Unreliable Transmissions Research Articles

A Fast Consensus for Permissioned Wireless Blockchains

With the wide deployment of Internet-of-Things (IoT), blockchain systems have been playing a crucial role to establish a trusted computing environment among potentially mistrusting agents without depending on a centralized server. Different from previous blockchain consensus protocols adopted in IoT, which rely on efficient and stable transmissions, in this paper, we consider how to reach blockchain consensus in wireless networks without reliable network support. Specifically, a realistic SINR model is adopted to depict the unreliable transmissions in wireless channels. Based on the SINR model, a distributed and randomized consensus algorithm is proposed to reach k-times consensus among n devices within O(k+logn) time steps with high probability. Note that the time complexity of our algorithm is asymptotically optimal since Ω(k+logn) is a lower bound to achieve k-times consensus in a distributed environment. We conduct both rigorous theoretical analysis and extensive simulations to validate our method. It is believed that our work can facilitate the implementation of blockchains in many wireless scenarios in which the reliable and fast transmissions cannot be guaranteed.

Optimal Sampling for Data Freshness: Unreliable Transmissions With Random Two-Way Delay

In this paper, we aim to design an optimal sampler for a system in which fresh samples of a signal (source) are sent through an unreliable channel to a remote estimator, and acknowledgments are sent back over a feedback channel. Both the forward and feedback channels could have random transmission times due to time varying channel conditions. Motivated by distributed sensing, the estimator can estimate the real-time value of the source signal by combining the signal samples received through the channel and the noisy signal observations collected from a local sensor. We prove that the estimation error is a non-decreasing function of the Age of Information (AoI) for the received signal samples and design an optimal sampling strategy that minimizes the long-term average estimation error subject to a sampling rate constraint. The sampling strategy is also optimal for minimizing the long-term average of general non-decreasing functions of the AoI. The optimal sampler design follows a randomized threshold strategy: If the last transmission was successful, the source waits until the expected estimation error upon delivery exceeds a threshold and then sends out a new sample. If the last transmission fails, the source immediately sends out a new sample without waiting. The threshold is the root of a fixed-point equation and can be solved with low complexity (e.g., by bisection search). The optimal sampling strategy holds for general transmission time distributions of the forward and feedback channels. Numerical simulations are provided to compare different sampling policies.

Stochastic predictive control under intermittent observations and unreliable actions

We propose a provably stabilizing and tractable approach for control of constrained linear systems under intermittent observations and unreliable transmissions of control commands. A smart sensor equipped with a Kalman filter is employed for the estimation of the states from incomplete and corrupt measurements, and an estimator at the controller side optimally feeds the intermittently received sensor data to the controller. The remote controller iteratively solves constrained stochastic optimal control problems and transmits the control commands according to a carefully designed transmission protocol through an unreliable channel. We present a (globally) recursively feasible quadratic program, which is solved online to yield a stabilizing controller for Lyapunov stable linear time invariant systems under any positive bound on control values and any non-zero transmission probabilities of Bernoulli channels.

The capacity of QoE for wireless networks with unreliable transmissions

Video streaming is anticipated to dominate wireless traffic in the near future. We study wireless systems where an access point delivers video streams to multiple clients over unreliable wireless channels. The performance of each client is measured by the amount of time that its video playback halts due to buffer underflow, which has been shown to have the most impact on clients’ perceived quality of experience (QoE). This performance measure is significantly different from traditional quality of service metrics. We develop an analytic framework that jointly captures the video playback process and the unreliable and heterogeneous wireless channels. We use a diffusion limit to approximate the short-term QoE performance. We derive the capacity region for QoE by establishing a lower bound of a weighted sum of video halt durations over all clients. We then propose a QoE-optimal policy that can achieve every point within the capacity region. Finally, we compare our policy against two commonly used policies. Both theoretical analysis and simulation results show that our policy greatly outperforms other policies.

An analytic traffic model with adaptive QoS control in an unreliable wireless sensor network

An analytic traffic model is developed for an unreliable wireless sensor network. We model the dynamics of traffic flow from the source node through a set of intermediate nodes to the sink node by using single-server queues. These single-server queues with finite buffers are linked in tandem. To analyze the performance of the sensor network, we decompose the tandem queuing network into individual nodes with modified arrival and service processes and modified queue capacities. In the individual node modeling, we consider the impact of the unreliable transmissions, i.e., node/link failure events, by involving the immediate upstream node and downstream node of the separated node. The steady-state solutions of the individual nodes are determined iteratively. A performance metric source-to-sink delay is derived and selected for studying the quality of service (QoS) control. Adaptive QoS control schemes are developed and their performance is validated by simulation.

Opportunistic broadcast of event-driven warning messages in Vehicular Ad Hoc Networks with lossy links

Multi-hop broadcast is a key technique to disseminate time-sensitive event-driven safety warning messages (WMs) in Vehicular Ad hoc Networks (VANETs). Due to the lossy nature of the vehicular wireless environment and the fact that the implementation of broadcast at the link layer uses unreliable transmissions (i.e., lack of positive ACKs), highly reliable, scalable, and fast multi-hop broadcast protocol is particularly difficult to design in VANETs with lossy links. Schemes that use redundant network layer broadcasts have been proposed. However, the tradeoff between reception reliability and transmission count in such schemes needs to be carefully considered. In this paper (The preliminary version of this paper appeared in [1], IEEE MASS 2009.,) we propose an opportunistic broadcast protocol (OppCast) that aims at simultaneously achieving high WM packet reception ratio (PRR) and fast multi-hop message propagation while minimizing the number of transmissions. A double-phase broadcast strategy is proposed to achieve fast message propagation in one phase and to ensure high PRR in the other. OppCast exploits opportunistic forwarding in each transmission to enhance the WM reception reliability and to reduce the hop delay, and to carry out reliable and efficient broadcast coordination, we propose the use of explicit broadcast acknowledgements (BACKs) which effectively reduces the number of redundant transmissions. OppCast is also extended to handle sparse and disconnected VANETs, where the protocol adaptively switches between fast opportunistic forwarding and the store-carry-and-forward paradigm. Extensive simulation results show that, compared with existing competing protocols, OppCast achieves close to 100% PRR and faster dissemination rate under a wide range of vehicular traffic densities, while using significantly smaller number of transmissions.

Throughput Capacity of Opportunistic Routing in Wireless Sensor Networks

Recently, the idea of opportunistic routing (OR) has been widely explored to cope with the unreliable transmissions by exploiting the broadcast nature and spatial diversity of the wireless medium in order to improve the performance of wireless sensor networks. However, there are few theoretical analyses on the maximum throughput of OR WSNs. This paper is the first attempt to conduct a theoretical analysis on aggregate throughput capacity of OR in multihop many-to-one WSNs with consideration for lossy link and transmission fairness. By capturing the key characteristics of forwarding candidate set in OR networks, we propose the cumulative delivery transmission model. Then we introduce the concept of concurrent schedulable set to represent the constraints imposed by the transmission conflicts of OR, and formulate the optimal aggregate throughput problem as a maximum concurrent flow linear programming problem. Simulation results demonstrate that the OR design derived from our analysis model often yields noticeably better throughput than traditional unicast routing protocols and the OR design derived from existing analysis model under a range of scenarios.

Location-Aided Opportunistic Forwarding in Multirate and Multihop Wireless Networks

Routing in multihop wireless networks is challenging, mainly due to unreliable wireless links/channels. Geographic opportunistic routing (GOR) was proposed to cope with the unreliable transmissions by exploiting the broadcast nature of the wireless medium and the spatial diversity of the network topology. Previous studies on GOR have focused on networks with a single channel rate. The capability of supporting multiple channel rates, which is common in current wireless systems, has not carefully been studied for GOR. In this paper, we carry out a study on the impacts of multiple rates, as well as candidate selection, prioritization, and coordination, on the performance of GOR. We propose a new local metric, i.e., the opportunistic effective one-hop throughput (OEOT), to characterize the tradeoff between one-hop packet advancement and packet forwarding time. We further propose a local rate adaptation and candidate-selection algorithm to approach the optimum of this metric. The simulation results show that the multirate GOR (MGOR) incorporating the rate adaptation and candidate-selection algorithm achieves higher throughput and lower delay than the corresponding single-rate and multirate traditional geographic routing and single-rate opportunistic routing protocols.

Capacity of opportunistic routing in multi-rate and multi-hop wireless networks

Opportunistic routing (OR) copes with the unreliable transmissions by exploiting the broadcast nature of the wireless medium and spatial diversity of the multi-hop wireless networks. In this paper, we carry out a comprehensive study on the impacts of multiple rates, interference, candidate selection and prioritization on the maximum end-to-end throughput or capacity of OR. Taking into account the wireless interference and unique properties of OR, we introduce the concept of concurrent transmitter sets to represent the constraints imposed by the transmission conflicts of OR, and formulate the maximum end-to-end throughput problem as a maximum-flow linear programming subject to the transmission conflict constraints. We also propose two multi-rate OR metrics: expected medium time (EMT) and expected advancement rate (EAR), and the corresponding distributed and local rate and candidate set selection schemes, one of which is least medium time OR (LMTOR) and the other is multi-rate geographic OR (MGOR). We compare the capacity of multi-rate OR with single-rate ones under different settings. We show that our proposed multi-rate OR schemes achieve higher throughput bound than any single-rate GOR. We observe some insights of OR: 1) although involving more forwarding candidates increases the end-to-end capacity, the capacity gained from involving more forwarding candidates decreases; 2) there exists a node density threshold, higher than which 24 Mbps GOR performs better than 12 Mbps GOR, and vice versa.

On Throughput Efficiency of Geographic Opportunistic Routing in Multihop Wireless Networks

Geographic opportunistic routing (GOR) has shown throughput efficiency in coping with unreliable transmissions in multihop wireless networks. The basic idea behind opportunistic routing is to take advantage of the broadcast nature and spacial diversity of the wireless medium by involving multiple neighbors of the sender into the local forwarding, thus improve transmission reliability. The existing GOR schemes typically involve as many as available next-hop neighbors into the local forwarding, and give the nodes closer to the destination higher relay priorities. In this paper, we show that it is not always the optimal way to achieve the best throughput. We introduce a framework to analyze the one-hop throughput of GOR, provide a deeper insight into the trade-off between the benefit (packet advancement and transmission reliability) and cost (medium time delay) associated with the node collaboration, and propose a local metric named expected one-hop throughput (EOT) to balance the benefit and cost. We also identify an upper bound of EOT and its concavity, which indicates that even if the candidate coordination delay were negligible, the throughput gain would become marginal when the number of forwarding candidates increases. Based on the EOT, we also propose a local candidate selection and prioritization algorithm. Simulation results validate our analysis and show that the EOT metric leads to both better one-hop and path throughput than the corresponding pure GOR and geographic routing.