Physical Sensor Network Research Articles

A fuzzy logic technique for virtual sensor networks

A virtual sensor network is designed to infer an approximation of real data when the physical sensor network cannot provide it, due to technical or economic reasons. In this paper, we propose an approach to model virtual sensor networks based on fuzzy logic. It relies on the hypothesis that the readings of the physical sensors are correlated. In particular, the virtual sensor network is able to learn the relationships between the physical quantities detected by the sensors and model them through fuzzy rules, which provide an approximation of the real values. In the proposed model, a set of fuzzy rules is derived for each physical sensor. It will provide an accurate approximation of the sensor readings when needed (e.g. sensor malfunction). The proposed approach has been designed as an extension of the well known Tree Routing protocol, used in sensor networks for transmitting sensed data to the base station. It has been implemented in Snlog, a language derived from Datalog that supports the development of distributed algorithms for Wireless Sensor Networks in a declarative way. The experimental evaluation reported in the paper shows the effectiveness and efficacy of our solutions.

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.

Using Sensor Graphs for Monitoring the Effect on the Performance of the OTAGO Exercise Program in Older Adults.

The OTAGO exercise program is effective in decreasing the risk for falls of older adults. This research investigated if there is an indication that the OTAGO exercise program has a positive effect on the capacity and as well as on the performance in mobility. We used the data of the 10-months observational OTAGO pilot study with 15 (m = 1, f = 14) (pre-)frail participants aged 84.60 y (SD: 5.57 y). Motion sensors were installed in the flats of the participants and used to monitor their activity as a surrogate variable for performance. We derived a weighted directed multigraph from the physical sensor network, subtracted the weights of one day from a baseline, and used the difference in percent to quantify the change in performance. Least squares was used to compute the overall progress of the intervention (n = 9) and the control group (n = 6). In accordance with previous studies, we found indication for a positive effect of the OTAGO program on the capacity in both groups. Moreover, we found indication that the OTAGO program reduces the decline in performance of older adults in daily living. However, it is too early to conclude causalities from our findings because the data was collected during a pilot study.

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.

Assessment of coverage quality of sensor networks for IoT applications

The advances in sensors and IOT technologies has prompted a plethora of applications, many of them require a large number of sensors distributed over a large geographical area. These sensors collectively form a network with a sole purpose of providing the application with high fidelity representation of the actual physical world. The Quality of Coverage (QoC) metric is typically refers to how well this representation is. The issue of coverage has been studied in the literatures, however, most research focused on various aspects of physical sensor network coverage with less attention to the diverse requirements of the applications which use the underlying sensor layer. In this paper, we introduce multiple metrics to measure the quality of coverage which can then be tailored to meet the requirements of different types of applications. In addition, we propose algorithms to measure these metrics and use evaluation techniques to demonstrate and test the performance of proposed algorithms.

A cloud based virtualisation protocol for wireless sensor networks

Virtual sensor networks (VSNs) allow linking physical wireless sensor networks (WSNs) and specialised services that permit sensor data gathering, storing and processing. In this manner VSNs provide concurrent, scalable and flexible access to a common pool of sensor nodes. Cloud computing technologies allow to deal with the WSNs heterogeneity (hardware footprint, communication protocols standards, etc.). This work proposes a real-time, centralised, cloud-based, service-oriented virtualisation protocol under a publish-subscribe model, capable of masking WSNs errors and their limitations. Protocol implementation tests on a single virtual machine show its capability to subscribe to more than 100 WSN and continuously receive, store, filter and predict data from over 1000 sensor nodes while also service reliably over 24,000 nodes from 500 requested VSN, using a stream of data from an original pool of 100 sensor nodes with an average VSN-publishing delay under 20 ms regardless of the WSN delays and errors.

Robust hardware-supported chaotic cryptosystems for streaming commutations among reduced computing power nodes

Most recent technological proposals, such as cyber–physical systems or wireless sensor networks, consist of a collection of tiny nodes designed to be seamless integrated into daily living objects. These nodes then, due to their miniaturized configuration, use to present very limited processing capabilities. Because of that, in general complex algorithms, as which are employed today to secure communications, cannot be implemented in these new systems. Thus, new instruments for security are needed, with a special mention to hardware-supported solutions. Therefore, in this paper different robust hardware-supported cryptosystems based on Chua’s circuit are proposed, studied ad compared. The described solutions are specifically designed to be employed in streaming communications among reduced computing power nodes. Moreover, an experimental validation is proposed comparing the performance of the proposed technologies and other existing solutions.

Hybrid energy aware clustered protocol for IoT heterogeneous network

IoT diverse applications face many challenges. The main challenge is to have efficient energy aware communication protocols that utilize the diversity and heterogeneity of the connected things through Internet. Saving energy is a vital requirement in the limited battery energy nodes and also for the outsourced energy nodes for green computing. IoT milieu has many diverse devices that are heterogeneous in their energies, their Internet availability, etc. These devices are usually distributed into regions with different heterogeneity levels; ranging from homogeneous to near homogenous, till reaching to the high heterogeneous regions. Many existed protocols efficiently treated either the homogenous devices or heterogeneous devices. This paper defeats the gap between the physical wireless sensor network environment and the real heterogeneous Cyber IoT milieu. This paper targets not only providing an efficient hybrid energy aware clustering communication protocol for green IoT network computing; Hy-IoT, but also provides a real IoT network architecture for examining the proposed protocol compared to commonly existed protocols. Efficient cluster-head selection boosts the utilization of the nodes energy contents and consequently increases the network life time as well as the packets transmission rate to the base station. Hy-IoT uses different weighted election probabilities for selecting a Cluster-head based on heterogeneity level of the region. Simulation shows that Hy-IoT prolongs the network life time and increases the throughput compared to the SEP, LEACH and Z-SEP. Hy-IoT provides prolonging for the network life time ranging from 47.8% to 92.5% based on the heterogeneity level and also the average throughput was boosted ranging from 11.5% to 70.1% based on the heterogeneity level.

Joint Application Admission Control and Network Slicing in Virtual Sensor Networks

We focus on the problem of managing a shared physical wireless sensor network (WSN) where a single network infrastructure provider leases the physical resources of the networks to application providers to run/deploy specific applications/services. In this scenario, we solve jointly the problems of application admission control (AAC), that is, whether to admit the application/service to the physical network, and wireless sensor network slicing (SNS), that is, to allocate the required physical resources to the admitted applications in a transparent and effective way. We propose a mathematical programming framework to model the joint AAC-SNS problem which is then leveraged to design effective solution algorithms. The proposed framework is thoroughly evaluated on realistic WSNs infrastructures.

A Cognitive Framework to Secure Smart Cities

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms.

Atomic Norm Minimization for Modal Analysis From Random and Compressed Samples

Modal analysis is the process of estimating a system's modal parameters such as its natural frequencies and mode shapes. One application of modal analysis is in structural health monitoring (SHM), where a network of sensors may be used to collect vibration data from a physical structure such as a building or bridge. There is a growing interest in developing automated techniques for SHM based on data collected in a wireless sensor network. In order to conserve power and extend battery life, however, it is desirable to minimize the amount of data that must be collected and transmitted in such a sensor network. In this paper, we highlight the fact that modal analysis can be formulated as an atomic norm minimization (ANM) problem, which can be solved efficiently and in some cases recover perfectly a structure's mode shapes and frequencies. We survey a broad class of sampling and compression strategies that one might consider in a physical sensor network, and we provide bounds on the sample complexity of these compressive schemes in order to recover a structure's mode shapes and frequencies via ANM. A main contribution of our paper is to establish a bound on the sample complexity of modal analysis with random temporal compression, and in this scenario we prove that the samples per sensor can actually decrease as the number of sensors increases. We also extend an atomic norm denoising problem to the multiple measurement vector (MMV) setting in the case of uniform sampling.

Wireless Sensor Network Softwarization: Towards WSN Adaptive QoS

Connecting wireless sensor networks (WSNs) to remote servers or clouds through the Internet of Things inspires the formation of a highly complex system. This has significant potential to encourage WSN softwarization. Such a network enables interaction between the physical and virtual sensor network, allowing for flexibility within the physical system to adapt with the dynamics of the service requirements. An architecture of a test environment with adaptive quality of service (AQoS) has been presented. The concept provides an avenue for a flexible system that is capable of reacting to dynamic changes of process demands. Physical network performance can be predicted by analyzing the historical data in the background on a network simulator or virtual network. This in turn allows for estimation of the necessary adjustments needed to improve the network performance without disturbing the physical system. This paper reports the experimentation and applicability of user-driven AQoS models on the system. Early stages of testing the organization, taking into account the system behavior and subsequent reaction to necessary adjustment of the WSN operational configuration, has shown encouraging results.

Physical and virtual sensor networks for Energy–Internet of Things

Physical and virtual sensor networks for Energy–Internet of Things

End-to-End Communication Delay Analysis in Industrial Wireless Networks

WirelessHART is a new standard specifically designed for real-time and reliable communication between sensor and actuator devices for industrial process monitoring and control applications. End-to-end communication delay analysis for WirelessHART networks is required to determine the schedulability of real-time data flows from sensors to actuators for the purpose of acceptance test or workload adjustment in response to network dynamics. In this paper, we consider a network model based on WirelessHART, and map the scheduling of real-time periodic data flows in the network to real-time multiprocessor scheduling. We then exploit the response time analysis for multiprocessor scheduling and propose a novel method for the delay analysis that establishes an upper bound of the end-to-end communication delay of each real-time flow in the network. Simulation studies based on both random topologies and real network topologies of a <inline-formula><tex-math>$74$</tex-math></inline-formula> -node physical wireless sensor network testbed demonstrate that our analysis provides safe and reasonably tight upper bounds of the end-to-end delays of real-time flows, and hence enables effective schedulability tests for WirelessHART networks.

Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies, Multiagent System Paradigm, and Natural Ecosystems

Wireless sensor networks (WSNs) are key components in the emergent cyber physical systems (CPSs). They may include hundreds of spatially distributed sensors which interact to solve complex tasks going beyond their individual capabilities. Due to the limited capabilities of sensors, sensor actions cannot meet CPS requirements while controlling and coordinating the operations of physical and engineered systems. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and communication mechanisms of living organisms. By mapping these organisms onto sensors and ecosystems onto WSNs, we highlight shortcomings that prevent WSNs from delivering the capabilities of ecosystems at several levels, including structure, topology, goals, communications, and functions. We then propose an agent-based architecture that migrates complex processing tasks outside the physical sensor network while incorporating missing characteristics of autonomy, intelligence, and context awareness to the WSN. Unlike existing works, we use software agents to map WSNs to natural ecosystems and enhance WSN capabilities to take advantage of bioinspired algorithms. We extend our architecture and propose a new intelligent CPS framework where several control levels are embedded in the physical system, thereby allowing agents to support WSNs technologies in enabling CPSs.

An Appraisal of Wireless Sensor Networks: Profiles and Characters

This paper presents an in-depth evaluation of Wireless Sensor Networks. Wireless Sensor Networks have been highlighted as the major component that enables the development of modern infrastructures, such as the Smart Grid. As part of an on-going edification process on the subject matter, this paper brings to fore the many important functions and components of Wireless Sensor Networks, including application areas, functional architectures, physical topological design, communication protocols, routing schemes and Wireless Sensor Network hardware capabilities.

Advanced Applications of Wireless Sensor Network Using Sensor Cloud Infrastructure

It is our pleasure to publish a special issue on advanced applications of wireless sensor network using sensor cloud infrastructure. This special issue contains seven high quality research papers related to the growing technologies of recent wireless sensor networks and sensor cloud infrastructure. The paper “Sensor resource sharing approaches in sensorcloud infrastructure” by Y. Lim and J. Park presents a survey on sensor-cloud infrastructure (SCI) and discusses technical issues of SCI such as architecture, sensor description methods, middleware based on publish/subscribe data exchange model, and case studies. The paper “Multiple wide tables with vertical scalability in multitenant sensor cloud systems” by K. Ma and B. Yang proposes the multitenant multiple wide tables with vertical scalability by analyzing the features of multitenant data and shows the effectiveness of the proposed method by the experiments. The paper “Semantic reasoning with contextual ontologies on sensor cloud environment” by K. Park et al. presents a massive contextual events handling scheme on a big data framework based on a context-aware inference model with the contextual ontologies and shows that the proposed collaboration framework allows many cooperating parties to collaborate with each other by sharing constituent entities. The paper “Modeling and analysis on congestion control for data transmission in sensor clouds” by J. Huang et al. proposes an Improved Random Early Detection (IRED) algorithm that meets sensor cloud networking requirements better and develops a queueing model coupled with an analysis technique to evaluate the performance of the proposed congestion control scheme. The paper “Agriculture sensor-cloud infrastructure and routing protocol in the physical sensor network layer” by K. Kimet al. proposes an agriculture sensor-cloud infrastructure to provide various services efficiently and to process largescale sensor data effectively and evaluates the proposed techniques by simulation experiments. The paper “Competition-based device-to-device transmission scheduling to support wireless cloud multimedia communications” byR. Yao et al. presents a new algorithm to improve the data transmission quality for a mobile multimedia cloud network (MMCN) in a cloud device-to-device (D2D) communication and demonstrates the simulation results to show the effects of the proposed algorithm. The paper “Towards an environmental measurement cloud: delivering pollution awareness to the public” by F. Dominguez et al. proposes a cloud platform with a federated two-layer architecture which integrates an environmental sensor network with a sensor web, in order to offer geographical environmental data to the public and also to serve as a test bed for scientific research on noise and air pollution. We hope that the papers of this issue contribute to the progress of research and the development of Wireless Sensor Networks.

An OSGi middleware to enable the Sensor as a Service paradigm

The Internet of Things vision has recently stimulated many research efforts in different communities. The collection of diverse information produced by the proliferation of inter-connected sensing entities is one of the biggest challenge that should be adequately addressed. The huge amounts of raw data produced by IoT devices need to be structured, stored, analysed, correlated and mined in a reliable and scalable way. The size of the produced data, and the high rate at which data are being produced, suggest that we need new solutions that combine tools for data management and services capable of promptly structuring, aggregating and mining data even just at the time they are produced. In this paper we propose a middleware, to be deployed on top of physical sensors and sensor networks, capable of abstracting sensor devices from their proprietary interfaces, and offering them to third party applications in an as-a-Service fashion for prompt and universal use. The middleware also offers tool to elaborate real-time measurements produced by sensors. A prototype of the middleware has been implemented. In the paper a real use case scenario is also discussed.

ABAMA: An Agent-based Architecture for Mapping Natural Ecosystems onto Wireless Sensor Networks

Wireless Sensor Networks (WSNs) comprise of hundreds of spatially distributed sensors which may collaborate, compete, and self-organize in order to solve complex tasks which are beyond their individual capabilities. The efficiency of these actions is commonly restricted by the limited energy, the environmental context, and the processing capabilities of the sensors. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and strong communication mechanisms used by living organisms. While mapping these organisms onto sensors and ecosystems onto WSNs, we identify the similarities of both parties in terms of structure, active entities, topology, goals, communications, and functions and highlight shortcomings that would prevent WSNs from matching the behavior of ecosystems. We then propose an agent-based architecture that migrates the complex processing loads outside the physical sensor network while incorporating missing characteristics such as autonomy, intelligence, and context awareness to the WSN. In contrast to existing works, we use software agents to bridge the gap between WSNs and natural ecosystems and achieve an optimal mapping between both systems. Our ultimate goal is to enhance the capabilities of WSNs to take advantage of ecology-inspired algorithms.

A communication‐efficient framework for outlier‐free data reporting in data‐gathering sensor networks

AbstractIn this paper, we address the problem of reducing the communication cost and hence the energy costs incurred in data‐gathering applications of a sensor network. Environmental data depicts a huge amount of correlation in both the spatial and temporal domains. We exploit these temporal–spatial correlations to address the aforementioned problem. More specifically, we propose a framework that partitions the physical sensor network topology into a number of feature regions. Each sensor node builds a data model that represents the underlying structure of the data. A representative node in each feature region communicates only the model coefficients to the sink, which then uses them to answer queries. The temporal and spatial similarity has special meaning in outlier cleaning too. We use a modified z‐score technique to precisely label the outliers and use the spatial similarity to confirm whether the outliers are due to a true change in the phenomenon under study or due to faulty sensor nodes. Copyright © 2008 John Wiley &amp; Sons, Ltd.