IMPROVISED OPPORTUNISTIC ROUTING FOR UNDERWATER WIRELESS SENSOR NETWORKS

Transcription

1 IMPROVISED OPPORTUNISTIC ROUTING FOR UNDERWATER WIRELESS SENSOR NETWORKS 2 Mohd Mujeebuddin, 1 Md Ateeq Ur Rahman 2 SCET, Hyderabad, 1 Professor, Dept of CSE, SCET, Hyderabad, Abstract In the recent years, Underwater Wireless Sensor Networks (UWSNs) has attracted the wireless research communities. Resource Discovery (RD) and Ocean monitoring are the two concepts explored by the researchers. In order to achieve successful packet delivery ratio, the constraints in acoustic communication channel has to be explored. This paper concentrates over devising opportunistic routing protocols using acoustic communication channel.improved opportunistic routing protocols takes advantage of distributed beaconing, constructs the adjacency graph at each hop and selects a forwarding set that holds the best trade-off between reliability and energy efficiency. The unique feature of an improved opportunistic routing is the selection of accurate next-hop forwarder nodes which trade-off the node configuration issue. Our simulation result shows that improved opportunistic routing works better than prior routing protocols, GOR in terms of reduced packet loss, energy consumption, endto-end delay and propagation deviation factor. Keywords:Underwater wireless sensor networks, Resource discovery, Ocean monitoring, Acoustic communication channel, and Opportunistic routing I. INTRODUCTION Wireless sensor networks (WSN) are an acquisition of wireless sensors nodes that supervises environmental changes without any centralized control over infrastructure [1]. Nowadays, several studies has been conducted to develop sensor hardware and network infrastructure for different sort of applications. Prior WSNs requirements didn t satisfy the needs of all applications. Network parameters like sensing range, transmission range and node density have to be selectively chosen for an effective network performances. Underwater sensor network is a branch of study in Wireless Sensor Networks (WSN). It is a sensor network which composed of different sensor nodes with acoustic transceivers. The role of acoustic transceivers is to build communication path between sensing areas from shallow water and oceans. The applications of UWSNs are numerous that majorly helps to monitor the ocean s environmental changes. In order to achieve better performances, the network parameters have to be devised regularly [2]. Resource discovery is an essential goal of the Underwater Wireless Sensor Networks. It is analyzed for dependency rate of land resources. The networks maintenance of the UWSNs is a daunting task which eventually poses cost maximization analysis. It is the recent study that impressed the research communities. The radio waves explored from underwater is used for gathering the events took place in ocean [3].The specific data are collected using acoustic transmission model. The sink transmits the acquired information to the monitoring center for further investigation. The major challenge persist in UWSNs is data forwarding in the area of node movements, low bandwidth, propagation speed and cost. Since the Global Positioning System (GPS) [4]can t be used in underwater environment. In addition to, the deployed nodes are dynamic that states unaware about the position of the nodes. Similarly, the communication quality of the channel also varies under pressure, temperature and salinity. In some cases, the bandwidth consumption of nodes may varythat also reduces the channel quality. The available nodes in UWSNs are mutually connected, if the decoding process is efficiently designed. Energy consumption [5] of the underwater sensor nodes is predicted to be higher usage. Thus, routing protocols are also plays a vital role in Underwater Wireless Sensor Networks. Opportunistic routing is the novel sort of routing protocols that deployed for increasing the transmission capability. When a set of candidate nodes are transmitting the packets to the nodes which are out of range, will fail to capture the information about the transmitting nodes. Higher priorities nodes are formed as the set of forwarding nodes. Volume 5, Issue 10, October 2017 Page 55

2 The rest of the paper is organized as follows: Section II describes the related work; Section III describes the proposed work; Section IV describes the experimental analysis and results and concludes in Section V. II. RELATED WORK This section presents the prior works carried out by other researchers.variety of routing protocols is available for UWSNs to overcome from the issues like long propagation delay, low data rate and high error probability. The geographic routing protocols are designed for UWSNs in VBF. The VBF [6] is used for delivering the packets from source to destination in vector. The range R is defined for each nodes and the packets are transferred to the nodes within range. Localization service is used for obtaining the information about sensor node. E-PULRP is energy efficient routing protocols that depict the energy maintenance between intermediate relay nodes and packet transmission. To effectively select the relay node, the time intervals taken by neighboring nodes are used for energy consumption and received signal strength. Reverse Localization Scheme (RLS) [7]is a 3D centralized localization model for mobile underwater wireless sensor networks. Other routing protocol, Depth Based Routing (DBR) is used for predicting the location of the nodes. Sink node gets the details about sonobuoy drifted at each surface. Using greedy algorithm, the forwarded node with lowest pressure is elected as the neighboring node. The delay sensitive based applications in UWSNs have to be focused for routing algorithm development [8]. Minimization of the energy level is also a serious issue. An energy model is developed for the optimal packet size and redundancy for underwater communication. Relied upon the underwater communications, transmission power of each sensor node is adjusted. To manage the power of each sensor node,multi-path Power control Transmission scheme (MPT) is designed. Vector based communication model was deployed to eliminate the void area of the communication channel. Vector based void avoidance (VBVA) is suggested for vector based communication model. It suggested two approaches, vector shift and back pressure that investigate over the convex and concave voids. The recovery process of those failed nodes is a daunting task. A time series model was used for packet dropping avoidance in the concave hole. Relative Distance Based Forwarding (RDBF) is an alternate routing protocol that depicts the location based coordinates [9]. Relied upon the geographical distance to the sink node, the relay nodes are selected and processed. Then, a threshold value is defined to every geometric shape of those forwarding nodes. RDBF also suffers from the high bit error rate, because it mostly relies on the nodes with the shortest path to the sink. In addition, RDBF does not represent a recovery mode to deal with the packets that are stuck in a local maxima node. In other group of research, depth information is employed for routing the packets towards the destinations.dbr is the first depth-based routing protocol proposed for UWSNs. Nevertheless, forwarding set selection will not performed in an optimal way (having the duplicated packets problem), and neither proposes any recovery method to solve the void problem. Depth-Based Multi-hop Routing (DBMR) and Energy-Efficient Depth-Based Routing (EEDBR) [10] are also proposed in this category, which consider the residual energy of the nodes in their forwarding set selection; however, they still provide no solution for the local maxima nodes. III. PROPOSED WORK This section depicts working of ourproposed model. The proposed algorithm contains four phases which is explained as follows: a) Topology creation: Let us consider a number of sensor nodes n and number of sonobuoys nodes S n are deployed in 2265 * 1000 sizes. According to the individual sensor nodes, the data packets are generated using Poisson process.poisson process is the simple and widely used stochastic process that models the packet delivery over particular period of time. The probability of packets arrival over period of time is estimated using poisson process under low traffic load.the packet transmission range is from max 5 m/s to min 2.70 m/s. Node transmission range is given as 250m with data rate of 50kbps. Relied upon the payload size and space taken for next hop forwarder set, the size of the packet is determined. b) Enhanced beaconing: Beaconing is the recovery process used in packet transmission systems. GEDAR routing protocols composed of location information of the reachable sensor nodes and sonobuoys. In order to efficiently manage the static and dynamic nodes, the beaconing algorithm used should efficiently be designed. The beacon message should be as short as possible in transmission systems. Consider a node with its known sonobuoys location, the beacon message is defined without including lower layer headers which is given as follows: (1) Where, Volume 5, Issue 10, October 2017 Page 56

3 m denotes size of the sequence number n denotes size of the ID fields with its geographic coordinates Enhanced beacon algorithm resolves the issues large packet transmission in underwater acoustic channel. Each beacon message is embedded with sequence number, uniqueid, geographic position coordinates x,y and z nodes and sonobuoys information. Since the deployment of GPS is restricted in underwater wireless sensor networks, the intermediate nodes are aware about their nearest sonobuoys information S i (t) where t is the time period. Using the localization services, each sensor node has known their location to eliminate the issue like network flooding.if the sonobuoy receives the beacon message, the rest sensor nodes in the network are updated. Random filters, 0 and 1 are added to the beacon messages and then broadcasted. By doing do, we can eliminate the network collision and synchronization. c) Neighbor candidate set selection This step permits to find the appropriate neighbor node for efficient packet transmission systems. In the greedy search process is applied to determine the set of neighbors for forwarding the packets. The sonobuoy node presented in surface is used for packet transmission process. The neighbor candidate process is given as: (2) Where, n i Intended node used for packet delivery. N i (t) Set of nearest neighbors S i (t) Set of known sonobuoys at time period t. D (a, b) Euclidean distance between nodes a and b. d) Next hop forwarder set selection: Prior work makes use of one neighbor node as next hop forwarder.when the neighbor node overhears, then the link to the packet is dropped off. Each packet is broadcast to forwarding set comprised of several neighbors. Though opportunistic routing has pitfalls, it significantly reduces the cost of the energy. OR model is used to reduce the rate of packet retransmission process. Goodness is an evaluation metric that measures the performance of next-hop candidate node. Priority state of the neighboring nodes updated when the packet size is increased. It is given as: (3) Where, NADV (Normalized advance) with set of candidate node n c. p( d i c,m) is the packet delivery probability of m bits over distance d i c. Opportunistic routing selects highest priority node as next-hop forwarder and rest nodes executes when highest priority node fails. The i th waiting time of the delayed packet transmission is given as: Where T p is the propagation time T proc is the packet processing time. (4) Fig.1. Workflow of the Proposed System Volume 5, Issue 10, October 2017 Page 57

4 IV. EXPERIMENTAL RESULTS AND ANALYSIS This section depicts the simulation analysis of our proposed model. The performance of the proposed algorithm deployed over MAC protocols without using its Request to Send (RTS), Clear To Send (CTS) and Acknowledgement (ACK) mechanism.if the channel is free, the forwarder node broadcast the packets.for lowering theredundant transmissions and nodes can simply listen to the channel and later drop the packet if it is relayed byother candidates. The simulation parameters considered are: Table 1. Simulation parameters Transmission power 95dB Transmission range Data generation rate Channel bit rate 100m One packet per second 10kbps a) Packet Delivery ratio (PDR): Propagation speed at 1500m/s underwater environment Area of relay nodes 500m * 500m *1000m Packet Delivery Ratio is defined as the rate of packets delivered successfully to its intended node from the source node. The fig.2 presents the packet delivery rate to its node densities. It is evident from the results PDR rate is high when the no.of nodes also increases. Our proposed model reduces the no. of void areas in dense network. b) Energy Consumption: Fig.2 Packet delivery ratio Energy consumption is defined as the energy consumed by each nodes in the underwater environment. It is measured in millijoules (mj). Selecting a large radius may involve many nodes in packet forwarding moreover, it cause increases duplicated packets, which at the end leads to more energy waste. On the other hand, lower radius causes more packet failures. To desired this, in dense networks, the number of forwarding nodes has been slightly risesd or held constant by GEDAR, to control the energy dissipation. Fig.3 presents the energy consumption per node with different node densities. Energy consumption rate (mj) Node densities GEDAR GOR Fig.3. Energy consumption with different nodes densities Volume 5, Issue 10, October 2017 Page 58

5 c) Average End to- End delay: Average End-to-End delay is defined as the average delay time taken for packet creation and successful delivery of packets to the destination node. The average end-to-end delay for all protocols decreases by increasing the number of nodes, because the forwarding node can find more qualified nodes in its neighbourhood. This feature of GEDAR increases its delay despite the use of reachability information. Furthermore, in GEDAR, each node can hold a packet with less average holding time (by setting a lesser amount of T Delay ) due to the fact that candidate nodes are closer to each other on average. Fig.4 Average End-to End delay with different node densities d) Propagation deviation factor: Propagation deviation factor is the value obtained for estimating the distance travelled by each packet from source to the sink. It is given as: Where TD is the aggregate of distance travelled and SD is the straight line distance between sink node and source node. The pdf value is incremented, when longer distance is taken for packet delivery. Thus, efficient routing protocol is used for selecting the optimal route with least pdf value. By increasing the number of nodes, the shorter routing paths can be established by the routing protocols. V. CONCLUSION Dealing with the void issue in acoustic Underwater Wireless Sensor Networks (UWSNs) is a daunting task in routing protocols. In this paper, we have enhanced the beaconing process by selecting efficient next hop forwarder node can significantly increasing the routing s performance.we have also investigated opportunistic routing to show how it can overcome the drawback of unreliable acoustic transmission by taking advantage of intermediate nodes collaboration to relay packets. To this end, we have proposed GEDAR, an opportunistic routing protocol, to minimize the number of dropped packets by efficiently bypassing void areas and also to maximize the transmission reliability. GEDAR searches best route path by improving the local information extracted from periodic beaconing and then selects the optimal path within the cluster based on energy constraints.our simulation results have shown that GEDAR significantly reduced the packet loss, energy consumption, end-to-end delay and propagation deviation factor in sparse to dense scenarios. REFERENCES [1] Rodolfo W. L. Coutinho et al, Geographic and Opportunistic Routingfor Underwater Sensor Networks, IEEE transactions on computers, 65(2), [2]Heidemann, J.; Ye, W.; Wills, J.; Syed, A.; Li, Y. Research challenges and applications for underwater sensor networking. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC 2006), Las Vegas, NV, USA, 3 6 April 2006; Volume 1, pp [3] Akyildiz, I.F.; Pompili, D.; Melodia, T. Underwater acoustic sensor networks: Research challenges. Ad Hoc Netw. 2005, 3, Volume 5, Issue 10, October 2017 Page 59

6 [4] Akyildiz, I.F.; Pompili, D.; Melodia, T. Challenges for efficient communication in underwater acoustic sensor networks. ACM Sigbed Rev. 2004, 1, 3 8. [5] Vasilescu, I.; Kotay, K.; Rus, D.; Dunbabin, M.; Corke, P. Data collection, storage, and retrieval with an underwater sensor network. In Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems, San Diego, CA, USA, 2 4 November 2005; ACM: New York, NY, USA, 2005; pp [6] Ayaz, M.; Baig, I.; Abdullah, A.; Faye, I. A survey on routing techniques in underwater wireless sensor networks. J. Netw. Comput. Appl. 2011, 34, [7] Pompili, D.; Akyildiz, I.F. Overview of networking protocols for underwater wireless communications. IEEE Commun. Mag. 2009, 47, [8] Kheirabadi, M.T.; Mohamad, M.M. Greedy routing in underwater acoustic sensor networks: A survey. Int. J. Distrib. Sens. Netw. 2013, doi: /2013/ [9] Noh, Y.; Lee, U.; Wang, P.; Choi, B.S.C.; Gerla, M. VAPR: Void-aware pressure routing for underwater sensor networks. IEEE Trans. Mob. Comput. 2013, 12, [10] Fang, Q.; Gao, J.; Guibas, L.J. Locating and bypassing holes in sensor networks. Mob. Netw. Appl Volume 5, Issue 10, October 2017 Page 60