## 2 Related Works  
Some strategies to address the reported challenges associated with the densification of heterogeneity for the connections of LPWAN technologies with billions of end users in smart cities have been advocated in the literature. These challenges include interference, spectrum insufficiency, unmanaged coexistence, quality of service (QoS), and scalability issues. For instance, the need for spectrum optimization for improved spectrum efficiency, QoS, low latency, and appreciable throughput has been opined by the authors in [10, 11, 12]. The optimized spectrum enables unlicensed and licensed users to coexist. Works in this area of spectrum optimization are found in [13, 14, 15, 16]. Further, the authors of [17] suggested the use of machine learning to accurately identify the available spectrum channels of the LPWAN technologies that are present within an area based on the channel state and network information (CSNI), such as channel frequency, packet loss rates, typical sizes of error burst, and so on. In addition, dynamic frequency allocations, including the use of frequency hopping to mitigate interference problems, are presented in [18, 19]. Going forward, the inter-symbol, co-channel, and cross-layer interference in LPWAN can be mitigated by multiple input-multiple outputs (MIMO) and a smart antenna [20]. This method is unsuitable for LPWAN due to its low complexity and low energy capability. The issue of co-channel interference has been addressed in [21], in which the authors used maximum rational combiner in mitigating co-channel interference of sensor nodes in a smart grid environment. The work in [22] proposed a joint reception mechanism as a solution to the degradation encountered due to interference from other overlapping technologies. Furthermore, another method of overcoming the challenges is the coexistence of technologies with multiple IoT radio access technologies (multi-RAT) on a single device. This will offload some of the connectivity to other LPWAN technology devices, thereby reducing the interference and congestion that would have been encountered by a single LPWAN device. However, the coexistence should be managed using a coordinated coexistence mechanism [8]. The authors in [23] proposed the support of multi-RAT on a single LPWAN device. They considered two RATs, namely, LoRaWAN and NB-IoT, in a smart city environment. Other works that considered multi-RAT on a single device are found in [24, 25, 26, 27, 28]. Further, enabling different IoT application services via a single infrastructure using a network slicing mechanism has been considered in [29, 30, 31]. Optimization of the LPWAN device physical (PHY) and media access (MAC) layer protocol settings, including the radio resource allocation management and deployment model, have been considered in [32, 33, 34, 35, 36, 37, 38] to improve the QoS in the IoT LPWAN-IoT network. In addressing the issue of scalability, the authors of [39] suggested the optimization of LPWAN technologies to support the Internet Protocol version 6 (IPv6) addressing scheme since IPv6 is not yet fully supported in LPWAN. This will enable multiple trillion LPWAN devices in a heterogenous dense environment. This is because the existing IPv4 addressing scheme does not support such an enormous capacity of devices to be connected. Regarding the various mechanisms used to address the problems of dense smart cities networks in LPWAN coexistence scenarios, an end-to-end solution model is rarely investigated, i.e., optimization at the base station/gateway side and the end user device side. Most of the authors mainly focused on modifications at the base station or the end-user device. However, this paper considers a model that consists of optimization at the base station/gateway side as well as protocol optimization of the LPWAN end-user device. Further, the existing literature does not consider the use of LPWAN in the unlicensed spectrum jointly with 5G private and public cellular network coexistence.