2022_1 - Infocommunications

2022. 1^st Issue

Volume XIV, Number 1

MESSAGE FROM THE EDITOR-IN-CHIEF

Pal Varga
Diverse Infocommunication Technologies to Assist Heterogeneous Distributed Systems
WE live in the age of infocommunication technology boom, where research results are applied in various fields. This first 2022 issue of the Infocommunications Journal presents a colorful blend of technologies used in various fields, most of which can be categorized as distributed systems. The range of application areas is wide: from predicting housing prices through transportation, from cloud-based robotic control through multi-core batch-scheduling to semiconductor supply chains.

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PAPERS FROM OPEN CALL

Qinghe Pan, Zeguo Qiu, Yaoqun Xu and Guilin Yao
Predicting the Price of Second-Hand Housing Based on Lambda Architecture and KD Tree
In this paper a system is designed and implemented to predict the price of second-hand housing. This system based on Lambda architecture can execute prediction in both real-time and batch modes so it can give two kinds of different price predictions that reflect current and historical conditions respectively. The kNN related algorithms are used for price prediction. By comparing the performance of brute kNN, kd tree and ball tree, kd tree is selected as the price prediction model of the system. In system implementation the kd tree model is chosen to predict prices in both real-time and batch services. The kd tree model can also recommend housings to user besides price prediction. The experiment shows the effectiveness of our system. Time and space performance of brute kNN, kd tree and ball tree are compared by experiments. And the evaluation metrics of other available maching learning models are compared. The reason of choosing the kd tree model is also explained by the experimental results.

Reference
DOI: 10.36244/ICJ.2022.1.1
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Es-said Azougaghe, Abderrazak Farchane, Said Safi and Mostafa Belkasmi
Turbo decoding of concatenated codes based on RS codes using adapted scaling factors
Iteratively decoded block turbo codes are product codes that exhibit excellent performance with reasonable complexity. In this paper, a generalization of parallel concatenated block codes (GPCBs) based on RS codes is presented. We propose an efficient decoding algorithm with modifications of the Chase-Pyndiah algorithm is written by using Weighting factor α and Reliability factor β. In this work, we studied the effect of diverse parametres such as the effect of various component codes, interleaver size (number of sub-blocks) and number of iterations. The simulation results shows the relevance of the adapted parameters to decode generalized parallel concatenated block codes based on RS codes. The proposed algorithm (MCP) using the adapted parameters performs better than the one using with empirical parameters (CP).

Reference
DOI: 10.36244/ICJ.2022.1.2
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Dávid Papp and Regő Borsodi
Determining Hybrid Re-id Features of Vehicles in Videos for Transport Analysis
The research topic presented in this paper belongs to computer vision problems in the transport application area, where the statistical data of the results give the input for the transport analysis. Although object tracking in a controlled environment could be performed with good results in general, accurate and detailed annotation of vehicles is a common problem in traffic analysis. Such annotation includes static and dynamic attributes of numerous vehicles. Most recent object trackers employ CNNs to compute the so-called re-identification features of the bounding boxes. In this paper we introduce hybrid re-identification features, which combine latent, static, and dynamic attributes to improve tracking. Furthermore, we propose a lightweight solution that could be integgrated in a real-time multi-camera tracking system.

Reference
DOI: 10.36244/ICJ.2022.1.3
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Hassan Farran, David Khoury, and László Bokor
A comprehensive survey on the application of blockchain/hash chain technologies in V2X communications
The Vehicle-to-Everything (V2X) technology and protocols are the main cornerstones for advanced transportation and autonomous vehicle applications. V2X has several subsets, including Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication contexts. The main benefit of applying V2X technologies is increased safety by facilitating predicted warnings supporting automated driving and traffic applications. Wirelessly transmitted messages are the information sources; therefore, security is critical in V2X systems. The V2X exchanged messages are sent wirelessly and must fulfill the security requirements, such as integrity, authenticity, and privacy support. The messaging between vehicles and networks must be trusted. Lately, promising and proliferating blockchain/hash chain technologies have been introduced in V2X communications and cope with the cooperative vehicular applications security and related efficiency aspects. This paper provides a comprehensive survey about the V2X use-cases based blockchain/hash chain and introduces the available solutions and methods in this domain.

Reference
DOI: 10.36244/ICJ.2022.1.4
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Marcell Balogh and Attila Vidács
Optimizing Camera Stream Transport in Cloud-Based Industrial Robotic Systems
Combining visual-guided robotics with cloud networking brought a new era into industrial robotic research and development. New challenges have to be tackled with a focus on providing proper communication and data processing setup: sensor data processing as well as the control software should be decoupled from the local robot hardware and should move into the cloud. In the emerging field of cloud robotics, there are trade-offs that have to be handled. More and more sensors such as cameras are being integrated but it comes with a cost. All sensory data have to be sent through often limited networking resources, while latency must be kept as low as possible. In this paper we propose a general solution for efficient camera stream transportation in cloud robotic systems. After introducing our test scenario with the used hardware and software elements, a detailed overview of the architecture is presented with describing each task of the components. The goal of this paper is to examine the current stream transportation implementations in ROS environment and implement a more efficient method. The performance of the proposed method is investigated and compared with other solutions evidenced by measurements.

Reference
DOI: 10.36244/ICJ.2022.1.5
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Tamás Lévai and Gábor Rétvári
Batch-scheduling Data Flow Graphs with Service-level Objectives on Multicore Systems
Data flow graphs are a popular program representation in machine learning, big data analytics, signal processing, and, increasingly, networking, where graph nodes correspond to processing primitives and graph edges describe control flow. To improve CPU cache locality and exploit data-level parallelism, nodes usually process data in batches. Batchy is a scheduler for data flow graph based packet processing engines, which uses controlled queuing to reconstruct fragmented batches inside a data flow graph in accordance with strict Service-Level Objectives (SLOs). Earlier work showed that Batchy yields up to 10x performance improvement in real-life use cases, thanks to maximally exploiting batch processing gains. Batchy, however, is fundamentally restricted to single-threaded execution. In this paper, we generalize Batchy to parallel execution on multiple CPU cores. We extend the analytical model to the parallel setting and present a primal decomposition framework, where each core runs an unmodified Batchy controller to schedule batch-processing on a subset of the data flow graph, orchestrated by a master controller that distributes the delay-SLOs across the cores using subgradient search. Evaluations on a real software switch provide experimental evidence that our decomposition framework produces 2.5x performance improvement while accurately satisfying delay SLOs that are otherwise not feasible with single-core Batchy.

Reference
DOI: 10.36244/ICJ.2022.1.6
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Nour Ramzy, Hans Ehm, Sandra Durst, Konstanze Wibmer, and Werner Bick
Knowgraph-TT: Knowledge-Graph-Based Transit Time Matching in Semiconductor Supply Chains
The semiconductor supply chain is characterized by a global and complex production network in a competitive market. The time when work at one location ends and can be resumed at another is defined as Transit Time (TT). Therefore, planning Transit Time accurately and minimizing delays is crucial as it is used in the execution system to determine the Available to Promise (ATP) and thus important for daily order confirmation. By determining the ATP, the customer receives a response to the resource availability and a due date to the customer requests. Due to tool inherent differences, we choose semantic integration via Knowledge Graph (KG) to match the planned TT used in the execution system and the actual TT measured in the monitoring tool. KnowGraph-TT thereby serves as a role model for further matching and alignment tasks using KG. It connects actual and planned TT, highlights the gaps via applied queries, and enables an optimized update of planned TT. With our solution, deviations of actual and planned TT can be minimized and confirmations of unrealizable deliverable times are avoided.

Reference
DOI: 10.36244/ICJ.2022.1.7
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