Network Server Research Articles

Greedy cooperative cache placement for mobile edge networks with user preferences prediction and adaptive clustering

Caching the most likely to be requested content at the base stations in a cooperative manner can facilitate direct content delivery without fetching content from the remote content server in a mobile edge network. This alleviates the user-perceived latency, reduces the burden on backhaul and minimizes the duplicated content transmissions. In addition to static content popularity, it is also important to consider the users’ dynamic behaviour for real-time applications. Unevenly distributed users, heterogeneity of the user preferences, and activity level in large-scale mobile edge networks are important issues that have been addressed in this work. The user preference has been predicted with the long short-term memory (LSTM) to capture the dynamic user behaviour and then users are clustered based on the predicted user preferences. The cache placement problem is formulated as Integer linear programming to maximize the saved delay with deadline and capacity constraints. Further, we devise a greedy algorithm by proving the submodular property of the objective function with matroid constraints, which can achieve (1−1e) optimality with linear computational complexity. Extensive simulation results demonstrate that the proposed cooperative caching mechanism significantly improves the performance in terms of acceleration ratio, hit ratio, and cache utilization compared with existing mechanisms.

Effective spectrum sensing using cognitive radios in 5G and wireless body area networks

Wireless Body Area Network (WBAN) is one of the wireless networks vertical purviews, which supports for constant physiological signal monitoring of human body and attracts both academic and industry in the field of research. In this wireless communication incorporating cognitive radio supports for providing opportunistic wireless link to user optimally by sensing spectral environment. Since the sensors used are battery dependent, increasing the lifetime of network is an essential task, implementing machine learning approach dynamically indicate path for effective data transmission between network intermediate and server. Consequently, energy harvesting and standard deviation of utilised energy estimation also need to be concentrated in this work while number sensors are incorporated in WBAN. Proposed scheme is validated with effectiveness of numerical results and proposes modified algorithm with low computational complexity. Reducing energy consumption of mobile communication networks has gained significant attentions since it takes a major part of the total energy consumption of information and communication technology (ICT).

Online Orchestration of Collaborative Caching for Multi-Bitrate Videos in Edge Computing

In the traditional video streaming service provisioning paradigm, users typically request video contents through nearby Content Delivery Network (CDN) server(s). However, because of the uncertain wide area networks delays, the (remote) users usually suffer from long video streaming delay, which affects the quality of experience. Multi-Access Edge Computing (MEC) offers caching infrastructures in closer proximity to end users than conventional Content Delivery Networks (CDNs). Yet, for video caching, MEC's potential has not been fully unleashed as it overlooks the opportunities of collaborative caching and multi-bitrate video transcoding. In this paper, we model and formulate an Integer Linear Program (ILP) to capture the long-term cost minimization problem for caching videos at MEC, allowing joint exploitation of MEC with CDN and real-time video transcoding to satisfy arbitrary user demands. While this problem is intractable and couples the caching decisions for adjacent time slots, we design a polynomial-time online orchestration framework which first relaxes and carefully decomposes the problem into a series of subproblems solvable in each individual time slot and then converts the fractional solutions into integers without violating constraints. We have formally proved a parameterized-constant competitive ratio as the performance guarantee for our approach, and also conducted extensive evaluations to confirm its superior practical performance. Simulation results demonstrate that our proposed algorithm outperforms the state-of-the-art algorithms, with 13.6% improvement on average in terms of total cost.

A PEER-TO-PEER AIR QUALITY MONITORING SYSTEM USING IOT SENSORS AND CLOUD PLATFORMS

This paper presents the design and implementation of an air quality monitoring system that combines Internet of Things (IoT) sensors, cloud computing platforms, and peer-to-peer (P2P) networking. The system uses IoT sensors to collect real-time air quality data from multiple geographic areas. This data is sent to cloud platforms, including AWS IoT Core, for processing and storage. A P2P network of servers, with each server responsible for a specific area, enables distributed access to the air quality data. The system also incorporates machine learning models deployed on the Google Cloud Platform to provide air quality predictions. The proposed architecture for wide-area air quality monitoring is feasible and scalable based on experimental results.

Evaluation of LoRaWAN class B performances and its optimization for better support of actuators

LoRaWAN technology is of particular importance in the Internet of Things realm. It is a simple network architecture with a default-working mode optimized to sensors with limited power autonomy and a few downlink exchanges with the network server. In addition, LoRaWAN offers an optional operation mode, Class B, suitable for IoT applications based on actuators, generating periodic downlink data and requiring limited end-to-end delay. Today, Class B performance is not adequately quantified. This paper proposes a LoRaWAN Class B performance evaluation that considers the downlink transmission efficiency and delivery delay. We provide optimization for the class B working mechanism to enhance the performance. In addition, we evaluate the effects of class B configurable parameters: DL transmission opportunities and retransmission retries. The results and conclusions help optimally configure a LoRaWAN network for actuator-based IoT applications.

Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach

The term “distributed denial of service” (DDoS) refers to one of the most common types of attacks. Sending a huge volume of data packets to the server machine is the target of a DDoS attack. This results in the majority of the consumption of network bandwidth and server, which ultimately leads to an issue with denial of service. In this paper, a majority vote-based ensemble of classifiers is utilized in the Sever technique, which results in improved accuracy and reduced computational overhead, when detecting attacks. For the experiment, the authors have used the CICDDOS2019 dataset. According to the findings of the experiment, a high level of accuracy of 99.98% was attained. In this paper, the classifiers use random forest, decision tree, and naïve bayes for majority voting classifiers, and from the results and performance, it can be seen that majority vote classifiers performed better.

Interference Mitigation and Decoding Through Gateway Diversity in LoRaWAN

Long Range (LoRa) represents an efficient low power solution for the Internet of Things. Specifically, LoRa defines a physical layer technology, while access control and network issues are handled by the LoRaWAN protocol. Channel access is essentially unslotted ALOHA, so LoRaWAN performance suffer from packet collision events. Such problem becomes more penalizing in dense network scenarios, where the large number of devices simultaneously connected makes the collision probability significantly grow. The largest part of solutions proposed to overcome LoRaWAN inefficiency is oriented to collision avoidance. Differently, we firstly present an algorithm for mitigating the interference among superposing LoRa signals, allowing collided packets to be detected anyway. Then, we propose a novel combining mechanism, implemented at the LoRaWAN network server, that exploits the information carried by the same packet, but received by different gateways, to achieve a more robust decoding. Hence, packet detection is reliably performed even in the presence of interference, thus reducing the need of retransmission and providing energy saving for end devices. Furthermore, simulation results show that the proposed solutions allow the end users to transmit exploiting low spreading factors, thus reducing the channel use that will be fundamental when dealing with larger scale network scenarios.

Efficient Load Balancing for Heterogeneous Radio-Replication-Combined LoRaWAN

LoRa wide area network (LoRaWAN), an emerging IoT protocol, has been popularized in large-scale applications, given its long-range and low-power properties. Hitherto, there is no appropriate traffic model for LoRaWAN to estimate the heterogeneous arriving traffic at the network server cluster (NSC). Inefficient computation power planning or even processing failure might be further caused. Radio replication, commonly existed in the arriving traffic at NSC in LoRaWAN, also causes difficulty estimating the makespan (i.e., mean processing time in NSC). To overcome the abovementioned limitations, a heterogeneous radio-replication-aware traffic aggregation model is proposed to estimate the arriving traffic for LoRaWAN. In addition, a radio-replication-combined supermarket model (RRC-SM), on top of HTAM, is proposed to achieve load balancing among servers in LoRaWAN. Furthermore, a nondominated sorting genetic algorithm based on multiobjective optimization is developed to simultaneously minimize cost and latency on NSC. Experiments reveal that the proposed HTAM and RRC-SM agree well with the simulation outcome. Under the arriving traffic estimated as 6.16 erlangs with four radio replications of each arriving packet on average, the proposed RRC-SM provides more than 50% reduction on the total processing latency and 75% reduction on the number of servers in NSC than other existing models.

A Novel Approach to Improve the Adaptive-Data-Rate Scheme for IoT LoRaWAN

The long-range wide-area network (LoRaWAN) uses the adaptive-data-rate (ADR) algorithm to control the data rate and transmission power. The LoRaWAN ADR algorithm adjusts the spreading factor (SF) to allocate the appropriate transmission rate and transmission power to reduce power consumption.The updating SF and transmission power of the standard ADR algorithm are based on the channel state, but it does not guarantee efficient energy consumption among all the nodes in complex environments with high-varying channel conditions. Therefore, this article proposes a new enhancement approach to the ADR+ algorithm at the network server, which only depends on the average signal-to-noise ratio (SNR). The enhancement ADR algorithm ADR++ introduces an energy-efficiency controller α related to the total energy consumption of all nodes, to use it for adjusting the average SNR of the last records. We implement our new enhanced ADR at the network server (NS) using the FLoRa module in OMNET++. The simulation results demonstrate that our proposed ADR++ algorithm leads to a significant improvement in terms of the network delivery ratio and energy efficiency that reduces the network energy consumption up to 17.5% and improves the packet success rate up to 31.55% over the existing ADR+ algorithm.

Stock administration framework utilizing AWS

Unsuccessful stock results in discouraged customers and a significant investment in more delayed deals and distribution facilities. Proposed work has eliminated paperwork, human errors, manual delays, and slow processes. This stock management system can track sales and available inventory, letting a shop know how much to replenish and when to do so. With distributed computing, tasks can be delegated to a combination of software and administrations across an enterprise. The cloud is this network of servers. Distributed computing can help businesses transform their static server foundations into dynamic ones that can expand and contract in response to client demands. A powerful distributed computing stage individually configures, designs, reconfigures, and deprovisions servers. cloud-based servers can be actual machines or virtual machines.

The Comparative Anatomy of Nanopublications and FAIR Digital Objects

Beginning in 1995, early Internet pioneers proposed Digital Objects as encapsulations of data and metadata made accessible through persistent identifier resolution services (Kahn and Wilensky 2006). In recent years, this Digital Object Architecture has been extended to include the FAIR Guiding Principles (Wilkinson et al. 2016), resulting in the concept of a FAIR Digital Object (FDO), a minimal, uniform container making any digital resource machine-actionable. Intense effort is currently underway by a global community of experts to clarify definitions around an FDO Framework (FDOF) and to provide technical specifications (FAIR DO group 2020, FAIR Digital Object Forum 2020 , Bonino da Silva Santos (2021)) regarding their potential implementation. Beginning in 2009, nanopublications were independently conceived (Groth et al. 2010) as a minimal, uniform container making individual semantic assertions and their associated provenance metadata, machine-actionable. They represent minimal units of structured data as citable entities (Mons and Velterop 2009). A nanopublication consists of an assertion, the provenance of the assertion, and the provenance of the nanopublication (publication info). Nanopublications are implemented in and aligned with Semantic Web technologies such as RDF, OWL, and SPARQL (World Wide Web Consortium (W3C) 2015) and can be permanently and uniquely identified using resolvable Trusty URIs (Groth et al. 2021). The existing Nanopublication Server Network provides vital services orchestrating nanopublications (Kuhn et al. 2021) including identifier resolution, storage, search and access. Nanopublications can be used to expose quantitative and qualitative data, as well as hypotheses, claims, negative results, and opinions that are typically unavailable as structured data or go unpublished altogether. The first practical application of nanopublications occurred in 2014, with the publication of millions of nanopublications as part of the FANTOM5 Project (The FANTOM Consortium and the RIKEN PMI and CLST (DGT) 2014, Lizio et al. 2015). Since then, millions of real-world examples spanning diverse knowledge domains are now available on the nanopublication server network. Like nanopublication, the FDOF also posits an ultra-minimal approach to structured, self-contained, machine-readable data and metadata. An FDO consists of: the object itself (subsequently referred to here as the resource to avoid confusion with other meanings of the term “object”); the metadata describing the resource; and a globally unique and persistent identifier with predictable resolution behaviors. These two technologies share the same vision of a data infrastructure, and act as instances of Machine-Actionable Containers (MACs) that make use of minimal uniform standards to enable FAIR operations. Here, we compare the structure and computational behaviors of the existing nanopublication infrastructure, to those in the proposed FAIR Digital Object Framework. Although developed independently there are clear parallels between the vision and the approach of nanopublication and FDOF. Each aspires to minimal standards for the encapsulation of digital information into free-standing, publishable (citable, referenceable) entities. The minimal standards involve globally unique and persistent identifiers that resolve to standardized semantically enabled metadata descriptions that include machine actionable paths to the resource itself. At the same time, there are also differences. The scope of nanopublications is limited to the assertional data type and, as the name suggests, nanopublications should remain small in size (limited to single assertions as individual triples or small RDF graphs). In contrast FDOs are unlimited in their scope, accommodating digital resources of arbitrarily large size, type and complexity, so long as their type can be ontologically described. Furthermore, whereas nanopublications represent a moderately mature technology, the FDOF is a specification still under development. If it were possible to formally draw points of contact between the two approaches, then it would be possible to leverage the vast practical experience gained in the nanopublishing of assertions for the FDO community. Here, inspired by recent applications of nanopublications in the FIP Wizard tool (Schultes et al. 2020), and their extension to research claims (Kuhn 2022, McNamara 2022) and data using Schultes (2022a), Schultes (2022b), we attempt a point-by-point comparison of the specifications between nanopublication and FDOs. We find a remarkable congruence between the currently proposed FDO requirements and the existing nanopublication infrastructure, including several FDO-like qualities already embodied in the nanopublication ecosystem.

A Fuzzy-Logic Based Adaptive Data Rate Scheme for Energy-Efficient LoRaWAN Communication

Long Range Wide Area Network (LoRaWAN) technology is rapidly expanding as a technology with long distance connectivity, low power consumption, low data rates and a large number of end devices (EDs) that connect to the Internet of Things (IoT) network. Due to the heterogeneity of several applications with varying Quality of Service (QoS) requirements, energy is expended as the EDs communicate with applications. The LoRaWAN Adaptive Data Rate (ADR) manages the resource allocation to optimize energy efficiency. The performance of the ADR algorithm gradually deteriorates in dense networks and efforts have been made in various studies to improve the algorithm’s performance. In this paper, we propose a fuzzy-logic based adaptive data rate (FL-ADR) scheme for energy efficient LoRaWAN communication. The scheme is implemented on the network server (NS), which receives sensor data from the EDs via the gateway (GW) node and computes network parameters (such as the spreading factor and transmission power) to optimize the energy consumption of the EDs in the network. The performance of the algorithm is evaluated in ns-3 using a multi-gateway LoRa network with EDs sending data packets at various intervals. Our simulation results are analyzed and compared to the traditional ADR and the ns-3 ADR. The proposed FL-ADR outperforms the traditional ADR algorithm and the ns-3 ADR minimizing the interference rate and energy consumption.

NELoRa

Low-Power Wide-Area Networks (LPWANs) has emerged as a promising mechanism to connect billions of low-cost Internet of Things (IoT) devices for wide-area data collection. Long Range (LoRa) [1] is a commercialized and widely deployed wireless technology that facilitates the establishment of LPWANs. As illustrated in Figure 1, a LoRaWAN consists of end nodes, gateways, a network server, and an application server. The collected sensory data (e.g., temperature, humidity) transmitted from the distributed end nodes are relayed by several gateways to the network server.

StateAFL: Greybox fuzzing for stateful network servers

Fuzzing network servers is a technical challenge, since the behavior of the target server depends on its state over a sequence of multiple messages. Existing solutions are costly and difficult to use, as they rely on manually-customized artifacts such as protocol models, protocol parsers, and learning frameworks. The aim of this work is to develop a greybox fuzzer (StateAFL) for network servers that only relies on lightweight analysis of the target program, with no manual customization, in a similar way to what the AFL fuzzer achieved for stateless programs. The proposed fuzzer instruments the target server at compile-time, to insert probes on memory allocations and network I/O operations. At run-time, it infers the current protocol state of the target server by taking snapshots of long-lived memory areas, and by applying a fuzzy hashing algorithm (Locality-Sensitive Hashing) to map memory contents to a unique state identifier. The fuzzer incrementally builds a protocol state machine for guiding fuzzing. We implemented and released StateAFL as open-source software. As a basis for reproducible experimentation, we integrated StateAFL with a large set of network servers for popular protocols, with no manual customization to accomodate for the protocol. The experimental results show that the fuzzer can be applied with no manual customization on a large set of network servers for popular protocols, and that it can achieve comparable, or even better code coverage and bug detection than customized fuzzing. Moreover, our qualitative analysis shows that states inferred from memory better reflect the server behavior than only using response codes from messages.

Federated Edge Network Utility Maximization for a Multi-Server System: Algorithm and Convergence

We propose a novel Federated Edge Network Utility Maximization (FEdg-NUM) architecture for solving a large-scale distributed network utility maximization (NUM) problem. In FEdg-NUM, clients with private utilities communicate to a peer-to-peer network of edge servers. This represents a departure from the classical distributed NUM master-slave configuration and enables distributed computing harnessing local communications. Compared to a solution using cloud synchronization via Ring AllReduce, we prove that our federated edge computing model has shorter run-time in the presence of network congestion, thanks to its configuration and its ability to make progress in the presence of intermittent links. The paper studies its convergence and run-time performance both analytically and numerically, and illustrates several possible networking applications.

Tapping into NFV Environment for Opportunistic Serverless Edge Function Deployment

Even with Network Function Virtualization (NFV), many commodity network servers have spare cycles. Despite that they are small and irregularly occur, spare cycles are fit for deploying short-lived serverless computing functions at the network edge. In this work, we perform detailed analyses of the benefits and limitations of co-locating serverless functions on NFV-ready servers. We propose <i>NEMO</i> , a novel platform that enables efficient serverless edge function deployment in the NFV environment. NEMO can intelligently harvest spare cycles of network functions to warm up the serverless functions and speed up the function invocation in an agile manner. Besides, NEMO can judiciously manage the thread conflict in a resource-limited environment. We build a prototype of NEMO. Our thorough evaluations show that NEMO can harvest up to 41% spare cycles and achieve about 12.5 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 25X performance improvement compared with straightforward co-location.

Cost-Effective Edge Server Network Design in Mobile Edge Computing Environment

<i>Mobile edge computing</i> (MEC) deploys edge servers at the base station in the proximity of users to provide cloud computing-like computing and storage functionalities, which can achieve applications’ low latency requirement at the network edge. The <i>edge server network</i> (ESN), constituted by edge servers in an area and the links between them, can host app vendors’ services for serving nearby users. Many existing studies have demonstrated that a high ESN density allows for high service performance because edge servers can communicate and share resources with each other effectively over the ESN. However, in the real-world MEC environment, constructing a high-density ESN may incur high construction costs. The trade-off between construction cost and network density plays a vital role in the design of an ESN. Unfortunately, existing studies of MEC have commonly and simply assumed the densities of the ESNs in their experiments. In this paper, we make the first attempt to study the design of cost-effective ESNs with the aim to trade off between the network construction cost and the network density. We model this novel <i>Edge Server Network Design</i> (ESND) problem as a constrained optimization problem and prove its <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hardness. ESND-O as an optimal approach is proposed based on integer programming to solve small-scale ESND problems. Another approximation approach named ESND-A is designed to solve large-scale ESND problems efficiently. We conduct extensive experiments to test the performance of ESND-O and ESND-A on a real-world dataset, and the experimental results demonstrate their effectiveness and efficiency against four representative approaches.

Mobile LoRa Device 의 네트워크 연결성 개선을 위한 FCR-ADR 알고리즘

Long Range Wide Area Network (LoRaWAN), a communication technology for the Internet of Things (IoT), consumes low energy and enables long-range communication. LoRaWAN supports Adaptive Data Rate (ADR), which can conserve energy by optimizing the data rate and Time on Air (ToA). ADR is executed on an end device and on the network server side. While ADR on the end device side is an algorithm to recover the connection when it is lost, ADR on the network server side aims to set appropriate transmission parameters with the recently uplinked signal-to-noise ratio (SNR) value and to deliver the parameter change command to the end device. However, the existing ADR on the end device side requires a considerable amount of time to restore the connection when the network condition deteriorates, leading to the loss of many packets. This study presents Fast Connection Recovery ADR (FCR-ADR), a novel, end device side ADR algorithm that reduces the number of packets lost while an end device moves. The evaluation results of simulation study using the OMNeT++ simulator and FLoRa (Framework for LoRa) show that the proposed FCR-ADR algorithm surpasses the existing ADR in terms of packet delivery ratio (PDR) with no significant difference in energy consumption.

Implementation of signature-based intrusion detection system using SNORT to prevent threats in network servers

Security is an important factor in today's digital era. In a network, implementing a security system is the focus of a network developer. One of the most basic network securities is in the form of access. To manage the security of a system must be known in advance who is involved in the system and what activities are carried out. Just like a security alarm, which monitors work conditions, this is the function of the Intrusion Detection System (IDS). IDS has several effective methods for detecting threats, one of which is the Signature-based method. IDS can be implemented through the open-source SNORT application, and the method works with rules which are commands to IDS to recognize various attacks. IDS rules will be included in the signature matching process, which means matching between rules and incoming attacks and views of both protocols, then the IDS will generate alerts that contain notifications. This study conducted a reading of the MIT-DARPA 1999 dataset on 1,252,412 packages and tested alerting with Network Scanning and DoS attacks. Analyze Package Data runs at a speed of 83,494 packets /second and gets a true positive percentage reaching 100% and an accuracy of 98.10%.

Quantitative and Real-Time Evaluation of Human Respiration Signals with a Shape-Conformal Wireless Sensing System.

Respiration signals reflect many underlying health conditions, including cardiopulmonary functions, autonomic disorders and respiratory distress, therefore continuous measurement of respiration is needed in various cases. Unfortunately, there is still a lack of effective portable electronic devices that meet the demands for medical and daily respiration monitoring. This work showcases a soft, wireless, and non-invasive device for quantitative and real-time evaluation of human respiration. This device simultaneously captures respiration and temperature signatures using customized capacitive and resistive sensors, encapsulated by a breathable layer, and does not limit the user's daily life. Further a machine learning-based respiration classification algorithm with a set of carefully studied features as inputs is proposed and it is deployed into mobile clients. The body status of users, such as being quiet, active and coughing, can be accurately recognized by the algorithm and displayed on clients. Moreover, multiple devices can be linked to a server network to monitor a group of users and provide each user with the statistical duration of physiological activities, coughing alerts, and body health advice. With these devices, individual and group respiratory health status can be quantitatively collected, analyzed, and stored for daily physiological signal detections as well as medical assistance.