Search

The massive growth in popularity of household IoT devices has brought new capabilities to our lives while also bringing new challenges to network providers. In particular, large numbers of devices have been used to cause disruptions to critical Internet services. Understanding which devices are connected to a network empowers administrators to mitigate threats with target-specific interventions. This information is obtained by analyzing traffic through a process known as device fingerprinting. Current device fingerprinting solutions face major scalability issues in high-speed and high-volume networks, either relying on middleboxes to perform their tasks or targeting a single household. This paper introduces a novel in-network fingerprinting system, PoirIoT, capable of real-time, accurate, and scalable IoT device fingerprinting. Specifically, PoirIoT takes advantage of recent programmable switches and use standard packet metadata, length, and direction information to gain high-throughput and per-packet fingerprinting granularity. We show the effectiveness (100% device detection accuracy) of our solution using a publicly available dataset on a testbed consisting of Intel Tofino switches. Moreover, PoirIoT adds no additional latency to the regular traffic flow and utilizes minimal switch resources (e.g., memory).

Welcome to our fifth edition of the Internet of Things Series of IEEE Communications Magazine!

The IEEE Communications Magazine publishes the Internet of Things (IoT) Series, which aims to bring together the most recent findings from industry and academia in terms of research and development efforts in fast-growing Internet of Things (IoT) ecosystem. The series offers insights into the theoretical and practical issues behind the breakthroughs in this exciting subject, while also delving deeply into the concept of IoT and its relationship to the larger internet. It also showcases recent efforts and accomplishments in this area. We are very happy to bring you 2024 July issue of IoT Series, in which you can find six papers solicited through a rigorous review process.

Welcome to our fourth edition of the Internet of Things Series of IEEE Communications Magazine! The ever-evolving domain of the Internet of Things (IoT) continues its trajectory of growth, propelled by relentless technological innovations that both broaden its scope and deepen its impact. At the heart of this revolution lies the potential of devices, networks, and systems to interact in unprecedented ways, paving the path for transformative experiences and solutions across various sectors. However, this increasing interconnectivity and sophistication bring to the fore numerous challenges that researchers and innovators must navigate. From ensuring the security of billions of connected devices to maintaining their efficient operation in diverse environments, the future of IoT is as complex as it is promising.

Welcome to our third edition of the Internet of Things Series of IEEE Communications Magazine! Since our last edition, we have added Sarah Johnson from the University of Newcastle in Australia to our editorial team. Welcome!

The “first wave” of wireless ad hoc routing protocols was heavily influenced by the routing algorithms used in wired networks. However, their overheads due to topology changes and the implicit restriction to point-to-point control messages meant that extensive effort had to be expended in “fixing” them to fit the intricacies and opportunities offered by wireless environments. In this paper, we analyze and compare the most widely used routing protocols in mobile ad hoc networks (MANETs). In particular, we consider routing and control overheads, storage requirements, and network setup costs of these protocols to see how well they fit in MANETs with nodes that have scarce resources.

In this paper, we consider the indoor localization problem, i.e., identifying the Cartesian coordinates of an object or a person under the roof. To solve this problem we consider an RF-based localization method called profiling, a two-step process, where a radio map of the monitored area is first constructed by collecting signal strength from known locations. An unknown location is then predicted using this radio map as a reference. In this paper, we first propose a K nearest neighbor (KNN) profiling-based localization method dubbed LEMON (location estimation by mining oversampled neighborhoods). It is based on a low-cost, low-power wireless devices and ensures good accuracy compared to the state-of-the-art. We then propose a variant of LEMON called combinatorial localization which exhaustively searches for the best possible set of nearest neighbors. We further define a Bayesian network model for the same localization problem. The performance of these methods is evaluated through extensive experiments in various indoor areas. We found an interesting outcome that the simple KNN-based approach can offer better localization accuracy compared to other complex localization methods. Thus we further enhance the performance of the KNN-based approach using multiple RF channels.