High security and low latency are important in mission-critical data transmission, such as the end-to-end transmission in Industrial IoT (IIoT). However, existing schemes often struggle to simultaneously meet these demanding requirements due to hardware limitations and the lack of a packet lossless forwarding protocol in the network layer data plane. To address this challenge, we propose OCEAN (Optional Capability-based En route Acknowledgement in Network layer). OCEAN includes (1) an in-network caching hardware, which is a programmable Application Specific Integrated Circuit (ASIC) integrated with a Field Programmable Gate Array (FPGA), and (2) a packet lossless forwarding protocol in the network layer data plane. In OCEAN, each packet was generated by an authorized end device, while each en route node verifies the packet, and caches it until receiving the acknowledgment from the next en route node. It incurs negligible latency to packet forwarding when there is no packet loss while retransmitting the packet at the en route node after a short timeout, which reduces the packet forwarding latency. Besides that, the per-packet verification guarantees that the adversary could not subvert the forwarding protocol. Our simulation in the BMv2 environment confirms its functionality, and the hardware implementation demonstrates that it can process packets at line rate with a total processing latency ranging from 2519 ns to 6160 ns, which is negligible in end-to-end transmission.

The paradigm of Intelligent DataPlane (IDP) embeds deep learning (DL) models on the network dataplane to enable intelligent traffic analysis at line-speed. However, the current use of the match-action table (MAT) abstraction on the dataplane is misaligned with DL inference, leading to several key limitations, including accuracy degradation, limited scale, and lack of generality. This paper proposes Pegasus to address these limitations. Pegasus translates DL operations into three dataplane-oriented primitives to achieve generality: Partition, Map, and SumReduce. Specifically, Partition "divides" high-dimensional features into multiple low-dimensional vectors, making them more suitable for the dataplane; Map "conquers" computations on the low-dimensional vectors in parallel with the technique of Fuzzy Matching, while SumReduce "combines" the computation results. Additionally, Pegasus employs Primitive Fusion to merge computations, improving scalability. Finally, Pegasus adopts full-precision weights with fixed-point activations to improve accuracy. Our implementation on a P4 switch demonstrates that Pegasus can effectively support various types of DL models, including Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and AutoEncoder models on the dataplane. Meanwhile, Pegasus outperforms state-of-the-art approaches with an average accuracy improvement of up to 22.8%, along with up to 248× larger model size and 212× larger input scale.

In-Network Computing (INC) has found many applications for performance boosts or cost reduction. However, given heterogeneous devices, diverse applications, and multi-path network typologies, it is cumbersome and error-prone for application developers to effectively utilize the available network resources and gain predictable benefits without impeding normal network functions. Previous work is oriented to network operators more than application developers. We develop ClickINC to streamline the INC programming and deployment using a unified and automated workflow. Click-INC provides INC developers a modular programming abstractions, without concerning to the states of the devices and the network topology. We describe the ClickINC framework, model, language, workflow, and corresponding algorithms. Experiments on both an emulator and a prototype system demonstrate its feasibility and benefits.

This letter proposes a new method for boresight misalignment calibration of the charge-coupled device (CCD) camera which is one component of an airborne light detection and ranging (LiDAR) system without ground control points (GCPs). In the calibration, tie points in overlapping areas are first selected, and then, a multibaseline forward intersection is used for calculating object coordinates of these points. In the intersection, exterior elements of the CCD camera are obtained directly from positioning and orientation system (POS) data of the LiDAR system, which are error contaminated mainly due to the unparallel relation between the frameworks of the inertial measurement unit of the POS and the CCD camera. Elevation values of the ground points are then refined by those obtained from LiDAR point clouds by interpolation, which can be considered to be more accurate than those obtained by multibaseline forward intersection. Through projecting the ground points with refined elevation values into the image space by collinear equations and minimizing distances between the image points selected manually and those projected from ground points, the boresight misalignment is removed effectively. Therefore, the proposed method without GCPs in the whole process is more flexible than other traditional photogrammetric ways.