scholarly journals Energy and Memory Efficient Data Loss Prevention in Wireless Sensor Networks

2018 ◽  
Author(s):  
Pooya Hejazi ◽  
Gianluigi Ferrari
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Storage ◽  
Data Dissemination ◽  
Wireless Sensor ◽  
Distributed Data ◽  
Data Loss ◽  
Loss Mechanism ◽  
Distributed Data Storage

Load balancing, energy efficiency and fault tolerance are among the most important data dissemination issues in Wireless Sensor Networks (WSNs). In order to successfully cope with the mentioned issues, two main approaches (namely, Data-centric Storage and Distributed Data Storage) have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the mentioned problems, the proposed solutions typically focus on one issue at a time. In this paper, we integrate the Data-centric Storage (DCS) features into Distributed Data Storage (DDS) mechanisms and present a novel approach, denoted as Collaborative Memory and Energy Management (CoMEM), to overcome both problems and bring memory and energy efficiency to the data loss mechanism of WSNs. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms existing approaches in various WSN scenarios.

scholarly journals Distributed Data Storage Strategy in Wireless Sensor Networks

2016 ◽  
Vol 12 (11) ◽  
pp. 52
Author(s):  
Song-juan Zhang ◽  
Jian Yang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Storage ◽  
Hot Spot ◽  
Wireless Sensor ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
Storage Node ◽  
Approach Method ◽  
Hot Spot Problem

In order to solve the problem of large scalability and low energy efficiency in distributed data storage in wireless sensor networks, the author proposed a temporal-centric storage approach method. By using this method the sensing data are stored in some storage node indexed by a hash function parameterized with detecting time. These nodes act as rendezvous among sink and source nodes. Simulation results show that the proposed approach mitigates the hot-spot problem and can thus improve overall system performance substantially.

A distributed data storage protocol for heterogeneous wireless sensor networks with mobile sinks

2013 ◽  
Vol 11 (5) ◽  
pp. 1588-1602 ◽  
Author(s):  
Guilherme Maia ◽  
Daniel L. Guidoni ◽  
Aline C. Viana ◽  
Andre L.L. Aquino ◽  
Raquel A.F. Mini ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Storage ◽  
Wireless Sensor ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
Mobile Sinks ◽  
Heterogeneous Wireless Sensor Networks

scholarly journals A Practical Data-Gathering Algorithm for Lossy Wireless Sensor Networks Employing Distributed Data Storage and Compressive Sensing

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3221 ◽  
Author(s):  
Ce Zhang ◽  
Ou Li ◽  
Guangyi Liu ◽  
Mingxuan Li
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Compressive Sensing ◽  
Data Storage ◽  
Data Gathering ◽  
Wireless Sensor ◽  
Distributed Data ◽  
Measurement Matrix ◽  
Distributed Data Storage

Reliability and energy efficiency are two key considerations when designing a compressive sensing (CS)-based data-gathering scheme. Most researchers assume there is no packets loss, thus, they focus only on reducing the energy consumption in wireless sensor networks (WSNs) while setting reliability concerns aside. To balance the performance–energy trade-off in lossy WSNs, a distributed data storage (DDS) and gathering scheme based on CS (CS-DDSG) is introduced, which combines CS and DDS. CS-DDSG utilizes broadcast properties to resist the impact of packet loss rates. Neighboring nodes receive packets with process constraints imposed to decrease the volume of both transmissions and receptions. The mobile sink randomly queries nodes and constructs a measurement matrix based on received data with the purpose of avoiding measuring the lossy nodes. Additionally, we demonstrate how this measurement matrix satisfies the restricted isometry property. To analyze the efficiency of the proposed scheme, an expression that reflects the total number of transmissions and receptions is formulated via random geometric graph theory. Simulation results indicate that our scheme achieves high precision for unreliable links and reduces the number of transmissions, receptions and fusions. Thus, our proposed CS-DDSG approach effectively balances energy consumption and reconstruction accuracy.

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