Distributed Destination Search Routing for 5G and beyond Networks

Fifth-generation and beyond networks target multiple distributed network application such as Internet of Things (IoT), connected robotics, and massive Machine Type Communication (mMTC). In the absence of a central management unit, the device need to search and establish a route towards the destination before initializing data transmission. In this paper, we proposes a destination search and routing method for distributed 5G and beyond networks. In the proposed method, the source node makes multiple attempts to search for a route towards the destination by expanding disk-like patterns originating from the source node. The source node increases the search area in each attempt, accommodating more nodes in the search process. As a result, the probability of finding the destination increases, which reduces energy consumption and time delay of routing. We propose three variants of routing for high, medium, and low-density network scenarios and analyze their performance for various network configurations. The results demonstrate that the performance of the proposed solution is better than previously proposed techniques in terms of time latency and reduced energy consumption, making it applicable for 5G and beyond networks.

Acute Gravitational Stress Selectively Impairs Dynamic Cerebrovascular Reactivity in the Anterior Circulation Independent of Changes to the Central Respiratory Chemoreflex

Cerebrovascular reactivity (CVR) to changes in the partial pressure of arterial carbon dioxide (PaCO2) is an important mechanism that maintains CO2 or pH homeostasis in the brain. To what extent this is influenced by gravitational stress and corresponding implications for the regulation of cerebral blood flow (CBF) remain unclear. The present study examined the onset responses of pulmonary ventilation (V̇E) and anterior middle (MCA) and posterior (PCA) cerebral artery mean blood velocity (Vmean) responses to acute hypercapnia (5% CO2) to infer dynamic changes in the central respiratory chemoreflex and cerebrovascular reactivity (CVR), in supine and 50° head-up tilt (HUT) positions. Each onset response was evaluated using a single-exponential regression model consisting of the response time latency [CO2-response delay (t0)] and time constant (τ). Onset response of V̇E and PCA Vmean to changes in CO2 was unchanged during 50° HUT compared with supine (τ: V̇E, p = 0.707; PCA Vmean, p = 0.071 vs. supine) but the MCA Vmean onset response was faster during supine than during 50° HUT (τ: p = 0.003 vs. supine). These data indicate that gravitational stress selectively impaired dynamic CVR in the anterior cerebral circulation, whereas the posterior circulation was preserved, independent of any changes to the central respiratory chemoreflex. Collectively, our findings highlight the regional heterogeneity underlying CBF regulation that may have translational implications for the microgravity (and hypercapnia) associated with deep-space flight notwithstanding terrestrial orthostatic diseases that have been linked to accelerated cognitive decline and neurodegeneration.

A Novel Blockchain-Based Encryption Model to Protect Fog Nodes from Behaviors of Malicious Nodes

The world has experienced a huge advancement in computing technology. People prefer outsourcing their confidential data for storage and processing in cloud computing because of the auspicious services provided by cloud service providers. As promising as this paradigm is, it creates issues, including everything from data security to time latency with data computation and delivery to end-users. In response to these challenges, the fog computing paradigm was proposed as an extension of cloud computing to overcome the time latency and communication overhead and to bring computing and storage resources close to both the ground and the end-users. However, fog computing inherits the same security and privacy challenges encountered by traditional cloud computing. This paper proposed a fine-grained data access control approach by integrating the ciphertext policy attribute-based encryption (CP-ABE) algorithm and blockchain technology to secure end-users’ data security against rogue fog nodes in case a compromised fog node is ousted. In this approach, we proposed federations of fog nodes that share the same attributes, such as services and locations. The fog federation concept minimizes the time latency and communication overhead between fog nodes and cloud servers. Furthermore, the blockchain idea and the CP-ABE algorithm integration allow for fog nodes within the same fog federation to conduct a distributed authorization process. Besides that, to address time latency and communication overhead issues, we equip each fog node with an off-chain database to store the most frequently accessed data files for a particular time, as well as an on-chain access control policies table (on-chain files tracking table) that must be protected from tampering by rogue fog nodes. As a result, the blockchain plays a critical role here because it is tamper-proof by nature. We assess our approach’s efficiency and feasibility by conducting a simulation and analyzing its security and performance.

Latency-aware secure transmission in MEC-assisted HSR communication systems

Mobile edge computing can provide short-range cloud computing capability for the mobile users, which is considered to be a promising technology in 5G communication. The mobile users offload some computing tasks to the edge server through the wireless backhaul link, which can reduce the energy consumption and the time latency. Meanwhile, due to the open characteristics of the wireless channel, the offloading tasks through the backhaul link may face the risk of eavesdropping. Therefore, the secure transmission based on physical layer security for the offloading tasks to the edge server is considered. The optimization problem of minimizing the energy consumption for the vehicular stations (VSs) in mobile edge computing-assisted high-speed railway communication system is studied in this paper. The energy consumption of the mobile users is generated by executing the local computing task and by transmitting the partial offloading task to the edge server. In this paper, a novel joint iterative optimization algorithm is proposed. By jointly optimizing the task scheduling, the task offloading and the transmission power, the energy consumption of all VSs is minimized under the constraint of the time latency. Numerical simulation results verify the effectiveness of the proposed algorithm.

Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing

Smart manufacturing employs embedded systems such as CNC machine tools, programable logic controllers, automated guided vehicles, robots, digital measuring instruments, cyber-physical systems, and digital twins. These systems collectively perform high-level cognitive tasks (monitoring, understanding, deciding, and adapting) by making sense of sensor signals. When sensor signals are exchanged through the abovementioned embedded systems, a phenomenon called time latency or delay occurs. As a result, the signal at its origin (e.g., machine tools) and signal received at the receiver end (e.g., digital twin) differ. The time and frequency domain-based conventional signal processing cannot adequately address the delay-centric issues. Instead, these issues can be addressed by the delay domain, as suggested in the literature. Based on this consideration, this study first processes arbitrary signals in time, frequency, and delay domains and elucidates the significance of delay domain over time and frequency domains. Afterward, real-life signals collected while machining different materials are analyzed using frequency and delay domains to reconfirm its (the delay domain’s) significance in real-life settings. In both cases, it is found that the delay domain is more informative and reliable than the time and frequency domains when the delay is unavoidable. Moreover, the delay domain can act as a signature of a machining situation, distinguishing it (the situation) from others. Therefore, computational arrangements enabling delay domain-based signal processing must be enacted to effectively functionalize the smart manufacturing-centric embedded systems.

SAGE: A S plit- A rchitecture Methodolo g y for E fficient End-to-End Autonomous Vehicle Control

Autonomous vehicles (AV) are expected to revolutionize transportation and improve road safety significantly. However, these benefits do not come without cost; AVs require large Deep-Learning (DL) models and powerful hardware platforms to operate reliably in real-time, requiring between several hundred watts to one kilowatt of power. This power consumption can dramatically reduce vehicles’ driving range and affect emissions. To address this problem, we propose SAGE: a methodology for selectively offloading the key energy-consuming modules of DL architectures to the cloud to optimize edge, energy usage while meeting real-time latency constraints. Furthermore, we leverage Head Network Distillation (HND) to introduce efficient bottlenecks within the DL architecture in order to minimize the network overhead costs of offloading with almost no degradation in the model’s performance. We evaluate SAGE using an Nvidia Jetson TX2 and an industry-standard Nvidia Drive PX2 as the AV edge, devices and demonstrate that our offloading strategy is practical for a wide range of DL models and internet connection bandwidths on 3G, 4G LTE, and WiFi technologies. Compared to edge-only computation, SAGE reduces energy consumption by an average of 36.13% , 47.07% , and 55.66% for an AV with one low-resolution camera, one high-resolution camera, and three high-resolution cameras, respectively. SAGE also reduces upload data size by up to 98.40% compared to direct camera offloading.

Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology

Wastewater-based epidemiology is a recognised source of information for pandemic management. In this study, we investigated the correlation between a SARS-CoV-2 signal derived from wastewater sampling and COVID-19 incidence values monitored by means of individual testing programs. The dataset used in the study is composed of timelines (duration approx. five months) of both signals at four wastewater treatment plants across Austria, two of which drain large communities and the other two drain smaller communities. Eight regression models were investigated to predict the viral incidence under varying data inputs and pre-processing methods. It was found that population-based normalisation and smoothing as a pre-processing of the viral load data significantly influence the fitness of the regression models. Moreover, the time latency lag between the wastewater data and the incidence derived from the testing program was found to vary between 2 and 7 days depending on the time period and site. It was found to be necessary to take such a time lag into account by means of multivariate modelling to boost the performance of the regression. Comparing the models, no outstanding one could be identified as all investigated models are revealing a sufficient correlation for the task. The pre-processing of data and a multivariate model formulation is more important than the model structure.

QoS aware web service selection using orthogonal array learning on fruit fly optimization approach

Purpose This paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale service environment (web service repository) while maintaining Quality-of-Service (QoS), is referred to as web service selection (WSS). With the explosive growth of internet services, managing and selecting the proper services (or say web service) has become a pertinent research issue. Design/methodology/approach In this paper, to address WSS problem, the authors propose a new modified fruit fly optimization approach, called orthogonal array-based learning in fruit fly optimizer (OL-FOA). In OL-FOA, they adopt a chaotic map to initialize the population; they add the adaptive DE/best/2mutation operator to improve the exploration capability of the fruit fly approach; and finally, to improve the efficiency of the search process (by reducing the search space), the authors use the orthogonal learning mechanism. Findings To test the efficiency of the proposed approach, a test suite of 2500 web services is chosen from the public repository. To establish the competitiveness of the proposed approach, it compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC). The empirical results show that the proposed approach outperforms its counterparts in terms of response time, latency, availability and reliability. Originality/value In this paper, the authors have developed a population-based novel approach (OL-FOA) for the QoS aware web services selection (WSS). To justify the results, the authors compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC) over the four QoS parameter response time, latency, availability and reliability. The authors found that the approach outperforms overall competitive approaches. To satisfy all objective simultaneously, the authors would like to extend this approach in the frame of multi-objective WSS optimization problem. Further, this is declared that this paper is not submitted to any other journal or under review.

A Real-Time Approach for Automatic Food Quality Assessment Based on Shape Analysis

Products sorting is a task of paramount importance for many countries’ agricultural industry. An accurate quality check assures that good products are not wasted, and rotten, broken and bent food are properly discarded, which is extremely important for food production chains. Such products sorting and quality controls are often performed with consolidated instruments, since simple systems are easier to maintain, validate, and they speed up the processing in terms of production line speed and products per second. Moreover, industries often lack advanced formation, required for more sophisticated solutions. As a result, the sorting task for many food products is mainly done by color information only. Sorting machines typically detect the color response of products to specific LEDs with various light wavelengths. Unfortunately, a color check is often not enough to detect some very common defects. The shape of a product, instead, reveals many important defects and is highly reliable in detecting external objects mixed with food. Also, shape can be used to take detailed measurements of a product, such as its area, length, width, anisotropy, etc. This paper proposes a complete treatment of the problem of sorting food by its shape. It treats real-world problems such as accuracy, execution time, latency and it provides an overview of a full system used on state-of-the-art measurement machines.