Resources Of Nodes Research Articles

An efficient heterogeneous signcryption scheme for internet of things

In today’s world, Internet of Things (IoT) has penetrated every aspect of us. The security of IoT is challenging because of the open nature of wireless communication and limited resources of sensor nodes (SNs). In this paper, we propose an efficient heterogeneous signcryption scheme from IoT to an Internet server, in which the server is in a public key infrastructure (PKI) environment, while the IoT is in a certificateless cryptosystem (CLC) environment. The proposed scheme is briefly referred to as CP-EHSC. CLC can settle the drawbacks of key escrow in identity-based cryptography (IBC) and public key certificate management in PKI. At the same time, PKI is suitable for the server since it has been widely adopted on the Internet. Our proposed scheme can provide security proof in the random oracle model (ROM). Performance analysis shows that the proposed scheme not only achieves great optimization in calculation cost and communication consumption but also makes great progress in total energy consumption. In addition, we illustrate the practical applicability of our proposed scheme by providing a realistic use case scenario.

Self-triggered Control with Energy Harvesting Sensor Nodes

Distributed embedded systems are pervasive components jointly operating in a wide range of applications. Moving toward energy harvesting powered systems enables their long-term, sustainable, scalable, and maintenance-free operation. When these systems are used as components of an automatic control system to sense a control plant, energy availability limits when and how often sensed data are obtainable and therefore when and how often control updates can be performed. The time-varying and non-deterministic availability of harvested energy and the necessity to plan the energy usage of the energy harvesting sensor nodes ahead of time, on the one hand, have to be balanced with the dynamically changing and complex demand for control updates from the automatic control plant and thus energy usage, on the other hand. We propose a hierarchical approach with which the resources of the energy harvesting sensor nodes are managed on a long time horizon and on a faster timescale, self-triggered model predictive control controls the plant. The controller of the harvesting-based nodes’ resources schedules the future energy usage ahead of time and the self-triggered model predictive control incorporates these time-varying energy constraints. For this novel combination of energy harvesting and automatic control systems, we derive provable properties in terms of correctness, feasibility, and performance. We evaluate the approach on a double integrator and demonstrate its usability and performance in a room temperature and air quality control case study.

Delay-Aware Container Scheduling in Kubernetes

Kubernetes is a powerful tool to manage containerized applications, which is also regarded as one promising platform to support microservices in edge computing. The scheduler is a key component of Kubernetes. It allocates each pod (i.e., a set of running containers) to one worker node (i.e., a machine). The default scheduler in Kubernetes is designed for the cloud environment containing homogeneous nodes. However, IoT edge nodes usually have various computing power and network bandwidth. The paper proposes a delay-aware container scheduling (DACS) algorithm to address the issue of node heterogeneity in edge computing. To efficiently assign pods to worker nodes, DACS takes account of not only residual resources of worker nodes but also potential delays caused by the pod assignment. We build a Kubernetes cluster by VMware to evaluate system performance. Experimental results reveal that DACS can significantly reduce both processing and network delays, thereby helping Kubernetes perform more efficiently in an edge environment.

A Realtime Adaptive Trust Model Based on Artificial Neural Networks for Wireless Sensor Networks

Wireless sensor networks (WSNs) are vulnerable to security attacks due to the unbounded nature of the wireless medium, restricted node resources, and cooperative routing. Standard cryptography and authentication mechanisms help protect against external attacks, but a compromised node can easily bypass them. This work aims to protect WSNs against internal attacks, which are mostly launched from compromised nodes to disrupt the network’s operation and/or reduce its performance. The trust and reputation management framework provides a routing cost function for selecting the best secure next hop. Tuning the trust weights is essential to cope with the constant changes in the network environment, such as the sensor nodes’ behaviours and locations. To allow real-time operation, the proposed framework introduces an artificial neural network (ANN) in each sensor node that automatically adjusts the weights of the considered trust metrics according to the WSN state. A large dataset is generated to train and test the ANN using a multitude of simulated cases. A prototype is developed and tested using the J-Sim simulator to show the performance gain resulting from applying the adaptive trust model. The experimental results showed that the adaptive model has robust performance and has achieved an improved packet delivery ratio with reduced power consumption and reduced average packet loss. The results showed that when sensor nodes were static and malicious nodes were present, the average accuracy was 99.6%, while when they were in motion, it was 88.1%.

Robustness Optimization of Cloud Manufacturing Process under Various Resource Substitution Strategies

Cloud manufacturing is characterized by large uncertainties and disturbances due to its networked, distributed, and loosely coupled features. To target the problem of frequent cloud resource node failure, this paper proposes (1) three resource substitution strategies based on node redundancy and (2) a new robustness analysis method for cloud manufacturing systems based on a combination of the complex network and multi-agent simulation. First, a multi-agent simulation model is constructed, and simulation evaluation indexes are designed to study the robustness of the dynamic cloud manufacturing process (CMP). Second, a complex network model of cloud manufacturing resources is established to analyze the static topological robustness of the cloud manufacturing network. Four types of node failure modes are defined, based on the initial and recomputed topologies. Further, three resource substitution strategies are proposed (i.e., internal replacement, external replacement, and internal–external integration replacement) to enable the normal operation of the system after resource node failure. Third, a case study is conducted for a cloud manufacturing project of a new energy vehicle. The results show that (1) the proposed robustness of service index is effective at describing the variations in CMP robustness, (2) the two node failure modes based on the recalculated topology are more destructive to the robustness of the CMP than the two based on the initial topology, and (3) under all four failure modes, all three resource substitution strategies can improve the robustness of the dynamic CMP to some extent, with the internal–external integration replacement strategy being most effective, followed by the external replacement strategy, and then the internal replacement strategy.

OUTLIER DETECTION WITH MACHINE LEARNING IN WIRELESS SENSOR NETWORKS

In recent decades, Wireless sensor networks have made significant strides, drawing interest from the scientific and industrial communities. Such networks' scattered sensor nodes function autonomously in challenging environments, leaving them susceptible to mistakes and attacks that could reduce the reliability and accuracy of sensor readings. Sensor readings are categorized as aberrant data, outliers/anomalies when they considerably vary from the predicted healthy behaviors. Such outliers can have a significant influence on the decision-making process and subsequent results in data analytics. As a result, the academic community has recognized the use of machine learning algorithms for outlier detection in WSNs as an innovative and promising methodology. On the basis of numerous viewpoints taken from the body of current research, we present a thorough definition of outliers in this work. We offer a novel and creative method to identify sensor irregularities by utilizing machine learning techniques. By utilizing pattern recognition and anomaly detection methods, machine learning enables us to analyze sensor data and find outliers. We give a comparative assessment of several approaches using machine learning paradigms for outlier detection in WSNs in order to provide a thorough understanding. For academics and practitioners looking to choose the best strategies for their unique application settings, this overview is an invaluable resource. In the end, we explore the main issues surrounding the identification of outliers in WSNs. The dynamic nature of WSNs, the finite resources of sensor nodes, the changing climatic conditions, and the requirement for real-time detection are only a few of the problems that these difficulties cover.

MetaVSID: A Robust Meta-Reinforced Learning Approach for VSI-DDoS Detection on the Edge

The explosive growth of end devices that generate massive amounts of data requires close-proximity computing resources for processing at the network’s edge. Having geographic distributions and limited resources of edge nodes or servers opens several doors for attackers to exploit them primarily to the detriment of deployed services; one of the recent attacks is Very Short Intermittent Distributed Denial of Services (VSI-DDoS). Deep learning-based models have been developed to detect and mitigate such attacks but cause the degrading quality of models due to covariate shifts when deployed in real-world environments. Therefore, we propose a new approach, called MetaVSID, to detect VSI-DDoS attacks in edge clouds using meta-reinforcement learning followed by ensemble learning to increase the robustness of the model in detecting VSI-DDoS attacks early. The proposed model can capture dynamic patterns of VSI-DDoS attacks, from which it identifies manipulated services and increase service availability when covariate shifts at deployment time. We carry out extensive experiments to validate the MetaVSID using both testbed and benchmark datasets. Via the meta-reinforced downsampling process, the proposed method improves sample efficiency, leading to cost-effective policies. Moreover, the optimized policies are generalized to adapt to dynamic changes in the training distribution. Our experimental results demonstrate that MetaVSID stably achieves better performance in multiple evaluation settings with the difference from baseline models from 1.5% to 7.5% in terms of AUC for both VSI-DDoS and DDoS detection, especially under covariate shift settings.

Global Path Link Prediction Method Based on Improved Resource Allocation

Link prediction is a basic method to study complex networks, which has important practical application value. This paper proposes a global path link prediction method based on resource allocation for complex network topology information. Firstly, we improve the current quantization method of node resource transmission capability, and then consider the global path information in the network structure comprehensively. Finally, the link prediction method is defined based on the quantized resource transmission capability and global path information. Compared with 11 classical similarity algorithms, the experimental results show that the link prediction accuracy of this method has been improved to some extent, among which the highest improvement is 22.85%.

An event‐driven fusion framework with auto‐scaling of edge intelligence for resilient smart applications in developing countries

AbstractInternet of Things has been a popular technology in recent years for deploying a large‐scale, smart environment monitoring application across the country, using fog computing and the cloud. However, most locations of the developing countries suffer from power outages and limited network connectivity. Moreover, varied population in different locations, may lead to either frequent or rare changes in the state of the monitored environment. Due to these stochastic conditions, there may be substantial increase in service time of the application with unnecessary battery and resource consumption of the fog node. For efficient utilization of fog node resources in dynamic environment conditions, an event‐driven information fusion framework is proposed using docker containerization technology and MQTT (Message Queuing Telemetry Transport) as application layer protocol. The proposed framework provides resilience to the application in the presence of stochastic conditions and auto‐scales edge intelligence with minimum scaling operations. The performance of the framework is validated on a smart sanitation system use‐case application, proposed for autonomous monitoring of restrooms deployed in Indian rural and semi‐urban environment, and the experimental result show deviation of 2.8% in average response time of the application with average accuracy of 98.9%.

An energy-efficient command broadcast protocol in dynamic WSNs

In wireless sensor networks (WSNs), a sink must broadcast periodically a command message to a group of nodes or all nodes to control the operation of the network. However, the sparse distribution and limited resources of nodes bring difficulties for efficient broadcasting. In this paper, we propose an energy-efficient command broadcast protocol based on the construction of a spanning tree which includes maximizing the number of leaf nodes, and the allocation of one sharable slot to each tree level. The sharable slot that allocates to tree level will be divided into multiple mini slots, and non-leaf nodes at the same level will compete for broadcasting; in this way, collision can be reduced. Moreover, a node that moves to another level can use the sharable slot allocated to the new level without reallocating the slots. Extensive simulations prove that this approach not only achieves high reliability of message delivery but also energy efficiency.

Security Resource Scheduling Algorithm for Intelligent UAV Communication Network Based on Optimized Subgraph Isomorphism and Link Partition

The resource scheduling problem of UAV communication networks has always been a hot research issue in the current environment. However, most of the current research focuses on meeting the user’s time cost requirements and rarely considers the user’s security requirements in the scheduling process. To solve the problems of low efficiency, more resource fragments, low network reliability, and unbalanced load, this paper improves the virtual network mapping model into a three-layer structure consisting of a security service request layer, virtual network provider layer, and security resource layer, so that it can be applied to the special scenario of security resource scheduling in an unmanned aerial communication network. An intelligent UAV communication network security resource scheduling algorithm based on optimized subgraph isomorphism and link segmentation is proposed. Intelligent UAVs can predict all possible access nodes and intelligently provide network services. The algorithm first improves and enriches the node constraint conditions and evaluation indicators, integrates the node resource attributes and topology attributes, optimizes the sorting process, then calculates the mapping domain in advance, considers the regional comprehensive resource capacity to balance the load, and finally combines the subgraph isomorphism and link segmentation technology to carry out a one-stage mapping scheduling to achieve the security service request in the network and the efficient allocation of network resources required to provide security services. The simulation results of the OMNeT++ show that the proposed algorithm improves the mapping success rate and the long-term average cost-benefit ratio of the UAVs.

A survey on artificial neural network based energy-efficient and robust routing scheme for pollution monitoring in WSNs

Despite the significant limits put on the resources of the sensor nodes, such as storage, processing power, communication range, and energy, the spectrum of applications for wireless sensor networks (WSNs) is expanding. This is the case even though WSNs have severe limitations. The primary goals of a WSN are to reduce the amount of energy used and the amount of time it takes to transmit data to the sink node. When a significant number of nodes are being installed, as is the situation with monitoring industrial pollutants, this becomes an extremely important point of discussion. We present an energy-efficient and resilient routing method for wireless sensor networks (WSNs) dubbed ELDC that is based on artificial neural networks. In this method, the network is trained on a massive data set that encompasses the vast majority of possible situations. This helps the network become more dependable and responsive to its surroundings. In addition to this, it makes use of a technique that is based on groups, with the understanding that the sizes of the groups might vary. This helps to extend the life of the network as a whole. An artificial neural network enables intelligent, efficient, and robust group organization by providing efficient threshold values for the selection of a group’s chief node and a cluster head based on the back propagation technique. These threshold values are provided by an artificial neural network. As a result, the method that we have suggested is very good at saving energy and is able to extend the life of sensor nodes.

Rendered Tile Reuse Scheme Based on FoV Prediction for MEC-Assisted Wireless VR Service

Interactive real-time wireless Virtual Reality (VR) has become increasingly popular because it provides an immersive experience for VR users anytime, anywhere. Introducing multi-access edge computing (MEC) to wireless VR service can solve the problem that VR devices having insufficient rendering power. However, the problem that some tiles in the overlapping field of views (FoVs) may be rendered repeatedly usually be ignored. Repeated rendering would waste the computing resources of edge nodes and damage users' quality of experience (QoE). This paper proposes a rendered tile reuse scheme based on FoV prediction and 3 C (caching, computing, and communication) optimization for the MEC-assisted VR service. Firstly, we model the above scheme as an optimization problem that aims to maximize the total users' QoE value under the motion to photons (MTP) delay constraints. Secondly, we use the recurrent neural network model with gated recurrent unit (GRU) architecture to dynamically predict the users' FoV in the next time slot. Thirdly, we use the proximal policy optimization (PPO)to learn the question's solution iteratively based on the results of FoV prediction. The simulation results show that our proposed algorithm is superior to other algorithms in improving the value of total users' QoE and reducing the MTP delay.

Spatio-Temporal Routing, Redundant Coding and Multipath Scheduling for Deterministic Satellite Network Transmission

Widespread deployment of Small Satellite Networks (SSN) fosters the foreseen integration of space-air-ground networks to provide worldwide Internet access to oceanic and remote airspace. However, the dynamic topology of SSN, the lossy wireless link, and the limited transmission resources induce unprecedented challenges to Deterministic Satellite Network Transmission (DSNT) for the sake of improving the utility of the SSN facility. Motivated by these challenges, this work aims to develop a Deterministic Satellite Network Transmission approach with deterministic Spatio-temporal routing, Redundant coding and Multipath scheduling (DSNT-SRM) for bolstering superior communications of SSN. DSNT-SRM uses the ephemeris information and dynamic resource update mechanism to predict all upcoming communication opportunities and construct the deterministic spatio-temporal routing paths. By combining sparse and redundant network coding mechanisms, DSNT-SRM no longer cares about the arrival of each packet, but the number of coded packets it receives, since the lost packets can be compensated with deterministic redundant traffic. Moreover, the adaptive traffic balance between multiple spatio-temporal paths is designed to provide a deterministic delay guarantee when facing limited node resources and multi-user competition. Extensive experiments show that DSNT-SRM can achieve satisfactory performance improvement in reducing delay and improving delivery rate.

Load balancing scheduling mechanism for OpenStack and Docker integration

With the development of cloud-edge collaborative computing, cloud computing has become crucial in data analysis and data processing. OpenStack and Docker are important components of cloud computing, and the integration of the two has always attracted widespread attention in industry. The scheduling mechanism adopted by the existing fusion solution uses a uniform resource weight for all containers, and the computing nodes resources on the cloud platform is unbalanced under differentiated resource requirements of the containers. Therefore, considering different network communication qualities, a load-balancing Docker scheduling mechanism based on OpenStack is proposed to meet the needs of various resources (CPU, memory, disk, and bandwidth) of containers. This mechanism uses the precise limitation strategy for container resources and a centralized strategy for container resources as the scheduling basis, and it generates exclusive weights for containers through a filtering stage, a weighing stage based on weight adaptation, and a non-uniform memory access (NUMA) lean stage. The experimental results show that, compared with Nova-docker and Yun, the proposed mechanism reduces the resource load imbalance within a node by 57.35% and 59.00% on average, and the average imbalance between nodes is reduced by 53.53% and 50.90%. This mechanism can also achieve better load balancing without regard to bandwidth.

Secure Key Management and Mutual Authentication Protocol for Wireless Sensor Network by Linking Edge Devices using Hybrid Approach

Wireless Sensor Networks (WSNs) play a crucial role in developing the Internet of Things (IoT) by collecting data from hostile environments like military and civil domains with limited resources. IoT devices need edge devices to perform real-time processing without compromising the security with the help of key management and authentication schemes. The above applications are prone to eavesdropper due to cryptographic algorithms' weaknesses for providing security in WSNs. The security protocols for WSNs are different from the traditional networks because of the limited resource of sensor nodes. Existing key management schemes require large key sizes to provide high-security levels, increasing the computational and communication cost for key establishment. This paper proposes a Hybrid Key Management Scheme for WSNs linking edge devices which use Elliptic Curve Cryptography (ECC) and a hash function to generate key pre-distribution keys. The Key establishment is carried out by merely broadcasting the node identity. The main reason for incorporating a hybrid approach in the key pre-distribution method is to achieve mutual authentication between the sensor nodes during the establishment phase. The proposed method reduces computational complexity with greater security and the proposed scheme can be competently applied into resource constraint sensor nodes.

Intelligent Salp Swarm Scheduler With Fitness Based Quasi-Reflection Method for Scientific Workflows in Hybrid Cloud-Fog Environment

The burgeoning volume of data from the IoT applications and intelligent devices processed on the cloud data centers can lead to network congestion and transmission delay. Compared to cloud computing, fog computing focuses on ubiquitous connected heterogeneous devices and addresses the transmission latency by placing the fog nodes at the network edge. Concerning the limited resources of fog nodes enable the computationally intensive tasks to offload on the cloud resources. Scheduling of deadline-constrained workflows with minimum execution cost is challenging due to complex and uncertain computation offloading problems. Therefore, an intelligent fuzzy scheduler is designed to offload tasks characterized with uncertain parameters to the appropriate resources. A new salp swarm algorithm has been exploited to learn and optimize fuzzy task-resource allocation rules. In addition to this, to overcome the shortcomings of the salp swarm algorithm, it is employed with one of the best opposition methods named: Fitness-based quasi-reflection method. The inclusion of the opposition method enhances the proposed ISSS-FQR (Intelligent salp swarm scheduler with the fitness-based quasi-reflection method) approach and improves the learning process. Simulation studies on the benchmark workflows are carried out to demonstrate the efficacy of ISSS-FQR. ISSS-FQR has been compared with the classical algorithms, including chemical reaction optimization and ant colony optimization algorithms for workflow scheduling problems (CR-AC), Cost-Makespan aware scheduling (Deadline-based CMaS), and Directional and non-local convergent particle swarm optimization (DNCPSO). From the analyzed result, ISSS-FQR outperforms the rest of the classical algorithms, which proves the effectiveness of ISSS-FQR. Note to Practitioners—This paper provides a novel method called ISSS-FQR for minimizing the cost of execution of IoT applications while satisfying the deadline constraint. An intelligent fuzzy scheduler is designed to offload tasks characterized with uncertain parameters to the appropriate resources. The ISSS-FQR combines the Salp Swarm Algorithm and the OBL method named FQR to learn the task-resource allocation rules. It is compared with three state-of-the-art algorithms called CR-AC, Deadline-based CMaS, and DNCPSO. From the analyzed result, it has been observed that ISSS-FQR outperforms the previous algorithms.

A Security-Enhanced and Privacy-Preserving Certificateless Aggregate Signcryption Scheme-Based Artificial Neural Network in Wireless Medical Sensor Network

With the recent advances in mobile sensing and beyond-5G technologies, wireless medical sensor networks (WMSNs) have become an emerging hot topic in smart healthcare. Under the condition of limited resources of medical sensor nodes (MSNs), ensuring the accuracy and integrity of a large number of patients’ medical data is a crux for security and data privacy issues. In order to overcome the limitations of medical sensors in communication and computing capabilities, and to achieve privacy in WMSN, we propose a low complexity certificateless aggregate signcryption (CL-ASC) scheme-based artificial neural network (NN) for safely achieving authentication and unforgeability of medical nodes. We use an adaptive sigma filter for denoising, adopt Levenshtein entropy for encoding, and design cryptographic protocol for signcrypting in the hidden layer of deep feedforward artificial NN (DFANN). The CL-ASC can authenticate all sensing bioinformation at once in a privacy-preserving way. Given the challenge of applying a discrete logarithm and computational Diffie-Hellman problems based on an elliptic curve, the proposed scheme satisfies the indistinguishability against chosen-ciphertext attacks (IND-CCA) and the adaptive chosen message attack (UF-CMA) security properties under the random oracle model (ROM). Finally, the benchmark evaluation shows that ours is ahead of similar state-of-the-art schemes, due to its low complexity and higher computational efficiency. Researchers interested in the provable security protocol design and framework of NNs can gain useful insights from this research and identify future cross domain of cybersecurity and NNs research directions based on the proposed scheme herein.

Kubernetes-Oriented Microservice Placement With Dynamic Resource Allocation

Microservices and Kubernetes are widely used in the development and operations of cloud-native applications. By providing automated placement and scaling, Kubernetes has become the main tool for managing microservices. However, existing work and Kubernetes fail to consider the dynamic competition and availability of microservices as well as the problem of shared dependency libraries among multiple microservice instances. To this end, this paper proposes an integer nonlinear microservice placement model for Kubernetes with the goal of cost minimization. Specifically, we calculate the number of instances based on microservice availability and construct a model in which the total resource demand of multiple microservice instances exceeds the appropriate proportion of node resources when dynamic resource competition exists and the size of the shared dependency library is less than the node storage capacity. Finally, this paper solves the microservice placement model using an improved genetic algorithm. The experimental results demonstrate that higher throughput is obtained with the same costs and that the same throughput is obtained with lower costs.

Node Essentiality Assessment and Distributed Collaborative Virtual Network Embedding in Datacenters

Network virtualization (NV) has extensive and significant applications in cloud computing and parallel and distributed systems. Virtual network embedding (VNE) is a key issue in NV, which is an effective means to advance systems' performance. While existing VNE research lacks resource allocation coordination between mappings of different virtual network requests, resulting in insufficient resource utilization and high overhead. In this paper, we propose a novel node essentiality evaluation model for data center networks (DCNs), and design an efficient distributed collaborative virtual network embedding. Firstly, we propose a node essentiality evaluation scheme based on dynamic model, which combines the characteristics of network topology and nodes to make the evaluation results more comprehensive. Secondly, we establish the two-stage node importance evaluation criteria for the deviation mean of the data center dynamic model and the variance based on the deviation mean. Furthermore, we investigate a nodal importance assessment method based on the data center dynamic model for perturbation testing. Finally, we design a distributed coordinated VNE algorithm ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CNI-VNE</b> ) which calculates the importance index of physical nodes through topology awareness. The proposed algorithm can increase the coordination between different request mappings, thereby reducing the mapping cost of physical node resources and minimizing the cost of VNE. We use the real Fat-tree DCN of 128 servers and 80 switches as testbed, and evaluate them from indicators such as average reliability, average bandwidth consumption, average energy consumption, and average mapping time. Massive simulation results in different scenarios show that our algorithm achieves the best performance on most indicators compared with the existing state-of-the-art proposals, mapping acceptance and average revenue increased by 19.4% and 21.3%, respectively, and DCN reduced bandwidth consumption by about 30%.