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EGM: An Efficient Generative Model for Unrestricted Adversarial Examples
Unrestricted adversarial examples allow the attacker to start attacks without given clean samples, which are quite aggressive and threatening. However, existing works for generating unrestricted adversary examples are quite inefficient and cannot achieve ...
AI-Based Mean Field Game against Resource-Consuming Attacks in Edge Computing
With the rapid development of edge computing, a new paradigm has formed for providing the nearest end service close to the data source. However, insufficient supply of resources makes edge computing devices vulnerable to attacks, especially sensitive to ...
An Energy-efficient and Privacy-aware Decomposition Framework for Edge-assisted Federated Learning
Deep Learning (DL) is an essential technology for modern intelligent sensor network and interactive multimedia applications, having problems with user data privacy when training on a central cloud. While Federated Learning (FL) motivates to preserve user ...
Secure and Efficient Item Traceability for Cloud-Aided IIoT
Cloud computing is an essential technique to provide item traceability for industrial internet of things (IIoT) systems by providing item data sharing services. However, a malicious cloud server may prevent industrial participants from acquiring accurate ...
ID-Based Multireceiver Homomorphic Proxy Re-Encryption in Federated Learning
Data privacy has become a growing concern with advances in machine learning. Federated learning (FL) is a type of machine learning invented by Google in 2016. In FL, the main aim is to train a high-accuracy global model by aggregating the local models ...
An Energy-efficient And Trustworthy Unsupervised Anomaly Detection Framework (EATU) for IIoT
Many anomaly detection techniques have been adopted by Industrial Internet of Things (IIoT) for improving self-diagnosing efficiency and infrastructures security. However, they are usually associated with the issues of computational-hungry and “black box.”...
XNAS: A Regressive/Progressive NAS for Deep Learning
Deep learning has achieved great and broad breakthroughs in many real-world applications. In particular, the task of training the network parameters has been masterly handled by back-propagation learning. However, the pursuit on optimal network structures ...
LSAB: Enhancing Spatio-temporal Efficiency of AoA Tracking Systems
Estimating the angle-of-arrival (AoA) of an RF source by using a large-sized antenna array is a classical topic in wireless systems. However, AoA tracking systems are not yet used for Internet of Things (IoT) in the real world due to their unaffordable ...
Breaking Cellular IoT with Forged Data-plane Signaling: Attacks and Countermeasure
We devise new attacks exploiting the unprotected data-plane signaling in cellular IoT networks (a.k.a. both NB-IoT and Cat-M). We show that, despite the deployed security mechanisms on both control-plane signaling and data-plane packet forwarding, novel ...
Energy-Aware Algorithm for Assignment of Relays in LP WAN
The Low-Power Wide-Area Networks allow maintaining connectivity with devices over a distance of a few kilometres. However, due to the presence of obstacles, the communication range in an urban environment may be shorter, which creates multiple blind spots ...
Chipnet: Enabling Large-scale Backscatter Network with Processor-free Devices
Differing from tremendous existing works that mainly focus on optimizing backscatter communication, Radio-to-Bus (R2B) communication utilizes backscatter to offload processors from IoT devices to the gateway, achieving processor-free devices of ...
Exploiting Rateless Codes and Cross-layer Optimization for Low-power Wide-area Networks
Long communication range and low energy consumption are the two most important design goals of Low-power Wide-area Networks (LPWANs); however, many prior works have revealed that the performance of LPWAN in practical scenarios is not satisfactory. ...
Exploring Partially Overlapping Channels for Low-power Wide Area Networks
Supporting a massive amount of Internet of Things applications requires a large pool of spectrum. DSM is a promising ecosystem to improve the spectrum efficiency. In the era of LoRaWAN, the physical hardware constraints, along with the bandwidth-hungry ...
From Demodulation to Decoding: Toward Complete LoRa PHY Understanding and Implementation
LoRa, as a representative of Low Power Wide Area Network technology, has attracted significant attention from both academia and industry. However, the current understanding of LoRa is far from complete, and implementations have a large performance gap in ...
Measurement-Based Optimization of Cell Selection in NB-IoT Networks
Narrowband-Internet of Things (NB-IoT) is an emerging cellular communication technology designed for low-power wide-area applications. Cell selection determines the channel of user device and hence is an important issue in cellular networks. In this ...
PCube: Scaling LoRa Concurrent Transmissions with Reception Diversities
This article presents the design and implementation of PCube, a phase-based parallel packet decoder for concurrent transmissions of LoRa nodes. The key enabling technology behind PCube is a novel air-channel phase measurement technique that is able to ...
Recent Advances in LoRa: A Comprehensive Survey
The vast demand for diverse applications raises new networking challenges, which have encouraged the development of a new paradigm of Internet of Things (IoT), e.g., LoRa. LoRa is a proprietary spread spectrum modulation technique that provides a solution ...
A Novel Time-Interval Based Modulation for Large-Scale, Low-Power, Wide-Area-Networks
Wireless communication over long distances has become the bottleneck for battery-powered, large-scale deployments. Low-power protocols like Zigbee and Bluetooth Low Energy have limited communication range, whereas long-range communication strategies like ...
DSME-LoRa: Seamless Long-range Communication between Arbitrary Nodes in the Constrained IoT
Long-range radio communication is preferred in many IoT deployments, as it avoids the complexity of multi-hop wireless networks. LoRa is a popular, energy-efficient wireless modulation but its networking substrate LoRaWAN introduces severe limitations to ...
LoRaWAN Security: An Evolvable Survey on Vulnerabilities, Attacks and their Systematic Mitigation
The changing vulnerability and threat landscape constantly challenge the security of wireless communication standards and protocols. For the Internet of Things (IoT), LoRaWAN is one of the dominant technologies for urban environments, industrial settings, ...
On the Global Maximization of Network Lifetime in Wireless Rechargeable Sensor Networks
In a Wireless Rechargeable Sensor Network (WRSN), a mobile charger (MC) moves and supplies energy for sensor nodes to maintain the network operation. Hence, optimizing the charging schedule of MC is essential to maximize the network lifetime in WRSNs. The ...
Evaluation of Low-cost Air Quality Sensor Calibration Models
We contribute a novel model evaluation technique that divides available measurements into training and testing sets in a way that adheres to the requirements imposed on professional monitoring stations. We perform extensive and systematic experiments with ...