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International Journal of Sensor Networks

 

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International Journal of Sensor Networks (40 papers in press)

 

Regular Issues

 

  • Recent Advances in Wireless Sensor Networks with Environmental Energy Harvesting
    by Lei Shu, Wanjiun Liao, Jaime Lloret, Lei Wang 
    Keywords: .

  • A Novel Task Recommendation Model for Mobile Crowdsourcing Systems   Order a copy of this article
    by Yingjie Wang, Xiangrong Tong, Kai Wang, Baode Fan, Zaobo He, Guisheng Yin 
    Abstract: With the developments of sensors in mobile devices, mobile crowdsourcing systems are attracting more and more attention. How to recommend user-preferred and trustful tasks for users is an important issue to improve efficiency of mobile crowdsourcing systems. This paper proposes a novel task recommendation model for mobile crowdsourcing systems. Considering both user similarity and task similarity, the recommendation probabilities of tasks are derived.\r\nBased on dwell-time, the latent recommendation probability of tasks can be predicted. In addition, trust of tasks is obtained based on their reputations and participation frequencies. Finally, we perform comprehensive experiments towards the Amazon metadata and YOOCHOOSE data sets to verify the effectiveness of the proposed recommendation model.
    Keywords: Mobile crowdsourcing systems; Recommendation model; Similarity; Dwell-time; Trust.
    DOI: 10.1504/IJSNET.2017.10007414
     
  • Accuracy-aware Data Collection in Wireless Sensor Networks   Order a copy of this article
    by Ran Bi, Xu Zheng, Guozhen Tan 
    Abstract: Data collection is a fundamental task in Wireless Sensor Networks. In many practical applications, approximate results with error bound guarantee can satisfy user requirements. To approximate sensed data, the filter based approach is to maintain the filters of each node at both sensor node and base station. The filter of each node is represented by an interval. For a given filter, the sensor node sends the update to the base station if the sensed data is beyond the range of the filter. In this paper, we investigate the accuracy-aware approach for approximate data collection. The filter assignment for optimizing the average of valid filter time is formalized as an integer optimization problem and the hardness of this problem is proven to be NP-Complete. A greedy heuristic based algorithm with low computation overhead is proposed. Moreover, to balance the valid filter time, the filter assignment for optimizing the minimum valid time is formalized as a general max-min problem. We analyze the hardness of the problem and propose an approximation algorithm. The experimental results show that our algorithms achieve better results in terms of communication cost and expected time of valid filters.\r\n
    Keywords: Data Collection; Sensed Data Model; Approximate Algorithms; Wireless Sensor Networks.
    DOI: 10.1504/IJSNET.2017.10007416
     
  • APRS : Adaptive Real-Time Payload Data Reduction Scheme for IoT/ WSN Sensor board with Multivariate Sensors   Order a copy of this article
    by NAYEF ALDUAIS, Jiwa Abdullah, Ansar Jamil 
    Abstract: In the applications of the Internet of Things (IoT) based on wireless sensor network (WSN), sensor board depends on battery that having a limited lifetime to function. Multivariate sensing boards pose additional challenges over the battery life time by additional data transmissions, thus draining the battery. In this paper, a new simple mechanism called as Adaptive Real-Time Payload Data Reduction Scheme (APRS) for energy-efficiency purpose is proposed. APRS aims to reduce the transmitted packet size for each sensed payload, moreover it prevents any transmissions if no significant change is reported by the payload sensing block. In the experiment, the APRS used a single variable to represent the row of the measured data (n-variables). It was based on the current relative difference and compared to the last measured data that had been transmitted to the fusion center. The APRS was able to reconstruct the original real-time sensed data (n-variables) from the representing variable at the fusion center. The performance of the APRS was evaluated through simulation by utilising various real-time environmental datasets. In addition, the APRS was successfully implemented in Libelium-Waspmote Gas sensor board for real-time IoT application. In conclusion, the APRS has managed to show its simplicity and flexibility for the real-time IoT/WSN application when it is compared with the other algorithms and its reduction ratio during a transmission is within acceptable range of 81 94 %. The average of the total percentage of energy saved by applied APRS in all nodes is 95%. Overall, the APRS has high performance in the reduction ratio of data and efficiency in energy consumption when it is compared with other recent multivariate data reduction methods.
    Keywords: WSN; IoT; multivariate data; Accuracy; Energy Consumption; data reduction.

  • A Lightweight Multicast Approach based on Bloom Filters for Actuator-Sensor-Actuator Communication in WSANs   Order a copy of this article
    by Marcelo Guimarães, Valério Rosset 
    Abstract: Wireless Sensor and Actuator Networks (WSANs) are a subclass of Wireless Sensor Networks (WSNs) where the presence of actuator devices allows the interaction within a controlled environment. Some applications require WSANs to operate in large-scale remote environments with natural barriers (relief and vegetation) for the direct communication between the devices. Natural disaster monitoring and relief are examples of such applications. In this context, to ensure the communication between multiple actuators, through the network of sensors, is essential. In the literature one may find few approaches regarding this issue. However, most of them rely on a costly unicast routing approach. In line with this, we propose a novel routing protocol, named Multicast Border Oriented Forward Routing Protocol (M-BOFP), whose communication between actuators is performed by multicast implemented on top of the network of sensor nodes. The M-BOFP takes advantage of the Bloom filters, a probabilistic data structure with low memory cost, to perform the multicast communication requiring low and fixed message overhead. We also present a comparative analysis between protocols of the literature and the M-BOFP. Additionally, we carry out a performance evaluation of the proposed protocol considering different scenarios representing small, moderate and large-scale WSANs. To sum up, we show the primary results that support the improvements on data delivery rate and energy conservation achieved by the proposed M-BOFP.
    Keywords: WSAN; Routing; Delivery Efficiency; Multicast; Bloom Filters.

  • Optimal Time and Channel Assignment for Data Collection in Wireless Sensor Networks   Order a copy of this article
    by Yanhong Yang, Huan Yang, Liang Cheng, Xiaotong Zhang 
    Abstract: This paper studies the joint assignment of time slots and frequency channels in tree-based wireless sensor networks (WSNs) for data collection applications. In order to approximate the optimal solution, we propose a series of algorithms that exploit the network topology and maximize concurrent communications within each time slot through dynamic programming. Unlike peer approaches established upon idealized link-layer models, our algorithms are designed to be resilient to link errors and they are evaluated with the presence of unreliable links and in various deployment scenarios. Evaluation results show that our new algorithms outperform the state of the art in terms of data collection delay performance under unreliable conditions with moderate node deployment. Finally, the impacts of assorted implementation-oriented network parameters are investigated and summarized as design guidelines.
    Keywords: Data collection; TDMA; convergecast; wireless sensor network.

  • Energy Efficient Data Collection in Periodic Sensor Networks Using Spatio-Temporal Node Correlation   Order a copy of this article
    by Hassan Harb, Abdallah Makhoul, Ali Jaber, Samar Tawbi 
    Abstract: In wireless sensor networks (WSNs), the densely deployment and the dynamic phenomenon provide strong correlation between sensor nodes. This correlation is typically spatio-temporal. This paper proposes an efficient data collection technique, based on spatio-temporal correlation between sensor data, aiming to extend the network lifetime in periodic WSN applications. In the first step, our technique proposes a new model based on an adapted version of Euclidean distance which searches, in addition to the spatial correlation, the temporal correlation between neighboring nodes. Based on this correlation and in a second step, a subset of sensors are selected for collecting and transmitting data based on a sleep/active algorithm. Our proposed technique is validated via experiments on real sensor data readings. Compared to other existing techniques, the results show the effectiveness of our technique in terms of reducing energy consumption and extending network lifetime while maintaining the coverage of the monitored area.
    Keywords: Periodic Sensor Networks (PSNs); Spatio-Temporal Data Correlation; Sleep/Active Sensors; Real Data Readings.

  • Improving Smart Home Security; Integrating Behaviour Prediction into Smart Home   Order a copy of this article
    by Arun Cyril Jose, Reza Malekian 
    Abstract: The paper highlights various security issues in existing smart home technology and its inhabitant behaviour prediction techniques and proposes a novel behaviour prediction algorithm to improve home security. The algorithm proposed in this work identifies legitimate user behaviour and distinguishes it from attack behaviour. The work also identifies the parameters necessary to predict user behaviour during the seven week learning period. The paper identified three factors namely time parameter, light parameter, users key placement behaviour to successfully predict user behaviour. The algorithm learned normal and suspicious user behaviours during the seven week training period and na
    Keywords: Home automation; Smart homes; Wireless sensor networks; Access control; ZigBee.

  • Node Placement Approaches for Pipelines Monitoring: Simulation and Experimental Analysis   Order a copy of this article
    by Abdullatif Albaseer, Uthman Baroudi 
    Abstract: Monitoring oil, gas and water pipeline networks is a critical problem; its impact has serious consequences on the ecosystem. This problem has attracted, and for long time, the attention of industry and academia. One of the challenges in the pipeline monitoring process is how to optimally place the sensors that monitor the pipeline, detect and report any anomaly. Naturally, the deployed sensors are placed in a linear topology. This linear topology requires careful attention in placing sensors to ensure robustness against anomaly, minimize the energy consumption and maximize the network lifetime. In this work, we have studied two existing greedy node placement approaches via simulation and experimental analysis. First, we have validated experimentally the 31 power levels of CC2420 TelosB chipon and their corresponding transmission ranges. Having more power-level resolution provides more flexible power assignment, which yields less energy consumption and longer lifetime compared to traditional 8 power levels. Second, Extensive simulation and real experiments have been conducted. The results demonstrate a considerable drop in power consumption which can reach 73% and 23% extension in the network lifetime when all 31 power levels are adopted.
    Keywords: Leak detection; Wireless Sensor Network; Pipeline Monitoring; Equal-Power Placement; TelosB; Linear Node placement; Reliability; On-line monitoring.

  • A Profile Based Data Segmentation for In-Home Activity Recognition   Order a copy of this article
    by Mohammed AL Zamil, Rania AL Nadi 
    Abstract: A major problem in smart-home activity recognition is the ambiguity of interpreting the actions that formulate activities in smart home environments. Such ambiguity resulted from the redundancy of irrelevant actions and the concurrent interleaving among activities themselves. In this paper, we present a framework to minimize the effect of such ambiguity using profile based data segmentation and actions refinement. The proposed methodology relies on defining a profile for each sensor in the environment for the purpose of enriching existing features with semantic ones. Furthermore, according to these profiles, irrelevant actions within data segments are removed. Moreover, the proposed methodology addresses the connectivity among actions and their designated activities for the purpose of resolving interleaving among them. Experiments have been conducted to measure the performance of the proposed framework on a well-known datasets in this domain. We evaluated our methodology using three different classifiers: J48 (decision tree), Na
    Keywords: Internet of Things (IoT); Smart Home; Activity Recognition; Data Segmentation; Data Mining; Sensor Profile.

  • Entropy Correlation based Clustering Method for Representative Data Aggregation in Wireless Sensor Networks   Order a copy of this article
    by Nga Nguyen Thi Thanh, Khanh Nguyen Kim, Son Hong Ngo 
    Abstract: One of the popular data aggregation method in wireless sensor network (WSN) is collecting only local representative data based on correlation of sample data. To recognize the local representative nodes, it is necessary to determine the correlation regions. However, recent correlation models are distance based that is not general and need to be determined beforehand or complicated with high computing cost. Thus, in this paper, a novel entropy correlation model is proposed based on joint entropy approximation. Using the proposed model, an entropy correlation-based clustering method is presented and the selection of representative data that satisfying the desired distortion is proposed. The algorithm is validated with practical data.
    Keywords: WSN; correlation; entropy; clustering; representative nodes.

  • Distributed Recursive Least-Squares Fusion Method for Gas Leakage Source Localization   Order a copy of this article
    by Zhang Yong, Zhang Liyi, Han Jianfeng, Yang Yi, Ban Zhe 
    Abstract: Gas leakage source localization has received considerable attention in the field of environmental monitoring and protection. In this study, an adaptive distributed recursive least-squares fusion method is presented with sensor networks, in which the estimator of gas source parameters and the corresponding error are updated with observations and results from the neighboring nodes. The method could be implemented with two sensor node scheduling schemes: the global and local methods. This study aimed to propose an information fusion objective function for optimizing estimation accuracy and energy consumption to balance the performance and resource utilization of sensor nodes. The performance of the two different methods was analyzed. Compared with the global method, the local method was found to achieve the desired performance with a significant reduction of the required sensor nodes, along with a decrease in congestion, energy consumption, and time latency in communication.
    Keywords: Parameter estimation; sensor scheduling; source localization.

  • An Improved MDS Localization Algorithm for a WSN in a Sub-Surface Mine   Order a copy of this article
    by Heng Xu, Qiyue Li, Jianping Wang, Keqiong Chen, Wei Sun 
    Abstract: Wireless sensor networks (WSNs) have been successfully applied in a wide range of application domains. However, because of the properties of wireless signals, WSN applications in underground environments have been limited. In this paper, we present a Kalman-filter-based localization algorithm for use in a WSN deployed in a sub-surface mine for environmental monitoring to identify the positions of a large number of miners, each carrying a wireless mobile node. To improve the positioning accuracy even when current data are not available, we enhance the estimates of the Received Signal Strength Indication signal intensity and range obtained from the Kalman filter by adjusting them using the elastic particle model. Then, we obtain the distance matrix of the WSN based on AoA and the cosine theorem. Finally, we determine the final positions of all mobile nodes using a multidimensional scaling (MDS) algorithm.
    Keywords: Wireless Sensor Networks; Mobile Nodes; Localization; Sub-surface Mine; Kalman Filter; Elastic Particle Model; Angle of Arrival; Multidimensional Scaling Algorithm; No Line of Sight; Clustering.

  • Sensor Management Based on Collaborative Information Fusion Algorithm for Gas Source Localization   Order a copy of this article
    by Zhang Yong, Zhang Liyi, Han Jianfeng, Yang Yi, Ma Xinyuan 
    Abstract: Gas source localization based on sensor networks is of great importance in many fields such as environmental monitoring, security protection and pollution control. Considering the sensor node scheduling and path planning problem in the gas source localization process, an improved collaborative sensor management method based on genetic and ant colony fusion algorithm was proposed in this paper. Simulation results show that, under different certain environmental assumptions, the mobile sensor nodes could achieve accurate gas source prediction positioning based on the proposed fusion method and it is superior to the genetic algorithm and ant colony algorithm with higher localization accuracy and faster convergence speed.
    Keywords: mobile sensor networks; sensor management; gas source localization.

  • An IoT Prototype System for Environmental Sustainability   Order a copy of this article
    by Cynthia Fu, David Cruz, Bo Sun, Guang Sun 
    Abstract: Applications based on Internet of Things (IoT) have opened great potential opportunities to provide seamless and pervasive data collection capabilities for many research disciplines. Unfortunately, the ability of IoT to enable relevant research disciplines is still hampered by the poor integration of IoT into various scientific and engineering fields. In this paper, we present an Intel Edison and Raspberry Pi 3 based proof-of-concept IoT prototype to collect environmental data in a low-cost manner, to advance understanding of water quantity and quality research. By utilizing the Intel Edison board and Raspberry Pi 3 as the central computing platform, we create an IoT prototype to remotely monitor important environment variables, including air temperature, light intensity, and water temperature, pH, and conductivity. Specifically, for Intel Edison, by leveraging existing off-the-shelf sensor probes (Water Temperature Sensor Probe H377, low-voltage precision centigrade sensor probe TMP36, and light sensor SEN-9088) and open source embedded Linux system Yocto, we have built a prototype IoT to collect water temperature, air temperature, and ambient light. For Raspberry Pi 3, we leverage EZO class pH and conductivity sensor probes from AtlasScientific and Raspbian Jessie, to collect water pH and conductivity data. The collected readings are then calibrated and transmitted to a public channel from www.ThingSpeak.com, a cloud-based data storage platform, which will facilitate data sharing with researchers. Our IoT prototype provides a web service to facilitate online data access and data visualization. Our presented architecture is general. Therefore, other sensor probes could be integrated to collect more types of data for different research purposes. We illustrate our detailed design for the prototype, present preliminary data results, and further point out important future work to extend our proposed IoT full-fledged.
    Keywords: Internet of things; sensor networks; security; prototype; IoT.

  • SmartData: an IoT-Ready API for Sensor Networks   Order a copy of this article
    by Antonio Frohlich 
    Abstract: Despite intense research on Wireless Sensor Networks (WSN) in the last two decades, programmers still do not have a cohesive, highly expressive API to model their sensing applications and to connect them to the Internet of Things (IoT). In this paper, we introduced emph{SmartData}, a high-level API for WSN aiming at leveraging the myriad of features available on such networks while delivering a common abstraction for sensed data that facilitates application development without incurring any significant overhead. SmartData are enriched with enough metadata to become self-contained in terms of semantics, spatial location, timing, and trustfulness. It is meant to be the only application-visible construct in the sensing platform and therefore implicitly mediates all system-level services, including communication, synchronization, and the interaction with transducers and actuators. Reading a SmartData implies in sensing, while writing to it defines a new setpoint for actuators. Both, local and remote sensors utilize the same interface. We demonstrate the concept through the automation of a solar building, using an embedded OS and the Trustful Space-Time Protocol to implement a set of SmartData in C++.
    Keywords: Wireless Sensor Network; Internet of Things; Sensing API; Sensing Data Management.

  • Self-Stopping Strategies for Tractable Information Dissemination in Dense Mobile Sensor Networks   Order a copy of this article
    by Chao Chen, Zesheng Chen 
    Abstract: Epidemic routing is a simple forwarding mechanism and considered useful for wireless mobile sensor networks where infrastructure support is limited and sensed information has to be disseminated in a timely manner. The relaying overhead of epidemic routing, however, needs to be reduced to conserve energy. In this paper, we study a new problem in epidemic routing in wireless mobile sensor networks: what is a good strategy to timely stop message forwarding when a certain percentage of nodes have received a copy? The goal of such a strategy is to disseminate the information to a certain percentage of nodes in the network in a timely and predictable manner, and at the same time to suppress further spreading when the goal has been reached. As a first attempt, we focus on dense sensor networks where nodes move rapidly and randomly around and a synchronous time model is applied. We first select an accurate mathematical model to characterize the information dissemination in wireless mobile sensor networks. We then apply and extend the model to analyze and design different distributed self-stopping strategies. The probability-based self-stopping strategy adjusts the stopping probability to subdue the message forwarding when a sensor node meets a neighbor that is already informed. Such a strategy can reach the percentage goal accurately, but it does not stop timely and cannot control the information dissemination to a smaller scale than 82% of the network size. Using the message life time as a basic guideline, we propose two new self-stopping strategies by either setting a hop count limit or adopting a final forwarding probability. Such self-stopping strategies not only stop fast and save energy, but also are able to control the scope of message spreading to an arbitrary preset goal. We further test the self-stopping epidemic routing strategies in an emulated network with random waypoint mobility. We then present guidelines of selecting self-stopping strategies for tractable information dissemination based on different application requirements.
    Keywords: Epidemic routing; self-stopping strategies; mobile sensor networks.

  • ICRA: Index based Cache Replacement Algorithm for Cloud Storage   Order a copy of this article
    by Yuwei Zhao, Tinghuai Ma 
    Abstract: Meteorological data set is usually big and has a clear eld or some otherrncharacteristics. Moreover, it is mainly in the form of text. Each read and display will take up more system resources. In addition, the loading speed of the web page also affects the user's online experience to some extent. Based on these problems, this paper designs a storage mechanism for the original document and page data content using index and caching index. We proposed a cache replacement algorithm combined with indexing algorithm called Index Cache Replacement Algorithm. The main innovation of this paper is that we create index while reading the document and the page data at first time and analyze, sort, and cache the indexing data at the same time. When users browse the samerndocument or the page, we can directly index file to query and return the corresponding data to reduce the document reading time and page loading time. Thus, it enhances the effectiveness of cached data, increases the eciency of the cache query and improves the query file and byte hit rate which finally improving the performance of the network.
    Keywords: Meteorological data; Documents; Pages; Cache replacement algorithms;rnIndexes; Queries.

  • An Extensible Framework for ECG Anomaly Detection in Wireless Body Sensor Monitoring Systems   Order a copy of this article
    by Le Sun, Jinyuan He 
    Abstract: The wireless body sensor monitoring system is becoming more and more important in both academic and industry area. Wearing a number of body sensors enables a real time health monitoring, especially for the heart disease monitoring and detection. Automatic anomaly detection from the sensor streams of the Electrocardiography requires online techniques of data stream processing and analysis. The significant increase in the availability of the Electrocardiography collected from wireless sensor networks has attracted heaps of research interests and attempts in identifying the anomalies in an Electrocardiography. However, most existing models focus on a particular type of anomaly, like arrhythmia, and they are not dynamically extensible for the identification of an unknown type of anomaly. This work proposes an extensible method named shapelet-base (SH-BASE) to solve this problem. Essentially, SH-BASE is a knowledge-base of shapelets, in which the shapelets are classified into different groups (we call them branches), and each branch stores the most distinctive shapelets of a particular type of anomaly. Experimental results show that the SH-BASE can achieve a very competitive performance in anomaly detection for Electrocardiography sensor streams by comparing with the state-of-the-art models.
    Keywords: ECG; data streams; sensor networks; pseudo-periodic time-series; anomaly detection.

  • Urbihoc: A Delay Tolerant Approach for Data Acquisition in Urban Areas using a Mobile Wireless Sensor Network   Order a copy of this article
    by Martha Montes De Oca, Javier Gomez, Miguel Lopez Guerrero 
    Abstract: In recent years several pieces of research have proposed the use of wireless mobile nodes to sense a wide diversity of phenomena in urban areas. Data collected by mobile sensors are typically sent to a central server in order to be shared with other users through cellular or WiFi networks. Unfortunately, the cost of deploying and maintaining such infrastructure may be prohibitively high. Furthermore, a disaster situation on the server side may cause the failure of the whole system. As an alternative approach, in this work we introduce Urbihoc, a data acquisition method that uses opportunistic trans- missions to directly share data among mobile nodes. In this way, from local exchanges, Urbihoc builds up global knowledge about a monitored phenomenon. Our results sug- gest that, in some applications, it is possible to monitor a phenomenon over a large metropolitan area by using only the resources contributed by mobile users.
    Keywords: Wireless sensors networks; mobile crowdsensing; energy saving; urban sensing; delay tolerant approach.

  • WSN-aided Haze Pollution Governance: Modeling Public Willingness Based on Structural Equations   Order a copy of this article
    by Jibo Chen, Yingxi Song, Guizhi Wang, Qi Liu 
    Abstract: In recent decades, Wireless Sensor Networks have been well investigated and developed, and been widely used in environmental surveillance, climate data collection, meteorological prediction, etc. However, research activities on statistical analysis and decision making aided by Wireless Sensor Networks, especially in the area of environmental hazards and risk governance, are rarely presented. In this paper, common factors are extracted from exploratory factor analysis based on closed questionnaire data in this study. The optimal model of the haze governance willingness among the common factors is designed according to the Structural Equation Modeling, and then the relationships between in the influencing factors of public haze governance willingness are analyzed. The results show that haze governance and willingness will be affected by haze risk perception, perception quality, perception measures and economic expenditure factors, and the most important influential factors are the degree of physical damage, and the willing to take measures to prevent haze and improve rescuing operations. The impact of haze on life, the inconvenience of haze to trip, the maximum duration of haze, and the times suffering from haze are the most basic factors affecting the haze governance willingness. In order to achieve the governance of haze, the public desires the government to take firm measures to govern the haze (e.g. via a Wireless Sensor Network-based measurement network), with the collaboration from the public governance.
    Keywords: Haze Pollution; Wireless Sensor Networks-aided Measurement; Structural Equation Modeling; Haze Governance Willingness; Policy Suggestions.

  • A Cooperative Spectrum Sensing Method Based on Signal Decomposition and K-medoids Algorithm   Order a copy of this article
    by Yonghua Wang, Shunchao Zhang, Yongwei Zhang, Pin Wan, Shikun Wang 
    Abstract: In order to solve the problem of low sensing performance and low accuracy of threshold estimation in traditional spectrum sensing systems with low signal-to-noise ratio, we proposes a cooperative spectrum sensing method based on empirical mode decomposition and K-medoids clustering algorithm. At the same time, in order to improve the effectiveness of K-medoids clustering algorithm and improve the sensing performance of the system in the case of fewer collaborative secondary users, a feature extraction method based on empirical mode decomposition algorithm and matrix decomposition and recombination is proposed. The method can accurately acquire the characteristic information of the sampled signal and improve the feature accuracy. The method firstly uses the empirical mode decomposition algorithm to process the spectrum signal, reduce the noise components in the signal, and then extract the signal features using a matrix split and recombination method. Finally, the features are classified using the K-medoids clustering algorithm. In the experimental part, we verify the performance of the method under different signal-to-noise ratio. The experimental result shows that the method can effectively improve the sensing performance of the spectrum sensing system at low signal-to-noise ratio.
    Keywords: Spectrum sensing; Decomposition and recombination; K-medoids clustering algorithm; Feature extraction.

  • Spiderweb Strategy: Application for Area Coverage with Mobile Sensor Nodes in 3D Wireless Sensor Network   Order a copy of this article
    by BOUALEM Adda, DAHMANI Youcef, D.E. RUNZ Cyril, AYAIDA Marwane 
    Abstract: The problem of area coverage in 3D wireless sensor networks is a NP-Hard problem; the approaches used to optimize this problem are not effective because of the difficulty of ensuring 3D connectivity, communication,rnand monitoring. The fundamental problem of all these constraints is the difficulty of deploying the sensor nodes with fair densities on the 3D areas, in order to guarantee connectivity and coverage over all the network. In this paper, well take advantage of spider canvas. Indeed, spider web are made to catch insects, have fortes of strength and resilience remarkable. The mobility of the sensor nodes according to Archimedes spiral function facilitates the proper node positioning.rnPositioning the mobile sensor nodes by miming a spider canvas in 3D ensuresrnthe equitable distribution of nodes in the area of interest, as well; the ability to vary communication and surveillance radii makes ensuring connectivity and the entire coverage. We have make some simulations to assess performance of our algorithms. Our simulation show that the spider canvas strategy outperforms the area coverage in 3D scheme in both effectiveness and efficiency strategy.
    Keywords: Area Coverage; 3D WSN; Spiderweb Strategy; Efficient-Energy; Spiral Archimedes.

  • Average Distance Estimation in Randomly Deployed Wireless Sensor Networks (WSNs): An Analytical Study   Order a copy of this article
    by Cüneyt Sevgi 
    Abstract: A Wireless Sensor Network (WSN) is an energy-scarce network in which the energy is primarily dissipated by the sensor nodes during data transmission to the base station (BS). The location of the BS in a WSN dramatically affects the energy dissipation, the throughput, and the lifetime. While in a number of studies the optimal positioning of a BS is considered, the system parameters are optimized when the BS location is known in advance in many others. In this paper, we provide a general-purpose mathematical framework to find the expected distance value between every point within any n-sided simple polygon shaped sensing field and an arbitrarily located BS. Having the knowledge of this value is very imperative particularly in random deployment as it is used for energy-efficient clustering. Although similar derivations appear in the related literature, to the best of our knowledge, this study departs from them, since our derivations do not depend on the shape of the sensing field and the orientation of BS relative to it.
    Keywords: WSNs; wireless sensor networks; average distance; estimation; random deployment; base station.

  • A New Path Planning Strategy of a Data Collection Problem Utilizing Multi-mobile Nodes in Wireless Sensor Networks   Order a copy of this article
    by Ye Miao, Chou Hongbing, Wang Mei, Wang Yong, Feng Hao 
    Abstract: The prevalent research on the path planning problem in mobile node-based data collection techniques only considers the simple situation involving a single mobile node or path endpoint located at the centre of the communication. This paper considers additional situations involving both of the above two factors and abstracts from these scenarios to formulate a hybrid optimization problem. This optimization problem has the characteristics of high dimensionality and a large search space. To solve this problem, the following modifications were made. First, k sub-paths based on the k-SPLITOUR algorithm were obtained. Second, a method to eliminate path intersections was designed to optimize the discrete components. Finally, a hybrid glowworm swarm optimization algorithm (HGSO) was proposed to optimize the positions of access points along the communication circle to optimize the continuous components. The global convergence analysis of the proposed HGSO algorithm is given. Simulations and comparisons with other algorithms verified that the proposed strategy can solve the path planning problem in data collection utilizing multi-mobile nodes effectively.
    Keywords: Wireless Sensor Networks; Multi-mobile Nodes; Travelling Salesman Problem with Neighbourhoods (TSPN); Hybrid Glowworm Swarm Optimization (HGSO) Algorithm.

  • Spatial-Temporal Variability of PM2.5 Concentration in Xuzhou based on Satellite Remote Sensing and Meteorological data   Order a copy of this article
    by Xi Kan, Linglong Zhu, Yonghong Zhang, Yuan Yuan 
    Abstract: Accurate estimation of the spatiotemporally continuous distribution of PM2.5 concentration is of great significance for the research on atmospheric pollution. Previous studies have used satellite-derived AOD and meteorological parameters to estimate the PM2.5 concentration successfully. However, the effect of aerosol characteristics such as aerosol types was seldom considered in PM2.5 estimation model. In this manuscript, authors applied an aerosol classification-based method to generate ground-level PM2.5 concentration datasets in Xuzhou from 2014 to 2017. The coefficient of determination (R2) of aerosol classification-based model increases from 0.57 to 0.61 verified by ground station measurements, comparing to the empirical model. The results of spatiotemporal analysis show that the PM2.5 concentration has a slowly decreased trend in last three years, despite has an extreme high value in the winter of 2016 due to the heavy haze pollution occurred in Xuzhou. The diurnal variations of PM2.5 concentration displays a typically single peak mode, with the highest peak observed in the morning (7:00-11:00) and the lowest occurring at dusk (15:00-18:00). With regard to the spatial distribution of estimated PM2.5 over Xuzhou, there is a high-PM2.5 area anchoring over the urban district, while low concentration occurs in county town. Thus, the PM2.5 concentration has the high correlation with weather change and anthropogenic activities.
    Keywords: PM2.5; AOD; Satellite remote sensing; Aerosol classification; Spatial-Temporal Variation.

  • A Portable Roadside Vehicle Detection System Based on Multi-sensing Fusion   Order a copy of this article
    by Jianying Zheng, Bin Xu, Xiang Wang, Xueliang Fan, Hao Xu, Guang Sun 
    Abstract: In order to solve traffic congestion problem and improve traffic efficiency, the intelligent transportation system is widely used in urban roads. The sensing system is preferred to be deployed at roadside to detect vehicles in the adjacent lane. The advantage is that the normal traffic wouldnt be interfered. In the field of the magnetic vehicle detection, one of the most challenging problems is that the disturbance from the other lane. Most existing magnetic detection systems need more than one magnetic sensor to solve the disturbance. However, these magnetic sensors need well designed and installed or these systems wouldnt work well. In this paper, we propose a portable roadside vehicle detection system based on multi-sensing fusion. Cooperated with the magnetic sensor and the ultrasonic sensor, the disturbance from non-detected lane can be excluded and the traffic volume of the detected lane can be calculated accurately. Through on-road experiments, the vehicle detection accuracy can achieve 97.14% in a two-lane road. The experimental results demonstrate that the proposed system in this paper can achieve high accuracy and the sensing system is able to be deployed at roadside in multilane roads to get traffic flow reliably.
    Keywords: Vehicle detection; magnetic sensor; ultrasonic sensor; multi-sensing fusion.

  • Energy Efficient Virtual Network Embedding for Wireless Multi-hop Cellular Networks Using Multi-commodity Flow Algorithm   Order a copy of this article
    by Yifei Wei, Li Li, Zihan Jia, Xiaojun Wang 
    Abstract: With the rapid development of wireless mobile communication technologies, the trend of generalization and centralization requires variable communication devices and flexible network structures. To improve the transmission quality and extend the cell coverage, multi-hop relay network, as a wireless access network, has become an important part of communication system framework. Furthermore, to support seamless communication of multiple devices and services in heterogeneous wireless networks with high resources utilization, network virtualization has been proposed to offer a flexible and scalable management. In this paper, we study the wireless multi-hop cellular network and the problem of virtual network embedding. We first analyze the wireless multi-hop cellular network scenario and establish a virtual network embedding model. After that we propose a minimum cost flow algorithm based on the multi-commodity flow algorithm. Thus the problem is transformed into a multi-commodity flow problem. We finally put forward an optimization algorithm of Lagrange relaxation and sub-gradient algorithm to solve the problem. The simulation results show that the proposed multi-commodity flow algorithm can make full use of network resources, and improve the acceptance rate of virtual network requests, so as to improve the quality of service.
    Keywords: Virtual network embedding; Multi-commodity flow algorithm; Multi-hop relay; Lagrangian relaxation.

  • DAO-R: Integrating Data Aggregation and Offloading in Sensor Networks Via Data Replication   Order a copy of this article
    by Basil Alhakami, Bin Tang, Jianchao Han, Mohsen Beheshti 
    Abstract: When sensor network applications are deployed in an inaccessible or inhospitable region, or under extreme weather, it is usually not viable to install a long-term base station in the field to collect data. The generated sensory data is therefore stored inside the network first, waiting to be uploaded. However, when more data is generated than available storage spaces in the entire network can possibly store, and uploading opportunities have not arrived, data loss becomes inevitable. We refer to this problem as {em overall storage overflow} in sensor networks. To overcome overall storage overflow, existing research designs a two-stage approach as below. First, by taking advantages of spatial correlation that commonly exists among sensory data, it aggregates overflow data to the size that can be accommodated by the available storage capacity in the network. Then, it offloads the aggregated data into the network to be stored. We refer to this naive two-stage approach as DAO-N. DAO-N is NP-hard. In this paper, we demonstrate that this approach does not necessarily achieve good performance in terms of energy consumption. We propose a more unified framework that is based upon data replication techniques in order to solve overall storage overflow and improve the performance of DAO-N. We refer to our approach as DAO-R. Specifically, we design two energy-efficient data replication algorithms to integrate data aggregation and data offloading seamlessly. We also give a sufficient condition under which DAO-R can be solved optimally. Via extensive simulations, we show that DAO-R outperforms DAO-N by around $30%$ in terms of energy consumption under different network parameters.
    Keywords: Data Aggregation; Data Offloading; Overall Storage Overflow; Sensor Networks; Algorithms; Energy-Efficiency.

  • A theoretical investigation on moving average filtering solution for fixed-path map matching of noisy position data   Order a copy of this article
    by Baris Baykant Alagoz 
    Abstract: Precisely estimation of moving object locations from position sensors promises useful implications for many fields of engineering. The mapping of a moving object on a predefined path is an important process for object tracking and remote control applications. Due to measurement noises of sensors and uncertainties, the measured object location may not precisely match to paths or roads in a map. This study presents a numerical method for a low computational-complexity solution of point to arc type mapping problems. This method has two main tasks: a noise reduction task by short-time moving average filtering of noisy two-dimensional position data, and a map matching task to estimate exact position of an object on a map. To evaluate performance of the investigated method, the algorithm is applied for bus route tracking simulations and results are discussed for several road scenarios and various levels of noise.
    Keywords: Map matching; noisy position data; short-time moving average filtering; point to arc mapping; object tracking.

  • Development of a Wireless Intravenous Drip Rate Monitoring Device   Order a copy of this article
    by Pratyush Kumar Patnaik, Suraj Kumar Nayak, Anilesh Dey, Sirsendu Sekhar Ray, Kunal Pal 
    Abstract: Biotelemetry increases the efficiency of the healthcare professionals. This study proposes the development of a wireless communication-enabled low-cost, automated drip rate monitoring system, used for intravenous (IV) therapy. The proposed system monitors the falling fluid drops inside the drip chamber, and subsequently calculates and displays the drip rate on a Liquid crystal display (LCD) panel. It is capable of wirelessly notifying the central monitoring station when the drip rate is outside the set range. The system was designed using a light emitting diode (LED), a light dependent resistor (LDR), a microcontroller and a Xbee wireless module. The proposed device is simple, user-friendly and can reduce the workload of the healthcare givers by reducing their regular visit to the patient site.
    Keywords: Drip Rate; Intravenous Infusion; LED; LDR; Microcontroller; Arduino Uno; Zigbee; Wireless Network; Mesh Topology; Graphical User Interface.

  • A New Optimization Model and Algorithm for Virtual Optical Networks   Order a copy of this article
    by Shanshan Hao, Yuping Wang, Beicong Lv 
    Abstract: Virtual Optical Network is considered to be the next generation internet architecture and is of big value. Currently, it has many issues to be solved, e.g., node mapping, link mapping and spectrum assignment. More and more challenges will be faced when there are privileged nodes through which certain connection requests can not pass in the physical topology. To solve the above issues, this paper put forward a new optimization model in virtual optical network considering privileged nodes, and aims to minimize the maximum index of used frequency slots. A routing algorithm is designed for the model, an adaptive strategy is put forward to determine the crossover rate and mutation rate, whats more, a new crossover and mutation operator are proposed. Based on these, a novel genetic algorithm is proposed to solve the problem. To evaluate the efficiency of the model and the algorithm, numerous experiments are conducted on four widely used network topologies. Simulation results show the feasibility of the model and the effectiveness of the algorithm.
    Keywords: Network Scheduling; Optimization Model; Virtual Optical Network; Genetic Algorithm.

  • A Mixed-Coding Adaptive Differential Evolution for Optimizing the Architecture and Parameters of Feedforward Neural Networks   Order a copy of this article
    by Li Zhang, Hong Li 
    Abstract: This paper presents an adaptive differential evolution with mixed-coding strategy to evolve feedforward neural networks (FNNs). This algorithm with adaptive control parameters which can handle effectively binary variables and real variables, is used to optimize simultaneously FNN architecture and connection parameters (weights and biases) by a specific individual representation and evolutionary scheme. The performance of the algorithm has been evaluated on several benchmarks. The results demonstrate that the proposed algorithm can produce compact FNNs with good generalization ability.
    Keywords: Feedforward Neural Network; Evolutionary Neural Network; Differential Evolution; Generalization Ability.

  • Maximization of the number of $beta$-view covered targets in visual sensor networks   Order a copy of this article
    by Ling Guo, Deying Li, Yongcai Wang, Zhao Zhang, Guangmo Tong, Weili Wu, Dingzhu Du 
    Abstract: In some applications using visual sensor networks, the facing directions of targets are bounded. Existing full-view coverage (all the facing directions of a target constitutes a disk) is not necessary. Therefore, a novel model called $beta$-view coverage, where only necessary facing directions of a target are effectively viewed, is proposed but uses much fewer cameras than those used by full-view coverage model.rnrnBased on this new model, a new problem called $beta$-view covered Target Maximization (BVCTM) problem is proposed to maximize the number of $beta$-view covered targets given some fixed and freely rotatable camera sensors. The NP-hardness of this problem is proven. We transform BVCTM problem into an Integer Linear Programming problem equivalently. Thereafter, a $displaystyle{(1-e^{-1})}$-factor approximate algorithm and a camera-utility based greedy algorithm are given to it. Finally, we conduct many experiments and investigate the influence of many parameters on these two algorithms.
    Keywords: Visual Sensor Networks; Target Coverage; $beta$ View Coverage.

  • Maximising influence in sensed heterogeneous social network with privacy preservation   Order a copy of this article
    by Meng Han, Qilong Han, Lijie Li, Ji Li, Yingshu Li 
    Abstract: Maximising influence to improve marketing performance has a significant impact on targeted advertisements and viral product promotion, which has become a fundamental problem in social data analysis. Most existing works neglect the fact that location data could also play an important role in the influence prorogation. This paper considers maximising influence towards both sensed location data and online social data with privacy concern. We merge location data from cyber-physical networks and relationship data from online social networks into a unified, then propose an efficient algorithm to solve the influence maximisation problem. Furthermore, our privacy-preserving mechanism could protect the sensitive location and link information during the whole process of data analysis. Real-life datasets are empirically tested with our framework and demonstrate the power of sensed and online data combination to influence maximisation. The experiment results suggest that our framework is outperforming most existing alternative resolutions and succeeds in preserving privacy.
    Keywords: sensed data; location; social; data privacy; influence maximisation.
    DOI: 10.1504/IJSNET.2017.10007412
     
  • A novel meteorological sensor data acquisition approach based on unmanned aerial vehicle   Order a copy of this article
    by Chuanlong Li, Xingming Sun 
    Abstract: Meteorological sensor data acquisition is critical for various applications and researches. Currently, meteorological sensor data monitoring and acquisition mainly relies on the automatic weather station (AWS), wireless sensor network, satellites, and airborne remote sensing, etc. However, these conventional methods have some insuperable deficiencies. Recent advances in the unmanned aerial vehicle (UAV) for scientific use make drones easily overcome some of the paucity generated by these means. UAV as a sensor bearing platform can be used to collect sensor data in a more responsive, timely, three dimensional, and cost-effective manners. By implementing sensor network alike methods, drones can simulate the mobile ad-hoc networks in a mission and reduce the communication energy cost between each sensor node. This paper first demonstrates the feasibility of this approach, then proposes a sensor bearing framework aiming to bridge UAV remote sensing technique and customised meteorological sensor. A system prototype is developed and some real-world field tests indicate the application of the proposed framework is practical.
    Keywords: UAV; meteorological sensor; UAS; unmanned aerial systems; sensor data acquisition; remote sensing.
    DOI: 10.1504/IJSNET.2017.10013468
     
  • RECrowd: a reliable participant selection framework with truthful willingness in mobile crowdsensing   Order a copy of this article
    by Xiaohui Wei, Yao Tang, Xingwang Wang, Yuanyuan Liu, Bingyi Sun 
    Abstract: In crowdsensing systems, participant selection as one of main problems attracts a lot of attention. Most studies focus on how to select reliable participants for task allocation to assure task quality and minimise incentive cost. Conventional methods are mainly based on historical reputation, which is from the statistical result of participant behaviours during a past period. However, these methods could cause unreliable assignment that reduces the task quality, since historical reputation cannot exactly reflect the current state of participants. In this paper, we advocate RECrowd, a reliable participant selection framework that considers both historical records and current truthful willingness. In RECrowd, we formulate an optimisation problem with the objective of minimising incentive cost while ensuring task quality, and design a two-stage online greedy algorithm with the pre-assignment step. During the participant selection, we also consider participants' position privacy. Experiment results with real datasets demonstrate our algorithm outperforms other methods.
    Keywords: mobile crowdsensing; participant selection; truthful willingness; incentive cost; task quality; privacy.
    DOI: 10.1504/IJSNET.2018.10016113
     
  • Three dimensional power efficient distributed node localisation in wireless sensor networks   Order a copy of this article
    by Reza Shahbazian, Seyed Ali Ghorashi 
    Abstract: Wireless sensor networks (WSNs) are usually used in applications in which information is only applicable when the location of informative node is known. Although the high accuracy is the main advantage of centralised algorithms, the distributed ones are more efficient in power consumption, robustness and reliability. In this paper, we first propose a three dimensional (3D) distributed node localisation algorithm for WSNs in which sensor nodes cooperate to estimate the unknown location in a distribute manner. Then, we propose a node selection algorithm extending the life time of the network. We further propose a weighting function to improve the performance of proposed algorithm. We further analyse the proposed method and prove its convergence. Simulation results confirm that the proposed algorithms are applicable in 3D environments while achieving acceptable performance in the case of localisation error, improving the robustness of the network and reducing the power consumption at least by 20%.Ghorash
    Keywords: localisation; distributed; WSN; wireless sensor network; three dimensional; power efficient; node selection; weighting function.
    DOI: 10.1504/IJSNET.2018.10016111
     
  • Passive and greedy beaconless geographic routing for real-time data dissemination in wireless networks   Order a copy of this article
    by Yongbin Yim, Jeongcheol Lee, Euisin Lee, Sang-Ha Kim 
    Abstract: Real-time geographic routing is one of the most popular examples relying on a greedy algorithm to deliver real-time data in wireless networks. Each sender node decides a next-hop node among one-hop neighbours in stateless manner. However, this sender-side decision paradigm suffers from periodic and network-wide beaconing to discover neighbour nodes. To overcome the limitation, this paper suggests a passive and greedy beaconless real-time routing, called PGBR. To forward real-time data by receiver-side selection, PGBR focuses on two major challenging issues: a delay estimation procedure and a contention function design. The delay estimation procedure estimates both waiting delay and packet transmission delay used for the contention function. PGBR also redesigns receiver-side contention function with deliberating the estimated delay and discuss combinations of important metrics for the contention. The experimental results show that PGBR could improve the energy-efficiency as well as keeps high delivery deadline success ratio.
    Keywords: beaconless; real-time; delay estimation; contention function; routing protocol.
    DOI: 10.1504/IJSNET.2018.10016110
     
  • On the coverage effects in wireless sensor networks based prognostic and health management   Order a copy of this article
    by Ahmad Farhat, Christophe Guyeux, Abdallah Makhoul, Ali Jaber, Rami Tawil 
    Abstract: In this paper, we used wireless sensor network (WSN) techniques for monitoring an area under consideration, in order to diagnose its state. These networks composed by a large number of sensor nodes having very limited and almost nonrenewable energy. Thus, maintain the quality of service (QoS) of a wireless sensor network for a long period is very important in order to ensure accurate data and diagnostics. One of the most important indexes of the QoS in WSN is the coverage. Many studies have been conducted to study the problem of detecting and eliminating redundant sensors in order to improve energy efficiency, while preserving the network's coverage. However, in this paper, we discuss the coverage problem in WSNs and its relation with prognostic and health management. The aim of this paper is to study the impact of coverage issues in WSN on these processes. We emphasised several issues and studied various parameters related to the coverage problem: like scheduling mechanisms, density, deployment of sensors, energy consumption. To reach this goal, evaluating both prognostic and health management with the coverage issues in WSN, we have used four diagnostic algorithms.
    Keywords: WSNs; wireless sensor networks; coverage; density; scheduling mechanisms; prognostic and health management; diagnostics.
    DOI: 10.1504/IJSNET.2018.10016112