Node Replacement Research Articles

Оптимізація траєкторії руху датчиків з урахуванням важливості ділянок території моніторингу та ймовірності виявлення об’єктів

У зв’язку з поширенням використання датчиків у задачах збору та обробки даних одними із ключових критеріїв є об’єм накопиченої інформації та енергоефективність. Під час моніторингу території поширений рух об’єктів дослідження, в результаті можлива зміна ймовірності їх виявлення у ділянці території. Також ділянки території можуть бути різної важливості. Врахування даних факторів суттєво збільшить кількість накопиченої інформації. Представлено метод побудови оптимальної траєкторії руху датчиків з урахуванням важливості ділянок території та ймовірності виявлення об’єктів. Метод ґрунтується на представленні розподілу ймовірності виявлення об’єктів та важливості ділянок території у вигляді шарів та їх об’єднання у шар ймовірної цінності виявлених об’єктів. Розглянуто сім класів ймовірної цінності виявлених об’єктів з відповідними числовими та графічними еквівалентами. Під оптимальною траєкторією розглядаємо траєкторію руху датчика, що забезпечує мінімальні енерговитрати. Енергоефективність досягається шляхом побудови траєкторії мінімальної довжини. Задача побудови траєкторії мінімальної довжини, що проходить всі задані точки, знаходиться як розв’язок задачі комівояжера. Набір точок, за якими будується траєкторія, формується на основі шару ймовірної цінності виявлених об’єктів після проведення процедури заміни вузлів. Для кожного класу ймовірної цінності виявлених об’єктів запропоновано окремий клас заміни вузлів або суперпозицію класів їх заміни. Описано заміну п’яти, трьох та двох вузлів. Для пошуку розв’язку представленої задачі використано генетичний алгоритм з модифікацією правил схрещування та селекції. З використанням запропонованого алгоритму побудовано сімейство траєкторій. Аналіз отриманих результатів підтвердив ефективність розробленого методу та дозволив збільшити енергоефективність при покритті конкретної території на 76 %.

OPTIMIZATION OF THE TRAJECTORY OF SENSORS MOTION TAKING INTO ACCOUNT THE IMPORTANCE OF THE AREAS OF THE MONITORING AREA SEGMENTS AND THE PROBABILITY OF DETECTION OF OBJECTS

Due to the widespread use of sensors in data collection and processing, one of the key criteria is the amount of information accumulated and energy efficiency. While monitoring the territory, the movement of research objects is common. As a result there is a change in the probability of their detection in the segment of the territory. Also, segments may be of varying importance. Taking these factors into account will significantly increase the amount of information accumulated. The article presents a method of constructing the optimal trajectory of sensors motion taking into account the importance of territory segments and the probability of detection of objects. The method is based on the representation of distribution of the probability of detection of objects and the importance of territory segments in the form of layers and their integration into a layer of the probable value of detected objects. Seven classes of the probable value of detected objects with corresponding numerical and graphical equivalents are considered. As optimal trajectory of sensors motion the trajectory which provides minimum energy expenditure is meant. Energy efficiency is achieved by constructing a trajectory of minimum length as a solution to the salesman’s problem. The set of points at which the trajectory is built is formed on the basis of the layer of the probable value of the detected objects after the procedure of replacing the nodes. A separate node replacement class, or superposition of node replacement classes, is proposed for each class of probable value of detected objects. Replacement of five, three and two nodes is described. A genetic algorithm with modification of crossing and selection rules was used to find a solution to this problem. A set of trajectories is constructed using the proposed algorithm. The analysis of the obtained results confirmed the efficiency of the developed method and allowed to increase the energy efficiency when covering a given area by 76 %.

Optimization of photonic reservoir computing using short internal cavity laser

With the advent of the high-speed information age and the explosive growth of the information, higher requirements have been placed on the information processing speed. In recent years, the delay-based reservoir computing (RC) systems have been extensively investigated. Meanwhile, the information processing rate is improved mainly around the replacement of nonlinear nodes in the system. Nevertheless, as the most commonly used distributed feedback semiconductor (DFB) laser, many researchers only use ordinary commercial DFB products for research, and they have not noticed the improvement of RC performance caused by changes in internal parameters of laser. With the development of photonic integration technology, the processing technology of DFB turns more mature, so that the size of DFB can be fabricated in a range of 100 μm–1 mm when it still generates laser, and the photon lifetime of the laser will also change. The shorter photon lifetime in the laser leads to a faster dynamic response, which has the potential to process the information at a higher rate in the RC system. According to the laser rate equation (Lang-Kobayashi), changing the internal cavity length will affect the feedback strength, injection strength and other parameters required for the laser to enter into each dynamic state, which in turn affects the parameter space required for the RC system to exhibit high performance. According to this, we study the relationship between the internal cavity length (120 μm–900 μm) and the information processing rate of the RC system. In addition, the influences of different internal cavity lengths on the parameter space of the RC system are analyzed. The results show that when the internal cavity length is in a range from 120 μm to 171 μm, the system can achieve 20-Gbps low-error information processing. It is worth noting that when the internal cavity length decreases from 600 μm to 128 μm, the parameter space with better prediction performance of the RC system is greatly improved. When performing the Santa-Fe chaotic time series prediction task, the normalized mean square error (NMSE) is less than 0.01, and the parameter range of the injection strength is increased by about 22%. The range of parameter with NMSE no more than 0.1 is improved by nearly 40% for the 10<sup>th</sup> order nonlinear auto-regressive moving average (NARMA-10) task. When the number of virtual nodes is 50, the system can achieve a high-precision prediction for the above two tasks. This is of great significance for the practical development of the system.

Coverage hole detection using social spider optimized Gaussian Mixture Model

Wireless sensors are backed by their battery life. The battery depletion creates a communication hole in the network. The replacement of certain energy-depleted sensor nodes necessitates their detection. The work in this article presents a novel solution for the detection of coverage hole in the WSN. An undirected graph was employed in this study to divide the WSN region into two parts: the outer and inner nodes. The Gaussian Mixture Model (GMM) clustering detects the outer/edge nodes at the coverage hole area. Node degree, closeness, betweenness, and page rank are the four graph metrics that GMM uses for clustering in this work. GMM’s clustering performance is governed by the static mean and covariance. A novel combination of social spider optimization and GMM is then leveraged to fine-tune the cluster to decrease false detection rates of holes. Distinct holes, such as round, E-shape, and rectangular, are used to conduct the analysis. Using the SSO-EM GMM approach, the comparison shows that it outperforms the current methods of coverage hole identification.

An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks

Coverage is a fundamental issue in wireless sensor networks (WSNs). It plays a important role in network efficiency and performance. When sensor nodes are randomly scattered in the network environment, an ON/OFF scheduling mechanism can be designed for these nodes to ensure network coverage and increase the network lifetime. In this paper, we propose an appropriate and optimal area coverage method. The proposed area coverage scheme includes four phases: (1) Calculating the overlap between the sensing ranges of sensor nodes in the network. In this phase, we present a novel, distributed, and efficient method based on the digital matrix so that each sensor node can estimate the overlap between its sensing range and other neighboring nodes. (2) Designing a fuzzy scheduling mechanism. In this phase, an ON/OFF scheduling mechanism is designed using fuzzy logic. In this fuzzy system, if a sensor node has a high energy level, a low distance to the base station, and a low overlap between its sensing range and other neighboring nodes, then this node will be in the ON state for more time. (3) Predicting the node replacement time. In this phase, we seek to provide a suitable method to estimate the death time of sensor nodes and prevent possible holes in the network, and thus the data transmission process is not disturbed. (4) Reconstructing and covering the holes created in the network. In this phase, the goal is to find the best replacement strategy of mobile nodes to maximize the coverage rate and minimize the number of mobile sensor nodes used for covering the hole. For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. To evaluate the performance of the proposed scheme, we simulate it using NS2 simulator and compare our scheme with three methods, including CCM-RL, CCA, and PCLA. The simulation results show that our proposed scheme outperformed the other methods in terms of the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime.

Collaborator recommendation integrating author’s cooperation strength and research interests on attributed graph

Collaborator recommendation aims to seek suitable collaborators for a given author. In this paper, we model all authors and their features as an attributed graph, and then perform community search on the attributed graph to locate the best collaborator community. From the early collaborative filtering-based methods to the recent deep learning-based methods, most existing works usually unilaterally weigh the network structure or node attributes, or directly search the community via the given node. We argue that the inherent disadvantage of these methods is that the quality of the node to be recommended may not be high, which can lead to suboptimal recommendation results. In this work, we develop a new recommendation framework, i.e., Collaborator Recommendation Integrating Author’s Cooperation Strength and Research Interests (CRISI) on an attributed graph. It improves the quality of recommended node via double-weighting the structure and attributes as well as adopting the node replacement method. This can effectively recommend collaborators who have a close cooperative relationship with the recommended node. We conduct extensive experiments on two real-world datasets, and further analysis shows that the performance of our proposed CRISI model is superior to existing methods.

Smart biological sensor network for efficient energy consumption

SummaryIn this paper, a link is proposed for wireless body area sensor networks (WBASNs) for the transmission of electrocardiogram (ECG) signal. When the quantity of signal is very high, then it is beneficial that signal should be compressed before transmission. In that situation, the signal compression and error control schemes played an important role in the designing of link for WBASNs. In case of biological sensor network, it is very important that node consumed less power in transmission because the replacement of nodes is a very complex surgical procedure in many medical situations. A compressed sensing concept is introduced here. The main aspect of this paper is to present transmission strategy that provides fast and accurate transmission of ECG signals. In this the smart sensor nodes are utilized. These nodes having sensing, transformation and transmission capability. The nodes sense the ECG signals, and these signal are compressed by Walsh–Hadamard transform method. This compressed signal is transmitted to the module outside the human body. In this module, various error control coding and modulation operations are to be performed on signals, and after that, signals are transmitted to the final destination. The compressed signal occupied less storage space, and ECC provides high data rate. In that manner, the fast transmission of ECG data becomes possible on low data rate sensor network link.

Network Lifetime Improved Optimal Routing in Wireless Sensor Network Environment

In the field of Wireless Sensor Networks, major problem arises due to the unbalanced consumption of energy. The unbalanced consumption will reduce the lifetime of a network performance. In the existing work, replacement of node is a difficult process and conservation of network parameters is not concentrated. It leads to the failure of network in different situations. These issues are resolved by introducing a density aware optimal clustering which is used to perform clustering to ensure the data transmission in a reliable manner. In the proposed work, Energy Balancing and Optimal Routing Based Secured Data Transmission based on clustering is introduced. It enhances the lifetime uniformly deployed data-gathering sensor networks using a balanced energy consumption technique. Ant Colony Optimization method is used to select the optimum head of the cluster to enhance lifetime of a network. Different techniques have been proposed for optimal edge disjoint routing. The data packets are forwarded in an optimum way by these techniques. Hybrid Genetic Particle Swarm Optimization algorithm is used to select the optimum edge disjoint route paths. The parameters of Quality of Service like reliability, capacity of processing, bandwidth and energy are considered to make an optimum selection. Based on the similarity values, sensor data’s are grouped and these data are aggregated to ensure the consumption energy which makes an optimum data transmission.

Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure

After a disaster happens, effective communication and information sharing between emergency response team members play a crucial role in a successful disaster response phase. With dedicated roles and missions are assigned to responders, role-based communication is a pivotal feature that an emergency communication network needs to support. Previous works have shown that Named Data Networking (NDN) has many advantages over traditional IP-based networks in providing this feature. However, these studies are only simulation-based. To apply NDN in disaster scenarios, real implementation of a deployment architecture over existing infrastructure during the disaster should be considered. Not only should it ensure efficient emergency communication, but the architecture should deal with other disaster-related challenges such as responder mobility, intermittent network, and replacement possibility due to disaster damage. In this paper, we designed and implemented an NDN-based disaster response support system over Edge Computing infrastructure with KubeEdge as the chosen edge platform to solve the above issues. Our proof-of-concept system performance shows that the architecture achieved efficient role-based communication support, fast mobility handover duration, quick network convergence time in case of node replacement, and loss-free information exchange between responders and the management center on the cloud.

A geometrical interpretation of the addition of nodes to an interpolatory quadrature rule while preserving positive weights

A novel mathematical framework is derived for the addition of nodes to univariate and interpolatory quadrature rules. The framework is based on the geometrical interpretation of the Vandermonde matrix describing the relation between the nodes and the weights and can be used to determine all nodes that can be added to an interpolatory quadrature rule with positive weights such that the positive weights are preserved. In the case of addition of a single node, the derived inequalities that describe the regions where nodes can be added are explicit. Besides addition of nodes these inequalities also yield an algorithmic description of the replacement and removal of nodes. It is shown that it is not always possible to add a single node while preserving positive weights. On the other hand, addition of multiple nodes and preservation of positive weights is always possible, although the minimum number of nodes that need to be added can be as large as the number of nodes of the quadrature rule. In case of addition of multiple nodes the inequalities describing the regions where nodes can be added become implicit. It is shown that the well-known Patterson extension of quadrature rules is a special case that forms the boundary of these regions and various examples of the applicability of the framework are discussed. By exploiting the framework, two new sets of quadrature rules are proposed. Their performance is compared with the well-known Gaussian and Clenshaw–Curtis quadrature rules, demonstrating the advantages of our proposed nested quadrature rules with positive weights and fine granularity.

IDBR: Iot Enabled Depth Base Routing Method for Underwater Wireless Sensor Network

Underwater wireless sensor networks (UWSNs) gained the attention of researchers due to their substantial applications in various fields. The major application areas of UWSN are environmental monitoring, underwater oil and gas extraction and military surveillance, smart farming, communication, and others. However, UWSNs are also prone to significant issues, such as limited network lifetime, the low processing capability of nodes, high energy consumption to run routing protocols, and difficult node replacement. Therefore, enhancing the lifetime of UWSN by reducing energy consumption and processing is a research issue. In this research, we proposed the IoT enabled depth base routing method (IDBR) to utilize energy efficiently. The performance of the proposed IDBR method is compared with conventional DBR protocol using simulation conducting in MATLAB. The performance of both the methods (i.e., IDBR and DBR) is evaluated in network energy consumption, the number of alive nodes, sink utilization, and end‐to‐end delay. The simulation‐based experiment results show that IDBR consumes 27.7% less energy and increases network stability than the DBR. Similarly, the utilization of the surface sinks in IDBR is more as compared to DBR as in IDBR, and sinks work as relay forward data to the base station without processing which increases the power of field nodes. The proposed mechanism improves the network’s lifetime and increases the accessibility and security of the sensed data.

A graph grammar and $$K_{4}$$-type tournament-based approach to detect conflicts of interest in a social network

In this paper, we introduce a new approach based on properties of graph grammars to detect conflicts of interest (COIs) in a field represented in the form of a social network. The approach consists of specializing the adaptive star graph grammar (ASGG) of Drewes et al. (Theor Comput Sci 411:3090–3109, 2010) to express kind of subgraphs that we call $$K_4$$ -type tournament graphs, corresponding to COIs, that cannot be generated by the node replacement graph grammar. This approach, called graph grammar and $$K_{4}$$ -type tournament-based approach to detect conflicts of interest $$(GGK_{4}T-COIs)$$ , is applied to detect COIs in the review process of papers accepted in an international conference which is represented through a social network. In this contribution, the principle of the used graph grammar is not to consider all the generated language but only subgraphs with some properties (corresponding to special graph queries), which identify parts of the social network representing COIs. For evaluating the performances and the efficiency of our proposition, experimentations have been done by comparing it with concurrent methods in the literature. The obtained results have shown that the approach GG $$K_{4}$$ T-COIs performs better than the investigated state-of-the-art approaches in terms of type and number of detected COIs.

Axillary lymph node metastasis: Fine-needle aspiration biopsy sensitivity as a function of node size, percent replacement of lymph node by tumor and tumor deposit size.

Fine-needle aspiration (FNA) is commonly used to investigate lymphadenopathy of suspected metastatic origin. While diagnostic accuracy of FNA for lymph node disease is well described, the relationship between node size, percent tumor replacement, and size of metastatic deposit with diagnostic accuracy is less well documented. All axillary lymph nodes undergoing ultrasound-guided FNA for suspected breast metastases were correlated with subsequent surgical excision specimens. FNAs were judged as positive or negative for malignancy and the percent of false negative FNAs was correlated with node size, percent tumor replacement and size of metastatic deposit RESULTS: Sensitivities were calculated for nodes greater than 15 mm (92%), nodes 11 to 14.9 mm (83%), nodes 7 to 10.9 mm (61%), and for nodes less than 7 mm (60%). Sensitivity increases with increasing node size (P = .001). Percent tumor replacement correlated with sensitivity: 90% or greater replacement (85%) 60% to 89.9% replacement (75%), 40% to 59.9% replacement (75%) and less than 39.9% replacement (64%)(P < .001). Metastases size correlated with sensitivity: metastases greater than 10 mm (94%), 6 to 9.9 mm (70%), 4 to 5.9 mm (54%), and less than 4 mm (72%). Percentage of false negative FNAs associate with investigation of metastatic disease correlates with node size, size of metastatic deposit and percentage of nodes replaced by tumor. Lymph nodes smaller than 7 mm, deposit diameter less than 6 mm and percentage replacement of less than 40% have the highest percentage of false negative results.

HARCI: A Two-Way Authentication Protocol for Three Entity Healthcare IoT Networks

With the recent use of IoT in the field of healthcare, a lot of patient data is being transmitted and made available online. This necessitates sufficient security measures to be put in place to prevent the possibilities of cyberattacks. In this regard, several authentication techniques have been designed in recent times to mitigate these challenges, but the physical security of the healthcare IoT devices against node tampering and node replacement attacks, in particular, is not addressed sufficiently in the literature. To address these challenges, a two-way two-stage authentication protocol using hardware security primitives called Physical Unclonable Functions (PUFs) is presented in this paper. Considering the memory and energy constraints of healthcare IoT devices, this protocol is made very lightweight. A formal security evaluation of this protocol is done to prove its validity. We also compare it with relevant protocols in the healthcare IoT scenario in terms of computation time and security to show its suitability and robustness.

ECBCM: A prestige‐based edge computing blockchain security consensus model

AbstractThe explosive growth of data in the network has brought huge burdens and challenges to traditional centralized cloud computing data processing. To solve this problem, edge computing technology came into being. Because the edge is closer to the user, processing part of the data at the edge can also bring a faster response to the user and improve their experience. However, the existing edge computing platforms have problems such as data storage security and multiparty data mutual trust. Blockchain technology has become an important means to solve the above data storage and sharing problems due to its excellent characteristics. The core of blockchain technology is consensus, and its speed and security will directly affect the efficiency and stability of the blockchain system. Therefore, this study uses the consensus mechanism as an entry point to reduce the resource consumption of the edge computing blockchain system and improve its security. In order to reduce the resource consumption of traditional consensus algorithms, improve their adaptability in the edge computing environment, and solve the security problem caused by the concentration of node rights, a prestige‐based edge computing blockchain security consensus model (ECBCM) is proposed. ECBCM is a general model based on prestige rewards and penalties. It also introduces a node replacement mechanism to ensure the fault tolerance of the consensus process. According to the results of multiple sets of performance comparison experiments and security verification experiments after embedding the existing consensus algorithm, the validity of the consensus model is confirmed.

A Proficient Bee Colony-Clustering Protocol to Prolong Lifetime of Wireless Sensor Networks

In wireless sensor network, replacement of node’s battery is very tough task in hostile environments. Therefore, to maximize network lifetime is the ultimate solution. Dividing the sensing region of wireless sensor network into clusters is an excellent approach to gain high energy efficiency and to enhance lifetime of the network. On the other hand, heads of the cluster need additional energy because of additional work such as obtaining data from its member nodes, aggregation of their data, and finally sending it to the base station. To enhance the lifetime of these networks, proper selection of heads plays a vital role. In this paper, we propose proficient bee colony-clustering protocol (PBC-CP) which is based on artificial bee colony algorithm. In PBC-CP approach, we have taken important factors for selection of heads such as node’s energy, degree of node, and distance from base station to node. For transmitting the data from cluster head to base station, it chooses the energy-efficient path which further minimizes the energy consumption of sensor network. Simulation experiments show the effectiveness of our proposed approach.

Energy Efficient Routing Structure to Avoid Energy Hole Problem in Multi-Layer Network Model

Wireless sensor network (WSN) is a communication network with several sensor nodes connected without any wired connection. Sensor nodes in the WSN are battery operated; therefore, recharging or replacement of the sensor node is a crucial task. Efficient energy consumption of sensor nodes is one of the significant challenges in WSN. In cluster based WSN, sensor nodes are grouped to form clusters. Gateway collects data from sensor nodes, aggregates data and transmits to the base station directly or through other gateways using the routing path. This causes heavy traffic load at the sink and forms an energy hole around the sink. In this paper, an energy efficient routing structure using the multi-layer network model is proposed to avoid the energy hole around the sink. The routing structure considers the one-to-one connection for all gateways to balance the overall load on the network. In the designed network model, the number of gateways increases in each layer towards the sink to minimize the traffic load around the sink. The extensive simulations on four different scenarios are performed in terms of network parameters such as the first gateway dies, first sensor node dies, half of the sensor nodes alive over the number of rounds, energy consumption rate and residual energy of sensor nodes and gateways. The experimental results are significant and proven the competency of the proposed algorithm.

Majority rule dynamics between a double coalition and a third opinion: coalition profit models and majority coalition ties

This article explores the opinion dynamics of a double coalition opinion against a third opinion under majority rule updates on odd fixed size connected groups. For this purpose, coalition benefit criteria and three opinion formation models which extend the 2-state majority rule model on lattices are introduced. The proposed models focus on the coalition profit of its constituent coalition opinions and cover the possible final scenarios from coalition alliance perspective: either minor opinion or major opinion is favored, or dynamics do not favor to any coalition opinion. Opinion exchanges take place on a torus embedded lattice network of a 3-state system having in consideration tie configurations and two rules to break them: either by random choice or leaving ties unaltered. Models were analyzed in the statistical mechanics spirit through Monte Carlo simulations without node replacement. Estimations for coalition benefits, the growth of coalition ties, and consensus probabilities are reported. The loss of coalition strengths due to coalition ties and its indecision is indicated. In particular, the logistic decay of consensus probability is due to the logistic adaptive growth of coalition ties. Scaling behaviors for consensus time and coalition ties in terms of network size are suggested. The results of numerical simulations are discussed in the context of social influence and social dynamics.

Fast Adaptive Gradient RBF Networks For Online Learning of Nonstationary Time Series

For a learning model to be effective in online modeling of nonstationary data, it must not only be equipped with high adaptability to track the changing data dynamics but also maintain low complexity to meet online computational restrictions. Based on these two important principles, in this paper, we propose a fast adaptive gradient radial basis function (GRBF) network for nonlinear and nonstationary time series prediction. Specifically, an initial compact GRBF model is constructed on the training data using the orthogonal least squares algorithm, which is capable of modeling variations of local mean and trend in the signal well. During the online operation, when the current model does not perform well, the worst performing GRBF node is replaced by a new node, whose structure is optimized to fit the current data. Owing to the local one-step predictor property of GRBF node, this adaptive node replacement can be done very efficiently. Experiments involving two chaotic time series and two real-world signals are used to demonstrate the superior online prediction performance of the proposed fast adaptive GRBF algorithm over a range of benchmark schemes, in terms of prediction accuracy and real-time computational complexity.

TIDAL-CAN: Differential Timing Based Intrusion Detection and Localization for Controller Area Network

Since the first reports on its lack of security, the Controller Area Network (CAN) was in focus for numerous research works. A specific area of research has employed physical layer characteristics that can be used to uniquely identify network nodes. But there are common downsides in existing approaches such as vulnerabilities in front of attacks involving node replacement or insertion or the inability to locate the intruder node within the network. In this work, we propose a new intrusion detection system for CAN which is based on monitoring the propagation time of the physical signals sent on the bus. Indeed, quite a number of recent works addressed the use of physical or timing characteristics to identify network nodes or to create covert channels. In our approach, by accounting for intrinsic delay characteristics of the bus and by monitoring the difference in signal arrival time at the two bus ends, we can identify nodes by location-related differential delays and provide relevant information for estimating the relative location of a transmitter node on the bus. The results of our experimental evaluation show that our approach provides very high identification rates and accurate localization in case of attacks from compromised nodes. The ability to detect attacks that replace an existing node or plug new adversarial nodes on the bus is also illustrated along with discussions on estimating sender location in these cases.