In recent years, owing to research on, and development of, 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 (additive links on-line Hawaii area) protocol in the medium access control (MAC) sublayer. Thus, there is a high possibility that multiple nodes transmit packets simultaneously on the common frequency channel, resulting in packet collisions. Carriersense 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 may not work successfully. Thus, this paper proposes a simple but effective machine-learningbased 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.INDEX TERMS Internet of Things (IoT), low power wide area networks (LPWAN), LoRaWAN , machine learning, resource allocation.
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. INDEX TERMS LPWA, LoRaWAN, index modulation.
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