Sensors In Wireless Sensor Networks Research Articles

Characterizing unpredictable patterns in Wireless Sensor Network data

Wireless Sensor Network (WSN) monitoring takes a primary role in many industrial and research processes. Huge amounts of WSN sensor readings are nowadays available and can be analyzed to discover fruitful knowledge.This paper focuses on analyzing historical WSN sensor readings to identify the combinations of sensors whose readings show an unexpected trend. Although significant variations of single sensor readings may be easily detected, discovering correlations between multiple sensor readings is challenging without using advanced data analytics tools. To tackle this issue, we present an itemset-based data mining approach to analyzing WSN data. It identifies the combinations of sensors (of arbitrary size) whose readings are unexpectedly low in a given time period. Since the readings acquired by multiple sensors may decrease in an alternate fashion, the discovered patterns provide new information compared to single sensor analysis. To make the mined patterns manageable by domain experts for manual inspection, the mining algorithm is driven by spatial constraints defined on the WSN topology.The experimental results, achieved on real WSN data, demonstrate the effectiveness of the proposed approach in detecting heating system malfunctioning.

Energy Consumption Analysis in WSN Based on Node Placement Strategies

Wireless Sensor Networks (WSNs) is a collection of devices and sensor nodes connected with wireless network and communicate with one another via radio signals. Sensor in WSN is an autonomous (self-configuring) device used to sense the light, heat, motion, moisture and pressure etc that communicate with their neighbor nodes. Node placement is a technique that places the nodes effectively in the specified network environment. In WSN basically, wireless sensor network includes different topologies namely star, point-to-point, ring, bus, mesh and hybrid. In recent years, research has been carried out on different node placement strategies and produced different results based on its performance that includes power distribution and energy consumption of sensors. Energy consumption and network lifetime are considered to be the critical issues as the nodes are powered by the batteries which have finite energy reservoirs. In this paper, three different node placements namely Random, Uniform and Grid with respect to AODV (Ad hoc On-Demand Distance Vector) protocol is evaluated in order to analyze the energy factor during wireless communication. The performance metrics used to measure the analysis are Energy Consumption Average Jitter, Average End-to-End Delay, Average Throughput and Average Packet Delivery Ratio. The comparison results suggests that Grid node placement performs well in grid scenarios and shows best for specific performance metrics.

An Intrusion Detection and Prevention Framework for Internet-Integrated CoAP WSN

End-to-end communications between Internet devices and Internet-integrated constrained wireless sensing platforms will provide an important contribution to the enabling of many of the envisioned IoT applications and, in this context, security must be addressed when employing communication technologies such as 6LoWPAN and CoAP. Considering the constraints typically found on sensing devices in terms of energy, memory, and computational capability, the integration of Wireless Sensor Networks (WSN) with the Internet using such technologies will open new threats and attacks that must be dealt with, particularly those originated at devices without the constraints of WSN sensors (e.g., Internet hosts). Existing encryption strategies for communications in IoT environments are unable to protect Internet-integrated WSN environments from Denial of Service (DoS) attacks, as well as from other forms of attacks at the network and application layers using CoAP. We may thus fairly consider that anomaly and intrusion detection will play a major role in the materialization of most of the envisioned IoT applications. In this article, we propose a framework to support intrusion detection and reaction in Internet-integrated CoAP WSN, and in the context of this framework we design and implement various approaches to support security against various classes of attacks. We have implemented and evaluated experimentally the proposed framework and mechanisms, considering various attack scenarios, and our approach was found to be viable, from the point of view of its impact on critical resources of sensing devices and of its efficiency in dealing with the considered attacks.

Electric Power Self-Supply Module for WSN Sensor Node Based on MEMS Vibration Energy Harvester.

This paper proposes an electric power self-supply module for the wireless sensor network (WSN) sensor node. The module includes an electromagnetic vibration energy harvester based on micro-electro-mechanical system (MEMS) technology and a processing circuit. The vibration energy harvester presented in this paper is fabricated by an integrated microfabrication process and consists of four similar and relatively independent beam vibration elements. The main functions of the processing circuit are to convert the output of the harvester from unstable alternating current (AC) to stable direct current (DC), charge the super capacitor, and ensure the stable output of the super capacitor. The preliminary test results of the harvester chip show that the chip can output discontinuous pulse voltage, and the range of the voltage value is from tens to hundreds of millivolts in the vibration frequency range of 10–90 Hz. The maximum value that can be reached is 563 mV (at the vibration frequency of 18 Hz). The results of the test show that the harvester can output a relatively high voltage, which can meet the general electric power demand of a WSN sensor node.

Large scale integration of wireless sensor network technologies for air quality monitoring at a logistics shipping base

Abstract The future of logistics shipping bases will be to seek efficient flows of materials to meet the needs of business partners. Supply chain and operations managers of supply bases will need to integrate technologies that allow for greater automation, digitalization, flexibility and improved communications among stakeholders. The technologies that are likely to boost integration will consist of a plethora of Industrial Internet-of-Things (IIoT) technologies that may include Wireless Sensor Network (WSN) technologies and could be applied for improved monitoring of healthy and safe industrial workplaces for workers. However, little is known regarding how WSN technologies can be implemented on a larger scale and its implications when integrated on standard logistics and operations of industrial workplaces such as a shipping base. The WSN sensor units represent an integrating resource that are capable of monitoring air temperature, humidity and levels of carbon dioxide (CO2) and other gasses and of disseminating this information to different actors in the production system. Air quality factors play a critical role in the perceived levels of workers' comfort and in reported medical health. The low cost of wireless sensor network (WSN) technologies offer potential for continuous, autonomous and importantly networked assessment of industrial workplace air quality that may have implications for operations management and quality of production. This paper initially presents a case study that monitors air quality that is collected with WSN technologies from two workshops carried out by a large on-shore logistics base that supports offshore petroleum logistics. The case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. However, with the advancement in IIoT technologies and the emergence of smart sensing and actuating devices, it is possible to form a digital closed-loop system that we argue is essential for managers to link together information about air quality with supply chain and operations management decisions. We propose that central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. We discuss how WSN technologies can be integrated into the logistics management and operations of the shipping base. Through an analytical discussion of BD we explore how to extend the potential application of IIoT and Visual Analytics to facilitate a smart workplace for the Industry 4.0 era.

Reliability assessment of photovoltaic wireless sensor networks for forest fire propagation detection

The objective of this paper is to assess the reliability of a wireless sensor network (WSN) equipped with mini photovoltaic cells (PV-WSN), simulating forest fire conditions. Our assessment considers the hardware specifications of WSN sensors, photovoltaic (PV) cells, rechargeable batteries, communication protocols, and elements required for efficient fire detection. Our study’s main assumptions are that: the PV-WSN is structured randomly with one sink per cluster and identical sensors used, random losses due to environmental interference to the network and variability of PV-cell orientation are accounted for, the shape of forest fire propagation is uniform and elliptical, the surrounding vegetation is homogeneous, and packets lost during transmission are retransmitted with the use of a dynamic source routing protocol. Physical hardware specifications were included in our reliability assessments for each network type and a realistic battery re-charge/depletion scenario based on sensor states, sun availability, and PV efficiency was considered for a PV-WSN. We developed a simulator to study and to compare how the PV-WSN behaved differently under different forest fire conditions. Our results support the use of a PV-WSN, as network lifetime can be efficiently prolonged in the context of large-scale fire detection with this setup.

A Survey Report on Wireless Sensor Network

Wireless Sensor Network (WSN) is the combination of various sensing nodes. WSN is used to produce meaningful information from raw local data collected by each sensor. The characteristics of sensor network create many new and existing application areas for remote sensing. Also now a day there is much application in many field and WSN faces security challenges so I have discussed it with architecture of WSN. With advancement in WSN, small and low cost sensor nodes become available, which have capabilities of wireless communication, sensing various types of environmental conditions and data processing. Now a day, we are living in advance technology environment, which has a great advantage on every person life. Wireless sensor network is one of them which is the collection of nodes organized into a cooperative network and in every node consist of processing capability, and may contains multiple types of memory. In this paper there is review of introduction of Wireless Sensor Network and its application in various fields with security challenges. Also I have discussed various factors that affect the sensor network design. Wireless Sensor Network has tremendous advantages in the worlds which makes every person life much easier and also have various applications.

An Approach to Secure Data Aggregation in Wireless Sensor Networks (WSN) using Asymmetric Homomorphic Encryption (Elliptic Curve Cryptography) Scheme

Wireless sensor networks (WSN) are a collection of autonomous collection of motes. Sensor motes are usually Low computational and low powered. In WSN Sensor motes are used to collect environmental data collection and pass that data to the base station. Data aggregation is a common technique widely used in wireless sensor networks. [2] Data aggregation is the process of collecting the data from multiple sensor nodes by avoiding the redundant data transmission and that collected data has been sent to the base station (BS) in single route. Secured data aggregation deals with Securing aggregated data collected from various sources. Many secured data aggregation algorithms has been proposed by many researchers. Symmetric key based cryptography schemes are not suitable when wireless sensor network grows. Here we are proposing an approach to secured data aggregation in wireless sensor networks using Asymmetric key based Elliptic Curve cryptography technique. Elliptic curve cryptography (ECC) [1] is an approach to public-key cryptography based on the algebraic structure of elliptic curves over finite fields. Elliptic Curve Cryptography requires smaller keys compared to non-Elliptic curve cryptography (based on plain Galois fields) to provide equivalent security. The proposed technique of secure data aggregation is used to improve the sensor network lifetime and to reduce the energy consumption during aggregation process.

Cost-effective barrier coverage formation in heterogeneous wireless sensor networks

Barrier coverage is a critical issue in wireless sensor networks (WSNs) for security applications, which however cannot be guaranteed to be formed after initial random deployment of sensors. Existing work on barrier coverage mainly focus on homogeneous WSNs, while little effort has been made on exploiting barrier coverage formation in heterogeneous WSNs where different types of sensors are deployed with different sensing models and costs. In this paper, we study how to efficiently form barrier coverage by leveraging multiple types of mobile sensors to fill in gaps between pre-deployed stationary sensors in heterogeneous WSNs. The stationary sensors are grouped into clusters and a cluster-based directional barrier graph is proposed to model the barrier coverage formation problem. We prove that the minimum cost of mobile sensors required to form a barrier with stationary sensors is the length of the shortest path on the graph. Moreover, we propose a greedy movement algorithm for heterogeneous WSNs to efficiently schedule different types of mobile sensors to different gaps while minimizing the total moving cost. In particular, we formulate the movement problem for homogeneous WSNs as a minimum cost bipartite assignment problem, and solve it in polynomial time using the Hungarian algorithm. Extensively experimental results on homogeneous and heterogeneous WSNs demonstrate the effectiveness of the proposed algorithms.

Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering.

An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering. To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE).

Optimizing Detection Quality and Transmission Quality of Barrier Coverage in Heterogeneous Wireless Sensor Networks

This paper proposes an algorithm, called the Optimized Barrier Coverage Algorithm (OBCA), to optimize the quality of the barrier coverage formed by a wireless sensor network (WSN). OBCA aims at optimizing the barrier coverage quality in terms of the detection degree, the detection quality, and the transmission quality (i.e., the expected transmission time). This paper also proposes a model to formulate the minimum detection probability between two WSN sensors with different sensing ranges. With the model, OBCA can be applied to heterogeneous WSNs whose sensors have various sensing ranges. OBCA’s optimization is proved, and its time complexity is analyzed. The performance of OBCA is simulated and compared with those of two related algorithms.

PAS: policy-based assistance in sensor networks

During the last one and a half decades, wireless sensor networks (WSNs) have witnessed significant growth and tremendous development. While enjoying the wide variety of applications, the sensors in WSN have constrained resources, affecting their living lives, working times, assigned tasks, etc. Though a large number of efforts have been devoted into designing protocols that save energy as much as possible, they are still a passive solution. In this paper, we propose a novel approach called policy-based assistance system (PAS) that addresses the issue of resource limitation in a proactive manner. Complementary to existing works which focus on saving energy in individual nodes, our proposed approach further allows sensors with limited resources to make requests to other sensors to help complete the tasks. In this way, the resources in the whole WSN are better utilized and more tasks can be completed with little communication overhead.

Network Coding Based Converge-Cast Scheme in Wireless Sensor Networks

The converge-cast in wireless sensor networks (WSNs) is widely applied in many fields such as medical applications and the environmental monitoring. WSNs expect not only providing routing with high throughput but also achieving efficient energy saving. Network coding is one of the most promising techniques to reduce the energy consumption. By maximizing the encoding number, the message capacity per package can be extended to the most efficient condition. Thus, many researchers have focused their work on this field. Nevertheless, the packages sent by the outer nodes need to be temporary stored and delayed in order to maximize the encoding number. To find out the balance between inserting the delay time and maximizing the encoding number, a Converge-cast Scheme based on data collection rate prediction (CSRP) is proposed in this paper. To avoid producing the outdated information, a prediction method based on Modifying Index Curve Model is presented to deal with the dynamic data collection rate of every sensor in WSNs. Furthermore, a novel coding conditions based on CDS is proposed to increase the coding opportunity and to solve the collision problems. The corresponding analysis and experimental results indicate that the feasibility and efficiency of the CSRP is better than normal conditions without the prediction.

Optimization Design of Network Structure for WSN Data Collection Design Based on Delay Perception

In order to solve such problems as large delay and large energy consumption in current wireless sensor network (WSN) during data collection, a network structure for WSN data collection based on delay perception is designed in this article. Firstly, the tree structure thought is introduced therein to divide WSN sensor nodes into multiple single-layer clusters with different sizes so as to construct a new network structure, thus to improve the topological structure and enable the cluster heads to complete the data communication in an interlaced mode as well as significantly reduce the delay during data collection; secondly, the model for calculating the delay and the energy consumption during WSN data collection is established in this article; thirdly, Top-Down technology is introduced therein to design the network structure formation algorithm, thus to optimize the energy consumption of the data collection mechanism through the communication distance minimization. The experiment shows: compared with existing network structure for WSN data collection, the proposed network structure can not only effectively reduce the delay during WSN data collection, but also maintain the whole communication energy consumption at a relatively low level.

Maintaining Large-Scale Rechargeable Sensor Networks Perpetually via Multiple Mobile Charging Vehicles

Wireless energy transfer technology based on magnetic resonant coupling has been emerging as a promising technology for wireless sensor networks (WSNs) by providing controllable yet perpetual energy to sensors. In this article, we study the deployment of the minimum number of mobile charging vehicles to charge sensors in a large-scale WSN so that none of the sensors will run out of energy, for which we first advocate a flexible on-demand charging paradigm that decouples sensor energy charging scheduling from the design of sensing data routing protocols. We then formulate a novel optimization problem of scheduling mobile charging vehicles to charge life-critical sensors in the network with an objective to minimize the number of mobile charging vehicles deployed, subject to the energy capacity constraint on each mobile charging vehicle. As the problem is NP-hard, we instead propose an approximation algorithm with a provable performance guarantee if the energy consumption of each sensor during each charging tour is negligible. Otherwise, we devise a heuristic algorithm by modifying the proposed approximation algorithm. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are very promising, and the solutions obtained are fractional of the optimal ones. To the best of our knowledge, this is the first approximation algorithm with a nontrivial approximation ratio for a novel scheduling problem of multiple mobile charging vehicles for charging sensors.

Non-Parametric and Semi-Parametric RSSI/Distance Modeling for Target Tracking in Wireless Sensor Networks

This paper introduces two main contributions to the wireless sensor network (WSN) society. The first one consists of modeling the relationship between the distances separating sensors and the received signal strength indicators (RSSIs) exchanged by these sensors in an indoor WSN. In this context, two models are determined using a radio-fingerprints database and kernel-based learning methods. The first one is a non-parametric regression model, while the second one is a semi-parametric regression model that combines the well-known log-distance theoretical propagation model with a non-linear fluctuation term. As for the second contribution, it consists of tracking a moving target in the network using the estimated RSSI/distance models. The target's position is estimated by combining acceleration information and the estimated distances separating the target from sensors having known positions, using either the Kalman filter or the particle filter. A fully comprehensive study of the choice of parameters of the proposed distance models and their performances is provided, as well as a study of the performance of the two proposed tracking methods. Comparisons with recently proposed methods are also provided.

Distributed $k$ -Means Algorithm and Fuzzy $c$ -Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory.

This paper is concerned with developing a distributed k-means algorithm and a distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is first proposed to find the initial centroids before executing the distributed k-means algorithm and the distributed fuzzy c-means algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group and large out-group distances, while the proposed distributed fuzzy c-means algorithm is capable of partitioning the data observed by the nodes into different measure-dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.

An Energy-Balanced Heuristic for Mobile Sink Scheduling in Hybrid WSNs

Wireless sensor networks (WSNs) are integrated as a pillar of collaborative Internet of Things (IoT) technologies for the creation of pervasive smart environments. Generally, IoT end nodes (or WSN sensors) can be mobile or static. In this kind of hybrid WSNs, mobile sinks move to predetermined sink locations to gather data sensed by static sensors. Scheduling mobile sinks energy-efficiently while prolonging the network lifetime is a challenge. To remedy this issue, we propose a three-phase energy-balanced heuristic. Specifically, the network region is first divided into grid cells with the same geographical size. These grid cells are assigned to clusters through an algorithm inspired by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{k}}$</tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-d</i> imensional tree algorithm, such that the energy consumption of each cluster is similar when gathering data. These clusters are adjusted by (de)allocating grid cells contained in these clusters, while considering the energy consumption of sink movement. Consequently, the energy to be consumed in each cluster is approximately balanced considering the energy consumption of both data gathering and sink movement. Experimental evaluation shows that this technique can generate an optimal grid cell division within a limited time of iterations and prolong the network lifetime.

A novel method for probabilistic coverage estimation of sensor networks based on 3D vector representation in complex urban environments

GIScience 2016 Short Paper Proceedings A novel method for probabilistic coverage estimation of sensor networks based on 3D vector representation in complex urban environments A. Afghantoloee 1 , F. Karimipour 2 , M. A. Mostafavi 1 Center for Research in Geomatics, Department of Geomatics, Universite Laval, Quebec, Canada Email: ali.afghantoloee.1@ulaval.ca;mir-abolfazl.mostafavi@scg.ulaval.ca Dept. of Geomatic Engineering, University of Tehran, Amir-abad Street, Tehran, Iran Email: fkarimipr@ut.ac.ir Abstract Wireless Sensor Networks (WSNs) are widely used for monitoring and observation of dynamic phenomena. A sensor in WSNs covers only a limited region, depending on its sensing and communicating ranges, as well as the environment configuration. For efficient deployment of sensors in a WSN the coverage estimation is a critical issue. Probabilistic methods are among the most accurate models proposed for sensor coverage estimation. However, most of these methods are based on raster representation of the environment for coverage estimation which limits their quality. In this paper, we propose a probabilistic method for estimation of the coverage of a sensor network based on 3D vector representation of the environment. 1. Introduction Nowadays, WSNs have found various applications in industry, security, agriculture, military and disaster management. Efficient monitoring and management of dynamic phenomena in the real world necessitates its efficient and accurate coverage. The efficiency of the coverage of a sensor network depends on optimal position of each sensor node within the network. An individual sensor covers only a limited area, which depends on its sensing capacity, range of communication as well as the environment’s complexity. The total area covered by a WSN is obtained from the union of the regions covered by individual sensors. Therefore, efficient deployment of sensors in a WSN is a critical issue that affects the coverage as well as communication between sensors. Several optimization methods (i.e., global or local, deterministic or stochastic, etc.) have been proposed to detect and eliminate coverage holes and hence increase the coverage of sensor networks (Argany et al. 2015). One of the key issues of all deployment optimization algorithms is accurate estimation of the coverage of an individual sensor. Sensing model of individual sensors ─which could be binary or probabilistic, omnidirectional or directional─ has significant impact on the precise coverage estimation of a sensor network using diverse optimization algorithms. Most of the sensor coverage estimation methods use a raster representation of the environment (Akbarzadeh et al. 2013) for optimization purposes that limits their precision and efficiency. This is because raster representations are constrained by their spatial resolution, and their regular shapes result in redundant data for unoccupied areas. Few vector-based optimization algorithms are proposed in the literature, which are mostly based on 2D vector-based representation of the environment and do not adequately consider the presence of manmade and natural obstacles in the sensing areas (Wang and Cao 2011). To overcome these limitations, in this paper we propose a probabilistic sensor coverage estimation method based on precise 3D vector-base representation of the environment and we present some results of an ongoing research project that aims at better optimization of a sensor network in a 3D complex urban area.

A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems

Wireless sensor networks (WSNs) as one of the key technologies for delivering sensor-related data drive the progress of cyber-physical systems (CPSs) in bridging the gap between the cyber world and the physical world. It is thus desirable to explore how to utilize intelligence properly by developing the effective scheme in WSN to support data sensing and fusion of CPS. This paper intends to serve this purpose by proposing a prediction-based data sensing and fusion scheme to reduce the data transmission and maintain the required coverage level of sensors in WSN while guaranteeing the data confidentiality. The proposed scheme is called GM–KRLS, which is featured through the use of grey model (GM), kernel recursive least squares (KRLS), and Blowfish algorithm (BA). During the data sensing and fusion process, GM is responsible for initially predicting the data of next period with a small number of data items, while KRLS is used to make the initial predicted value approximate its true value with high accuracy. The KRLS as an improved kernel machine learning algorithm can adaptively adjust the coefficients with every input, while making the predicted value more close to actual value. And BA is used for data encoding and decoding during the transmission process due to its successful applications across a wide range of domains. Then, the proposed secure data sensing and fusion scheme GM–KRLS can provide high prediction accuracy, low communication, good scalability, and confidentiality. In order to verify the effectiveness and reasonableness of our proposed approach, we conduct simulations on actual data sets that are collected from sensors in the Intel Berkeley research lab. The simulation results have shown that the proposed scheme can significantly reduce redundant transmissions with high prediction accuracy.