Adaptive data synchronization algorithm for IoT-oriented low-power wide-area networks

The Internet of Things (IoT) is by now very close to be realized, leading the world towards a new technological era where people’s lives and habits will be definitively revolutionized. Furthermore, the incoming 5G technology promises significant enhancements concerning the Quality of Service (QoS) in mobile communications. Having billions of devices simultaneously connected has opened new challenges about network management and data exchange rules that need to be tailored to the characteristics of the considered scenario. A large part of the IoT market is pointing to Low-Power Wide-Area Networks (LPWANs) representing the infrastructure for several applications having energy saving as a mandatory goal besides other aspects of QoS. In this context, we propose a low-power IoT-oriented file synchronization protocol that, by dynamically optimizing the amount of data to be transferred, limits the device level of interaction within the network, therefore extending the battery life. This protocol can be adopted with different Layer 2 technologies and provides energy savings at the IoT device level that can be exploited by different applications

A Compression Technique Exploiting References for Data Synchronization Services

Department of Computer Science and EngineeringIn a variety of network applications, there exists significant amount of shared data between two end hosts. Examples include data synchronization services that replicate data from one node to another. Given that shared data may have high correlation with new data to transmit, we question how such shared data can be best utilized to improve the efficiency of data transmission. To answer this, we develop an encoding technique, SyncCoding, that effectively replaces bit sequences of the data to be transmitted with the pointers to their matching bit sequences in the shared data so called references. By doing so, SyncCoding can reduce data traffic, speed up data transmission, and save energy consumption for transmission. Our evaluations of SyncCoding implemented in Linux show that it outperforms existing popular encoding techniques, Brotli, LZMA, Deflate, and Deduplication. The gains of SyncCoding over those techniques in the perspective of data size after compression in a cloud storage scenario are about 12.4%, 20.1%, 29.9%, and 61.2%, and are about 78.3%, 79.6%, 86.1%, and 92.9% in a web browsing scenario, respectively.ope

CRDTs in highly volatile environments

Publisher Copyright: © 2022 The Author(s)The implementation of collaborative applications in highly volatile environments, such as the ones composed of mobile devices, requires low coordination mechanisms. The replication without coordination semantics of Conflict-Free Replicated Data Types (CRDTs) makes them a natural solution for these execution contexts. However, the current CRDT models require each replica to know all other replicas beforehand or to discover them on-the-fly. Such solutions are not compatible with the dynamic ingress and egress of nodes in volatile environments. To cope with this limitation, we propose the Publish/Subscribe Conflict-Free Replicated Data Type (PS-CRDT) model that combines CRDTs with the publish/subscribe interaction model, and, with that, enable the spatial and temporal decoupling of update propagation. We implemented PS-CRDTs in Thyme, a reactive storage system for mobile edge computing. Our experimental results show that PS-CRDTs require less communication than other CRDT-based solutions in volatile environments.publishersversionpublishe

Asynchronous spiking neurons, the natural key to exploit temporal sparsity

Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms

Conflict-Free Replicated Data Types in Dynamic Environments

Over the years, mobile devices have become increasingly popular and gained improved computation capabilities allowing them to perform more complex tasks such as collaborative applications. Given the weak characteristic properties of mobile networks, which represent highly dynamic environments where users may experience regular involuntary disconnection periods, the big question arises of how to maintain data consistency. This issue is most pronounced in collaborative environments where multiple users interact with each other, sharing a replicated state that may diverge due to concurrency conflicts and loss of updates. To maintain consistency, one of today’s best solutions is Conflict-Free Replicated Data Types (CRDTs), which ensure low latency values and automatic conflict resolution, guaranteeing eventual consistency of the shared data. However, a limitation often found on CRDTs and the systems that employ them is the need for the knowledge of the replicas whom the state changes must be disseminated to. This constitutes a problem since it is inconceivable to maintain said knowledge in an environment where clients may leave and join at any given time and consequently get disconnected due to mobile network communications unreliability. In this thesis, we present the study and extension of the CRDT concept to dynamic environments by introducing the developed P/S-CRDTs model, where CRDTs are coupled with the publisher/subscriber interaction scheme and additional mechanisms to ensure users are able to cooperate and maintain consistency whilst accounting for the consequent volatile behaviors of mobile networks. The experimental results show that in volatile scenarios of disconnection, mobile users in collaborative activity maintain consistency among themselves and when compared to other available CRDT models, the P/S-CRDTs model is able to decouple the required knowledge of whom the updates must be disseminated to, while ensuring appropriate network traffic values

Distributed multi-user MIMO transmission using real-time sigma-delta-over-fiber for next generation fronthaul interface

To achieve the massive device connectivity and high data rate demanded by 5G, wireless transmission with wider signal bandwidths and higher-order multiple-input multiple-output (MIMO) is inevitable. This work demonstrates a possible function split option for the next generation fronthaul interface (NGFI). The proof-of-concept downlink architecture consists of real-time sigma-delta modulated signal over fiber (SDoF) links in combination with distributed multi-user (MU) MIMO transmission. The setup is fully implemented using off-the-shelf and in-house developed components. A single SDoF link achieves an error vector magnitude (EVM) of 3.14% for a 163.84 MHz-bandwidth 256-QAM OFDM signal (958.64 Mbps) with a carrier frequency around 3.5 GHz transmitted over 100 m OM4 multi-mode fiber at 850 nm using a commercial QSFP module. The centralized architecture of the proposed setup introduces no frequency asynchronism among remote radio units. For most cases, the 2 x 2 MU-MIMO transmission has little performance degradation compared to SISO, 0.8 dB EVM degradation for 40.96 MHz-bandwidth signals and 1.4 dB for 163.84 MHz-bandwidth on average, implying that the wireless spectral efficiency almost doubles by exploiting spatial multiplexing. A 1.4 Gbps data rate (720 Mbps per user, 163.84 MHz-bandwidth, 64-QAM) is reached with an average EVM of 6.66%. The performance shows that this approach is feasible for the high-capacity hot-spot scenario