Zero-Shot Anomaly Detection (ZSAD) is an emerging AD paradigm. Unlike the traditional unsupervised AD setting that requires a large number of normal samples to train a model, ZSAD is more practical for handling data-restricted real-world scenarios. Recently, Multimodal Large Language Models (MLLMs) have shown revolutionary reasoning capabilities in various vision tasks. However, the reasoning of image abnormalities remains underexplored due to the lack of corresponding datasets and benchmarks. To facilitate research in AD & reasoning, we establish the first visual instruction tuning dataset, Anomaly-Instruct-125k, and the evaluation benchmark, VisA-D&R. Through investigation with our benchmark, we reveal that current MLLMs like GPT-4o cannot accurately detect and describe fine-grained anomalous details in images. To address this, we propose Anomaly-OneVision (Anomaly-OV), the first specialist visual assistant for ZSAD and reasoning. Inspired by human behavior in visual inspection, Anomaly-OV leverages a Look-Twice Feature Matching (LTFM) mechanism to adaptively select and emphasize abnormal visual tokens. Extensive experiments demonstrate that Anomaly-OV achieves significant improvements over advanced generalist models in both detection and reasoning. Extensions to medical and 3D AD are provided for future study. The link to our project page: https://xujiacong.github.io/Anomaly-OV/

This paper centers around a multiple-input-multiple-output (MIMO) visible light communication (VLC) system, where an unmanned aerial vehicle (UAV) benefits from a light emitting diode (LED) array to serve photo-diode (PD)equipped users for illumination and communication simultaneously. Concerning the battery limitation of the UAV and considerable energy consumption of the LED array, a hybrid dimming control scheme is devised at the UAV that effectively controls the number of glared LEDs and thereby mitigates the overall energy consumption. To assess the performance of this system, a radio resource allocation problem is accordingly formulated for jointly optimizing the motion trajectory, transmit beamforming and LED selection at the UAV, assuming that channel state information (CSI) is partially available. By reformulating the optimization problem in Markov decision process (MDP) form, we propose a soft actor-critic (SAC) mechanism that captures the dynamics of the problem and optimizes its parameters. Additionally, regarding the high mobility of the UAV and thus remarkable rearrangement of the system, we enhance the trained SAC model by integrating a meta-learning strategy that enables more adaptation to system variations. By defining energy efficiency as a trade-off between the data rate and power consumption, simulations verify that upgrading a single-LED UAV by an array of 10 LEDs, exhibits 47 % and 34 % improvements in data rate and energy efficiency, albeit at the expense of 8 % more power consumption.

The proliferation of battery-equipped smart devices in Internet of Things (IoT) applications has underscored the critical need for energy-efficient communication and computation solutions to extend lifetime. Meanwhile, the complex nature of mobile IoT networks’ communications poses significant challenges. Software-Defined Networking (SDN) offers minimized device-related overheads associated with processing and computations by centralizing energy-intensive tasks. The employed central controller in SDN can effectively follow, and manage the continuous topological alterations in dynamic mobile environments, thereby mitigating energy overhead imposed on individual IoT devices. On the other hand, clustering can further improve energy efficiency in IoT networks by reducing the number of transmissions, aggregating data efficiently, balancing the load among nodes, and optimizing routing paths. Accordingly, this paper introduces EDEN; an energy-aware SDN-based routing and clustering approach for mobile IoT networks to reduce energy consumption, increase network lifetime, and enhance reliability in the network in terms of Packet Delivery Ratio (PDR). To determine the optimal number of clusters and ensure a balanced distribution, EDEN utilizes a dynamic genetic algorithm to adaptively determine mutation and crossover rates. In selecting cluster heads, EDEN incorporates multiple objective function parameters, including node centrality, remaining energy, and distance, to optimize energy efficiency. Furthermore, EDEN employs a path prediction algorithm based on the LSTM Neural Network (NN) to forecast the trajectory of the mobile nodes to maintain cluster stability and reduce the frequency of re-clustering, thereby enhancing both energy efficiency and reliability. Extensive simulations in the NS3 environment demonstrate the effectiveness of the proposed solution, showing improvements in energy consumption by at least 32% while improving PDR by more than 98% compared to the state-of-the-art.

Employing point cloud and 3D data has recently proliferated with the advent of emerging IoT applications, e.g., Autonomous Vehicles (AV), and Augmented Reality/Virtual Reality (AR/VR), with their resource-limited IoT devices equipped with Light Detection and Ranging (LiDAR) sensors. While offloading the point cloud computations could improve energy-efficiency, the imposed bandwidth and energy dissipation due to transmitting a significant volume of 3D data is still challenging. This issue gets more complicated due to the instability caused by the noisy channel conditions, which leads to the cliff effect and reduced object detection accuracy. Accordingly, this paper introduces NORRIS; a semantic-based point cloud data transmission technique that mitigates transmitted data while enhancing resilience against channel noise. NORRIS utilizes joint source-channel coding in combination with transfer learning. It emphasizes efficiency and performance, providing a lightweight model that sustains excellent performance across different channel conditions, making it ideal for resource-constraint IoT devices. While NORRIS provides high detection accuracy, it significantly reduces the inference time and model complexity. The effectiveness of NORRIS is evaluated and compared with state-of-the-art through a comprehensive set of experiments. Results demonstrate that NORRIS successfully overcomes the cliff effect while guaranteeing a higher performance in lower Signal-to-Noise Ratio (SNR) conditions. It also reduces the transmitted data volume by up to 10x, while accelerating meaning extraction by 5x. It also enhances accuracy by approximately 10% in various SNR scenarios. Furthermore, NORRIS maintains stable classification performance under noise, with only a 5% accuracy drop, in contrast to the 60% decrease observed in existing approaches.

RPL is introduced to conduct path selection in Low-power and Lossy Networks (LLN), including IoT. A routing policy in RPL is governed by its objective function, which corresponds to the requirements of the IoT application, e.g., energy-efficiency, and reliability in terms of Packet Delivery Ratio (PDR). In many applications, it is not possible to connect the nodes to the power outlet. Also, since nodes may be geographically inaccessible, replacing the depleted batteries is infeasible. Hence, harvesters are an admirable replacement for traditional batteries to prevent energy hole problem, and consequently to enhance the lifetime and reliability of IoT networks. Nevertheless, the unstable level of energy absorption in harvesters necessitates developing a routing policy, which could consider harvesting aspects. Furthermore, since the rates of absorption, and consumption are incredibly dynamic in different parts of the network, learning-based techniques could be employed in the routing process to provide energy-efficiency. Accordingly, this paper introduces LANTERN; a learning-based routing policy for improving PDR in energy-harvesting IoT networks. In addition to the rate of energy absorption, and consumption, LANTERN utilizes the remaining energy in its routing policy. In this regard, LANTERN introduces a novel routing metric called Energy Exponential Moving Average (EEMA) to perform its path selection. Based on diversified simulations conducted in Cooja, with prolonging the lifetime of the network by$5.7\times $, and mitigating the probability of energy hole problem, LANTERN improves the PDR by up to 97%, compared to the state-of-the-art. Also, the consumed energy per successfully delivered packet is reduced by 76%.

Due to the real-time requirements in several IoT applications, fog computing has emerged to overcome the long latency and other constraints of cloud computing. Due to the high probability of packet loss, energy limitation of IoT devices, and the external disturbances that may frequently occur on the fog infrastructure, the timing constraints of real-time tasks may be compromised. Therefore, the reliability of executing real-time tasks has always been a significant challenge in fog computing. In addition to the correct execution of the tasks, it is also important to execute them before their deadlines according to their real-time classification. State-of-the-art methods generally focus on the delay or functionality of tasks in fog computing systems. However, those methods do not widely focus on the reliability of tasks with real-time constraints in dynamic environments. In this article, a novel primary backup task assignment strategy based on machine learning (ReLIEF) is proposed to improve the reliability of fog-based IoT systems. To identify suitable nodes for the execution of the primary and backup tasks, ReLIEF employs a reinforcement learning (RL) approach, which has an outstanding performance in dynamic environments by establishing a balance between communication delay and workload on each fog device. Based on the simulations, our newly proposed technique has been able to reduce the amount of task dropping rate by up to 84% against the state of the art. Moreover, it is capable of balancing the workload distribution while increasing the reliability of the system by nearly 72% compared with its counterparts.

Mobile portable embedded devices are becoming an integral part of our daily activities in the vision of Internet of Things (IoT). Nevertheless, due to lack of mobility support in the IPv6 routing protocol for low-power and lossy networks (RPLs), which is standardized for multihop IoT infrastructures, providing reliable communications in terms of packet delivery ratio (PDR) in mobile IoT applications has become significantly challenging. While several studies tried to enhance the adaptability of RPL to network dynamics, their utilized routing metrics have prevented them from establishing long-lasting reliable paths. Furthermore, the stochastic parent replacement policy in the standard version of RPL has intensified this challenge. Aside from this, due to the existing tradeoff between reliability and power efficiency, most of the existing approaches have only concentrated on one of these concerns without paying attention to the other one. To address these issues, this article introduces ARMOR, a routing mechanism built upon RPL, which employs a novel mobility-aware routing metric, i.e., time to reside (TTR), and a corresponding parent replacement policy. According to the motion characteristics of the mobile objects, TTR provides an estimation of how long the nodes will be in the transmission range of each other. This enables ARMOR to select nodes, which provide longer connection period and consequently higher reliability. In comparison with the state of the art, while keeping the power consumption constant, ARMOR significantly improves the amount of PDR in the network by up to$2.5\times $, while it enhances the reliability against the original version of this protocol by up to$4.2\times $.

Reliability of routing mechanisms in wireless networks is typically measured with Packet Delivery Ratio (PDR). Basically, PDR is reported with an optimistic assumption that the topology is fully constructed, and the nodes have started their packet transmission. This is despite the fact that prior to being able to transmit packets, nodes must first join the network, and then try to keep connected as much as possible. This is a key factor in the overall reliability provided by the routing protocols, especially in mobile IoT applications, where disconnections occur frequently. Nevertheless, there is a lack of appropriate metrics, which could evaluate the routing mechanisms from this perspective. Accordingly, this paper introduces attachability; a new metric for evaluating the capability of routing protocols in assisting the mobile or stationary nodes in joining, and maintaining their connections to the network. Our newly proposed metric is calculated via Markov chain analysis along with the sample frequency-based estimating technique. To evaluate attachability, we have simulated a mobile IoT infrastructure, and conducted a comprehensive set of experiments on different versions of the IPv6 Routing Protocol for Low-power and lossy networks (RPL). Based on our observations, attachability is significantly dependent on the employed metrics and path selection policies in the routing mechanisms. Among the three different versions of RPL, including the original version (ORPL), which is standardized for stationary IoT applications, and two mobility-aware versions, i.e., MARPL, and OMARPL, OMARPL showed up to 42%, and 10% of improvement in terms of attachability against ORPL, and MARPL, respectively.

Energy consumption is a major challenge in IoT devices, which was aimed to be improved by employing energy-efficient objective functions (OFs) in the structure of the RPL routing protocol. Meanwhile, the majority of the existing OFs mainly perform the parent selection based on the gathered information from the routing layer. Nevertheless, based on our investigations, there exists a series of transmission operations in the medium access control (MAC) layer, which significantly affects the energy consumption in IoT devices. Therefore, in this article, we propose ELITE, an energy-efficient cross-layer OF, which introduces a novel routing metric, called strobe per packet ratio (SPR). SPR indicates the number of transmitted strobes per packet due to radio duty cycling (RDC) policies in the MAC layer. This newly defined metric, which has been designed to be coupled with asynchronous MAC protocols, could be differentiated node by node and based on the existing relative phase shift between the communicating nodes. In this regard, the ELITE tries to select a path, which imposes less number of strobe transmissions to its nodes. According to the evaluation results, while ELITE could reduce the average amount of required strobes per packet by up to 25%, it can significantly improve the average amount of consumed energy in an IoT node by up to 39% compared to its counterpart OFs.