Network Entities Research Articles

A notification-oriented solution for data-intensive enterprise information systems – A cloud manufacturing case

ABSTRACTThe application of Industrial Internet of Things (IIoT) has brought a series of new challenges to the traditional Enterprise Information Systems (EIS). More and more networked manufacturing entities are combined with data sensing, collection, transmission, and actuation capabilities. EIS are now facing large amounts of heterogeneous data. To address this issue, this paper proposes a notification-oriented solution for the data-intensive EIS within a distributed, collaborative, and heterogeneous environment. The solution is then applied in terms of model to a cloud-based smart factory prototype. This work indicates the suitability of the proposition and discusses the existing drawbacks and perspectives that include the manual modelling process and the application on a prototype.

Secure and Energy-Efficient Handover in Fog Networks Using Blockchain-Based DMM

Modern fog network architectures, empowered by IoT applications and 5G communications technologies, are characterized by the presence of a huge number of mobile nodes, which undergo frequent handovers, introducing a significant load on the involved network entities. Considering the distributed and flat nature of these architectures, DMM can be the only viable option for efficiently managing handovers in these scenarios. The existing DMM solutions are capable of providing smooth handovers, but lack robustness from the security point of view. Indeed, DMM depends on external mechanisms for handover security and uses a centralized device, which has obvious security and performance implications in flat architectures where hierarchical dependencies can introduce problems. We propose a new DMM schema based on the blockchain, capable of resolving hierarchical security issues without affecting the network layout, and also satisfying fully distributed security requirements with less consumption of energy.

Community Detection in Attributed Graphs: An Embedding Approach

Community detection is a fundamental and widely-studied problem that finds all densely-connected groups of nodes and well separates them from others in graphs. With the proliferation of rich information available for entities in real-world networks, it is useful to discover communities in attributed graphs where nodes tend to have attributes. However, most existing attributed community detection methods directly utilize the original network topology leading to poor results due to ignoring inherent community structures. In this paper, we propose a novel embedding based model to discover communities in attributed graphs. Specifically, based on the observation of densely-connected structures in communities, we develop a novel community structure embedding method to encode inherent community structures via underlying community memberships. Based on node attributes and community structure embedding, we formulate the attributed community detection as a nonnegative matrix factorization optimization problem. Moreover, we carefully design iterative updating rules to make sure of finding a converging solution. Extensive experiments conducted on 19 attributed graph datasets with overlapping and non-overlapping ground-truth communities show that our proposed model CDE can accurately identify attributed communities and significantly outperform 7 state-of-the-art methods.

Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover

In this paper, we address the problem of cell association during a handover performed in a dense heterogeneous network, where the preference of a mobile user’s equipment in terms of uplink traffic is not the same as for the downlink traffic. Therefore, since mobility is an intrinsic element of cellular networks, designing a handover from the perspective of the uplink and downlink is mandatory in the context of 5G cellular networks. Based on this arena, we propose a decoupled uplink-downlink handover scheme while making use of femtocells in order to maximize the overall network entity utilities and avoid overloading macrocells. However, the fact that the handover process is performed in a dense heterogeneous network makes the issue NP-hard. Therefore, taking into account the need for self-organizing solutions, we modeled the handover process as a matching game with externalities. Thus, we will provide an aspect of intelligence for the execution of the handover process to mobile user’s equipment (UE). To make the proposition more efficient, we integrate an assignment step to assist the matching game. Hence, the base stations will be investigated and filtered, keeping only the helpful base stations as the players in terms of the quality of service for the uplink and downlink. The numerical results verify the superiority of the proposed context-aware algorithm over traditional downlink handover and traditional decoupled uplink and downlink handover schemes, by improving the load balancing, increasing rates and reducing delays.

Evolutionary task allocation in Internet of Things-based application domains

Internet of Things (IoT) is an environment that contains different embedded devices interacting with each other to perform tasks related to information collection, communications and processing. Successful applications of IoT aim to interconnect objects with various capabilities within the same heterogeneous network. The goal is to allow network entities to cooperate and make their resources available in order to perform the demanded task. However, assigning tasks to group of heterogeneous objects that are equipped with limited resources poses a challenging task. The most limited and valuable resource for variety of IoT objects is battery power. Therefore, minimizing the energy consumption in task allocation process is one of the primary objectives. Currently, almost all existing studies employ heuristic optimizations to cope with different aspects of task allocation problems without considering different energy levels of network entities. In this paper, we address the problem of task allocation in IoT applications. We model the problem as a single objective optimization problem with the main goal to minimize the energy consumption. First, we propose two protocols with different objective models to minimize the energy expenditures. Then, considering the different energy levels of network entities, we inject heterogeneity-aware heuristics into these protocols to maintain well balanced energy consumption. Thus, we develop two more protocols to maximize the stability periods, viz., to prolong the time interval before the death of the first virtual object in each task group. We extensively evaluate the proposed protocols using different measurements and benchmarking parameters and compare them with the most relevant algorithm in the literature. The results show that the proposed protocols are superior to existing work in the literature in terms of both total energy consumption and stability.

Ontology-Based Modelling and Information Extracting of Physical Entities in Semantic Sensor Networks

ABSTRACTThe semantic sensor web adds semantic web technologies such as ontology to sensor network. Semantic technologies can help the better management of query and data aggregation of the sensor network. So far, several ontologies have been presented for the semantic presentation of sensor networks concepts. However, applications and end-users require physical entities information rather than technical details and information regarding sensors and sensor network. This paper semantically models physical entities whose information is collected by sensor networks at a level higher than sensors and their observations. Hence, first, an ontology is presented for semantically modelling physical entities in the real world. Then, essential extensions are added to the model for modelling time, place, and relations of the entities with each other. In the end, a method was proposed for extracting the semantic information of entities from the data collected based on the presented model. Result of the modelling and simulation of the presented strategies on climate data indicates the desirable performance of modelling and entity-based information reasoning as compared to works carrying out sensor-based semantic modelling.

A Comprehensive Survey of the Current Trends and Extensions for the Proxy Mobile IPv6 Protocol

Network based mobility management has attracted significant research interest due to its salient feature of relieving mobile nodes from participating in the mobility process. This feature of relying the mobility functions on the network entities would indeed eases the deployment of mobility solutions. Proxy Mobile IPv6 (PMIPv6) is considered as a promising network-based mobility management protocol in the next-generation mobile network. However, since the emergence of basic specification of the PMIPv6 protocol, it is still being developed in different directions to enhance its performance in order to ensure the best service for mobile users. This paper presents the PMIPv6 basic specifications and surveys the different extensions that have been considered by both the standardization bodies and researchers to enhance the basic PMIPv6 protocol with interesting features needed to offer a richer mobility experience, namely, clustering, fast handoff, route optimization, network mobility support, and load sharing. The research works conducted for these extensions are analyzed to specify the main issues that should be considered during the design of such extensions. Also, an integrated solution is proposed to show the possibility of combining more than one enhancement feature into a single integrated scheme.

Political Leadership in Modern France

The paper analyses the relation between political elites and political leaders. It is demonstrated that social shifts in the ranks of the elite are reflected in the profiles of heads of state, and the coming to power of a new president results in the renewal of elites. For years of existence of the Fifth republic several generations of political elite were replaced. At the time of Che. De Gaulle highest public servants were the main political actors. The logic of appointments in the system of executive power changed, a new type of political career was created. During the rule of F. Mitterrand, decentralization expanded the ranks of political elite, strengthened the positions of local elites. The president’s fellow party members came to power, political parties turned into an effective mechanism of recruitment of elite cadres. With E. Macron’s election there was a renewal of the deputy corps; reforms which will lead to further changes in the ranks of elites are planned. The paper investigates political biographies and career paths of presidents of the Fifth republic: the path of a notable, the path of a party functionary and the path of a member of administrative bureaucracy. The path of a notable assumes that the politician starts their career from election in local authorities and gradually works their way up through the ranks of power. The path of a party functionary demonstrates that the politician is rooted in party structures. The path of a bureaucrat assumes ascent to the political Olympus through promotion in the executive power branch and also by entering the immediate environment of the president, prime minister, key ministers. Local rootedness, good knowledge of public administration are characteristic of French presidents. Until recently it was thought that to achieve presidency in France it was necessary to be supported by a strong party. The new French president is an exception to this rule, and the movement he created is not a party in the traditional sense but a network entity. The study further brings to light qualities which the leader has to possess. The role of communication in the life of the French president is explained and it is shown how communication shapes the president’s image and influences public trust in the institution of presidency.

SHINE+: A General Framework for Domain-Specific Entity Linking with Heterogeneous Information Networks

Heterogeneous information networks that consist of multi-type, interconnected objects are becoming increasingly popular, such as social media networks and bibliographic networks. The task of linking named entity mentions detected from unstructured Web text with their corresponding entities in a heterogeneous information network is of practical importance for the problem of information network population. This task is challenging due to name ambiguity and limited knowledge existing in the network. Most existing entity linking methods focus on linking entities with Wikipedia and cannot be applied to our task. In this paper, we present SHINE+, a general framework for linking named entitie S in Web free text with a H eterogeneous I nformation NE twork. We propose a probabilistic linking model, which unifies an entity popularity model with an entity object model. As the entity knowledge contained in the information network is insufficient, we propose a knowledge population algorithm to iteratively enrich the network entity knowledge by leveraging the context information of mentions mapped by the linking model with high confidence, which subsequently boosts the linking performance. Experimental results over two real heterogeneous information networks (i.e., DBLP and IMDb) demonstrate the effectiveness and efficiency of our proposed framework in comparison with the baselines.

Prioritized Relationship Analysis in Heterogeneous Information Networks

An increasing number of applications are modeled and analyzed in network form, where nodes represent entities of interest and edges represent interactions or relationships between entities. Commonly, such relationship analysis tools assume homogeneity in both node type and edge type. Recent research has sought to redress the assumption of homogeneity and focused on mining heterogeneous information networks (HINs) where both nodes and edges can be of different types. Building on such efforts, in this work, we articulate a novel approach for mining relationships across entities in such networks while accounting for user preference over relationship type and interestingness metric. We formalize the problem as a top- k lightest paths problem, contextualized in a real-world communication network, and seek to find the k most interesting path instances matching the preferred relationship type. Our solution, PROphetic HEuristic Algorithm for Path Searching (PRO-HEAPS), leverages a combination of novel graph preprocessing techniques, well-designed heuristics and the venerable A* search algorithm. We run our algorithm on real-world large-scale graphs and show that our algorithm significantly outperforms a wide variety of baseline approaches with speedups as large as 100X. To widen the range of applications, we also extend PRO-HEAPS to (i) support relationship analysis between two groups of entities and (ii) allow pattern path in the query to contain logical statements with operators AND, OR, NOT, and wild-card “.”. We run experiments using this generalized version of PRO-HEAPS and demonstrate that the advantage of PRO-HEAPS becomes even more pronounced for these general cases. Furthermore, we conduct a comprehensive analysis to study how the performance of PRO-HEAPS varies with respect to various attributes of the input HIN. We finally conduct a case study to demonstrate valuable applications of our algorithm.

Resisting re-identification mining on social graph data

Varieties of sensitive personal information become a privacy concern for social networks. However, characteristics of social graphs could be utilized by attackers to re-identify target entities of social networks. In this paper, we first analyze a new attack model named bin-based attack, which re-identifies social individuals in social networks, according to their graph structure characteristics. For bin-based attack, we propose a novel k-anonymity scheme. With this scheme, social individuals are completely k-anonymity protection. Experiments illustrate the effectiveness of the proposed scheme. The utility of anonymized networks are demonstrated with the results of vertex degree, and betweenness.

Energy-Aware Radio Resource Management in D2D-Enabled Multi-Tier HetNets

Hybrid networks consisting of both millimeter wave (mmWave) and microwave $(\mu W)$ capabilities are strongly contested for next-generation cellular communications. A similar avenue of current research is device-to-device (D2D) communications, where users establish direct links with each other rather than using central base stations. However, a hybrid network, where D2D transmissions coexist, requires special attention in terms of efficient resource allocation. This paper investigates dynamic resource sharing between network entities in a downlink transmission scheme to maximize energy efficiency (EE) of the cellular users (CUs) served by either $(\mu W)$ macrocells or mmWave small cells while maintaining a minimum quality-of-service (QoS) for the D2D users. To address this problem, first, a self-adaptive power control mechanism for the D2D pairs is formulated, subject to an interference threshold for the CUs while satisfying their minimum QoS level. Subsequently, an EE optimization problem, which is aimed at maximizing the EE for both CUs and D2D pairs, has been solved. Simulation results demonstrate the effectiveness of our proposed algorithm, which studies the inherent tradeoffs between system EE, system sum rate, and outage probability for various QoS levels and varying densities of D2D pairs and CUs.

Performance Evaluation of a Live Multi-Site LTE Network

In this paper, to quantify the field performance of a live long term evolution (LTE) network, we design and implement a multi-layered performance monitoring system. This monitoring system delivers an accurate and thorough picture of the network behavior including switching, cellular, transmission, IP, and data networks. The designed system enables measurement results gathered from different network layers and from both user and control planes through a mediation platform, paving the way for the collection of synchronous performance measurement results, including latency, handover ratios, and throughput from network entities. Network performance metrics and the terminal-based measurement are jointly reported to reflect the subscriber perspective. Two field trials are conducted to highlight performance results of LTE networks, in a congested multi-cell with release 12 support, and a sparsely populated cell with release 14 support. Different multiple-input multiple-output (MIMO) configurations, including spatial multiplexing, transmit diversity, beamforming, and $64\times 64$ massive MIMO support, along with a selection of bandwidths and frequency bands are considered. The measurement results show that, without any significant latency cost, using more antenna elements provides higher user throughput, which also affects the overall cell throughput. However, doubling the number of antennas may not necessarily double the average data rates. Additionally, the average intrafrequency and interfrequency handover success ratios are observed to be acceptable, hence the changes among the selected the diversity and multiplexing technique does not have a visible deteriorating effect on the handover performance.

Bitcoin Concepts, Threats, and Machine-Learning Security Solutions

The concept of Bitcoin was first introduced by an unknown individual (or a group of people) named Satoshi Nakamoto before it was released as open-source software in 2009. Bitcoin is a peer-to-peer cryptocurrency and a decentralized worldwide payment system for digital currency where transactions take place among users without any intermediary. Bitcoin transactions are performed and verified by network nodes and then registered in a public ledger called blockchain, which is maintained by network entities running Bitcoin software. To date, this cryptocurrency is worth close to U.S. $150 billion and widely traded across the world. However, as Bitcoin’s popularity grows, many security concerns are coming to the forefront. Overall, Bitcoin security inevitably depends upon the distributed protocols-based stimulant-compatible proof-of-work that is being run by network entities called miners, who are anticipated to primarily maintain the blockchain (ledger). As a result, many researchers are exploring new threats to the entire system, introducing new countermeasures, and therefore anticipating new security trends. In this survey paper, we conduct an intensive study that explores key security concerns. We first start by presenting a global overview of the Bitcoin protocol as well as its major components. Next, we detail the existing threats and weaknesses of the Bitcoin system and its main technologies including the blockchain protocol. Last, we discuss current existing security studies and solutions and summarize open research challenges and trends for future research in Bitcoin security.

A Security Scheme of 5G Ultradense Network Based on the Implicit Certificate

The ultradense network (UDN) is one of the most promising technologies in the fifth generation (5G) to address the network system capacity issue. It can enhance spatial reuse through the flexible, intensive deployment of small base stations. A universal 5G UDN architecture is necessary to realize the autonomous and dynamic deployment of small base stations. However, the security of the 5G UDN is still in its infancy, and the data communication security among the network entities is facing new challenges. In this paper, we proposed a new security based on implicit certificate (IC) scheme; the scheme solves the security problem among the access points (APs) in a dynamic APs group (APG) and between the AP and user equipment (UE). We present each phase regarding how two network entities obtain the Elliptic Curve Qu‐Vanstone (ECQV) implicit certificate scheme, verify each other’s identity, and share keys in an UDN. Finally, we extensively analyze our lightweight security communication model in terms of security and performance. The simulation on network bandwidth evaluation is also conducted to prove the efficiency of the solution.

A Stochastic Approach for Designing Supply Chain Networks under Uncertainty

In this research, we develop a stochastic programming approach for multi-period supply chain design problems under uncertainty. The supply chain design problem involves making location and allocation decisions to support the required flows in a supply chain. It is assumed that the locations of facilities (manufacturing sites, warehouses and/or retailers) are determined at the strategic level, while selection of suppliers, production levels at manufacturing sites, inventory levels at warehouses, transportation modes and shipping quantities among the entities of the supply chain network over the planning horizon are all determined at the tactical level. Demands, processing, transportation and productions times at manufacturing sites are all considered as uncertain parameters following different distributions. To develop a robust model, an additional objective function is added into the model, which is the variance of the net present value of all cash flows.

Parked Vehicle Edge Computing: Exploiting Opportunistic Resources for Distributed Mobile Applications

Vehicular Edge Computing (VEC) has been studied as an important application of mobile edge computing in vehicular networks. Usually, the generalization of VEC involves large-scale deployment of dedicated servers, which will cause tremendous economic expense. We also observe that the Parked Vehicles (PVs) in addition to mobile vehicles have rich and underutilized resources for task execution in vehicular networks. Thus, we consider scheduling PVs as available edge computing nodes to execute tasks, and this leads to a new computing paradigm, called by parked vehicle edge computing (PVEC). In this paper, we investigate PVEC and explore opportunistic resources from PVs to run distributed mobile applications. PVs coordinate with VEC servers for collective task execution. First, a system architecture with primary network entities is proposed for enabling PVEC. We also elaborately design an interactive protocol to support mutual communications among them with security guarantee. Moreover, we measure the availability of opportunistic resources and formulate a resource scheduling optimization problem by using Stackelberg game approach. A subgradient-based iterative algorithm is presented to determine workload allocation among PVs and minimize the overall cost of users. Numerical results indicate that compared with existing schemes, PVEC serves more vehicles and reduces service fee for users. We also demonstrate that the Stackelberg game approach is effective and efficient.

SCOTRES: Secure Routing for IoT and CPS

Wireless ad-hoc networks are becoming popular due to the emergence of the Internet of Things and cyber-physical systems (CPSs). Due to the open wireless medium, secure routing functionality becomes important. However, the current solutions focus on a constrain set of network vulnerabilities and do not provide protection against newer attacks. In this paper, we propose SCOTRES—a trust-based system for secure routing in ad-hoc networks which advances the intelligence of network entities by applying five novel metrics. The energy metric considers the resource consumption of each node, imposing similar amount of collaboration, and increasing the lifetime of the network. The topology metric is aware of the nodes’ positions and enhances load-balancing. The channel-health metric provides tolerance in periodic malfunctioning due to bad channel conditions and protects the network against jamming attacks. The reputation metric evaluates the cooperation of each participant for a specific network operation, detecting specialized attacks, while the trust metric estimates the overall compliance, safeguarding against combinatorial attacks. Theoretic analysis validates the security properties of the system. Performance and effectiveness are evaluated in the network simulator 2, integrating SCOTRES with the DSR routing protocol. Similar schemes are implemented using the same platform in order to provide a fair comparison. Moreover, SCOTRES is deployed on two typical embedded system platforms and applied on real CPSs for monitoring environmental parameters of a rural application on olive groves. As is evident from the above evaluations, the system provides the highest level of protection while retaining efficiency for real application deployments.

Link prediction in signed networks based on connection degree

Link prediction has recently received considerable attention in signed networks. Most of the existing methods assume that the signed network topology is certain, such as network structure, entities relations and entities attributes. However, the assumption is not applicable, since the signed network is uncertain. As a result, the prediction accuracy cannot be ensured if the uncertainty of the signed networks is ignored. In this paper, we regard the signed network as an identical-discrepancy-contrary system employing the set pair theory, and propose a new link prediction measure SNCD which integrates both the certain and uncertain relations, local and global information at the same time. After a series of experiment, the experimental results show that our proposed method provides better prediction accuracy and correctness.

Research on Knowledge Intelligent Learning Method in Network Operation and Maintenance

Network operator confronts diverse challenges in the procedure of network operation and maintenance with massive applications emerging, in this paper, we design a framework to achieve the objective of Knowledge Intelligent Learning in Network operations. Our framework focuses on augmented reality technology, knowledge mapping technology, man-machine conversation assisted operation and maintenance technology, on-site fault diagnosis, etc. Our work also builds a channel between network operation knowledge and entities, which dramatically promotes the ability of network operators.