Long range (LoRa) is one of the most successful low-power wide-area networking technologies because it is ideally suited for long-distance, low-bit rate, and low-power communications in the unlicensed sub-GHz spectrum utilized for Internet of things (IoT) networks. The effectiveness of LoRa depends on the link budget (i.e., spreading factor (SF), bandwidth (BW), and transmission power (TX)). Due to the near–far effect, the allocation of a link budget to LoRa devices (LDs) in large coverage regions is unfair between them depending on their distance to the GW. Thus, more transmission opportunities are given to some LDs to the detriment of other LD’s opportunities. Numerous studies have been conducted to address the prevalent near–far fairness problem. Due to the absence of a tractable analytical model for fairness in the LoRa network, however, it is still difficult to solve this problem completely. Thus, we propose an SF-partition-based clustering and relaying (SFPCR) scheme to achieve enormous LD connectivity with fairness in IoT multihop LoRa networks. For the SF partition, the SFPCR scheme determines the suitable partitioning threshold point for bridging packet delivery success probability gaps between SF regions, namely, the lower SF zone (LSFZ) and the higher SF zone (HSFZ). To avoid long-distance transmissions to the GW, the HSFZ constructs a density-based subspace clustering that generates clusters of arbitrary shape for adjacent LDs and selects cluster headers by using a binary score representation. To support reliable data transmissions to the GW by multihop communications, the LSFZ offers a relay LD selection that ideally chooses the best relay LD to extend uplink transmissions from LDs in the HSFZ. Through simulations, we show that the proposed SFPCR scheme exhibits the highest success probability of 65.7%, followed by the FSRC scheme at 44.6%, the mesh scheme at 34.2%, and lastly the cluster-based scheme at 29.4%, and it conserves the energy of LDs compared with the existing schemes.

Owing to the increasing capabilities of mobile devices and the development of mobile communication techniques in vehicular ad hoc networks (VANETs), mobile multimedia services have focused on supporting high Quality of Service (QoS) and Quality of Experience (QoE) for the subscribers in video streaming services. In VANETs, high-quality video streaming services aim to provide subscribers with safety and various infotainment applications. Due to the dynamic topology and frequent connectivity changes in moving vehicles, video streaming services require elastic and continuous vehicle information updates to present interactive real-time views of nontrivial road scenarios. The QoS and QoE are affected due to obstacles, re-location tracking, network traffic, and bandwidth factors that occur due to mobility problems and significantly influence the ability of vehicles with mobility to provide video streaming services. To achieve high QoS and QoE of video streaming services in VANETs, this paper proposes a novel protocol named PSOstreaming based on a particle swarm optimization (PSO) which is one of the mainstream and nature-inspired algorithms for swarm intelligence (SI). PSOstreaming calculates the PSQ score, which is the scoring method of the node in the topology in terms of the data communication capability measurement to analyze and optimize topology information in real-road circumstances. In addition, PSOstreaming utilizes a 3D vector mobility prediction algorithm for the mobility prediction method for the vehicles to address the characteristics of VANETs. In the topology, PSOstreaming defines Global PSO members and Local PSO members to disseminate video packets for video-streaming services to the service requester by computational equations. Experimental results indicate that PSOstreaming achieves high-quality video streaming services with a flexible response to dynamic topology changes and a high frame delivery ratio in terms of QoS and QoE.

Long range (LoRa) is a low-power wide-area technology because it is eminent for robust long-distance, low-bitrate, and low-power communications in the unlicensed sub-GHz spectrum used for the Internet of things (IoT) networks. Recently, several multi-hop LoRa networks have proposed schemes with explicit relay nodes to partially mitigate the path loss and longer transmission time bottlenecks of the conventional single-hop LoRa by focusing more on coverage expansion. However, they do not consider improving the packet delivery success ratio (PDSR) and the packet reduction ratio (PRR) by using the overhearing technique. Thus, this paper proposes an implicit overhearing node-based multi-hop communication (IOMC) scheme in IoT LoRa networks, which exploits implicit relay nodes for performing the overhearing to promote relay operation while satisfying the duty cycle regulation. In IOMC, implicit relay nodes are selected as overhearing nodes (OHs) among end devices with a low spreading factor (SF) in order to improve PDSR and PRR for distant end devices (EDs). A theoretical framework for designing and determining the OH nodes to execute the relay operations was developed with consideration of the LoRaWAN MAC protocol. Simulation results verify that IOMC significantly increases the probability of successful transmission, performs best in high node density, and is more resilient to poor RSSI than the existing schemes.

Long Range (LoRa) as a low-power wide-area technology is distinguished by its robust long-distance communications tailored for Internet of Things (IoT) networks. Because LoRa was primarily designed for stationary devices, when applied to mobile devices, they become susceptible to frequent channel attenuation. Such a condition can result in packet loss, higher energy consumption, and extended transmission times. To address these inherent challenges posed by mobility, we propose an adaptive mobility-based IoT LoRa clustering communication (AMILCC) scheme, which employs the 2D random waypoint mobility model, strategically partitions the network into optimal spreading factor (SF) regions, and incorporates an adaptive clustering approach. The AMILCC scheme is bolstered by a hybrid adaptive data rate (HADR) mechanism categorized into two approaches, namely intra-SF and inter-SF region HADRs, derived from the standard network-based ADR mechanism for stationary devices, to ensure efficient resource allocation for mobile IoT LoRa devices. Evaluation results show that, based on simulations at low mobility speeds of up to 5 m/s, AMILCC successfully maximizes the packet success ratio to the gateway (GW) by over 70%, reduces energy consumption by an average of 55.5%, and minimizes the end-to-end delay by 47.62%, outperforming stationary schemes. Consequently, AMILCC stands as a prime solution for mobile IoT LoRa networks by balancing the high packet success ratio (PSR) with reliability with energy efficiency.

By leveraging the development of mobile communication technologies and due to the increased capabilities of mobile devices, mobile multimedia services have gained prominence for supporting high-quality video streaming services. In vehicular ad-hoc networks (VANETs), high-quality video streaming services are focused on providing safety and infotainment applications to vehicles on the roads. Video streaming data require elastic and continuous video packet distributions to vehicles to present interactive real-time views of meaningful scenarios on the road. However, the high mobility of vehicles is one of the fundamental and important challenging issues for video streaming services in VANETs. Nevertheless, previous studies neither dealt with suitable data caching for supporting the mobility of vehicles nor provided appropriate seamless packet forwarding for ensuring the quality of service (QoS) and quality of experience (QoE) of real-time video streaming services. To address this problem, this paper proposes a video packet distribution scheme named Clone, which integrates vehicle-to-vehicle and vehicle-to-infrastructure communications to disseminate video packets for video streaming services in VANETs. First, an indicator called current network quality information (CNQI) is defined to measure the feature of data forwarding of each node to its neighbor nodes in terms of data delivery ratio and delay. Based on the CNQI value of each node and the trajectory of the destination vehicle, access points called clones are selected to cache video data packets from data sources. Subsequently, packet distribution optimization is conducted to determine the number of video packets to cache in each clone. Finally, data delivery synchronization is established to support seamless streaming data delivery from a clone to the destination vehicle. The experimental results show that the proposed scheme achieves high-quality video streaming services in terms of QoS and QoE compared with existing schemes.

Intermittently connected vehicular networks (ICVNs) consist of vehicles moving on roads and stationary roadside units (RSUs) deployed along roads. In ICVNs, the long distances between RSUs and the large volume of vehicular content lead to long download delays to vehicles and high traffic overhead on backhaul links. Fortunately, the improved content storage size and the enhanced vehicular mobility prediction afford opportunities to ameliorate these problems by proactively caching (i.e., precaching) content. However, existing precaching schemes exploits RSUs and vehicles individually for content precaching, even though the cooperative precaching between them can reduce download delays and backhaul link traffic. Thus, this paper proposes a cooperative content precaching scheme that exploits the precaching ability of both vehicles and RSUs to enhance the performance of content downloads in ICVNs. Based on the trajectory and velocity information of vehicles, we first select the optimal relaying vehicle and the next RSUs to cache the requested content proactively and provide it to the requester vehicle optimally. Next, we calculate the optimal content precaching amount for each of the relaying vehicle and the downloading RSUs by using a mathematical model that exploits both the dwell time in an RSU and the contact time between vehicles. To compensate for the error of the mobility prediction in determining both the dwell time and the contact time, our scheme adds a guardband to the optimal content precaching amount by considering the expected reduced delay. Finally, we evaluate the proposed scheme in various simulation environments to prove the achievement of efficient content download performance by comparing with the existing schemes.