Integrating non-fungible tokens (NFTs) into decentralized user-facing applications like virtual worlds presents significant performance and security challenges. Traditional NFT marketplaces struggle with scalability and security, particularly when off-chain solutions are employed to enhance performance. To tackle these issues, we introduce DecentEdge (Decentralized Edge), a trusted off-chain NFT Marketplace (NFTM) designed to operate closer to the user at the edge. DecentEdge stands out by enabling concurrent, secure processing of multiple transactions on the same NFT off-chain, significantly boosting scalability. This is achieved through two key design ingredients: leveraging Trusted Execution Environments (TEEs) to secure off-chain operations, and implementing a Multi-Stage NFT transaction processing protocol. This protocol splits NFT transactions into off-chain and on-chain stages, effectively reducing the impact of on-chain latency in typical execution paths. However, this approach introduces potential conflicts between off-chain and on-chain stages, which we address with a carefully designed conflict resolution mechanism to ensure safety and security. Our experiments-conducted on a blockchain network-show that DecentEdge achieves over 300-fold throughput improvement when compared to the state-of-the-art NFTM, OpenSea.

Metastable failure is a recent abstraction of a pattern of failures that occurs frequently in real-world distributed storage systems. In this paper, we propose a formal analysis and modeling of metastable failures in replicated storage systems. We focus on a foundational problem in distributed systems—the problem of consensus—to have an impact on a large class of systems. Our main contribution is the development of a queuing-based analytical model, MSF-Model, that can be used to characterize and predict metastable failures. MSF-Model integrates novel modeling concepts that allow modeling metastable failures, which was intractable to model prior to our work. We also perform real experiments to reproduce and validate our model. Our real experiments show that MSF-Model predicts metastable failures with high accuracy by comparing the real experiment with the predictions from the queuing-based model.

Mobile health apps are a digital era revolution, facilitating direct patient-physician communication, lab and test orders, and medication refills. Despite these benefits, security and privacy issues arise due to handling sensitive data. This paper assesses the security and privacy of Saudi Arabian mobile healthcare apps, gauging compliance with the Personal Data Protection Law (PDPL). Results highlight varied PDPL compliance, underscoring the imperative for enhanced security measures in the digital healthcare landscape.

Recent advancements of the Industrial Internet of Things (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contain rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time are challenging. Recent advancements in artificial intelligence (AI) allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this article, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. First, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real time. Second, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

Low-cost networked IoT devices are fast becoming commonplace. From implanted medical devices to motion-activated surveillance cameras and from driverless smart cars to voice-operated home management systems, IoT devices continue to permeate further and deeper into our lives. However, this widespread adoption of IoT devices has also given rise to a wide range of security, privacy and trust issues that are unique to the IoT ecosystem. Conventional solutions are ill-suited for the IoT domain due to limited resources, network dynamics, and evolving trust boundaries. Hence, novel mechanisms are needed to address the specific challenges of the IoT landscape. To this end, we propose a user-centric cloud-based service that allows device owners to have fine-grained control over what kind and how much data is shared through their IoT devices. Our scheme builds on top of Intel Software Guard Extensions (SGX) to instantiate secure virtual clones (shadows) of actual devices in the cloud, substantially reducing the attack surface for IoT networks. Furthermore, a scalable infrastructure in the cloud allows us to deploy sophisticated policy enforcement and data scrubbing mechanisms on a per application basis giving users explicit control over data sharing. The presented approach requires little effort on part of device vendors and users as the service provider handles the bulk of the work. We demonstrate the effectiveness of our approach empirically by implementing the service on SGX hardware and deploying advanced data cleansing policies on device-generated data.

Frame aggregation is a major enhancement in the IEEE 802.11 family to boost the network performance. The increasing awareness about energy efficiency motivates the re-think of frame aggregation design. In this paper, we propose a novel Green Frame Aggregation (GFA) scheduling scheme that optimizes the aggregate size based on channel quality in order to minimize the consumed energy. GFA selects an optimal sub-frame size that satisfies the loss constraint for real-time applications as well as the energy budget of the ideal channel. This scheme is implemented and evaluated using a testbed deployment. The experimental analysis shows that GFA outperforms the conventional frame aggregation methodology in terms of energy efficiency by about 6× in the presence of severe interference conditions. Moreover, GFA outperforms the static frame sizing method in terms of network goodput while maintaining the same end-to-end latency.

Wireless full-duplex radios can simultaneously transmit and receive using the same frequency. In theory, this can double the throughput. In fact, there is only little work addressing aspects other than throughput gains in full-duplex systems. Over-buffering in today's networks or the so-called “bufferbloat” phenomenon creates excessive end-to-end delays resulting in network performance degradation. Our analysis shows that full-duplex systems may suffer from high latency caused by bloated buffers. In this paper, we address the problem of buffer management in full-duplex networks by using Wireless Queue Management (WQM), which is an active queue management technique for wireless networks. Our solution is based on Relay Full-Duplex MAC (RFD-MAC), an asynchronous media access control protocol designed for relay full-duplexing. We compare the performance of WQM in full-duplex environment to Drop Tail mechanism over various scenarios. Our solution reduces the end-to-end delay by two orders of magnitude while achieving similar throughput in most of the cases.

In this paper, we analyze the impact of large, persistently-full buffers (`bufferbloat') on various network dynamics in IEEE 802.11n wireless networks. Bufferbloat has mostly been studied in the context of wired networks. We study the impact of bufferbloat on a variety of wireless network topologies, including wireless LAN (WLAN) and multi-hop wireless networks. Our results show that a single FTP transfer between two Linux wireless hosts can saturate the buffers in the network stack, leading to RTT delays exceeding 4.5 s in multi-hop configurations. We show that well-designed Aggregate MAC Protocol Data Unit (A-MPDU) MAC-layer frame aggregation can reduce RTT delays while simultaneously increasing network throughput. However, additional measures may still be required to meet the constraints of real-time flows (such as VoIP). Our experiments show that large buffers can deteriorate the fairness in rate allocation in parking lot based multi-hop networks.