Proposing an optimal routing protocol for internet of vehicles with reduced overhead has endured to be a challenge owing to the incompetence of the current architecture to manage flexibility and scalability. The proposed architecture, therefore, consolidates an evolving network standard named as software defined networking in internet of vehicles. Which enables it to handle highly dynamic networks in an abstract way by dividing the data plane from the control plane. Firstly, road-aware routing strategy is introduced: a performance-enhanced routing protocol designed specifically for infrastructure-assisted vehicular networks. In which roads are divided into road segments, with road side units for multi-hop communication. A unique property of the proposed protocol is that it explores the cellular network to relay control messages to and from the controller with low latency. The concept of edge controller is introduced as an operational backbone of the vehicle grid in internet of vehicles, to have a real-time vehicle topology. Last but not least, a novel mathematical model is estimated which assists primary controller in a way to find not only a shortest but a durable path. The results illustrate the significant performance of the proposed protocol in terms of availability with limited routing overhead. In addition, we also found that edge controller contributes mainly to minimizes the path failure in the network.

In Internet of Vehicles (IoV), numerous routing metrics have been used to assess the performance of routing protocols such as, packet delivery ratio, throughput, end-to-end delay and path duration. Path duration is an influential design parameter, among these routing metrics, that determines the performance of vehicular networks. For instance, in highly dynamic scenarios, it can be used to predict link life time in on-demand routing protocols. In this paper, we propose an infrastructure-assisted hybrid road-aware routing protocol which is capable of enhanced vehicle-to-vehicle and vehicle-to-infrastructure communication. A remarkable aspect of the proposed protocol is that it establishes a link between path duration and fundamental design parameters like vehicular velocity, density, hop count and transmission range. Although, a lot of research has been previously performed, a well defined analytical model for IoV is not available in the literature. Precisely, a relation between path duration and vehicular velocity has not been validated in the previous studies. Experimental results show that the increased packet delivery ratio with reduced end-to-end delay can be achieved by the prediction of path duration. Proposed model for path duration is validated by getting experimental results from network simulator 3 (NS3) and analytical results from MATLAB. In addition, SUMO simulator was used to generate real time traffic on the roads of Gangnam district, South Korea.

The Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy efficiency in mind. This paper conducts a comprehensive survey on energy-saving solutions in 3GPP-based CIoT networks. In comparison to current studies, the contribution of this paper is the classification and an extensive analysis of existing energy-saving solutions for CIoT, e.g., configuration of particular parameter values and software modifications of transport- or radio-layer protocols, while also stressing key parameters impacting the energy consumption such as the frequency of data reporting, discontinuous reception cycles (DRX), and Radio Resource Control (RRC) timers. In addition, we discuss shortcomings, limitations, and possible opportunities which can be investigated in the future to reduce the energy consumption of CIoT devices.

Deploying Cellular Internet of Things (CIoT) devices in urban and remote areas faces significant energy efficiency challenges. This is especially true for Narrowband IoT (NB-IoT) devices, which are expected to function on a single charge for up to 10 years while transmitting small amounts of data daily. The 3rd Generation Partnership Project (3GPP) has introduced energy-saving mechanisms in Releases 13 to 16, including Early Data Transmission (EDT) and Preconfigured Uplink Resources (PURs). These mechanisms extend battery life and reduce latency by enabling data transmission without an active Radio Resource Control (RRC) connection or Random Access Procedure (RAP). This paper examines these mechanisms using the LENA-NB simulator in the ns-3 environment, which is a comprehensive framework for studying various aspects of NB-IoT. The LENA-NB has been extended with PURs, and our analysis shows that PURs significantly enhance battery life and latency efficiency, particularly in high-density environments. Compared to the default RAP method, PURs reduce energy consumption by more than 2.5 times and increases battery life by 1.6 times. Additionally, PURs achieve latency reductions of 2.5–3.5 times. The improvements with PURs are most notable for packets up to 125 bytes. Our findings highlight PURs’ potential to enable more efficient and effective CIoT deployments across various scenarios. This study represents a detailed analysis of latency and energy consumption in a simulated environment, advancing the understanding of PURs’ benefits.

The deployment of Cellular Internet of Things (CIoT) is expected to reach over six billion devices by 2030. Many of these devices will be located in remote areas where replacing or recharging their batteries would be difficult and expensive. Therefore, it is crucial to configure these devices for efficient energy use to avoid frequent battery replacements or recharging. However, optimizing the energy consumption of CIoT devices, considering their applications and operating environmental conditions, presents a complex challenge. In response to this challenge, we propose the Gradient-Boosted Learning Optimization for Battery Efficiency (GLOBE) framework for dynamic configuration of Narrowband Internet of Things (NB-IoT) devices. GLOBE adjusts the radio layer of NB-IoT devices based on data transmission patterns and network conditions, enabling swift and automated reconfiguration. Our results demonstrate that GLOBE reduces energy consumption by 30% to 75% compared to baseline configurations, offering significant benefits for both network operators and end devices by improving energy efficiency.