Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach

Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

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Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach Vani Krishnaswamy ⇑, Sunilkumar S. Manvi ** School of Computing and Information Technology, REVA University, Bangalore, Karnataka 560064, India

a r t i c l e

i n f o

Article history: Received 28 February 2019 Revised 14 May 2019 Accepted 14 June 2019 Available online xxxx Keywords: UWASN Data aggregation Multiple agent Routing

a b s t r a c t The primary constraints of Underwater Wireless Acoustic Sensor Networks (UWASNs) are limited bandwidth, node energy and latency. The process of data aggregation will ease the constraints of UWASN. In this paper, we propose a scheme for data aggregation and routing in UWASN using static and mobile agents based on palm tree structure. The main components of palm tree structure include leaflets, spines, rachis and petioles. The leaflets and smaller leaflets (fronds) are connected to the petiole through spines. The junction of petioles connects to the sink node. The proposed scheme works as follows.Firstly, fronds and leaflets connected to the petioles are created through spines. Secondly, master center nodes are selected on petiole junction using mobile agent based on the parameters such as residual energy, Euclidean distance, petiole angle and connectivity. Thirdly, local center nodes are identified on either side of leaflet and connected to the master center using a mobile agent. Fourthly, the process of local aggregation at local centers happens by taking into account of nodes along the leaflets and carry to a connected master center. Finally, the process of master aggregation at the junction of petioles and delivering the aggregated data to the sink node using a mobile agent is performed. To assess the efficacy of the scheme, simulation in different UWASN scenarios are carried out. The parameters of performance analyzed are master and local center selection time, aggregation ratio, aggregation energy, energy consumption, number of local and master centers involved in the aggregation process and lifespan of the network. We observed that proposed scheme performs better than the existing aggregation scheme. Ó 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Contents 1. 2. 3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Palm tree structure based data aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Network environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Proposed agency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Sink Node Agency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Underwater Sensor Node Agency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6. Petiole/master center selection process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7. Leaflet/Local center selection process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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⇑ Corresponding author. ⇑⇑ Principal corresponding author. E-mail addresses: [email protected] (V. Krishnaswamy), [email protected] (S.S. Manvi). Peer review under responsibility of King Saud University.

Production and hosting by Elsevier https://doi.org/10.1016/j.jksuci.2019.06.007 1319-1578/Ó 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article as: V. Krishnaswamy and S. S. Manvi, Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.06.007

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V. Krishnaswamy, S.S. Manvi / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

3.8.

4.

5.

Routing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8.1. Example state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9. Aggregation mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Simulation parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Performance parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Result analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction The Underwater Wireless Acoustic Sensor Networks (UWASNs) are seizing the attention of the researchers due to its advancements. The numerous applications of UWASN such as monitoring the environment, pollution and habitat, marine data collection, detection of oil leakages, prevention of disasters etc., require huge number of sensors which are networked for its better performance and greater accuracy (Heidemann et al., 2012; Felemban et al., 2015; Akyildiz et al., 2005). Nevertheless due to unique features of underwater environment such as less bandwidth, long propagation delay, attenuation, limited energy etc., researches find tough to work in UWASN (Krishnaswamy and Manvi, 2015). Each UWASN comprises of small UW-sensor nodes, Autonomous Underwater Vehicles (AUVs) and Gateway nodes which have the potentiality to sense, compute and communicate among each other or to the external sink node using wireless channel. Acoustic frequency ð1:5  103 m=sÞ is means of communication in underwater environment. Thorough research is under progress to enhance the network lifetime which depends on the sensor nodes energy, architecture of the network, aggregation scheme and routing (Heidemann et al., 2006). The UW-sensor nodes in UWASN are deployed randomly to collect the information from different aquatic mediums such as rivers, lakes etc., and transmit it to the sink node.Many UWASN protocols are used to monitor such aquatic mediums (Xu et al., 2015; Curiac, 2016). But these protocols do not fit in for the large aquatic mediums such as oceans and seas. To trounce such situations, data aggregation techniques are used along with routing protocols to deliver the data aggregated to the sink node. The main purpose of data aggregation techniques are to lessen the redundant data, consumption of energy and delay. We have employed a palm tree structure in the proposed scheme for the process of data aggregation and routing. The palm tree structure is created by sink node for collecting the data inside the network using aggregation technique to increase the scalability of the network. The proposed palm tree structure as depicted in Fig. 1 for data aggregation and routing in UWASN is explained as follows. The crown represents the canopy of the palm tree. The crown comprises of spines with leaflets where all the spines are connected to the junction through petiole. The leaflet is connected to the leaves through rachis (spine of the leaf). In this work we have defined the following. (1) The leaflets of the palm tree covers all the nodes in UWASN. (2) Junction of petioles connected through spines will have master center node for each spine. (3) A local center node represents rachis connectivity to the spine. The software agents are employed in the aggregation process due to their flexible and customizable services. The static agents are the programs located within an environment to perform actions upon sensing the local factors. The mobile agent is a program which can move one node to another node within an environment to perform actions based on the occurrence of certain events. An agency represents set of static and mobile agents to perform certain tasks.

Fig. 1. Structure of palm tree.

The recommended scheme uses the agents which are static and mobile along with UW-sensor nodes to form the palm tree structure. The two kinds of software agencies used in the proposed scheme are as follows. (1) At the Sink (Base Station) called Sink Node Agency (SNA) and (2) at each UW-sensor node called Underwater Sensor Node Agency (UWSNA). The software agency at sink and each UW-sensor node comprises of a knowledge base and many agents. The following are the steps involved in the operation of the scheme. (1) Initially a mobile agent is triggered at SNA which is called Petiole/Master Aggregator Selection Agent (PASA) at an angle between 0° to 360° with a given reference provided in UWASN. (2) The crown of the palm tree is constructed by PASA using master nodes on it based on residual energy, euclidean distance, petiole angle and connectivity. (3) During the formation of crown of the palm tree, PASA records identification of all master center sensor nodes along the 360° of the crown using petioles. (4) At every master center node, a mobile agent is triggered by UWSNA which is called Leaflet/Local Aggregator Selection Agent (LASA) at leaflet angle made with the leaflets to identify all the local center nodes. (5) At the local center level, an aggregation agent which is mobile visits every local center, collects and aggregates the data. Every local center node transmits the data which is aggregated to its master center node along the petiole. (6) Even at the master center level, an aggregation agent which is mobile is triggered by last master center node, visits every master center node across the petioles, collects and aggregates the data. The aggregated data is delivered by each master center node to the sink node. The contribution of this paper as compared to the existing works are in the following ways.

Please cite this article as: V. Krishnaswamy and S. S. Manvi, Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.06.007

V. Krishnaswamy, S.S. Manvi / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

 Employment of palm tree structure for data aggregation.  Using agent technology which are suitable for providing autonomous, flexible and customizable services.  Optimal selection of local and master center nodes.  Design of paths for mobile agents traversal.  Three level aggregation for efficient elimination of redundant data. The rest of the paper is arranged in the following ways. Section 2 briefs about the overview of the related works carried out in data aggregation and routing. Section 3 discusses about our proposed palm tree structure for data aggregation. This section also briefs about local and master center aggregation. Section 4 explains about performance evaluation highlighting on the simulation parameters and analysis of the results. Finally, Section 5 concludes the paper with few future enhancements. 2. Related works The authors in Goyal et al. (2017) have done a study on aggregation of data in underwater wireless sensor networks discussing various techniques of data aggregation, highlighting their benefits and limitations. The performances of various clustering schemes are related and measured with respect to delay, consumption of energy and packet drop with and without aggregation. The authors in Wang et al. (2016) have discussed about energy efficient data aggregation scheme created on the concepts of distributed compressed sensing for cluster based underwater acoustic networks to reduce the cost and increase the lifetime of the network. This scheme let effectual reduction of data and calculate sensed values. However, it is nearly complex, occasionally create communication overhead and sometimes the sink require few transmissions to identify failures. The authors in Bharamagoudra and Manvi (2017) have discussed about secured routing scheme based on agent that increases the quality of service. They worked on identifying the wormhole flexible secure neighbors to route the data through secure path. In this scheme the agents ease supple and adjustable services for safe routing. The works given in Manjula and Manvi (2012) and Krishnaswamy and Manvi (2018) have discussed about cluster based data aggregation scheme. This scheme adopts cluster formation, identification of cluster heads and data transmission to the sink node in UWASN. This schemes accomplishes savings of energy, enhances the lifetime of the network and decreases the bandwidth. The authors in Sutagundar and Manvi (2014) and Sutagundar and Manvi (2012) have proposed data aggregation and routing schemes based on the different structures like fish bone and wheel built on wireless sensor nodes to minimize the consumption of energy and maximize the lifetime of the network. The schemes utilizes the agents which are static and mobile to form these structures and data aggregation is performed at three levels providing reliability in data transmissions by various paths. However in such algorithms the major part of the computations are held at sink node in place of being performed at individual nodes. They also sometimes lack scalability. The authors in Rahman et al. (2018) have proposed a totally opportunistic routing algorithm for UWASN to avoid horizontal transmissions, void nodes and reduce the delay in order to increase the throughput and energy efficiency. This scheme utilizes the idea of multi hoping to evade void holes. However the concepts of complexity and reliability are not discussed in the scheme. The authors in Akbar et al. (2016) have recommended an energy efficient data gathering scheme in UWASN wherein, it has mobile

3

sink node and courier nodes. These nodes gather the data at specific stops to minimize the overall energy consumption of the network. This 3-D linear scheme is nevertheless precise in routing however it is too difficult for real-time implementation in task precarious applications. The authors in Javaid et al. (2015) have proposed an efficient reliable AUV aided routing protocol for data delivery using shortest path algorithm in UWASNs. This protocol helps in limiting the number of connected nodes with gateways to avoid overloading. This aids to minimize the consumption of energy and enhance the lifetime of the network. This scheme holds the advantage of less transmission delay. The authors in Harb et al. (2017) have recommended a data aggregation scheme built on the distance parameter for periodic sensor networks. The approach explains about two phases where the initial one searches for the similarities of measures by each node and the latter uses distance based functions to identify the similarities among the set of nodes. The prime objective in their approach is to minimize the transmission of redundant data from sensors and Cluster Heads (CHs) in the cluster based network. However the scheme focuses on reducing the size for data aggregation. The authors in Faheem et al. (2016) have presented cluster based routing protocol for underwater environment to evade the problems of hotspots that arise nearby sink. In addition, the coordination given by the CH enables sensor nodes to rest for stretched out period and help to spare more vitality in every sensor node. Therefore, clustering improves to organize versatility and life span by decreasing both the traffic and the conflict for the channel. The authors in Faheem et al. (2018) have presented a cluster based routing protocol to improve the dependability of information move in UWASNs. The mechanism utilized for sorting out sensor nodes is as small clusters which are associated progressively for dispersed vitality and information exchange uniformly. The proposed convention progresses parcel conveyance proportion, and diminishes normal start to finish deferral and by large system vitality utilization. The authors in Faheem et al. (2015) have proposed three algorithms for cluster formation, active sensing mechanisms and to solve the issues related to inter and intra cluster routing. Ongoing documented tests and estimations have demonstrated that sorting out sensor nodes in a few clusters and determining a specific sensor node in each group to perform CH task, permits total of information, yet additionally constrains information transmission basically inside and among the clusters. Palm tree structure can be suitable for the large network since the leaflets of the palm tree extend up to last sensor node and covers the entire boundary of the network. Since the fish bone and the wheel topology of the networks have been successfully proved to cover all the nodes in the network, palm tree structure is an combination of both the topologies. The leaflet of the palm tree almost depicts the fish bone structure with few modifications and the junction of petioles (base of leaflet) form the wheel structure. Since the sink is placed at the arbitrary center of the network area, all the data from entire 360° directions covers all nodes in UWASN. In this way a flexible palm tree structure can be designed for all architectures of UWASN. 3. Palm tree structure based data aggregation This section highlights on the topics such as network environment, describing the different agencies used for the process of data aggregation, identification of master and local centers, routing and scheme for aggregation.

Please cite this article as: V. Krishnaswamy and S. S. Manvi, Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.06.007

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V. Krishnaswamy, S.S. Manvi / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

3.1. Network environment 

many UW-sensor nodes connected in the form of leaf of a palm tree. Master center node: It is in-between node placed at the junction of petioles. Aggregated data at each leaflet connected through master aggregation. Local center node: It is also in-between node placed along the rachis of the leaflet. The local aggregation procedure is performed along the rachis to remove the redundant data beyond the data collected from neighboring UW-sensor nodes. Number of neighboring UW-sensor nodes: All the active UWsensor nodes which are available within the specified range of communication form the overall count of neighboring UWsensor nodes. Petiole angle: The angle at which master center node is located with respect to reference direction from sink to neighboring UW-sensor nodes. Leaflet angle: The angle between the leaflets on the rachis and the local center on the spine with reference to midrib axis.

The underwater wireless sensor network considered for data gathering, aggregation and routing schemes encompasses numerous heterogeneous UW-sensor nodes, Gateway nodes and a Sink node. Following are the assumptions made with respect to UWsensor nodes which are distributed geographically in a given area. They are static in nature, have same energy and capable to reconfigure the transmission power. The data is sensed periodically by these UW-sensor nodes and transmitted to the sink node. To have unbeaten communication between the UW-sensor nodes, all the UW-sensor nodes are set with processor and transceiver. Every UW-sensor node in the network is designed to posses a platform for the agents which manages the communication among the agents. An active UW-sensor node partake in the aggregation process under the condition that the values sensed at an assumedthreshold for a given time window drift. The data aggregation framework is as shown in Fig. 2 which depicts location of sink, local and master centers along the leaflets and junction of petioles of the palm tree structure nodes for aggregation. The data from all the neighboring UW-sensor nodes are gathered and aggregated by all the local centers. The local centers’ data is aggregated by Local Aggregation Agent (LAA) which is triggered by last local center and is delivered to the corresponding master center. Similarly the master center’s data is aggregated by Master Aggregation Agent (MAA) which is triggered by last master center and is delivered to the sink.

This section explains about a set of static, mobile agents and the agencies used in the identification of master and local centers. It comprises of two agencies one at sink node called Sink Node Agency (SNA) and other at each UW-sensor node called Underwater Sensor Node Agency (UWSNA). Some of the notations used in description of the scheme are given in Table 1.

3.2. Preliminaries

3.4. Sink Node Agency

The key terms used in the process of data aggregation scheme are explained in this section.

The Sink Node Agency residing at sink node comprises of Underwater Sink Monitoring Agent (UWSMA), Petiole Aggregator Selection Agent (PASA) and Underwater Sink Knowledge Base (UWSKB) for inter-agent communication. The constituents of the SNA and their connections are shown in Fig. 3. Underwater Sink Monitoring Agent (UWSMA): The monitoring and maintenance of neighboring UW-sensor nodes information is performed by a static agent located in sink node called UWSMA. The user runs the application which will trigger the agent at the data collecting centers. UWSMA performs the following functions. (1) It calculates the weight factor and Euclidean distance of

 Palm tree structure: It consists of leaflets/fronds nodes and a sink node with many in-between nodes. Every leaflet has









3.3. Proposed agency

Table 1 Notations. Descriptions

Symbols

Communication range of UW-sensor node Neigbor node count Weight factor of Master/Local center selection Abitrary radius of junction of petioles Number of petioles Initial energy of every node Residual/Utilized energy of every node Euclidean distance between nodes l and m

R Nc W f /W l r Np Ei ERt / Eu Edl;m

Threshold distance of master/local center Angle between master center/petiole and node i Total number of leaflets connected to the spine Degree of neighbor nodes Distance between petioles Petiole angle Angle between petioles Probability of occurrence of redundant data Redundant data set Angle between local center/leaflet and node i Probability of aggregated data Data aggregation time at master/local centers Time required to aggregate from leaflet Total time for aggregation

Dpth / Dlth hp i Latotal Dnth Pd hp hpetiole P Rda Rda h li P Ag Maagtime / Laagtime T leaf Toagtime

Fig. 2. Network environment.

Please cite this article as: V. Krishnaswamy and S. S. Manvi, Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.06.007

V. Krishnaswamy, S.S. Manvi / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

5

Fig. 3. Sink Node Agency.

neighboring UW-sensor nodes and also updates information on aggregation in UWSKB. (2) It creates PASA based on the requirement to data aggregate from the UW-sensor nodes. (3) It selects the master center based on the distance. (4) It maintains the threshold values Dpth and Dlth . Petiole Aggregator Selection Agent (PASA): UWSMA triggers a mobile agent based on weight factor (Eq. (9)) and Euclidean distance between neighboring nodes called PASA. The selection of master centers at the junction of petioles of the palm tree structure is performed by PASA. The master center is updated with the information such as petiole angle, leaflet angle by PASA. It fixes the master centers at the junction of petioles and provides the identification (id) for all the traversed master center nodes till the last one in the crown of the palm tree. Underwater Sink Knowledge Base (UWSKB): UWSMA and PASA reads and updates the knowledge base around the UW-sensor nodes connected to the sink such as: identification of the node, residual energy, neighboring count, strength of signal, geographical location and indices, threshold of petiole angle and leaflet angle, previously aggregated data, threshold data and reception time.

Fig. 4. Underwater Sensor Node Agency.

Fig. 5. Leaflet structure of palm tree.

3.5. Underwater Sensor Node Agency The Underwater Sensor Node Agency (UWSNA) residing at every UW-sensor node comprises of Underwater Node Monitoring Agent (UWNMA), Leaflet Aggregator Selection Agent (LASA), Local and Master Aggregation agent (LAA and MAA respectively) and Underwater Node Knowledge Base (UWNKB) for inter agent communication. The constituents of the UWSNA and their connections are shown in Fig. 4. Underwater Node Monitoring Agent (UWNMA): The accessible monitoring information such as data related to temperature, depth, salinity, strength of the signal, residual energy, transmission range, etc., is performed by the static agent residing in every UW-sensor node of the UWASN is called UWNMA. It also updates the aggregation information in UWNKB. The process of aggregation is updated regularly by UWNMA, by comparing previously sensed data with present data of the each UW-sensor node. If there is a change in drift value between the two data, then the UW-sensor node is made to participate in the aggregation process. The status of the residual energy of each UW-sensor node is updated to the nearest node by UWNMA. UWNMA also updates UWSKB with each nodes’ node id, position and its weight factor (see Figs. 5 and 6). Leaflet/Local Aggregator Selection Agent (LASA): The mobile agent residing in each node is triggered by UWNMA. Since LASA gets weight factor and Euclidean distance of near by nodes, it is accountable for electing the local center node. It gets leaflet angle from UWNKB and directs it to one hop neighbor nodes. It fixes the

Fig. 6. Master/local center in a leaflet.

local centers in the region of leaf and provides the node identification (id) for all the traversed local centers till the last one in the leaf of the tree. Leaflet/Local Aggregation Agent (LAA): Each time when LASA instigates the process of aggregation, a mobile agent called LAA is triggered by UWNMA of the last local center. The path information of local center and aggregated data locally are updated from UWNKB to LAA. After obtaining these data from each local center, LAA continues with aggregation process, then it migrates to next local center along the spine to continue its journey to reach the master center at the base of rachis called petiole. At each leaflet on either sides of the spine, in order to aggregate the data LAAs are activated in the last local centers. Lastly the aggregated data is delivered to the consistent master center on the petiole.

Please cite this article as: V. Krishnaswamy and S. S. Manvi, Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.06.007

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V. Krishnaswamy, S.S. Manvi / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

Master Aggregation Agent (MAA): Each time when PASA commences the aggregation process, a mobile agent is triggered by UWNMA of the last master center called MAA. MAA gets the path information of the master center and data aggregated locally on the connected spines from the UWNKB and travel to next corresponding master center. The aggregation process is initiated at each visited master center by MAA and prolong its ride until it completes all the master centers to reach the sink node. Underwater Node Knowledge Base (UWNBB): UWNMA and LASA reads and updates the knowledge base about the UW-sensor nodes connected to the each UW-sensor node such as: identification of the node, residual energy, neighboring count, strength of signal, geographical location and indices, threshold of leaflet angle, previously aggregated data, data threshold and time of reception. 3.6. Petiole/master center selection process Petioles originate from the junction of petioles which is at the top of stem/trunk of the palm tree. P d distance between petioles is given by Eq. (1).

Pd ¼ 2  R  Dnth

ð1Þ

Number of petioles in the junction region of the crown of palm tree is given by Eq. (2).

Np ¼

2pr Pd

ð2Þ

The arbitrary radius of the junction is calculated using Eq. (3).

r ¼ R  Dnth

ð3Þ

The value of Dnth is selected based on number of direct and indirect neighbors to be contacted for data aggregation. Petiole angle hpetiole is given by Eq. (4). The petiole angle facilitates angle between the petioles and is given by Eq. (5).

hpetiole ¼

360 Np

ð4Þ

The angle of petioles p, i.e., hp , for p = 1, 2,. . ., N p , is computed by the Eq. (5).

hp ¼ hpetiole  p

ð5Þ

Petiole angle between the neighbor UW-sensor nodes in the junction (considering node l and m) is computed by Eq. (6).

hpðiÞ ¼ tan1

ðyl  ym Þ ðxl  xm Þ

ð6Þ

where ðxl ; yl Þ and ðxm ; ym Þ are the locations of node l and node m, respectively. The neighbor UW-sensor nodes for master center selection can be obtained through the following cases as petiole can be in any of the four quadrants of a circle. The junction of petioles is assumed to be circle.  Case I: Master center selection in the first quadrant is performed, when the UW-sensor nodes are in the location xl > xm and yl > ym .  Case II: Master center selection in the second quadrant is performed, when the UW-sensor nodes are in the location xl < xm and yl > ym .  Case III: Master center selection in the third quadrant is performed, when the UW-sensor nodes are in the location xl < xm and yl < ym .  Case IV: Master center selection in the fourth quadrant is performed, when the UW-sensor nodes are in the location xl > xm and yl < ym .

The residual energy ERt of each UW-sensor node in the network is given by Eq. (7).

ERt ¼ jEi  Eu j

ð7Þ

where the initial energy and utilized energy of the node is depicted as Ei and Eu respectively. The Euclidean distance between a UW-sensor node l to its neighbor node m is given by Eq. (8).

Edl;m ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi jxl  xm j2 þ jyl  ym j2

ð8Þ

The weight factor W f of an UW-sensor node is based on neighbor node count N c and residual energy at time t ERt . The process of master selection is instigated by UWSMA of sink node. A query message will be sent by UWSMA to its neighbors in the network. As a reply message, UWNMA of neighbors nodes compute their W f and send location of the node along with W f to UWSMA as given in Eq. (9).

W f ¼ KðERt  Nc Þ

ð9Þ

where K is a constant ranges between 0 and 1. In the process of master center selection at the junction of petioles, the UW-sensor nodes placed at angle hp are measured. The threshold distance of UW-sensor nodes is represented as Dpth . If Edð l;mÞ > Dpth , then the UW-sensor node can participate in the master center selection process. The UW-sensor node with highest W f , is selected as master center (Petiole Aggregator-PA) among competitive neighboring nodes by UWSMA. If none of the competitor is found based on the threshold condition ðEdð l;mÞ > Dpth Þ, then the Dpth is incremented or decremented to identify other competitors for PA by UWSMA. Since the selection of first PA from the competitor nodes at given petiole angle is performed. PASA is triggered by UWSMA of master center to identify the remaining PAs until PASA reaches the last master center in a network. PASA transports the node id’s every master center to find the path i.e., linking all the master centers. 3.7. Leaflet/Local center selection process The process of selecting the local centers among the neighboring UW-sensor nodes is instigated by master center. These neighbor UW-sensor nodes of the corresponding master center, find the node weight W f considering the neighbor count N c and residual energy at time t ERðtÞ . Similar to selection of master centers, UWNMA of master centers find hpðiÞ , residual energy ERðtÞ and euclidean distance between neighbor UW-sensor nodes and master center as in Eqs. 6,7,9 respectively. Neighboring UW-sensor nodes located on the leaflets at an angle of 45°–60° and 135°– 150° on the either side of the rachis are denoted as the nodes located at leaflet angle. If the consideration of the angles is taken apart from these angles, then there will be a chance of overlapping coverage area. Based on leaflet angle, Euclidean distance and neighbor node count N c , UWNMA of master center selects the local center. The node weight W l of local center is computed as shown in Eq. (10).

W l ¼ KðERt  Nc Þ

ð10Þ

where K is a constant ranges between 0 and 1. The local center selection process is carried out by considering the UW-sensor nodes located at leaflet angle. The threshold distance of nodes is represented as Dlth . If Edð l;mÞ > Dlth , then the UWsensor node can participate in the process of selection of local centers. The UW-sensor node with maximum W l , is chosen as local center (leaflet Aggregator-LA) amongst competitive neighboring nodes by UWNMA. If none of the competitor is found based on

Please cite this article as: V. Krishnaswamy and S. S. Manvi, Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.06.007

V. Krishnaswamy, S.S. Manvi / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

the threshold condition ðEdð l;mÞ > Dlth Þ, then the Dlth is incremented or decremented to identify other competitors for LA by UWNMA. Since the selection of first LA from the competitor UW-sensor nodes at given leaflet angle is performed, LASA is triggered by UWNMA of local center to identify the remaining LAs till LASA touches the final local center in a network. LASA transports the node id’s every local center to find the path i.e., linking all the local centers. As the value of Edðl;mÞ varies, the threshold values Dpth and Dlth are maintained by UWSMA in the following ways. To begin with, a threshold parameter T i is fixed by UWSMA to get few master/ local centers. If the centers are not found for this value of x, then UWSMA increments T i as ðT i þ dÞ. If the centers are not found for incremented value of threshold then UWSMA continue incrementing the threshold value as ðT i þ BdÞ, where B ¼ 2p , and ‘p’ is an integer. The probability of obtaining centers is better by considering 2p than by incrementing the threshold values similarly ðT i þ dÞ; ðT i þ 2dÞ; ðT i þ 3dÞ, and so on. When the master center selects the initial local center, LASA is triggered by UWNMA of master center to recognize the left over local centers along the rachis until it covers all the local centers in a network. LASA carries the node id’s of all the local centers to identify the path by linking all the local centers in the form of leaf of a palm tree. 3.8. Routing

ERð minÞ ¼ MinERð 1Þ ; ERð 2Þ ; . . . ERð nÞ

ð11Þ

ERð maxÞ ¼ MaxERð1Þ ; ERð2Þ ; . . . ERðnÞ

ð12Þ

The Energy factor Efact of path is calculated as in Eq. (13).

ERðminÞ ERðmaxÞ

ð13Þ

The distance factor Dfact for every path is calculated as in Eq. (14).

Dfact ¼

Pd Ph

ð14Þ

where the path distance and number of hops are represented by P d and P h respectively. The Cost function C fun for every path is calculated as in Eq. (15).

C fun ¼ Efact þ Dfact

3.8.1. Example state The operation of the proposed scheme is illustrated with an example state. Fig. 2 depicts such an example, where in the underwater scenario consists of 50 UW-sensor nodes (sn1-50), Master Center nodes (MC1-8) and the sink/base station. (i) UWSMA at Sink/Base Station activates PASA to recognize the master centers. In view of the weight factor and Euclidean distance of UWsensor nodes at the junction of petioles, PASA allocates master center status to UW-sensor nodes MC1-8. (ii) UWNMA of master centers MC1-8 activates LASA to recognize local centers. Here also in view of the weight factor and Euclidean distance LASA recognizes the local centers moving along the leaflet angle. For master centers MC1, local center is sn10. master centers MC2, local center is sn2, master centers MC3, local center is sn46, master centers MC4, local center is sn41, master centers MC5, local center is sn36, master centers MC6, local center is sn30, master centers MC7, local center is sn25, master centers MC8, local center is sn18. (iii) After the selection of master and local centers, the remaining UW-sensor nodes in the leaflet direct their data to their corresponding local centers. For example UW-sensor nodes sn11-17 direct the data to local center sn10 and in this order all the local centers direct the aggregated data to their master centers. For example local center sn10 to MC1. Finally all the master centers direct the gathered data to the sink. The flow of gathered data is in this way, II quadrant sn11  17 ! sn10 ! MC1 ! sink. 3.9. Aggregation mechanism

Mobile agents at local and master level performs routing using mobile agents. These mobile agents traveling from one node to another node gathers the information such as residual energy of the UW-sensor node, distance between the neighboring UWsensor nodes, number of hops and convey this collected data to the sink node. Then, the sink node computes the following parameters using the data obtained from the nodes. (1) Minimum and maximum of the residual energy in the path for n nodes. (Refer Eqs. (11) and (12)). (2) Energy factor for the path. (Refer Eq. (13)). (3) Distance factor for the path. (Refer Eq. (14)). (4) Cost function of the path. (Refer Eq. (15)). Finally depending on how critical the information is and the value of cost function, sink node ranks the paths. For less critical information, the path with highest cost function is selected for transmission whereas for highest critical information, the path with cost function in order are selected. The minimum and maximum residual energy ERð minÞ and ERð maxÞ among the nodes in a path are calculated using Eqs. (11) and (12) respectively.

Efact ¼

7

ð15Þ

The process of aggregation is performed at three stages: First stage at node, second and third stages at local and master centers respectively. UWNMA in each UW-sensor node follows the mentioned steps. (1) Each individual UW-sensor node in the network has UWNMA which in a given time window uses statistical averaging of the data and store the average and variance. (2) To have loss less aggregation, UWNMA of all local centers collects the data from neighboring UW-sensor nodes to put on union set theory where the redundant sets are eliminated and new set (S) is generated. (3) Similarly the data collected by UWNMA of master center which is provided the near by local centers also put on union set theory to eliminate duplicate sets of data to have loss less aggregation. UWNMA also plays a major role in each UW-sensor node through link failure as follows. If the node/link fails due to which local/master center fails, then UWNMA initiates recovery mechanism. Every UW-sensor node in the network is informed with weight factors of the neighbor nodes, at any time if the link fails along the aggregated propagation route, then UWNMA pursuits the UW-sensor node with maximum weight factor. As a substitution the UW-sensor node with maximum weight factor is selected as master/local center for the failed master/local center respectively. The study of data aggregation is as explained. The gathered data at every node i is represented as DGi ¼ DG1; DG2; DG3; . . . ; DGn with respect to time window t1 ! tn ¼ t1; t2; t3; . . . ; tn, respectively. The redundant data in the data set DGi is Rda . The probability of occurrence of redundant data PRda is given in Eq. (16).

PRda ¼

Rda DGi

ð16Þ

The number of master centers at the junction of petioles be n. The total number of leaflets connected to either side of the spine is given by Eq. (17).

Latotal ¼ 2  n

ð17Þ

Hence the probability of data aggregated out of data sensed DGi is provided by the Eq. (18).

Please cite this article as: V. Krishnaswamy and S. S. Manvi, Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.06.007

8

V. Krishnaswamy, S.S. Manvi / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

PAg ¼ 1  PRda

ð18Þ

The aggregation time at the respective local centers are represented as LAt1 ; LAt2 ; LAt3 ; . . . :LAtn . Then the time for aggregation at each spine of the leaflet is given by Eq. (19).

Laagtime ¼

n X

ai  LAni

ð19Þ

i¼0

where the value of a is between 0.001 to 0.003; ai < aiþ1 ; i 6 n. Subsequent to the process of aggregation at the local center, the size of the aggregated data differs as it is forwarded from one to another local center. The data gathering is performed at each leaflet. As the data size increases, aggregation time process also increases. In turn to optimize the aggregation time for local aggregation, the threshold value of a are chosen as 0.001–0.003 s. Time for data aggregation at local centers is depicted as T leaf and is given by Eq. (20).

T leaf ¼ Laagtime  Latotal

ð20Þ

The master aggregation time at the corresponding master centers are represented as MAt1 ; MAt2 ; MAt3 ; . . . :MAtn . Then the total time for data aggregation at master center is depicted by Eq. (21).

Maagtime ¼

m X bi  MAni

ð21Þ

i¼0

where the value of b is between 0.003 to 0.005; bi < aiþ1 ; i 6 m. In the proposed palm tree structure based data aggregation, the process of aggregation assumed to begin at the last master center of the palm tree. Subsequent to the process of aggregation at the master center, the size of the aggregated data differs as it is forwarded from one to another master center. The data gathering is performed at each petiole. As the data size increases, aggregation time process also increases. In turn to optimize the aggregation time for master aggregation, the threshold value of b are chosen as 0.003–0.005 s The total time for aggregation is provided by Eq. (22).

Toagtime ¼ Maagtime  T leaf

ð22Þ

4. Performance evaluation In this section the discussion is about performance parameters, simulation parameters and results. The proposed scheme is simulated for various network scenarios and compared with works given in Sutagundar and Manvi (2012) and Rahman et al. (2018) in terms of lifetime of network, time required to select local and master centers, aggregation time and energy. 4.1. Simulation parameters We have considered the scenario for simulation which consists of heterogeneous underwater wireless static and mobile sensor nodes within the boundary of 600 m by 600 m with a varying depth of 100 m–500 m. These UW-sensor nodes are placed deterministically for testing performance evaluation with transmission power between 2 mW and 4 mW. The data rate follows the Poisson process varied from 0.002 to 0.1 pkts/ms. UW-CSMA/CA MAC protocol (Fang et al., 2010) is utilized for MAC layer to avoid collision of packets. The data packets are assumed to be transmitted in discrete time and the channel is error free. The modem used for each UW-sensor node is a model of the WHOI acoustic modem. The discrete event network simulator tool, NS-3 is used for simulation. Table 2 represents the parameters for simulation used for examine the proposed scheme.

Table 2 Notations. Parameters

Symbols

Values

Set of UW-sensor nodes Bandwidth Communication range of sensor node Leaflet angle threshold Petiole angle threshold Initial energy of each node Local center time constant Master center time constant Threshold distance of local center Threshold distance of master center Incremental threshold communication range value Size of sensor data at each node

sn1 ; sn2 ; sn3 ; . . . snm BW R

50–200 4000 Hz 400–600 m

hlth hlth Ei b Dlth Dpth d

45°–60° 0°–360° 5J 1–3 ms 3–5 ms 400–500 m 300–500 m 10 m

Sd

512 bytes

a

4.2. Performance parameters Following are the performance parameters assessed.  Selection time for Local/Master centers: The time needed to select the Local/Master centers respectively.  Local centers: The total number of local centers connected to all master centers in the network.  Master centers: The total number of master centers identified for the network.  Aggregation energy: The average energy consumed by all nodes, local and master centers for data aggregation.  Aggregation time: The time possessed by all nodes, local and master centers to aggregate the data  Aggregation ratio: The ratio of data aggregated to the total accessible data of the network.  Network lifetime: The number of rounds utilized to die the first node in the network.

4.3. Result analysis Total number and selection time for Local centers: It is observed from Fig. 7 that, as there is increase in the number of nodes and communication range, LASA finds many connected nodes for the process of selection of local centers. It also depicts that communication range is directly proportional to number of connected nodes. As a result when communication range (R) increases from 400 m to 500 m, LASA find many connected nodes as well as it would find many repeated nodes involved to the process of selection of local centers. But when R increases to 600 m, LASA finds secluded and connected nodes along the leaflet angle of the palm tree structure. Hence in the graph it shows that when compared to lower communication ranges (R = 400 m and 500 m), there is slight linear increase for the case R = 600 m. As a result upsurge in the communication range rises the number of local centers. It is observed from the Figs. 7 and 8 that the time required for selecting local centers with respect to number of UW-sensor nodes in the network. As the number of local centers increases the time required for selecting these local centers also increases because LASA needs to visit and identify the local centers based on weight factor and Euclidean distance.As a result when the communication range (R) increases from 400 m to 500 m, LASA would find less time to identify the local centers when compared to R = 600 m because LASA finds secluded nodes. If it is a repeated node, then LASA neglects it and again continues it’s search for local centers. Hence in the graph, time is increasing and stays for a while and again starts increasing.

Please cite this article as: V. Krishnaswamy and S. S. Manvi, Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.06.007

V. Krishnaswamy, S.S. Manvi / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

Fig. 7. Local center node.

Fig. 9. Master center node.

Fig. 8. Local center selection time.

Fig. 10. Master center selection time.

Total number and selection time for Master centers: It is observed from Fig. 9 that similar to local centers, the number of master centers increases with increase in communication range (R). As a result when R increases, PASA finds connected nodes and repeated nodes involved to the process of selection of master centers. As R increases to 600 m, PASA would find secluded and connected nodes along the petiole angle at the junction of the petioles in the palm tree structure. Hence in the graph it shows that when compared to lower communication ranges (R = 400 m and 500 m), there is slight linear increase for the case R = 600 m. With increase in coverage area, there is rise in neighboring nodes available for the process of choosing the master centers resulting in efficient aggregation scheme. It is observed from Fig. 10 that similar to time required for selecting local centers, the time required for selecting the master centers with respect to number of UW-sensor nodes in the network. As a result when the communication range (R) increases from 400 m to 500 m, PASA would find less time to identify the local centers when compared to R = 600 m because PASA finds secluded nodes. If it is a repeated node, then PASA neglects it and again continues it’s search for master centers. Hence in the graph, time is increasing and stays for a while and again starts increasing. With the rise in the number of UW-sensor nodes in the communication range, the selection time for master centers also rises. Time and energy for aggregation: It is observed from Fig. 11 that the time required for data aggregation with respect to number of UW-sensor nodes in the network. As the communication range is increased results in more number of UW-sensor nodes, hence enormous redundant data is generated by these nodes. To cumulatively collect these data from sensor nodes,more time requires resulting in increasing of time for data aggregation in the network. In our proposed scheme, the process of aggregation is carried out

9

Fig. 11. Aggregation time vs. Nodes.

by software agents which lessens the time for aggregation when compared to other aggregation schemes. It is observed from Fig. 12 about consumption of energy for data aggregation. In our proposed scheme the reliability in the aggregation process is realized by status of master and local centers in the network. The status of these centers are specific and fixed, as a result it performs better when compared other aggregation schemes to lessen the data amount to be processed in the network. Hence the consumption of energy also reduces. Aggregation ratio and Lifetime of network: Fig. 13 shows the relation between the aggregation ratio and number of UW-sensor nodes with various communication range. It is observed that as the communication range upsurges, the number of UW-sensor nodes associated to it in the network along with amount of redundant data also rises. But in our proposed scheme due to three levels of aggregation, the redundant data is efficiently suppressed.

Please cite this article as: V. Krishnaswamy and S. S. Manvi, Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.06.007

10

V. Krishnaswamy, S.S. Manvi / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

Fig. 12. Aggregation energy vs. Nodes.

master levels using palm tree structure starting from last node in leaflet up to the sink node. Based on the factors like weight, residual energy of UW-sensor nodes and Euclidean distance, the aggregation nodes are selected. In our proposed scheme we have attained the lifetime of the network at the cost of increase in time for selecting the number of local centers when compared to master centers. Our scheme tried to balance this trade-off resulting in increase of network lifetime. Simulation analysis shows that the proposed scheme performs better with respect to aggregation energy, aggregation time and aggregation ratio. The future works that can be considered for the proposed scheme are developing a cognitive agent based aggregation and routing, mobility of UWsensor nodes and sink node. Effective compressing techniques can be adopted to reduce the cost of the network.

References

Fig. 13. Aggregation Ratio vs. Nodes.

Fig. 14. Network lifetime vs. Nodes.

Lifetime of network: Fig. 14 shows the relation between lifetime of network and number of UW-sensor nodes with various communication range. Increased communication range results in increased connectivity but using our proposed scheme the redundant data are eliminated which conserves the energy of the nodes. As a result the network lifetime is increased. 5. Conclusion In this paper, we have proposed a palm tree structure based data aggregation and routing in underwater acoustic sensor networks using agents. The proposed scheme works on three stages of aggregation: at node stage, at master center stage (along the junction of petioles) and at local center stage (along the leaflets of the tree). The agents which are static and mobile are used to form palm tree structure with UW-sensor nodes and sink node. These agents facilitate in selecting aggregation nodes at local and

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Please cite this article as: V. Krishnaswamy and S. S. Manvi, Palm tree structure based data aggregation and routing in underwater wireless acoustic sensor networks: Agent oriented approach, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.06.007