Self-Selective Correlation Ship Tracking Method for Smart Ocean System

In recent years, with the development of the marine industry, navigation environment becomes more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count the sailing ships to ensure the maritime security and facilitates the management for Smart Ocean System. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly include: 1) A self-selective model with negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of classifier at the same time; 2) A bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were higher than Discriminative Scale Space Tracking (DSST) by over 8 percentage points on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 Frames Per Second (FPS)

EASR: Graph-based Framework for Energy Efficient Smart Routing in MANET using Availability Zones

Energy consumption in MobileAdhoc Network (MANET) is a topic of research from more than a decade. Althoughthere are multiple archival of literatures, that have proposed variousenergy-efficient algorithms for reducing the energy consumption to improveenergy efficiency. Establishing correct and reliable route is important designissue in MANET, but a more challenging goal is to provide energy efficientroute. But, it was observed that majority of such energy efficient routingprotocols just give symptomatic solution which addresses and mitigated theenergy issues overlooking various associated issues like quality of services.Moreover, in majority of research previous studies it is found that AODV andDSDV are highly in adoption rate among the researcher for solving energy issuesusing routing protocols. This manuscript after reviewing some of thesignificant literatures in past explored issues in existing AODV and DSDVand  proposes a novel energy efficientrouting protocols by incorporating a new actor called availability zone. Theproposed model shows better energy efficiency and QoS compared to AODV andDSDV

Robust geometric forest routing with tunable load balancing

Although geometric routing is proposed as a memory-efficient alternative to traditional lookup-based routing and forwarding algorithms, it still lacks: i) adequate mechanisms to trade stretch against load balancing, and ii) robustness to cope with network topology change. The main contribution of this paper involves the proposal of a family of routing schemes, called Forest Routing. These are based on the principles of geometric routing, adding flexibility in its load balancing characteristics. This is achieved by using an aggregation of greedy embeddings along with a configurable distance function. Incorporating link load information in the forwarding layer enables load balancing behavior while still attaining low path stretch. In addition, the proposed schemes are validated regarding their resilience towards network failures

A self-selective correlation ship tracking method for smart ocean systems

In recent years, with the development of the marine industry, the ship navigation environment has become more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count sailing ships to ensure maritime security and facilitate management for Smart Ocean systems. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly includes: (1) A self-selective model with a negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of the classifier at the same time; (2) a bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were over 8 % higher than Discriminative Scale Space Tracking (DSST) on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 frames per second (FPS).This research was supported by the National Natural Science Foundation of China under Grant (No. 61772387 and No. 61802296), the Fundamental Research Funds of Ministry of Education and China Mobile (MCM20170202), the Fundamental Research Funds for the Central Universities (JB180101), China Postdoctoral Science Foundation Grant (No. 2017M620438), and supported by ISN State Key Laboratory

Routing of Time-Sensitive Data in Mobile Ad Hoc Networks

This research focuses on routing of time-sensitive data in mobile ad hoc networks. Routing protocols that were developed for ad hoc networks have been reviewed. Ad Hoc On-demand Distance Vectoring (AODV) routing protocol, which is a prominent routing protocol among these protocols have been implemented in OPNET, a discrete-event simulation tool. Taking AODV as a point of departure, a routing protocol named Real Time Routing Protocol (RTRP) has been developed and also implemented in simulation environment. The performance of these routing protocols have been observed by simulating them in various experiments where the workload has been chosen to be time-sensitive data. Results show that for lightly loaded networks, AODV and RTRP have similar performance. When the workload introduced to the network is increased, RTRP outperforms AODV significantly

09451 Abstracts Collection -- Geometric Networks, Metric Space Embeddings and Spatial Data Mining

From November 1 to 6, 2009, the Dagstuhl Seminar 09451 ``Geometric Networks, Metric Space Embeddings and Spatial Data Mining\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Low Power Wide Area Networks (LPWAN): Technology Review And Experimental Study on Mobility Effect

In the past decade, we have witnessed explosive growth in the number of low-power embedded and Internet-connected devices, reinforcing the new paradigm, Internet of Things (IoT). IoT devices like smartphones, home security systems, smart electric meters, garage parking indicators, etc., have penetrated deeply into our daily lives. These IoT devices are increasingly attached and operated in mobile objects like unmanned vehicles, trains, airplanes, etc. The low power wide area network (LPWAN), due to its long-range, low-power and low-cost communication capability, is actively considered by academia and industry as the future wireless communication standard for IoT. However, despite the increasing popularity of mobile IoT, little is known about the suitability of LPWAN for those mobile IoT applications in which nodes have varying degrees of mobility. To fill this knowledge gap, in this thesis:1. We present a thorough review on LPWAN technology focusing on the mobility effect. 2. We conduct an experimental study to evaluate, analyze, and characterize LPWAN in both indoor and outdoor mobile environments.Our experimental results indicate that the performance of LPWAN is surprisingly susceptible to mobility, even to minor human mobility, and the effect of mobility significantly escalates as the distance to the gateway increases. These results call for development of new mobility-aware LPWAN protocols to support mobile IoT