# Beacon Success Rate versus Gateway Density in Sub-GHz Sensor Networks  
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Quality of Service (QoS). These devices are installed by the general public in locations in and around houses or neighborhoods. Given the use of sub-GHz carrier frequencies within the Industrial, Scientific, and Medical (ISM) bands, these WAN networks will experience interference from other networks, such as Sigfox, LoRa-WAN, and Zigbee, etc. This study presents an in-depth model for evaluating the performance of such networks in relation to the size of coexisting devices. The performance evaluation is provided for the coverage area, GW density, latency, and Packet Success Rates (PSR). GWs have access to a power outlet and the Internet; hence, they can be used as central devices with more sophisticated system operations to serve and simplify the WAN sensors. In this paper, a star network topology is assumed, as depicted in Figure 1, where IoT sensors are represented by different colored circles connecting to the GW [1]. Having a multi-hop mesh topology is not recommended due to the already low bit-rates available in each link. Coverage issues are overcome by GW diversity instead [2]. With GW diversity provided by the network operator, sensors can access cloud services via the closest neighbor's GW or via the user's own GW. ENs serve as sensors for various IoT applications and are typically battery operated. These applications can include motion sensing, smart lock and contact sensing, geo-fencing, smart metering, temperature sensing, asset/pet tracking, and any other IoT applications, as illustrated in Figure 2. All GWs can connect to the same cloud server, where the reach of each GW can be extended to the scale of a WAN. Since the WAN operates in an unlicensed frequency spectrum, it needs to coexist among other WANs, as illustrated in Figure 2. Each of the star networks, depicted by an orange circle centered on its GW, functions autonomously without any dependence on other star networks. Each GW can access to the same cloud server. For such a network, further system optimizations among the GWs can be performed centrally via the cloud server. With narrow-band connectivity hardware suitable for low-bitrate IoT applications, the coverage area of each GW can span to few hundred meters, causing interference to the neighboring GWs. Hence, it is important to understand the system performance within such an interference prone environment. Figure 1: Star network topology for each Gateway, facilitating the discovery and connection of multiple sensors. In this study, system capacity is defined in terms of the number of Gateways (GWs) needed to serve a given geographical area. The analysis is provided for PSR and latency versus the number of ENs as they join the network. The analysis is extended to include the neighboring GWs that interfere with the primary GW. The paper presents how the distance to a GW is mapped to a PSR and Co-Channel Carrier-to-Interference (C/I) ratio. Channel access is assumed to be over the sub-GHz ISM band, also known as the 915 MHz band in the U.S. and 868 MHz band in Europe. Hence, the system is impacted not only by intra-network interference from neighboring GWs, but also by inter-network interference from other networks. While interference in the environment cannot be controlled, intra-network interference can be reduced by using efficient channel access algorithms such as Listen Before Talk (LBT) and frequency hopping, or Time Slotted Channel Hopping (TSCH) [1; 3]. To control the collisions in random medium access, various techniques such as carrier sense and random backoff mechanisms can be used [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. Collisions refer to the simultaneous use of the same radio channel by two or more devices transmitting at the same time.