Ambient Backscattering-Enabled SWIPT Relaying System with a Nonlinear Energy Harvesting Model.

Since radio frequency (RF) signals can be used for both information transmission and energy harvesting, RF-based energy harvesting is capable of integrating with other existing communication techniques for providing better rate-energy tradeoff and quality-of-service. Within the context of an RF-based energy harvesting relaying network, a relay node not only acts as an intermediate node to help the delivery from source to destination, but also harvests energy from an RF dedicated source to prolong its lifetime. Thus, it brings diversity gain and coverage extension as well as provides extra energy for data transmission.

Inter-Relay Interference Mitigation for Chirp-Based Two-Path Successive Relaying Protocol.

Since the chirp spread spectrum (CSS) system is considered as a communication technology for the Internet of things (IoT), long-range communication and a high data rate are required. In wireless communications, in order to increase spectral efficiency and to extend transmission coverage, a two-path successive relaying (TPSR) protocol has been proposed. Thus, in order to improve transmission performance of the CSS system, in this paper we apply the TPSR protocol to the CSS system.

An Efficient RSS Localization for Underwater Wireless Sensor Networks.

Localization is a key-enabling technology for many applications in underwater wireless sensor networks. Traditional approaches for received signal strength (RSS)-based localization often require uniform distribution for anchor nodes and suffer from poor estimates according to unpredictable and uncontrollable noise conditions. In this paper, we establish an RSS-based localization scheme to determine the location of an unknown normal sensor from a certain measurement set of potential anchor nodes.

An Efficient Hybrid RSS-AoA Localization for 3D Wireless Sensor Networks.

Wireless sensor networks (WSNs) enable many applications such as intelligent control, prediction, tracking, and other communication network services, which are integrated into many technologies of the Internet-of-Things. The conventional localization frameworks may not function well in practical environments since they were designed either for two-dimensional space only, or have high computational costs, or are sensitive to measurement errors. In order to build an accurate and efficient localization scheme, we consider in this paper a hybrid received signal strength and angle-of-arrival localization in three-dimensional WSNs, where sensors are randomly deployed with the transmit power and the path loss exponent unknown.

Signal Detection for Ambient Backscatter Communication with OFDM Carriers.

Ambient backscatter communication (AmBC) is considered as a promising future emerging technology. Several works on AmBC have been proposed thanks to its convenience and low cost property. This paper focuses on finding the optimal energy detector at the receiver side and estimating the corresponding bit error rate for the communication system utilizing the AmBC.

A Distance Boundary with Virtual Nodes for the Weighted Centroid Localization Algorithm.

In wireless sensor networks, accurate location information is important for precise tracking of targets. In order to satisfy hardware installation cost and localization accuracy requirements, a weighted centroid localization (WCL) algorithm, which is considered a promising localization algorithm, was introduced. In our previous research, we proposed a test node-based WCL algorithm using a distance boundary to improve the localization accuracy in the corner and side areas.

Matrix Completion Optimization for Localization in Wireless Sensor Networks for Intelligent IoT.

Localization in wireless sensor networks (WSNs) is one of the primary functions of the intelligent Internet of Things (IoT) that offers automatically discoverable services, while the localization accuracy is a key issue to evaluate the quality of those services. In this paper, we develop a framework to solve the Euclidean distance matrix completion problem, which is an important technical problem for distance-based localization in WSNs. The sensor network localization problem is described as a low-rank dimensional Euclidean distance completion problem with known nodes.

Deterministic sensing matrices in compressive sensing: a survey.

Compressive sensing is a sampling method which provides a new approach to efficient signal compression and recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements. One of the most concerns in compressive sensing is the construction of the sensing matrices. While random sensing matrices have been widely studied, only a few deterministic sensing matrices have been considered.