Search

The localization is a very important task in many internet of things (IoT) based applications. Due to high requirements of communication cost and power consumption, however, existing localization methods are hard to apply for practical IoT applications with low power and narrow frequency. To answer this problem, in this paper, we propose a fully distributed low energy localization method by incorporating the distributed quantization aware least mean square (DQA-LMS) algorithm in a mobile diffusion narrowband IoT (NB-IoT) network. Each mobile network node in this case is assumed to use a low-power and low-resolution analog to digital converter (ADC). First, we address the performance analysis of the mobile networks in the presence of the quantized data. Second, we evaluate the localization performance of the proposed method with differently leveled quantization data and confirm that it can perform suitably even in the one-bit data case. Finally, we collate the localization performance of the Mobile DQA-LMS with the mobile distributed LMS (Mobile DLMS) and show that for all the quantized data cases, the performance of the mobile DQA-LMS is better than the conventional DLMS in terms of mean square deviation (MSD). [ABSTRACT FROM AUTHOR]

This article describes advancements towards the creation of the Internet of biological things and introduces a paper that introduces one such milestone. Biological systems, such as bees and dandelions, exhibit remarkable capabilities like efficient energy use and long-range seed dispersal, which current IoT and embedded systems cannot match. Recent advancements aim to bridge this gap by developing tiny, bio-integrated devices like mSAIL, a lightweight sensor capable of tracking monarch butterflies over long distances using innovative methods like neural network-based location estimation. This research represents a significant step toward creating a network of bio-inspired devices that could transform fields such as environmental monitoring and healthcare by harnessing the strengths of biological systems.

Federated learning (FL) enables the full utilization of decentralized training without raw data. However, various attacks still threaten the training process of FL. To address these concerns, differential privacy (DP) and secure multi-party computation (SMC) are applied, but these methods may result in low accuracy and heavy training load. Moreover, the high communication consumption of FL in resource-constrained devices is also a challenging problem. In this paper, we propose a novel SMC algorithm for the FL (FL-IPFE) to protect the local gradients. It does not require a trusted third party (TTP) and is more suitable for FL. Furthermore, we propose a secure and efficient FL algorithm (SEFL), which applies compressed sensing (CS) and all-or-nothing transform (AONT) to minimize the number of transmitted and encrypted model updates. Additionally, our FL-IPFE is used to encrypt the last element of the preprocessed parameters for guaranteeing the security of the entire local model updates. Meanwhile, the issue of participant dropouts is also taken into account. Theoretical analyses demonstrate that our proposed algorithms can aggregate model updates with high security. Finally, experimental evaluation reveals that our SEFL possesses higher efficiency compared to other state-of-the-art works, while providing comparable model accuracy and strong privacy guarantees. [ABSTRACT FROM AUTHOR]

Obtaining location information of the multi-sensor internet of things (IoT) is a fundamental requirement. But, two kinds of the incomplete measurement data enhance the difficulty of positioning: 1) The location information is partially missing. 2) The errors of the measurement. Furthermore, the complex environmental impact and the characters of the measurement methods make the error model more changeable at the same time. This paper tries to study a universal method to address these problems, through combining IoT positioning and affinity graph assessment with arbitrary model of the measurement errors or the missing measurement data. First of all, the relationship between the graph signal and the combinatorial graph Laplacian matrix is constructed to link the graph topology learning and the IoT localization problem. Later, the Gaussian mixture model is applied to describe the measurement errors as a general model. Then, the calculations of the graph signal, Laplacian matrix and hyper parameters are obtained via variational Bayesian inference and message passing. The numerical results show the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Mobile Crowdsensing (MCs) is a widely applicable and inexpensive data-obtaining method that leverages mobile devices to sense and report data without deploying sensors. The rapid development of the Integration of IoT systems, as well as the widespread use of communication satellites, make MCs further develop and receive extensive attention from researchers. One key issue is how to ensure the reliability of the reported data to avoid the presence of malicious participants leading to poor IoT service quality. In this paper, we proposed an Active and Trace Back based Trust Management (ATBTM) algorithm to evaluate the trust of participants and handle malicious participants. The main innovations are: (1) An active trust evaluation approach for MCs is proposed, which uses an Unmanned Aerial Vehicle (UAV) to collect data as a baseline to verify the reliability of the data reported by participants. Then, the data reported by high reputation participants can also be used as a sub-baseline to verify the trust of some participants. (2) A traceback based trust evaluation method is also proposed. In this approach, some reliable devices provide historical sensing data that has been collected but not reported. Then the system compares it to real data with corresponding timestamps to evaluate the trust of other participants. Sufficient theoretical analysis and experimental results demonstrate that the proposed ATBTM framework can effectively identify the malicious workers, and conquer the drawbacks of lacking trust evaluation method in existed MCs. [ABSTRACT FROM AUTHOR]

• Reviewed 182 articles on BCT's application in IoMTs contexts. • Provided a summary framework for the function of BCT within an IoMTs environment. • A road map was provided for promoting the adoption of BCT in IoMTs settings. • Successful deployment and adoption of BCT in an IoMTs setting were discussed. The Internet of Medical Things (IoMTs) has rapidly gained popularity recently, and the need for self-health assessment and the resulting reduction in treatment waiting periods have sped up the usage of IoMTs. Unfortunately, the growing usage of IoMTs has also brought up issues that have unintended effects, including a lack of data management, an unsafe network environment, privacy and security concerns. To address the problems with IoMT applications, distributed ledger technology, which forms the basis of blockchain technology (BCT), has emerged as a practical solution. The utility of BCT to support IoMT-based settings has been demonstrated by academic research. However, no analysis of the literature has been performed to comprehend BCT performance in IoMT scenarios. Therefore, the present study offers a thorough literature review of 182 studies on the use of BCT in an IoMT environment, drawn from 70 pioneering journals included in the WoS database. It also outlines recommendations for future research areas. This study adds to the body of literature in three main ways using a mix of bibliographic and content analysis. It starts out by outlining the structure of how BCT works in an IoMT setting. Secondly, it provides a roadmap that takes into account the factors that might aid in BCT proliferation in IoMT situations. Thirdly, the paper outlines the prerequisites for BCT's effective adoption and use in an IoMT environment. The implications of the recognized clusters and the factors influencing the properties and outcomes of BCT are covered in the article's conclusion. This is the first effort to investigate BCT's function within the context of the IoMT environment; this has become crucial since BCT has developed popularity in IoMT contexts. The article highlights many aspects that may help to improve comprehension of this important issue by utilizing the provided outline as a guide and identifying major knowledge gaps. [ABSTRACT FROM AUTHOR]

The time series database is a specialized type of database specifically designed for storing and analyzing time series data. Compression of time series data is crucial for its performance. However, efficiently compressing time series data, particularly floating-point data, remains challenging. Existing compression algorithms are efficient for only a limited range of data patterns, indicating a lack of self-adaptation. In this paper, we propose an effective and A daptive lossless F loating-point C ompression algorithm AFC for time series databases. We devise four unique compression strategies, and based on the data patterns, AFC dynamically selects the appropriate strategy. These strategies handle data compression for diverse data patterns, enhancing the compression ratio and efficiency. The most suitable strategy is employed to achieve an optimal compression ratio. We compared our AFC algorithm with four state-of-the-art compression algorithms, namely Gorilla, FPC, TSXor, and Chimp, as well as various general-purpose compression algorithms such as LZ4 and Snappy. Experimental results demonstrate that our algorithm achieves an improvement of at least 20% in compression ratio and even up to 100% on certain datasets. [ABSTRACT FROM AUTHOR]

Federated learning is a promising approach in the Internet of Things (IoT) that enables collaborative and distributed machine learning among massive IoT devices without sharing private data, thereby constructing smart IoT applications. However, traditional federated learning approaches are unable to monitor the local training process of devices. Some malicious devices can exploit the vulnerability to conduct Byzantine attacks, which may potentially lead to the failure or compromise of shared global model. In this paper, we propose a new Byzantine-robust federated learning framework called rFedFW, which aims to achieve secure and trustable federated learning in the IoT. Specifically, we propose a dual filtering mechanism to identify and discard malicious gradients. Furthermore, we design an adaptive weight adjustment scheme that dynamically reduces the aggregated weight of potentially malicious gradients, ultimately achieving robust model aggregation. Additionally, we propose a dynamic clipping method to reduce the magnitude of various gradients, and we incorporate an additive model aggregation scheme with momentum to smooth the effects of local gradients and achieve efficient model aggregation. Extensive experimental results on various datasets demonstrate the effectiveness and robustness of rFedFW. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) are considered a key paradigm to improve the coverage of beyond-fifth-generation (B5G) networks as signal reflectors. Moreover, device-to-device (D2D) communications and non-orthogonal multiple-access (NOMA) schemes will enable networks with a large number of devices for the Internet of Things. This paper analyzes the performance of the downlink of B5G network, where both a NOMA-empowered BS and a RIS support cellular communications co-existing with inband D2D transmissions. We derive approximate expressions of the outage probability, the cumulative distribution function (CDF), and the probability density function (PDF) of the signal-to-noise-plus-interference ratio (SINR) of the NOMA-connected users and the D2D receivers. We further derive similar expressions under orthogonal multiple access (OMA) transmission for comparison purpose. Then, we numerically compute the ergodic rate of each user for a given power allocation. The derived expressions are validated by extensive simulations. • Outage probability for RIS-Assisted NOMA-empowered system with D2D transmissions. • CDF and PDF of the SINR of downlink users and D2D receivers. • Achieved ergodic rate for the downlink users and D2D receivers. [ABSTRACT FROM AUTHOR]

Low-delay and low-cost communication are the main concerns of the wireless sensor-based Internet of Things (IoT), which is widely deployed in various kinds of applications. Adopting wake-up radio (WuR)-enabled wireless sensor nodes can effectively reduce delay, but the deployment costs of the network increase. In this paper, we propose a delay differentiated services routing (DDSR) scheme to reduce the deployment costs for WuR-enabled wireless sensor networks (WSNs). Urgent data prefer to select the awake non-WuR node to forward data. If there is no available awake non-WuR node to forward data at the instant, it will awaken WuR nodes alternatively to maintain a relatively low delay. However, normal data are forwarded by the awake non-WuR node to reduce the number of deployed WuR nodes. Our theoretical analysis results demonstrate that the DDSR scheme can effectively reduce the deployment costs by 93.63%–98.91% while meeting the delay requirement of forwarding urgent data and maintaining a long lifetime. [ABSTRACT FROM AUTHOR]