Underwater backscatter is an emerging technology for low-cost and low-power communication and sensing in underwater environments. However, past underwater backscatter systems have been largely limited to simple on-off modulation schemes, which inherently limit their robustness, adaptivity, and spectral efficiency.

Underwater backscatter is a recent networking technology that enables net-zero-power communication and sensing in underwater environments. Existing research on underwater backscatter has focused on designing and demonstrating early systems with impressive capabilities; however, what remains critically missing is an end-to-end analysis of the underwater backscatter communication channel, which is necessary to understand the potential of this technology to scale to real-world applications and practical deployments.

Acoustic backscatter is a recent communication technology that enables ultra-low power sensing and connectivity in underwater environments. This demo presents an interactive graphical tool (Matlab App) for quickly evaluating the performance of acoustic backscatter in real-world scenarios. Our tool enables users to customize a backscatter system with practical underwater transducers and channel properties, then uses an analytical framework to predict the system's power-up and communication range. Researchers and IoT designers with no acoustics background can leverage our tool to make informed design choices and optimize their system's performance. A demo video of the tool can be found here: https://youtu.be/CJBYtOHUjC8

We present the design, implementation, and evaluation of Van Atta Acoustic Backscatter (VAB), a technology that enables long-range, ultra-low-power networking in underwater environments. At the core of VAB is a novel, scalable underwater backscatter architecture that bridges recent advances in RF backscatter (Van Atta architectures) with ultra-low-power underwater acoustic networks. Our design introduces multiple innovations across the networking stack, which enable it to overcome unique challenges that arise from the electro-mechanical properties of underwater backscatter and the challenging nature of low-power underwater acoustic channels. We implemented our design in an end-to-end system, and evaluated it in over 1,500 real-world experimental trials in a river and the ocean. Our evaluation in stationary setups demonstrates that VAB achieves a communication range that exceeds 300m in round trip backscatter across orientations (at BER of 10−3). We compared our design head-to-head with past state-of-the-art systems, demonstrating a 15× improvement in communication range at the same throughput and power. By realizing hundreds of meters of range in underwater backscatter, this paper presents the first practical system capable of coastal monitoring applications. Finally, our evaluation represents the first experimental validation of underwater backscatter in the ocean.

With the emergence of Zero Trust (ZT) Architecture, industry leaders have been drawn to the technology because of its potential to handle a high level of security threats. The Zero Trust Architecture (ZTA) is paving the path for a security industrial revolution by eliminating location-based implicant access and focusing on asset, user, and resource security. Software Defined Perimeter (SDP) is a secure overlay network technology that can be used to implement a Zero Trust framework. SDP is a next-generation network technology that allows network architecture to be hidden from the outside world. It also hides the overlay communication from the underlay network by employing encrypted communications. With encrypted information, detecting abnormal behavior of entities on an overlay network becomes exceedingly difficult. Therefore, an automated system is required. We proposed a method in this paper for understanding the normal behavior of deployed polices by mapping network usage behavior to the policy. An Apache Spark collects and processes the streaming overlay monitoring data generated by the built-in fabric API in order to do this mapping. It sends extracted metrics to Prometheus for storage, and then uses the data for machine learning training and prediction. The cluster-id of the link that it belongs to is predicted by the model, and the cluster-ids are mapped onto the policies. To validate the legitimacy of policy, the labeled polices hash is compared to the actual polices hash that is obtained from blockchain. Unverified policies are notified to the SDP controller for additional action, such as defining new policy behavior or marking uncertain policies.

Due to the mega-scale of next-generation networks, dynamic network policy management and assessment is a continuous struggle for network operators. As a result, networks have evolved towards automated Softwarized intent-based policy platforms. IBN (Intent-based Networking) administers high-level interpretation of intents and translates them as policies to the lower-level infrastructures. As a Softwarized platform, it defines an SSOT (Single Source of Truth) where every high-level intention has well-defined policy action at every stage of orchestration from top to bottom. However, due to the dynamic nature of the network, the static quantitative measures may result in excessive reservation or under-allocation of network resources. As network service orchestration is performed for the future, deciding on service parameters per resource availability is crucial. Hence, this work utilizes machine-learning-based resource forecasting to determine quantitative measures for intent activation proactively. This work empowers IBN policy activation through LSTM (Long Short-Term Memory) based compute resource forecasting and RouteNet-driven transport network path link utilization forecasting. The goal of IBN is to ensure service level agreement, as there are two primary service activation rules, GBR and Non-GBR (Guaranteed Bit Rate); hence using forecasting, the proposed model ensures the service agreement accomplishment proactively.

Zero Trust Networking (ZTN) is a security model where no entity in a network infrastructure is trusted. The first bastion of security for achieving ZTN is to have strong identity verification. Several standard methods for assuring a robust identity exist (E.g., OAuth2.0, OpenID Connect). These standards employ the use of JSON Web Tokens (JWT) during the authentication process. However, the use of JWT for One Time Token (OTT) enrollment has a latent security issue. A JWT can be intercepted by a third party and the information of the payload can be exposed, revealing the details of the enrollment server. Furthermore, an intercepted JWT could be used for enrollment by an impersonator as long as the JWT remains active. Our proposed mechanism aims to secure the ownership of the OTT by including the JWT as encrypted metadata into a Non-Fungible Token (NFT). The mechanism uses the blockchain Public Key of the intended owner for encrypting the JWT, and the blockchain assures the JWT ownership by mapping it to the intended owner's blockchain public address. Our proposed mechanism is applied to an emerging Zero Trust framework (OpenZiti) alongside a permissioned Ethereum blockchain using Hyperledger Besu. The Zero Trust Framework provides the enrollment functionality, while our proposed mechanism based on blockchain and NFT assures the secure distribution of OTTs that is used for the enrollment of identities.

Network ecosystems have grown to encompass multiple application domains. SDN and NFV technologies have helped pave the road for the evolution of the core and edge networking systems, allowing for numerous services to be served by the same physical infrastructure. Guaranteeing the operability of the network has become an ever-increasing requirement in order to sustain the underlying services deployed on the network. For this, Intent-Based Networking (IBN) aims to abstract network management by introducing high-level rules/policies that are translated to network configurations per service requirements. By following this principle, we proposed an anomaly detection and mitigation mechanism that exploits the characteristics of IBN for collecting and analyzing flows, using Machine Learning for interpreting traffic patterns, and automatic deployment of high-level policies for corrective actions related to anomalous traffic occurrences. The complete system is deployed on the Korea Advanced Research Network (KOREN), where the abstraction provided by IBN for network anomaly detection and mitigation is a key factor in closing the gap to achieve complete network automation.

The amount of data generation is exponentially increasing over the past decade due to the widespread use of multimedia applications and social media platforms. Advanced real-time applications such as virtual reality, augmented reality, automated vehicles, smart homes, and intelligent traffic control systems have increased the demand for low latency. Many of these applications are delay-sensitive and put enormous stress on the core network to respond in real-time. CDN (Content Delivery Network) brings storage service to end-users proximity to provide low latency, high data throughput, and low traffic pressure to handle the problems mentioned above. Due to the limited storage capacity of the edge, only in-demand content should cache. Therefore, to optimally utilized the cache space, an efficient content caching and replacement policy is needed. To this end, in this paper, we propose an optimal content replacement algorithm. In this algorithm, a video request pattern is first generated based on a publicly available dataset. After that, a machine learning model is trained on cache logs data. As a result, the predicted video is deleted from the edge to make space for new videos. A real-time testbed is built on KOREN to check the performance of our model. The results based on MAE, MSE, and R-2 show that our model performs well in real-time scenarios.

Intent-Based Networking (IBN) is a model that enables proactive network control and automation to satisfy high-level demands. It follows a closed-loop mechanism that abstracts the network complexity and dynamically allows updates to the network monitoring and intelligence. Hence, it eliminates the traditionally practiced error-prone manual network control. In contrast with the traditional reactive approach, IBN-based approach promotes the proactive and dynamic solution. To this end, IBN uses machine learning (ML) to predict the future and keeps a balance between the intended and actual state. Hence, this work considers an IBN-driven routing mechanism to ensure service provisioning with the desired quality of service (QoS). In contrast to the traditional static routing scheme, this work considers AI-driven dynamic and proactively updatable routing schemes to ensure QoS. This work demonstrates AI-driven service route control mechanism for ensuring quality of service (QoS) on top of Korea Advanced Research Network-Software Defined Infrastructure (KOREN-SDI). More precisely, an ML-based approach is used to find the best path between source and destination nodes. In addition, it incorporates IBN closed-loop process to monitor and update service routes proactively based on predicted future link utilization. This novel approach introduces proactive updates on runtime to avoid future failures and ensures seamless service provisioning with the fulfillment of QoS demands by changing traffic routes dynamically.