Quantized fusion rules for energy-based distributed detection in wireless sensor networks

We consider the problem of soft decision fusion in a bandwidth-constrained wireless sensor network (WSN). The WSN is tasked with the detection of an intruder transmitting an unknown signal over a fading channel. A binary hypothesis testing is performed using the soft decision of the sensor nodes (SNs). Using the likelihood ratio test, the optimal soft fusion rule at the fusion center (FC) has been shown to be the weighted distance from the soft decision mean under the null hypothesis. But as the optimal rule requires a-priori knowledge that is difficult to attain in practice, suboptimal fusion rules are proposed that are realizable in practice. We show how the effect of quantizing the test statistic can be mitigated by increasing the number of SN samples, i.e., bandwidth can be traded off against increased latency. The optimal power and bit allocation for the WSN is also derived. Simulation results show that SNs with good channels are allocated more bits, while SNs with poor channels are censored

Distributed Combining Techniques for Distributed Detection in Fading Wireless Sensor Networks

We investigate distributed combining techniques for distributed detection in wireless sensor networks (WSNs) over Rayleigh fading multiple access channel (MAC). The MAC also suffers from with path loss and additive noise. The WSN is modelled as a Poisson point process (PPP). Two distributed transmit combining techniques are proposed to mitigate fading; distributed equal gain transmit combining (ddEGTC) and distributed maximum ratio transmit combining (dMRTC). The performance of the previous methods is analysed using stochastic geometry tools, where the mean and variance of the detector’s test statistic are found thus enabling the fitting of the received signal distribution by a log-normal distribution. Surprisingly, simulation results show a that ddEGTC outperforms dMRTC

Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML

The channel state information (CSI) of the sub-carriers employed in orthogonal frequency division multiplexing (OFDM) systems has been employed traditionally for channel equalisation. However, the CSI intrinsically is a signature of the operational RF environment and can serve as a proxy for certain activities in the operational environment. For instance, the CSI gets influenced by scatterers and therefore can be an indicator of how many scatterers or if there are mobile scatterers etc. The mapping between the activities whose signature CSI encodes and the raw data is not deterministic. Nevertheless, machine learning (ML) based approaches can provide a reliable classification for patterns of life. Most of these approaches have only been implemented in lab environments. This is mainly because the hardware requirements for capturing CSI, processing it and performing signal-processing algorithms are too complex to be implemented in commercial devices. The increased proliferation of IoT sensors and the development of edge-based ML capabilities using the TinyML framework opens up possibilities for the implementation of these techniques at scale on commercial devices. Using RF signature instead of more invasive methods e.g. cameras or wearable devices provide ease of deployment, intrinsic privacy and better usability. The design space of device-free wireless sensing (DFWS) is complex and involves device, firmware and ML considerations. In this article, we present a comprehensive overview and key considerations for the implementation of such solutions. We also demonstrate the viability of these approaches using a simple case study

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

Distributed Detection Fusion in Clustered Sensor Networks over Multiple Access Fading Channels

In this paper, we tackle decision fusion for distributed detection in a randomly-deployed clustered Wireless Sensor Networks (WSNs) operating over a non-ideal multiple access channels (MACs), i.e. considering Rayleigh fading, path loss and additive noise. To mitigate fading, we propose the distributed equal gain transmit combining (dEGTC) and distributed maximum ratio transit combining (dMRTC). The first and second order statistics of the received signals were analytically computed via stochastic geometry tools. Then the distribution of the received signal over the MAC are approximated by Gaussian and log-normal distributions via moment matching. This enabled the derivation of moment matching optimal fusion rules (MOR) for both distributions. Moreover, suboptimal simpler fusion rules were also proposed, in which all the CHs data are equally weighed, which is termed moment matching equal gain fusion rule (MER). It is shown by simulations that increasing the number of clusters improve the performance. Moreover, MOR-Gaussian based algorithms are better under free-space propagation whereas their lognormal counterparts are more suited in the ground-reflection case. Also, the latter algorithms show better results in low SNR and SN numbers conditions. We have proved that the received power at the CH in MAC is proportional O(λ 2 R2) and to O(λ 2 ln 2 R) in the free-space propagation and the ground-reflection cases respectively, where λ is SN deployment intensity and R is the cluster radius. This implies that having more clusters decreases the required transmission power for a given SNR at the receiver

Optimal Linear Fusion Rule for Distributed Detection in Clustered Wireless Sensor Networks

In this paper we consider the distributed detection of intruders in clustered wireless sensor networks (WSNs). The WSN is modelled by a homogeneous Poisson point process (PPP). The sensor nodes (SNs) compute local decisions about the intruder's presence and send them to the cluster heads (CHs). Hence, the CHs collect the number of detecting SNs in the cluster. The fusion center (FC), on the other hand, combines the the CH's data in order to reach a global detection decision. We propose an optimal cluster-based linear fusion (OCLR), in which the CHs' data are linearly fused. Interestingly, the OCLR performance is very close to the optimal clustered fusion rule (OCR) previously proposed in literature. Furthermore, the OCLR performance approaches the optimal Chair-Varshney fusion rule as the number of SNs increases

On the leverage of superimposed training for energy-efficient spectrum sensing in cognitive radio

Abstract The efficient utilization of the radio-electric spectrum (or simply spectrum) is essential to satisfy the ever- increasing amount of bandwidth required by future wireless communications networks. Cognitive radio (CR) networks aim to improve this efficiency by dynamically exploiting the underutilized spectrum (also called spectrum opportunities). To identify these transmission opportunities, cognitive users might draw on spectrum sensing, although this task increases the energy consumption. For battery-powered terminals, this increment might represent a challenge, also considering that spectrum sensing must be recurrently performed. For a scenario in which the CR user first senses the spectrum and then, if allowed, transmit data, the average energy consumption depends on the time used for spectrum sensing and for data transmission, which also impacts the spectrum-efficiency. Thus, improving the energy-efficiency might implicate a reduction on the spectrum-efficiency. This paper analyses the energy-efficiency in the context of spectrum sensing of superimposed training-based transmissions, showing the advantages of using an enhanced spectrum sensing method in terms of the relationship between the spectrum and energy- efficiency