Energy harvest and information transmission design in internet-of-things wireless communication systems

Wireless energy transmission is considered in the Internet-of-Things (IoT) dual-hop communication systems. The power splitting method is employed at the relay node to receive the wireless energy and the wireless information simultaneously from the source to the relay node in the first resource block. With the harvested energy at the relay node, one wireless information node and one wireless energy harvest node are served simultaneously by the relay node in the second resource block. The optimization problem is formulated to maximize the energy efficiency defined as the ratio of the wireless information throughput of the first destination node over the power consumed by the whole IoT systems, which is subject to the required minimum energy harvested at the second destination node of the energy transfer. To tackle the non-convex problem, the exact expression of the wireless information throughput is approximated by the high signal to noise ratio (SNR) approximation method, which is shown very tight to the exact expression. Hence, the asymptotically optimal solution is derived in analytical expression by employing the Lagrangian method. Numerical simulations verify the performance of the proposed scheme.     

基于SLIPT的混合可见光/射频协作通信系统性能分析

传统无线通信传输和网络接入依赖射频通信技术.随着海量移动设备涌入,频谱资源匮乏、移动设备电能受限等问题对新一代物联网的可持续发展提出了严峻挑战.本文通过融合传统无线射频通信技术与新型可见光通信技术,构建一种基于可见光信息能量同传(simultaneous lightwave information and power ...     

Towards machine-learning-driven effective mashup recommendations from big data in mobile networks and the Internet-of-Things

A large number of Web APIs have been released as services in mobile communications, but the service provided by a single Web API is usually limited. To enrich the services in mobile communications, developers have combined Web APIs and developed a new service, which is known as a mashup. The emergence of mashups greatly increases the number of services in mobile communications, especially in mobile networks and the Internet-of-Things (IoT), and has encouraged companies and individuals to develop even more mashups, which has led to the dramatic increase in the number of mashups. Such a trend brings with it big data, such as the massive text data from the mashups themselves and continually-generated usage data. Thus, the question of how to determine the most suitable mashups from big data has become a challenging problem. In this paper, we propose a mashup recommendation framework from big data in mobile networks and the IoT. The proposed framework is driven by machine learning techniques, including neural embedding, clustering, and matrix factorization. We employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the mashups. We also develop a novel Joint Matrix Factorization (JMF) model to complete the mashup recommendation task, where we design a new objective function and an optimization algorithm. We then crawl through a real-world large mashup dataset and perform experiments. The experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.     

Proactive human–robot collaboration: Mutual-cognitive,predictable, and self-organising perspectives

Human–Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human-centricity, sustainability, and resilience. However, existing HRC development mainly undertakes either a human-dominant or robot-dominant manner, where human and robotic agents reactively perform operations by following pre-defined instructions, thus far from an efficient integration of robotic automation and human cognition. The stiff human–robot relations fail to be qualified for complex manufacturing tasks and cannot ease the physical and psychological load of human operators. In response to these realistic needs, this paper presents our arguments on the obvious trend, concept, systematic architecture, and enabling technologies of Proactive HRC, serving as a prospective vision and research topic for future work in the human-centric smart manufacturing era. Human–robot symbiotic relation is evolving with a 5C intelligence — from Connection, Coordination, Cyber, Cognition to Coevolution, and finally embracing mutual-cognitive, predictable, and self-organising intelligent capabilities, i.e., the Proactive HRC. With proactive robot control, multiple human and robotic agents collaboratively operate manufacturing tasks, considering each others’ operation needs, desired resources, and qualified complementary capabilities. This paper also highlights current challenges and future research directions, which deserve more research efforts for real-world applications of Proactive HRC. It is hoped that this work can attract more open discussions and provide useful insights to both academic and industrial practitioners in their exploration of human–robot flexible production.     

A quality status encoding scheme for PCB-based products in IoT-enabled remanufacturing

In this paper, a binary-extensible quality status encoding scheme, named IQSCT (IoT quality status code table), is proposed for the PCB-based product with available recovery options in remanufacturing. IQSCT is achieved by code evolution based on binary logic, in which the product flow and the quality information flow are integrated, and three key features of PCB-based product (PCB-module association, assemblydisassembly logic, and disassembly risk) are involved in production costing.With IQSCT, the manufacturer can have better decisions to reduce remanufacturing cost and improve resource utilization, which is verified by a case study based on the real data from BOM cost and corresponding estimation of Apple iPhone 11 series.     

Toward AI-enabled augmented reality to enhance the safety of highway work zones: Feasibility,requirements, and challenges

Highway work zones are considered among the most hazardous working environments. In 2018 alone, 124 workers lost their lives to fatal accidents. The lack of predictive safety systems that notify workers of upcoming dangers in advance is a major reason to blame in the highway maintenance and operation community. This article presents an integrative design framework for bringing recent advances in Augmented Reality (AR) and Artificial Intelligence (AI) to enhance the safety of highway workers through real-time multimodal notifications on-spot. To this end, this article conceptualizes and co-designs three major pillars: (1) AR user interface design for multimodal notification, (2) real-time AI at the edge for vehicle detection/classification from distance, and (3) real-time wireless communication in work zone setting to enable latency-aware operation between AI and AR components. Our early results demonstrate that we can achieve 24.83 FPS end-to-end execution latency on the Xavier AGX Jetson board with 48.7% mAP on BDD100K dataset, and a real-time communication covering 120 meters with an average latency of 5.1 ms at the farthest distance. Our mixed-method user research also reveals an acceptable level of excitement and engagement from the body of highway workers toward both the proposed technology and the designed user interface. Overall, this article provides a proof-of-concept toward AI-enabled AR safety systems in highway work zones.     

Drone services: issues in drones for location-based services from human-drone interaction to information processing

Recently, drones (or unmanned aerial vehicles (UAVs)) are getting increasing attention and taking mobile computing to a new era. Due to the support of highly advanced technologies, soon they might be ubiquitous and networks of drones might be used in providing civilian drone services. In this paper, we provide a survey of drone services and applications, data management for drones, data services using drones, distributed computing trends fuelled by drones and a range of human-drone interaction research which is useful if drones are to regularly serve non-technical users, while highlighting the specific concerns in data management and airborne Internet-of-Things (IoT) computing infrastructure. We present concepts such as drones-as-a-service and fly-in, fly-out computing infrastructure, and note data management and system design issues that arise in these situations. Issues of Big Data arising from such applications, optimising the configuration of airborne and ground infrastructure to provide the best QoS and QoE, situation-awareness, scalability, reliability, scheduling for efficiency, interaction with users and drones using different methods are noted.     

Real-time air pollution monitoring with sensors on city bus

This paper presents an experimental study on real-time air pollution monitoring using wireless sensors on public transport vehicles. The study is part of the GreenIoT project in Sweden, which utilizes Internet-of-Things to measure air pollution level in the city center of Uppsala. Through deploying low-cost wireless sensors, it is possible to obtain more fine-grained and real-time air pollution levels at different locations. The sensors on public transport vehicles complement the readings from stationary sensors and the only ground level monitoring station in Uppsala. The paper describes the deployment of wireless sensors on Uppsala buses and the integration of the mobile sensor network with the GreenIoT testbed. Extensive experiments have been conducted to evaluate the communication quality and data quality of the system.     

Impact of Transmission Power Control in multi-hop networks

Many Transmission Power Control (TPC) algorithms have been proposed in the past, yet the conditions under which they are evaluated do not always reflect typical Internet-of-Things (IoT) scenarios. IoT networks consist of several source nodes transmitting data simultaneously, possibly along multiple hops. Link failures are highly frequent, causing the TPC algorithm to kick-in quite often. To this end, in this paper we study the impact that frequent TPC actions have across different layers. Our study shows how one node’s decision to scale its transmission power can affect the performance of both routing and MAC layers of multiple other nodes in the network, generating cascading packet retransmissions and forcing far too many nodes to consume more energy. We find that crucial objectives of TPC such as conserving energy and increasing network capacity are severely undermined in multi-hop networks.