Takeo Fujii

Large-scale wireless sensor networks (WSNs) comprising multiple sensors accessing the network simultaneously have gained considerable attention owing to the proliferation of the Internet of Things (IoT). However, the increasing collision rates and header redundancy in packet-oriented WSNs severely degrade the transmission efficiency. In this study, we proposed a wide-area environmental recognition scheme that integrates Physical Wireless Parameter Conversion Sensor Networks (PhyC-SN) featuring headerless transmissions using a Mobile Data Gathering Station. It helps control the number of simultaneous accesses while exploiting the location information to localize the source sensors. Furthermore, we designed constraint-aware database updating algorithms: the Integrity Verification Algorithm (IVA) and enhanced IVA (EIVA) that prevent breakdowns in the sensor information correspondence to address the missing data and misestimated sensor counts arising from the upper bound of the sensor count identification of PhyC-SN. The results indicate that the proposed method achieves high-accuracy environmental recognition both in simulated and real-world settings.

To enhance capabilities of an interference avoidance at the end devices, the authors theoretically and numerically investigated the characteristics of carrier sense based on energy detection, which detects target signals using an average energy in a certain period, in low power wide area networks (LPWANs) and their performances. Conventional studies have been conducted mainly through computer simulation based analyses, and studies have never been reported using actual LPWA modules. In this study, the characteristics of the energy detection based carrier sense implemented on a commercially available LPWA modules, which consist of a long range (LoRa) chip and a micro controller unit (MCU). Two types of carrier sense, that is, the energy detection and conventional peak detection based, are implemented on the modules to experimentally evaluate the characteristics. The evaluation results show that not only the signal processing parameters such as the accuracy of the power, but also the performance of the RF front end of the LoRa chip is an important factor that determines the signal detection performance.

Carrier sense allows end devices to improve the communication quality through autonomous decentralization by consuming power. In particular, energy detection-based carrier sense can improve communication quality compared with peak detection-based carrier sense. To improve the trade-off between communication quality and energy consumption in low-power wide-area networks (LPWANs), this study proposes a self-tuning method for the signal detection level of an energy detection-based carrier sense, that is, the carrier sense level in sub-GHz band LPWANs. In the proposed method, the carrier sense level of each end device is determined based on the reception success probability of the acknowledgment packet, such that they become low carrier sense levels for an end device with low probability and high carrier sense levels for an end device with high probability. The proposed method enables autonomous decentralized derivation of the carrier sense level using only existing protocols. Numerical examples show that the proposed method can improve the performance of end devices with a high path loss to a gateway.

Low-power wide-area (LPWA) is a communication technology for the IoT that allows low power consumption and long-range communication. Additionally, packet-level index modulation (PLIM) can transmit additional information using multiple frequency channels and time slots. However, in a competitive radio access environment, where multiple sensors autonomously determine packet transmission, packet collisions occur when transmitting the same information. The packet collisions cause a reduction in the throughput. A method has been proposed to design a mapping table that shows the correspondence between indexes and information using a packet collision minimization criterion. However, the effectiveness of this method depends on how the probability of the occurrence of the information to be transmitted is modeled. We propose an environment-aware adaptive data-gathering method that identifies the location of factors affecting sensor information and constructs a model for the probability of the occurrence of sensor information. The packet collision rate of the environment-aware adaptive data-gathering method was clarified through computer simulations and actual experiments on a 429 MHz LPWA. We confirm that the proposed scheme improves the packet collision rate by 15% in the computer simulation and 30% in the experimental evaluation, respectively.

Low-power wide area networks (LPWANs), such as long-range wide area networks, are increasingly adopted as a communication standard for wireless sensor networks. LPWAN has been adopted for systems that periodically collect data from sensors, such as in environmental monitoring. However, continuous packet collisions may occur in periodic traffic systems owing to the adoption of a simple random-access scheme in LPWAN. In addition, the use of low-cost nodes results in clock drift, rendering the synchronization of nodes in the system challenging. Thus, this study proposes a wireless resource allocation scheme to avoid continuous packet collisions under the adverse effect of such clock drift. In the proposed scheme, the gateway determines the transmission offset and frequency channel for each node utilizing the periodic traffic feature and clock drift. Based on computer simulation results, the proposed scheme can improve the packet delivery rate by over 20% compared with benchmark methods.

In recent years, long-range wide-area network (LoRaWAN) has attracted considerable attention due to its ability to realize massive machine-type communication (MTC); however, its throughput is limited by the duty cycle (DC). Packet-level index modulation (PLIM) can increase throughput by utilizing a data packet’s frequency channel and transmission timing as the information-bearing index. In PLIM, a node selects a specific transmission resource (frequency channel and timing) within a frame and transmits a packet. If the node cannot transmit the packet at the specific transmission resource, it discards the packet. This packet discard results in throughput degradation of each node. Thus, this paper proposes an index mapping scheme that divides a frame into multiple subframes to provide each node with multiple transmission opportunities. A node performs channel activity detection (CAD) at the selected transmission resource within a subframe; if the node cannot transmit a packet, it moves to the next subframe. The proposed scheme adaptively maps the information bit sequence onto a transmission resource within each subframe based on the wireless environment and communication quality. Computer simulation results show that the proposed scheme improves throughput by increasing the transmission opportunities and reducing the packet discard rate under the constraint of DC.

Extensive research has been conducted on imperfect carrier sense based on energy detection; however, the effect of the finite packet length, finite carrier sense period, and arbitrary start time of the carrier sense for the arrived signal on the result of the energy detection based carrier sense has never been investigated. This paper presents a detailed discussion on the performances of an energy detection based carrier sense in low-power wide-area network (LPWAN) to detect a target signal with power below the noise power and employ it over a wide communication area. We theoretically derive the finite packet length, finite carrier sense period, and arbitrary start time of the carrier sense as the signal detection deterioration factors. The performance of LPWANs with the energy detection based carrier sense is numerically verified based on these theoretical analyses. The numerical examples demonstrate that the energy detection based carrier sense improves the performance of LPWANs, and an optimum carrier sense level is determined based on the width of the communication area and the packet length on air around the end devices.

Radio environment map (REM) provides detailed information on the coverage of the primary terminals of wireless communication systems. The spatial estimation of the channel condition is directly related to the radio environment. However, the open characteristics of a system are always accompanied by life-threatening conditions. This study addresses this by proposing the Kriging-based trust nodes aided (KTNA) REM construction method with a channel estimation (KTNA+) system. The proposed method adds a small number of trust nodes to avoid malicious impacts and precisely estimate the channel conditions. The simulation results show that the proposed method succeeded in estimating the average path-loss and shadowing impacts under threatening environmental conditions.

In recent years, there have been increased demands for aggregating sensor information from several sensors owing to the spread of the Internet of Things (IoT). However, packet communication, which is a conventional multiple-access technology, is hindered by packet collisions owing to simultaneous access by sensors and waiting time to avoid packet collisions; this increases the aggregation time. The physical wireless parameter conversion sensor network (PhyC-SN) method, which transmits sensor information corresponding to the carrier wave frequency, facilitates the bulk collection of sensor information, thereby reducing the communication time and achieving a high aggregation success rate. However, when more than one sensor transmits the same frequency simultaneously, the estimation accuracy of the number of accessed sensors deteriorates significantly because of multipath fading. Thus, this study focuses on the phase fluctuation of the received signal caused by the frequency offset inherent to the sensor terminals. Consequently, a new feature for detecting collisions is proposed, which is a case in which two or more sensors transmit simultaneously. Furthermore, a method to identify the existence of 0, 1, 2, or more sensors is established. In addition, we demonstrate the effectiveness of PhyC-SNs in estimating the location of radio transmission sources by utilizing three patterns of 0, 1, and 2 or more transmitting sensors.

Establishing a highly accurate positioning of radio sources for radio wave monitoring and frequency spectrum sharing is attracting considerable attention. Because the positioning method generally requires specific processing of the position target, it is not applicable when the position target cannot perform any processing. The location fingerprinting method uses multiple sensors to observe the received signal strength indication (RSSI) emitted by a radio source and estimates the position from the radio wave propagation characteristics. This does not require a certain process for the target position. However, it takes time to gather RSSI from many sensors by wireless communication. In this study, we propose an RSSI gathering method using physical wireless parameter conversion sensor networks (PhyC-SN) for the high-speed positioning of radio sources. In the proposed method, each sensor selects the radio carrier frequency corresponding to its measured RSSI and transmits the signal. Projecting the RSSI distribution of each sensor onto the frequency distribution of the received signal enables the center to detect the RSSI of multiple sensors simultaneously. Furthermore, to improve the gathering accuracy, we established a sensor group method by considering the regional characteristics and access timing control based on each sensor group. Computer simulations and experimental evaluations show that the proposed method significantly reduces the data-gathering time compared with conventional packet communications and achieves a high positioning accuracy.