scholarly journals Application of Fuzzy Logic for Selection of Actor Nodes in WSANs —Implementation of Two Fuzzy-Based Systems and a Testbed

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5573 ◽  
Author(s):  
Donald Elmazi ◽  
Miralda Cuka ◽  
Makoto Ikeda ◽  
Keita Matsuo ◽  
Leonard Barolli
Keyword(s):  
Fuzzy Logic ◽  
Physical World ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Smart Devices ◽  
Different Types ◽  
Actor Networks ◽  
Prompt Action ◽  
Simulation Systems ◽  
Selection Of

The development of sensor networks and the importance of smart devices in the physical world has brought attention to Wireless Sensor and Actor Networks (WSANs). They consist of a large number of static sensors and also a few other smart devices, such as different types of robots. Sensor nodes have responsibility for sensing and sending information towards an actor node any time there is an event that needs immediate intervention such as natural disasters or malicious attacks in the network. The actor node is responsible for processing and taking prompt action accordingly. But in order to select an appropriate actor to do one task, we need to consider different parameters, which make the problem NP-hard. For this reason, we consider Fuzzy Logic and propose two Fuzzy Based Simulation Systems (FBSS). FBSS1 has three input parameters such as Number of Sensors per Actor (NSA), Remaining Energy (RE) and Distance to Event (DE). On the other hand, FBSS2 has one new parameter—Transmission Range (TR)—and for this reason it is more complex. We will explain in detail the differences between these two systems. We also implement a testbed and compare simulation results with experimental results.

A Secure Energy Aware Game Theory (SEGaT) Mechanism for Coordination in WSANs

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Game Theory ◽  
Initial Step ◽  
Packet Delivery Ratio ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Energy Aware ◽  
Packet Delivery ◽  
Actor Networks ◽  
Selection Of

A novel secure energy aware game theory (SEGaT) method has proposed to have better coordination in wireless sensor actor networks. An actor has a cluster of sensor nodes which is required to perform different action based on the need that emerge in the network individually or sometime with coordination from other actors. The method has different stages for the fulfilment of these actions. Based on energy aware actor selection (EAAS), selection of number of actors and their approach is the initial step followed by the selection of best team of sensors with each actor to carry out the action and lastly the selection of reliable node within that team to finally nail the action into place in the network for its smooth working and minimum compromise in the energy The simulations are done in MATLAB and result of the energy and the packet delivery ratio are compared with game theory (GaT) and real time energy constraint (RTEC) method. The proposed protocol performs better in terms of energy consumption, packet delivery ratio as compared to its competitive protocols.

scholarly journals NEEF: A Novel Energy Efficient Fuzzy Logic based Clustering Protocol for Wireless Sensor Network

2020 ◽  
Vol 21 (3) ◽  
pp. 555-568
Author(s):  
Anshu Kumar Dwivedi ◽  
A. K. Sharma
Keyword(s):  
Fuzzy Logic ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy Efficient ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Heads ◽  
Clustering Protocol ◽  
Selection Of

The uttermost requirement of the wireless sensor network is prolonged lifetime. Unequal energy degeneration in clustered sensor nodes lead to the premature death of sensor nodes resulting in a lessened lifetime. Most of the proposed protocols primarily choose cluster head on the basis of a random number, which is somewhat discriminating as some nodes which are eligible candidates for cluster head role may be skipped because of this randomness. To rule out this issue, we propose a deterministic novel energy efficient fuzzy logic based clustering protocol (NEEF) which considers primary and secondary factors in fuzzy logic system while selecting cluster heads. After selection of cluster heads, non-cluster head nodes use fuzzy logic for prudent selection of their cluster head for cluster formation. NEEF is simulated and compared with two recent state of the art protocols, namely SCHFTL and DFCR under two scenarios. Simulation results unveil better performance by balancing the load and improvement in terms of stability period, packets forwarded to the base station, improved average energy and extended lifetime.

scholarly journals Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

2014 ◽  
Vol 5 (1) ◽  
pp. 20-45 ◽  
Author(s):  
Osman Salem ◽  
Alexey Guerassimov ◽  
Ahmed Mehaoua ◽  
Anthony Marcus ◽  
Borko Furht
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Patient Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Smart Devices ◽  
Support Vector ◽  
Processing Unit

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

scholarly journals Efficient Node and Sensed Module Management for Multisensory Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2328 ◽  
Author(s):  
Juan Feng ◽  
Xiaozhu Shi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Management Strategies ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Tracking Accuracy ◽  
Node Selection ◽  
Information Utility ◽  
Save Energy ◽  
Selection Of

In target tracking wireless sensor networks, choosing a part of sensor nodes to execute tracking tasks and letting the other nodes sleep to save energy are efficient node management strategies. However, at present more and more sensor nodes carry many different types of sensed modules, and the existing researches on node selection are mainly focused on sensor nodes with a single sensed module. Few works involved the management and selection of the sensed modules for sensor nodes which have several multi-mode sensed modules. This work proposes an efficient node and sensed module management strategy, called ENSMM, for multisensory WSNs (wireless sensor networks). ENSMM considers not only node selection, but also the selection of the sensed modules for each node, and then the power management of sensor nodes is performed according to the selection results. Moreover, a joint weighted information utility measurement is proposed to estimate the information utility of the multiple sensed modules in the different nodes. Through extensive and realistic experiments, the results show that, ENSMM outperforms the state-of-the-art approaches by decreasing the energy consumption and prolonging the network lifetime. Meanwhile, it reduces the computational complexity with guaranteeing the tracking accuracy.

An Automated Optimize Utilization of Water and Crop Monitoring in Agriculture Using IoT

2021 ◽  
pp. 37-45
Author(s):  
Ajith Krishna R ◽  
◽  
◽  
◽  
Ankit Kumar ◽  
...  
Keyword(s):  
Soil Analysis ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Leaf Wetness ◽  
Productivity Analysis ◽  
Factors Affecting ◽  
Crop Monitoring ◽  
Different Types ◽  
Smart Agriculture ◽  
Climate Analysis

Agriculture is the primary occupation in our country for ages. But now due to migration of people from rural to urban there is hindrance in agriculture. So, to overcome this problem we go for smart agriculture techniques using IoT. This paper includes various features like GPS based remote controlled monitoring, moisture & temperature sensing, intruders scaring, security, leaf wetness and proper irrigation facilities. It makes use of wireless sensor networks for noting the soil properties and environmental factors continuously. Various sensor nodes are deployed at different locations in the farm. Controlling these parameters are through any remote device or internet services and the operations are performed by interfacing sensors, Wi-Fi, camera with microcontroller. This concept is created as a product and given to the farmer’s welfare. AI Solution for Farmers perform soil analysis, climate analysis, and productivity analysis using linear regression. It helps farmers to understand about the crop to be sown as well as the factors affecting their productivity with the help of different types of graphs and tables. Farmers need not to do anything on the application as it is highly interactive as by using speech API.

A framework for indoor construction resources tracking by applying wireless sensor networks1This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.

2012 ◽  
Vol 39 (9) ◽  
pp. 1083-1088 ◽  
Author(s):  
Xuesong Shen ◽  
Ming Lu
Keyword(s):  
Radio Frequency Identification ◽  
Cost Effective ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Mobile Nodes ◽  
Received Signal Strength Indicator ◽  
Construction Engineering ◽  
Global Positioning ◽  
Frequency Identification ◽  
Selection Of

The state-of-the-art tracking technologies, such as the global positioning system (GPS) and the radio frequency identification (RFID), lend themselves well to applications in relatively open areas, while falling short of accuracy and reliability in indoor or partially covered application settings due to signal blockage, distortion or deterioration. This research aims to address this challenge in construction engineering by exploring a cost-effective positioning methodology to realize automated and continuous tracking of construction resources. The emerging ZigBee-based wireless sensor networks (WSN) technology is introduced. A framework of WSN application is proposed for indoor construction resources tracking, which consists of a group of stationary and mobile sensor nodes that can communicate with one another. Real-time locations of the mobile nodes can be determined by applying the localization method based on received signal strength indicator (RSSI) and geometric trilateration.

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