The mutual interference among wireless nodes is a critical factor in the Internet-of-Things (IoT) era due to its dense deployment. Due to its large coverage area, wireless nodes may not be able to detect the on-going communication of other nodes in a long range wide area network (LoRaWAN), which is one of the low power wide area (LPWA) standards. This results in packet collision. The packet collision among LoRaWAN nodes significantly deteriorates network performance functions such as packet delivery rate (PDR). Furthermore, if packet collision happens, LoRaWAN nodes must retransmit packets, draining their limited battery power. Thus, mutual interference management among LoRaWAN nodes is important from the perspectives of both network performance and network lifetime. However, due to its large network size, it is difficult to explicitly comprehend the wireless channel environment around each LoRaWAN node, such as the relation among other LoRaWAN nodes. Thus, in this paper, we utilize the powerful machine learning technique. The wireless environment around LoRaWAN nodes are learned, and the knowledge is utilized for resource allocation in order to improve PDR performance. In the proposed method, Q-learning is adopted in a LoRaWAN system, and the weighted sum of the number of successfully received packets is treated as a Q-reward. The gateway (GW) allocates resources to maximize this Q-reward. The numerical results considering LoRaWAN elucidate that the proposed scheme can improve average PDR performance by about 20% compared to the random resource allocation scheme.

The long range wide area network (LoRaWAN) is one of the enabling technologies for low power wide area (LPWA) networks. In LoRaWAN, a node transmits its packet to a gateway (GW) in an autonomous and decentralized manner. The quantity of data that can be transmitted by each node is limited by the duty cycle (DC). Furthermore, it is not easy for the nodes to perform sophisticated techniques for increasing the data quantity due to their limited functionality, especially when the nodes are battery-powered. If the network size increases, packet collision may occur more frequently. Since the packet transmission drains the LoRaWAN nodes’ battery, the packet collision results in a waste of limited power. Thus, it is necessary to develop a simple but effective transmission strategy that efficiently utilizes limited battery at each LoRaWAN node. This study proposes packet-level index modulation (PLIM), which is suitable for such LPWA networks. PLIM takes advantage of the sparse data packet transmission in time and the selection of a frequency channel among multiple ones by each node. A time slot and frequency channel combination is selected, i.e., the index, to increase the data quantity. For long-range communication, it is necessary to use a higher spreading factor (SF) in LoRaWAN, which results in a lower data rate due to the DC. The proposed PLIM can compensate for such data rate loss by taking advantage of the sparse transmission in time. Numerical evaluation elucidates that the proposed PLIM can increase the data quantity of LoRaWAN system without requiring any modification in the specification. When the SF is 10, the proposed PLIM can increase the data quantity up to 32.5%, compared to the conventional LoRaWAN system.

In this paper, we discuss change of read range between a UHF passive RFID tag and reader when a tag is close to another tag. The read range is determined by the strength of forward link and reverse link, which are represented by received power at a tag and reader, respectively. Approaching of tags causes electromagnetic interference between antennas of tags and results in change in impedance matching condition between an antenna and IC of a tag. This change in impedance matching condition decreases in read range. Therefore, we measure the change in received power of a tag and reader experimentally so as to identify which link limits read range and to restrict the existence range of IC impedance. In addition, a technique for extending read range when tags are close is proposed.

Owing to the recent research and development on the Internet-of-Things (IoT) and machine-to-machine (M2M) communication, wireless sensor networks have attracted considerable attention. Among these networks, low power wide area networks (LPWANs), which realize low power, low data rate, and wide communication area, are most commonly used for long-range communication. These networks adopt asynchronous random-access protocols, such as the pure ALOHA protocol in the medium access control (MAC) layer. Thus, there is a high possibility that multiple nodes transmit packets simultaneously on the same frequency channel, resulting in packet collisions. Carrier-sense multiple access/collision avoidance (CSMA/CA) and centralized resource allocation are effective for avoiding packet collisions. However, these schemes increase the energy consumption of battery-powered LPWAN nodes. In addition, LPWAN has a large coverage area; hence, there is a high possibility that the carrier sense will not work successfully. Thus, this paper proposes a simple but effective machine-learning-based scheme that tackles the packet collision problem by offsetting the transmission timings and avoiding unnecessary packet transmission in an autonomous decentralized manner. Each LPWAN node adjusts the transmission probability and timing using the Q-learning technique. The proposed scheme provides effective packet collision avoidance for LPWAN nodes without the need for an additional control signal. The computer simulation results show that the proposed scheme can improve the average packet delivery ratio (PDR) by 60% compared to the pure ALOHA protocol.

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.

Low-power wide-area network (LPWAN), which includes the long-range wide-area network (LoRaWAN) protocol, is an enabling technology that satisfy low power consumption and long-range communication. In many applications of LPWAN, an end node (EN) transmits data at regular intervals. Based on this, we had previously proposed the concept of packet-level index modulation (PLIM) to increase the number of information bits transmitted by one data packet. In PLIM, the generation interval between two consecutive packets is split into multiple time slots. Each EN transmits its packet in a specific combination of time slot and frequency channel, which represents the index, to convey additional information bits. To retrieve additional information, the gateway (GW) detects the time slot in which the data packet is being transmitted. Therefore, accurate synchronization between EN and GW is essential. However, clock drift occurs due to the inexpensive real-time clock oscillator on each EN, which results in timing misalignment between each node and the GW. This article proposes a simple clock drift estimation and compensation method for the PLIM. An experimental measurement is performed to model the clock drift. The numerical results obtained using the clock drift model show that it can accurately detect the time slot index under the influence of clock drift. Furthermore, PLIM is implemented on a commercial LoRaWAN node and GW to demonstrate its practicability.