in both independent and correlated Gaussian noise is analyzed, and compared in terms of their Receiver Operating Characteristics (ROCs). Various factors that affect the fusion performance are considered in the analysis. By varying the local decision thresholds, the Rocs under the influence of the number of sensors, signal-to-noise ratio (SNR), the deviation of local decision probabilities, and correlation coefficient, are computed and plotted, respectively. Several interesting and key observations on the performance of fusion rules are drawn from the analysis. As an application of the above theory, a decentralized or distributed scheme in which each fusion center is connected with three widely spaced base stations is proposed for digital cellular code-division multi-access communications. Detected results at each base station are transmitted to the fusion center where the final decision is made by optimal fusion. The theoretical analysis shows that this novel structure can achieve an error probability at the fusion center which is always less than or equal to the minimum of the three respective base station. The performance comparison for binary coherent signaling in Rayleigh fading and log-normal shadowing demonstrates that the decentralized detection has a significant increased system capacity over conventional macro selection diversity. This dissertation analyzes the performance of the adaptive fusion method for macroscopic diversity combination in the wireless cellular environment when the error probability information from each base station detection is not available. The performance analysis includes the derivation of the minimum achievable error probability. An alternative realization with lower complexity of the optimal fusion scheme by using selection diversity is also proposed. The selection of the information bit in this realization is obtained either from the most reliable base station or through the majority rule from the participating base stations

Decentralised Control of Adaptive Sampling in Wireless Sensor Networks

The efficient allocation of the limited energy resources of a wireless sensor network in a way that maximises the information value of the data collected is a significant research challenge. Within this context, this paper concentrates on adaptive sampling as a means of focusing a sensor’s energy consumption on obtaining the most important data. Specifically, we develop a principled information metric based upon Fisher information and Gaussian process regression that allows the information content of a sensor’s observations to be expressed. We then use this metric to derive three novel decentralised control algorithms for information-based adaptive sampling which represent a trade-off in computational cost and optimality. These algorithms are evaluated in the context of a deployed sensor network in the domain of flood monitoring. The most computationally efficient of the three is shown to increase the value of information gathered by approximately 83%, 27%, and 8% per day compared to benchmarks that sample in a naive non-adaptive manner, in a uniform non-adaptive manner, and using a state-of-the-art adaptive sampling heuristic (USAC) correspondingly. Moreover, our algorithm collects information whose total value is approximately 75% of the optimal solution (which requires an exponential, and thus impractical, amount of time to compute)

On Power Allocation for Distributed Detection with Correlated Observations and Linear Fusion

We consider a binary hypothesis testing problem in an inhomogeneous wireless sensor network, where a fusion center (FC) makes a global decision on the underlying hypothesis. We assume sensors observations are correlated Gaussian and sensors are unaware of this correlation when making decisions. Sensors send their modulated decisions over fading channels, subject to individual and/or total transmit power constraints. For parallel-access channel (PAC) and multiple-access channel (MAC) models, we derive modified deflection coefficient (MDC) of the test statistic at the FC with coherent reception.We propose a transmit power allocation scheme, which maximizes MDC of the test statistic, under three different sets of transmit power constraints: total power constraint, individual and total power constraints, individual power constraints only. When analytical solutions to our constrained optimization problems are elusive, we discuss how these problems can be converted to convex ones. We study how correlation among sensors observations, reliability of local decisions, communication channel model and channel qualities and transmit power constraints affect the reliability of the global decision and power allocation of inhomogeneous sensors

Collaborative spectrum sensing optimisation algorithms for cognitive radio networks

The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance

Distributed Optimization in Energy Harvesting Sensor Networks with Dynamic In-network Data Processing

Energy Harvesting Wireless Sensor Networks (EH- WSNs) have been attracting increasing interest in recent years. Most current EH-WSN approaches focus on sensing and net- working algorithm design, and therefore only consider the energy consumed by sensors and wireless transceivers for sensing and data transmissions respectively. In this paper, we incorporate CPU-intensive edge operations that constitute in-network data processing (e.g. data aggregation/fusion/compression) with sens- ing and networking; to jointly optimize their performance, while ensuring sustainable network operation (i.e. no sensor node runs out of energy). Based on realistic energy and network models, we formulate a stochastic optimization problem, and propose a lightweight on-line algorithm, namely Recycling Wasted Energy (RWE), to solve it. Through rigorous theoretical analysis, we prove that RWE achieves asymptotical optimality, bounded data queue size, and sustainable network operation. We implement RWE on a popular IoT operating system, Contiki OS, and eval- uate its performance using both real-world experiments based on the FIT IoT-LAB testbed, and extensive trace-driven simulations using Cooja. The evaluation results verify our theoretical analysis, and demonstrate that RWE can recycle more than 90% wasted energy caused by battery overflow, and achieve around 300% network utility gain in practical EH-WSNs