Kiseok Kim, Sangmin Lee, Taehoon Yoo, Hwangnam Kim
In performing various missions using various types of vehicles or other moving objects, the positioning of each agent within the swarm is essential. In particular, for missions that require precise location estimation, the case of malicious attacks through data forgery cannot be excluded. In this paper, we propose a highly secure and accurate localization framework utilizing a Directed-Acyclic-Graph (DAG)-based distributed ledger as an intelligent vehicle network for an Ultra-Wideband (UWB) positioning system. When performing UWB positioning, the data generated from each node are used to calculate the Time of Flight (ToF), and if any of them are tampered with, the overall positioning performance is greatly reduced. We prevented positioning performance degradation by ensuring the safety and integrity of the data by applying a chain-based logical network between each node utilized for UWB positioning. The experimental results indicated that the proposed framework was effective at providing system stability and security without affecting the UWB positioning performance. In addition, the performance of the framework was verified by presenting defense indicators for various attack scenarios.
Federated learning (FL) enables clients to participate in machine learning tasks in a private way. Applying blockchain into FL for decentralization and security has attracted much attention recently. The blockchain with a directed acyclic graph (DAG) structure enables mobile devices to participate in decentralized FL more flexibly while reducing resource consumption and is more suitable for implementing decentralized FL in mobile networks than traditional blockchains. Non-independent and identically distributed (non-IID) data is a common problem in FL. Existing work on DAG-based FL lacks a suitable optimization method for non-IID data. In this paper, we briefly describe a DAG-based FL approach in mobile networks. In order to mitigate the negative effects of non-IID data, consensus in DAG is improved by utilizing a new tip (Unconfirmed blocks in the DAG ledger) selection algorithm proposed in this paper to help clients find suitable models more easily in DAG-based FL. Experiments on multiple datasets show that the method proposed in this paper has better results than existing work and is closer to traditional FL.
Ali Shakerian, Ali Eghmazi, Justin Goasdoué, René Landry
This paper proposes a novel Blockchain-based indoor navigation system that combines a foot-mounted dual-inertial measurement unit (IMU) setup and a zero-velocity update (ZUPT) algorithm for secure and accurate indoor navigation in GNSS-denied environments. The system estimates the user's position and orientation by fusing the data from two IMUs using an extended Kalman filter (EKF). The ZUPT algorithm is employed to detect and correct the error introduced by sensor drift during zero-velocity intervals, thus enhancing the accuracy of the position estimate. The proposed Low SWaP-C blockchain-based decentralized architecture ensures the security and trustworthiness of the system by providing an immutable and distributed ledger to store and verify the sensor data and navigation solutions. The proposed system is suitable for various indoor navigation applications, including autonomous vehicles, robots, and human tracking. The experimental results provide clear and compelling evidence of the effectiveness of the proposed system in ensuring the integrity, privacy, and security of navigation data through the utilization of blockchain technology. The system exhibits an impressive ability to process more than 680 transactions per second within the Hyperledger-Fabric framework. Furthermore, it demonstrates exceptional accuracy and robustness, with a mean RMSE error of 1.2 m and a peak RMSE of 3.2 during a 20 min test. By eliminating the reliance on external signals or infrastructure, the system offers an innovative, practical, and secure solution for indoor navigation in environments where GNSS signals are unavailable.
Mobile sign language apps have drawn a lot of interest recently as a way to minimize communication barriers between hearing people and people with hearing impairments. However, there are issues with the criteria and standards that should be taken into account when developing these apps. This study proposes a set of development criteria for sign language mobile apps and standardizes these criteria using the Fuzzy Delphi approach. Furthermore, the Fuzzy-Weighted Zero Inconsistency (FWZIC) approach is utilized to assign weights to the criteria and establish a ranking order. An initial set of requirements is developed based on the literature review. The Fuzzy Delphi technique is used, involving a panel of experts made up of developers, sign language experts, and users of sign language mobile apps, to assess the validity and reliability of the criteria. The FWZIC technique is used to give the criterion weights and determine their ranking order in order to further improve the decision-making process. The relative relevance of each criterion is determined by the FWZIC technique, which involves expert input and makes use of their knowledge and expertise. A thorough ranking is generated by taking into account the effects of each criterion on several zones, assisting in efficient decision-making during the creation of sign language mobile apps. Six Malaysian Sign Language apps that have been shortlisted are being utilized as a proof of concept to test the idea. The result of 6 apps is obtained based on the final standard criteria, their weights, and rankings.
Unmanned aerial vehicle (UAV) contributes substantial strategic benefits on the Internet of Military Things (IoMT). However, the untrusted party’s misuse of the UAV may violate the security and even demolish the critical operation in the IoMT system. In addition, data manipulation and falsification using unauthorized access are the significant challenges of the IoMT system. In response to this problem, this study proposes a blockchain-integrated convolution neural network (CNN)-based intelligent framework named IoMT-Net for identification and tracking illegal UAV in the IoMT system. Blockchain technology prevents illicit access, data manipulation, and illegal intrusions, as well as stored data on the central control server (CCS). Concurrently, the proposed CNN analyzed the radio-frequency (RF) signal sent by the antenna array element to determine the Direction of Arrival (DoA) for the localization of the illegal UAV. Therefore, a signal model is designed to process the received signal array through IoMT-Net. Moreover, the proposed CNN model is designed with two different functional modules, such as the resource accuracy tradeoff (RAT) module and the unique feature extraction and accuracy boosting (UAB) module, by adopting depthwise and grouped convolution layers. These sparsely connected convolution layers offer high DoA estimation accuracy while maintaining the network more lightweight. In addition, the skip connection is also leveraged into the subunits of RAT and UAB modules for sharing features and handling the vanishing gradients problem. Based on the simulation results, the proposed network achieves superior DoA estimation accuracy (approximately 97.63% accuracy at 10-dB SNR) and outperforms other state-of-the-art models.
The proliferation of IoT-based services for smart cities, and especially those related to mobility, are ever becoming more relevant and gaining attention from a number of stake-holders. In our work, we tackle the problem of characterizing people movements in a urban environment by using WiFi sensors connected to the cellular network. In particular, we leverage WiFi probe requests transmitted by people’s smartphones and a machine learning approach to detect people’s flows, while preserving users’ privacy. We validate our approach through a proof-of-concept testbed deployed in the proximity of our campus area. We consider two types of devices, namely, commercial, off-the-shelf WiFi scanners and ad-hoc designed scanners implemented with Raspberry PIs. They provide different levels of visibility of the captured traffic, preserving in different ways the privacy of the people’s movements. In our current work, we investigate the different trade-offs between mobility tracking accuracy and the level of provided people’s privacy.
Speech is a natural user interface for the Internet of Things system. However, the presence of noise affects severely the performance of such system. With the deployment of smart devices with microphones, one can form a powerful acoustic sensor network to enhance the speech via beamforming techniques. On the other hand, reliability of data transmission also determines the beamforming performance, since faulty data will drift the beamformer steering location randomly. Currently, there is no protection scheme for acoustic data transmitted over the wireless network in order to keep steady beamforming performance. In this article, we design a compound distributed beamformer, where nodes are grouped and the system is embedded with blockchain technology to protect the data integrity during transmission. It attempts to provide more possible reliable connections between groups. Simulated experiments show that the distributed beamformer with blockchain protection is able to maintain steady beamforming performance.
Autonomic computing has become increasingly popular during recent years. Many mobile autonomic and context-aware applications exhibit self-organization in dynamic environments adopted from multi-agent, or swarm, research. The basic paradigm behind swarm systems is that tasks can be more efficiently dispatched through the use of multiple, simple autonomous agents instead of a single, sophisticated one. Such systems are much more adaptive, scalable, and robust than those based on a single, highly capable, agent. A swarm system can generally be defined as a decentralized group (swarm) of autonomous agents (particles) that are simple, with limited processing capabilities. Particles must cooperate intelligently to achieve common tasks.
Martin Hoffmann, Michael Wittke, Jörg Hähner, Christian Müller-Schloer
We propose a decentralized, self-organizing system architecture for wireless networked smart cameras (SCs) with pan, tilt, and zoom abilities. Each SC communicates with its neighbors and independently calculates the optimal position for its field of view. Thereby, SCs autonomously organize themselves and spatially partition the area they observe. This is achieved by a decentralized algorithm that makes way for self-organization in SC systems. The system quickly adapts to new situations caused by joining and failing nodes. Simulations with hundreds of cameras show that scalability and reliability are achieved. We analyze the performance of different camera densities and show that optimal surveillance coverage is achieved in a short time (20 s) while maintaining low communication traffic using a simulated 350-node outdoor SC system.
In this paper, a non-interactive zero-knowledge proof scheme is proposed for secure identification in wireless networks, and it uses a timed oblivious transfer technique to enable a single verifier to identify multiple provers. The verifier and the prover do not need to be synchronized in this scheme. This scheme also enjoys the distance bounding property which makes the proposed scheme invulnerable to the relay attack. We propose to use the order statistic for the detection of relay attackers. We show that it is optimal in terms of minimum variance. Finally, we will shed some light on implementation issues of our proposed scheme.