Emerging IoT applications are transitioning from battery-powered to grid-powered nodes. DRP, a contention-based data dissemination protocol, was developed for these applications. Traditional contention-based protocols resolve collisions through control packet exchanges, significantly reducing goodput. DRP mitigates this issue by employing a distributed delay timer mechanism that assigns transmission-start delays based on the average link quality between a sender and its children, prioritizing highly connected nodes for early transmission. However, our in-field experiments reveal that DRP is unable to accommodate real-world link quality fluctuations, leading to overlapping transmissions from multiple senders. This overlap triggers CSMA’s random back-off delays, ultimately degrading the goodput performance. To address these shortcomings, we first conduct a theoretical analysis that characterizes the design requirements induced by real-world link quality fluctuations and DRP’s passive acknowledgments. Guided by this analysis, we design EDRP, which integrates two novel components: (i) Link-Quality Aware CSMA (LQ-CSMA) and (ii) a Machine Learning-based Block Size Selection (ML-BSS) algorithm for rateless codes. LQ-CSMA dynamically restricts the back-off delay range based on real-time link quality estimates, ensuring that nodes with stronger connectivity experience shorter delays. ML-BSS algorithm predicts future link quality conditions and optimally adjusts the block size for rateless coding, reducing overhead and enhancing goodput. In-field evaluations of EDRP demonstrate an average goodput improvement of 39.43% than the competing protocols.

As the number of wireless devices supporting multiple communication interfaces increases, the connection redundancy is being considered for efficient bandwidth utilization and QoS improvement. Accordingly, network technologies must adapt to emerging multi-interface devices to improve network performance. Multipath TCP (MPTCP) is default multipath transport protocol desired for networks with multi-interface devices and has achieved success in computer networks. However, it has not been well studied for wireless networks, especially for carrier sense multiple access (CSMA) based wireless networks, which present great challenges to round trip time (RTT) computation and multipath scheduling. This paper introduces MPTCP techniques for heterogeneous WiFi and 5G networks. We first model a proposed 5-state congestion control algorithm and WiFi CSMA function. We then present an innovative RTT computation method and a novel loss-ware multipath scheduling mechanism. We evaluated the proposed MPTCP techniques under varying network configurations. Our MPTCP can significantly outperform conventional MPTCP.

This paper explores the transformative potential of the IoT paradigm in promoting smart agriculture. Key challenges lie in how to connect agriculture sensors to remote cloud servers in the absence of feasible communication infrastructure and the unreliable wireless links in rural areas. To address these issues, we propose an innovative two-tier smart agriculture architecture: an Unmanned Aerial Vehicle (UAV) aided agriculture network model, which leverages UAVs as intermediaries to collect and route data from agriculture sensors to cloud servers. This novel architecture leads to two particular problems, i.e., data packet scheduling in the first-tier networks and multi-hop routing in the second-tier UAV mesh network. To that end, we present formal Markov decision process (MDP) based problem formulations for both tiers, with a primary focus on the more challenging multi-hop routing problem in the second-tier network. This problem is approached as a multi-agent reinforcement learning (MARL) framework, for which we introduce a novel distributed algorithm - Focus Coordination: attention-guided Multi-Agent Deep Deterministic Policy Gradient (FC-MADDPG). This algorithm reduces communication overhead and mitigates the risks associated with single-node failures. We evaluated the performance of the proposed FC-MADDPG algorithm, demonstrating its efficacy in enhancing data transmission reliability and efficiency.

In distributed learning settings, models are iteratively updated with shared gradients computed from potentially sensitive user data. While previous work has studied various privacy risks of sharing gradients, our paper aims to provide a systematic approach to analyze private information leakage from gradients. We present a unified game-based framework that encompasses a broad range of attacks including attribute, property, distributional, and user disclosures. We investigate how different uncertainties of the adversary affect their inferential power via extensive experiments on five datasets across various data modalities. Our results demonstrate the inefficacy of solely relying on data aggregation to achieve privacy against inference attacks in distributed learning. We further evaluate five types of defenses, namely, gradient pruning, signed gradient descent, adversarial perturbations, variational information bottleneck, and differential privacy, under both static and adaptive adversary settings. We provide an information-theoretic view for analyzing the effectiveness of these defenses against inference from gradients. Finally, we introduce a method for auditing attribute inference privacy, improving the empirical estimation of worst-case privacy through crafting adversarial canary records.

In single photon lidar (SPL), the laser repetition rate sets the maximum distance that can be recovered unambiguously. Conventional SPL extends this maximum recordable depth by reducing the repetition rate; however, the slower acquisition speed limits the number of received photons, which may be insufficient to track fast-moving objects. Inspired by recent successes in modulo sensing, we leverage the smoothness of typical trajectories to achieve long-range tracking beyond the unambiguous range. Although SPL naturally acquires modulo time-of-flight measurements, it introduces several challenges—including random sampling times, multiple noise sources, and absolute distance uncertainty—that are not addressed by the current modulo sensing literature. Hence, we propose an interpolation and denoising method that operates directly over the modulo samples. We further disambiguate the absolute distance based on the changing reflectivity fall-off. Monte Carlo simulations considering realistic trajectories under practical conditions show that, when properly unwrapped, the normalized mean squared error of our depth estimate decreases by over 20 dB with respect to a lidar setup whose repetition period leads to no ambiguity.

With the advent of 5G and beyond communication technologies, the consumer IoT devices are evolving from current generation to next generation. Next generation IoT devices can support multiple communication interfaces and perform more functions. Accordingly, IoT network technologies must adapt to the emerging multi-link devices to improve network performance. Multipath TCP (MPTCP) is desired for networks with multi-link devices and has achieved success in computer networks. However, MPTCP has not been well studied for wireless networks. To that end, this paper presents MPTCP techniques for heterogeneous wireless IoT networks consisting of IEEE 802.15.4 nodes and 5G nodes. We propose a path builder, an adaptive congestion controller and an innovative path scheduler. We evaluated our MPTCP techniques under varying network configurations. Compared with conventional MPTCP, the proposed MPTCP can significantly reduce the number of packet transmissions, shorten packet delivery time, improve network throughput and packet delivery rate.

Sub-1 GHz Wireless Communications of LPWAN (Low Power Wide Area Network) are attracting attention in IoT applications. In addition to battery-powered devices, the number of grid-powered and solar-powered sensor devices using LPWAN are also rapidly increasing for various IoT applications. We aim to improve reliability and efficiency of IEEE 802.15.4 CSMA/CA mechanism in the network consisting of devices without power constraint. We propose Suspendable Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) algorithms for IEEE 802.15.4 to mitigate packet loss by channel access failure while maintaining compatibility with conventional IEEE 802.15.4 CSMA/CA. We have performed extensive simulations to valid the Suspendable CSMA/CA mechanism. Simulation results show that the proposed Suspendable CSMA/CA improves Packet Delivery Rate (PDR) by 9.7 points (89.9 % to 99.6 %) compared to the conventional IEEE 802.15.4g CSMA/CA and therefore, can lead higher spectrum efficiency for IoT applications operate in the limited Sub-l G Hz wireless bandwidth. The proposed Suspendable CSMA/CA mechanism has been also approved and adopted for the next IEEE 802.15.4 amendment by IEEE 802.15 Working Group.

With the advent of 5G and beyond communication technologies, the consumer IoT devices are evolving from current generation to next generation. The next generation IoT devices are capable of supporting multiple communication modes and performing more functions. During the migration phase, it is impractical to completely remove the deployed current generation devices. Accordingly, the next generation IoT networks will consist of the mixed current and next generation devices. To that end, how to efficiently route diverse data in next generation IoT networks needs to be addressed. This paper presents a two-topology routing architecture for next generation IoT networks, one topology for regular data delivery and another topology for priority data delivery. The priority routes are discovered to minimize route overlap. We evaluated our route discovery algorithms under varying network configurations. Compared with standard RPL baseline, the proposed routing algorithms can simultaneously reduce route overlap, route transmission time and route length.

In frequency-modulated continuous-wave (FMCW) lidar, the distance to an illuminated target is proportional to the beat frequency of the interference signal. Laser phase noise often limits the range accuracy of FMCW lidar, and existing frequency estimation methods make overly simplistic assumptions about the noise model. In this work, we propose an algorithm that performs frequency estimation via phase unwrapping by explicitly accounting for correlations in the phase noise. Given a candidate frequency, we approximately recover the maximum likelihood unwrapping sequence using the Viterbi algorithm and the phase noise statistics. The algorithm then alternates between unwrapping and frequency estimate refinement until convergence. Compared to state-of-the-art alternatives, our algorithm consistently achieves superior performance at long range or with large-linewidth lasers when the signal-to-noise ratio is sufficiently high.

System logs record rich information for system events. Practical anomaly detection from system logs should be able to address three challenges: 1) understanding complicated attributes in event logs; 2) extracting complex context relations among events; and 3) providing concrete explanations to human analysts. In this paper, we develop an attention-equipped encoder-decoder system to capture context from system logs for explainable anomaly detection. For each target event, we collect its nearby events in chronological order as its context events. Instead of using a recurrent neural network-based encoder like previous works, we adopt a Transformer-based encoder to extract complex relations among context events and their attributes. Then, a context vector is generated and passed to the decoder, where an attention matrix is learned and used to weigh the context events for detecting the anomalies. Evaluation on the large-scale real-world Los Alamos National Laboratory dataset shows that, compared with existing works, our methods can provide fine-grained one-to-one attention to help explain the importance of each attribute in the context events to the prediction, without sacrificing detection performance.

The software distribution in advanced IoT networks is inevitable. However, distributing software in multi-hop wireless networks consumes enormous communication bandwidth and can also suffer from reliability challenge. This paper proposes an innovative dynamic relay point (DRP) protocol to reduce the number of software packet transmissions. It also introduces a network condition based rateless coding scheme to improve the packet transmission reliability. The NS3 simulator is employed for performance evaluation. The proposed DRP protocol outperforms multi-point relay (MPR) baseline by reducing the software packet transmissions and improving the effective throughput.

This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification problem. We propose a variational quantum circuit (VQC) design as a new denoising solution. For a practical grant-free communications system having correlated device activities, variational quantum parameters for Pauli rotation gates in the proposed VQC system are optimized to facilitate to the non-linear estimation. Numerical results show that the VQC method can outperform modern compressed sensing techniques using an element-wise denoiser.

Optical coherence tomography (OCT) using Fourier domain processing can resolve micrometer-scale depth information. However, the conventional volumetric reconstruction approach is unnecessary for opaque samples with only one reflector per lateral position, and the required sample interpolation degrades performance. In this paper, we show that surface depth profilometery with a Fourier-domain OCT system simplifies to a sinusoidal parameter estimation problem. We derive approximate maximum likelihood estimators for the sample depth and reflectivity, which can easily be computed by backprojecting the data without interpolating. Iterative refinement further improves results at high signal-to-noise ratio (SNR). We demonstrate the performance of the technique compared to the conventional Fourier transform approach on both simulated and experimental data collected with a spectral-domain OCT system. Our results show that maximum likelihood profilometry is fast and more robust to noise than the Fourier approaches at moderate SNR.

While privacy concerns entice connected and automated vehicles to incorporate on-board federated learning (FL) solutions, an integrated vehicle-to-everything communication with heterogeneous computation power aware learning platform is urgently necessary to make it a reality. Motivated by this, we propose a novel mobility, communication and computation aware online FL platform that uses on-road vehicles as learning agents. Thanks to the advanced features of modern vehicles, the on-board sensors can collect data as vehicles travel along their trajectories, while the on-board processors can train machine learning models using the collected data. To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold. To satisfy this threshold, the central server accepts partially trained models, the distributed roadside units (a) perform downlink multicast beamforming to minimize global model distribution delay and (b) allocate optimal uplink radio resources to minimize local model offloading delay, and the vehicle agents conduct heterogeneous local model training. Using real-world vehicle trace datasets, we validate our FL solutions. Simulation shows that the proposed integrated FL platform is robust and outperforms baseline models. With reasonable local training episodes, it can effectively satisfy all constraints and deliver near ground truth multi-horizon velocity and vehicle-specific power predictions.

With the explosive proliferation of wireless devices, our lives are improved by various applications supported by heterogeneous wireless technologies, such as WiFi and ZigBee. However, the coexistence of WiFi and ZigBee also results in the degradation of the network performance, which cannot be avoided if the WiFi devices are even unaware of the ambient ZigBee devices. To better accommodate the heterogeneous wireless devices, this paper presents X-Disco, the first cross-technology neighbor discovery mechanism, for a WiFi device to detect ZigBee neighbors, without modification to hardware or firmware. With the help of the recently proposed cross-technology communication, X-Disco enables a commodity WiFi device to trigger responses, containing ZigBee neighbor information, from the ambient ZigBee coordinators (including routers). Through exploring the WiFi PHY-layer information accessible by WiFi driver, X-Disco decodes the responded ZigBee messages and obtains the ZigBee neighbor information. To improve X-Disco's reliability, we also propose ZigBee neighbor validation and interruption mitigation to exclude hidden node terminals and mitigate the interference caused by the ambient WiFi traffic respectively. The evaluation of X-Disco is performed on the commodity devices (TP-Link WDR 4300 WiFi router, TelosB motes) and USRP B210. The results demonstrate X-Disco successfully detects nine ZigBee neighbors within 70ms in the office.

This paper proposes an anomaly detection algorithm for a factory automation system, which jointly performs data pre-processing and time-delay autoencoder (TDAE) with a hybrid loss function. The source data are pre-processed by digital filters before feeding into a TDAE for anomaly detection. The digital filters extract analog signals from a variety of frequency bands to facilitate identifying anomalies. The pre-processed data then takes time-delay reform to explore temporal relationship of data signals. In addition, two anomaly diagnosis algorithms, a statistical based method and an autoencoder based method, are presented. Numerical results show that time-delay reform can improve the anomaly detection accuracy compared to the conventional autoencoder. Data pre-processing can further improve the anomaly detection accuracy. Moreover, we confirm that our anomaly diagnosis algorithms outperform traditional method that does not perform data pre-processing and time-delay reform.

Adversarial examples have recently exposed the severe vulnerability of neural network models. However, most of the existing attacks require some form of target model information (i.e., weights/model inquiry/architecture) to improve the efficacy of the attack. We leverage the information-theoretic connections between robust learning and generalized rate-distortion theory to formulate a universal adversarial example (UAE) generation algorithm. Our algorithm trains an offline adversarial generator to minimize the mutual information between the label and perturbed data. At the inference phase, our UAE method can efficiently generate effective adversarial examples without high computation cost. These adversarial examples in turn allow for developing universal defenses through adversarial training. Our experiments demonstrate promising gains in improving the training efficiency of conventional adversarial training.

A novel multi-task federated learning (FL) framework is proposed in this paper to optimize the traffic prediction models without sharing the collected data among traffic stations. In particular, a divisive hierarchical clustering is first introduced to partition the collected traffic data at each station into different clusters. The FL is then implemented to collaboratively train the learning model for each cluster of local data distributed across the stations. Using the multi-task FL framework, the route planning is studied where the road map is modeled as a time-dependent graph and a modified A * algorithm is used to determine the route with the shortest traveling time. Simulation results showcase the prediction accuracy improvement of the proposed multi-task FL framework over two baseline schemes. The simulation results also show that, when using the multi-task FL framework in the route planning, an accurate traveling time can be estimated and an effective route can be selected.

A distribution matcher (DM) maps a binary input sequence into a block of nonuniformly distributed symbols. To facilitate the implementation of shaped signaling, fast DM solutions with high throughput and low serialism are required. We propose a novel DM architecture with parallel amplitudes (PA-DM) for which m-1 component DMs, each with a different binary output alphabet, are operated in parallel in order to generate a shaped sequence with m amplitudes. With negligible rate loss compared to a single nonbinary DM, PA-DM has a parallelization factor that grows linearly with m, and the component DMs have reduced output lengths. For such binary-output DMs, a novel constant-composition DM (CCDM) algorithm based on subset ranking (SR) is proposed. We present SR-CCDM algorithms that are serial in the minimum number of occurrences of either binary symbol for mapping, and fully parallel for demapping. For distributions that are optimized for the additive white Gaussian noise (AWGN) channel, we numerically show that PA-DM combined with SR-CCDM can reduce the number of sequential processing steps by more than an order of magnitude, while having a rate loss that is comparable to conventional nonbinary CCDM with arithmetic coding.

Distribution matching is a fixed-length invertible mapping from a uniformly distributed bit sequence to shaped amplitudes and plays an important role in the probabilistic amplitude shaping framework. With conventional constant-composition distribution matching (CCDM), all output sequences have identical composition. In this paper, we propose multiset-partition distribution matching (MPDM), where the composition is constant over all output sequences. When considering the desired distribution as a multiset, MPDM corresponds to partitioning this multiset into equal-sized subsets. We show that MPDM allows addressing more output sequences and, thus, has a lower rate loss than CCDM in all nontrivial cases. By imposing some constraints on the partitioning, a constructive MPDM algorithm is proposed which comprises two parts. A variable-length prefix of the binary data word determines the composition to be used, and the remainder of the input word is mapped with a conventional CCDM algorithm, such as arithmetic coding, according to the chosen composition. Simulations of 64-ary quadrature amplitude modulation over the additive white Gaussian noise channel demonstrate that the block-length saving of MPDM over CCDM for a fixed gap to capacity is approximately a factor of 2.5-5 at medium to high signal-to-noise ratios.

Routing algorithm can significantly impact network performance. Routing in a network containing heterogeneous nodes differs from routing in a network with homogeneous nodes. If the routing algorithm is designed to fit less powerful nodes, the resources of more powerful nodes are wasted and network performance can be degraded. If the routing algorithm is developed to suit more powerful nodes, less powerful nodes may not have sufficient resources to run the algorithm and network may break down. Routing algorithms developed for homogeneous networks do not work well for heterogeneous networks. The IETF designed the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) by taking into account resource heterogeneity and defined four modes of operation. However, RPL only allows one mode of operation for all routers in a network. This paper proposes a resource-aware adaptive mode RPL (RAM-RPL) to achieve adaptive mode of operation in heterogeneous wireless machine-to-machine (M2M) networks. RAM-RPL not only allows routers to have mixed modes of operation in a network but also allows routers to adaptively adjust their modes of operation during network operation. Acting parent and acting root techniques are introduced to realize adaptive mode of operation and route compression. RAM-RPL exploits resource heterogeneity and shifts routing workload from less powerful nodes to more powerful nodes. Simulation results show that RAM-RPL can improve data packet delivery rate by 26% and reduce control message overhead by 53% while maintaining similar packet latency.

The IETF standardized the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) to meet routing requirements of the emerging applications. RPL is a distributed routing protocol and shows good scalability and fast network setup. However, RPL does not support sleep operation well. To provide efficient energy management and enhance RPL for sleep operation support, this paper presents battery energy management solutions for heterogeneous wireless machine-to-machine networks containing both battery powered nodes and mains powered nodes. We introduce a distributed sleep model for battery powered nodes to manage their own sleep schedules based on their internal parameters and observed network conditions. We propose two broadcast message delivery methods for battery operated networks that use distributed sleep control. Two battery node aware routing metrics are introduced to discover more battery energy efficient routes. We also present a battery energy efficient routing protocol called B-RPL to leverage distributed sleep model and introduced routing metrics. A battery energy efficient data packet transmission and forwarding method is provided to select the most battery energy efficient route among multiple active routes to transmit and forward data packets. Simulation results show that compared with standard RPL, the proposed B-RPL can extend network lifetime by two times and improve data packet delivery rate by 75%.

To design a routing protocol for applications over low-power and lossy networks (LLNs), the IETF ROLL Working Group standardized the IPv6 Routing Protocol for LLNs (RPL), which organizes nodes in a LLN into a tree-like topology called Destination Oriented Directed Acyclic Graph (DODAG). RPL shows good scalability and fast network setup. However, it may suffer from severe unreliability due to the selection of suboptimal routes with low quality links. To optimize the reliability of RPL routes, this paper proposes a stability metric based routing protocol named sRPL for reliable routing and data collection in LLNs. We introduce a new routing metric for RPL called stability index (SI), which exploits stability characteristics of RPL nodes to select more stable routes. In addition, we present a passive and lightweight network layer technique to measure the bi-directional expected transmission count (ETX) for wireless links in LLNs. As a use case of SI, we combine SI metric with ETX metric to make routing decisions. Simulation results show that sRPL can improve packet delivery rate of RPL routing protocol by 20%.

The RPL routing protocol published in RFC 6550 was designed for efficient and reliable data collection in low-power and lossy networks. Specifically, it constructs a Destination Oriented Directed Acyclic Graph (DODAG) for data forwarding. However, due to the uneven deployment of sensor nodes in large areas, and the heterogeneous traffic patterns in the network, some sensor nodes may have much heavier workload in terms of packets forwarded than others. Such unbalanced workload distribution will result in these sensor nodes quickly exhausting their energy, and therefore shorten the overall network lifetime. In this paper, we propose a load balanced routing protocol based on the RPL protocol, named LB-RPL, to achieve balanced workload distribution in the network. Targeted at the low-power and lossy network environments, LB-RPL detects workload imbalance in a distributed and non-intrusive fashion. In addition, it optimizes the data forwarding path by jointly considering both workload distribution and link-layer communication qualities. We demonstrate the performance superiority of our LB-RPL protocol over original RPL through extensive simulations.