共找到 20 条结果
More and more smart connected things and services turn our homes into smart environments. They promise comfort, efficiency and security. These devices often integrate simple sensors, e.g. for temperature, light or humidity, etc. However, these smart but yet simple sensors can pose a sincere privacy risk. The sensor data enables sense-making of home attendance, domestic activities and even health conditions, often a fact that neither users nor developers are aware of or do not know how to address. Nevertheless, not all is lost or evil. This article makes a plea for how we, the ThingsCon community, might rethink smart connected things and services in our homes. We show this in our approaches and research projects that we initiated.
With the advancement of information technology, the Social Internet of Things (SIoT) has fostered the integration of physical devices and social networks, deepening the study of complex interaction patterns. Text Attribute Graphs (TAGs) capture both topological structures and semantic attributes, enhancing the analysis of complex interactions within the SIoT. However, existing graph learning methods are typically designed for complete attributed graphs, and the common issue of missing attributes in Attribute Missing Graphs (AMGs) increases the difficulty of analysis tasks. To address this, we propose the Topology-Driven Attribute Recovery (TDAR) framework, which leverages topological data for AMG learning. TDAR introduces an improved pre-filling method for initial attribute recovery using native graph topology. Additionally, it dynamically adjusts propagation weights and incorporates homogeneity strategies within the embedding space to suit AMGs' unique topological structures, effectively reducing noise during information propagation. Extensive experiments on public datasets demonstrate that TDAR significantly outperforms state-of-the-art methods in attribute reconstruction and dow
With the rapid development of artificial intelligence, robotics, and Internet of Things, multi-robot systems are progressively acquiring human-like environmental perception and understanding capabilities, empowering them to complete complex tasks through autonomous decision-making and interaction. However, the Internet of Robotic Things (IoRT) faces significant challenges in terms of spectrum resources, sensing accuracy, communication latency, and energy supply. To address these issues, a reconfigurable intelligent surface (RIS)-aided IoRT network is proposed to enhance the overall performance of robotic communication, sensing, computation, and energy harvesting. In the case studies, by jointly optimizing parameters such as transceiver beamforming, robot trajectories, and RIS coefficients, solutions based on multi-agent deep reinforcement learning and multi-objective optimization are proposed to solve problems such as beamforming design, path planning, target sensing, and data aggregation. Numerical results are provided to demonstrate the effectiveness of proposed solutions in improve communication quality, sensing accuracy, computation error, and energy efficiency of RIS-aided IoR
In the world of Internet of Things (IoT), obtaining the physical location of devices has always been a task of great interest for developing increasingly complex location-based services (LBS). That is why in recent years wireless communication standards have been incorporating new additions focused on providing localization mechanisms to technologies widely used in the IoT world, such as Wi-Fi or Bluetooth. In particular, the IEEE 802.11-2016 Wi-Fi standard introduced ranging estimation between two devices through the so-called fine time measurement (FTM) protocol, defined by the IEEE 802.11mc. FTM is not yet widespread in the IoT field, but commercial modules capable of offering this functionality at a reasonable price are starting to appear. In early 2021, the most widespread system on a chip (SOC) family among IoT devices, the ESP32-XX series, added support for this Wi-Fi standard, enabling, for the first time, the use of a standard designed for location-based systems. This article analyzes the performance of this FTM implementation by carrying out and studying several measurement campaigns in different indoor and outdoor scenarios. Additionally, this work proposes an alternativ
Federated Learning (FL) is a promising machine learning approach for Internet of Things (IoT), but it has to address network congestion problems when the population of IoT devices grows. Hierarchical FL (HFL) alleviates this issue by distributing model aggregation to multiple edge servers. Nevertheless, the challenge of communication overhead remains, especially in scenarios where all IoT devices simultaneously join the training process. For scalability, practical HFL schemes select a subset of IoT devices to participate in the training, hence the notion of device scheduling. In this setting, only selected IoT devices are scheduled to participate in the global training, with each of them being assigned to one edge server. Existing HFL assignment methods are primarily based on search mechanisms, which suffer from high latency in finding the optimal assignment. This paper proposes an improved K-Center algorithm for device scheduling and introduces a deep reinforcement learning-based approach for assigning IoT devices to edge servers. Experiments show that scheduling 50% of IoT devices is generally adequate for achieving convergence in HFL with much lower time delay and energy consump
Cooperative molecular communication (MC) is a promising technology for facilitating communication between nanomachines in the Internet of Bio-Nano Things (IoBNT) field. However, the performance of IoBNT is limited by the availability of energy for cooperative MC. This paper presents a novel transmitter design scheme that utilizes molecule movement between reservoirs, creating concentration differences through the consumption of free energy, and encoding information on molecule types. The performance of the transmitter is primarily influenced by energy costs, which directly impact the overall IoBNT system performance. To address this, the paper focuses on optimizing energy allocation in cooperative MC for enhanced transmitter performance. Theoretical analysis is conducted for two transmitters. For scenarios with more than two users, a genetic algorithm is employed in the energy allocation to minimize the total bit error rate (BER). Finally, numerical results show the effectiveness of the proposed energy allocation strategies in the considered cooperative MC system.
Blockchain provides decentralization and trustlessness features for the Industrial Internet of Things (IIoT), which expands the application scenarios of IIoT. To address the problem that the blockchain cannot actively obtain off-chain data, the blockchain oracle is proposed as a bridge between the blockchain and external data. However, the existing oracle schemes are difficult to solve the problem of low quality of service caused by frequent data changes and heterogeneous devices in IIoT, and the current oracle node selection schemes are difficult to balance security and quality of service. To tackle these problems, this paper proposes a secure and reliable oracle scheme that can obtain high-quality off-chain data. Specifically, we first design an oracle node selection algorithm based on Verifiable Random Function (VRF) and reputation mechanism to securely select high-quality nodes. Second, we propose a data filtering algorithm based on a sliding window to further improve the consistency of the collected data. We verify the security of the proposed scheme through security analysis. The experimental results show that the proposed scheme can effectively improve the service quality of
The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems. In this article, we explore the emerging opportunities brought by 6G technologies in IoT networks and applications, by conducting a holistic survey on the convergence of 6G and IoT. We first shed light on some of the most fundamental 6G technologies that are expected to empower future IoT networks, including edge intelligence, reconfigurable intelligent surfaces, space-air-ground-underwater communications, Terahertz communications, massive ultra-reliable and low-latency communications, and blockchain. Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely Healthcare Internet of Things, Vehicular Internet of Things and Autonomous Driving, Unmanned Aerial Vehicles, Satellite Internet of Things, and Industrial Internet of Things. Finally, we highlight interesting research challenges and point out potential directions to spur further rese
We present "The HI Nearby Galaxy Survey (THINGS)", a high spectral (<=5.2 km/s) and spatial (~6") resolution survey of HI emission in 34 nearby galaxies obtained using the NRAO Very Large Array (VLA). The overarching scientific goal of THINGS is to investigate fundamental characteristics of the interstellar medium (ISM) related to galaxy morphology, star formation and mass distribution across the Hubble sequence. Unique characteristics of the THINGS database are the homogeneous sensitivity as well as spatial and velocity resolution of the HI data which is at the limit of what can be achieved with the VLA for a significant number of galaxies. A sample of 34 objects at distances 2<D<15Mpc (resulting in linear resolutions of ~100 to 500pc) are targeted in THINGS, covering a wide range of star formation rates (10^-3 to 6 M_sun/yr), total HI masses M_HI (0.01 to 14x10^9 M_sun), absolute luminosities M_B (-11.5 to -21.7 mag) and metallicities (7.5 to 9.2 in units of 12+log[O/H]). We describe the setup of the VLA observations, the data reduction procedures and the creation of the final THINGS data products. We present an atlas of the integrated HI maps, the velocity fields, the s
Physical-layer authentication is a popular alternative to the conventional key-based authentication for internet of things (IoT) devices due to their limited computational capacity and battery power. However, this approach has limitations due to poor robustness under channel fluctuations, reconciliation overhead, and no clear safeguard distance to ensure the secrecy of the generated authentication keys. In this regard, we propose a novel, secure, and lightweight continuous authentication scheme for IoT device authentication. Our scheme utilizes the inherent properties of the IoT devices' transmission model as its source for seed generation and device authentication. Specifically, our proposed scheme provides continuous authentication by checking the access time slots and spreading sequences of the IoT devices instead of repeatedly generating and verifying shared keys. Due to this, access to a coherent key is not required in our proposed scheme, resulting in the concealment of the seed information from attackers. Our proposed authentication scheme for IoT devices demonstrates improved performance compared to the benchmark schemes relying on physical channels. Our empirical results f
The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Currently, such Internet-connected objects or `things' outnumber both people and computers connected to the Internet and their population is expected to grow to 50 billion in the next 5 to 10 years. To be able to develop IoT applications, such `things' must become dynamically integrated into emerging information networks supported by architecturally scalable and economically feasible Internet service delivery models, such as cloud computing. Achieving such integration through discovery and configuration of `things' is a challenging task. Towards this end, we propose a Context-Aware Dynamic Discovery of {Things} (CADDOT) model. We have developed a tool SmartLink, that is capable of discovering sensors deployed in a particular location despite their heterogeneity. SmartLink helps to establish the direct communication between sensor hardware and cloud-based IoT middleware platforms. We address the challenge of heterogeneity using
Internet of Things devices are envisioned to penetrate essentially all aspects of life, including homes and urbanspaces, in use cases such as health care, assisted living, and smart cities. One often proposed solution for dealing with the massive amount of data collected by these devices and offering services on top of them is the federation of the Internet of Things and cloud computing. However, user acceptance of such systems is a critical factor that hinders the adoption of this promising approach due to severe privacy concerns. We present UPECSI, an approach for user-driven privacy enforcement for cloud-based services in the Internet of Things to address this critical factor. UPECSI enables enforcement of all privacy requirements of the user once her sensitive data leaves the border of her network, provides a novel approach for the integration of privacy functionality into the development process of cloud-based services, and offers the user an adaptable and transparent configuration of her privacy requirements. Hence, UPECSI demonstrates an approach for realizing user-accepted cloud services in the Internet of Things.
The Internet of Things combines various earlier areas of research. As a result, research on the subject is still organized around these pre-existing areas: distributed computing with services and objects, networks (usually combining 6lowpan with Zigbee etc. for the last-hop), artificial intelligence and semantic web, and human-computer interaction. We are yet to create a unified model that covers all these perspectives - domain, device, service, agent, etc. In this paper, we propose the concept of cells as units of structure and context in the Internet of things. This allows us to have a unified vocabulary to refer to single entities (whether dumb motes, intelligent spimes, or virtual services), intranets of things, and finally the complete Internet of things. The question that naturally follows, is what criteria we choose to demarcate boundaries; we suggest various possible answers to this question. We also mention how this concept ties into the existing visions and protocols, and suggest how it may be used as the foundation of a formal model.
A massive current research effort focuses on combining pre-existing 'Intranets' of Things into one Internet of Things. However, this unification is not a panacea; it will expose new attack surfaces and vectors, just as it enables new applications. We therefore urgently need a model of security in the Internet of Things. In this regard, we note that IoT descends directly from pre-existing research (in embedded Internet and pervasive intelligence), so there exist several bodies of related work: security in RFID, sensor networks, cyber-physical systems, and so on. In this paper, we survey the existing literature on RFID and WSN security, as a step to compiling all known attacks and defenses relevant to the Internet of Things.
In the next few years, the W3C Web of Things (WoT) platform will represent a reference solution toward the deployment of fully interoperable systems, hence unlocking the potential of the IoT paradigm on several use-cases characterized by the current fragmentation of devices and technologies. At the same time, the worlwide adoption of the W3C WoT architecture depends on many factors, including also the availability of support tools that might facilitate the deployment of novel WoT applications or the integration with traditional IoT systems. To this purpose, the paper presents the WoT Store, a complete software platform enabling the discovery and management of W3C Things, the monitoring of its properties and events, and the invoking of actions, all within the same dashboard. In addition, the platform leverages on the semantic description of each Thing with the goal of easing and automatizing the installation and execution of WoT applications, e.g. defining the behaviour of a Thing or implementing mash-up operations from multiple Things. We sketch the main features, the architecture and the prototypal implementation of the WoT Store. Moreover, we discuss the WoT Store capabilities on
Since the beginning of the Internet thirty years ago, we have witnessed a number of changes in the application of communication technologies. Today, the Internet can be described to a large extent as a ubiquitous infrastructure that is always accessible. After the era of connecting places and connecting people, the Internet of the future will also connect things. The idea behind the resulting Internet of Things is to seamlessly gather and use information about objects of the real world during their entire lifecycle. In this paper, we consider different approaches to technological protection of user data privacy in the world of Internet of Things. In particular,we consider what kind of security problems are being faced and what level of protection can be provided by applying approaches based on secure multi-party computations.
The battlefield of the future will be densely populated by a variety of entities ("things") -- some intelligent and some only marginally so -- performing a broad range of tasks: sensing, communicating, acting, and collaborating with each other and human warfighters. We call this the Internet of Battle Things, IoBT. In some ways, IoBT is already becoming a reality, but 20-30 years from now it is likely to become a dominant presence in warfare. To become a reality, however, this bold vision will have to overcome a number of major challenges. As one example of such a challenge, the communications among things will have to be flexible and adaptive to rapidly changing situations and military missions. In this paper, we explore this and several other major challenges of IoBT, and outline key research directions and approaches towards solving these challenges.
Federated learning (FL) was proposed to facilitate the training of models in a distributed environment. It supports the protection of (local) data privacy and uses local resources for model training. Until now, the majority of research has been devoted to "core issues", such as adaptation of machine learning algorithms to FL, data privacy protection, or dealing with the effects of uneven data distribution between clients. This contribution is anchored in a practical use case, where FL is to be actually deployed within an Internet of Things ecosystem. Hence, somewhat different issues that need to be considered, beyond popular considerations found in the literature, are identified. Moreover, an architecture that enables the building of flexible, and adaptable, FL solutions is introduced.
The Internet is smoothly migrating from an Internet of people towards an Internet of Things (IoT). By 2020, it is expected to have 50 billion things connected to the Internet. However, such a migration induces a strong level of complexity when handling interoperability between the heterogeneous Inter- net things, e.g., RFIDs (Radio Frequency Identification), mobile handheld devices, and wireless sensors. In this context, a couple of standards have been already set, e.g., IPv6, 6LoWPAN (IPv6 over Low power Wireless Personal Area Networks), and M2M (Machine to Machine communications). In this paper, we focus on the integration of wireless sensor networks into IoT, and shed further light on the subtleties of such integration. We present a real-world test bed deployment where wireless sensors are used to control electrical appliances in a smart building. Encountered problems are highlighted and suitable solutions are presented.
We present rotation curves of 19 galaxies from THINGS, The HI Nearby Galaxy Survey. The high spatial and velocity resolution of THINGS make these the highest quality HI rotation curves available to date for a large sample of nearby galaxies, spanning a wide range of HI masses and luminosities. The high quality of the data allows us to derive the geometrical and dynamical parameters using HI data alone. We do not find any declining rotation curves unambiguously associated with a cut-off in the mass distribution out to the last measured point. The rotation curves are combined with 3.6 um data from SINGS (Spitzer Infrared Nearby Galaxies Survey) to construct mass models. Our best-fit, dynamical disk masses, derived from the rotation curves, are in good agreement with photometric disk masses derived from the 3.6 um images in combination with stellar population synthesis arguments and two different assumptions for the stellar Initial Mass Function (IMF). We test the Cold Dark Matter-motivated cusp model, and the observationally motivated central density core model and find that (independent of IMF) for massive, disk-dominated galaxies, all halo models fit apparently equally well; for lo