No abstract

The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors because they use fixed path loss models. In this paper, we propose an RSSI real-time correction method based on Bluetooth gateway which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusts them by the RSSI fluctuation information stored on the server in real-time. The adjusted RSSIs can be used for calculation and achieve smaller positioning error. Moreover, it is difficult to accurately fit the RSSI distance model with the logarithmic distance loss model due to the complex electromagnetic environment in the room. Therefore, the back propagation neural network optimized by particle swarm optimization (PSO-BPNN) is used to train the RSSI distance model to reduce the positioning error. The experiment shows that the proposed method has better positioning accuracy than the traditional method.

In Underwater Wireless Sensor Networks (UWSNs), localization is one of most important technologies since it plays a critical role in many applications. Motivated by widespread adoption of localization, in this paper, we present a comprehensive survey of localization algorithms. First, we classify localization algorithms into three categories based on sensor nodes’ mobility: stationary localization algorithms, mobile localization algorithms and hybrid localization algorithms. Moreover, we compare the localization algorithms in detail and analyze future research directions of localization algorithms in UWSNs.

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Mapping global shipping density, including vessel density and traffic density, is important to reveal the distribution of ships and traffic. The Automatic Identification System (AIS) is an automatic reporting system widely installed on ships initially for collision avoidance by reporting their kinematic and identity information continuously. An algorithm was created to account for errors in the data when ship tracks seem to ‘jump’ large distances, an artefact resulting from the use of duplicate identities. The shipping density maps, including the vessel and traffic density maps, as well as AIS receiving frequency maps, were derived based on around 20 billion distinct records during the period from August 2012 to April 2015. Map outputs were created in three different spatial resolutions: 1° latitude by 1° longitude, 10 minutes latitude by 10 minutes longitude, and 1 minute latitude by 1 minute longitude. The results show that it takes only 56 hours to process these records to derive the density maps, 1·7 hours per month on average, including data retrieval, computation and updating of the database.

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In this paper, we present a real-time communication protocol, called SPEED, for sensor networks. The protocol provides three types of real-time communication services, namely, real-time unicast, real-time area-multicast and real-time area-anycast. SPEED is specifically tailored to be a stateless, localized algorithm with minimal control overhead. End-to-end real-time communication guarantees are achieved using a novel combination of feedback control and non-deterministic QoS-aware geographic forwarding with a bounded hop count. SPEED is a highly efficient and scalable protocol for the sensor networks where node density is high while the resources of each node are scarce. Theoretical analysis and simulation experiments are provided to validate our claims.

Underwater Sensor Network (UWSN) is a representative three-dimensional wireless sensor network. Due to the unique characteristics of underwater acoustic communication, providing energy-efficient and low-latency routing protocols for UWSNs is challenging. Major challenges are water currents, limited resources, and long acoustic propagation delay. Network topology of UWSNs is dynamic and complex as sensors have always been moving with currents. Some proposed protocols adopt geographic routing to address this problem, but three-dimensional localization is hard to obtain in underwater environment. As depth-based routing protocol (DBR) uses depth information only which is much more easier to obtain, it is more practical for UWSNs. However, depth information is not enough to restrict packets to be forwarded within a particular area. Packets may be forwarded through multiple paths which might cause energy waste and increase end-to-end delay. In this paper, we introduce underwater time of arrival (ToA) ranging technique to address the problem above. To maintain all the original advantages of DBR, we make the following contributions: energy-efficient depth-based routing protocol that reduces redundancy energy cost in some blind zones; low-latency depth-based routing protocol that is able to deliver a packet through an optimal path. The proposed protocols are validated through extensive simulations.