Hyperspectral (HS) sensing provides detailed high-dimensional spectral data to identify subtle anomalies and material variations in complex scenes. HS anomaly detection (HS-AD) aims to identify small, spectrally distinct objects or materials in HS imagery without prior target information. However, most HS-AD methods assume that the training and test data are drawn from the same distribution (single-domain). This assumption is often violated in operational sensing because of differences in sensor characteristics, scene composition, atmospheric conditions, and acquisition geometry. To address this challenge, we propose Generalized MoE-AD (GMoE-AD), a neural Mixture-of-Experts (MoE) architecture for robust cross-domain hyperspectral anomaly detection. The framework fuses the outputs of six unsupervised base detectors with representations from a pretrained HS foundation model, combines four neural experts through learned top-2 routing, and applies gradient-reversal-based domain-adversarial training to improve robustness under distribution shifts. A drift-aware test-time adaptation (DTA) variant is evaluated separately. We evaluate GMoE-AD on six public real-world HS benchmark datasets—San-Diego, Salinas, HYDICE-Urban, ABU-Airport, ABU-Beach, and ABU-Urban—and one private Arizona dataset comprising 22 images acquired by four different sensors. Under all-domain training, the model uses the training portions of all seven datasets and is evaluated without access to domain identity or dataset-specific information. GMoE-AD achieves an average ROC-AUC of 0.943, PR-AUC of 0.623, and F1-macro of 0.821 in this setting. In a leave-one-dataset-out (LODO) evaluation, where the target dataset is completely unseen during training, the model maintains an average ROC-AUC of 0.910 and F1-macro of 0.773. The optional DTA variant increases mean ROC-AUC from 0.938 to 0.953 but reduces F1-macro from 0.822 to 0.815, indicating a metric- and dataset-dependent adaptation trade-off. These results suggest that combining neural expert routing, transfer learning, and domain-adversarial representation learning improves robustness across heterogeneous HS datasets and provides a unified approach to anomaly detection in high-dimensional sensor data, supporting deployment in real-world hyperspectral applications where training and deployment conditions differ.

Name-based Access Control (NAC) in Named Data Networking (NDN) enforces fine-grained access control by encrypting data at production time with keys across multiple granularities following structured name hierarchies. NAC-ABE extends NAC with Attribute-Based Encryption (ABE) to improve scalability. However, the current NAC-ABE library relies on Ciphertext-Policy ABE (CP-ABE), which requires access policies to be fixed at encryption time. This is problematic for mobile health (mHealth) applications where access policies evolve dynamically. NAC-ABE also generates a new content key for each data packet, incurring significant overhead for high-frequency data streams, and lacks trust-schema-based data validation, leaving it vulnerable to unauthorized data injection. We present enhancements to the NAC-ABE library in the context of mGuard, a secure real-time mHealth data sharing system. Evaluation on Mini-NDN shows that our approach reduces content key overhead by several orders of magnitude, enabling practical real-time encrypted data sharing for mHealth and beyond.

The TCP/IP protocol stack uses IP addresses for two distinct roles: identifying hosts and locating their attachment points in the network topology. This dual purpose creates a fundamental tension that has led to routing and forwarding scalability challenges throughout the history of the Internet in unicast packet delivery and, more notably, in multicast delivery. This paper reviews the evolution of routing scalability solutions over the years and makes four observations. First, map-and-encap is a recurring architectural solution shared by all scalable unicast and multicast delivery methods, developed independently across different problem contexts. Second, a new solution tends to succeed when it can bring immediate local gains to early adopters without requiring coordination across administrative domains. Third, network routing and forwarding designs that depend on external factors, such as the number of distinct end sites or even application-specific deliveries, inherently preclude an upper bound on their scalability. Fourth, today's inter-domain routing protocol, BGP, lacks a topological abstraction equivalent to an egress router within a routing domain, thereby inherently preventing a map-and-encap solution for scalability. These observations offer insights into the design of future scalable routing system architectures.

A 1995 SIGCOMM paper, "A Reliable Multicast Framework for Light-weight Sessions and Application-Level Framing", commonly known as SRM, explored a fundamentally new approach to reliable multiparty data delivery. Rather than adapting established sender-driven reliable unicast mechanisms to multicast, as most contemporaneous proposals did, SRM introduced a data-centric model in which data receivers recover losses by explicitly requesting missing data. Thirty years later, we revisit the SRM framework, examining the challenges it faced, the lessons learned, and its influence on the later development of Named Data Networking (NDN). Experimentations with SRM revealed a fundamental semantic mismatch between its data-centric framework and IP's address-based delivery; while the application layer named data, the network layer remained 'blind' to those names, resulting in inefficient loss recovery. NDN resolves this architectural friction by aligning network delivery with the data-retrieval model and by securing data directly rather than securing communication channels. This retrospective highlights how early insights from SRM informed key design decisions in NDN and illustrates how NDN's design emerged from the cumulative insights gained over decades of networking research and development.

Accurate crop mapping-identifying both the location and types of crops-is crucial for effective agricultural planning and informed decision-making. Advances in remote sensing, notably hyperspectral imagery from unmanned aerial vehicles (UAV), greatly enhance the efficiency and accuracy of crop mapping, reducing the reliance on traditional, labor-intensive field surveys. However, applying deep classifiers directly to hyperspectral data can lead to overfitting. Conversely, deep semantic segmentation models may struggle due to limited annotated hyperspectral imagery. To address this problem, we propose enhancing a U-Net-style model-originally trained on RGB imagery-by incorporating task adaptation, a custom loss function, and a spectral attention mechanism to better optimize it for hyperspectral data and improve crop mapping performance. Our proposed segmentation network achieved 76.6% accuracy and a 74.9% Dice score on a UAV-acquired hyperspectral agricultural dataset, which is competitive and well-rounded compared to other state-of-the-art methods while offering significantly lower computational complexity.

The Bluetooth Special Interest Group (SIG) introduced the Bluetooth Low Energy Mesh (BLE Mesh) network specification in 2017, enabling multi-to-multi communication capability for devices operating on the Bluetooth Low Energy protocol. This specification has made BLE mesh network versatile for a range of Internet of Things (IoT) applications, particularly in building lighting and smart home systems. However, the existing BLE mesh network specification employs a managed-flood-based mechanism at the network layer for message dissemination, resulting in both message redundancy and unnecessary energy expenditure. This paper makes two innovative contributions to address these shortcomings: 1) Introduction of a broadcast routing protocol based on Q-learning algorithms. This approach enables network nodes to optimally select the next-hop relay node utilizing localized Q-value tables, thereby substantially mitigating data packet redundancy within the network. 2) Formulation of a comprehensive set of scanning-broadcasting strategies. These strategies not only ensure the reliable transmission of data packets but also facilitate a low-power standby mode for the majority of the network nodes' operational time, thereby enhancing the overall energy efficiency of the network. Based on the results of our simulation experiments, the proposed methodology significantly enhances the longevity of nodes while concurrently minimizing message redundancy within BLE mesh network.

Named Data Networking (NDN) takes a data-centric design approach to data delivery, which intrinsically enables asynchronous communication. That is, communicating entities can exchange data effectively even when they are not directly connected or online at the same time, as long as everyone can receive all its requested data. NDN makes data available through persistent in-network data repository, or repo for short, which is an integral component in the NDN architecture. In this paper, we first articulate the important role repos play in an NDN network, and then present the design of a simple repo protocol, PythonRepo, which has been used in several NDN applications. We also identify remaining work to be done to make PythonRepofullfil the needs of future NDN applications.

Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport–Beach–Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach.

Wireless Sensor Networks (WSN) play a important part in collecting and transmitting information. However, the limited energy supply of sensor nodes poses a significant challenge to the longevity and performance of these networks. Energy Harvesting Wireless Sensor Networks (EHWSN) have been proposed as a solution to this problem. This paper presents a novel routing algorithm for EHWSN with the aim of improving energy efficiency, stability, and reliability. Initially, a centralized approach is employed, utilizing the k-means algorithm for clustering and genetic algorithms for optimizing cluster head routing. Subsequently, a distributed approach is employed to replace cluster heads if their energy levels fall below the predetermined threshold. Simulations conducted in MATLAB showcase advantages over conventional routing algorithms in terms of prolonging the lifespan of networks and enhancing energy utilization.

Although multiple routing protocols and forwarding strategies have been proposed for NDN, there is a lack of understanding about the synergy between them. In this work, we investigate the effectiveness of NDN forwarding strategy, routing, and the combination of the two in maximizing data fetching success in the face of network failures. Through emulation experiments, we first evaluate the ASF (Adaptive SRTT-based Forwarding) strategy and NLSR (Named-data Link State Routing) protocol separately. Our results show that ASF with static routing outperforms NLSR with best-route strategy in most cases, although its data delivery performance exhibits a bias toward popular producers. We then conduct experiments that combine ASF and NLSR. The results show that the combination of ASF with frequent probing and NLSR with slow routing adaptation leads to better data delivery performance than using either ASF or NLSR alone. Our results provide insights into the future design and deployment of routing and forwarding strategies in NDN networks.

Packet encapsulation is a general network technique that provides an essential building block for constructing secure networks. While extensively used in IP networks over the last few decades, secure packet encapsulation remains largely unexplored in the context of Named Data Networking (NDN) networks. NDN represents a radical departure from traditional endpoint-oriented networking by making secured data the centerpiece of communication. This new data-centric design brings both advantages and new challenges for the development of secure packet encapsulation that can preserve essential properties of an NDN network, including in-network data caching and builtin multicast data delivery. In this paper, we first identify the major differences between encapsulation solution designs in IP and NDN, highlighting the ensuing challenges, both inherent and practical. We then present a novel design to achieve secure NDN data packet encapsulation, and showcase an implementation suite that enables efficient fetching of securely encapsulated data. 1 1 The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. Distribution Statement “A” (Approved for Public Release, Distribution Unlimited)

Name-based access control (NAC) over NDN provides fine-grained data confidentiality and access control by encrypting and signing data at the time of data production. NAC utilizes specially crafted naming conventions to define and enforce access control policies. NAC-ABE, an extension to NAC, uses an attribute-based encryption (ABE) scheme to support access control with improved scalability and flexibility. However, existing NAC-ABE libraries are based on ciphertext-policy ABE (CP-ABE), which requires knowledge of the access policy when encrypting data packets. In some applications, including mHealth, the data access policy is unknown at the time of data generation, while data attributes and properties are known. In this paper, we present an extension to the existing NDN-ABE library which can be used by mHealth and other applications to enforce fine-granularity access control in data sharing. We also discuss the challenges we encountered during the application deployment, and remaining open issues together with potential solution directions.

Millions of matrix dimensions in matrix multiplication will have high requirements on node computing power and storage space. Coded Distributed Computing (CDC) can solve this problem by dividing large-dimensional matrices into small matrices and then assigning them to machines in the computing cluster to perform matrix multiplication in parallel. In order to adapt to the reality that computer clusters are usually composed of heterogeneous workers with different computing capabilities, and overcome the performance limitations of CDC based on the isomorphism of computing power, Coded Elastic Computing (CEC) is proposed. However, the existing CEC discards the received information and directly starts a new round of computation after an elastic event occurs, resulting waste of computing time and resources. In this paper, we propose to employ the received information to redesign the allocation scheme. We first determine the offline machine number and the data segment it should have returned as the missing part of decoding that needs to be recomputed. We then count the total number of lost data for each segment of data and calculate the amount of tasks that each machine should undertake. Finally, the amount of tasks actually undertaken by each machine is calculated by solving the system of linear equations. Through experiments, we show the effectiveness of our proposed allocation scheme, in terms of saving resources and time, and accelerating the calculation speed, when compared with the original scheme.

Different types of 3D sensors, such as LiDAR and RGB-D cameras, capture data with different resolution, range, and noise characteristics. It is often desired to merge these different types of data together into a coherent scene, but automatic alignment algorithms generally assume that the characteristics of each fragment are all similar. Our goal is to evaluate the performance of these algorithms on data with different characteristics to enable the integration of data from multiple types of sensors. We use the Redwood dataset, which has high-resolution scans of several different environments captured using a stationary LiDAR scanner. We first develop a method to emulate the capture of these environments as viewed by different types of sensor by leveraging OpenGL and a mesh creation process. Next, we take fragments of these captures which represent scenarios in which each type of sensor would be used, using our scanning experience to inform the selection process. Finally, we attempt to merge the fragments together using several automatic algorithms and evaluate how the results compare with the original scenes. We evaluate based on transformation similarity to ground truth, algorithm speed and ease of use, and subjective quality assessments.

An accurate model of building interiors with detailed annotations is critical to protecting first responders and building occupants during emergencies. First responders and building occupants can use these 3D building models to navigate indoor environments or vacate the building safely. In collaboration with the City of Memphis, we have collected extensive LiDAR and video data from seven buildings in Memphis. We apply machine learning techniques to the video frames to detect and classify objects of interest to first responders. We then utilize data fusion methods on the LiDAR and image data to create a comprehensive colored 3D indoor point cloud dataset with labeled safety-related objects. This paper documents the challenges we encountered in data collection and processing, and it presents a complete 3D mapping and labeling system for the environments inside and adjacent to buildings. Moreover, we used two of the scanned buildings as a case study to illustrate our process and show detailed evaluation results. Our results show that the deep neural network Mask R-CNN with transfer learning and hard-negative mining performs well in labeling public-safety objects in our image dataset, especially for large objects.

Exploratory efforts in mobile health (mHealth) data collection and sharing have achieved promising results. However, fine-grained contextual access control and real-time data sharing are two of the remaining challenges in enabling temporally-precise mHealth intervention. We have developed an NDN-based system called mGuard to address these challenges. mGuard provides a pub-sub API to let users subscribe to real-time mHealth data streams, and uses name-based access control policies and key-policy attribute-based encryption to grant fine-grained data access to authorized users based on contextual information. We evaluate mGuard's performance using sample data from the MD2K project.

Distributed dataset synchronization, or Sync in short, plays the role of a transport service in the Named Data Networking (NDN) architecture. A number of NDN Sync protocols have been developed over the last decade. In this paper, we conduct a systematic examination of NDN Sync protocol designs, identify common design patterns, reveal insights behind different design approaches, and collect lessons learned over the years. We show that (i) each Sync protocol can be characterized by its design decisions on three basic components - dataset namespace representation, namespace encoding for sharing, and change notification mechanism, and (ii) two or three types of choices have been observed for each design component. Through analysis and experimental evaluation, we reveal how different design choices influence the latency, reliability, overhead, and security of dataset synchronization. We also discuss the relationship between transport and application naming, the implications of namespace encoding for Sync group scalability, and the fundamental reason behind the need for Sync Interest multicast.

Named Data Networking (NDN) provides native support for multi-party communication. However, the current NDN forwarder lacks a duplicate suppression mechanism for multicasting in a multi-access network, potentially leading to network congestion and significant degradation in overall packet delivery performance. In this paper, we introduce Adaptive Duplicate Suppression (ADS) for one-hop multicasting in multi-access NDN networks. ADS utilizes the duplicate count per Interest and Data name observed in the network to dynamically adjust the suppression time that a node waits before forwarding a packet. We have implemented ADS in the NDN forwarding daemon (NFD) and assessed its performance using Mini-NDN. Our evaluation demonstrates that ADS can effectively reduce redundant network traffic under various network conditions, resulting in significantly improved application goodput and reduced transfer times.

Service discovery is a crucial component in today's massively distributed applications. In this paper, we propose NDNSD - a fully distributed and general-purpose service discovery protocol for Named Data Networking (NDN). By leveraging NDN's data synchronization capability, NDNSD offers a high-level API for service publishing and discovery. We present NDNSD's main design features including hierarchical naming, service information specification, and service accessibility. We also implemented two other discovery schemes, one reactive and one proactive, and compared them with NDNSD. Our evaluation shows that NDNSD achieves (a) lower latency, lower overhead, and same reliability compared to the reactive scheme, and (b) comparable latency, lower overhead at larger scale, and higher reliability compared to the proactive scheme.

As more and more mobile data is collected continuously in space and time for a variety of purposes such as health monitoring and driving behavior tracking, people are increasingly concerned about their privacy when sharing their data. To minimize privacy leakage, data owners often want to restrict the access to their data based on space and time constraints while meeting each data user’s specific needs. In this paper, we introduce an access control system over Named Data Networking (NDN) that allows data owners to schematize and control data access at a fine granularity with respect to time, space, and user needs. More specifically, we designed spatio-temporal access control policies using hierarchically structured names, extended Name-based Access Control to support spatio-temporal policies, and incorporated publish-subscribe functionality for real-time data sharing. Moreover, we developed an NDN-based prototype based on our design and evaluated its performance in sharing both real-time and historical data.