Virtual Wireless Sensor Network Research Articles

A framework of survivability model virtualized wireless sensor networks for IOT‐assisted wireless sensor network

AbstractUsing traditional non‐virtualized Wireless Sensor Networks (WSNs) efficiently is difficult due to the embedded applications, which make the sensor nodes inaccessible to other applications. The proposed study considered both the node‐level and network‐level virtualization of wireless sensor networks to examine dynamic virtual network embedding. WSNs can leverage their shared sensing capabilities through network virtualization. Infrastructure providers earn more revenue by mapping more virtual network embedding (VNE) onto their substrate networks. VNE must therefore improve its acceptance ratio. The proposed RLE‐SVNE is demonstrated to be more efficient than state‐of‐the‐art in respect to acceptance, recovery, failure recovery delay, and revenue cost through simulation results. It compares the RLF‐SVNE method with C‐SVNE and N‐SVNE to demonstrate its superiority.

A Novel SDWSN-Based Testbed for IoT Smart Applications

Wireless sensor network (WSN) environment monitoring and smart city applications present challenges for maintaining network connectivity when, for example, dynamic events occur. Such applications can benefit from recent technologies such as software-defined networks (SDNs) and network virtualization to support network flexibility and offer validation for a physical network. This paper aims to present a testbed-based, software-defined wireless sensor network (SDWSN) for IoT applications with a focus on promoting the approach of virtual network testing and analysis prior to physical network implementation to monitor and repair any network failures. Herein, physical network implementation employing hardware boards such as Texas Instruments CC2538 (TI CC2538) and TI CC1352R sensor nodes is presented and designed based on virtual WSN- based clustering for stationary and dynamic networks use cases. The key performance indicators such as evaluating node (such as a gateway node to the Internet) connection capability based on packet drop and energy consumption virtually and physically are discussed. According to the test findings, the proposed software-defined physical network benefited from “prior-to-implementation” analysis via virtualization, as the performance of both virtual and physical networks is comparable.

Survivability Mapping Strategy for Virtual Wireless Sensor Networks for Link Failures in the Internet of Things

In the case of virtual wireless sensor networks, a link-failure-oriented survivable virtual sensor network mapping algorithm (F-SVNE) is proposed to address the issue of link failure in the underlying wireless sensor network. First, the algorithm utilizes the fast routing strategy and creates a backup route set based on a multi-path selection algorithm to reduce the delay in fault recovery caused by path selection. Second, the survivable virtual sensor network mapping algorithm is adopted based on the routing set. Finally, in the mapping stage, the efficiency and reliability of the algorithm are comprehensively considered, and the path with the largest survival probability is selected from the backup route set of the faulty link to remap the virtual link affected by the link failure. Empirical results demonstrate that the F-SVNE algorithm can efficaciously lessen the failure recovery delay, and improve the long-term average revenue–cost ratio and average failure recovery rate.

Trust-Aware Virtual Network Embedding in Wireless Sensor Networks

Network virtualization in wireless sensor networks (WSNs) enables the utilization of their shared sensing capabilities. Efficient assignment of WSN resources to maximize the infrastructure provider’s revenue can be achieved by virtual network embedding (VNE) while considering quality of information (QoI) (as the accuracy of sensing), quality of service (QoS) (as the reliability), and wireless interference handling constraints. Improving the acceptance ratio of VNE is essential because the more the virtual networks can be mapped onto the substrate network, the more revenue they will generate for the infrastructure provider. However, the shared and complex nature of VNE exposes the WSNs to security risks. This article develops a novel offline trust-aware virtual WSN (TA-VWSN) algorithm to maximize the virtual networks acceptance rate while minimizing the cost. This algorithm improves the QoI, QoS, and security by adding required trust level constraints to virtual nodes and links and trust level constraints to the substrate counterparts. Our algorithm embeds virtual nodes and links on substrate nodes and links with the required trust levels. The additional constraints increase the complexity of computation required to achieve an optimal solution. However, our TA-VWSN algorithm achieves a high-quality suboptimal solution in a short duration, enabling us to investigate the tradeoff between solution quality and search time. Our algorithm is also evaluated in large-scale network scenarios to verify all enforced limitations by the WSN substrate. While adding security constraints limits the acceptance ratio, the simulation results demonstrate its superiority in terms of average network throughput, measurement error efficiency, and processing time when the trust attributes are assigned, making the VNE algorithm more practical.

A Multi-Objective Crowding Optimization Solution for Efficient Sensing as a Service in Virtualized Wireless Sensor Networks

The Internet of Things (IoT) encompasses a wide range of applications and service domains, from smart cities, autonomous vehicles, surveillance, medical devices, to crop control. Virtualization in wireless sensor networks (WSNs) is widely regarded as the most revolutionary technological technique used in these areas. Due to node failure or communication latency and the regular identification of nodes in WSNs, virtualization in WSNs presents additional hurdles. Previous research on virtual WSNs has focused on issues such as resource maximization, node failure, and link-failure-based survivability, but has neglected to account for the impact of communication latency. Communication connection latency in WSNs has an effect on various virtual networks providing IoT services. There is a lack of research in this field at the present time. In this study, we utilize the Evolutionary Multi-Objective Crowding Algorithm (EMOCA) to maximize fault tolerance and minimize communication delay for virtual network embedding in WSN environments for service-oriented applications focusing on heterogeneous virtual networks in the IoT. Unlike the current wireless virtualization approach, which uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), EMOCA uses both domination and diversity criteria in the evolving population for optimization problems. The analysis of the results demonstrates that the proposed framework successfully optimizes fault tolerance and communication delay for virtualization in WSNs.

Intelligent Multilevel Fusion System for Wireless Sensor Network Virtualization Using Deep Reinforcement Learning in Education

wireless sensor networks (WSN) in ubiquitous learning environments to enhance teaching and learning quality. WSNs can serve as a learner-to-context interface, enabling learners to interact with the learning environment while collecting contextual information. With the help of WSN virtualization technology, learners can leverage different virtualized characteristics of the state-of-the-art WSN and engage with the ubiquitous learning paradigm to gain knowledge and skills. The report examines the current state of WSN virtualization and its potential for sharing in this context. Research concerns are discussed in-depth, and an in-depth overview of the current state of the art is provided. This paper presents the fundamentals of WSN virtualization and argues for its usefulness. By allowing learners to learn while on the go in an environment that interests them, gadgets and embedded computers work together to keep students connected to their learning environment. Recent years have seen an increase in interest in deep reinforcement learning technologies. Despite the availability of several internet resources for researching this field, it might be challenging for those just getting started to design effective teaching systems for autonomous vehicles. This article offers a model for a highly effective and interactive ubiquitous learning environment system based on ubiquitous computing technology. An educational system based on deep reinforcement learning and system development is developed in this project using the WSNV-ES method. The web-based system that has been designed can do the following: settings for reinforcing student success, learning scripts to run, and the learning state to monitor are described.

Effective and Secure vWSN Applications in a Virtualized Cloud Computing Environment

Security is one of the key concerns in cloud computing. We are proposing Virtual Wireless Sensor Network (vWSN) which is one of the key features of cloud computing in the area of agriculture. Our main focuses are effective and secure vWSN in virtualized cloud computing environment with enhanced security to detect environmental monitoring, humidity monitoring, soil moisture, air quality (pollution) monitoring, insect monitoring, pest and disease control, which is based on virtualization and cloud computing technology, centrally managed device having flexible and configurable parameter with less maintenance and operational cost. To the best of our knowledge, vWSN is the first approach to agriculture based on extensional WSN information. WSN contains both control plane (signaling) and data plane (forwarding) coupled together.WSN is a specific purpose computer containing hardware whose main components are memory, CPU, IO, registers, Data and address bus, timer, sensor, computing logic and decision making logic. WSN is a physical device which needs to be placed in the field in certain topology (Ring, mesh, tree, star) which may need to be protected for damage and erosion due to various parameter including but not limited to physical theft, damage due to weather, animal and various other reason. In this paper our approach is to propose virtualized model to replace physical wireless sensor network (WSN) to virtual machine (VM) based vWSN which can be deployed on the cloud. Since security is one of the major concern for VM in cloud computing therefore we also proposed the enhanced 3-tier security model for vWSN.

Ensuring Energy Efficiency When Dynamically Assigning Tasks in Virtualized Wireless Sensor Networks

Traditional non-virtualized Wireless Sensor Networks (WSNs) suffer from high deployment and maintenance costs, mainly because their applications are embedded in sensor nodes. Virtualization technologies address these challenges by allowing multiple sensing tasks to run over the same deployed WSN infrastructure. However, virtualization comes at an energy-delay cost, making it both essential and challenging to allocate physical and/or virtual resources efficiently to applications with different sensing tasks, especially for delay-sensitive applications. Our goal is to address the challenge of task assignment in virtualized WSNs while minimizing the overall energy consumption and meeting the given deadlines. After formulating the problem as an Integer Linear Programming (ILP), we propose a scalable heuristic. We evaluate the performance of our proposed heuristic in different scenarios and compare it with the optimal solution as well as a recent work from literature. The results indicate that our proposed heuristic leads close-to-optimal solutions with good performance in terms of execution time. It shows that the proposed DTA solution can not only achieve up to a 97% reduction of the execution time for small-scale scenarios, as compared to the optimal solution, but it also outperforms the existing benchmarks in terms of successful task execution rate by 100%.

Integration of Machine Learning Techniques in Virtual Wireless Sensor Network for insect monitoring

Agriculture industries are comparatively slow in adopting emerging technologies than any other industries despite lot of exciting research. The use of Wireless sensor network (WSN) is very important role in agriculture for more productive and sustainable growth. The structure of WSN is tightly application dependent. Every WSN have sensors, processing unit, low frequency radio wave transmitter and power supply using battery. With increasing number of interconnected WSN devices, there is substantial increase in data generation. It contains both control messages as well as application dependent data, collected by the sensors. The collected data are frequently sent to the nearest centralized controller for further processing and decision making. For continuous functioning of WSN, uninterrupted power supply is needed. Many researches are carried out to overcome these challenges. In this manuscript we are proposing a simple and effective machine learning techniques combined with pause and play method to increase battery life of WSN. This could be achieved in three stages play-pause-play (PPP) model. First by gathering data for some time (play mode), Second by putting WSN to sleep (pause mode), in the backend, apply machine learning algorithm that helps to build model from training data to predict the future data. At the final stage, put WSN back to play mode. Compare the result with actual data and fine tune the model to reduce the error. Using this method WSN will get enough sleep time to increase overall life by simulating the normal behaviour of sensor node. The sleep time will be calculated dynamically.

Survivability-Enhanced Virtual Network Embedding Strategy in Virtualized Wireless Sensor Networks.

With the widespread application of wireless sensor networks (WSNs), WSN virtualization technology has received extensive attention. A key challenge in WSN virtualization is the survivable virtual network embedding (SVNE) problem which efficiently maps a virtual network on a WSN accounting for possible substrate failures. Aiming at the lack of survivability research towards physical sensor node failure in the virtualized sensor network, the SVNE problem is mathematically modeled as a mixed integer programming problem considering resource constraints. A heuristic algorithm—node reliability-aware backup survivable embedding algorithm (NCS)—is further put forward to solve this problem. Firstly, a node reliability-aware embedding method is presented for initial embedding. The resource reliability of underlying physical sensor nodes is evaluated and the nodes with higher reliability are selected as mapping nodes. Secondly, a fault recovery mechanism based on resource reservation is proposed. The critical virtual sensor nodes are recognized and their embedded physical sensor nodes are further backed up. When the virtual sensor network (VSN) fails caused by the failure physical node, the operation of the VSN is restored by backup switching. Finally, the experimental results show that the strategy put forward in this paper can effectively guarantee the survivability of the VSN, reduce the failure penalty caused by the physical sensor nodes failure, and improve the long-term operating income of infrastructure provider.

Resource Allocation in Wireless Powered Virtualized Sensor Networks

As an important technology for Internet of Things, wireless sensor networks have been receiving widespread attention in recent years. Traditional wireless sensor network deploys resources for specific applications, which causes the problem of low resource utilization. Additionally, the energy consumption brought by the diversification of application requirements has also raised the burden on sensor nodes. Therefore, based on the introduction of wireless energy transfer technology, this paper proposes a resource allocation strategy for virtualized wireless sensor networks. Specifically, physical resources are pooled by the the wireless sensor network service provider. Then, virtual sensor networks are built through network slicing technology to provide one-to-one services based on the application requirements and the current status of the physical sensor nodes. Furthermore, in order to minimize the overall network energy consumption, a system-friendly resource allocation strategy is proposed to optimize the jointly configurations of sensing frequency, time slot and transmission power. Simulation results validate that the proposed strategy can not only effectively save the network energy, but also meet the diverse personalized needs of applications.

Effective and Secure vWSN Applications in a Virtualized Cloud Computing Environment

Security is one of the key concerns in cloud computing. We are proposing Virtual Wireless Sensor Network (vWSN) which is one of the key features of cloud computing in the area of agriculture. Our main focuses are effective and secure vWSN in virtualized cloud computing environment with enhanced security to detect environmental monitoring, humidity monitoring, soil moisture, air quality (pollution) monitoring, insect monitoring, pest and disease control, which is based on virtualization and cloud computing technology, centrally managed device having flexible and configurable parameter with less maintenance and operational cost. To the best of our knowledge, vWSN is the first approach to agriculture based on extensional WSN information. WSN contains both control plane (signaling) and data plane (forwarding) coupled together.WSN is a specific purpose computer containing hardware whose main components are memory, CPU, IO, registers, Data and address bus, timer, sensor, computing logic and decision making logic. WSN is a physical device which needs to be placed in the field in certain topology (Ring, mesh, tree, star) which may need to be protected for damage and erosion due to various parameter including but not limited to physical theft, damage due to weather, animal and various other reason. In this paper our approach is to propose virtualized model to replace physical wireless sensor network (WSN) to virtual machine (VM) based vWSN which can be deployed on the cloud. Since security is one of the major concern for VM in cloud computing therefore we also proposed the enhanced 3-tier security model for vWSN.

Sensor virtualization Middleware design for Ambient Assisted Living based on the Priority packet processing

Wireless Sensor Network (WSN) is leading towards a new and more advanced paradigm that is Internet of Everything (IoE). Ambient Assisted Living (AAL), which aims to support elderly and physically challenged persons, is based on sensor networks. WSNs are mostly installed for a particular single application. Though, future of WSNs hides in WSN virtualization, which supports multiple applications. The aggregation of the sensor network resources in the form of software or hardware are supported by WSN virtualization. In Virtualizing WSN, the role of middleware layer is of supreme importance. However, a middleware layer faces overhead challenges of high processing time, delay, and the power consumption. The major challenge faced by AAL application in the paradigm of WSN virtualization is the delay caused by the aggregation of the sensed data. In this research paper efforts have been made to design a priority based multi-threading middleware design for sensor virtualization. The proposed middleware resides at the sensor node as well as the gateway that reduces the delay caused by the sensor virtualization. The proposed middleware design has been implemented and tested on the AAL testbed.

Overlay Virtualized Wireless Sensor Networks for Application in Industrial Internet of Things: A Review.

In recent times, Wireless Sensor Networks (WSNs) are broadly applied in the Industrial Internet of Things (IIoT) in order to enhance the productivity and efficiency of existing and prospective manufacturing industries. In particular, an area of interest that concerns the use of WSNs in IIoT is the concept of sensor network virtualization and overlay networks. Both network virtualization and overlay networks are considered contemporary because they provide the capacity to create services and applications at the edge of existing virtual networks without changing the underlying infrastructure. This capability makes both network virtualization and overlay network services highly beneficial, particularly for the dynamic needs of IIoT based applications such as in smart industry applications, smart city, and smart home applications. Consequently, the study of both WSN virtualization and overlay networks has become highly patronized in the literature, leading to the growth and maturity of the research area. In line with this growth, this paper provides a review of the development made thus far concerning virtualized sensor networks, with emphasis on the application of overlay networks in IIoT. Principally, the process of virtualization in WSN is discussed along with its importance in IIoT applications. Different challenges in WSN are also presented along with possible solutions given by the use of virtualized WSNs. Further details are also presented concerning the use of overlay networks as the next step to supporting virtualization in shared sensor networks. Our discussion closes with an exposition of the existing challenges in the use of virtualized WSN for IIoT applications. In general, because overlay networks will be contributory to the future development and advancement of smart industrial and smart city applications, this review may be considered by researchers as a reference point for those particularly interested in the study of this growing field.

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks.

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

Simplified Agent-Based Resource Sharing Approach for WSN-WSN Interaction in IoT/CPS Projects

This paper focuses on the problem of interoperability and resource sharing in wireless sensor networks (WSNs) running under the Internet of Things (IoT) and cyber-physical systems (CPSs). Considering the scale of IoT/CPS projects, conventional WSN virtualization techniques remain incapable because of the hardware/software constraints and heterogeneity. To this end, in this paper, an agent-based server system approach, which improves the resource sharing between heterogeneous WSNs in IoT/CPS providers, is proposed. In line with this approach, a software agent framework is introduced. With the help of the framework, called Firat Virtual WSN framework (FVWSN), the clients can move the commands/queries and data fusion/aggregation algorithms, which use on their local networks, to the provider side and run them remotely or automatically. This process is carried out by logical agent entities, called virtual nodes, which are created with the help of FVWSN. In this way, since the client evaluation mechanism is performed at a closer point to the shared resources, a shorter response time can be achieved in time-critical applications. The most important features that differentiate the developed agent framework from other agent-based technologies are that it is semi-autonomous and uses a specific resource selection/allocation algorithm. With the improvement that FVWSN provides for IoT/CPS WSN providers, it is possible to achieve a shorter response time and allow more client applications to share the same limited WSN resources. In this paper, first, the analysis and the necessity of the proposed system are discussed. Then, the system is simulated in the OPNET Modeler platform to make comparisons with well-known conventional WSN resource sharing mechanisms. Finally, the physical comparison tests of the system are carried out on an OpenStack-based cloud system, and the success of the system is shown.

Percolation analysis for constructing a robust modular topology based on a binary-dynamics model

In the context of Internet of Things, virtualization of wireless sensor networks is a crucial technology for sharing sensors as infrastructure. In our previous work, we proposed a brain-inspired method for constructing a robust and adaptive virtual wireless sensor network topology and showed that the method of constructing links between modules has crucial effect on robustness and adaptivity of the constructed virtual wireless sensor network topology. However, the best way of constructing a robust and adaptive virtual wireless sensor network topology is still unclear. Therefore, in this article, we use an analytical approach and propose a method for clarifying robustness of a topology according to the method of constructing links between modules. We add a new tool to a binary-dynamics model which is an analytical method for investigating percolation dynamics on a modular network. Evaluation by simulation showed that graphs in which the number of nodes selected as endpoint nodes of inter-module links and the degrees of the endpoint nodes before the link addition are large have robust connectivity in terms of the point of fragmentation of the network into modules when we fix the degree of the endpoint nodes after the link addition. After the point, the internal structure of modules may matter more. We additionally investigate an applicable range of our proposed method.

Virtual Wireless Sensor Networks: Adaptive Brain-Inspired Configuration for Internet of Things Applications.

Many researchers are devoting attention to the so-called “Internet of Things” (IoT), and wireless sensor networks (WSNs) are regarded as a critical technology for realizing the communication infrastructure of the future, including the IoT. Against this background, virtualization is a crucial technique for the integration of multiple WSNs. Designing virtualized WSNs for actual environments will require further detailed studies. Within the IoT environment, physical networks can undergo dynamic change, and so, many problems exist that could prevent applications from running without interruption when using the existing approaches. In this paper, we show an overall architecture that is suitable for constructing and running virtual wireless sensor network (VWSN) services within a VWSN topology. Our approach provides users with a reliable VWSN network by assigning redundant resources according to each user’s demand and providing a recovery method to incorporate environmental changes. We tested this approach by simulation experiment, with the results showing that the VWSN network is reliable in many cases, although physical deployment of sensor nodes and the modular structure of the VWSN will be quite important to the stability of services within the VWSN topology.

Wireless sensor network virtualization: A survey

Wireless Sensor Networks (WSNs) are the key components of the emerging Internet-of-Things (IoT) paradigm. They are now ubiquitous and used in a plurality of application domains. WSNs are still domain specific and usually deployed to support a specific application. However, as WSN nodes are becoming more and more powerful, it is getting more and more pertinent to research how multiple applications could share a very same WSN infrastructure. Virtualization is a technology that can potentially enable this sharing. This paper is a survey on WSN virtualization. It provides a comprehensive review of the state-of-the-art and an in-depth discussion of the research issues. We introduce the basics of WSN virtualization and motivate its pertinence with carefully selected scenarios. Existing works are presented in detail and critically evaluated using a set of requirements derived from the scenarios. The pertinent research projects are also reviewed. Several research issues are also discussed with hints on how they could be tackled.

Wireless sensor network virtualization: early architecture and research perspectives

Wireless sensor networks (WSNs) have become pervasive and are used in many applications and services. Usually the deployments of WSNs are task oriented and domain specific; thereby precluding re-use when other applications and services are contemplated. This inevitably leads to the proliferation of redundant WSN deployments. Virtualization is a technology that can aid in tackling this issue, as it enables the sharing of resources/infrastructure by multiple independent entities. In this paper we critically review the state of the art and propose a novel architecture for WSN virtualization. The proposed architecture has four layers (physical layer, virtual sensor layer, virtual sensor access layer and overlay layer) and relies on the constrained application protocol (CoAP). We illustrate its potential by using it in a scenario where a single WSN is shared by multiple applications; one of which is a fire monitoring application. We present the proof-of-concept prototype we have built along with the performance measurements, and discuss future research directions.