The Named-data Link State Routing protocol (NLSR) is a protocol for intra-domain routing in Named Data Networking (NDN). It is an application level protocol similar to many IP routing protocols, but NLSR uses NDN’s Interest/Data packets to disseminate routing updates, directly benefiting from NDN’s built-in data authenticity. The NLSR design, which was first developed in 2013 and deployed on the NDN testbed in August 2014, has undergone significant changes. Following an application-driven design approach, NLSR’s development helped drive the development of the trust/security functionality of NDN libraries as well as a number of features in NFD and ChronoSync. In this paper, we describe the current design and implementation of NLSR, with emphasis on those features that differentiate it from an IP-based link state routing protocol – (1) naming: a hierarchical naming scheme for routers, keys, and routing updates; (2) security: a hierarchical trust model for routing within a single administrative domain; (3) routing information dissemination: using ChronoSync to disseminate routing updates; and (4) multipath routing: a simple way to calculate and rank multiple forwarding options. Although NLSR is designed in the context of a single domain, its design patterns may offer a useful reference for future development of inter-domain routing protocols.

A medical sensor network can wirelessly monitor vital signs of humans, making it useful for long-term health care without sacrificing patient comfort and mobility. For such a network to be viable, its design must protect data privacy and authenticity given that medical data are highly sensitive. We identify the unique security challenges of such a sensor network and propose a set of resource-efficient mechanisms to address these challenges. Our solution includes (1) a novel two-tier scheme for verifying the authenticity of patient data, (2) a secure key agreement protocol to set up shared keys between sensor nodes and base stations, and (3) symmetric encryption/decryption for protecting data confidentiality and integrity. We have implemented the proposed mechanisms on a wireless mote platform, and our results confirm their feasibility.

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.

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.

Task allocation systems in the Cloud have been recently proposed so that their performance is optimised in real-time based on reinforcement learning with spiking Random Neural Networks (RNN). In this paper, rather than reinforcement learning, we suggest the use of multi-layer neural network architectures to infer the state of servers in a dynamic networked Cloud environment, and propose to select the most adequate server based on the task that optimises Quality of Service. First, a procedure is presented to construct datasets for state classification by collecting time-varying data from Cloud servers that have different resource configurations, so that the identification of server states is carried out with supervised classification. We test four distinct multi-layer neural network architectures to this effect: multi-layer dense clusters of RNNs (MLRNN), the hierarchical extreme learning machine (H-ELM), the multi-layer perceptron, and convolutional neural networks. Our experimental results indicate that server-state identification can be carried out efficiently and with the best accuracy using the MLRNN and H-ELM.