Enabling LPWANs for Coexistence and Diverse IoT Applications in Smart Cities Using Lightweight Heterogenous Multihomed Network Model

Abstract

Smart cities have been envisioned to provide smartness in managing internet of things (IoT) application domains, such as transport and mobility, health care, natural resources, electricity and energy, homes and buildings, commerce and retail, society and workplace, industry, agriculture, and the environment. The growth trajectory in usage of these IoT domains has led to a heterogeneous dense network in a smart city environment. The heterogeneous dense network in smart cities has led to challenges, such as difficulties in the management of LPWAN coexistence, interference, spectrum insufficiency, QoS, and scalability issues. The existing LPWAN technologies cannot support the heterogeneous dense network challenges in smart cities. Further, it cannot support diverse IoT, including medium- to high-bandwidth applications, due to the power, complexity, and resource constraints of the LPWAN devices. Hence, this paper addresses high data rate IoT applications and heterogeneous dense networks. This paper proposes a lightweight heterogenous multihomed network (LHM-N) model for diverse smart city applications that will address dense heterogeneity network challenges in a smart city. The work aims to advocate and integrate a manageable license-free LPWAN that will coexist with 5G private and public cellular networks in the LHM-N model. This will help to provide a cost-effective solution model in a heterogeneous dense smart city environment. Further, a secured lightweight energy-efficient packet-size forwarding engine (PSFE) algorithm is presented using the discrete event simulation (DES) methodological approach in MATLAB for complexity evaluation. In addition, a 5G reduced capability (RedCap) IoT device is integrated into the (LHM-N) model to support smart city. Finally, the results show that the LHM-N model outperforms the conventional quadrature amplitude modulation (QAM) protocol scheme in terms of error rate, latency, and data throughput with reduced energy costs for medium- to high-bandwidth industrial IoT applications. This validates the suitability of the LHM-N model for high data rate IoT applications.

Global Models of Smart Cities and Potential IoT Applications: A Review

“…These simpler and lower cost cellular LPWA technologies support longer battery life (up to 10 years) and better coverage (for IoT devices underground and deep inside buildings) compared to traditional M2M-IoT cellular connectivity options. Though LP-WAN technologies have clear advantages over traditional IoT cellular connectivity options, they are only suited to meet very basic data requirements for IoT applications with limited/modest data needs and relaxed latency (in the order of few seconds) [3,181]. They are ideally suited for IoT applications, which require just extended coverage, but reliability, latency, and availability might be more or less important [181] as shown in Table 1.…”

“…Though LP-WAN technologies have clear advantages over traditional IoT cellular connectivity options, they are only suited to meet very basic data requirements for IoT applications with limited/modest data needs and relaxed latency (in the order of few seconds) [3,181]. They are ideally suited for IoT applications, which require just extended coverage, but reliability, latency, and availability might be more or less important [181] as shown in Table 1. LPWAN technologies are a group of wireless technologies that are designed to provide long-range, low-power connectivity for IoT devices.…”

Exploiting Multihoming Capabilities in 5G-Enabled IoT Nodes

“…Besides, in [24], is done a review of the related work taking into account the utilization of 5G technologies in IoT applications, considering the limitations due to network optimization and interferences. In this line, in [25], it is proposed a multihomed network model for coexistence and diverse IoT applications in smart cities from a theoretical point of view. Also in [26], the use of machine learning techniques has been applied for traffic steering in scenarios with heterogeneous ultra dense 5G networks.…”