Capabilities Of Nodes Research Articles

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where the IoT, edge, and cloud converge to form a computing continuum, is also known as the IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems’ architectural approach, including (i) the design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; (ii) the design of efficient task offloading algorithms aimed at optimizing services execution; (iii) the support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; (iv) scalability, flexibility, and reliability guarantees to face the expected mobility for IoT systems; and (v) the design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges will become even more significant towards the new era of sixth-generation (6G) networks, which will be based on the integration of various cutting-edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented from an IEC continuum perspective.

A Fully Privacy-Preserving Solution for Anomaly Detection in IoT using Federated Learning and Homomorphic Encryption

AbstractAnomaly detection for the Internet of Things (IoT) is a very important topic in the context of cyber-security. Indeed, as the pervasiveness of this technology is increasing, so is the number of threats and attacks targeting smart objects and their interactions. Behavioral fingerprinting has gained attention from researchers in this domain as it represents a novel strategy to model object interactions and assess their correctness and honesty. Still, there exist challenges in terms of the performance of such AI-based solutions. The main reasons can be alleged to scalability, privacy, and limitations on adopted Machine Learning algorithms. Indeed, in classical distributed fingerprinting approaches, an object models the behavior of a target contact by exploiting only the information coming from the direct interaction with it, which represents a very limited view of the target because it does not consider services and messages exchanged with other neighbors. On the other hand, building a global model of a target node behavior leveraging the information coming from the interactions with its neighbors, may lead to critical privacy concerns. To face this issue, the strategy proposed in this paper exploits Federated Learning to compute a global behavioral fingerprinting model for a target object, by analyzing its interactions with different peers in the network. Our solution allows the training of such models in a distributed way by relying also on a secure delegation strategy to involve less capable nodes in IoT. Moreover, through homomorphic encryption and Blockchain technology, our approach guarantees the privacy of both the target object and the different workers, as well as the robustness of the strategy in the presence of attacks. All these features lead to a secure fully privacy-preserving solution whose robustness, correctness, and performance are evaluated in this paper using a detailed security analysis and an extensive experimental campaign. Finally, the performance of our model is very satisfactory, as it consistently discriminates between normal and anomalous behaviors across all evaluated test sets, achieving an average accuracy value of 0.85.

A Trust and Node Capability Model for Reliable and Secure MANET Communication

The Mobile Ad-hoc Network (MANET) is a rapidly deployable network. That is valuable for industrial and domestic applications due to flexible, mobile, and wireless communication. But the network is constrained with resources and security. In this paper, we are presenting a node capability based trusted routing named TNC-AODV for MANET. It is a hybrid approach for maintaining route reliability and security. The model is composed of the property of node capability and Trust. The Node capability is defined by the quality of service parameters like remaining energy, available bandwidth, buffer length, and mobility pattern. The aim is to ensure the discovery of reliable routes. Additionally, the trust is implemented by using a local and global trust for securing the network. The TNC-AODV is implemented through modification of AODV routing. That routing technique has been tested on three security threats namely Black-hole, wormhole, and DOS flooding attack. The simulation has been carried out using the NS2 simulator. The experimental results demonstrate that TNC-AODV provides security against attacks. Additionally, improve the packet delivery ratio, and throughput. Finally, the possible and feasible future extension of the work has also been proposed.

Optimal configuration of distributed wind turbines with D-STATCOMs for improving voltage recovery capability of sensitive nodes

Optimal configuration of distributed wind turbines with D-STATCOMs for improving voltage recovery capability of sensitive nodes

A novel method for the dynamics of worms in wireless sensor networks with fuzzy partition

Wireless sensor networks (WSNs) have gained much interest due to their enormous potential in civil and military applications. The power and radio communication capabilities of the sensor nodes are limited. Because sensor nodes have limited resources, they have weak defense capabilities and are attractive targets for software attacks. Worm-based cyberattacks are among the most significant threats to computers and WSNs’ security and integrity. In this article, a five-compartmental WSN epidemic model is considered. We conducted an investigation into equilibrium analysis and the reproductive number, followed by the development of a nonstandard finite difference numerical scheme for our model. The outcomes of our numerical simulations are then presented. This method yields reliable predictions, which can be valuable for regulators when making decisions related to designing and implementing control strategies. Furthermore, some interesting properties of the developed scheme are investigated, such as positivity, convergence, and consistency. The developed scheme preserves the essential characteristics of disease epidemic models like positivity, convergence, and consistency.

Ultrasound and Doppler Diagnostic Capabilities of Lymph Nodes in Thyroid Cancer

nodular lesions, autoimmune thyroiditis and neoplastic processes are the leading diseases of the thyroid gland (TG), when it is characterized by the cervical lymph nodes (LN) enlargement. They can undergo macro- and microscopic changes due to inflammatory processes of bacterial or viral origin, benign and malignant lesions of the neck, head and mediastinum. Doppler ultrasonography (US) plays a significant role among the new methods of screening for altered LN and differential diagnosis of pathological processes. The aim of this work is to determine the prospects for the use of LN lesions’ ultrasound diagnostics in inflammatory and oncological processes of the TG. Ultrasound diagnostics was performed to determine Doppler signs (EDV, PSV, RI) and changes in neck LN in 175 patients with and without TG pathologies. According to the results of histopathology, the patients were divided into 4 groups: 46 healthy patients without any thyroid pathology, 44 patients with inflammatory thyroid diseases, 42 patients with thyroid cancer but without metastatic LN involvement, 43 patients with metastatic LN involvement – Thyroid cancer. B-mode ultrasound diagnostic data demonstrated positive complementation with echostructural signs and changes in vascularization. It manifested in a wide range of velocities and atypical angioarchitectonics. 13 (31%) patients had increased vascularization with PSV – 23-45 cm/s, EDV – 14- 8 cm/s, RI – 0.32-0.46. It shows the progression of proliferative processes without necrotic changes in the hilus and its periphery. In 18 (43%) patients dynamic Doppler observation indicated the appearance of accelerated blood flow and an atypical vascular concentration outside nodular capsules. It is an indicator of the cancer spread. Color Doppler mapping and energy Doppler coding of cervical metastases are important diagnostic techniques, that qualitatively and quantitatively complements and details B-mode ultrasound diagnosis.

A dynamic and multi-level key management method in wireless sensor networks (WSNs)

Wireless sensor networks include a set of ultralight sensor nodes with limited energy, low storage capacity, and constrained processing power. Security is a challenging issue in these networks. One approach to maintain security in these networks is key management. When designing key management schemes, the main challenge is to achieve the acceptable security level and manage resources, especially energy. Today, many key management methods use authentication mechanisms. However, these methods are faced with different challenges due to the limited computational capability of nodes and high energy consumption in the network. In this paper, we present a dynamic and multi-level key management method for homogeneous wireless sensor networks. The proposed key management approach uses an authentication mechanism based on message authentication code (MAC), which is calculated by the shared symmetric keys between the transmitter and receiver nodes. In our scheme, the network is divided into five levels. Then, the clustering process is performed at each level, so that the size of the clusters varies in different levels. The proposed approach includes four phases: (1) Network leveling phase (2) Clustering phase (3) Key establishment phase (cluster key, pairwise key, and gateway key) (4) Rekeying phase. In the proposed method, a symmetric encryption algorithm called RC5 is used for producing keys. Our method contributes to the state of the art in wireless sensor networks by proposing a dynamic and multi-level key management approach. The use of a MAC-based authentication mechanism and the division of the network into different levels and clusters allows for efficient key establishment and rekeying, while minimizing energy consumption and communication overhead. Our proposed method is implemented using the NS2 simulator. Then, the simulation results are compared with SKWN and KMP methods. The results show that our proposed method outperforms SKWN and KMP in terms of average energy consumption, required memory, and communication overhead. However, its computational overhead is slightly more than SKWN and KMP.

DDoS attack detection in smart home applications

AbstractIn smart applications, edge nodes are deployed to perform faster computations. Due to the limited computational capability of edge nodes, collaborative computing is used in which multiple edge nodes collaborate for request processing. For faster processing, these edge nodes are used in many applications namely, smart homes, smart farming, healthcare and so forth. In this paper, we have discussed the use of edge nodes in smart home applications. The smart home application contains different types of sensors and these sensors generate various types of data. Edge nodes are used in these applications for the immediate processing of data. A data classifier is used to classify the data and to reduce delay in data processing. However, the data classifier is more susceptible to DDoS attacks. Hence, an efficient attack detection mechanism is required to detect DDoS attacks. We have used a Feature Selection SVM (FSSVM) algorithm to select optimal parameters for attack recognition. In this algorithm, the information gain ratio is used for optimal parameter selection, and SVM is used for classification. The FSSVM algorithm has been compared with KPCA‐SVM, SVM, and Naive Bayes. Simulation results show that the FSSVM algorithm provides better accuracy compared to KPCA‐SVM, SVM, and Naive Bayes algorithms.

Software Defined Network-Based Multi Service Routing Algorithm for Spatial Information Network

In order to solve the problems of unbalanced utilization of link resources due to the different capabilities of nodes in the spatial information network and the difficulty of satisfying the quality of service(QoS) with the change of task requirements, this paper proposes a multi-service routing algorithm for a spatial information network based on Software Defined Network(SDN), which divides the network transmission services into three categories: delay sensitive, bandwidth sensitive and packet loss rate sensitive. The link state information between satellite nodes is collected by an SDN controller, and then the topology-based bidirectional search algorithm is used to establish a “Source-Destination” set of routes between satellite nodes, the algorithm counts the delay, bandwidth, and packet loss rate of each route in the set, and selects routes that meet the needs of different business classes for data forwarding. The results show that the routing cost of this algorithm is lower than other algorithms, and it has better throughput performance.

Simulation of Coherent Electromagnetic Waves in Wavelength Division Multiplexing (WDM) Transmission

This study analyzes the application of Wavelength Division Multiplexing (WDM) in fiber optic networks which aims to find the wavelength, WDM optical spectrum and modes, as well as the CPR estimated phase and modes. In this study WDM allows the simultaneous transmission of different data streams through a single optical fiber, using different wavelengths. This research was conducted using the python OptiCommPy module. This module is used to perform modeling of complex optical fiber transmission systems by considering the various parameters and disturbances involved in optical transmission. The results obtained from this study are that WDM networks can use full or limited wavelength conversion, depending on the wavelength conversion capability of each network node. Whereas multifiber networks use fiber pools between network nodes, and multifiber WDM networks can be implemented without or with full wavelength conversion. This research can be a guide for designing coherent electromagnetic waves in WDM transmissions using the OptiCommPy python module.

Comparison of the new version of DEEC protocol to extend WSN lifetime

Wireless sensor networks (WSNs) are used in many different fields. One of the most recent and rapidly expanding applications is the Internet of Things (IoT), which enables the connectivity of numerous objects or devices over the Internet. However, WSNs have a bigger concern about battery power than mobile ad hoc networks, which shortens the network's lifetime. As a result, extensive research has been done on lowering WSNs' energy usage. Designing a hierarchical clustering algorithm is one of many methods for lowering the energy consumption of WSNs. Given the restricted hardware capabilities of the sensor nodes, modeling and building energy-efficient routing solutions to enhance the overall network lifetime has emerged as one of the most crucial approaches in wireless sensor networks. Cluster-based heterogeneous routing protocols, a typical feature of routing technology, have demonstrated efficacy in managing topology, energy consumption, data collection or fusion, dependability, or stability in distributed sensor networks. The existing Distributed Energy-Efficient Clustering (DEEC) clustering protocol is changed in this study by switching the power level between the nodes and adding a threshold limit for cluster head selection. The number of packets sent to a base station increases by 843% when comparing the proposed improved DEEC protocol to the existing DEEC protocol, increasing the number of living nodes to more than 10,000 rounds, or over 30,000 rounds, which can be used to extend the WSN lifetime. The suggested algorithm outperforms alternative energy-efficient protocols in terms of stability period and network lifetime under various scenarios of area, energy, and node density.

Identifying influential nodes in complex contagion mechanism

Identifying influential nodes in complex networks is one of the most important and challenging problems to help optimize the network structure, control the spread of the epidemic and accelerate the spread of information. In a complex network, the node with the strongest propagation capacity is known as the most influential node from the perspective of propagation. In recent years, identifying the key nodes in complex networks has received increasing attention. However, it is still a challenge to design a metric that has low computational complexity but can accurately identify important network nodes. Currently, many centrality metrics used to evaluate the influence capability of nodes cannot balance between high accuracy and low time complexity. Local centrality suffers from accuracy problems, while global metrics require higher time complexity, which is inefficient for large scale networks. In contrast, semi-local metrics are with higher accuracy and lower time cost. In this paper, we propose a new semi-local centrality measure for identifying influential nodes under complex contagion mechanisms. It uses the higher-order structure within the first and second-order neighborhoods of nodes to define the importance of nodes with near linear time complexity, which can be applied to large-scale networks. To verify the accuracy of the proposed metric, we simulated the disease propagation process in four real and two artificial networks using the SI model under complex propagation. The simulation results show that the proposed method can identify the nodes with the strongest propagation ability more effectively and accurately than other current node importance metrics.

A Cascading Failure Model of the Air Traffic Control Network Based on the Theory of Interdependent Networks

With the continuous development of information technology, a spontaneous interdependent network has formed within the air traffic control network. Due to the internal interdependence, any small, failed node may trigger a cascade failure of the entire system. The purpose of this study is to investigate the resilience of air traffic control networks. Based on air traffic management regulations, a new cascading failure model for air traffic control networks is proposed, which is based on the theory of interdependent networks. The model establishes a dual-layer dependency relationship between the control coordination network and the air route facility network, including control dependency and service dependency. Through experiments, targeted measures are proposed to improve the safety and reliability of air traffic control. This model introduces parameters such as control cost and node control capability, and reflects the resilience of the air traffic control network, based on the final number of failed nodes after the steady-state of the cascade failure, the network’s cascade failure rate, and the system’s load failure threshold. Simulation results show that enhancing the control capability and increasing the number of control positions can improve the control cost of the air traffic control network. The higher the control cost, the better the resilience of the air traffic control network. Improving the control capability of control nodes has a greater impact on the resilience of the air traffic control network, compared to increasing the number of control nodes. The degree attack on route nodes has a greater impact on the cascade failure of the air traffic control network, compared to random attacks and facility node degree attacks. The cascade failure model proposed in this paper provides a new method for guiding the air traffic control network to resist cascade failure attacks and enhance its resilience.

Impedance-Based Online Estimation of Nodal Inertia and Primary Frequency Regulation Capability

Accurate estimation of inertia and primary frequency regulation (PFR) capability is important to ensure frequency stability in low-inertia power systems. This paper investigates how the inertia and PFR capability of a power source node can be evaluated using a non-invasive online estimation method from the impedance perspective. Based on small perturbation injection, a novel approximate relationship between the impedance and corresponding frequency response transfer function (FR(s)) is deduced. The features of the FR(s)&#x2019;s magnitude response curve, i.e., resonance frequency and DC gain, are then used to develop an overall implementation framework for nodal inertia and PFR capability estimation in real time. This method provides new insights with respect to obtain the synchronous and non-synchronous converter interfaced generator&#x0027;s inertia constant and PFR capability for grid operators via impedance calculation and measurements. Case studies on a modified 9-bus system and NETS-NYPS 68-bus system demonstrate the effectiveness of the proposed impedance-based estimation approach.<fn id="fn4" fn-type="other"> This work was supported in part by the National Natural Science Foundation of China (52077066), in part by the Hunan Provincial Natural Science Foundation for Excellent Young Scholars (2020JJ3011), and in part by the Huxiang Young Talents Science and Technology Innovation Program (2020RC3015). (<i>Corresponding author: Yunfeng Wen</i>) W. Zhang and Y. Wen are with the College of Electrical and Information Engineering, Hunan University, Changsha 410082, China (e-mail: ; ). C. Y. Chung is with the Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China (e-mail: C.Y.). </fn>

Clustered Distributed Learning Exploiting Node Centrality and Residual Energy (CINE) in WSNs

With the explosion of big data, the implementation of distributed machine learning mechanisms in wireless sensor networks (WSNs) is becoming required for reducing the number of data traveling throughout the network and for identifying anomalies promptly and reliably. In WSNs, the above need has to be considered along with the limited energy and processing resources available at the nodes. In this paper, we tackle the resulting complex problem by designing a multi-criteria protocol CINE that stands for “Clustered distributed learnIng exploiting Node centrality and residual Energy” for distributed learning in WSNs. More specifically, considering the energy and processing capabilities of nodes, we design a scheme that assumes that nodes are partitioned in clusters and selects a central node in each cluster, called cluster head (CH), that executes the training of the machine learning (ML) model for all the other nodes in the cluster, called cluster members (CMs). In fact, CMs are responsible for executing the inference only. Since the CH role requires the consumption of more resources, the proposed scheme rotates the CH role among all nodes in the cluster. The protocol has been simulated and tested using real environmental data sets.

Formation of wireless sensor network protection system parameters for intrusion detection in the form of false event flows

Wireless sensor networks with stationary and mobile sensor nodes are studied. For mobile nodes, in addition to sensor nodes, the influence of node movement speed on the duration of the network life cycle for mobile AdHoc networks was also studied. When studying the impact of erroneous events on the sensor field, it was established that providing sensor nodes with mobility allows increasing the life cycle of the network. A model of intrusion into a wireless sensor network with the aim of shortening its life cycle has been developed, which differs from known models in that false event streams are used to achieve this goal. The model is developed based on typical geometric, quantitative and energy parameters of wireless sensor networks using a basic clustering algorithm for a homogeneous mobile sensor network under conditions of Poisson network intrusion and deterministic error event flows. It is established that the duration of the life cycle of a wireless sensor network can significantly depend on the type of the flow of erroneous events and, other things being equal, under the influence of a deterministic flow can be almost half as long as under the influence of a flow of erroneous events. the impact of the flow of false events. the Poisson flow effect. Detection of false events in a wireless sensor network can be considered as a target tracking task, and to detect false events with a given probability, taking into account the limited capabilities of sensor nodes, it is advisable to use the architectural characteristics of the network, the distribution of the density of nodes on the sensor field.

Energy Efficient Multi-hop routing scheme using Taylor based Gravitational Search Algorithm in Wireless Sensor Networks

A group of small sensors can participate in the wireless network infrastructure and make appropriate transmission and communication sensor networks. There are numerous uses for drones, including military, medical, agricultural, and atmospheric monitoring. The power sources available to nodes in WSNs are restricted. Furthermore, because of this, a diverse method of energy availability is required, primarily for communication over a vast distance, for which Multi-Hop (MH) systems are used. Obtaining the optimum routing path between nodes is still a significant problem, even when multi-hop systems reduce the cost of energy needed by every node along the way. As a result, the number of transmissions must be kept to a minimum to provide effective routing and extend the system's lifetime. To solve the energy problem in WSN, Taylor based Gravitational Search Algorithm (TBGSA) is proposed, which combines the Taylor series with a Gravitational search algorithm to discover the best hops for multi-hop routing. Initially, the sensor nodes are categorised as groups or clusters and the maximum capable node can access the cluster head the next action is switching between multiple nodes via a multi-hop manner. Initially, the best (CH) Cluster Head is chosen using the Artificial Bee Colony (ABC) algorithm, and then the data is transmitted utilizing multi-hop routing. The comparison result shows out the extension of networks longevity of the proposed method with the existing EBMRS, MOGA, and DMEERP methods. The network lifetime of the proposed method increased by 13.2%, 21.9% and 29.2% better than DMEERP, MOGA, and EBMRS respectively.

A New Method for Identifying Influential Spreaders in Complex Networks

Abstract Social networks have an important role in the distribution of ideas. With the rapid development of the social networks, identifying the influential nodes provides a chance to turn the new potential of global information spread into reality. The measurement of the spreading capabilities of nodes is an attractive challenge in social networks analysis. In this paper, a novel method is proposed to identify the influential nodes in complex networks. The proposed method determines the spreading capability of a node based on its local and global positions. The degree centrality is improved by the Shannon entropy to measure the local influence of nodes. The k-shell method is improved by the clustering coefficient to measure the global influence of nodes. To rank the importance of nodes, the entropy weighting method is used to calculate the weight for the local and global influences. The Vlsekriterijumska Optimizacija I Kompromisno Resenje method is used to integrate the local and global influences of a node and obtain its importance. The experiments are conducted on 13 real-world networks to evaluate the performance of the proposed method. The experimental results show that the proposed method is more powerful and accurate to identify influential nodes than other methods.

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

Most recent edge and fog computing architectures aim at pushing cloud-native traits at the edge of the network, reducing latency, power consumption, and network overhead, allowing operations to be performed close to data sources. To manage these architectures in an autonomous way, systems that materialize in specific computing nodes must deploy self-* capabilities minimizing human intervention across the continuum of computing equipment. Nowadays, a systematic classification of such capabilities is missing, as well as an analysis on how those can be implemented. For a system owner in a continuum deployment, there is not a main reference publication to consult to determine what capabilities do exist and which are the sources to rely on. In this article, a literature review is conducted to analyze the self-* capabilities needed to achieve a self-* equipped nature in truly autonomous systems. The article aims to shed light on a potential uniting taxonomy in this heterogeneous field. In addition, the results provided include conclusions on why those aspects are too heterogeneously tackled, depend hugely on specific cases, and shed light on why there is not a clear reference architecture to guide on the matter of which traits to equip the nodes with.

Optimal relaying nodes selection for IEEE 802.15.6-based two-hop star topology WBAN

Wireless body area networks (WBANs) are a promising communication technology that supports various types of medical and non-medical applications with heterogeneous requirements. Generally, IEEE 802.15.6-based WBAN use one-hop star topology to establish direct communication between the hub and body sensors. The standard also supports two-hop star topology to establish communication where direct transmission between the hub and sensor nodes is not possible due to poor link quality caused by body shadowing. Data transmission reliability and network lifetime are key factors in WBANs. Therefore, we propose an optimal relaying nodes selection mechanism as an alternative to the two-hop star topology extension of IEEE 802.15.6 to enhance the packet delivery ratio, network lifetime, and throughput. In this work, while choosing a relaying node for a disconnected node, we consider the fitness value of all the relaying capable nodes and select the fittest node as relaying node. We calculate the fitness value of all the relaying capable nodes based on their link quality, residual energy, and current traffic load using the multi-criteria decision making method “Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)”, which was originally developed by Ching-Lai Hwang and Yoon in 1981. We use the Mamdani fuzzy inference system (FIS) to calculate the link quality index of relaying capable nodes based on various link quality parameters such as Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Packet Error Rate (PER). Moreover, we also designed a schedule slot allocation mechanism for all the sensor nodes based on their packet generation rate (PGR). The proposed scheme significantly improved packet delivery ratio, throughput, and network lifetime compared to the IEEE 802.15.6 standard’s two-hop extension of star topology and other relay selection mechanisms. The simulation result of the proposed mechanism shows that the packet delivery ratio and network lifetime increase by (5–30)% and 50% respectively, compared to the IEEE 802.15.6 two-hop star topology.