This paper presents an empirical study on the feasibility of using Checkpoint/Restore In Userspace (CRIU) for run-time application migration between hosts, with a particular focus on edge computing and cloud infrastructures. The paper provides experimental support for CRIU in Docker and offers insights into the impact of application memory usage on checkpoint size, time, and resources. Through a series of tests, we establish that the time to checkpoint is linearly proportional to the size of the memory allocation of the container, while the restore is significantly lower. Our findings contribute to the understanding of CRIU’s performance and its potential use in edge computing scenarios. To obtain accurate and meaningful findings, we monitored system telemetry while using CRIU to observe its impact on the host machine’s CPU and RAM utilization. Although our results may not be groundbreaking, they offer a good overview and a technical report on the feasibility of using CRIU on edge devices, which are typically resource constrained. This study’s findings and experimental support for CRIU in Docker could serve as a useful reference for future research on performance optimization and application migration using CRIU.

The large number of devices characterizing Wireless Sensor Networks (WSNs) provide the benefits of observing, tracking, and recording everything; nonetheless, the cumulative computing power of those devices is typically not utilized, and the few implementations taking advantage of it neglect privacy or are application-specific. This manuscript describes a privacy-preserving protocol that enables WSN nodes to jointly compute an arbitrary function without disclosing their own private inputs. The computation takes place at data source nodes, while computation instructions and intermediate results move across the network secured by cryptography. The protocol relies on the Onion Routing technique to provide uniformly distributed network traffic and confine the knowledge a foreign actor can gain from monitoring messages traveling the network. We show that the communication protocol is privacy-preserving against the external and internal attacker models, and we validate our protocol implementation using the NS3 network simulator.

Gathering information is the primary purpose of a Sensor Network. The task is performed by spatially distributed nodes equipped with sensing, processing, and communication capabilities. However, data gathered from a sensor network must be processed, and often the collective computation capability of nodes forming the sensor network is neglected in favor of data processing on cloud systems. Nowadays, Edge Computing has emerged as a new paradigm aiming to migrate data processing close to data sources. In this contribution, we focus on the development of a sensor network designed to detect a person’s fall. We named this sensor network the smart floor. Fall detection is tackled with a Convolutional Neural Network, and we propose an approach for in-network processing of convolution layers on grid-shaped sensor networks. The proposed approach could lead to the development of a sensor network that detects falls by performing CNN inference processing on the edge. We complement our work with a simulation using the simulator ns-3. The simulation is designed to emulate the communication overhead of the proposed approach applied to a wired sensor network that resembles the smart floor. Simulation results provide evidence on the feasibility of the proposed concept applied to wired grid shaped sensor networks.

To anonymous internet traffic, many popular protocols route traffic through a network of nodes in order to conceal information about the request. However, routing traffic through other nodes inherently introduces added latency. Over the past two decades, there were many attempts to improve the path selection in order to decrease latency with little or no trade-off in terms of security, and anonymity. In this paper, we show the potential use of geo-sharding in decentralized routing networks to improve fault-tolerance, and latency. Such networks can be used as a communication layer for Edge devices computing huge amounts of data. Specifically, we focus our work on Low Latency Anonymous Routing Protocol (LLARP), a protocol built on top of Oxen blockchain that aims to achieve internet privacy. We analyse the existing network of Service Nodes(SN), observe cloud provider centralisation, and propose a high level protocol that provides incentives for a better geographical distribution mitigating potential cloud provider/country wide service dropouts. Additionally, the protocol level information about geographical location can be used to improve client’s path (the string of nodes that will participate in the transaction) selection, decreasing network latency. We show the feasibility of our approach by comparing it with the random path selection in a simulated environment. We observe marginal drops in average latency when selecting paths geographically closer to each other.