scholarly journals Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 85
Author(s):  
Marcin Lewandowski ◽  
Bartłomiej Płaczek ◽  
Marcin Bernas
Keyword(s):  
Sensor Networks ◽  
Sensor Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Network Lifetime ◽  
Data Transmission ◽  
Activity Monitoring ◽  
Sensor Nodes ◽  
Wearable Sensor ◽  
Transmission Reduction

The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction.

scholarly journals AR-RBFS: Aware-Routing Protocol Based on Recursive Best-First Search Algorithm for Wireless Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Farzad Kiani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Data Transmission ◽  
Search Algorithm ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Second Phase

Energy issue is one of the most important problems in wireless sensor networks. They consist of low-power sensor nodes and a few base station nodes. They must be adaptive and efficient in data transmission to sink in various areas. This paper proposes an aware-routing protocol based on clustering and recursive search approaches. The paper focuses on the energy efficiency issue with various measures such as prolonging network lifetime along with reducing energy consumption in the sensor nodes and increasing the system reliability. Our proposed protocol consists of two phases. In the first phase (network development phase), the sensors are placed into virtual layers. The second phase (data transmission) is related to routes discovery and data transferring so it is based on virtual-based Classic-RBFS algorithm in the lake of energy problem environments but, in the nonchargeable environments, all nodes in each layer can be modeled as a random graph and then begin to be managed by the duty cycle method. Additionally, the protocol uses new topology control, data aggregation, and sleep/wake-up schemas for energy saving in the network. The simulation results show that the proposed protocol is optimal in the network lifetime and packet delivery parameters according to the present protocols.

scholarly journals Energy Reckoning Distance Based Clustering for Spectrum Aware Cognitive Radio Wireless Sensor Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 842-850
Keyword(s):  
Wireless Sensor Networks ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Sensor Network ◽  
Network Lifetime ◽  
Spectrum Sharing ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cognitive Radio Sensor Network

Implementing cognitive radio sensor nodes in wireless sensor networks introduced a smart combination called cognitive radio sensor network (CRSN) which creates new challenges in the design of network topology. Conserving the nodes energy helps to extend the lifetime of the network. This stands as an important criterion while designing any algorithm. In order to achieve the same, two important criteria are to be considered – the communicating distance between the nodes or node to base station and proper spectrum sharing technique. In the proposed work, Energy Reckoning Distance-Based Clustering (ERDBC) algorithm, both the criterion is taken into consideration and designed in order to increase the lifetime of a cognitive radio sensor network. In the ERDBC algorithm, the whole network area is divided into three regions according to the distance and the cluster heads are elected based on energy, distance and common channel creates a greater impact on retaining the nodes energy. Also, implementing multi-hop routing using proper spectrum sharing technique helps to avoid data collision and retransmission thereby; the energy consumption of the nodes are reduced to a greater extent. The performance of the proposed ERDBC algorithm is measured on the basis of residual energy, throughput, channel usage, first node death, last node death, and network lifetime, and compared with the already existing LEACH, CogLEACH, LEAUCH and CEED algorithms. Thus the network lifetime of the proposed ERDBC algorithm is 78.18% more than LEACH, 73.6% more than CogLEACH, 29.88% more than CEED and 17.98% more than LEAUCH algorithms

scholarly journals An enhancement of path selection to cluster head based on multi-hop routing in two-tier wireless sensor network

2019 ◽  
Vol 9 (6) ◽  
pp. 5360
Author(s):  
Wan Isni Sofiah Wan Din ◽  
Asyran Zarizi Bin Abdullah ◽  
Razulaimi Razali ◽  
Ahmad Firdaus ◽  
Salwana Mohamad ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Data Transmission ◽  
Cluster Head ◽  
Threshold Value ◽  
Path Selection ◽  
Sensor Nodes ◽  
Wireless Sensor

<span lang="EN-US">Wireless Sensor Network (WSN) is a distributed wireless connection that consists many wireless sensor devices. It is used to get information from the surrounding activities or the environment and send the details to the user for future work. Due to its advantages, WSN has been widely used to help people to collect, monitor and analyse data. However, the biggest limitation of WSN is about the network lifetime. Usually WSN has a small energy capacity for operation, and after the energy was used up below the threshold value, it will then be declared as a dead node. When this happens, the sensor node cannot receive and send the data until the energy is renewed. To reduce WSN energy consumption, the process of selecting a path to the destination is very important. Currently, the data transmission from sensor nodes to the cluster head uses a single hop which consumes more energy; thus, in this paper the enhancement of previous algorithm, which is MAP, the data transmission will use several paths to reach the cluster head. The best path uses a small amount of energy and will take a short time for packet delivery. The element of Shortest Path First (SPF) Algorithm that is used in a routing protocol will be implemented. It will determine the path based on a cost, in which the decision will be made depending on the lowest cost between several connected paths. By using the MATLAB simulation tool, the performance of SPF algorithm and conventional method will be evaluated. The expected result of SPF implementation will increase the energy consumption in order to prolong the network lifetime for WSN.</span>

HUMAN ACTIVITY RECOGNITION BY FUSING MULTIPLE SENSOR NODES IN THE WEARABLE SENSOR SYSTEMS

2012 ◽  
Vol 12 (05) ◽  
pp. 1250084 ◽  
Author(s):  
YONGCAI GUO ◽  
WEIHUA HE ◽  
CHAO GAO
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Sensor Node ◽  
Recognition Rate ◽  
Fusion Rule ◽  
Sensor Nodes ◽  
Sensor Systems ◽  
Wearable Sensor ◽  
Activity Data ◽  
Multiple Sensor

This paper presents a novel method for recognizing human daily activity by fusion multiple sensor nodes in the wearable sensor systems. The procedure of this method is as follows: firstly, features are extracted from each sensor node and subsequently reduced in dimension by generalized discriminant analysis (GDA), to ensure the real-time performance of activity recognition; then, the reduced features are classified with the multiclass relevance vector machines (RVM); finally, the individual classification results are fused at the decision level, in consideration that the different sensor nodes can provide heterogeneous and complementary information about human activity. Extensive experiments have been carried out on Wearable Action Recognition Database (WARD). Experimental results show that if all the five sensor nodes are fused with the adaptive weighted logarithmic opinion pools (WLOGP) fusion rule, we can even achieve a recognition rate as high as 98.78%, which is far more better than the situations where only single sensor node is available or the activity data is processed by state-of-the-art methods. Moreover, this proposed method is flexible to extension, and can provide a guideline for the construction of the minimum desirable system.

scholarly journals AMPC: A Lightweight Hybrid Cryptographic Algorithm for Wireless Sensor Networks

2020 ◽  
Vol 5 (6) ◽  
pp. 1142-1146
Author(s):  
Md. Navid Bin Anwar ◽  
Maherin Mizan Maha
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Data Transmission ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Computational Time ◽  
Cryptographic Algorithm ◽  
Autonomous Sensor

Wireless sensor network (WSN) is a group of several autonomous sensor nodes attached to each other. Wireless sensor networks are commonly used in a lot of applications and are expected to have a cheap deployment cost. The network of sensors continues to grow aiding the need of the system. Due to that, sensors become vulnerable to attacks and need strong security mechanism. To strengthen the security of data which are transmitted through sensors in WSN, different cryptographic schemes are used. As WSN has limited energy source, therefore, complex cryptographic algorithms may require excessive computational time which not only make the data transmission slow but the life time of sensor network will be significantly affected. To overcome these challenges a new hybrid cryptographic scheme, AES and Modified Playfair Cipher (AMPC), is introduced in this paper.

scholarly journals Security from Denial of Sleep Attack in Wireless Sensor Network

2005 ◽  
Vol 4 (2) ◽  
pp. 419-425 ◽  
Author(s):  
Simerpreet Kaur ◽  
Md. Ataullah ◽  
Monika Garg
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Network Lifetime ◽  
Physical World ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Sleep Attack ◽  
Energy Constrained

With the advancement in Wireless Sensor Network (WSN) sensors are gaining importance in the physical world. Besides the low power of sensor nodes used, sensors are widely used in detecting temperature, pollution, pressure and other various applications. Energy-constrained sensor networks periodically place nodes to sleep in order to extend the network Lifetime. Denial of sleep attacks are a great threat to lifetime of sensor networks as it prevents the nodes from going into sleep mode. In this paper we are describing prevention against Denials of sleep attack. We have analyzed each of proposed solutions, identify their strengths and limitations.

scholarly journals Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 885 ◽  
Author(s):  
Zhongzheng Fu ◽  
Xinrun He ◽  
Enkai Wang ◽  
Jun Huo ◽  
Jian Huang ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Sensor Node ◽  
Learning Algorithm ◽  
Joint Probability ◽  
Human Activity Recognition ◽  
Sensor Technology ◽  
Measurement Unit ◽  
Wearable Sensor

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.

scholarly journals A Data-Based Fault-Detection Model for Wireless Sensor Networks

2019 ◽  
Vol 11 (21) ◽  
pp. 6171 ◽  
Author(s):  
Jangsik Bae ◽  
Meonghun Lee ◽  
Changsun Shin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Data Transmission ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sensors And Actuators ◽  
Detection Model ◽  
Management Platform

With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optimized for smart farms was implemented as an example, and resource management of sensors and actuators was provided using the oneM2M common platform. The effectiveness of the proposed fault-detection model was verified on an integrated management platform based on the Internet of Things by collecting and analyzing data. The results confirm that when a faulty sensor node is not separated from the network, unnecessary data transmission of other sensor nodes occurs due to continuous abnormal data transmission; thus, increasing energy consumption and reducing the network lifetime.

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