Federated learning is a distributed learning method by training the model in locally multiple clients, which has been used in numerous fields. Current convolutional neural networks (CNN)-based federated learning approaches face challenges from computational cost, communication efficiency, and robust communication. Recently, Hyper Dimensional Computing (HDC) has been recognized as a promising technique to address these challenges. HDC encodes data as high-dimensional vectors and enables lightweight training and communication through simple parallel vector operations. Several HDC-based federated learning methods have been proposed. Although existing methods reduce computational efficiency and communication cost, they are difficult to handle complex learning tasks and are not robust to unreliable wireless channels. In this work, we innovatively introduce a synergetic federated learning framework, FHDnn. With advantage of the complementary strengths of CNN and HDC, FHDnn can achieve optimal performance on complex image tasks while maintaining good computational and communication efficiency. Secondly, we demonstrate in detail the convergence of using HDC in a generalized federated learning framework, providing theoretical guarantees for HDC-based federated learning approach. Finally, we design three communication strategies to further improve the communication efficiency of FHDnn by 32×. Experiments demonstrate that FHDnn converges 3× faster than CNN-based federated learning methods, reduces the communication cost by 2,112×, and the local computation and energy consumption by 192×. In addition, it has good robustness to unreliable communication with bit errors, noise, and packet loss.

Automatic modulation classification for radio signals is an important task in many applications, including cognitive radio, radio spectrum monitoring and signal decoding in non-cooperative communications. Recent studies in this area apply various deep learning methods to achieve accurate classification. However, due to the nature of radio signals, distortions during transmission are often unforeseen and unpredictable, which poses a need for robust learning models. At the same time, there is the need for fast real-time modulation classification to meet strict timing requirements. In this work, we propose a lightweight deep learning model that accurately and quickly classifies the modulation of signals having different types of distortions, without the need to be trained using distorted signals. Our model trains 25% faster and classifies 36% faster compared to the state-of-the-art [1], with smaller accuracy degradation on datasets generated using distortion parameters that do not appear in the training set.

The emerging paradigm of edge computing envisions to overcome the shortcomings of cloud-centric Internet of Things (IoT) by providing data processing and storage capabilities closer to the source of data. Accordingly, IoT edge devices, with the increasing demand of computation workloads on them, are prone to failures more than ever. Hard failures in hardware due to aging and reliability degradation are particularly important since they are irrecoverable, requiring maintenance for the replacement of defective parts, at high costs. In this article, we propose a novel dynamic reliability management (DRM) technique for multigateway IoT edge computing systems to mitigate degradation and defer early hard failures. Taking advantage of the edge computing architecture, we utilize gateways for computation offloading with the primary goal of maximizing the battery lifetime of edge devices, while satisfying the Quality of Service (QoS) and reliability requirements. We present a two-level management scheme, which work together to 1) choose the offloading rates of edge devices; 2) assign edge devices to gateways; and 3) decide multihop data flow routes and rates in the network. The offloading rates are selected by a hierarchical multitimescale distributed controller. We assign edge devices by solving a bottleneck generalized assignment problem (BGAP) and compute optimal flows in a fully distributed fashion, leveraging the subgradient method. Our results, based on real measurements and trace-driven simulation, demonstrate that the proposed scheme can achieve a similar battery lifetime and better QoS compared to the state-of-the-art approaches while satisfying reliability requirements, where other approaches fail by a large margin.

Recent years have witnessed a significant expansion in Internet-of-Things (IoT) applications. Although the battery energy availability can be improved with energy harvesting, the overall device reliability management has been overlooked in the existing literature. State-of-the-art reliability models of solar panels, electronics and rechargeable batteries show exponential dependence of failures on temperature. This work is the first to develop a comprehensive reliability deployment framework for energy-harvesting IoT networks, reflecting the non-negligible thermal stresses on each hardware component. Our framework improves the reliability on both pre-deployment and post-deployment stages. Prior to deployment, given the historical temperature and solar radiation of the region, we formulate a Mixed Integer Linear Program (MILP) to place the minimum number of nodes, while ensuring (i) full target coverage, (ii) complete connectivity, (iii) energy-neutral operation, and (iv) reliability constraints at each deployed node. We propose a polynomial-time heuristic, R-TSH, to approximate the optimal placement in large-scale deployments. While R-TSH optimizes long-term reliability, the prompt temperature or link quality differences from the historical patterns can significantly degrade device reliability after deployment. The post-deployment section of our design consists of a reliability-driven routing algorithm, AODV-Rel, that adapts to real-time environmental and link quality changes. Extensive analysis is done using a real-world dataset from the National Solar Radiation Database. Simulations in ns-3 show that R-TSH meets all reliability constraints even after 5 years of deployment as compared to the state of the art. In addition, it is 2000x faster than the optimal solution, while placing only 28% more nodes. AODV-Rel further extends the minimal operational lifetime by 1.5 and 2.8 months under temperature deviation and wireless interference.

The advent of IoT and advances in edge computing inspired federated learning, a distributed algorithm to enable on device learning. Transmission costs, unreliable networks and limited compute power all of which are typical characteristics of IoT networks pose a severe bottleneck for federated learning. In this work we propose FHDnn, a synergetic federated learning framework that combines the salient aspects of CNNs and Hyperdimensional Computing. FHDnn performs hyperdimensional learning on features extracted from a self-supervised contrastive learning framework to accelerate training, lower communication costs, and increase robustness to network errors by avoiding the transmission of the CNN and training only the hyperdimensional component. Compared to CNNs, we show through experiments that FHDnn reduces communication costs by 66X, local client compute and energy consumption by 1.5 - 6X, while being highly robust to network errors with minimal loss in accuracy.

Today’s systems rely on sending all the data to the cloud and then using complex algorithms, such as Deep Neural Networks, which require billions of parameters and many hours to train a model. In contrast, the human brain can do much of this learning effortlessly. Hyperdimensional (HD) Computing aims to mimic the behavior of the human brain by utilizing high-dimensional representations. This leads to various desirable properties that other Machine Learning (ML) algorithms lack, such as robustness to noise in the system and simple, highly parallel operations. In this article, we propose 𝖧𝗒𝖣𝖱𝖤𝖠, a HyperDimensional Computing system that is Robust, Efficient, and Accurate. We propose a Processing-in-Memory (PIM) architecture that works in a federated learning environment with challenging communication scenarios that cause errors in the transmitted data. 𝖧𝗒𝖣𝖱𝖤𝖠 adaptively changes the bitwidth of the model based on the signal-to-noise ratio (SNR) of the incoming sample to maintain the accuracy of the HD model while achieving significant speedup and energy efficiency. Our PIM architecture is able to achieve a speedup of 28× and 255× better energy efficiency compared to the baseline PIM architecture for Classification and achieves 32 × speed up and 289 × higher energy efficiency than the baseline architecture for Clustering. 𝖧𝗒𝖣𝖱𝖤𝖠 is able to achieve this by relaxing hardware parameters to gain energy efficiency and speedup while introducing computational errors. We show experimentally, HD Computing is able to handle the errors without a significant drop in accuracy due to its unique robustness property. For wireless noise, we found that 𝖧𝗒𝖣𝖱𝖤𝖠 is 48 × more robust to noise than other comparable ML algorithms. Our results indicate that our proposed system loses less than 1% Classification accuracy, even in scenarios with an SNR of 6.64. We additionally test the robustness of using HD Computing for Clustering applications and found that our proposed system also looses less than 1% in the mutual information score, even in scenarios with an SNR under 7 dB, which is 57 × more robust to noise than K-means.

The Internet of Things (IoT) systems, as any electronic or mechanical system, are prone to failures. Hard failures in hardware due to aging and degradation are particularly important since they are irrecoverable, requiring maintenance for the replacement of defective parts, at high costs. In this paper, we propose a novel dynamic reliability management (DRM) technique for IoT edge computing systems to satisfy the Quality of Service (QoS) and reliability requirements while maximizing the remaining energy of the edge device batteries. We formulate a state-space optimal control problem with a battery energy objective, QoS, and terminal reliability constraints. We decompose the problem into low-overhead subproblems and solve it employing a hierarchical and multi-timescale control approach, distributed over the edge devices and the gateway. Our results, based on real measurements and trace-driven simulation demonstrate that the proposed scheme can achieve a similar battery lifetime compared to the state-of-the-art approaches while satisfying reliability requirements, where other approaches fail to do so.

Today's systems, especially in the age of federated learning, rely on sending all the data to the cloud, and then use complex algorithms, such as Deep Neural Networks, which require billions of parameters and many hours to train a model. In contrast, the human brain can do much of this learning effortlessly. Hyperdimensional (HD) Computing aims to mimic the behavior of the human brain by utilizing high dimensional representations. This leads to various desirable properties that other Machine Learning (ML) algorithms lack such as: robustness to noise in the system and simple, highly parallel operations. In this paper, we propose HyDREA, a HD computing system that is Robust, Efficient, and Accurate. To evaluate the feasibility of HyDREA in a federated learning environment with wireless communication noise, we utilize NS-3, a popular network simulator that models a real world environment with wireless communication noise. We found that HyDREA is 48× more robust to noise than other comparable ML algorithms. We additionally propose a Processing-in-Memory (PIM) architecture that adaptively changes the bitwidth of the model based on the signal to noise ratio (SNR) of the incoming sample to maintain the robustness of the HD model while achieving high accuracy and energy efficiency. Our results indicate that our proposed system loses less than 1% classification accuracy, even in scenarios with an SNR of 6.64. Our PIM architecture is also able to achieve 255× better energy efficiency and speed up execution time by 28× compared to the baseline PIM architecture.

Recent advances in low-power long-range communication schemes such as LoRa have opened up new potentials in large-scale Internet-of-Things (IoT) applications, especially environmental monitoring. However, the versatile environment and the long traveling distance have imposed significant challenges to maintenance. Previous research has shown that higher temperature exponentially accelerates electronics failure rates. The maintenance cost can take as much as 80% of the total deployment expenses if not managed carefully. In this article, we formulate a sensor deployment problem to preventively minimize maintenance costs while ensuring tolerable sensing quality and complete connectivity. We are the first to derive a maintenance cost model for IoT networks considering thermal degradation and battery depletion. To assess the spatial phenomena of interest, we adopt the sensing quality metric based on mutual information. While the proposed problem is nonconvex, we bring up a relaxed form and solve it with a sparse nonlinear optimizer. We further apply two population-based metaheuristics, i.e., particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm, to approximate the optimal solution. Extensive simulations are performed on two real-world datasets of the Southern California region in the U.S. Our metaheuristics save up to 40% of maintenance cost compared with the existing greedy heuristics under the same acceptable sensing quality.

The reliability and maintainability of the Internet of Things (IoT) devices become highly important as the number of "things" grows rapidly. The majority of the IoT devices have batteries which age, degrade, and eventually require maintenance. Existing work focuses on ensuring that batteries have sufficient amount of stored charge to operate until they can recharge, but does not consider battery degradation. This leads to high replacement and maintenance costs in large IoT networks. In this paper, we formulate the problem of minimizing battery degradation to improve the lifetime of IoT networks and solve it with Model Predictive Control (MPC) leveraging models for battery dynamics and State of Health (SoH). The battery SoH is modeled using a realistic non-linear model while taking ambient temperature into account. We demonstrate that our solution can improve network lifetime up to 68.5% compared to conventional energy consumption focused algorithms, which use simple linear battery models. The proposed approach achieves near-optimal performance in terms of preserving battery health, staying within 8.7% SoH with respect to an ideal oracle solution on average.