Article | 23-April-2018
A multi-cluster-head based clustering routing algorithm is researched and realized in order to achieve better balance the energy consumption of wireless sensor network nodes as well as promote the stability and extend the service life of the network. By taking cluster as the basic unit, it divides the wireless sensor network into multiple clusters, each of which includes a main cluster head node, an assistant cluster head node, a cluster management node and several ordinary nodes. The article
Jie Huang
International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 15–19
Article | 01-December-2014
Underwater wireless sensor network nodes deployment optimization problem is studied and underwater wireless sensor nodes deployment determines its capability and lifetime. Underwater wireless sensor network if no wireless sensor node is available in the area due to used up energy or any other reasons, the area which is not detected by any wireless sensor node forms coverage holes. The coverage holes recovery algorithm aiming at the coverage holes in wireless sensor network is designed in this
Hengchang Jing
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1890–1907
Article | 01-September-2016
This paper proposes a novel localization algorithm for wireless sensor network (WSN). Accurate localization is very important for WSN. WSN localization problem is sometimes regarded as an optimization problem. Plant growth simulation algorithm (PGSA) is a kind of new intelligent optimization algorithm, which is intelligent simulation of plant growth in natural way. In addition to the common characteristics of intelligent algorithms, PGSA show robustness and provides a global optimal solution
Yuqiang Qin,
Hui Ying
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 3, 1287–1304
Article | 01-September-2016
Wireless sensor network is a kind of brand-new information acquisition platform, which is realized by the introduction of self-organizing and auto-configuration mechanisms. Node localization technology represents a crucial component of wireless sensor network. In this paper, a localization method based on kernel principal component analysis and particle swarm optimization back propagation algorithm is carefully discussed. First of all, taking KPCA as the front-end system to extract the main
Wang Jun,
Zhang Fu,
Ren Tiansi,
Chen Xun,
Liu Gang
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 3, 1323–1340
Article | 11-April-2018
Wireless sensor network is composed of many wireless sensor nodes with the same or different functions. A typical sensor node consists of four parts: sensor unit, information processing unit, wireless communication unit and energy supply unit. In this paper, the existing ant colony algorithm is analyzed, and an improved ant colony optimization algorithm is proposed. The paper presents design of routing protocol and node structure in wireless sensor network based on improved ant colony
Yan Song,
Xiaomei Yao
International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 168–172
Article | 05-June-2013
sensor network. Furthermore, collected data are proved to be consistent with natural phenomenon. Compared with traditional monitoring method, the new method has better advantages.
He Yueshun,
Zhang Wei
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 3, 867–887
Article | 01-March-2016
Aiming at three-dimensional space localization problem in wireless sensor network, a method of three-dimensional centroid algorithm with spherical perception radius (3DCSPR) is proposed. The algorithm uses Gaussian probability density function to estimate the sensor node radius, and uses tetrahedron method and three-dimensional center to locate unknown node with estimated. Firstly, RSSI value between beacon nodes and destination node is selected by RSSI distribution density function, that
Xiang Hua,
Zhang Jinjin,
Bin Lei
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 1, 233–255
Article | 05-June-2013
Wireless sensor network (WSN) shows unique advantages in cold alpine area comparing with traditional in-situ monitoring approaches. This paper presents two WSN applications in cold alpine area, one in the Hulugou watershed and the other in the Babaohe watershed, both of which are situated in the upper mountainous reach of the Heihe River Basin of West China. Apart from introductions to the two WSN applications and experimental results, experiences learned and challenges met during designing
Chen Hao,
Nan Zhuotong
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 3, 932–952
Article | 01-March-2015
Coverage and connectivity are two important problems in wireless sensor network. This paper focuses on the wireless sensor network communication radius in the high density of sensor nodes deployed randomly and two times smaller than the sensing radius; put forward a distributed k coverage multi connected node deployment algorithm based on grid. Simulation results show that the algorithm in this paper while guaranteeing the wireless sensor network coverage and connectivity can reduce the number
Yin Zhouping
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 272–290
Article | 03-November-2017
The existing routing protocol for sensor networking can be divided into proactive routing protocol, reactive routing protocol and hybrid routing protocol. Each routing protocol has its merits and shortcomings. The lifetime will end when the working routing protocol can no longer support the whole wireless sensor network. An adaptive method based on redundancy node and dual routing protocol was proposed in the study. Redundancy node, when wireless sensor network is being deployed, can divide the
Jiann-Liang Chen,
Yu-Ming Hsu,
I-Cheng Chang
International Journal on Smart Sensing and Intelligent Systems, Volume 2 , ISSUE 4, 515–539
Research Article | 15-February-2020
Cycling assessment to increase performance during sport training is an important issue. In this article presents a wireless sensor network including multi-sensing channels for dynamic and cinematic measurement during cycling training. The Smart Mountain Bike is a system that includes the bicycle and the associated sports equipment, such as gloves, shoes and chest strap. The system is characterized by sensing channels as part of wireless sensor network which base station is expressed by embedded
José Barreiro,
Octavian Postolache,
Pedro Passos
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–6
Research Article | 15-February-2020
This paper presents the experiments and performance analysis of virtual fence unit consists of microwave motion detector and IEEE 802.15.4 wireless sensor network (WSN) for maximum sensing range. In particular, the analysis is focusing on the maximum sensing range in terms of azimuth angle, height, sensitivity level for indoor and outdoor implementation. The WSN platform is developed using Octopus II sensor nodes while the microwave motion detector is HB100 which detects the movement using
H.T Chan,
T.A Rahman,
A. Arsad
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–4
Article | 30-June-2013
Ying Guo,
Feng Hong,
Zhongwen Guo
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 3, 1054–1076
Research Article | 15-February-2020
Underwater Wireless Sensor Networks (UWSN) share the common challenges of terrestrial Wireless Sensor Network (WSN), however they are significantly different from terrestrial WSN. Mainly, because acoustic wireless communication is the main physical layer technology in UWSN. Acoustic communication offers longer range, but has limitations due to low speed of sound, high error probability, limited bandwidth capacity, node mobility and 3-dimensional network architecture. Most of the ground based
Beenish Ayaz,
Alastair Allen,
Marian Wiercigroch
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–5
Research Article | 15-February-2020
Nowadays, two of the most compelling challenges in the field of food safety and certification are the reduction of the multitude of food losses and wastes in the supply chain and the improvement of certification and monitoring procedures during each stage of production. The aim of this paper is to propose an effective solution to both problems: a wireless sensor network (WSN) combined with a further data processing for real-time monitoring and shelf life prediction.
V.F. Annese,
G.E. Biccario,
S. Cipriani,
D. De Venuto
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–6
Article | 01-June-2015
In this paper the development of a Wireless Sensor Network (WSN) for construction noise identification and sound locating is investigated using the novel application of Bluetooth Low Energy (BLE). Three WSNs using different system-on-chip (SoC) devices and networking protocols have been prototyped using a Raspberry Pi as the gateway in the network. The functionality of the system has been demonstrated with data logging experiments and comparisons has been made between the different WSN systems
Josie Hughes,
Jize Yan,
Kenichi Soga
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 2, 1379–1405
Article | 23-April-2018
In order to improve the network coverage, this paper presents the research on wireless sensor network coverage based on improved particle swarm optimization algorithmfor wireless sensor nodes that are randomly deployed in a certain area.In this paper, we use the regional network coverage as the target objective function, and combine various improved particle swarm optimization algorithms to optimize the deployment location of all nodes to enhance the area coverage. The experimental results show
Li Changxing,
Zhang Qing,
Zhang Long-yao
International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 20–25
Research Article | 20-February-2013
Peng Xiaohong,
Mo Zhi,
Liao Riyao
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 352–367
Article | 05-September-2013
Zhao Jindong,
Fan Baode,
Lu Yunhong,
Mu Chunxiao
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, –
Article | 03-December-2015
Energy control in Wireless Sensor Network is one of the most crucial technologies. Based on the acoustic object localization background, we designed a new structure of the sensor node to realize the energy efficiency in the power supply and artificial sleeping scheduling in this paper. The power control model is independent with data processing and control model and can separately realize the power supply for different part of the node. The node can transfer status according the different
ChengFang Zhen,
Wenyi LIU,
Hanming WEI
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 4, 1997–2017
Article | 17-January-2018
Md. Abdullah-al MAMUN,
Yuji koi,
Naoshi Nakaya,
Goutam Chakraborty
International Journal on Smart Sensing and Intelligent Systems, Volume 2 , ISSUE 2, 309–328
Article | 05-September-2013
We investigate the problem of adaptive control of traffic lights using real-time traffic information collected by a wireless sensor network (WSN). Previous studies mainly focused on optimizing the intervals of green lights in a fixed sequence of traffic lights, and ignored some traffic flow’s characteristics and special traffic circumstances. In this paper, an adaptive traffic light control scheme has been proposed, in which the sequence of traffic lights can be adjusted dynamically in
Binbin Zhou,
Jiannong Cao,
Jingjing Li
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, 1559–1581
Research Article | 01-March-2017
Sadik Kamel Gharghan,
Rosdiadee Nordin,
Mahamod Ismail
International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 1, 124–145
Research Article | 01-September-2017
Wireless Sensor Network (WSN) consists of three main components: nodes, gateways, and software. The spatially distributed measurement nodes interface with sensors to monitor assets or their environment. In a WSN network the devices are connected to WSN nodes wherein the entire nodes uses Zigbee network to transfer the status of connected applications to a controller which controls the whole applications but the main drawback of Wireless sensor networks is its high interference, low coverage
M.Anto Bennet,
B Thamilvalluvan,
C.A. Hema Priya,
B. Bhavani,
M. Shalini
International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 69–86
Article | 01-December-2016
This paper describes a harvesting and power management system that can be equipped with a Wireless Sensor Network (WSN) node in order to harvest energy presents in the environment to be used for sensor node power supply. The proposed scope is to develop a harvesting board exploiting available integrated circuits and devices for extending battery life-cycle of sensor node developed by Medinok SPA. The aim is to realize a WSN able to perform a monitoring of principal physical parameters deemed of
P. Visconti,
R. Ferri,
M. Pucciarelli,
E. Venere
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 1637–1667
Article | 05-June-2013
A quasi-optimal query propagation algorithm Bi-Filtered Forwarding (BFF) for quickly routing a query throughout a wireless sensor network is proposed in this paper. BFF is implemented in a limited flooding manner for guaranteeing quick query propagation and low message consumption in wireless sensor networks. The experimental results show that in comparison with the flooding algorithm, BFF can greatly reduce the redundant message consumption during the procedure of real time query propagation
Junhu Zhang,
Xiujuan Zhu,
Hui Peng
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 3, 993–1011
Research Article | 20-February-2013
To solve the problems of traditional wired on-line monitoring system which has lines too much, cost too high, fault diagnosing and maintaining difficulties and so on, an on-line working condition monitoring system for large electrical equipment based on wireless sensor network (WSN) is proposed, designed and implemented. CC2431 chips were used for hardware design of wireless sensor network node and base station, and the TinyOS transplanted into the sensor nodes and base stations are discussed
Xuejun Chen,
Yongming Yang
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 297–316
Article | 16-December-2013
As the world is moving towards the “Internet of Things” [1], Internet Protocol enabled wireless sensor network is becoming an important research area. In order to make it possible and to facilitate transmission of IPv6 packets over low powered networks, 6Lowpan [2,3] has been introduced. Since the IPWSN application domain is expanded to real-time applications such as health-care [17] and surveillance systems, a fast and seamless handover becomes an important criterion for supporting mobility in
Suman Sankar Bhunia,
Sarbani Roy,
Nandini Mukherjee
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 5, 2075–2102
Article | 01-December-2016
In order to reliable data transmission of wireless sensor network (WSN) in indoor environment, the indoor field intensity distribution and transmission characteristics of electromagnetic wave were researched. First of all, the 3D model in specific indoor environment was built by the finite difference time domain method (FDTD).Then, layout of room, different furniture, position of field source and field source frequency had an influence on indoor field intensity distribution that were studied
Song Yongxian,
Zhang xianjin
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 1943–1970
Research Article | 20-February-2013
Underwater wireless sensor network (UWSN) is a special kind of wireless sensor network which is composed of a large quantity number of wireless sensor nodes deployed in the water. While there are extensive studies on deploy-issue of terrestrial wireless sensor networks (WSN), UWSN has not been paid enough attention due to the challenges of UWSN, such as low available bandwidth, highly varying multipath, and large propagation delays. In this paper, we propose a depth-adjustment scheme to
Jiagao Wu,
Yinan Wang,
Linfeng Liu
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 244–258
Research Article | 15-February-2020
Avijit Mathur,
Thomas Newe
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–6
Article | 01-December-2016
WU Minghu,
CHEN Rui,
YANG Jie,
JIAO Liangbao
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 1822–1839
Research Article | 20-February-2013
In order to reduce the location estimation error in Wireless Sensor Network(WSN). A localization algorithm is proposed combining adaptive estimation, PI-learning and spring-relaxation techniques for wireless sensor networks in this paper. Our proposed method takes the advantages of the spring-relaxation technique, thus it inherits its simplicity. The overall accuracy of the location estimations is improved by introducing adaptive estimation and PI-learning. Moreover, it requires only a few
Li Haiyan,
Hu Yun-an,
Zhu Min
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 317–332
Article | 01-December-2016
Wireless Sensor Networks (WSN) is often deployed in hostile environments, and the attacker can easily capture nodes to inject false data to the sensor network, which can cause serious results. This paper has studied various false data filtering techniques recently in wireless sensor network. Based on encryption technology, we have analyzed and compared the difference of various existing filtering strategies, then have pointed out the merits and demerits of them in detailed. At last, we give the
Ze LUO,
Lingzhi ZHU,
Yunjie CHANG,
Qingyun LUO,
Guixiang LI,
Weisheng LIAO
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 1795–1821
Research Article | 01-September-2017
Ching-Ju Chen,
Jou-An Chen,
Yueh-Min Huang
International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 3, 696–717
Article | 05-September-2013
Masato Noto,
Seiji Yoneda
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, 1533–1558
Research Article | 03-September-2018
This research supports the possibility of implementing in a smart city, a wireless sensor network, with sensitive nodes in movements implemented in public transport system vehicles and static sink nodes located in a vehicular intersection. The methodology contemplates the influence of mobility in obtaining the parameters of network operation, through mathematical models and probabilistic calculations of the data collected through measurement campaigns carried out in a real traffic light
Jorge Gomez-Rojas,
Luis Camargo,
Ruben Montero
International Journal on Smart Sensing and Intelligent Systems, Volume 11 , ISSUE 1, 1–8
Research Article | 13-December-2017
Tien-Wen Sung,
Ting-Ting Wu,
Chu-Sing Yang,
Yueh-Min Huang
International Journal on Smart Sensing and Intelligent Systems, Volume 3 , ISSUE 3, 504–520
Research Article | 10-April-2013
Hao-Li Wang,
Rong-Guei Tsai,
Long-Sheng Li
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 2, 772–790
Article | 01-December-2016
Node deployment is the key problem of wireless sensor network technology in application. The existing study on deployment of deterministic perceived nodes is simplified to the randomly deployment. In this paper, we take the effective coverage , connectivity and probability threshold as the evaluation indices to analyze the different deployment models. Experimental results demonstrate that the effective coverage area of the triangle deployment is the largest when using the same number of nodes
Lei Yutong,
Wen jian,
Zhao Xuan,
Li Jianyu,
Zhang Junguo
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 2032–2050
Research Article | 20-February-2013
Cheng Chunling,
Wu Hao,
Yu Zhihu,
Zhang Dengyin,
Xu Xiaolong
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 18–37
Article | 01-June-2016
Yifan Zhao,
Shengjie Zhou,
Hongwei Ding,
Zhijun Yang,
Qianlin Liu
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 845–863
Research Article | 15-February-2020
Anindya Nag,
Subhas Chandra Mukhopadhya
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–10
Research Article | 20-February-2013
The main objective of wireless sensor network design is to maximize network lifetime. The network topology, which is the important foundation of upper layer protocols, serves as the supportive groundwork for this goal. We constructed the model of sensor networks, and investigated the property of topology with complex network theory. Three statistical parameters were used to describe the network structure, and then some ideal characteristics were concluded for topology. The characteristics of
Linfeng Liu,
Jiagao Wu,
Fu Xiao,
Ruchuan Wang
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 58–76
Research Article | 10-April-2013
In recent years, wireless sensor network (WSN) is a rapidly evolving technological platform with tremendous and novel applications. Many routing protocols have been specially designed for WSN because the sensor nodes are typically battery-power. To prolong the network lifetime, power management and energy-efficient routing techniques become necessary. In large scale wireless sensor networks, hierarchical routing has the advantage of providing scalable and resource efficient solutions. To find
Xiangyuan Yin,
Zhihao Ling,
Liping Guan
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 2, 523–547
Article | 16-December-2013
Ting CAO,
Yuhao WANG,
Xinmin XIONG,
Yan HAO
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 5, 2103–2118
Article | 01-June-2015
In this paper, we proposed to use the Efficient Indoor Thermal Time Constant (EITTC) to characterize the indoor thermal response in old buildings. Accordingly, a low cost, energy-efficient, wide-applicable indoor thermal modeling solution is developed by combining Wireless Sensor Network (WSN) and Artificial Neural Network (ANN). Experiments on both prototype and building room showed consistent results that the combination of WSN and ANN can provide accurate indoor thermal models. A linear
Yi Zhao,
Valentin Gies,
Jean-Marc Ginoux
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 2, 869–895
Research Article | 01-September-2017
A tree topology is used to construct to construct a Zigbee networks practices by wireless sensor network for data delivery applications. There are 3 types of nodes in zigbee networks; coordinator, router and mobile end devices. Coordinator performs the initialization and maintenance functions in the network. A router is responsible for routing data between the coordinator and mobile end device. In-order to avoid the delivery failures occurs due to node movements and network topology changes
R. Elankavi,
R. Kalaiprasath,
R. Udayakumar
International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 174–185
Research Article | 15-February-2020
Clustering, an energy efficient approach is used in Wireless Sensor Network. Clustering involves cluster formation and Cluster Head Selection. As the Cluster Head is involved in carrying out the entire communication, a high energy node has to be selected as Cluster Head. In this paper, a novel predictive Fuzzy based Cluster Head selection algorithm is proposed. The proposal suggests a new input parameter, Rate of recurrent Communication apart from the standard parameters namely the Residual
Hemavathi Natarajan,
Sudha Selvara
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–8
Article | 05-June-2013
Yujie Liang,
Rendong Ying,
Peilin Liu
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 3, 1180–1199
Research Article | 20-February-2013
is designed using modularization way, and this kind of wireless sensor loaded with ZigBee/IEEE 802.15.4 MAC protocol stack can self-organize wireless sensor network, measure the angle value and send the data to the coordinator. Then the deflection curve is displayed on PC. Finally, deflection measurement experiments are conducted on a bridge model and Beida Bridge. The experimental results show that, the presented deflection measurement method is feasible, practical and reliable; the wireless
Yan Yu,
Hang Liu,
Dongsheng Li,
Xingquan Mao,
Jinping Ou
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 38–57
Article | 01-December-2015
transmission are good for data aggregation in wireless sensor network.
Mingxin Yang
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 4, 1935–1955
Article | 01-September-2014
A distributed non-binary fault tolerant event detection technique is proposed for a wireless sensor network (WSN) consisting of a large number of sensors. The sensor nodes may be faulty due to harsh environment and manufacturing reasons. In the existing works on event detection, the detection of event is decided by only one threshold level. The objective of this paper is to extend the fault recognition and correction algorithm for non-binary event detection. The analysis presented here takes
B Victoria Jancee,
S Radha,
Nandita Das
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 1287–1309
Article | 05-June-2013
Nan Yan,
Ming-zheng Zhou,
Li Tong
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 3, 1032–1053
Article | 01-December-2015
This paper proposes a real-time urban air quality forecasting method based on monitoring sites and Thiessen polygon. Firstly, the concentration of pollutants affecting air quality is obtained through the real-time observations of the monitoring sites deployed in wireless sensor network, according to which the air quality index (AQI) can be calculated and air quality levels and categories can be graded. Then, Thiessen polygons are constructed based on the monitoring sites and the air quality
Xuefeng Liu,
Fenxiao Ye,
Yuling Liu,
Xiange Xie,
Jingjing Fan
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 4, 2065–2082
Article | 01-December-2014
HUANG Zhiwei,
ZHENG Zimu,
LI Zhicheng,
PENG Xinyi
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1663–1682
Article | 01-December-2014
H. Ghayvat,
A. Nag,
N. K. Suryadevara,
S.C. Mukhopadhyay,
X. Gui,
J. Liu
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1997–2013
Article | 23-April-2018
Zexin Lin,
Weixing Wang,
Huili Yin,
Sheng Jiang,
Guohui Jiao,
Jieping Yu
International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 5–9
Research Article | 01-September-2017
Wireless sensor networks (WSN) are being used for huge range of applications where the traditional infrastructure based network is mostly infeasible. The most challenging aspect of WSN is that they are energy resource-constrained and that energy cannot be replenish. the wireless sensor network of power limited sensing devices called sensor deployed in a region to sense various types physical information from the environment, when these sensors sense and transmit data to other sensors present in
G Vijayalakshmi,
M.Anto Bennet,
P. Shenbagavalli,
M. Vijayalakshmi,
S. Saranya
International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 395–413
Article | 01-September-2016
Sensor network is a data-centric network, which provides data collection, storage and query services. Data storage and query is one of the hot spot in the research of sensor networks. In order to solve the problem of low efficiency of storage and query，high energy consumption in sensor networks, we put forward a scheme that storing distributed data of wireless sensor network based on information processing cloud. Information processing cloud is made up of a group of sensor nodes around the
LUO Qing-Yun,
ZHU Ling-Zhi,
CHAGN Yun-Jie,
ZHAO Jin-Guo,
LIAO Wei-Sheng,
HE Rui
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 3, 1614–1636
research-article | 30-November-2019
, sustainable, and, most importantly, low cost RF underwater modem with energy harvesting capabilities that can be used as a building block of an underwater wireless sensor network where each device is a node. Monocrystalline solar cells are used to create an energy harvesting subsystem in order to recharge the battery.
The intention is to build a system that could be deployed for monitoring the turbulence and floods as the system will be a standalone where no human interaction is needed. The modems will be
Mohammad M. Abdellatif,
Salma M. Maher,
Ghazal M. Al-sayyad,
Sameh O. Abdellatif
International Journal on Smart Sensing and Intelligent Systems, Volume 13 , ISSUE 1, 1–11
research-article | 30-November-2020
EL IDRISSI Nezha,
Najid Abdellah,
El Alami Hassan
International Journal on Smart Sensing and Intelligent Systems, Volume 14 , ISSUE 1, 1–15
research-article | 18-April-2020
successfully receives data from slave node, the LCD display turns ON and displays “FIRE ALERT”. This alert gets transmitted to the user via SMS by GSM modem.
Figure 4:
Schematic diagram implementation of receiver wireless sensor network for fire alert system.
The major hardware blocks present in the alert system are fire sensor, smoke sensor, micro controller, reset, crystal oscillator, LED indicators, GSM modem, RF transmitter, RF receiver, regulated power supply and step down transformer as you can
Premsai Dasari,
Gundam Krishna Jayanth Reddy,
Abhishek Gudipalli
International Journal on Smart Sensing and Intelligent Systems, Volume 13 , ISSUE 1, 1–8
research-article | 31-August-2020
The wireless sensor network applications have evolved through various applications in multiple domains such as healthcare, smart cities Santos and Ferreira (2019), agriculture, industries, etc. Different parameters can be assessed and monitored with sensor nodes deployed across a given area. A wireless sensor node will comprise of the sensor, a controller/processor, battery to power the node, and the transmission protocol module. This sensor network that contains an array of these nodes is
Alice James,
Avishkar Seth,
Subhas C. Mukhopadhyay
International Journal on Smart Sensing and Intelligent Systems, Volume 13 , ISSUE 1, 1–18
research-article | 30-November-2019
S. S. Bamber
International Journal on Smart Sensing and Intelligent Systems, Volume 13 , ISSUE 1, 1–12
Article | 01-June-2016
Y. K. Benkouider,
M. Keche
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 1054–1072
Research Article | 15-February-2020
A Gaddam,
M Al-Hrooby,
W F Esmael
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–6