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In-Network Processing on Low-Cost IoT Nodes for Maritime Surveillance

Commercially available system of distributed wireless sensors could increase the Navy's intelligence collection footprint.

The effective distribution of offensive weapon capabilities to naval units at the tactical edge is a critical focus for Navy leaders. A direct byproduct of this priority is the need to employ sensor and data collection systems that can effectively guide the targeting of that offensive capability. In the recent past, wireless sensor networks have received limited use in the maritime domain due to the exploratory nature of technology, high system complexity and the high cost of system deployment. With the Internet-of-Things revolution, commercially available hardware and software components can be used to build low-cost, reliable, disposable wireless sensor networks that can leverage in-network processing schemes to greatly expand the intelligence collection footprint.

Centralized Sensor Network

The demonstrated application of the tactical low-cost sensor network (TLCSN) is the collection, processing, and dissemination of 802.11 messages—e.g. beacons, probe requests and data packets. This data provides enhanced situational awareness, identifying and displaying what clients and APs may exist in a specific operating area, as well as add context by analyzing and characterizing any trends and patterns of movement that may appear.

The overall system assists in characterizing the wireless environment by aggregating the data into a streamlined format for display. In colloquial terms, the data set is comparable to that provided by “war-driving”; however, this data serves as an initial demonstration of the sensor node and network capabilities. It provides enough volume over time to serve as an interesting data set for analysis while also providing useful data to a commander. Various other sensors can be added for different intelligence gain and used either interchangeably or in concert with each other.

There are four main components of the TLCSN system: the sensor nodes, the server/broker platform, the system controller, and the overall network structure.

Sensor Nodes

The sensor nodes conduct the actual data collection and reporting. They would be spread across a specified target area as dictated by the operational or testing requirements. When deployed, they would be automatically activated and establish connectivity with the network at large. Through this network connection, each node will be able to transmit and receive collected data across the network, reach back to a designated repository, and receive additional or modified tasking by a system controller. Nodes are assumed to be more or less static during their collection mode in this environment. Minimal movement, accounting for drift and currents, is acceptable.

Server / Broker Platform

The broker consolidates, processes and distributes data across the network as required. It has a complex role and provides various capabilities from acting as a VPN server allowing connectivity from outside the network, to serving as the broker for collected data. In some situations, it may also act as a WAP and DHCP server for the local network.

System Controller

The system controller serves as the user interface for the system. It provides mechanisms for accessing, viewing and analyzing the data, while providing additional tasking and control to various sensors. For example, if the controller wishes to use the sensor node for additional activities beyond the passive collection of data, that capability is available. To provide the maximum flexibility for this system, it is assumed that the controller resides at some external location. In all cases, it connects to the sensor network via a VPN connection and is never physically present on the same network.

Network

There are three general network architectural constructs that are explored. For the sake of simplicity, each one is illustrated using only five nodes. Actual implementation can be scaled to a much larger extent—both in the number of nodes working with a server (covering more area within a general region), as well as the total number of servers (covering more regions). This would serve to provide both greater breadth as well as depth of coverage.

This work was done by Andrew R. Belding for the Naval Postgraduate School. NPS-0006

Topics:
Aerospace Communications Computer software and hardware Computer-Aided Design (CAD) Computer-Aided Engineering (CAE) Computer-Aided Manufacturing (CAM) Connectivity Data Acquisition Data acquisition and handling Defense Education and training Electronic Components Electronics Electronics & Computers Internet of Things Internet of things (IoT) MEMs Mechanical Components People and personalities Sensors Sensors and actuators Software Surveillance Surveillance and Security Telecommunications Wireless
This Brief includes a Technical Support Package (TSP).
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In-Network Processing on Low-Cost IoT Nodes for Maritime Surveillance

(reference NPS-0006) is currently available for download from the TSP library.

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    Aerospace & Defense Technology Magazine

    This article first appeared in the June, 2018 issue of Aerospace & Defense Technology Magazine (Vol. 3 No. 4).

    Read more articles from this issue here.

    Read more articles from the archives here.

    Overview

    The document is a thesis authored by Andrew R. Belding at the Naval Postgraduate School in March 2017, focusing on the application of low-cost Internet-of-Things (IoT) nodes for maritime surveillance. The research addresses the critical need for effective sensor and data collection systems to enhance naval capabilities, particularly in the context of maritime operations.

    The thesis begins by outlining the challenges associated with traditional maritime surveillance methods, which often rely on expensive and complex systems. Belding proposes the use of IoT technology as a cost-effective alternative that can improve situational awareness and operational efficiency. By leveraging low-cost sensors and wireless communication, the research aims to create a more accessible and scalable solution for maritime monitoring.

    A significant portion of the thesis is dedicated to the design and implementation of a testbed for the proposed IoT-based system. This includes the development of a network architecture that supports in-network processing, allowing for data to be analyzed and acted upon closer to the source, rather than relying solely on centralized processing. This approach not only reduces latency but also minimizes the bandwidth required for data transmission, making it particularly suitable for maritime environments where connectivity can be limited.

    The document also discusses the testing and demonstration of the proposed system, highlighting its potential effectiveness in real-world scenarios. Belding emphasizes the importance of rigorous testing to validate the performance and reliability of the IoT nodes in various maritime conditions.

    In conclusion, the thesis presents a compelling case for the integration of low-cost IoT technology into maritime surveillance operations. By addressing the limitations of existing systems and proposing a novel approach that combines affordability with advanced capabilities, Belding's research contributes to the ongoing evolution of naval surveillance strategies. The findings suggest that adopting IoT solutions can significantly enhance the ability to monitor and respond to maritime threats, ultimately improving national security and operational readiness.

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