The authors propose, design, and implement a low‐cost universal smart energy meter (USEM) with demand‐side load management. The meter can be used in the postpaid and prepaid modes with flexible tariff plans such as time of use, block rate tariff, and their combination. The smart meter comprises of a potential transformer, current transformer, and microcontroller unit with an embedded communication module. The connectivity among the utility authority, the smart meter, and consumer is established by authority identification number, meter identification number, and user identification number using the cellular network. The load management option of the meter controls electrical loads and provides emergency power during the power shortage. The USEM can be configured and reconfigured remotely simply by short message service without changing hardware. Besides, energy consumption status, meter tampering, and fault at the distribution end can be monitored with the proposed metering system. Here, a prototype of the smart meter is presented, and its effectiveness, flexibility, and versatility are experimentally demonstrated.

Content Centric Networking (CCN), a future Internet architecture, brings new challenges in maintaining the Quality of Experience (QoE) for video streaming. Because of the universal caching capability of CCN routers, streaming from multiple sources will be common, and switching between content sources might affect QoE by inducing delays and consequently stalls in video playback. This paper proposes a new QoE-aware multi-source video streaming scheme for CCN. First, the content distributions of video files among CCN nodes for different caching methods are studied. Second, an adaptive video streaming with distributed caching (ASDC) algorithm is designed to guarantee QoE during the switching between content sources. The ASDC algorithm considers the delivery of scalable video streams. It automatically adapts the layers in a video stream when there is source switching, based on a QoE model that characterizes the effect of stalling. Experimental results show that the ASDC algorithm outperforms dynamic adaptive streaming over HTTP (DASH) in the CCN platform in terms of the QoE obtained from human subjective tests.

Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.

The increasing demand of global video traffic presents a grand challenge for Internet service providers. This is particularly important for Smart Cities, where the citizens will be surrounded by various multimedia services. The Information Centric Networking (ICN) paradigm has been proposed to handle the vast amount of data, mobility of users, and provide better scalability and efficiency in terms of bandwidth demand. However, the distributed caching mechanism in ICNs brings new challenges in maintaining satisfactory Quality of Experience (QoE) for video streaming applications. Because of the caching capability of ICN routers, streaming from multiple sources will be common, and switching between content sources might affect QoE by inducing delays and consequently stalls in video playback. This paper investigates the impact of content source switching, which is brought by distributed in-network caching, on the QoE for video streaming in ICNs. Human subjective tests are conducted using videos generated from an ICN emulation platform, in which the videos are streamed to human users under different caching strategies and available bandwidths. Test results indicate that, as the number of source switches increases, the satisfaction with stalling decreases and thus resulting in lower QoE scores. The impact of stalling on the overall QoE is further quantified under different video clarity levels through several statistical methods.

Unmanned aerial vehicles (UAVs) have been used in many surveillance and remote sensing applications due to their capabilities of flying over large areas with sensing devices. The sensed data such as weather information or surveillance footage is usually very time sensitive, and the mission will be successful only if the data could be captured and delivered to processing centers on time. Using the data ferrying mechanism, UAVs could collect sensed data from remote areas and ferry the data to appropriate locations for offloading before the on-board storage spaces are overloaded. The data collection and offloading strategies of UAVs should be carefully determined to ensure the timely delivery of information while saving the travel costs. This paper investigates a UAV path planning problem considering realistic data ferrying situations involving data offloading time windows and communication constraints for periodic remote sensing applications. A Mixed Integer Linear Programming (MILP) problem is formulated that finds paths for UAVs with the minimal cost to meet application deadline requirements under time window and communication constraints on data collection and offloading. A genetic algorithm (GA) based approach is designed to solve this problem. Evaluation results on different input parameters show that the proposed approach could produce near-optimal solutions with much faster execution time than the brute-force approach. The scalability of the proposed approach is validated by a major test case with 120 waypoints, in which stabilized near-optimal solutions could be found within 60,000 iterations.

Information-centric networking (ICN) is a promising approach to address the limitations of current host-centric IP-based networking. ICN models feature ubiquitous in-network caching to provide faster and more reliable content delivery, name-based routing to provide better scalability, and self-certifying contents to ensure better security. Due to the differences in the core architecture of ICN compared to existing IP-based networks, it requires special considerations to provide quality-of-service (QoS) or quality-of-experience (QoE) support for applications based on ICNs. This paper discusses the latest advances in QoS and QoE research for ICNs. First, an overview of ICN architectures is given, followed by a summary of different factors that influence QoS and QoE. Approaches for improving QoS and QoE in ICNs are then discussed in five main categories: in-network caching, name resolution and routing, transmission and flow control, software-defined networking, and media-streaming-based strategies. Finally, open research questions for providing QoS and QoE support in ICNs are outlined for future research.