scholarly journals Time and energy complexity of distributed computation in wireless sensor networks

2005 ◽  
Author(s):  
N. Khude ◽  
A. Kumar ◽  
A. Karnik
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Distributed Computation ◽  
Wireless Sensor ◽  
Time And Energy

Secured data aggregation in wireless sensor networks

Sensor Review ◽  
2018 ◽  
Vol 38 (3) ◽  
pp. 369-375 ◽  
Author(s):  
Sathya D. ◽  
Ganesh Kumar P.
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Computation Time ◽  
Wireless Sensor ◽  
Content Type ◽  
Homomorphic Cryptosystem ◽  
Secured Data ◽  
Time And Energy

PurposeThis study aims to provide a secured data aggregation with reduced energy consumption in WSN. Data aggregation is the process of reducing communication overhead in wireless sensor networks (WSNs). Presently, securing data aggregation is an important research issue in WSNs due to two facts: sensor nodes deployed in the sensitive and open environment are easily targeted by adversaries, and the leakage of aggregated data causes damage in the networks, and these data cannot be retrieved in a short span of time. Most of the traditional cryptographic algorithms provide security for data aggregation, but they do not reduce energy consumption.Design/methodology/approachNowadays, the homomorphic cryptosystem is used widely to provide security with low energy consumption, as the aggregation is performed on the ciphertext without decryption at the cluster head. In the present paper, the Paillier additive homomorphic cryptosystem and Bonehet al.’s aggregate signature method are used to encrypt and to verify aggregate data at the base station.FindingsThe combination of the two algorithms reduces computation time and energy consumption when compared with the state-of-the-art techniques.Practical implicationsThe secured data aggregation is useful in health-related applications, military applications, etc.Originality/valueThe new combination of encryption and signature methods provides confidentiality and integrity. In addition, it consumes less computation time and energy consumption than existing methods.

scholarly journals Online fault diagnosis of wireless sensor networks

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Arunanshu Mahapatro ◽  
Pabitra Khilar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Diagnosis ◽  
Wireless Sensor ◽  
Transient Faults ◽  
Intermittent Faults ◽  
Detection Latency ◽  
Time Redundancy ◽  
Simulation Results ◽  
Time And Energy

AbstractThis paper proposes an adaptive online distributed solution for fault diagnosis in wireless sensor networks (WSNs). Fault diagnosis is achieved by comparing the heartbeat message generated by neighboring nodes and dissemination of decision made at each node. Time redundancy is used to detect the intermittent faults since an intermittent fault will not occur consistently. The diagnosis performance degradation due to intermittent faults in sensing and transient faults in communication is analyzed. A near optimal trade-off between detection latency and number of tests required to detect intermittent faults is obtained. Simulation results are provided and they show that this work performs better, from both time and energy complexity viewpoint.

The Amalgamative Sharp Wireless Sensor Networks Routing and with Enhanced Machine Learning

2019 ◽  
Vol 16 (9) ◽  
pp. 3766-3769 ◽  
Author(s):  
Sowjanya Ramisetty ◽  
Kavita ◽  
Sahil Varma
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Environmental Conditions ◽  
Information Transfer ◽  
Processing Time ◽  
Human Interaction ◽  
Wireless Sensor ◽  
Energy Calculation ◽  
Time And Energy

Wireless sensor networks (WSN) are most prominent domain in present networking world. Sensors are the devices used to sense the information transfer, routing, processing time, energy calculation and also to know the physical and environmental conditions. Many researchers worked on WSN to improve the performance of the sensors. Various algorithms are discussed about routing in WSN, information transfer and many sub domains. Machine Learning is used to control the process without any human interaction. In this paper, the amalgamative sharp WSN algorithm is introduced to show the performance based on information transfer, routing, processing time, and energy calculation. For the better results the ML algorithms are adopted with proposed algorithm. Results show the performance of the ASWSN algorithm.

Distributed Computation in Wireless Sensor Networks

2015 ◽  
Vol 4 (3) ◽  
pp. 14-32 ◽  
Author(s):  
Tejaswini Devanaboyina ◽  
Balakrishna Pillalamarri ◽  
Rama Murthy Garimella
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Transport System ◽  
Vehicular Ad Hoc Networks ◽  
Distributed Computation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Recursive Functions ◽  
User Friendly ◽  
Primitive Recursive

Wireless Sensor Networks are used to perform distributed sensing in various fields like health, military, home etc where the sensor nodes communicate among themselves and do distributed computation over the sensed values to identify the occurrence of an event. The architecture for distributed computation of primitive recursive functions and median is presented in this paper. This paper assumes the no memory computational model of sensor nodes; in the architecture for primary recursive functions i.e. the sensor nodes only have two registers. This assumption is not made for the computation of median. This paper also explores the applications of wireless sensor networks in building a smart, hassle free transportation system. In purview of emerging technologies like Internet of things and Vehicular Ad Hoc networks, the transport system can be made user friendly by including itinerary planning, dynamic speed boards etc. Already research is moving in the direction of making transport system efficient and user-friendly. This paper serves as a one more step in the process of achieving it.

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