A Robust Diffusion Estimation Algorithm for Asynchronous Networks in IoT

Chen, Feng; Hu, Limei; Liu, Pengfei; Feng, Minyu

doi:10.1109/jiot.2020.3002778

Cited by 21 publications

(8 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For memoryless continuous channels, given a mapping function w : Y → R and choose Y r with parameter r as the Gallager region, the GFBT-based bound in (12) can be written as…”

Section: Sphere Bound For Awln Channelmentioning

confidence: 99%

“…Nevertheless, there are a growing number of scenarios, such as urban, indoor, and underwater, where the noises exhibit impulsive nature and the Gaussian noise assumption does not hold. For instance, the impulsive noises widely exist in the industrial internet-of-things [12], smart grid and smart home presented environments and have a severe impact on the systems like NOMA [13], [14] and power line communication [15]- [17]. The underwater acoustic channel in a shallow water environment can also be characterized by the presence of impulsive noises [18], [19].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Fast Performance Evaluation of Linear Block Codes over Memoryless Continuous Channels

Pan¹,

Mow²

2022

Preprint

View full text Add to dashboard Cite

There are rising scenarios in communication systems, where the noises exhibit impulsive behavior and are not adequate to be modeled as the Gaussian distribution. The generalized Gaussian distribution instead is an effective model to describe real-world systems with impulsive noises. In this paper, the problem of efficiently evaluating the error performance of linear block codes over an additive white generalized Gaussian noise (AWGGN) channel is considered. The Monte Carlo (MC) simulation is a widely used but inefficient performance evaluation method, especially in the low error probability regime. As a variance-reduction technique, importance sampling (IS) can significantly reduce the sample size needed for reliable estimation based on a well-designed IS distribution. By deriving the optimal IS distribution on the one-dimensional space mapped from the observation space, we present a general framework to designing IS estimators for memoryless continuous channels. Specifically, for the AWGGN channel, we propose an L p -norm-based minimum-variance IS estimator. As an efficiency measure, the asymptotic IS gain of the proposed estimator is derived in a multiple integral form as the signal-tonoise ratio tends to infinity. Specifically, for the Laplace and Gaussian noises, the gains can be derived in a one-dimensional integral form, which makes the numerical calculation affordable. In addition, by limiting the use of the union bound to an optimized L 1 -norm sphere, we derive the sphere bound for the additive white Laplace noise channel. Simulation results verify the accuracy of the derived IS gain in predicting the efficiency of the proposed IS estimator.

show abstract

“…For memoryless continuous channels, given a mapping function w : Y → R and choose Y r with parameter r as the Gallager region, the GFBT-based bound in (12) can be written as…”

Section: Sphere Bound For Awln Channelmentioning

confidence: 99%

mentioning

confidence: 99%

Fast Performance Evaluation of Linear Block Codes over Memoryless Continuous Channels

Pan¹,

Mow²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Digital Object Identifier 2021/XX asynchronous networks [11], outlier detection [12], [13], and state estimation for wireless power transfer systems [14]. This wide range of applications stems from the fact that dynamical performance of the physical system controlled by IoT can be implemented mathematically by a state-space model.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Diffusion Kalman Filtering for Multiagent Networks in IoT

Khalili¹,

Vahidpour²,

Rastegarnia³

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

Increasing the energy efficiency of an Internet of Things (IoT) system is a major challenge for its successful implementation. To reduce the computation and storage burden and enhance the efficiency of traditional IoT, an energy-efficient diffusion-based algorithm for state estimation in multi-agent networks is proposed in this paper. In the proposed algorithm (referred to as reduced-link diffusion Kalman filter (RL-diffKF)) the nodes (agents) can communicate only with a fraction of their neighbors and each node runs a local Kalman filter to estimate the state of a linear dynamic system. This algorithm results in a significant reduction in communication cost during both adaptation and aggregation processes albeit at the expense of possible degradation in the network performance. To justify the stability and convergence of the RL-diffKF algorithm, an indepth analysis of the performance is reported. We also consider the problem of optimal selection of combination weights and use the idea of minimum variance estimation to analytically derive the adaptive combiners. The theoretical findings are verified through numerical simulations.

show abstract

“…Therefore, large scale distributed computing systems, where a computation task is distributed to many servers, are becoming very relevant. The MapReduce [9], Hadoop [2] are two of the popular distributed computing frameworks, and have wide application in many areas, for instance, [3]- [5], [10], [13], [14], [17], [18], [20], [21], [32]. In such frameworks, in order to compute the output functions, the computation consists of three phases: map phase, shuffle phase and reduce phase.…”

mentioning

confidence: 99%

Cascaded Coded Distributed Computing Schemes Based on Placement Delivery Arrays

Jiang

2020

IEEE Access

View full text Add to dashboard Cite

Li et al. introduced coded distributed computing (CDC) scheme to reduce the communication load in general distributed computing frameworks such as MapReduce. They also proposed cascaded CDC schemes where each output function is computed multiple times, and proved that such schemes achieved the fundamental trade-off between computation load and communication load. However, these schemes require exponentially large numbers of input files and output functions when the number of computing nodes gets large. In this paper, by using the structure of placement delivery arrays (PDAs), we construct several infinite classes of cascaded CDC schemes. We also show that the numbers of output functions in all the new schemes are only a factor of the number of computing nodes, and the number of input files in our new schemes is much smaller than that of input files in CDC schemes derived by Li et al.

show abstract

A Robust Diffusion Estimation Algorithm for Asynchronous Networks in IoT

Cited by 21 publications

References 37 publications

Fast Performance Evaluation of Linear Block Codes over Memoryless Continuous Channels

Fast Performance Evaluation of Linear Block Codes over Memoryless Continuous Channels

Energy-Efficient Diffusion Kalman Filtering for Multiagent Networks in IoT

Cascaded Coded Distributed Computing Schemes Based on Placement Delivery Arrays

Contact Info

Product

Resources

About