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ScienceDirect Procedia Computer Science 57 (2015) 33 – 40

3rd International Conference on Reecent Trends in Computing 2015 (ICRTC-2015)

Agent Based Predictive Data Collection in Opportunistic Wirelesss Sensor Network Ruppali Rohankara* a

Assitant Professor, Amity U University, Noida, India, [email protected]

Abstract Opportunistic wireless sensor networks have significcant applications like remote monitoring where node density is usually low than average. The traditional deployment of sennsor networks has larger density, confirmed communication lin nks and multiple routing paths. This does not apply to oppportunistic networks. Due to sensing nodes constraints and d sparse connectivity, the data collection in OWSN is a difficuult task. Mobile agents can be injected into the network for perfforming network exploration to collect metadata of nodes. A Ant-AODV routing protocol provides least overhead in mobille agent routing. Based on the metadata a predictive model caan be built to find the probable contact time and location of the mobile nodes and sink in OWSN. This paper simulates an aggent based predictive data collection algorithm (PLA). The sim mulation results are then compared with other opportunistic algorithms and the data collection performance improves alm most by 33.2%. © 2015 2015The TheAuthors. Authors. Published by Elsevier B.V.is an open access article under the CC BY-NC-ND license Published by Elsevier B.V. This (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing comm mittee of the 3rd International Conference on Recent Trends in Peer-review under of organizing committee of the 3rd International Conference on Recent Trends in Computing 2015 Computing 2015responsibility (ICRTC-2015). (ICRTC-2015)

Opportunistic sensor networks, mobille agents, wireless sensor networks, data collection, agen nt based data gathering Keywords—

1. Introduction In Wireless Sensor Networks (WSN) data collection is an important task as stated by [1]. Data collecction is ndomly either done periodically or whenever an evennt is triggered. Since the sensor nodes are usually ran deployed in large geographical area data collection has large volumes stated [25]. This data is then forw warded to a sink node for further processing. Since, the area covered is large and due to limited transmission range, not unding all the nodes can directly communicate with tthe sink node. Therefore, the data gathered from surrou nodes is communicated through intermediate reelay nodes using multi-hop communication [23]. On thee other hand, some of the applications where the moniitoring area involve thick forests, volcano sites and so on, o the deployment of sensor nodes is a tedious task aand they are dropped from air. The sensor nodes get un nevenly spread in the monitoring site and may not be in the transmission range of other relay nodes. The data collection

1877-0509 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of the 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015) doi:10.1016/j.procs.2015.07.361

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Rupali Rohankar / Procedia Computer Science 57 (2015) 33 – 40

then is done using mobile nodes as the full coverage is required and full connectivity is impractical [23]. They are known as Opportunistic Wireless Sensor Network (OWSN) [2]. In OWSN data collection is usually done using mobile sink and needs one-hop communication. The sensing nodes therefore temporarily store the data being sensed and wait till the sink node arrives in the communication range [21]. They make use of store-carry-forward mechanism for disseminating the sensed data. Since, the communication is opportunistic a definite routing path cannot be defined in OWSN and new routing methods are needed. In [4], data collection is done in one-hop or multi-hop by mules (mobile data collector) and then they forward the data collected to the sink node. Mobility graph based on contact time prediction between mobile buses and sensing nodes is simulated in [5]. In [6] mobile sink node is controlled and routed on a pre-decided path. A predictive data collection algorithm (PLA) is implemented in this paper. The graph is modeled based on the metadata collected by mobile agents. The sink is then programmed based on the calculations and graph to collect data from the sensing node [16]. A mobile agent migrates from one node to another and accesses the information or performs a task on a node [8]. Mobile agents have been efficiently deployed in WSN to perform data dissemination and fusion thus saving node energy and bandwidth. They best serve the distributed operations in an opportunistic network and well support disconnected, asynchronous operations [9-10]. In [12], data forward probability is calculated and only neighbors with high probability are forwarded the data. In [13], a friendship based routing is used to know the friendly relations based on their behavior between nodes (which are people in social networking group) and the messages are forwarded accordingly. [14] describes the fixed link structure for forwarding data in adhoc networks. [15-16] describe the applications of mobile agents in monitoring wireless sensor networks. The PLA algorithm, utilizes mobile agents to explore network. Network is explored by collecting metadata like residual node energy, statistics of node contact with mobile sink. The number of agents that are sent on the network is determined based on the task for which they will be created and adapt to changing network topology. Section II discusses predictive location pattern and agent based network exploration. An example predictive graph is created in Section III and mobile agent migration is described. Section IV illustrates simulation results followed by conclusion and future scope in section V. 2. Predictive Pattern & Agent Based Network Exploration 2.1. Network model Consider a homogeneous network with a sink node and N sensing nodes with unique id. The values of the parameters sensed from the environment periodically are store in a record on the node with a unique id. The nodes are constrained with energy and computation space and can identify their own locations. The nodes movement is cyclic with some kind of pattern. The sink is actuated accordingly for data communication. When two nodes are in transmission range of each other they temporarily establish the communication link to perform opportunistic data dissemination. i) Communication opportunity: Two nodes m and have communication opportunity if they are in contact with each other for more than a predetermined threshold. ii) Path: for a group of nodes if (m1, m2, … mk-1) if there is an opportunity for communication between m1 m2, m2 – m3, .. mk-1 - mk, then a path is said to exists between m1 and mk.


iii) Cost of data collection: For N sensing nodes, if average k-bits are exchanges during each contact and t is transmission cost then the cost of data collected across m1 - mk path is given as in equation (1) below:

Cost = ¦ ( k * t )

(1)

i

2.2. Predictive Location Based Algorithm This section describes the agent based predictive location algorithm (PLA). When the mobile nodes move around, the contact nodes change defining a new path to the sink. If there is a pattern in the node movement, we can predict the next possible contact between the nodes and sink. PLA has following phases: • Sink collects network metadata like node movement, contact duration, through mobile agents. • Sink creates a graph to predict contact duration and location with a node based on metadata. • Sink is actuated based on the predictive model for collecting sensed data from nodes. • Data collection points are updated whenever necessary. 2.3. Network Exploration using mobile agents The algorithm proposed PLA makes use of predictive model for data collection which is built from the information collected by mobile agents. Specifically the mobile agents collect the metadata of each node such as connection history with other nodes or sink node in the network, the contact duration, node residual energy value, and location information of the sensing nodes. Ant-AODV routing protocol for network exploration as the routing protocol [8]. The information gathered is further processed to compute the contact time and location of nodes and sink. Mobile agents are created and destroyed at sink node and they operate autonomously following a unique path. OWSN networks are sparsely populated, and therefore density of spatial nodes is negligible. The network is said to be completely explored if all the N nodes are visited by the mobile agent. The processing time needed by agent at a node is predictable [18]. Mobile agents are adaptable to the surrounding conditions such as latency requirements and energy available at each node. A mobile agent server is established at sink node and it exhibits various functions of agents like inter-agent communication, security features and migration of agents to different nodes and back to sink. Besides, each node has a static agent that maintains routing table on the host node, or finds alternate routes to sink node and stores node statistics. Mobile agent migrates to different nodes and collects the required information from static agent residing on the node. The routing table on mobile nodes is simultaneously updated with the information gathered from mobile agent by the static agent [19]. 3. Predictive Graph for Actuating Sink Mobile agent is first authenticated by the target node and then it processes and fuses the information on the node before carrying it along. Network metadata on sink is regularly updated at certain intervals. The sensing data and the node path history is collected by the agent and passed to the sink. This movement data of nodes is stored at sink in a table as record including parameters as shown in the table 1. The movement table is then frequently updated. Table 1: Time Stamp 10 12 19 27

Nodes movement table Nodes Contact Duration of Link Contact mÆn 5 nÆo 10 oÆp 2 pÆq 15

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m Æ r, m Æ o pÆs

42 55

13 21

From the information collected in the table 1, the temporal graph is created to predict contact locations for nodes and sink. For distant node contact the sink can be actuated to move to the appropriate location according to the graph. This results in saving unwanted transmission energy and effectively maximizing network lifetime. 3.1. Creating Temporal Graph The contact time T probability is calculates as in equation (2): T −1

Tc = ¦ 1 / 2T − K

(2)

K =1

Where, T is the current epoch and at kth epoch nodes m and n meet. If two nodes do not meet each other then k will become 0. The length of the contact time TL is given as duration for which the nodes m and n meet: T −1

TL = 1 /(T − 1)¦ p * k , T > 1

(3)

K =1

p denotes the duration for which nodes m and n are in contact with each other, k denotes the contact time. Consider T =4, and nodes (m, n) encounter at 3rd, 4th epoch then the probability of contact between them is CT = 1/ 8 – 3 + 1/ 8 – 4 = 0.45. if CT meets some threshold (h), then a link is added to the contact graph between m and n. Consider h 10, from nodes movement table 1. The table 2 shows contact probability CT values at different instances and the corresponding contact graph as shown in Fig. 2. Table 2: Contact Duration Probability CT

Time Stamp 7 10 15 23 32 39

Nodes Link n1 n2 n3 n4 n5 n6

Contact

Fig. 2: Nodes Contact Graph

Node Location (17,25) (20,30) (25,35) (40,55) (45,50) (33,40)

CT (x 10-1) 12 4 22 9 17 15


4. Simulation 4.1 Simulation Environment Extensive simulations have been performed forr comparing the proposed algorithm PLA with algorithm in [7], hereafter known as ADA, and algorithm in [8], known k as MAR. The implementation of PLA is done in Matlab. M Consider N = 20 sparsely scattered in an area of o 160m x 160m. The simulation is performed 30 times to find the average values for each result. A packet size is 512B and 1 packet per 5seconds is generated by a node. Since the communication cost is more importantt we consider only communication cost. 4.2 Simulation Results The comparison of algorithms is made on the baasis of communication cost for variable network size, radio

range and moving speed. Finally the impact of network exploration using mobile agents is discussed baased on the communication cost and latency [19-20]. A. Network Size mission As seen from fig. 3 the increase in network siize leads to significant increase in the number of transm messages. PLA has the smallest number of transmission t messages required. While MAR has the highest h messages transmitted. PLA reduces the transm mission messages by 33% as compared to MAR and is almost similar to ADA when the network size grows too its maximum. As in fig. 4, the data collected by PLA iss about 94% on average and is almost 10% higher than those of ADA and MAR. This is because PLA moves alo ong the A also buffers less number of messages saving storage space. pattern learnt from mobile agents Further, PLA Also, the sink in PLA is actuated according to the t data gathering points in contact graph which reduces energy consumption due to unnecessary transmissions. B. Varying radio range of nodes ollected From Fig. 5 & 6, it can be seen that number of message transmitted and number of data packets co LA is better as compared to ADA and MAR in transm mission increases with the increasing radio range. PL message as well as data collection with varying range. Radio range when grows maximal, the transmission and

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data collection grows to 90%, which is the best performance. Practically, achieving this performance depends on hardware and will be very expensive for each transmitted data unit. C. Speed of nodes The nodes in opportunistic networks are mobile and faster they move more nodes they can encounter and

exchange data packets. As seen in Fig. 7 & 8 data transmission and data packets collected increase with the speed of node. When the speed is low, the transmission messages needed by PLA are reduced by almost 33.2% when compared with ADA and MAR. While, the data collection increases by almost 7-10% by PLA. The reduction in message transmission and increased data packets is due to the fact that PLA initially builds a movement table and actuates the sink accordingly. The routing table is built by other algorithms only when nodes come in contact with each other. This further reduces the delay caused in data collection by PLA. Hence, PLA results in considerable energy saving thus maximizing network lifetime.


4.3 Discussion It can be concluded from the simulation results that by using ant like mobile agents and implementing the antAODV on demand routing protocol the route discovery latency and connectivity between nodes or sink is increased. The initial prediction based on metadata simplifies the sink movement to a certain location at a particular instance of time. Since, the routing table is frequently updated the unnecessary transmission of control messages is reduced thus saving energy of the node. Further, as discussed earlier if a node is low on energy the mobile agent does not carry any status packet for transmitting an event. Any node failure can be subsequently detected. Ant-AODV being hybrid and combines goodness of both the algorithms the routing overhead is the least and similar to AODV. 5. Conclusion & Future Scope In this paper, a mobile agent based predictive algorithm (PLA) is implemented for data collection in opportunistic sensor networks. The PLA is an efficient algorithm and is based on metadata of node movements. This metadata obtained from network exploration is done by mobile agents using ant-AODV routing protocol. The mobile agents send the status packet only when a new event is detected. A contact graph based on contact prediction CT is prepared. If the connection time TL is more than a certain threshold (h) then the communication link is established between the nodes for data exchange. As seen from the experimental results the number of messages transmitted between the nodes is reduced by 33.2% saving considerable energy of the nodes and increasing data collection rate by 10 to 12% when compared with other algorithms like ADA and MAR. In future, PLA implementation considering energy efficiency with certain additional features like data redundancy in OWSN and contact failure probability can be worked on. References 1. 2. 3.

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