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.

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.

scholarly journals Novel Energy-Efficient Data Gathering Scheme Exploiting Spatial-Temporal Correlation for Wireless Sensor Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Ying Zhou ◽  
Lihua Yang ◽  
Longxiang Yang ◽  
Meng Ni
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Data Gathering ◽  
Temporal Correlation ◽  
Wireless Sensor ◽  
Selection Scheme ◽  
Efficient Data ◽  
Transmission Energy Consumption

A novel energy-efficient data gathering scheme that exploits spatial-temporal correlation is proposed for clustered wireless sensor networks in this paper. In the proposed method, dual prediction is used in the intracluster transmission to reduce the temporal redundancy, and hybrid compressed sensing is employed in the intercluster transmission to reduce the spatial redundancy. Moreover, an error threshold selection scheme is presented for the prediction model by optimizing the relationship between the energy consumption and the recovery accuracy, which makes the proposed method well suitable for different application environments. In addition, the transmission energy consumption is derived to verify the efficiency of the proposed method. Simulation results show that the proposed method has higher energy efficiency compared with the existing schemes, and the sink can recover measurements with reasonable accuracy by using the proposed method.

An Energy-Efficient Data Dissemination Scheme in Large-Scale Wireless Sensor Networks

2013 ◽  
Vol 5 (3) ◽  
pp. 34-54
Author(s):  
Shiow-Fen Hwang ◽  
Han-Huei Lin ◽  
Chyi-Ren Dow
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Storage ◽  
Energy Efficient ◽  
Large Scale ◽  
Data Dissemination ◽  
Wireless Sensor ◽  
Event Data ◽  
Efficient Data

In wireless sensor networks, due to limited energy, how to disseminate the event data in an energy-efficient way to allow sinks quickly querying and receiving the needed event data is a practical and important issue. Many studies about data dissemination have been proposed. However, most of them are not energy-efficient, especially in large-scale networks. Hence, in this paper the authors proposed an energy-efficient data dissemination scheme in large-scale wireless sensor networks. First, the authors design a data storage method which disseminates only a few amount event data by dividing the network into regions and levels, and thus reducing the energy consumption. Then, the authors develop an efficient sink query forwarding strategy by probability analysis so that a sink can query events easily according to its location to reduce the delay time of querying event data, as well as energy consumption. In addition, a simple and efficient maintenance mechanism is also provided. The simulation results show that the proposed scheme outperforms TTDD and LBDD in terms of the energy consumption and control overhead.

scholarly journals Architecture of an MAS-Based Intelligent Communication in a WSN

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Mostefa Bendjima ◽  
Mohammed Feham
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Large Scale ◽  
Data Transfer ◽  
Base Station ◽  
Sensor Nodes ◽  
Process Data ◽  
Communication Architecture ◽  
Efficient Data ◽  
Intelligent Communication

Wireless sensor networks (WSNs) are designed to collect information across a large number of sensor nodes with limited batteries. Therefore, it is important to minimize energy consumption of each node, so as to extend the lifetime of the network. This paper proposes the use of an intelligent WSN communication architecture based on a multiagent system (MAS), to ensure optimal data collection. MAS refers to a group of agents that interact and cooperate to achieve a specific goal. To ensure this objective, we propose the integration of a migrating agent into each node to process data and enhance cooperation between neighboring nodes, while mobile agents (MAs) can be used to reduce data transfer between the nodes and send them to the base station (Sink). The collaboration of these agents generates a simple message that summarizes important information to be transmitted by an MA. To reduce the size of MAs, nodes in the network sectors are grouped in such way that, for each MA, an optimal itinerary is established, using a minimum amount of energy with efficient data aggregation within a minimum time. Successive simulations in large-scale sensor networks show the good performance of our proposal in terms of energy consumption and packet delivery rate.

scholarly journals A balanced energy consumption-based routing protocol for efficient data gathering in underwater ASNs

2018 ◽  
Vol 5 (17) ◽  
pp. 154443
Author(s):  
Obaida Abdul Karim ◽  
Nadeem Javaid ◽  
Arshad Sher ◽  
Zahid Wadud ◽  
Sheeraz Ahmed
Keyword(s):  
Energy Consumption ◽  
Routing Protocol ◽  
Data Gathering ◽  
Efficient Data ◽  
Balanced Energy Consumption

scholarly journals Energy and performance costs evaluation for BLE mesh links

2018 ◽  
Author(s):  
Henrique Carvalho Silva ◽  
Cíntia Borges Margi
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Mesh Networks ◽  
Lower Boundary ◽  
Low Energy ◽  
Trade Offs ◽  
Main Challenge ◽  
And Performance ◽  
Large Scale Networks ◽  
The Internet Of Things

Bluetooth Low Energy (BLE for short) is among the favorites to become a de facto standard in the context of the Internet of Things (IoT). However, its main challenge is the lack of standards for efficient mesh networking. Furthermore, the literature lacks works analyzing energy consumption trade-offs for BLE mesh networks. We address this issue by experimentally evaluating three minimal topologies for linking separate BLE star networks. We aim to determine a lower boundary in terms of energy and performance costs using the metrics of energy consumption, delivery rate, and goodput. We perform our experiments using a testbed comprised of TI CC1350 nodes running Contiki OS. Our results enable us to estimate similar costs for large scale networks.

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