Distributed Detection in Wireless Sensor Networks under Multiplicative Fading via Generalized Score-tests

In this paper, we address the problem of distributed detection of a non-cooperative (unknown emitted signal) target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an (unknown) deterministic signal with attenuation depending on the unknown distance between the sensor and the target, multiplicative fading, and additive Gaussian noise. To model energy-constrained operations within Internet of Things (IoT), one-bit sensor measurement quantization is employed and two strategies for quantization are investigated. The Fusion Center (FC) receives sensor bits via noisy Binary Symmetric Channels (BSCs) and provides a more accurate global inference. Such a model leads to a test with nuisances (i.e. the target position xT) observable only under H1 hypothesis. Davies framework is exploited herein to design the generalized forms of Rao and Locally-Optimum Detection (LOD) tests. For our generalized Rao and LOD approaches, a heuristic approach for threshold-optimization is also proposed. Simulation results confirm the promising performance of our proposed approaches.acceptedVersio

Fusion Rules for Distributed Detection in Clustered Wireless Sensor Networks with Imperfect Channels

In this paper we investigate fusion rules for distributed detection in large random clustered-wireless sensor networks (WSNs) with a three-tier hierarchy; the sensor nodes (SNs), the cluster heads (CHs) and the fusion center (FC). The CHs collect the SNs' local decisions and relay them to the FC that then fuses them to reach the ultimate decision. The SN-CH and the CH-FC channels suffer from additive white Gaussian noise (AWGN). In this context, we derive the optimal log-likelihood ratio (LLR) fusion rule, which turns out to be intractable. So, we develop a sub-optimal linear fusion rule (LFR) that weighs the cluster's data according to both its local detection performance and the quality of the communication channels. In order to implement it, we propose an approximate maximum likelihood based LFR (LFR-aML), which estimates the required parameters for the LFR. We also derive Gaussian-tail upper bounds for the detection and false alarms probabilities for the LFR. Furthermore, an optimal CH transmission power allocation strategy is developed by solving the Karush-Kuhn-Tucker (KKT) conditions for the related optimization problem. Extensive simulations show that the LFR attains a detection performance near to that of the optimal LLR and confirms the validity of the proposed upper bounds. Moreover, when compared to equal power allocation, simulations show that our proposed power allocation strategy achieves a significant power saving at the expense of a small reduction in the detection performance

Exploiting the Structure in DHT Overlays for DoS Protection

Peer to Peer (P2P) systems that utilize Distributed Hash Tables (DHTs) provide a scalable means to distribute the handling of lookups. However, this scalability comes at the expense of increased vulnerability to specific types of attacks. In this paper, we focus on insider denial of service (DoS) attacks on such systems. In these attacks, nodes that are part of the DHT system are compromised and used to flood other nodes in the DHT with excessive request traffic. We devise a distributed lightweight protocol that detects such attacks, implemented solely within nodes that participate in the DHT. Our approach exploits inherent structural invariants of DHTs to ferret out attacking nodes whose request patterns deviate from "normal" behavior. We evaluate our protocol's ability to detect attackers via simulation within a Chord network. The results show that our system can detect a simple attacker whose attack traffic deviates by as little as 5\% from a normal request traffic. We also demonstrate the resiliency of our protocol to coordinated attacks by up to as many as 25\% of nodes. Our work shows that DHTs can protect themselves from insider flooding attacks, eliminating an important roadblock to their deployment and use in untrusted environments

MAC-PHY Frameworks For LTE And WiFi Networks\u27 Coexistence Over The Unlicensed Band

The main focus of this dissertation is to address these issues and to analyze the interactions between LTE and WiFi coexisting on the unlicensed spectrum. This can be done by providing some improvements in the first two communication layers in both technologies. Regarding the physical (PHY) layer, efficient spectrum sensing and data fusion techniques that consider correlated spectrum sensing readings at the LTE/WiFi users (sensors) are needed. Failure to consider such correlation has been a major shortcoming of the literature. This resulted in poorly performing spectrum sensing systems if such correlation is not considered in correlated-measurements environments

When Decision Meets Estimation: Theory and Applications

In many practical problems, both decision and estimation are involved. This dissertation intends to study the relationship between decision and estimation in these problems, so that more accurate inference methods can be developed. Hybrid estimation is an important formulation that deals with state estimation and model structure identification simultaneously. Multiple-model (MM) methods are the most widelyused tool for hybrid estimation. A novel approach to predict the Internet end-to-end delay using MM methods is proposed. Based on preliminary analysis of the collected end-to-end delay data, we propose an off-line model set design procedure using vector quantization (VQ) and short-term time series analysis so that MM methods can be applied to predict on-line measurement data. Experimental results show that the proposed MM predictor outperforms two widely used adaptive filters in terms of prediction accuracy and robustness. Although hybrid estimation can identify model structure, it mainly focuses on the estimation part. When decision and estimation are of (nearly) equal importance, a joint solution is preferred. By noticing the resemblance, a new Bayes risk is generalized from those of decision and estimation, respectively. Based on this generalized Bayes risk, a novel, integrated solution to decision and estimation is introduced. Our study tries to give a more systematic view on the joint decision and estimation (JDE) problem, which we believe the work in various fields, such as target tracking, communications, time series modeling, will benefit greatly from. We apply this integrated Bayes solution to joint target tracking and classification, a very important topic in target inference, with simplified measurement models. The results of this new approach are compared with two conventional strategies. At last, a surveillance testbed is being built for such purposes as algorithm development and performance evaluation. We try to use the testbed to bridge the gap between theory and practice. In the dissertation, an overview as well as the architecture of the testbed is given and one case study is presented. The testbed is capable to serve the tasks with decision and/or estimation aspects, and is helpful for the development of the JDE algorithms