Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/)

A Review of Energy Conservation in Wireless Sensor Networks

In wireless sensor networks, energy efficiency plays a major role to determine the lifetime of the network. The network is usually powered by a battery which is hard to recharge. Hence, one major challenge in wireless sensor networks is the issue of how to extend the lifetime of sensors to improve the efficiency. In order to reduce the rate at which the network consumes energy, researchers have come up with energy conservation techniques, schemes and protocols to solve the problem. This paper presents a brief overview of wireless sensor networks, outlines some causes of its energy loss and some energy conservation schemes based on existing techniques used in solving the problem of power management. Keywords: Wireless sensor network, Energy conservation, Duty cycling and Energy efficiency

The Dynamics of Vehicular Networks in Urban Environments

Vehicular Ad hoc NETworks (VANETs) have emerged as a platform to support intelligent inter-vehicle communication and improve traffic safety and performance. The road-constrained, high mobility of vehicles, their unbounded power source, and the emergence of roadside wireless infrastructures make VANETs a challenging research topic. A key to the development of protocols for inter-vehicle communication and services lies in the knowledge of the topological characteristics of the VANET communication graph. This paper explores the dynamics of VANETs in urban environments and investigates the impact of these findings in the design of VANET routing protocols. Using both real and realistic mobility traces, we study the networking shape of VANETs under different transmission and market penetration ranges. Given that a number of RSUs have to be deployed for disseminating information to vehicles in an urban area, we also study their impact on vehicular connectivity. Through extensive simulations we investigate the performance of VANET routing protocols by exploiting the knowledge of VANET graphs analysis.Comment: Revised our testbed with even more realistic mobility traces. Used the location of real Wi-Fi hotspots to simulate RSUs in our study. Used a larger, real mobility trace set, from taxis in Shanghai. Examine the implications of our findings in the design of VANET routing protocols by implementing in ns-3 two routing protocols (GPCR & VADD). Updated the bibliography section with new research work

MARITIME DOMAIN AWARENESS THROUGH THE CHARACTERIZATION OF SHIP BEHAVIOR WITH AIS DATA

Maritime Domain Awareness (MDA), as defined in the 2005 National Strategy for Maritime Security, is the “effective understanding of anything associated with the global maritime domain that could impact the security, safety, economy, or environment of the United States.” Thus, it is imperative for the U.S. Navy to develop approaches that enhance understanding of the maritime domain in order to maintain operational effectiveness. One such way to enhance this understanding is to develop approaches that automate the analysis of Automatic Identification System (AIS) data to characterize the behavior of ships in the maritime domain. By the sheer amount of AIS data available, it quickly becomes challenging for a human operator to identify ship behaviors throughout the world. When timeliness is important for decision makers, it becomes even more important that the characterization of ship behavior is done quickly and accurately to identify potential issues or threats. Thus, a major contribution of this thesis is the development of an autonomous machine learning system that characterizes ship behavior quickly and accurately in order to achieve MDA in a particular environment. This includes an autonomous system for the identification of ship tracks in a region. Two major contributions of this work are the development of a taxonomy of ship behaviors, which is currently lacking in the literature, and a report on the characterization of such behaviors through machine learning methods.Ensign, United States NavyApproved for public release. Distribution is unlimited