Related papers: Expectation Maximization Aided Modified Weighted S…
Estimation problems in wireless sensor networks typically involve gathering and processing data from distributed sensors to infer the state of an environment at the fusion center. However, not all measurements contribute significantly to…
The performance of object detection, to a great extent, depends on the availability of large annotated datasets. To alleviate the annotation cost, the research community has explored a number of ways to exploit unlabeled or weakly labeled…
Reliable spectrum sensing is a key functionality of a cognitive radio network. Cooperative spectrum sensing improves the detection reliability of a cognitive radio system but also increases the system energy consumption which is a critical…
Due to non-homogeneous spread of sunlight, sensing nodes possess non-uniform energy budget in recharge- able Wireless Sensor Networks (WSNs). An energy-aware workload distribution strategy is therefore nec- essary to achieve good data…
Energy preservation is one of the most important challenges in wireless sensor networks. In most applications, sensor networks consist of hundreds or thousands nodes that are dispersed in a wide field. Hierarchical architectures and data…
We develop an energy-efficient routing protocol in order to enhance the stability period of wireless sensor networks. This protocol is called weighted election protocol (WEP). It introduces a scheme to combine clustering strategy with chain…
Transition-edge sensors (TESs) are capable of highly accurate single particle energy measurement. TESs have been used for a wide range of photon detection applications, particularly in astronomy, but very little consideration has been given…
This paper presents EnergyAnalyzer, a code-level static analysis tool for estimating the energy consumption of embedded software based on statically predictable hardware events. The tool utilises techniques usually used for worst-case…
Compressive sampling has great potential for making wideband spectrum sensing possible at sub-Nyquist sampling rates. As a result, there have recently been research efforts that leverage compressive sampling to enable efficient wideband…
In this paper, we use statistical tools to analysis dependency between Wireless Sensor Network (WSN) parameters and overall Energy Consumption (EC). Our approach has two main phases: profiling, and effective parameter extraction. In former,…
The integration of Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT), which is usually referred to as Wireless Powered Mobile Edge Computing (WP-MEC), has been recognized as a promising technique to enhance the lifetime and…
Cognitive radio (CR) is a promising scheme to improve the spectrum utilization. Spectrum sensing (SS) is one of the main tasks of CR. Cooperative spectrum sensing (CSS) is used in CR to improve detection capability. Due to its simplicity…
We consider distributed estimation of a random source in a hierarchical power constrained wireless sensor network. Sensors within each cluster send their measurements to a cluster head (CH). CHs optimally fuse the received signals and…
Energy beamforming (EB) is a key technique for achieving efficient radio-frequency (RF) transmission enabled wireless energy transfer (WET). By optimally designing the waveforms from multiple energy transmitters (ETs) over the wireless…
We consider the non-coherent single-input multiple-output (SIMO) multiple access channel with general signaling under spatially correlated Rayleigh block fading. We propose a novel soft-output multi-user detector that computes an…
In this paper, we consider a non-Bayesian sequential change detection based on the Cumulative Sum (CUSUM) algorithm employed by an energy harvesting sensor where the distributions before and after the change are assumed to be known. In a…
This paper considers the distributed sparse identification problem over wireless sensor networks such that all sensors cooperatively estimate the unknown sparse parameter vector of stochastic dynamic systems by using the local information…
The robust application of wireless sensor networks has increased during the past decade due to the potential use of wireless nodes in transmission of information by decreasing latency for surveillance and monitoring. The study proposes an…
A model-based windowed chi-squared procedure is proposed for identifying falsified sensor measurements. We employ the widely-used static chi-squared and the dynamic cumulative sum (CUSUM) fault/attack detection procedures as benchmarks to…
Expectation maximisation (EM) is an unsupervised learning method for estimating the parameters of a finite mixture distribution. It works by introducing "hidden" or "latent" variables via Baum's auxiliary function $Q$ that allow the joint…