Related papers: Is Our Model for Contention Resolution Wrong?

Binary exponential backoff (BEB) is a decades-old algorithm for coordinating access to a shared channel. In modern networks, BEB plays an important role in WiFi (IEEE 802.11) and other wireless communication standards. Despite this track…

Contention resolution addresses the problem of coordinating access to a shared communication channel. Time is discretized into synchronized slots, and a packet can be sent in any slot. If no packet is sent, then the slot is empty; if a…

We study the problem of medium access control in domain of event-driven wireless sensor networks (WSNs). In this kind of WSN, sensor nodes send data to sink node only when an event occurs in the monitoring area. The nodes in this kind of…

The exponential growth of wireless devices and stringent reliability requirements of emerging applications demand fundamental improvements in distributed channel access mechanisms for unlicensed bands. Current Wi-Fi systems, which rely on…

Exponential backoff (EB) is a widely adopted collision resolution mechanism in many popular random-access networks including Ethernet and wireless LAN (WLAN). The prominence of EB is primarily attributed to its asymptotic throughput…

Randomized backoff protocols, such as exponential backoff, are a powerful tool for managing access to a shared resource, often a wireless communication channel (e.g., [1]). For a wireless device to transmit successfully, it uses a backoff…

Binary Neural Networks (BNNs), which constrain both weights and activations to binary values, offer substantial reductions in computational complexity, memory footprint, and energy consumption. These advantages make them particularly well…

Collisions are a main cause of throughput degradation in WLANs. The current contention mechanism used in IEEE 802.11 networks is called Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). It uses a Binary Exponential Backoff…

The commonly used asynchronous bounded delay (ABD) network models assume a fixed bound on message delay. We propose a probabilistic network model, called asynchronous bounded expected delay (ABE) model. Instead of a strict bound, the ABE…

Empirical Bayes (EB) improves the accuracy of simultaneous inference "by learning from the experience of others" (Efron, 2012). Classical EB theory focuses on latent variables that are iid draws from a fitted prior (Efron, 2019). Modern…

Recent work in time series forecasting has explored reformulating regression as a classification task. By discretizing the continuous target space into bins and predicting over a fixed set of classes, these approaches benefit from more…

Leader-based consensus algorithms are vulnerable to liveness and performance downgrade attacks. We explore the possibility of replacing leader election in Multi-Paxos with random exponential backoff (REB), a simpler approach that requires…

The IEEE 802.11 backoff algorithm is very important for controlling system throughput over contentionbased wireless networks. For this reason, there are many studies on wireless network performance focus on developing backoff algorithms.…

One of the important issues in wireless networks is the Routing problem that is effective on system performance, in this article the attempt is made to propose a routing algorithm using the bee colony in order to reduce energy consumption…

When developing a new networking algorithm, it is established practice to run a randomized experiment, or A/B test, to evaluate its performance. In an A/B test, traffic is randomly allocated between a treatment group, which uses the new…

\emph{Contention Resolution} is a fundamental symmetry-breaking problem in which $n$ devices must acquire temporary and exclusive access to some \emph{shared resource}, without the assistance of a mediating authority. For example, the $n$…

Randomized ensemble classifiers (RECs), where one classifier is randomly selected during inference, have emerged as an attractive alternative to traditional ensembling methods for realizing adversarially robust classifiers with limited…

Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an ImageNet-trained convolutional neural network. However, the…

The Beeping Network (BN) model captures important properties of biological processes. Paradoxically, the extremely limited communication capabilities of such nodes has helped BN become one of the fundamental models for networks. Since in…

Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function. We argue that likelihood-based training strategies may fail because the objective does…