Logical Clusters Research Articles

Enhancing Feature Extraction in Sensor Fault Detection

The large amount of data provided by sensors and sensor networks deployed in industrial plants presents an opportunity for advancing process monitoring and informed asset maintenance management. Data-driven anomaly detection systems can be developed for both process anomalies and monitoring systems but the approach must be tailored to effectively capture and categorize relevant features, establishing a robust approach. The use of multi-source signals facilitates the retention of maximal information without reducing the number of available features, as sensor signals may show high degrees of correlation even if the sensors measure different variables across different equipment. The scope of the paper is to execute a preliminary analysis of industrial data, obtaining a logical clustering from a physical one using Canonical Correlation Analysis. This data mining methodology is employed to gather highly correlated data for training an Artificial Neural Network aimed at identifying sensor anomalies. A benchmark is provided by comparing the performance of the ANN trained using logical clusters rather than physical clusters. This comparison serves to evaluate the effectiveness and advantages of utilizing logical clusters for anomaly detection and classification.

Modelling the operational maturity challenges faced by online food ordering and delivery enterprises during Covid-19 lockdown in Oman: a fuzzy interpretive structural modelling approach

Purpose This research aims to ascertain the various operational maturity challenges faced by the online food ordering and delivery enterprises (OFODE), their nature and their interactive relationships. In particular, this paper aims to (a) identify the most relevant operational maturity challenges faced by the OFODE during the COVID-19 lockdown in Oman, (b) explore and establish any likely structural relationship among these challenges and (c) put them into logical clusters. Design/methodology/approach Experts helped to reduce the 18 initially identified maturity challenges to 13 most pressing ones. Mutual relationships, dominance of interactions and their classifications were explored using fuzzy interpretive structural modeling (FISM) and fuzzy MICMAC analysis. Findings The study of situation-specific operational maturity challenges convinced the authors to propose a distinct FISM model that depicts the relationship among these challenges. Keeping commissions and fees reasonable emerges as the challenge which all other challenges seemingly culminate into. One of the most important situation-specific challenges (i.e. customer confidence about infection free delivery) emerges as a linkage challenge which aggravates as well as is aggravated by certain challenges. Research limitations/implications Besides enriching literature, the proposed model has implications for practitioners particularly when the similar lethal waves are experienced anywhere. The number of respondents, subjective approach, specific context as well as the geographical area coverage are the key limitations. Originality/value To the best of the authors’ knowledge, this study is the first known scientific effort which attempts to model the operational maturity challenges faced by the OFODE during COVID-19 lockdown period. The authors used the FISM modeling approach to forge these interrelated challenges into a structural model.

SeAC: SDN-Enabled Adaptive Clustering Technique for Social-Aware Internet of Vehicles

Since millions of smart vehicles in Internet-of-Vehicles (IoV) produce and relay data to analyze road conditions, creating social networks of vehicles in IoV is an important factor for the future Intelligent Transportation System (ITS). Likewise, the IoV architecture has seen vertical fragmentation of approaches used to meet the needs of different work domains. Therefore, IoV in combination with social networking, called Social IoV (SIoV), was created to address these alleged problems. However, one of the challenges in SIoV is that the social relations between vehicles grow and deplete very fast due to the extremely dynamic and unstable nature of the IoV. Therefore, a clustering-based scheme for SIoV, which is efficient in terms of stability can overcome this problem. We propose : an SDN-enabled adaptive clustering technique for SIoV. uses a 3D modeling approach to construct logical clusters that are based on factors such as physical location, social tie, and interest similarity among vehicles. Therefore, improves the stability of clusters and the efficiency of the underlying SIoV architecture. Additionally, by minimizing the trade-off between social and physical distances, lowers communication and computation costs. We evaluate, and the simulation results show that for two different topologies, the adaptive approach using can produce better results in terms of a stable cluster formation.

Software-Defined Small Cell-Linked Vehicular Networks: Architecture and Evaluation

Vehicle-to-everything services are in the implementation phase, and automakers agree that V2X would improve the safety-critical applications already deployed. 3GPP Release 12 introduces LTE-V for V2V and V2I services. The LTE-V is extended to C-V2X to support V2N. Because of the challenge of high mobility in the V2X system, cutting-edge technologies, such as SDN and small cell in 5G networks, pave the way to the next generation of vehicular networks. SDN is a network technology concept that divides the data and control planes. The OpenFlow protocol is used for communication between the control layer and the network layer in SDN. Different from wireless traditional cellular base stations, small cells are lower-power cell sites that are deployed every few blocks. Small cells can transmit data using mid- and high-band spectrums. Small cell-linked road side unit (RSU) is considered a key enabling technology because it has the capability to create a logical cluster platform residing at the edge of the network, which provides high computation performance. Accordingly, we consider a novel distributed software-defined small cell-linked road side unit vehicular network architecture (diSRsVN). Based on diSRsVN, logical software-defined on-board wireless vehicle, and topology discovery over diSRsVN are presented. The proposed architecture is evaluated under an omnet++ network simulator. The simulation results show the effectiveness of the proposed architecture, which improves the packet delivery ratio and minimizes end-to-end delay.

Leveraging the Influence of Power Grid Links in Renewable Energy Power Generation

Smart grid construction provides the basic conditions for grid connection of renewable energy power generation. However, the grid connection of large-scale intermittent renewable energy sources increases the complexity of operational control of power systems. With the increase in intermittent renewable energy grid connections, distribution system operators must optimize and integrate these new participants to ensure the flexibility and stability of smart grids. Dividing the smart grid into logical clusters helps to overcome the problems caused by intermittent renewable energy grid connections. In this study, we propose a 2-step modeling approach that includes both link and leverage analyses to detect the network partitioning and assess the stability of the smart grids. Our experimental results of the link analysis show that, despite the identical scores in modularity and Silhouette Coefficients (SC), the total computational time of the linear programming model for linkage (CD1) is 29.8% shorter than that of the quadratic programming model for linkage (CD2) on 7 networks with fewer than 200 nodes, whereas CD2 is 29.5% faster than CD1 on 19 larger networks with more than 200 nodes. The leverage results of benchmark networks indicate that the computational time of each instance with the proposed linear programming model for leverage (ID1) and quadratic programming model for leverage (ID2) was substantially reduced, and the Critical Node Problem (CNP) results of medium- and large-scale networks were better than those reported in the literature, which play a significant role in smart grid optimization.

Supporting SLA via Adaptive Mapping and Heterogeneous Storage Devices in Ceph

This paper proposes a new resource management scheme that supports SLA (Service-Level Agreement) in a bigdata distributed storage system. Basically, it makes use of two mapping modes, isolated mode and shared mode, in an adaptive manner. In specific, to ensure different QoS (Quality of Service) requirements among clients, it isolates storage devices so that urgent clients are not interfered by normal clients. When there is no urgent client, it switches to the shared mode so that normal clients can access all storage devices, thus achieving full performance. To provide this adaptability effectively, it devises two techniques, called logical cluster and normal inclusion. In addition, this paper explores how to exploit heterogeneous storage devices, HDDs (Hard Disk Drives) and SSDs (Solid State Drives), to support SLA. It examines two use cases and observes that separating data and metadata into different devices gives a positive impact on the performance per cost ratio. Real implementation-based evaluation results show that this proposal can satisfy the requirements of diverse clients and can provide better performance compared with a fixed mapping-based scheme.

Перспективы развития сетевого взаимодействия в транспортно-логистическом комплексе региона

The article deals with the current issue of development of the transport and logistics system and transport and logical clusters on the territory of the Russian Federation on the basis of network interaction. The subject of the study is the transport and logistics clusters of Russian regions, their importance in the development of the transport and logistics complex and economy of Russian regions. Scientific and theoretical concepts on the research problem and their hierarchy in the transport and logistics complex of the region are considered. The economic essence of the transport and logistics cluster and its structure elements are described, and the economic characteristics of various groups of enterprises depending on the degree of participation in the main business process are presented. A comparative assessment of the state and functioning of transport and logistics clusters on the territory of Russia with the European network of transport and logistics clusters formed during the strengthening of integration of European economies into a single space is carried out and proposals for their development based on network interaction are made. The main research methods are formal logical, system and situational approaches, and statistical analysis. The need to support the formation of transport and logistics clusters through state programs and strategies, forms of public-private partnership in economy, activation of integration ties and integrated development based on basic enterprises is identified. The indicators of functioning of transport and logical clusters on the example of Belgorod region, Primorye Krai, including the level of development of the regional infrastructure, the degree of involvement of the economic power of the region, regions position on major domestic and global transport corridors are analyzed and studied. Based on the results of the work, proposals for the development of network interaction in the transport and logistics complex of the regions were formulated, prospects and directions for improving the economic efficiency of transport enterprises were determined, and the percentage of losses in the industry was reduced. The application of the results is useful in the development of regional socio-economic and transport development programs.

Off-Street Vehicular Fog for Catering Applications in 5G/B5G: A Trust-Based Task Mapping Solution and Open Research Issues

One of the key enablers in serving the applications requiring stringent latency in 5G networks is fog computing as it is situated closer to the end users. With the technological advancement of vehicles’ on-board units, their computing capabilities are becoming robust, and considering the underutilization of the off-street vehicles, we envision that the off-street vehicles can be an enormously useful computational source for the fog computing. Additionally, clustering the vehicles would be advantageous in order to improve the service availability. As the vehicles become highly connected, trust is needed especially in distributed environments. However, vehicles are made from different manufacturers, and have different platforms, security mechanisms, and varying parking duration. These lead to the unpredictable behavior of the vehicles where quantifying trust value of vehicles would be difficult. A trust-based solution is necessary for task mapping as a task has a set of properties including expected time to complete, and trust requirements that need to be met. However, the existing metrics used for trust evaluation in the vehicular fog computing such as velocity and direction are not applicable in the off-street vehicle fog environments. In this paper, we propose a framework for quantifying the trust value of off-street vehicle fog computing facilities in 5G networks and forming logical clusters of vehicles based on the trust values. This allows tasks to be shared with multiple vehicles in the same cluster that meets the tasks’ trust requirements. Further, we propose a novel task mapping algorithm to increase the vehicle resource utilization and meet the desired trust requirements while maintaining imposed latency requirements of 5G applications. Results obtained using iFogSim simulator demonstrate that the proposed solution increases vehicle resource utilization and reduces task drop noticeably. This paper presents open research issues pertaining to the study to lead...

Logical measurement-based quantum computation in circuit-QED

We propose a new scheme of measurement-based quantum computation (MBQC) using an error-correcting code against photon-loss in circuit quantum electrodynamics. We describe a specific protocol of logical single-qubit gates given by sequential cavity measurements for logical MBQC and a generalised Schrödinger cat state is used for a continuous-variable (CV) logical qubit captured in a microwave cavity. To apply an error-correcting scheme on the logical qubit, we utilise a d-dimensional quantum system called a qudit. It is assumed that a three CV-qudit entangled state is initially prepared in three jointed cavities and the microwave qudit states are individually controlled, operated, and measured through a readout resonator coupled with an ancillary superconducting qubit. We then examine a practical approach of how to create the CV-qudit cluster state via a cross-Kerr interaction induced by intermediary superconducting qubits between neighbouring cavities under the Jaynes-Cummings Hamiltonian. This approach could be scalable for building 2D logical cluster states and therefore will pave a new pathway of logical MBQC in superconducting circuits toward fault-tolerant quantum computing.

Enhanced dynamic data storage for enabling low cost space astronomy observations for capital constrained astronomy organizations

Mechanisms that reduce the capital and operational costs are important for increased participation in astronomy. It is important that capital constrained organizations can engage in astronomy in cost effective manner. Approaches such as telescope conversion and using small satellites reduce the cost of astronomy observations. However, astronomy data observed by converted and small satellite telescopes require storage and processing by high performance computing infrastructure. High performance computing infrastructure acquisition is expensive for capital constrained astronomy organizations. The reduction in costs obtained by using converted and small satellite telescopes is not matched by a corresponding reduction in high performance computing. This paper addresses this challenge and proposes using a software defined space data storage system. The software defined space data storage system considers space telescopes as primary satellites and telecommunication and earth observation satellites as secondary satellites. The primary and secondary satellites are grouped in logical clusters. Secondary satellites are temporal data centers that store the astronomy data that cannot be held on primary satellites. The discussion in this paper presents algorithms that enable the identification of suitable secondary satellites and also influence the entry and exit of secondary satellite into dynamic clusters.

IFCIoT: Integrated Fog Cloud IoT: A novel architectural paradigm for the future Internet of Things

The Internet of Things (IoT) is a network of physical things, objects, or devices, such as radio-frequency identification tags, sensors, actuators, mobile phones, and laptops. The IoT enables objects to be sensed and controlled remotely across existing network infrastructure, including the Internet, thereby creating opportunities for more direct integration of the physical world into the cyber world. The IoT becomes an instance of cyberphysical systems (CPSs) with the incorporation of sensors and actuators in IoT devices. Objects in the IoT have the potential to be grouped into geographical or logical clusters. Various IoT clusters generate huge amounts of data from diverse locations, which creates the need to process these data more efficiently. Efficient processing of these data can involve a combination of different computation models, such as in situ processing and offloading to surrogate devices and cloud-data centers.

Logical clusters in a DHT-Paradigm for scalable routing in MANETs

Connectivity of nodes in the logical network is the minimal requirement for the functionality of a distributed hash table (DHT)-based routing protocol. In case of high mobility, frequent changes to the network topology introduce higher maintenance overhead for updating the mapping information that worsens not only the performance of these protocols, but also restricts their pertinence only to the networks with low mobility. In addition, the route to the destination node in such protocols is not immediately available which result in high lookup latency for the requesting node.In this paper, we proposes a novel 3-dimensional logical cluster-based DHT routing protocol for mobile ad hoc network (MANETs) that use logical clustering and an effective replication strategy to reduce the routing overhead and lookup latency introduced by the above problems. Simulation results substantiate the effectiveness of the proposed protocol in terms of reducing the delay by slightly increasing the control overhead.

A statistically compiled test battery for feasible evaluation of knee function after rupture of the Anterior Cruciate Ligament - derived from long-term follow-up data.

PurposeClinical test batteries for evaluation of knee function after injury to the Anterior Cruciate Ligament (ACL) should be valid and feasible, while reliably capturing the outcome of rehabilitation. There is currently a lack of consensus as to which of the many available assessment tools for knee function that should be included. The present aim was to use a statistical approach to investigate the contribution of frequently used tests to avoid redundancy, and filter them down to a proposed comprehensive and yet feasible test battery for long-term evaluation after ACL injury.MethodsIn total 48 outcome variables related to knee function, all potentially relevant for a long-term follow-up, were included from a cross-sectional study where 70 ACL-injured (17–28 years post injury) individuals were compared to 33 controls. Cluster analysis and logistic regression were used to group variables and identify an optimal test battery, from which a summarized estimator of knee function representing various functional aspects was derived.ResultsAs expected, several variables were strongly correlated, and the variables also fell into logical clusters with higher within-correlation (max ρ = 0.61) than between clusters (max ρ = 0.19). An extracted test battery with just four variables assessing one-leg balance, isokinetic knee extension strength and hop performance (one-leg hop, side hop) were mathematically combined to an estimator of knee function, which acceptably classified ACL-injured individuals and controls. This estimator, derived from objective measures, correlated significantly with self-reported function, e.g. Lysholm score (ρ = 0.66; p<0.001).ConclusionsThe proposed test battery, based on a solid statistical approach, includes assessments which are all clinically feasible, while also covering complementary aspects of knee function. Similar test batteries could be determined for earlier phases of ACL rehabilitation or to enable longitudinal monitoring. Such developments, established on a well-grounded consensus of measurements, would facilitate comparisons of studies and enable evidence-based rehabilitation.

Space-based parallel program design process with high-level communication channels

In this research, we reduce design complexity of parallel programs by performing the design task at the high level. We devised a formalism of the parallel program design process to support space-based parallel program design. In this formalism, program designs are captured in entity spaces. Entity spaces, including data spaces, tasks, etc. can be subdivided and freely distributed into logical node clusters that are assigned to processes. We designed high-level communication channels for communication among divided subtasks in a parallel program for space-based communication integration. Supported program models range in single program multiple data, multiple program multiple data, and their hybrid model. We designed single-space and cross-space high-level communication channels with various communication topologies. They are specified externally and decoupled from a parallel program. This makes the program logical and easily designed and reused. A practical example is used for illustrating the design process.

A Hierarchical and Role Based Secure Routing Protocol for Mobile Wireless Sensor Networks

Nodes in wireless sensor networks are highly resource constrained and topology of such networks remains highly dynamic due to sudden death of sensor nodes and break of links. When mobility to such nodes is introduced, it gives rise to a highly complex network system since the topology becomes even more dynamic. Providing security in the routing layer of wireless sensor network is a highly critical issue mainly due to the stringent resource constraints and dynamic topology. In this paper, we propose a Secure Hierarchical and Role based Routing Protocol for Mobile Wireless Sensor Network (SHaRP) which is a combination of symmetric and asymmetric cryptographic approach. In the SHaRP framework, the sensor field is divided into some logical clusters and each cluster contains nodes with different roles such as ordinary sensor node (OSN), cluster head (CH) and gateway node (GN). Different keys are used by the nodes with distinct roles for message encryption and message decryption. The overall security has been entrusted on different roles in a distributed manner instead of relying on one authority and that is why this approach is safe from single point of failure problem. This also enhances the security in the network system. In this paper, we focus more on various security aspects in the routing process rather than the issues like cluster formation and role distribution. We shift most of the computing overhead to the base station as it is assumed to be a resourceful and reliable node. This obviously improves the energy efficiency of the protocol. We also report a security analysis along with implementation issues of the proposed protocol.

Distributed Resource Reservation in Hybrid MAC With Admission Control for Wireless Mesh Networks

Providing guaranteed quality of service (QoS) for multimedia applications is a challenging topic in wireless mesh networks (WMNs). This paper proposes a distributed resource reservation (RR) mechanism with novel admission control to guarantee QoS performance of real-time sessions (RTSNs) while improving bandwidth utilization in WMNs. The proposed RR mechanism establishes logical clusters (LCs) for identifying interference relationships between nodes and avoids simultaneous transmissions from interfering nodes. Following the established LCs, a cooperative contention and collision-free channel access is achieved in hybrid medium access control (MAC) for multihop data forwarding in WMNs. To efficiently utilize the scarce resources in a contention-free period, soft and hard resources are reserved for RTSNs according to their QoS demands. This is achieved by a novel admission control algorithm. Furthermore, an analytical model is derived for investigating the RR parameters of the proposed mechanism. Simulation results show that the proposed mechanism guarantees the QoS for RTSNs while improving bandwidth utilization.

The Research of Improved Abnormal ECG Cluster Analysis Algorithm

In order to solve the problem of ECG being complex, and the problem of abnormal ECG signal analysis standards being not uniform, this paper analyzes Lin Zetao’s clustering algorithm research achievements in the classification of abnormal heart rate. According to the characteristics of the clustering algorithm which is suitable for dealing with the rare data and the large amounts of data, this paper makes the analysis of the method of setting parameters in key process of the application of the clustering algorithm. This paper proposes an accurate clustering algorithm (LCFCM) about abnormal ECG which combines logical judgment, cluster analysis and fuzzy clustering together. Based on the algorithm design requirements, this algorithm perfects the logical judgment criteria and the key technologies of the ECG waveform vector extraction method. Ultimately, the MIT-BIH database is used as the sample to make experiment. The experiment shows that, this paper proposes LCFCM algorithm, whose accurate rate toward the classification of the abnormal heart rate reaches 93%. And at the same time, the good self-adaptive algorithm on different individuals ECG, has good practical value.

Multidimensional Canopy Clustering on Iterative MapReduce Framework Using Elefig Tool

ABSTRACTA number of applications today deal with the processing and analysis of Big Data. As the size of the data increases, it becomes important to process it to reveal many new and interesting patterns. One such task of processing huge data is to group records into logical clusters. Most of the clustering algorithms are iterative in nature. Hence, these clustering algorithms exceptionally outperform if modelled using iterative distributed framework like Twister. Here, a canopy clustering algorithm was modelled as a series of MapReduce jobs. Once the overlapping canopies are generated, k-means clustering is applied to form actual clusters. A comparative study was performed on the variants of MapReduce framework like Twister and Hadoop. Experimental results show that, even for a large number of data points, the implementation of canopy clustering on Twister was more than three times as fast as its implementation on Hadoop. The speedup of canopy clustering using Twister was considerable – more than 24 times faster as compared to the implementation of a k-means algorithm on Hadoop. In addition to this, a new tool called Elefig is designed to facilitate the master node to automatically find the location of slave nodes in a Hadoop cluster. Without Elefig tool, one has to manually fix the problem by updating the hosts file on each node of the cluster whenever cluster is booted up.

Improving ZigBee AODV Mesh Routing Algorithm Topology and simulation analysis

This paper used AODV mesh topology methods to send and receive data from source to destination and vice versa. In this paper we present an improved version of ZigBee-AODV mesh network, which utilizes and divide the topology of network into one or more logical clusters and restricts the flooding of route request outside the cluster. ZigBee-AODV mesh network uses nodes of the same cluster to share routing information, which significantly reduces the route path discovery and created cluster formation to decrease the routing path. The proposed protocol has higher reliability and lower overhead than AODV. We have done to figure out the various proposed modified algorithms based on the standard routing protocols, which have better network performance in terms of Average MAC control traffic sent/received , Average MAC data traffic sent/received , Average end to end delay, Packet loss (%), Network Lifetime, Throughput, Average Numbers of Hops, Average Packets Dropped , Normalized Routing Overheadetc.as compare to standard routing algorithms used in ZigBee-AODV mesh network. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3576

A Microclimate Monitor Sensor Network with an Effective Data Aggression Algorithm

Wireless sensor network technology has the potential to reveal fine-grained, dynamic changes in monitored variables of outdoor landscape. But there are significant problems to be overcome in order to realize the vision in working systems, such as effective utilize of energy, prolong network life and improve sensor accuracy. This paper describes the design and evaluation of a sensor network with an effective data aggression algorithm applied in orchard microclimate monitor. A novel feature of the solution is its data compression algorithm design, in which all sensors were encoded with Morton code and a logical multi-lays cluster was constructed among the nodes. Making use of the similarity of the output of the sensors, the algorithm reduces network data amount and power cost significantly to prolong life of the network. Tests and experiments results are shown in diagrammatic form. The system was field tested over one month in Nankou farm located in Beijing province of China. The experimental results demonstrate that effective collecting of environmental information can be achieved by using our proposed system.