Joint species distribution modelling (JSDM) is a widely used statistical method that analyzes combined patterns of all species in a community, linking empirical data to ecological theory and enhancing community-wide prediction tasks. However, fitting JSDMs to large datasets is often computationally demanding and time-consuming. Recent studies have introduced new statistical and machine learning techniques to provide more scalable fitting algorithms, but extending these to complex JSDM structures that account for spatial dependencies or multi-level sampling designs remains challenging. In this study, we aim to enhance JSDM scalability by leveraging high-performance computing (HPC) resources for an existing fitting method. Our work focuses on the Hmsc R-package, a widely used JSDM framework that supports the integration of various dataset types into a single comprehensive model. We developed a GPU-compatible implementation of its model-fitting algorithm using Python and the TensorFlow library. Despite these changes, our enhanced framework retains the original user interface of the Hmsc R-package. We evaluated the performance of the proposed implementation across various model configurations and dataset sizes. Our results show a significant increase in model fitting speed for most models compared to the baseline Hmsc R-package. For the largest datasets, we achieved speed-ups of over 1000 times, demonstrating the substantial potential of GPU porting for previously CPU-bound JSDM software. This advancement opens promising opportunities for better utilizing the rapidly accumulating new biodiversity data resources for inference and prediction.

The industrial environment augments resource-constrained devices to bring services closer to autonomous devices. However, over time, these devices get overburdened due to computational workload, which results in degraded network performance. Therefore, the devices are programmed to share resources with nearby devices. However, owing to real-time collaboration, there is the possibility that the device moves to an undefined state and starts behaving maliciously. This can impact the entire collaborative environment laid to meet the industrial product deadline. In this article, we propose an industrial simulation framework that enables the resource-sharing environment and identifies the undefined device behavior. Furthermore, our detection scheme is based on an intelligent model trained on device behavior through the machine-in-a-loop mechanism and deployed at network intersections, i.e., edge nodes. The proposed technique improves the efficiency of the collaborative network by 30%.

Connected vehicle technology is rapidly evolving. In the U.S., the government targets that 50% of all vehicles be electric by 2030 in order to reduce climate change. This initiative comes in the wake of an increased spate of ransomware attacks targeting transportation companies. Unlike traditional ransomware targeting computer networks, the compromise of vehicles can have disastrous consequences on businesses and ultimately the national economy as was evident in the recent Colonial pipeline attack. This research proposes a novel framework to study the spread of ransomware and its impact on connected vehicles. Our contribution is fourfold: 1) three different ransomware infection vectors are considered for connected vehicles, namely, a hotspot attack, OBD dongle attack, and malicious over-the-air updates; 2) business impact of the ransomware is analyzed on a ride-hailing service; 3) a fog computing architecture is used to reduce latency in vehicle-to-vehicle communication and vehicle to base station communication; and 4) the effectiveness of safeguard controls are studied in mitigating the ransomware spread. Our results show that ransomware can have a debilitating impact on connected vehicle businesses due to their high mobility and connectivity with attacks on average impacting earnings by 45% per hour.

With an ever-increasing number of connected devices on roads, it becomes unsustainable to provide nearby specialized execution resources (compute and storage) for servicing innovative applications. Moreover, the vehicular environment being inherently ad hoc and opportunistic, not to mention highly mobile, makes it unsuitable to use traditional cloud computing due to delayed and interrupted services. Thus, there is a possibility to introduce potential collaboration among nearby connected vehicles. However, the underlying decision model for the selection of the most suitable vehicle for task offloading is challenging in such a dynamic environment. In this study, we propose a collaborative vehicular computing framework that adopts online learning for efficient task assignment between local and neighboring computing resources. The underlying workload adaptive task offloading intends to balance out the workload across neighboring vehicles. The framework is compared against three techniques including two adaptive learning techniques in terms of service delay, efficiency, task delivery rate, task failures, and learning regret. The results demonstrate the effectiveness of the proposed resource-sharing network, improving service quality and throughput for servicing innovative intelligent transportation applications.

With recent advancements in software-defined vehicles, over-the-air (OTA) software updates are crucial to roll out new software and patches for connected vehicles. Traditionally, outdated vehicles are recalled by the manufacturers, however, owners are notoriously difficult to reach with recall notices. Also, organizational and procedural challenges result in many outdated vehicles with insecure and unstable software. In this paper, we propose a dissemination framework for OTA updates using a federated fog environment. Pushing the update to all vehicles via the fog nodes may congest the network, leading up to a single-point failure for update dissemination, as well as, disruption to other installed services at the fog nodes. We propose a software-defined mechanism to select a pivot, which gets the update from the fog node and streams it to other vehicles. Moreover, a timer-barrier mechanism is proposed to identify any malicious vehicles that are barred from participating in pivot selection. The experimental evaluation demonstrates that the proposed scheme improves network convergence time by 40% with 95% node trustworthiness.

The industrial Internet of Things (IIoT) has emerged as an essential paradigm to enhance industrial operations and productivity through efficient utilization of available resources. The paradigm allows industrial devices to share computing resources based on locality constraints to support innovative services. In this article, we propose a trusted multihop collaborative computing model for the efficient utilization of nearby devices in an IIoT environment. To establish trust, we explore a social-aware incentive scheme managed by network edge, using distributed ledgers. The extensive simulation results demonstrate the effectiveness of the proposed model in the presence of malicious nodes in the industrial environment.

Collaborative robots are an emerging area where robots share resources among each other for mutual benefit. They are particularly becoming popular in a modern industrial environment, primarily to improve production efficiency. This often involves some sort of decision making at the robots. Traditionally, the robots are connected to the cloud infrastructure and edge locations to offload compute-intensive tasks. But due to the dynamic workload generated and additional network delay, cloud, and edge locations are deemed unsuitable computing paradigms for use in industrial robots. In this article, we propose a symbiotic robotics framework where robots share their onboard computing capabilities for effective task offloading and its execution. Furthermore, the underlying symbiotic paradigm incorporates the concept of repute based on every successful task offloading and execution. The experimental results demonstrate a significant performance gain in terms of offloading time, task completion time, and overall efficiency when compared to two classical task offloading schemes.

Streaming large amounts of data to cloud data centers cause network congestion resulting in high network and energy consumption. The concept of fog computing is introduced to reduce workload from backbone networks and support delay-sensitive Internet of Things (IoT) applications. The concept places compute, storage, and network services closer to the source of the requests. In general fog-based simulators are used for better understanding and optimum fog resource allocation. Unfortunately, most of the simulators lack core features like network delay, latency, packet error rate, energy consumption, and distributed fog node management. In this paper, we propose a fog simulation framework termed as xFogSim to support latency-sensitive applications at the fog layer with multi-objective optimization to trade-off cost, availability, and performance among the fog federation. Moreover, during peak load, the framework provides locality-aware distributed broker node management that enables borrowing resources from nearby fog locations to meet service and energy requirements. The results show that the framework is lightweight, configurable, and scalable, capable of handling a large number of user requests using dynamic resource provisioning across the fog federation.

Abstract Cloud adoption has significantly increased using the infrastructure-as-a-service (IaaS) paradigm, in order to meet the growing demands of computing, storage and networking, in small as well as large enterprises. Different vendors provide their customized solutions for OpenStack deployment on bare metal or virtual infrastructure. Among these many available IaaS solutions, OpenStack stands out as being an agile and open-source platform. However, its deployment procedure is a time-taking and complex process with a learning curve. This paper addresses the lack of basic infrastructure automation in almost all of the OpenStack deployment projects. We propose a flexible framework to automate the process of infrastructure bring up for deployment of several OpenStack distributions, as well as resolving dependencies for a successful deployment. Our experimental results demonstrate the effectiveness of the proposed framework in terms of automation status and deployment time, that is, reducing the time spent in preparing a basic virtual infrastructure by four times, on average.

Smart cities are based on connected devices generating large quantities of data every instant. These data can be stored at a nearby edge location for initial processing but later sending the data to the backend data centers for storage and further analysis consumes considerable network bandwidth. In this article, we propose a large-scale data migration framework using vehicles. The framework uses a neural network to identify suitable vehicles as data mules, ones moving toward the data destination, potentially reducing the load from backend networks in terms of bandwidth usage and overall energy consumption. We compare the framework with data transfers using the traditional Internet and an approach without machine intelligence. The proposed framework performs well in terms of data loss, transfer time, energy, and CO2 emissions. From experiments, we demonstrate that the approach achieves a 67% success rate with data transfers $193\times $ faster than the average Internet bandwidth of 21.28 Mb/s. Moreover, the resulting CO2 emissions for 30-TB data transfers stood at 6.403 kg, which is significantly lower compared to 1172.8 kg for the Internet.

The concept of smart farming has led to the use of technology to enhance agricultural productivity. With access to low-cost sensors and management systems, more farmers are adopting this technology to achieve sustainable growth. However, in literature, there are no simulation platforms to help researchers and users understand sensor deployment, and data collection and processing. In this article, we propose a framework designed to provide a complete farming ecosystem. The toolkit facilitates users to simulate custom farming scenarios, specifically to identify sensor placement, coverage area, line-of-sight deployment, and data gathering through the relay mechanism or airborne systems, mobility models for mobile nodes, energy models for on-ground sensors and airborne vehicles, and backend computing support using the fog computing paradigm. Furthermore, in most of the existing works, network parameters are ignored, which can impact the overall performance of any deployed system. Therefore, the proposed framework also provides a benchmark in terms of transmission delay, packet delivery ratio, energy consumption, and system resources usage.

Advancement of technology has enabled access to innovative applications for connected devices. To handle growing computation requirements, the backend cloud data centers become an inefficient solution due to the caused network overhead. This is generally alleviated using edge locations deployed to meet the increasing computing demands. This article proposes a micro-level fog unit deployment to facilitate delay-sensitive applications. To manage imbalanced workloads due to the traffic density, the framework established a fog federation acting as a consortium where underutilized resources are shared to provide service quality. Moreover, we implement a price-based workload balancing algorithm to limit offloading among fog units relative to other consortium members. The experimental results show a balanced offload rate compared to traditional algorithms. Moreover, other measures, such as queue length, end-to-end delay, and workload balancing, demonstrate performance gain under the federation. Overall, 72% energy reduction is achieved through the proposed technique in comparison with the traditional nonfederated model.

Summary Existing simulators are designed to simulate a few thousand nodes due to the tight integration of modules. Thus, with limited simulator scalability, researchers/developers are unable to simulate protocols and algorithms in detail, although cloud simulators provide geographically distributed data centers environment but lack the support for execution on distributed systems. In this paper, we propose a distributed simulation framework referred to as CloudSimScale. The framework is designed on top of highly adapted CloudSim with communication among different modules managed using IEEE Std 1516 (high‐level architecture). The underlying modules can now run on the same or different physical systems and still manage to discover and communicate with one another. Thus, the proposed framework provides scalability across distributed systems and interoperability across modules and simulators.

Parallel discrete event simulation frameworks have been widely used to analyze the performance of traditional applications under different scenarios. The existing frameworks are designed to work on a cluster and cloud-based computing environments. With the current advances in the Internet of Things, there is a strong need to revamp such traditional frameworks and make use of the smart connected-devices as an underlying infrastructure to perform simulations. In this article, we propose a new simulation framework, which has been specifically designed to work with diverse heterogeneous devices. The framework allows these heterogeneous mobile devices to participate in a distributed simulation while managing network latency, using device profiles that are maintained by the simulation framework. Moreover, in the proposed framework, random and context-aware simulation task distributions have been explored to manage the devices’ sporadic connectivity. Evaluation results using the well-known PHOLD benchmark demonstrate a gain in the overall efficiency of the proposed simulation system.

Players in computer games continue to rely on assistance for navigation in the game environment, even after hours of gameplay. This behavior is in contrast to the real world where spatial knowledge of an unfamiliar environment develops with experience and reliance on navigational assistance declines. The slow development of spatial knowledge in virtual environments can be attributed to the use of turn-by-turn navigational aids. In the context of computer games, the most common form of these aids is a “mini-map.” The use of such aids in computer games is necessitated by the demands of immersion and entertainment and, hence, they cannot be entirely discarded. The need, then, is to design navigational aids that support, rather than inhibit, the development of spatial knowledge. The authors propose landmark-based verbal directions as an alternative to mini-maps and report the results of a randomized comparative study conducted to examine the impact of mini-maps and their proposed aid on the development of spatial knowledge in a virtual urban environment. The results confirm the superiority of their verbal aid in terms of spatial knowledge, while mini-maps perform better with respect to navigational efficiency. The authors hope that this study provides a first step toward defining design parameters that govern the tradeoff between navigational efficiency and spatial learning.

A spectral clustering based video object segmentation technique is proposed in this work. A foreground separation model is introduced which uses thresholding by different features to produce an initial labeling for each frame of the input sequence. We use a combination of color, optical flow, spatial-coordinates, spatiotemporal saliency and the initial foreground labeling to construct an interframe graph showing the relationship between superpixels of the entire video. The graph is solved using spectral clustering to obtain the final segmentation results. We compare our segmentation maps against state-of-the-art techniques and experimental results show that our solution is comparable to them.