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Software defined network based self-diagnosing faulty node detection scheme for surveillance applications
Journal Pre-proof Software defined network based self-diagnosing faulty node detection scheme for surveillance applications R. Palanikumar, K. Ramasamy
PII: DOI: Reference:
S0140-3664(19)31152-1 https://doi.org/10.1016/j.comcom.2019.12.034 COMCOM 6091
To appear in:
Computer Communications
Received date : 10 September 2019 Revised date : 10 December 2019 Accepted date : 19 December 2019 Please cite this article as: R. Palanikumar and K. Ramasamy, Software defined network based self-diagnosing faulty node detection scheme for surveillance applications, Computer Communications (2019), doi: https://doi.org/10.1016/j.comcom.2019.12.034. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
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Software Defined Network Based Self-Diagnosing Faulty Node Detection Scheme for surveillance applications Dr. K.Ramasamy2
Associate Professor, Department of CSE, P.S.R Engineering College, Sivakasi, Tamilnadu
Professor, Department of ECE, P.S.R.R. College of Engineering for Women, Sivakasi, Tamilnadu [email protected]
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R.Palanikumar1
[email protected]
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Abstract – Unmanned aerial vehicles (UAV) can be used as basic elements of the sensor network or an upgrade of existing network that are built with static wireless sensor nodes. Wireless Networks (WN) are utilized across in surveillance in all domains, like natural disasters, agriculture, water, forest, military, buildings, health monitoring, disaster relief & emergency
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management, area and industrial surveillance , due to its wider applicability. Though the performance of WN is satisfactory, it still suffers from several challenges such as energy efficiency, reliability and security. This work attempts to render reliability to the wireless network as sensor physical node in network by detecting the faulty sensor nodes. The faulty
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sensor nodes degrade the performance of the complete sensor network and hence, it is necessary to detect the faulty sensor nodes to attain better Quality of Service (QoS) with software defined in network. The proposed method detects the faulty sensor nodes using software defined network approach by means of Reward-and-Punishment Model (RPM) and the hypothetical analysis of Dempster-Shafer (DS) theory protocol approach. The performance of the proposed approach is observed to be satisfactory in terms of Quality of Service parameters of faulty node detection accuracy, energy consumption and network lifetime. Keywords – Unmanned aerial Vehicles, surveillance, Software defined network, wireless networks, Quality of Service, Faulty sensor node detection, energy efficiency, and network lifetime.
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1. Introduction
A Wireless Networks (WN) is formed by numerous distributed sensor nodes with
one or more sink nodes. Each and every sensor node of WN is equipped with the three basic components, which are microcontroller, transceiver and power components. The sensor nodes utilize these components for performing local data processing, data transmission and all these
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functionalities are performed by the power backup. The data transmission is achieved by exploiting the radio channels and the communication can take place in two ways such as direct and indirect means.
The direct mean of communication allows the source node to directly
communicate with the destination node. Similarly, indirect mode of communication involves
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some number of sensor nodes between the source and destination node. Due to the simplicity and work efficiency, WN is employed in numerous real-time applications [1-3]. Some of the most prominent applications of WN are environmental sensing, security based, monitoring and
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tracking based applications [4-5].
Energy efficiency is the crucial requirement of WSN, as the sensor nodes are packed with limited energy and in most of the cases, the batteries are irreplaceable or not rechargeable [8]. The work highlights of this article are as follows.
Faulty nodes are dangerous to the network, as all the operations involved in the WN take
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the faulty nodes into account for estimating all the basic operations such as routing, data collection and so on. Hence, this work presents an approach to detect faulty nodes in the
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network.
Faulty node detection helps the network to plan accordingly without including the faulty nodes and this idea conserves energy.
The proposed approach detects faulty nodes based on RPM, which relies on the data of the neighbourhood nodes. Hence, this technique is simple and consumes minimal energy.
The lifetime of the network is satisfactory, as the energy consumption is reasonable.
2. Review of Literature
In [1] Akyildiz et al. , proposed about the Wireless sensor networks. In [2] Harun et al. , proposed about the WSN application in led plant factory using continuous lighting (CL)
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method. In [3] et al. Lu, I proposed about the WSN for machine area network applications. In [4] K. Mikhaylov, et al. proposed about the Wireless Sensor Networks in industrial environment. In [5] G. Alderisi et al. proposed about the Simulative assessments of the IEEE 802.15.4e DSME and TSCH in realistic process automation scenarios. In [6] T. Senthil et al. proposed about the Energy Conserving Trustworthy Multipath Routing Algorithm Based on Cuckoo Search Algorithm.
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In [7] Li et al. proposed about the Enhancing real-time delivery in wireless sensor networks with two-hop information. In [8] Kerasiotis et al. proposed about a Battery lifetime prediction model for a WSN platform. In [9], proposed about the distributed faulty node detection technique for delay tolerant networks is presented. This work is meant for Delay Tolerant Networks (DTN)
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and detects the nodes that produce faulty data. The behaviour of the nodes is modelled by continuous time state equations. A Round Trip Delay (RTD) based matrix calculus (MCS) method is proposed in [10] for finding multiple failure nodes. This work suits only for 3 × 3
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square matrix and gives optimal results for 3 × 3 matrix and not for other matrices. So, it is not well suited for all other forms of matrices. A technique to detect anomalies and array diagnosis is proposed for WSN with several antennas [11]. The goal of this work is achieved by fusing relational data and measured signals. Initially, the anomalies are detected and the array diagnosis is performed by fusion of data and signal. A threshold tuning based wearable sensor fault
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detection scheme for medical monitoring system is proposed in [12]. In [13] Seyed Ahmad Soleymani proposed a Secure Trust Model Based on Fuzzy Logic in Vehicular Ad Hoc Networks With Fog Computing. In [14] Kasilingam Rajeswari et al. proposed about the Genetic
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algorithm based fault tolerant clustering in wireless sensor network. In [15] Amir Mehdi Pasdar et al. proposed about the Detecting and Locating Faulty Nodes in Smart Grids Based on High Frequency Signal Injection.Motivated by the existing works, the proposed work aims to present a self-diagnosing technique for detecting faulty nodes, which results in increased QoS (Quality of Service) and decreases the energy consumption of the network. The proposed approach is described in the following section.
3. Proposed Software defined based Self-diagnosing Fault Detection Technique for WSN 3.1 Work Overview
In addition to this, all the operations of the network consider the sensor nodes without any discrimination. This leads to serious issue in case of real-time applications. In order
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to deal with these issues, it is suggested to detect the faulty nodes in advance, such that they can be excluded in performing network operations. This idea ensures the QoS and boosts up the reliability of the system. Additionally, the time and computational complexity are minimized due to the negligence of the faulty nodes. This in turn conserves the energy and leads to network lifetime maximization. Hence, detection of faulty nodes introduces numerous advantages to the network. Considering all these points, this paper intends to propose a self-diagnosing faulty node
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detection system for WirelessNetwork. This work analyses the nature of a sensor node by taking the data provided by the neighbourhood sensor nodes into account. This idea is simple and efficient. Figure. 1 represents the faulty sensor nodes. The faulty behavior of sensor nodes may result from many situations, such as a faulty decision from the signal processing in a senor node
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due to noise , malfunctions due to low-cost hardware, environmental interference, or battery
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depletion.
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Figure.1. Faulty and Perfect nodes
3.2 Node Cluster Formation
This work assumes that all the participating sensor nodes of this work are immobile and the BS alone is mobile. This work clusters the deployed sensor nodes, so as to attain better manageability and control. When the sensor nodes are clustered, each and every cluster is assigned with the cluster manager node. The main job of cluster manager node is to have control over the cluster member nodes with respect to communication and general maintenance. Generally, a cluster manager node is selected in such a way that it is reliable and energy packed. The concept of clustering brings in energy efficiency and easy management. There are several techniques in the existing literature to select better cluster manager amidst all sensor nodes.
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However, the scope of the work is limited to fault detection of sensor nodes and hence, the concept of clustering is made simple by considering energy backup alone. In order to form cluster, the sensor nodes are monitored for a time period 𝑡𝑝 . The participating nodes of the network passes an 𝑖𝑛𝑖𝑡 energy backup 𝑐𝑢𝑟
, which is comprised of the node’s unique identifier and the current
. On reception of an 𝑖𝑛𝑖𝑡
, the sensor node measures the distance
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between the message forwarded node and the node itself with the help of the signal power. The energy consumption style of the sensor node is computed as per in eqn. (1). 𝐸𝑁 𝑖
(1)
𝑡𝑖𝑚𝑒
𝑖
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In the above equation, 𝐸𝑁 𝑖 indicates the energy consumption model of the 𝑖 sensor node, 𝑐𝑜𝑛𝑠 𝑖 is the used energy and 𝑐𝑢𝑟 𝑖 is the presently available energy of the 𝑖 that contains the unique identifier of node. The time consumption for broadcasting the 𝑐𝑜𝑛 the sensor node is calculated by eqn.(2). 𝑖 . 𝑟𝑎𝑛𝑑
𝑐𝑢𝑟
(2)
In eqn.(2), 𝑟𝑎𝑛𝑑 is a random number that can exist between 0.5 and 1. The rand value stops the 𝑐𝑜𝑛
from being forwarded at the same time, which reduces the collision rate.
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Additionally, the energy consumption rate is reduced. The complete work flow of the proposed fault node detection algorithm is given in figure. 2. The nearby nodes join the corresponding cluster manager to work co-operatively. Now, the RPM model is computed by the neighbourhood nodes for all the sensor nodes and reported to the cluster manager. The RPM of
3.3 RPM Model
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the cluster manager is computed by the nearby cluster manager node and reported to the BS.
When the cluster manager node is selected, it forwards a 𝐻𝐼 message to all its member nodes. The rewards or punishments of all the sensor nodes are computed by the following equations. The member nodes send an 𝐴𝐶𝐾 message to the cluster manager node, based on which the reward 𝑅 and punishment 𝑃 is computed. 𝑅
,
𝑃
,
,
,
(3) (4)
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The cluster manager 𝐶𝑀 node collects the 𝑅 and 𝑃 data from the neighbourhood
nodes of every corresponding node. The 𝑅 and 𝑃 value of the cluster member sensor nodes are collected from 𝑠 number of nodes to represent the nodes as perfect 𝑃 neither perfect nor faulty 𝑄
, faulty 𝐹
and
. The 𝑅 and 𝑃 values of the corresponding node collected from
the neighbourhood nodes are represented by 𝑟
and 𝑝
, where 𝑟
∈ 𝑅, 𝑝
∈ 𝑃.
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(5)
𝐹
(6)
𝑄
(7)
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𝑃
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Proposed Faulty Sensor Node Detection Algorithm Input : Sensor Nodes Output : Faulty node detection Begin //Clustering Observe the nodes for 60 seconds; Circulate 𝑖𝑛𝑖𝑡 among the sensors; Select cluster manager node based on eqn.(2); For each cluster manager Do Forward 𝐽𝑂𝐼𝑁 message; Receive acknowledgement; If (node count<20) Add the neighbourhood node to the cluster; End; End do; End for; //RP computation Compute R and P values and obtain from neighbourhood nodes; Normalize the values by eqn.(8); Apply DS theory to perform hypothetical analysis; Detect faulty sensor nodes; End;
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Observe the nodes for 60 seconds
For each cluster manager
Join request
If node count is 20
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Msg Initialization
Fault node detection
Yes
Apply D.S theory
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Values normalization
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Figure. 2. Proposed software defined based Faulty Sensor Node Detection Algorithm
These reward and punishment are collected by the cluster manager node from the neighbourhood nodes of all the nodes in the cluster. Hence, each sensor node has two reward and punishment data. The values range from 0 to 2 and the values are normalised by the following equation. 𝑁𝑜𝑟𝑚
(8)
In the above given equation, the value of 𝑥 is the original value of the RP model which is to be normalized. 𝑎𝑐 and 𝑎𝑐 are the least and highest values of the attribute. 𝑛 and 𝑛 are the required range of values for performing normalization. The reason for normalizing the values is
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to end up with easy data processing. In this work, the values of 𝑛
and 𝑛 are 1 and 0
respectively. Finally, the DS theory is utilized for performing the hypothetic analysis [22], as presented below. 𝑁𝑁1 𝑃
𝑁𝑁1 𝑄
⊕ 𝑁𝑁2 𝑃 𝑁𝑁2 𝑃
𝑁𝑁1 𝑃
𝑁𝑁2 𝑃
𝑁𝑁1 𝑃
𝑁𝑁2 𝑄 (9)
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𝑁𝑁1 𝐹 𝑁𝑁1 𝑄 𝑁𝑁1 𝑄
⊕ 𝑁𝑁2 𝐹
𝑁𝑁1 𝐹
𝑁𝑁2 𝐹
𝑁𝑁1 𝐹
𝑁𝑁2 𝑄
𝑁𝑁2 𝐹
(10)
⊕ 𝑁𝑁2 𝑄
𝑁𝑁1 𝑄
𝑁𝑁2 𝑄
(11)
𝑁𝑁1 𝑃
𝑁𝑁2 𝑃
𝑁𝑁1 𝐹
𝑁𝑁2 𝐹
𝑁𝑁1 𝑄
𝑁𝑁2 𝑄
𝑁𝑁1 𝑃 𝑁𝑁1 𝐹
𝑁𝑁2 𝑄
𝑁𝑁2 𝑄
(12)
𝑁𝑁1 𝑄 𝑁𝑁1 𝑄
𝑁𝑁2 𝑃
𝑁𝑁2 𝐹
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𝑤
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In the above equations (9), (10) and (11), 𝑤 is computed as follows.
By this way, the hypothetical analysis is performed. Each and every sensor node gets two values for RP scheme. This value ranges between 0 and 1, when the value is 1 the results is
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not faulty and 0 indicates the node is completely faulty. The nodes with value 0.5 or greater are considered as neither perfect nor faulty. However, this work fixes a threshold that the nodes with value greater than or equal to 0.6 are considered as perfect and the nodes with lesser than 0.6 are considered as faulty and are blocked by the cluster manager. The same process is performed for
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cluster manager nodes by the BS. The cluster manager is selected only by considering energy and so the cluster manager node can even be faulty. In order to deal with this, the performance of the cluster manager node is measured by the BS, which is mobile. The reliability of cluster manager node is tested in a regular time interval, which is 120 seconds. As the faulty nodes are blocked, there is no chance of performance deterioration and the QoS is improved. The following section analyses the performance of the proposed approach. 4. Results and Discussion
The proposed self-diagnosing faulty node detection scheme is simulated with the help of NS2 [21]. The performance of the proposed approach is tested by distributing 250 nodes in a random fashion. The simulation area is set to 500
500 𝑚 . All the nodes are aware of
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their location information and initially all the nodes are with full energy backup. The performance of the proposed approach is compared with the existing approaches such as threshold based [12] and RTD based in terms of faulty node detection accuracy, packet delivery rate, energy consumption, network lifetime. The faulty nodes are distributed in the ratio of 0.3. The faulty node detection accuracy is the most important metric, as it is meant for
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checking the basic functionality of the proposed approach. This work computes the faulty node detection accuracy of the proposed approach and the results are compared with the existing
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approaches as given in figure. 3.
Figure.3. Faulty node detection accuracy analysis This analysis is performed by randomly distributing the 10 faulty nodes here and there. On analysis, it is observed that the average faulty node detection accuracy rate decreases
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with the increase of sensor nodes. The greatest faulty node detection accuracy rate is found when the number of sensor nodes is 50 and is observed with the proposed approach. The greatest average faulty node detection rate of the proposed approach is 98 percent and the least faulty node detection rate is 89 percent. Both the existing approaches perform well but not equally to the proposed approach. In the second phase, the performance of the proposed approach is analysed by varying the count of faulty nodes in the network and the results are presented in figure 4. The count of faulty nodes is varied and the performance of the proposed approach is tested. As the faulty nodes increase, the detection accuracy decreases and is evident. However, the proposed approach performs a decent job by detecting maximal number of faulty nodes even when the count of faulty nodes is increased. The least performance is observed when the faulty
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node count is 25 and the detection accuracy of the proposed approach is 82 percent. On the other the existing approaches show 69 and 72 percent respectively. The figure. 5 presents the energy consumption analysis of the proposed approach with respect to the simulation time.
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Figure.4. Faulty node detection accuracy by varying faulty node count
Figure.5. Energy consumption analysis
The energy consumption pattern of the faulty node detection techniques are analysed and the experimental values are presented in table 1 and the results are depicted in figure 5. The proposed approach consumes minimal energy when compared to the existing approach. The
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main reason for minimal energy consumption is that the proposed approach introduces simple mechanism to detect faulty nodes, which results in reduced time and computational overhead. All these together reduce the energy consumption of the proposed approach and increase its energy efficiency. Energy efficiency is closely related to the network lifetime and is inversely proportional to network lifetime. As the proposed approach shows minimal energy consumption, the impact of it is seen in the network lifetime as shown in figure 5.
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Table 1: Comparison of Average Energy Consumption (J)
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100 150 200 250 300 350 400 450 500
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Simulation Time (s)
Average Energy Consumption (J) Proposed(SDN fault sensor Threshold based RTD based node detection) 0.18 0 0.0 0.23 0.1 0.03 0.25 0.15 0.09 0.28 0.19 0.1 0.29 0.18 0.15 0.3 0.23 0.19 0.35 0.28 0.23 0.38 0.32 0.27 0.42 0.39 0.3
Figure.6. Network lifetime analysis
The lifetime of the network can be analysed in two different ways and they are by noting the time at which the first node dies and the second way is by counting the number of live
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nodes. As time progresses, the number of live nodes decreases and the proposed approach shows greater number of live nodes when compared to the existing approaches. From the experimental results, it is noted that the number of live nodes in case of proposed approach is 6. On the other hand, the existing approaches show 158 and 167 nodes at the 500th second of simulation. The comparison values are presented in table 2 and the corresponding results are shown in figure. 6.
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As stated earlier, energy conservation increases the lifetime of the network. Hence, the proposed approach detects the faulty nodes in a better way by consuming reasonable energy. Table 2: Comparison of Number of live nodes No. of nodes alive Threshold based
RTD based
0 100 150 200 250 300 350 400 450 500
200 198 195 190 188 185 183 8 164 158
200 200 197 196 193 188 181 6 1 167
Proposed SDN fault sensor node detection 200 200 200 195 193 190 189 186 181 6
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Simulation Time (s)
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The major drawback of the existing threshold and the RTD method is that they often lack the sensitivity and specificity needed for accurate classification. Here the issues can be clarified. 5. Conclusion
In this paper presents a software defined network based self-diagnosing faulty sensor node detection scheme for WSN as part of Unmanned aerial vehicle. Faulty nodes degrade the performance of the sensor network and have to be detected, such that the Quality of Service is improved as security of network is improved. This work detects the faulty nodes by employing a simple approach that could conserve energy and thereby increasing the lifetime of the network. The proposed approach employs RP model and performs hypothetical analysis by DS theory.
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The performance of the proposed approach is tested in terms of faulty node detection accuracy, energy consumption and lifetime analysis. Also, the proposed approach shows better performance and in future, the faulty nodes can be detected by the hybrid of centralized and distributed approach. References
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[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless sensor networks: A survey,'' Comput. Netw., vol. 38, no. 4, pp. 393-422, 2002. [2] A. N. Harun, R. Ahmad, and N. Mohamed, ``WSN application in led plant factory using continuous lighting (CL) method,'' in Proc. IEEE Conf. Open Syst. (ICOS), Aug. 2015, pp. 56-61
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[3] X. Lu, I. H. Kim, A. Xhafa, and J. Zhou, "WSN for machine area network applications,'' in Proc. 46th Eur. Solid-State Device Res.Conf. (ESSDERC), Sep. 2016, pp. 23-28.
[4] K. Mikhaylov, J. Tervonen, J. Heikkilä, and J. Känsäkoski, "Wireless Sensor Networks in Commun., Apr. 2012, pp. 1-7.
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industrial environment: Real-life evaluation results,'' in Proc. 2nd Baltic Congr. Future Internet [5] G. Alderisi, G. Patti, O. Mirabella, and L. L. Bello, "Simulative assessments of the IEEE 802.15.4e DSME and TSCH in realistic process automation scenarios,'' in Proc. IEEE 13th Int. Conf. Ind. Inform. (INDIN), Jul. 2015, pp. 948-955
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[6] T. Senthil, Dr. B. Kannapiran, "ECTMRA: Energy Conserving Trustworthy Multipath Routing Algorithm Based on Cuckoo Search Algorithm", Wireless Personal Communications, Vol.88, No.3, 2016.
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[7] Y. Li, C. S. Chen, Y.-Q. Song, Z.Wang, and Y. Sun, "Enhancing real-time delivery in wireless sensor networks with two-hop information", IEEE Trans. Ind. Informat., vol. 5, no. 2, pp. 113-122, May 2009..
[8] F. Kerasiotis, A. Prayati, C. Antonopoulos, C. Koulamas, and G. Papadopoulos, "Battery lifetime prediction model for a WSN platform,'' in Proc. 4th Int. Conf. Sensor Technol. Appl., Jul. 2010, pp. 525-530.
[9] Wenjie Li ; Laura Galluccio ; Francesca Bassi ; Michel Kieffer, "Distributed Faulty Node Detection in Delay Tolerant Networks: Design and Analysis", IEEE Transactions on Mobile Computing, Vol., No.4, pp. 831-844, 2018.
[10] R. Palanikumar and K. Ramasamy, “Effective failure nodes detection using matrix calculus algorithm in wireless sensor networks”, Cluster Computing – The Journal of Networks, Software
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Tools and Applications, Vol 21, pp 1-10, 2018.
[11] Bo Wang ; Fengye Hu ; Yanping Zhao ; Terry N. Guo, "Anomaly Detection and Array Diagnosis in Wireless Networks with Multiple Antennas: Framework, Challenges and Tools", IEEE Network, Vol.32, No.1, pp. 152-159, 2018.
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[12] Haibin Zhang ; Jiajia Liu ; Nei Kato, "Threshold Tuning-Based Wearable Sensor Fault Detection for Reliable Medical Monitoring Using Bayesian Network Model", IEEE Systems Journal, Vol. 12, No.2, pp.1886-1896, 2018. [13] Seyed Ahmad Soleymani ; Abdul Hanan Abdullah ; Mahdi Zareei ; Mohammad Hossein Computing", IEEE Access, Vol.5, pp.15619-15629, 20.
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Anisi, "A Secure Trust Model Based on Fuzzy Logic in Vehicular Ad Hoc Networks With Fog [14] Kasilingam Rajeswari ; Subbu Neduncheliyan, "Genetic algorithm based fault tolerant
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clustering in wireless sensor network", IET Communications, Vol.11, No.12, pp.1927-1932, 20. [15] Amir Mehdi Pasdar ; Yilmaz Sozer ; Iqbal Husain, "Detecting and Locating Faulty Nodes in Smart Grids Based on High Frequency Signal Injection", IEEE Transactions on Smart Grid,
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Vol.4, No.2, pp.1067-1075, 2013.
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Conflict of Interest This paper has not communicated anywhere till this moment, now only it is communicated to your esteemed journal for the publication with the knowledge of all co-authors.
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Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
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AUTHORSHIP STATEMENT
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Manuscript title: SOFTWARE DEFINED NETWORK BASED SELF-DIAGNOSING FAULTY NODE DETECTION SCHEME FOR SURVEILLANCE APPLICATIONS
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All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Furthermore, each author certifies that this material or similar material has not been and will not be submitted to or published in any other publication before its appearance in the Computer Communications.
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Authorship contributions Please indicate the specific contributions made by each author. The name of each author must appear at least once in each of the three categories below. Category 1 Conception and designofstudy:R. Palanikumar ;
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,
Acquisition of data:R. Palanikumar
,
,
,
,
Analysis and/or interpretation of data:R. Palanikumar
,
,
Category 2 Drafting the manuscript:R. Palanikumar ,
,
,
,
Revising the manuscript critically for important intellectual content: R. Palanikumar,
.
;
,
.
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,
;
Category 3 Approval of the version of the manuscript to be published (the names of all authors must be listed):
R. Palanikumar , Dr. K. Ramasamy,
,
,
,
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, , , , . Acknowledgements Allpersonswhohavemadesubstantialcontributionstotheworkreportedinthemanuscript(e.g.,technical help, writing and editing assistance, general support), but who do not meet the criteria for authorship, are named in the Acknowledgements and have given us their written permission to be named. If we have not includedanAcknowledgements,thenthatindicatesthatwehavenotreceivedsubstantialcontributionsfro m non-authors.
This statement is signed by all the authors (a photocopy of this form may be used if there are more than 10 authors):
R.Palanikumar
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Dr. K. Ramasamy
Author’ssignature
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Author’sname(typed)
Date
09.12.2019 09.12.2019
PII: DOI: Reference:
S0140-3664(19)31152-1 https://doi.org/10.1016/j.comcom.2019.12.034 COMCOM 6091
To appear in:
Computer Communications
Received date : 10 September 2019 Revised date : 10 December 2019 Accepted date : 19 December 2019 Please cite this article as: R. Palanikumar and K. Ramasamy, Software defined network based self-diagnosing faulty node detection scheme for surveillance applications, Computer Communications (2019), doi: https://doi.org/10.1016/j.comcom.2019.12.034. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
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Software Defined Network Based Self-Diagnosing Faulty Node Detection Scheme for surveillance applications Dr. K.Ramasamy2
Associate Professor, Department of CSE, P.S.R Engineering College, Sivakasi, Tamilnadu
Professor, Department of ECE, P.S.R.R. College of Engineering for Women, Sivakasi, Tamilnadu [email protected]
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R.Palanikumar1
[email protected]
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Abstract – Unmanned aerial vehicles (UAV) can be used as basic elements of the sensor network or an upgrade of existing network that are built with static wireless sensor nodes. Wireless Networks (WN) are utilized across in surveillance in all domains, like natural disasters, agriculture, water, forest, military, buildings, health monitoring, disaster relief & emergency
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management, area and industrial surveillance , due to its wider applicability. Though the performance of WN is satisfactory, it still suffers from several challenges such as energy efficiency, reliability and security. This work attempts to render reliability to the wireless network as sensor physical node in network by detecting the faulty sensor nodes. The faulty
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sensor nodes degrade the performance of the complete sensor network and hence, it is necessary to detect the faulty sensor nodes to attain better Quality of Service (QoS) with software defined in network. The proposed method detects the faulty sensor nodes using software defined network approach by means of Reward-and-Punishment Model (RPM) and the hypothetical analysis of Dempster-Shafer (DS) theory protocol approach. The performance of the proposed approach is observed to be satisfactory in terms of Quality of Service parameters of faulty node detection accuracy, energy consumption and network lifetime. Keywords – Unmanned aerial Vehicles, surveillance, Software defined network, wireless networks, Quality of Service, Faulty sensor node detection, energy efficiency, and network lifetime.
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1. Introduction
A Wireless Networks (WN) is formed by numerous distributed sensor nodes with
one or more sink nodes. Each and every sensor node of WN is equipped with the three basic components, which are microcontroller, transceiver and power components. The sensor nodes utilize these components for performing local data processing, data transmission and all these
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functionalities are performed by the power backup. The data transmission is achieved by exploiting the radio channels and the communication can take place in two ways such as direct and indirect means.
The direct mean of communication allows the source node to directly
communicate with the destination node. Similarly, indirect mode of communication involves
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some number of sensor nodes between the source and destination node. Due to the simplicity and work efficiency, WN is employed in numerous real-time applications [1-3]. Some of the most prominent applications of WN are environmental sensing, security based, monitoring and
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tracking based applications [4-5].
Energy efficiency is the crucial requirement of WSN, as the sensor nodes are packed with limited energy and in most of the cases, the batteries are irreplaceable or not rechargeable [8]. The work highlights of this article are as follows.
Faulty nodes are dangerous to the network, as all the operations involved in the WN take
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the faulty nodes into account for estimating all the basic operations such as routing, data collection and so on. Hence, this work presents an approach to detect faulty nodes in the
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network.
Faulty node detection helps the network to plan accordingly without including the faulty nodes and this idea conserves energy.
The proposed approach detects faulty nodes based on RPM, which relies on the data of the neighbourhood nodes. Hence, this technique is simple and consumes minimal energy.
The lifetime of the network is satisfactory, as the energy consumption is reasonable.
2. Review of Literature
In [1] Akyildiz et al. , proposed about the Wireless sensor networks. In [2] Harun et al. , proposed about the WSN application in led plant factory using continuous lighting (CL)
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method. In [3] et al. Lu, I proposed about the WSN for machine area network applications. In [4] K. Mikhaylov, et al. proposed about the Wireless Sensor Networks in industrial environment. In [5] G. Alderisi et al. proposed about the Simulative assessments of the IEEE 802.15.4e DSME and TSCH in realistic process automation scenarios. In [6] T. Senthil et al. proposed about the Energy Conserving Trustworthy Multipath Routing Algorithm Based on Cuckoo Search Algorithm.
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In [7] Li et al. proposed about the Enhancing real-time delivery in wireless sensor networks with two-hop information. In [8] Kerasiotis et al. proposed about a Battery lifetime prediction model for a WSN platform. In [9], proposed about the distributed faulty node detection technique for delay tolerant networks is presented. This work is meant for Delay Tolerant Networks (DTN)
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and detects the nodes that produce faulty data. The behaviour of the nodes is modelled by continuous time state equations. A Round Trip Delay (RTD) based matrix calculus (MCS) method is proposed in [10] for finding multiple failure nodes. This work suits only for 3 × 3
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square matrix and gives optimal results for 3 × 3 matrix and not for other matrices. So, it is not well suited for all other forms of matrices. A technique to detect anomalies and array diagnosis is proposed for WSN with several antennas [11]. The goal of this work is achieved by fusing relational data and measured signals. Initially, the anomalies are detected and the array diagnosis is performed by fusion of data and signal. A threshold tuning based wearable sensor fault
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detection scheme for medical monitoring system is proposed in [12]. In [13] Seyed Ahmad Soleymani proposed a Secure Trust Model Based on Fuzzy Logic in Vehicular Ad Hoc Networks With Fog Computing. In [14] Kasilingam Rajeswari et al. proposed about the Genetic
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algorithm based fault tolerant clustering in wireless sensor network. In [15] Amir Mehdi Pasdar et al. proposed about the Detecting and Locating Faulty Nodes in Smart Grids Based on High Frequency Signal Injection.Motivated by the existing works, the proposed work aims to present a self-diagnosing technique for detecting faulty nodes, which results in increased QoS (Quality of Service) and decreases the energy consumption of the network. The proposed approach is described in the following section.
3. Proposed Software defined based Self-diagnosing Fault Detection Technique for WSN 3.1 Work Overview
In addition to this, all the operations of the network consider the sensor nodes without any discrimination. This leads to serious issue in case of real-time applications. In order
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to deal with these issues, it is suggested to detect the faulty nodes in advance, such that they can be excluded in performing network operations. This idea ensures the QoS and boosts up the reliability of the system. Additionally, the time and computational complexity are minimized due to the negligence of the faulty nodes. This in turn conserves the energy and leads to network lifetime maximization. Hence, detection of faulty nodes introduces numerous advantages to the network. Considering all these points, this paper intends to propose a self-diagnosing faulty node
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detection system for WirelessNetwork. This work analyses the nature of a sensor node by taking the data provided by the neighbourhood sensor nodes into account. This idea is simple and efficient. Figure. 1 represents the faulty sensor nodes. The faulty behavior of sensor nodes may result from many situations, such as a faulty decision from the signal processing in a senor node
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due to noise , malfunctions due to low-cost hardware, environmental interference, or battery
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depletion.
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Figure.1. Faulty and Perfect nodes
3.2 Node Cluster Formation
This work assumes that all the participating sensor nodes of this work are immobile and the BS alone is mobile. This work clusters the deployed sensor nodes, so as to attain better manageability and control. When the sensor nodes are clustered, each and every cluster is assigned with the cluster manager node. The main job of cluster manager node is to have control over the cluster member nodes with respect to communication and general maintenance. Generally, a cluster manager node is selected in such a way that it is reliable and energy packed. The concept of clustering brings in energy efficiency and easy management. There are several techniques in the existing literature to select better cluster manager amidst all sensor nodes.
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However, the scope of the work is limited to fault detection of sensor nodes and hence, the concept of clustering is made simple by considering energy backup alone. In order to form cluster, the sensor nodes are monitored for a time period 𝑡𝑝 . The participating nodes of the network passes an 𝑖𝑛𝑖𝑡 energy backup 𝑐𝑢𝑟
, which is comprised of the node’s unique identifier and the current
. On reception of an 𝑖𝑛𝑖𝑡
, the sensor node measures the distance
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between the message forwarded node and the node itself with the help of the signal power. The energy consumption style of the sensor node is computed as per in eqn. (1). 𝐸𝑁 𝑖
(1)
𝑡𝑖𝑚𝑒
𝑖
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In the above equation, 𝐸𝑁 𝑖 indicates the energy consumption model of the 𝑖 sensor node, 𝑐𝑜𝑛𝑠 𝑖 is the used energy and 𝑐𝑢𝑟 𝑖 is the presently available energy of the 𝑖 that contains the unique identifier of node. The time consumption for broadcasting the 𝑐𝑜𝑛 the sensor node is calculated by eqn.(2). 𝑖 . 𝑟𝑎𝑛𝑑
𝑐𝑢𝑟
(2)
In eqn.(2), 𝑟𝑎𝑛𝑑 is a random number that can exist between 0.5 and 1. The rand value stops the 𝑐𝑜𝑛
from being forwarded at the same time, which reduces the collision rate.
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Additionally, the energy consumption rate is reduced. The complete work flow of the proposed fault node detection algorithm is given in figure. 2. The nearby nodes join the corresponding cluster manager to work co-operatively. Now, the RPM model is computed by the neighbourhood nodes for all the sensor nodes and reported to the cluster manager. The RPM of
3.3 RPM Model
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the cluster manager is computed by the nearby cluster manager node and reported to the BS.
When the cluster manager node is selected, it forwards a 𝐻𝐼 message to all its member nodes. The rewards or punishments of all the sensor nodes are computed by the following equations. The member nodes send an 𝐴𝐶𝐾 message to the cluster manager node, based on which the reward 𝑅 and punishment 𝑃 is computed. 𝑅
,
𝑃
,
,
,
(3) (4)
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The cluster manager 𝐶𝑀 node collects the 𝑅 and 𝑃 data from the neighbourhood
nodes of every corresponding node. The 𝑅 and 𝑃 value of the cluster member sensor nodes are collected from 𝑠 number of nodes to represent the nodes as perfect 𝑃 neither perfect nor faulty 𝑄
, faulty 𝐹
and
. The 𝑅 and 𝑃 values of the corresponding node collected from
the neighbourhood nodes are represented by 𝑟
and 𝑝
, where 𝑟
∈ 𝑅, 𝑝
∈ 𝑃.
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(5)
𝐹
(6)
𝑄
(7)
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Proposed Faulty Sensor Node Detection Algorithm Input : Sensor Nodes Output : Faulty node detection Begin //Clustering Observe the nodes for 60 seconds; Circulate 𝑖𝑛𝑖𝑡 among the sensors; Select cluster manager node based on eqn.(2); For each cluster manager Do Forward 𝐽𝑂𝐼𝑁 message; Receive acknowledgement; If (node count<20) Add the neighbourhood node to the cluster; End; End do; End for; //RP computation Compute R and P values and obtain from neighbourhood nodes; Normalize the values by eqn.(8); Apply DS theory to perform hypothetical analysis; Detect faulty sensor nodes; End;
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Observe the nodes for 60 seconds
For each cluster manager
Join request
If node count is 20
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Msg Initialization
Fault node detection
Yes
Apply D.S theory
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Values normalization
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Figure. 2. Proposed software defined based Faulty Sensor Node Detection Algorithm
These reward and punishment are collected by the cluster manager node from the neighbourhood nodes of all the nodes in the cluster. Hence, each sensor node has two reward and punishment data. The values range from 0 to 2 and the values are normalised by the following equation. 𝑁𝑜𝑟𝑚
(8)
In the above given equation, the value of 𝑥 is the original value of the RP model which is to be normalized. 𝑎𝑐 and 𝑎𝑐 are the least and highest values of the attribute. 𝑛 and 𝑛 are the required range of values for performing normalization. The reason for normalizing the values is
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to end up with easy data processing. In this work, the values of 𝑛
and 𝑛 are 1 and 0
respectively. Finally, the DS theory is utilized for performing the hypothetic analysis [22], as presented below. 𝑁𝑁1 𝑃
𝑁𝑁1 𝑄
⊕ 𝑁𝑁2 𝑃 𝑁𝑁2 𝑃
𝑁𝑁1 𝑃
𝑁𝑁2 𝑃
𝑁𝑁1 𝑃
𝑁𝑁2 𝑄 (9)
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𝑁𝑁1 𝐹 𝑁𝑁1 𝑄 𝑁𝑁1 𝑄
⊕ 𝑁𝑁2 𝐹
𝑁𝑁1 𝐹
𝑁𝑁2 𝐹
𝑁𝑁1 𝐹
𝑁𝑁2 𝑄
𝑁𝑁2 𝐹
(10)
⊕ 𝑁𝑁2 𝑄
𝑁𝑁1 𝑄
𝑁𝑁2 𝑄
(11)
𝑁𝑁1 𝑃
𝑁𝑁2 𝑃
𝑁𝑁1 𝐹
𝑁𝑁2 𝐹
𝑁𝑁1 𝑄
𝑁𝑁2 𝑄
𝑁𝑁1 𝑃 𝑁𝑁1 𝐹
𝑁𝑁2 𝑄
𝑁𝑁2 𝑄
(12)
𝑁𝑁1 𝑄 𝑁𝑁1 𝑄
𝑁𝑁2 𝑃
𝑁𝑁2 𝐹
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𝑤
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In the above equations (9), (10) and (11), 𝑤 is computed as follows.
By this way, the hypothetical analysis is performed. Each and every sensor node gets two values for RP scheme. This value ranges between 0 and 1, when the value is 1 the results is
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not faulty and 0 indicates the node is completely faulty. The nodes with value 0.5 or greater are considered as neither perfect nor faulty. However, this work fixes a threshold that the nodes with value greater than or equal to 0.6 are considered as perfect and the nodes with lesser than 0.6 are considered as faulty and are blocked by the cluster manager. The same process is performed for
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cluster manager nodes by the BS. The cluster manager is selected only by considering energy and so the cluster manager node can even be faulty. In order to deal with this, the performance of the cluster manager node is measured by the BS, which is mobile. The reliability of cluster manager node is tested in a regular time interval, which is 120 seconds. As the faulty nodes are blocked, there is no chance of performance deterioration and the QoS is improved. The following section analyses the performance of the proposed approach. 4. Results and Discussion
The proposed self-diagnosing faulty node detection scheme is simulated with the help of NS2 [21]. The performance of the proposed approach is tested by distributing 250 nodes in a random fashion. The simulation area is set to 500
500 𝑚 . All the nodes are aware of
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their location information and initially all the nodes are with full energy backup. The performance of the proposed approach is compared with the existing approaches such as threshold based [12] and RTD based in terms of faulty node detection accuracy, packet delivery rate, energy consumption, network lifetime. The faulty nodes are distributed in the ratio of 0.3. The faulty node detection accuracy is the most important metric, as it is meant for
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checking the basic functionality of the proposed approach. This work computes the faulty node detection accuracy of the proposed approach and the results are compared with the existing
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approaches as given in figure. 3.
Figure.3. Faulty node detection accuracy analysis This analysis is performed by randomly distributing the 10 faulty nodes here and there. On analysis, it is observed that the average faulty node detection accuracy rate decreases
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with the increase of sensor nodes. The greatest faulty node detection accuracy rate is found when the number of sensor nodes is 50 and is observed with the proposed approach. The greatest average faulty node detection rate of the proposed approach is 98 percent and the least faulty node detection rate is 89 percent. Both the existing approaches perform well but not equally to the proposed approach. In the second phase, the performance of the proposed approach is analysed by varying the count of faulty nodes in the network and the results are presented in figure 4. The count of faulty nodes is varied and the performance of the proposed approach is tested. As the faulty nodes increase, the detection accuracy decreases and is evident. However, the proposed approach performs a decent job by detecting maximal number of faulty nodes even when the count of faulty nodes is increased. The least performance is observed when the faulty
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node count is 25 and the detection accuracy of the proposed approach is 82 percent. On the other the existing approaches show 69 and 72 percent respectively. The figure. 5 presents the energy consumption analysis of the proposed approach with respect to the simulation time.
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Figure.4. Faulty node detection accuracy by varying faulty node count
Figure.5. Energy consumption analysis
The energy consumption pattern of the faulty node detection techniques are analysed and the experimental values are presented in table 1 and the results are depicted in figure 5. The proposed approach consumes minimal energy when compared to the existing approach. The
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main reason for minimal energy consumption is that the proposed approach introduces simple mechanism to detect faulty nodes, which results in reduced time and computational overhead. All these together reduce the energy consumption of the proposed approach and increase its energy efficiency. Energy efficiency is closely related to the network lifetime and is inversely proportional to network lifetime. As the proposed approach shows minimal energy consumption, the impact of it is seen in the network lifetime as shown in figure 5.
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Table 1: Comparison of Average Energy Consumption (J)
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100 150 200 250 300 350 400 450 500
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Simulation Time (s)
Average Energy Consumption (J) Proposed(SDN fault sensor Threshold based RTD based node detection) 0.18 0 0.0 0.23 0.1 0.03 0.25 0.15 0.09 0.28 0.19 0.1 0.29 0.18 0.15 0.3 0.23 0.19 0.35 0.28 0.23 0.38 0.32 0.27 0.42 0.39 0.3
Figure.6. Network lifetime analysis
The lifetime of the network can be analysed in two different ways and they are by noting the time at which the first node dies and the second way is by counting the number of live
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nodes. As time progresses, the number of live nodes decreases and the proposed approach shows greater number of live nodes when compared to the existing approaches. From the experimental results, it is noted that the number of live nodes in case of proposed approach is 6. On the other hand, the existing approaches show 158 and 167 nodes at the 500th second of simulation. The comparison values are presented in table 2 and the corresponding results are shown in figure. 6.
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As stated earlier, energy conservation increases the lifetime of the network. Hence, the proposed approach detects the faulty nodes in a better way by consuming reasonable energy. Table 2: Comparison of Number of live nodes No. of nodes alive Threshold based
RTD based
0 100 150 200 250 300 350 400 450 500
200 198 195 190 188 185 183 8 164 158
200 200 197 196 193 188 181 6 1 167
Proposed SDN fault sensor node detection 200 200 200 195 193 190 189 186 181 6
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Simulation Time (s)
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The major drawback of the existing threshold and the RTD method is that they often lack the sensitivity and specificity needed for accurate classification. Here the issues can be clarified. 5. Conclusion
In this paper presents a software defined network based self-diagnosing faulty sensor node detection scheme for WSN as part of Unmanned aerial vehicle. Faulty nodes degrade the performance of the sensor network and have to be detected, such that the Quality of Service is improved as security of network is improved. This work detects the faulty nodes by employing a simple approach that could conserve energy and thereby increasing the lifetime of the network. The proposed approach employs RP model and performs hypothetical analysis by DS theory.
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The performance of the proposed approach is tested in terms of faulty node detection accuracy, energy consumption and lifetime analysis. Also, the proposed approach shows better performance and in future, the faulty nodes can be detected by the hybrid of centralized and distributed approach. References
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[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless sensor networks: A survey,'' Comput. Netw., vol. 38, no. 4, pp. 393-422, 2002. [2] A. N. Harun, R. Ahmad, and N. Mohamed, ``WSN application in led plant factory using continuous lighting (CL) method,'' in Proc. IEEE Conf. Open Syst. (ICOS), Aug. 2015, pp. 56-61
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[3] X. Lu, I. H. Kim, A. Xhafa, and J. Zhou, "WSN for machine area network applications,'' in Proc. 46th Eur. Solid-State Device Res.Conf. (ESSDERC), Sep. 2016, pp. 23-28.
[4] K. Mikhaylov, J. Tervonen, J. Heikkilä, and J. Känsäkoski, "Wireless Sensor Networks in Commun., Apr. 2012, pp. 1-7.
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industrial environment: Real-life evaluation results,'' in Proc. 2nd Baltic Congr. Future Internet [5] G. Alderisi, G. Patti, O. Mirabella, and L. L. Bello, "Simulative assessments of the IEEE 802.15.4e DSME and TSCH in realistic process automation scenarios,'' in Proc. IEEE 13th Int. Conf. Ind. Inform. (INDIN), Jul. 2015, pp. 948-955
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[6] T. Senthil, Dr. B. Kannapiran, "ECTMRA: Energy Conserving Trustworthy Multipath Routing Algorithm Based on Cuckoo Search Algorithm", Wireless Personal Communications, Vol.88, No.3, 2016.
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[7] Y. Li, C. S. Chen, Y.-Q. Song, Z.Wang, and Y. Sun, "Enhancing real-time delivery in wireless sensor networks with two-hop information", IEEE Trans. Ind. Informat., vol. 5, no. 2, pp. 113-122, May 2009..
[8] F. Kerasiotis, A. Prayati, C. Antonopoulos, C. Koulamas, and G. Papadopoulos, "Battery lifetime prediction model for a WSN platform,'' in Proc. 4th Int. Conf. Sensor Technol. Appl., Jul. 2010, pp. 525-530.
[9] Wenjie Li ; Laura Galluccio ; Francesca Bassi ; Michel Kieffer, "Distributed Faulty Node Detection in Delay Tolerant Networks: Design and Analysis", IEEE Transactions on Mobile Computing, Vol., No.4, pp. 831-844, 2018.
[10] R. Palanikumar and K. Ramasamy, “Effective failure nodes detection using matrix calculus algorithm in wireless sensor networks”, Cluster Computing – The Journal of Networks, Software
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Tools and Applications, Vol 21, pp 1-10, 2018.
[11] Bo Wang ; Fengye Hu ; Yanping Zhao ; Terry N. Guo, "Anomaly Detection and Array Diagnosis in Wireless Networks with Multiple Antennas: Framework, Challenges and Tools", IEEE Network, Vol.32, No.1, pp. 152-159, 2018.
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[12] Haibin Zhang ; Jiajia Liu ; Nei Kato, "Threshold Tuning-Based Wearable Sensor Fault Detection for Reliable Medical Monitoring Using Bayesian Network Model", IEEE Systems Journal, Vol. 12, No.2, pp.1886-1896, 2018. [13] Seyed Ahmad Soleymani ; Abdul Hanan Abdullah ; Mahdi Zareei ; Mohammad Hossein Computing", IEEE Access, Vol.5, pp.15619-15629, 20.
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Anisi, "A Secure Trust Model Based on Fuzzy Logic in Vehicular Ad Hoc Networks With Fog [14] Kasilingam Rajeswari ; Subbu Neduncheliyan, "Genetic algorithm based fault tolerant
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clustering in wireless sensor network", IET Communications, Vol.11, No.12, pp.1927-1932, 20. [15] Amir Mehdi Pasdar ; Yilmaz Sozer ; Iqbal Husain, "Detecting and Locating Faulty Nodes in Smart Grids Based on High Frequency Signal Injection", IEEE Transactions on Smart Grid,
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Vol.4, No.2, pp.1067-1075, 2013.
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Conflict of Interest This paper has not communicated anywhere till this moment, now only it is communicated to your esteemed journal for the publication with the knowledge of all co-authors.
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Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
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AUTHORSHIP STATEMENT
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Manuscript title: SOFTWARE DEFINED NETWORK BASED SELF-DIAGNOSING FAULTY NODE DETECTION SCHEME FOR SURVEILLANCE APPLICATIONS
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All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Furthermore, each author certifies that this material or similar material has not been and will not be submitted to or published in any other publication before its appearance in the Computer Communications.
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Authorship contributions Please indicate the specific contributions made by each author. The name of each author must appear at least once in each of the three categories below. Category 1 Conception and designofstudy:R. Palanikumar ;
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,
Acquisition of data:R. Palanikumar
,
,
,
,
Analysis and/or interpretation of data:R. Palanikumar
,
,
Category 2 Drafting the manuscript:R. Palanikumar ,
,
,
,
Revising the manuscript critically for important intellectual content: R. Palanikumar,
.
;
,
.
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,
;
Category 3 Approval of the version of the manuscript to be published (the names of all authors must be listed):
R. Palanikumar , Dr. K. Ramasamy,
,
,
,
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, , , , . Acknowledgements Allpersonswhohavemadesubstantialcontributionstotheworkreportedinthemanuscript(e.g.,technical help, writing and editing assistance, general support), but who do not meet the criteria for authorship, are named in the Acknowledgements and have given us their written permission to be named. If we have not includedanAcknowledgements,thenthatindicatesthatwehavenotreceivedsubstantialcontributionsfro m non-authors.
This statement is signed by all the authors (a photocopy of this form may be used if there are more than 10 authors):
R.Palanikumar
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Dr. K. Ramasamy
Author’ssignature
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Author’sname(typed)
Date
09.12.2019 09.12.2019