Objects In Wireless Sensor Networks Research Articles

Novel phase adjournment data capturing technique for a mobile object in wireless sensor network

Novel phase adjournment data capturing technique for a mobile object in wireless sensor network

Mobility Prediction Based Tracking of Moving Objects in Wireless Sensor Networks

Mobility Prediction Based Tracking of Moving Objects in Wireless Sensor Networks

Optimizing Storage for Energy Conservation in Tracking Wireless Sensor Network Objects

The amount of needed control messages in wireless sensor networks (WSN) is affected by the storage strategy of detected events. Because broadcasting superfluous control messages consumes excess energy, the network lifespan can be extended if the quantity of control messages is decreased. In this study, an optimized storage technique having low control overhead for tracking the objects in WSN is introduced. The basic concept is to retain observed events in internal memory and preserve the relationship between sensed information and sensor nodes using a novel inexpensive data structure entitled Ordered Binary Linked List (OBLL). Whenever an object passes over the sensor area, the recognizing sensor can immediately produce an OBLL along the object’s route. To retrieve the entire information, the OBLL can be traversed with logarithmic complexity which is much less than the traversing complexity of existing linked list structures. Performance evaluation and simulations were carried out to ensure that the suggested technique minimizes the number of messages and thus saving energy and extending the network life.

Sparsity-enabled radio tomographic imaging using quantized received signal strength observations

Detection and tracking of objects in wireless sensor networks (WSN) is one of the active research areas in the signal processing community, where device-free localization (DFL) techniques are the potential contributors. Radio tomographic imaging (RTI) is one such DFL technique where the loss field surrounded by any obstacle can be visualized with the help of the received signal strength (RSS) of various transceiver nodes. Due to strict communication constraints and the necessity of low-complexity sensors, we are motivated towards quantized RSS (q-RSS) observations in the RTI system. The RSS data is digitized before sending it to the fusion center (FC). Therefore, uncertainty due to RSS quantization error leads to momentous performance degradation of the RTI system. The proposed support vector regression (SVR) technique reduces this uncertainty. The ϵ-SVR and ν-SVR techniques are affected by noise levels, and their performance is enhanced by the use of fused l2-SVR (F-l2-SVR). However, these estimators have higher computational complexity due to the large number of support vectors. Hence, a robust sparsity-based estimator using the linear programming SVR (LP-SVR) is proposed to reduce the computational complexity. Further, a novel fused lasso-based l1-SVR (FL-l1-SVR) estimator is proposed that provides estimated spatial loss field (SLF) as close as to the true SLF. Finally, the performance metrics and the reduced number of support vectors lead to the efficacy of the proposed FL-l1-SVR estimator over other estimators under q-RSS observations in the RTI system.

DEVELOPMENT OF METHODS OF POSITIONING, LOCALIZATION AND DATA COLLECTION FROM NODES OF A FREE MOBILE SENSOR NETWORK USING INTELLIGENT ADAPTIVE TELECOMMUNICATION AEROPLATFORMS

Background. The article presents the results of a study of methods of positioning, localization and data collection from nodes of a mobile wireless sensor network using intelligent adaptive telecommunication air platforms. To implement the study of this research topic, an analysis of literary sources on this topic was carried out. Based on a fairly rich bibliographic material, this work has the main task of examining, analyzing and systematizing already known approaches to positioning objects in wireless sensor networks using intelligent adaptive telecommunication air platforms and suggesting options for their development.
 Objective. The aim of the work is to improve the methods of direct data collection of TA from the nodes of BSM, the general directions of synthesis of which are defined in the work.
 Methods. Methods of cluster analysis (network clustering), graph theory (research of analytical models of BSM with TA functioning, construction of cluster topology), theory of telecommunication networks (when calculating bandwidth in BSM with TA radio channels) and theory were used to solve the formulated problem. (when developing a positioning model for telecommunications air platforms)
 Results. A technique for evaluating the effectiveness of methods for collecting data from wireless sensor networks using intelligent adaptive telecommunication air platforms is proposed.
 Conclusions. The method of collecting TA monitoring data from the main nodes of clustered BSM has been improved. The method of estimation of efficiency of methods of data collection with BSM by telecommunication air platforms is offered.

An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks

In wireless sensor networks (WSNs), detection and report of continuous object, such as forest fire and toxic gas leakage, is one of the major applications. In large-scale continuous object tracking in WSNs, there might be many source nodes simultaneously, detecting the continuous object. Each nodes reports its data to both a base station and mobile workers in the industry field. For communication between the source nodes and a mobile worker, sink location service is needed to continuously notify the location of the mobile worker. But, as the application has a large number of sources, it causes a waste of energy consumption. To address this issue, in this paper, we propose a two-phase sink location service scheme. In the first phase, the proposed scheme constructs a virtual grid structure for merging the source nodes. Then, the proposed scheme aggregates the merging points from an originated merging point as the second phase. Simulation results show that the proposed scheme is superior to other schemes in terms of energy consumption.

Clustering objectives in wireless sensor networks: A survey and research direction analysis

Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of networks given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.

RECOD: reliable detection protocol for large-scale and dynamic continuous objects in wireless sensor networks

Whether individual objects such as enemy tanks or intruders have been reliably detected typically depends on the number of data reports successfully delivered to a sink node from the sensor nodes surrounding the object. When the number of data reports exceeds a required threshold, the sink recognizes the object that is detected by sensor nodes. Thus, previous studies exploited this framework for reliable detection as event reliability for individual objects, and proposed event-to-sink reliable-transport mechanisms that can reach a required threshold. Recently, in wireless sensor networks, research has focused on coverage detection for large-scale phenomena such as biochemical material and wild fires. Such phenomena are known as continuous objects because they generally cover wide areas and frequently change their shape as a result of wind or geographical features. Since continuous objects are large-scale and alterable, they present new challenges for the event reliability. In this paper, we first define new criteria for measuring the event reliability of large-scale phenomena. Then, we propose a novel event-to-sink transport protocol that is reliable, even when excessive data is generated from many sensor nodes detecting these phenomena. Analysis and simulation results demonstrate the event reliability of our protocol.

Efficient Aggregation Processing in the Presence of Duplicately Detected Objects in WSNs

Wireless sensor networks (WSNs) have received increasing attention in the past decades. Owing to an enhancement of MEMS technology, various types of sensors such as motion detectors, infrared radiation detectors, ultrasonic sensors (sonar), and magnetometers can detect the objects within a certain range. Under such an environment, an object without an identifier can be detected by several sensor nodes. However, existing studies for query processing in WSNs simply assume that the sensing regions of sensors are disjoint. Thus, for query aggregation processing, effective deduplication is vital. In this paper, we propose an approximate but effective aggregate query processing algorithm, called DE-Duplication on the Least Common Ancestor∗ (abbreviated as DELCA∗). In contrast to most existing studies, since we assume that each object does not have a unique identifier, we perform deduplication based on similarity. To recognize the duplicately detected events earlier, we utilize the locality-sensitive hashing (LSH) technique. In addition, since the similarity measures are not generally transitive, we adapt three duplicate semantics. In our experiments, by using a transmission cost model, we demonstrate that our proposed technique is energy-efficient. We also show the accuracy of our proposed technique.

BRTCO: A Novel Boundary Recognition and Tracking Algorithm for Continuous Objects in Wireless Sensor Networks

Continuous objects monitoring and tracking, which aims to detect the invasion of unauthorized materials of interest, is one of the most prominent applications in wireless sensor networks. In this paper, we propose a novel boundary recognition and tracking algorithm for continuous objects (BRTCO) to ensure the efficiency of objects contour extraction. On precondition of assuring the tracking accuracy, a collaborative filtering scheme is proposed to minimize the number of boundary nodes. Also, in the phase of data transmission, we take the advantage of clustering to ensure energy efficiency. A report node selection mechanism is designed based on the competition of cluster heads. Simulation results demonstrate that BRTCO can significantly reduce the total energy consumption, the number of boundary nodes, and the number of report nodes.

Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm

Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a central location. In fact, the environmental conditions such as sound, humidity, temperature, wind, pollution levels, etc., can be clearly determined by WSNs. The principal objective of WSNs is to organize the whole sensor nodes in their related positions, thereby developing an effective network. In WSNs, target COVerage (TCOV) and Network CONnectivity (NCON) are the main concern of the sensor deployment problem. Many research works aspire the evolvement of smart context awareness algorithm for sensor deployment issues in WSN. Here the TCOV and NCON process are deployed as the minimization problem. This article makes an analysis of different GA variations in attaining the objective. The GA variations are as follows: self-adaptive genetic algorithm (SAGA), deterministic-adaptive genetic algorithm (DAGA), Individual- Adaptive Genetic Algorithm (IAGA). Finally, the methods are compared to one another in terms of connectivity and coverage performance.

An Efficient Proposed Approach for Tracing of Moving Object in Wireless Sensor Network

An Efficient Proposed Approach for Tracing of Moving Object in Wireless Sensor Network

Energy-Efficient Tracking and Localization of Objects in Wireless Sensor Networks

The energy-efficient tracking and precise localization of continuous objects have long been key issues in research on wireless sensor networks (WSNs). Among various techniques, significant results are reported from applying a clustering-based object tracking technique, which benefits the energy-efficient and stable network in large-scale WSNs. As of now, during the consideration of large-scale WSNs, a continuous object is tracked by using a static clustering-based approach. However, due to the restriction of global information sharing among static clusters, tracking at the boundary region is a challenging issue. This paper presents a complete tracking and localization algorithm in WSNs. Considering the limitation of static clusters, an energy-efficient incremental clustering algorithm followed by Gaussian adaptive resonance theory is proposed at the boundary region. The proposed research is allowed to learn, create, update, and retain clusters incrementally through online learning to adapt to incessant motion patterns. Finally, the Trilateration algorithm is applied for the precise localization of dynamic objects throughout the sensor network. The performance of the proposed system is evaluated through simulation results, demonstrating its energy-efficient tracking and stable network.

Pre-Vaccination and Quarantine Approach for Defense Against Worms Propagation of Malicious Objects in Wireless Sensor Networks

Network security poses a challenge to wireless sensor networks (WSNs) achieving its true potential. It is hard to tackle due to operational constraints of networks. Worms have become an emergent threat to the wireless networks. The spread of worms in the network is epidemic in nature. This article proposes a novel mathematical model with pre-vaccination and quarantine for study of worm propagation dynamics in WSN that is based on epidemic model. Further, the authors have devised an expression to determine threshold communication radius and node density. The objective of this proposed model is to study the propagation dynamics of worms in wireless sensor networks. Through the model, investigate the stability condition of networks in the presence of malicious codes. The experimental studies indicate that the proposed model outperforms in terms of security and energy efficiency over other existing models. It is a leap toward worm-controlling mechanisms in sensor networks. Finally, the control mechanism and performance of the proposed model is validated through extensive simulation results.

Boundary Region Detection for Continuous Objects in Wireless Sensor Networks

Industrial Internet of Things has been widely used to facilitate disaster monitoring applications, such as liquid leakage and toxic gas detection. Since disasters are usually harmful to the environment, detecting accurate boundary regions for continuous objects in an energy-efficient and timely fashion is a long-standing research challenge. This article proposes a novel mechanism for continuous object boundary region detection in a fog computing environment, where sensing holes may exist in the deployed network region. Leveraging sensory data that have been gathered, interpolation algorithms have been applied to estimate sensory data at certain geographical locations, in order to estimate a more accurate boundary line. To examine whether estimated sensory data reflect that fact, mobile sensors are adopted to traverse these locations for gathering their sensory data, and the boundary region is calibrated accordingly. Experimental evaluation shows that this technique can generate a precise object boundary region with certain time constraints, and the network lifetime can be prolonged significantly.

Adaptive Importance Sampling: The past, the present, and the future

A fundamental problem in signal processing is the estimation of unknown parameters or functions from noisy observations. Important examples include localization of objects in wireless sensor networks [1] and the Internet of Things [2]; multiple source reconstruction from electroencephalograms [3]; estimation of power spectral density for speech enhancement [4]; or inference in genomic signal processing [5]. Within the Bayesian signal processing framework, these problems are addressed by constructing posterior probability distributions of the unknowns. The posteriors combine optimally all of the information about the unknowns in the observations with the information that is present in their prior probability distributions. Given the posterior, one often wants to make inference about the unknowns, e.g., if we are estimating parameters, finding the values that maximize their posterior or the values that minimize some cost function given the uncertainty of the parameters. Unfortunately, obtaining closed-form solutions to these types of problems is infeasible in most practical applications, and therefore, developing approximate inference techniques is of utmost interest.

GLT: Grouping Based Location Tracking for Object Tracking Sensor Networks

The use of wireless sensor networks (WSN) in tracking applications is growing rapidly. In these applications, the nodes detect, monitor, and track a target, object, or event. In this paper, we consider the problem of tracking mobile objects in wireless sensor networks (WSN). We present a novel tracking model, named Grouping based Location Tracking (GLT), scaling well with the number of nodes and the number of mobile objects. GLT is based on the Grouping Hierarchy Structure, GHS. In GHS, nodes are partitioned into groups (not clusters) according to their maximum covered region (MCR) such that each group contains a number of nodes and a number of leaders. GLT consists of two tiers. The first tier, which is called the Notification Tree (NT), enhances the activation mechanism, the data cleaning mechanism, and the energy balancing mechanism. On the other hand, the second tier, which is called the Hierarchical Spanning Tree (HST), supports the data reporting mechanism and the lifetime prolonging mechanism. Simulations results show that GLT reduces the communication node selections overhead without diminishing object tracking accuracy and achieves a significant energy consumption reduction and network lifetime extension compared with the state-of-the-art approaches.

A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization

An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

무선 센서 네트워크에서 이동 객체 간 대칭적인 상호 통신

Wireless Sensor Networks (WSNs) are special network which has purpose of usage for gathering information of certain area and observing situation. WSNs consist of small nodes with sensing event such as temperature, movement or certain objects. The sensor has the capabilities to collect data and route data back to the sink. The sensors route data either to other sensors or back to a sink in one direction. That is, traditional WSNs communicate asymmetrically. However, under the new paradigm of the Internet of Things (IoTs) or Cyber Physical system (CPS), WSNs have potential to be used as important area. So, more research is necessary to communicate with each moving objects symmetrically in WSNs. In this paper, we proposed symmetric communication scheme among mobile objects in wireless sensor network. Simulation results show that our scheme is superior th the existing ones in terms of energy consumption and transmission success ratio.

TRACKING OF MOVING OBJECT IN WIRELESS SENSOR NETWORK

A Wireless Sensor Network is a collection of sensor nodes distributed into a network to monitor the environmental conditions and send the sensed data to the Base Station. Wireless Sensor Network is one of the rapidly developing area in which energy consumption is the most important aspect to be considered while tracking, monitoring, reporting and visualization of data. An Energy Efficient Prediction-based Clustering algorithm is proposed to track the moving object in wireless sensor network. This algorithm reduces the number of hops between transmitter and receiver nodes and also the number of transmitted packets. In this method, the sensor nodes are statically placed and clustered using LEACH-R algorithm. The Prediction based clustering algorithm is applied where few nodes are selected for tracking which uses the prediction mechanism to predict the next location of the moving object. The Current Location of the target is found using Trilateration algorithm. The Current Location or Predicted Location is sent to active Cluster Head from the leader node or the other node. Based on which node send the message to the Cluster Head, the Predicted or Current Location will be sent to the base station. In real time, the proposed work is applicable in traffic tracking and vehicle tracking. The experiment is carried out using Network Stimulator-2 environment. Simulation result shows that the proposed algorithm gives a better performance and reduces the energy consumption.