scholarly journals ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 930 ◽  
Author(s):  
Rosana Lachowski ◽  
Marcelo Pellenz ◽  
Edgard Jamhour ◽  
Manoel Penna ◽  
Glauber Brante ◽  
...  
Keyword(s):  
Big Data ◽  
Sensor Networks ◽  
Large Scale ◽  
Data Gathering ◽  
Essential Element ◽  
State Recovery ◽  
Periodic Data ◽  
Soft State ◽  
Lossy Links ◽  
Large Scale Networks

Wireless Sensors Networks (WSNs) are an essential element of the Internet of Things (IoT), and are the main producers of big data. Collecting a huge amount of data produced by a resource-constrained network is a very difficult task, presenting several challenges. Big data gathering involves not only periodic data sensing, but also the forwarding of queries and commands to the network. Conventional network protocols present unfeasible strategies for large-scale networks and may not be directly applicable to IoT environments. Information-Centric Networking is a revolutionary paradigm that can overcome such big data gathering challenges. In this work, we propose a soft-state information-centric protocol, ICENET (Information Centric protocol for sEnsor NETworks), for big data gathering in large-scale WSNs. ICENET can efficiently propagate user queries in a wireless network by using a soft-state recovery mechanism for lossy links. The scalability of our solution is evaluated in different network scenarios. Results show that the proposed protocol presents approximately 84% less overhead and a higher data delivery rate than the CoAP (Constrained Application Protocol), which is a popular protocol for IoT environments.

Efficient group key management for secure big data in predictable large-scale networks

2015 ◽  
Vol 28 (4) ◽  
pp. 1174-1192 ◽  
Author(s):  
Shuangyu He ◽  
Qianhong Wu ◽  
Bo Qin ◽  
Jianwei Liu ◽  
Yan Li
Keyword(s):  
Big Data ◽  
Key Management ◽  
Large Scale ◽  
Group Key Management ◽  
Group Key ◽  
Large Scale Networks

Data Transmission Scheduling Based on RTS/CTS Exchange for Periodic Data Gathering Sensor Networks

2007 ◽  
Vol E90-B (12) ◽  
pp. 3410-3418 ◽  
Author(s):  
T. MATSUDA ◽  
M. ICHIEN ◽  
H. KAWAGUCHI ◽  
C. OHTA ◽  
M. YOSHIMOTO
Keyword(s):  
Sensor Networks ◽  
Data Transmission ◽  
Data Gathering ◽  
Transmission Scheduling ◽  
Periodic Data

scholarly journals A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 575 ◽  
Author(s):  
Yu Gao ◽  
Jin Wang ◽  
Wenbing Wu ◽  
Arun Sangaiah ◽  
Se-Jung Lim
Keyword(s):  
Energy Consumption ◽  
Mobile Agent ◽  
Hybrid Method ◽  
Large Scale ◽  
Data Gathering ◽  
Sensor Field ◽  
Efficient Data ◽  
Anchor Nodes ◽  
Large Scale Networks ◽  
Moving Path

Wireless Sensor Networks (WSNs) are usually troubled with constrained energy and complicated network topology which can be mitigated by introducing a mobile agent node. Due to the numerous nodes present especially in large scale networks, it is time-consuming for the collector to traverse all nodes, and significant latency exists within the network. Therefore, the moving path of the collector should be well scheduled to achieve a shorter length for efficient data gathering. Much attention has been paid to mobile agent moving trajectory panning, but the result has limitations in terms of energy consumption and network latency. In this paper, we adopt a hybrid method called HM-ACOPSO which combines ant colony optimization (ACO) and particle swarm optimization (PSO) to schedule an efficient moving path for the mobile agent. In HM-ACOPSO, the sensor field is divided into clusters, and the mobile agent traverses the cluster heads (CHs) in a sequence ordered by ACO. The anchor node of each CHs is selected in the range of communication by the mobile agent using PSO based on the traverse sequence. The communication range adjusts dynamically, and the anchor nodes merge in a duplicated covering area for further performance improvement. Numerous simulation results prove that the presented method outperforms some similar works in terms of energy consumption and data gathering efficiency.

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