scholarly journals Data Uploading Strategy for Underwater Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5265 ◽  
Author(s):  
Huang ◽  
Sun ◽  
Yang
Keyword(s):  
Decision Making ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Energy Saving ◽  
Wireless Sensor ◽  
High Similarity ◽  
Underwater Wireless Sensor Networks ◽  
Data Upload

Underwater wireless sensor networks (UWSNs) have become a popular research topic due to the challenges of underwater communication. The existing mechanisms for collecting data from UWSNs focus on reducing the data redundancy and communication energy consumption, while ignoring the problem of energy-saving transmission after compression. In order to improve the efficiency of data collection, we propose a data uploading decision-making strategy based on the high similarity of the collected data and the energy consumption of the high similarity data compression. This decision-making strategy efficiently optimizes the energy consumption of the networks. By analyzing the data similarity, the quality of network communication, and uploading energy consumption, the decision-making strategy provides an energy-efficient data upload strategy for underwater nodes, which reduces the energy consumption in various network settings. The simulation results show that compared with several existing data compression and uploading methods, the proposed data upload methods has better energy saving effect in different network scenarios.

scholarly journals Performance Analysis of Data Compression Algorithms for Energy Efficient Wireless Sensor Networks

2019 ◽  
Vol 9 (1S) ◽  
pp. 404-408
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Energy Saving ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Piecewise Linear Approximation ◽  
Compression Algorithms

Wireless Sensor Networks (WSN), is an intensive area of research which is often used for monitoring, sensing and tracking various environmental conditions. It consists of a number of sensor nodes that are powered with fixed low powered batteries. These batteries cannot be changed often as most of the WSN will be in remote areas. Life time of WSN mainly depends on the energy consumed by the sensor nodes. In order to prolong the networks life time, the energy consumption has to be reduced. Different energy saving schemes has been proposed over the years. Data compression is one among the proposed schemes that can scale down the amount of data transferred between nodes and results in energy saving. In this paper, an attempt is made to analyze the performances of three different data compression algorithms viz. Light Weight Temporal Compression (LTC), Piecewise Linear Approximation with Minimum Number of Line Segments (PLAMLIS) and Univariate Least Absolute Selection and Shrinkage Operator (ULASSO). These algorithms are tested on standard univariate datasets and evaluated using assessment metrics like Mean Square Error (MSE), compression ratio and energy consumption. The results show that the ULASSO algorithm outperforms other algorithms in all three metrics and contributes more towards energy consumption

scholarly journals A new scheme for evaluating energy efficiency of data compression in wireless sensor networks

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877692
Author(s):  
Shaoqiang Liu ◽  
Yanfang Liu ◽  
Xiang Chen ◽  
Xiaoping Fan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Energy Saving ◽  
Data Communication ◽  
Wireless Sensor ◽  
Compression Algorithms ◽  
Wireless Module

Data communication incurs the highest energy cost in wireless sensor networks, and restricts the application of wireless sensor networks. Data compression is a promising technique that can reduce the amount of data exchanged between nodes and results in energy saving. However, there is a lack of effective methods to evaluate the efficiency of data compression algorithms and to increase nodes’ energy efficiency. The energy saving of nodes is related to both hardware and software, this article proposes a new scheme for evaluating energy efficiency of data compression in wireless sensor networks according to the node’s hardware and software. The relationship between the energy efficiency and the hardware and software factors is expressed by a formula. In this formula, energy efficiency can be improved by increasing the compression ratio and decreasing the ratio of s/ k, in which k represents the node’s hardware factor related to energy consumption of processor, wireless module, and so on and s represents the software factor that reflects the energy consumption of the algorithm. Based on the scheme, a mechanism is proposed to improve the node’s energy efficiency by selecting effective algorithms in accordance with the node’s radio frequency power. The feasibility of the scheme is demonstrated with lossless data compression algorithms on the MSP430F2618 processor.

scholarly journals Hierarchical classification of time series data aggregation in underwater wireless sensor networks

2020 ◽  
Vol 37 (2) ◽  
pp. 53-64
Author(s):  
Durairaj Ruby ◽  
Jayachandran Jeyachidra
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Error Rate ◽  
Data Aggregation ◽  
Hierarchical Classification ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks

Environmental fluctuations are continuous and provide opportunities for further exploration, including the study of overground, as well as underground and submarine, strata. Underwater wireless sensor networks (UWSNs) facilitate the study of ocean-based submarine and marine parameters details and data. Hardware plays a major role in monitoring marine parameters; however, protecting the hardware deployed in water can be difficult. To extend the lifespan of the hardware, the inputs, processing and output cycles may be reduced, thus minimising the consumption of energy and increasing the lifespan of the devices. In the present study, time series similarity check (TSSC) algorithm is applied to the real-time sensed data to identify repeated and duplicated occurrences of data for reduction, and thus improve energy consumption. Hierarchical classification of ANOVA approach (HCAA) applies ANOVA (analysis of variance) statistical analysis model to calculate error analysis for realtime sensed data. To avoid repeated occurrences, the scheduled time to read measurements may be extended, thereby reducing the energy consumption of the node. The shorter time interval of observations leads to a higher error rate with lesser accuracy. TSSC and HCAA data aggregation models help to minimise the error rate and improve accuracy.

scholarly journals A data forwarding algorithm based on Markov thought in underwater wireless sensor networks

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771769198
Author(s):  
Dongwei Li ◽  
Jingli Du ◽  
Linfeng Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Success Rate ◽  
Three Dimensional ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Data Forwarding ◽  
Bottom To Top

The underwater wireless sensor networks composed of sensor nodes are deployed underwater for monitoring and gathering submarine data. Since the underwater environment is usually unpredictable, making the nodes move or be damaged easily, such that there are several vital objectives in the data forwarding issue, such as the delivery success rate, the error rate, and the energy consumption. To this end, we propose a data forwarding algorithm based on Markov thought, which logically transforms the underwater three-dimensional deployment model into a two-dimensional model, and thus the nodes are considered to be hierarchically deployed. The data delivery is then achieved through a “bottom to top” forwarding mode, where the delivery success rate is improved and the energy consumption is reduced because the established paths are more stable, and the proposed algorithm is self-adaptive to the dynamic routing loads.

scholarly journals Increasing network lifetime using data compression in wireless sensor networks with energy harvesting

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Chiwoo Cho ◽  
Kyung-Joon Park ◽  
Hyuk Lim
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Data Compression ◽  
Network Lifetime ◽  
Average Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Entire Network

In wireless sensor networks powered by battery-limited energy harvesting, sensor nodes that have relatively more energy can help other sensor nodes reduce their energy consumption by compressing the sensing data packets in order to consequently extend the network lifetime. In this article, we consider a data compression technique that can shorten the data packet itself to reduce the energies consumed for packet transmission and reception and to eventually increase the entire network lifetime. First, we present an energy consumption model, in which the energy consumption at each sensor node is derived. We then propose a data compression algorithm that determines the compression level at each sensor node to decrease the total energy consumption depending on the average energy level of neighboring sensor nodes while maximizing the lifetime of multihop wireless sensor networks with energy harvesting. Numerical simulations show that the proposed algorithm achieves a reduced average energy consumption while extending the entire network lifetime.

scholarly journals Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4273 ◽  
Author(s):  
Jianlin Liu ◽  
Fenxiong Chen ◽  
Dianhong Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Compression Ratio ◽  
Sensor Nodes ◽  
Learning Ability ◽  
Wireless Sensor ◽  
Compression Efficiency ◽  
Sensing Data

Data compression is very important in wireless sensor networks (WSNs) with the limited energy of sensor nodes. Data communication results in energy consumption most of the time; the lifetime of sensor nodes is usually prolonged by reducing data transmission and reception. In this paper, we propose a new Stacked RBM Auto-Encoder (Stacked RBM-AE) model to compress sensing data, which is composed of a encode layer and a decode layer. In the encode layer, the sensing data is compressed; and in the decode layer, the sensing data is reconstructed. The encode layer and the decode layer are composed of four standard Restricted Boltzmann Machines (RBMs). We also provide an energy optimization method that can further reduce the energy consumption of the model storage and calculation by pruning the parameters of the model. We test the performance of the model by using the environment data collected by Intel Lab. When the compression ratio of the model is 10, the average Percentage RMS Difference value is 10.04%, and the average temperature reconstruction error value is 0.2815 °C. The node communication energy consumption in WSNs can be reduced by 90%. Compared with the traditional method, the proposed model has better compression efficiency and reconstruction accuracy under the same compression ratio. Our experiment results show that the new neural network model can not only apply to data compression for WSNs, but also have high compression efficiency and good transfer learning ability.

scholarly journals Reinforcement Learning-Based Data Forwarding in Underwater Wireless Sensor Networks with Passive Mobility

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 256 ◽  
Author(s):  
Haotian Chang ◽  
Jing Feng ◽  
Chaofan Duan
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Mobility Model ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Data Forwarding ◽  
Energy Constrained

Data forwarding for underwater wireless sensor networks has drawn large attention in the past decade. Due to the harsh underwater environments for communication, a major challenge of Underwater Wireless Sensor Networks (UWSNs) is the timeliness. Furthermore, underwater sensor nodes are energy constrained, so network lifetime is another obstruction. Additionally, the passive mobility of underwater sensors causes dynamical topology change of underwater networks. It is significant to consider the timeliness and energy consumption of data forwarding in UWSNs, along with the passive mobility of sensor nodes. In this paper, we first formulate the problem of data forwarding, by jointly considering timeliness and energy consumption under a passive mobility model for underwater wireless sensor networks. We then propose a reinforcement learning-based method for the problem. We finally evaluate the performance of the proposed method through simulations. Simulation results demonstrate the validity of the proposed method. Our method outperforms the benchmark protocols in both timeliness and energy efficiency. More specifically, our method gains 83.35% more value of information and saves up to 75.21% energy compared with a classic lifetime-extended routing protocol (QELAR).

scholarly journals DMTC: Optimize Energy Consumption in Dynamic Wireless Sensor Network Based on Fog Computing and Fuzzy Multiple Attribute Decision-Making

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Abbas Varmaghani ◽  
Ali Matin Nazar ◽  
Mohsen Ahmadi ◽  
Abbas Sharifi ◽  
Saeid Jafarzadeh Ghoushchi ◽  
...  
Keyword(s):  
Decision Making ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Complexity Analysis ◽  
Multiple Attribute Decision Making ◽  
Wireless Sensor ◽  
Multiple Attribute

Advances in wireless technologies and small computing devices, wireless sensor networks can be superior technology in many applications. Energy supply constraints are one of the most critical measures because they limit the operation of the sensor network; therefore, the optimal use of node energy has always been one of the biggest challenges in wireless sensor networks. Moreover, due to the limited lifespan of nodes in WSN and energy management, increasing network life is one of the most critical challenges in WSN. In this investigation, two computational distributions are presented for a dynamic wireless sensor network; in this fog-based system, computing load was distributed using the optimistic and blind method between fog networks. The presented method with the main four steps is called Distribution-Map-Transfer-Combination (DMTC) method. Also, Fuzzy Multiple Attribute Decision-Making (Fuzzy MADM) is used for clustering and routing network based on the presented distribution methods. Results show that the optimistic method outperformed the blind one and reduced energy consumption, especially in extensive networks; however, in small WSNs, the blind scheme resulted in an energy efficiency network. Furthermore, network growth leads optimistic WSN to save higher energy in comparison with blinded ones. Based on the results of complexity analysis, the presented optimal and blind methods are improved by 28% and 48%, respectively.

scholarly journals Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3271 ◽  
Author(s):  
Arshad Sher ◽  
Aasma Khan ◽  
Nadeem Javaid ◽  
Syed Ahmed ◽  
Mohammed Aalsalem ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Destination Node ◽  
Transmission Range ◽  
Delivery Ratio ◽  
Underwater Wireless Sensor Networks ◽  
End To End Delay ◽  
End To End

Due to the limited availability of battery power of the acoustic node, an efficient utilization is desired. Additionally, the aquatic environment is harsh; therefore, the battery cannot be replaced, which leaves the network prone to sudden failures. Thus, an efficient node battery dissipation is required to prolong the network lifespan and optimize the available resources. In this paper, we propose four schemes: Adaptive transmission range in WDFAD-Depth-Based Routing (DBR) (A-DBR), Cluster-based WDFAD-DBR (C-DBR), Backward transmission-based WDFAD-DBR (B-DBR) and Collision Avoidance-based WDFAD-DBR (CA-DBR) for Internet of Things-enabled Underwater Wireless Sensor Networks (IoT, UWSNs). A-DBR adaptively adjusts its transmission range to avoid the void node for forwarding data packets at the sink, while C-DBR minimizes end-to-end delay along with energy consumption by making small clusters of nodes gather data. In continuous transmission range adjustment, energy consumption increases exponentially; thus, in B-DBR, a fall back recovery mechanism is used to find an alternative route to deliver the data packet at the destination node with minimal energy dissipation; whereas, CA-DBR uses a fall back mechanism along with the selection of the potential node that has the minimum number of neighbors to minimize collision on the acoustic channel. Simulation results show that our schemes outperform the baseline solution in terms of average packet delivery ratio, energy tax, end-to-end delay and accumulated propagation distance.

Sign in / Sign up

Export Citation Format

Share Document