scholarly journals Analytical Model of Energy Consumption in Cluster based Wireless Sensor Networks with Data Aggregation using M/M/1 Queuing Model

2012 ◽  
Vol 53 (13) ◽  
pp. 1-6
Author(s):  
Manijeh Keshtgary ◽  
Mohammad Reza Mansouri ◽  
Reza Mohammadi ◽  
Mohammad Mahmoudi
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Analytical Model ◽  
Data Aggregation ◽  
Wireless Sensor ◽  
Queuing Model

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 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 Enabling Green Wireless Sensor Networks: Energy Efficient T-MAC Using Markov Chain Based Optimization

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 534 ◽  
Author(s):  
Mahendra Ram ◽  
Sushil Kumar ◽  
Vinod Kumar ◽  
Ajay Sikandar ◽  
Rupak Kharel
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Markov Chain ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Access Control ◽  
Analytical Model ◽  
Wireless Sensor ◽  
Medium Access ◽  
Service Delay

Due to the rapidly growing sensor-enabled connected world around us, with the continuously decreasing size of sensors from smaller to tiny, energy efficiency in wireless sensor networks has drawn ample consideration in both academia as well as in industries’ R&D. The literature of energy efficiency in wireless sensor networks (WSNs) is focused on the three layers of wireless communication, namely the physical, Medium Access Control (MAC) and network layers. Physical layer-centric energy efficiency techniques have limited capabilities due to hardware designs and size considerations. Network layer-centric energy efficiency approaches have been constrained, in view of network dynamics and available network infrastructures. However, energy efficiency at the MAC layer requires a traffic cooperative transmission control. In this context, this paper presents a one-dimensional discrete-time Markov chain analytical model of the Timeout Medium Access Control (T-MAC) protocol. Specifically, an analytical model is derived for T-MAC focusing on an analysis of service delay, throughput, energy consumption and power efficiency under unsaturated traffic conditions. The service delay model calculates the average service delay using the adaptive sleep wakeup schedules. The component models include a queuing theory-based throughput analysis model, a cycle probability-based analytical model for computing the probabilities of a successful transmission, collision, and the idle state of a sensor, as well as an energy consumption model for the sensor’s life cycle. A fair performance assessment of the proposed T-MAC analytical model attests to the energy efficiency of the model when compared to that of state-of-the-art techniques, in terms of better power saving, a higher throughput and a lower energy consumption under various traffic loads.

Cluster Trisecting Based Data Aggregation Scheme for Wireless Sensor Networks

2013 ◽  
Vol 756-759 ◽  
pp. 1126-1130
Author(s):  
Jiang Hong Guo ◽  
De Li Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Wireless Sensor ◽  
Important Method ◽  
Cluster Aggregation ◽  
Aggregation Scheme ◽  
Reduce Energy Consumption

Data aggregation is an important method to reduce energy consumption in wireless sensor networks (WSN). Auth-ors proposed a cluster trisecting based data aggregation scheme for wireless sensor networks in which the cluster was trisected and some reporters were assigned to each region. The nodes have same reading and located in same region with reporter will keep silent in data aggregating, thus reducing the inner-cluster transmissions. Analysis and simulation show that the transmissions of inner-cluster aggregation in our scheme lower than that of related schemes and the decrease of trans-missions is obvious when redundancy of sensor readings is high.

scholarly journals A DISTRIBUTED ENERGY EFFICIENT CLUSTERING ALGORITHM FOR DATA AGGREGATION IN WIRELESS SENSOR NETWORKS

2018 ◽  
Vol 19 (1) ◽  
pp. 72-90
Author(s):  
Seyed Mohammad Bagher Musavi Shirazi ◽  
Maryam Sabet ◽  
Mohammad Reza Pajoohan
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Clustering Algorithm ◽  
Base Station ◽  
Wireless Sensor ◽  
Relay Nodes ◽  
Cluster Heads ◽  
Energy Efficient Clustering

Wireless sensor networks (WSNs) are a new generation of networks typically consisting of a large number of inexpensive nodes with wireless communications. The main purpose of these networks is to collect information from the environment for further processing. Nodes in the network have been equipped with limited battery lifetime, so energy saving is one of the major issues in WSNs. If we balance the load among cluster heads and prevent having an extra load on just a few nodes in the network, we can reach longer network lifetime. One solution to control energy consumption and balance the load among nodes is to use clustering techniques. In this paper, we propose a new distributed energy-efficient clustering algorithm for data aggregation in wireless sensor networks, called Distributed Clustering for Data Aggregation (DCDA). In our new approach, an optimal transmission tree is constructed among sensor nodes with a new greedy method. Base station (BS) is the root, cluster heads (CHs) and relay nodes are intermediate nodes, and other nodes (cluster member nodes) are the leaves of this transmission tree. DCDA balances load among CHs in intra-cluster and inter-cluster data communications using different cluster sizes. For efficient inter-cluster communications, some relay nodes will transfer data between CHs. Energy consumption, distance to the base station, and cluster heads’ centric metric are three main adjustment parameters for the cluster heads election. Simulation results show that the proposed protocol leads to the reduction of individual sensor nodes’ energy consumption and prolongs network lifetime, in comparison with other known methods. ABSTRAK: Rangkaian sensor wayarles (WSN) adalah rangkaian generasi baru yang terdiri daripada nod-nod murah komunikasi wayarles. Tujuan rangkaian-rangkaian ini adalah bagi mengumpul maklumat sekeliling untuk proses seterusnya. Nod dalam rangkaian ini dilengkapi bateri kurang jangka hayat, jadi simpanan tenaga adalah satu isu besar dalam WSN. Jika beban diimbang antara induk kelompok dan lebihan beban dihalang pada setiap rangkaian iaitu hanya sebilangan kecil nod pada tiap-tiap kelompok,  jangka hayat dapat dipanjangkan pada sesebuah rangkaian. Satu penyelesaian adalah dengan mengawal penggunaan tenaga dan mengimbangi beban antara nod menggunakan teknik berkelompok. Kajian ini mencadangkan kaedah baru pembahagian tenaga berkesan secara algoritma berkelompok bagi pembahagian data dalam WSN, dikenali sebagai Pembahagian Kelompok Kumpulan Data (DCDA). Melalui pendekatan baru ini, pokok transmisi optimum dibina antara nod sensor melalui kaedah baru. Stesen utama (BS) ialah akar, induk kelompok-kelompok (CHs) dan nod penyiar ialah nod perantara, dan nod-nod lain (nod-nod ahli kelompok) ialah daun bagi pokok trasmisi. DCDA mengimbangi beban CHs antara-kelompok dan dalam-kelompok komunikasi data daripada kelompok berbeza saiz. Bagi komunikasi berkesan dalam-kelompok, sebahagian nod penyampai akan memindahkan data antara CHs. Penggunaan tenaga, jarak ke stesen utama dan induk kelompok metrik sentrik adalah tiga parameter pelaras bagi pemilihan induk kelompok. Keputusan simulasi protokol yang dicadang menunjukkan pengurangan penggunaan tenaga pada nod-nod sensor individu dan memanjangkan jangka hayat rangkaian, berbanding kaedah-kaedah lain yang diketahui.

Analytical Model of Wireless Sensor Networks Based on Data Aggregation for Debris Flow Monitoring System

2014 ◽  
Vol 536-537 ◽  
pp. 314-319
Author(s):  
Nan Chen ◽  
Jing Fan ◽  
Min Min Xiang ◽  
Jin Shuai Qu
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Debris Flow ◽  
Real Time ◽  
Network Topology ◽  
Data Aggregation ◽  
Wireless Sensor ◽  
Time Data ◽  
Flow Monitoring

In view of debris flow monitoring for complicated mountainous terrain, network topology is impacted by environmental change, this paper designing a structure-free network topology to enhance network robustness. While applying the weighted resampling algorithm, to avoid monitoring data loss, make sure fairness transmission sensor nodes. To solve the problem of energy consumption and the time delay in wireless sensor networks, a real-time data aggregation algorithm proposed to reduce the redundant information transmission, to improve the energy efficiency. The simulation results show that adopted the network model of data aggregation is effective in reducing the energy consumption and improves the quality of network communication, while meeting the requirements of real-time monitoring.

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