Detection of Node Capture under Asynchronous Sleep Mode Based on Recurrent Neural Network

In recent years, wireless sensor networks (WSNs) have found numerousapplications in industrial manufacturing and people’s daily lives. However, security risks associated with the use of WSNs have also become increasingly pronounced. An attacker launching an internal attack on a WSNmust first physically capture several nodes, i.e., take control of the target nodes by acquiring, cracking, and analyzing important information carried by the target nodes, thus laying the groundwork for subsequent attack steps. Therefore, physical node capture is a critical step in an internal attack on aWSN. Detecting behaviorsthat indicatephysical capture of nodes provides an early warning of anetwork attack, allowing steps to be taken to prevent further attacks from being launched from the captured nodes. This paper proposes an RNN (recurrent neural network)-based detection method that can be used to detect node capture in WSNs with asynchronous sleep mode at an early stage (i.e., before captured nodes rejoin the network).Thus, the methodenables early detection of network attacks. During the decision-making process, a common monitoring mechanism that relies on cooperation between neighboring nodes is employed to improve detection accuracy. The proposed method obtains the sensor nodes’ states and makes a judgment with the help of RNN, achieving accurate detection of node capture under the condition of unsynchronized clocks. Simulation results demonstrate the proposed method’s capability to achieve high detection accuracy.

Robustness of Cyber-Physical Supply Networks in Cascading Failures

A cyber-physical supply network is composed of an undirected cyber supply network and a directed physical supply network. Such interdependence among firms increases efficiency but creates more vulnerabilities. The adverse effects of any failure can be amplified and propagated throughout the network. This paper aimed at investigating the robustness of the cyber-physical supply network against cascading failures. Considering that the cascading failure is triggered by overloading in the cyber supply network and is provoked by underload in the physical supply network, a realistic cascading model for cyber-physical supply networks is proposed. We conducted a numerical simulation under cyber node and physical node failure with varying parameters. The simulation results demonstrated that there are critical thresholds for both firm’s capacities, which can determine whether capacity expansion is helpful; there is also a cascade window for network load distribution, which can determine the cascading failures occurrence and scale. Our work may be beneficial for developing cascade control and defense strategies in cyber-physical supply networks.

S.O.V.O.R.A.: A Distributed Wireless Operating System

Due to the growth of users and linked devices in networks, there is an emerging need for dynamic solutions to control and manage computing and network resources. This document proposes a Distributed Wireless Operative System on a Mobile Ad-hoc Network (MANET) to manage and control computing resources in relation to several virtual resources linked in a wireless network. This prototype has two elements: a local agent that works on each physical node to manage the computing resources (e.g., virtual resources and distributed applications) and an orchestrator agent that monitors, manages, and deploys policies on each physical node. These elements arrange the local and global computing resources to provide a quality service to the users of the Ad-hoc cluster. The proposed S.O.V.O.R.A. model (Operating Virtualized System oriented to Ad-hoc networks) defines primitives, commands, virtual structures, and modules to operate as a distributed wireless operating system.

A Node Virtualization Scheme for Structured Overlay Networks Based on Multiple Different Time Intervals

Sensor data which relate to the specific geographical positions, areas, and time are strongly expected in IoT. The author has studied overlay networks to efficiently process interval queries which have specific time intervals and the actual users tend to request. However, unfairness and a concentration of the loads occur for the specific processing computer (node) in the previous method because the density of data or those generators/providers is different from those related values. In this paper, the author proposes the enhanced scheme for structured overlay networks based on multiple different time intervals. The proposed method uses node virtualization to equalize the loads of each real (physical) node. The simulation results showed that the proposed method can increase the fairness of the number of the assigned data among physical nodes.

A Virtual Machine Migration Mechanism Based on Firefly Optimization for Cloud Computing

Background: Cloud computing is one of the prominent technology revolutions around us. It is changing the ways the consumer expends services, changing the ways the organization develop and run applications and is completely reshaping the old business models in multiple industries. Cloud service providers need large-scale data centres for offering cloud resources to users, the electric power consumed by these data centres has become a concrete and prudential concern. Most of the energy is dissipated in these data centres due to underutilized hosts which also subsidies to global warming. The broadly adept technology is virtual machine migration in cloud computing so our main focus is to save energy. Objective: Virtual machine (VM) migration can reap various objectives like load balancing, ubiquitous computing, power management, fault tolerance, server maintenance, etc. This paper presents an energy-oriented mechanism for VM migration based on firefly optimization that reduces energy consumption and no. of VM migrations to a great extent. Method: A Firefly optimization (FFO) oriented VM migration mechanism has been proposed which allocates tasks to the physical machines in cloud data centres. It strives to migrates high loaded VMs from one physical node to another physical node, which induces minimum energy consumption after VM migration. Results: The empirical result shows that the FFO based mechanism implemented in the CloudSim simulator performs better in terms of number of hosts saved up to 13.91% as contrast to First Fit Decreasing (FFD) algorithm and 8.21% as compared to Ant Colony Optimization (ACO). It reduced energy consumption up to 12.76% as compared to FFD and 7.78% as compared to ACO and ultimately lesser number of migrations up to 52.49% when compared to FFD and 44.51% as compared to ACO. Conclusion: The proposed scheme performs better in terms of saving hosts, reduced energy consumption and lesser number of migrations in contrast to FFD and ACO techniques. The research paper also presents challenges and issues in cloud computing, VM migration process, VM migration techniques, their comparative review as well.

Virtual Network Embedding Based on Topology Potential

To improve the low acceptance ratio and revenue to cost ratio caused by the poor match between the virtual nodes and the physical nodes in the existing virtual network embedding (VNE) algorithms, we established a multi-objective optimization integer linear programming model for the VNE problem, and proposed a novel two-stage virtual network embedding algorithm based on topology potential (VNE-TP). In the node embedding stage, the field theory once used for data clustering was introduced and a node embedding function designed to find the optimal physical node. In the link embedding stage, both the available bandwidth and hops of the candidate paths were considered, and a path embedding function designed to find the optimal path. Extensive simulation results show that the proposed algorithm outperforms other existing algorithms in terms of acceptance ratio and revenue to cost ratio.

Study on the Stability of Virtual Network Mapping Algorithm Based on the Improved Genetic Algorithm

A based on the improved genetic algorithm of the stability is presented, for the current virtual network mapping study based on the underlying resources load imbalance. The algorithm consider for the constraint of the underlying physical node, link resources and the parameters of virtual network requests. Join control threshold α to decide to accept the request. Use the improved genetic algorithm to automatically adapt to the current load overheating network node, choose the best physical link and line up a virtual mapping. As can be seen through the analysis of simulation results, the algorithm can process the request maps faster than others algorithm, improve the stability and the load balancing capability.