An efficient routing algorithm for void hole avoidance in underwater wireless sensor networks Ghazanfar Latif1 , Nadeem Javaid1,∗ , Arshad Sher1 , Muhammad Khan1 , Tayyab Hameed2 , Waseem Abbas1 1
COMSATS Institute of Information Technology, Islamabad 44000, Pakistan1 2 Center for Advanced Studies in Engineering, Islamabad 44000, Pakistan2
[email protected], www.njavaid.com
Abstract—Routing hole problem is one of the most important issues in the underwater wireless sensor networks (UWSNs). It aims to analyze the routing hole boundary to prevent the formation of routing hole such that network lifetime and all other parameters are also increased. In this paper, we have proposed the two techniques to transmit the data in the presence of routing hole. In our protocol, the nodes transmit the data on the basis of an energy gradation (EG) and depth adjustment (DA) of the void nodes. However, these techniques are used for avoidance of routing hole. The efficient load balanced routing (ELBAR) using DA performed better than other two schemes. Simulation results depict that our schemes perform the better role in terms of energy efficiency, lifetime, deviation of energy consumption, path length and euclidean stretch. Index Terms—Routing hole, UWSNs, ELBAR, EG, DA
I. I NTRODUCTION sensor networks (WSNs) have been investigated because of the different potential applications. In the terrestrial network, the routing hole creates the problem in the absence of the forwarder node [1]. The approximate polygon determines the exact boundary of the hole. It is based on the hole covering parallelogram and the hole view angle of a given point. Data can send through escape mode and default mode. The default mode is that mode in which data transmits normally. However, the escape mode is used to transmit the data along the boundary of routing hole [1]. The limitations of default mode and escape mode are given as follows. In default mode, because of the data transmission via normally causes the energy consumption is an imbalance. While in an escape mode the routing path bends around the routing hole. Because of this, degrades the network performance in terms of lifetime. Now, we explain the underwater wireless sensor networks (UWSNs) which are used in the depth of the sea and they are not suitable for the human life due to the greater pressure and less visibility. Therefore, UWSNs are important for many applications i.e., disaster prevention, tsunami, environmental monitoring and military defense etc. The underwater environment is not suitable for terrestrial WSNs due to limited bandwidth, long propagation delay. In UWSNs, the acoustic signal is used and in terrestrial WSNs used the radio signal for communication. The speed of underwater channel is 1500m/s and the speed of radio channel is 300000000m/s. The underwater channel
W
IRELESS
is also known as an acoustic channel which is five orders of magnitude less than the radio signal. Moreover, it changes with the depth and other parameters. This is because the higher propagation speed causes the greater end-to-end delay. So, the land-based communication is not feasible for the acoustic environment. However, the multi-path fading, path loss, and attenuation cause high bit error rate (BER). The movements of sensor nodes occur with the mobility of water [2]. The limitation of this protocol is routing hole problem. The routing hole degrades the network performance in terms of network lifetime, energy efficiency, deviation of energy consumption etc. The void nodes are that nodes which are located in the void region. Sometimes the data transmission failing upon the void node region then the routing protocol transmits the data via some techniques. Contributions: In this paper, we have proposed the energy gradation (EG) and depth adjustment (DA) schemes to prevent the routing hole in communication phase. The proposed routing protocols are important to avoid the routing hole and unnecessary transmission. The EG scheme forwards data on the basis of energy comparison if the energy of a forwarder node is greater, then the forwarder node transmits data directly to sink. However, if the energy is less, then the node transmits data via the backward transmission for determining the higher energy node. Moreover, the idea of the DA topology of a void node moves to the new depth and it starts the data transmission. Simulation results depict the efficient load balanced routing (ELBAR) is capable of avoidance the routing hole through EG and DA topology routing schemes. So, due to these schemes are to improve the energy efficiency, network lifetime, path length, deviation energy and the euclidean stretch. The euclidean stretch defines the ratio between the actual path and threshold routing path. The rest of the paper is organized as follows. Related work is discussed in section II. The problem statement of our system is given in section III. The system model explains in section IV. The simulation and results are presented in section V and conclusions are in section VI and finally, references are listed for related work. II. RELATED WORK Recently, researchers have interested in terrestrial wireless sensor networks (WSNs) due to the distinctive characteristics
of UWSNs. In this section, we explain some existing literature in this domain. Jungmin et al., proposed the routing protocol called hop-byhop dynamic addressing based protocol (H2-DAB) in UWSNs. This protocol gets the unique ID to next all nodes in the network. The small ID gets to those nodes which are nearer to the sink while larger ID to away from the sink. However, the data is transmitted from greater ID to smaller ID nodes. This protocol solves energy hole problem near the sink due to the lower ID nodes. Therefore, the energy consumption and load is relatively higher [2]. Heejung et al., proposed an opportunistic routing (ORR) in WSNs. Because of this protocol reduces the sender waiting time, it also avoids the loop formation and achieves the network lifetime. However, the limitation is redundant packet forwarding due to the several received data simultaneously [3]. In [4], authors proposed the mixed routing technique and corona based network. This is because, the energy consumption is balanced among coronas. The limitation of this paper is larger energy consumption due to long distance. This paper achieves the maximum network lifetime. In [5], authors proposed logical ring topology and the sensor nodes are uniformly deployed. The limitation is higher energy consumption near the sink due to the transmission distance is decreases with sink. However, it achieves the better performance in terms of network lifetime and also avoids the energy hole problem near the sink. Jarnet et al., proposed the routing protocol called focused beam routing (FBR) in UWSNs [6]. This protocol is to minimize the extra flooding. In this article, before transmitting the packets, the nodes increase the transmitting range time-bytime according to adjust the flooding angle and the communication power level via power gradient. Moreover, the nodes need to determine the request to send (RTS) message in the sparse network. Due to this scheme, the wastage of energy consumption and propagation delay are high. However, the flooding angle is affected by the network performance. In [7], authors proposed the routing protocol called energy aware routing protocol (EASR). This protocol used for sink mobility behavior in a land based network. The sensor nodes earlier die due to imbalance load at near the sink. Due to the earlier death of nodes, the network lifetime is reduce. So, EASR adaptively to adjust the forwarding range of sensor nodes and the sink is relocated according to the uniform load among the nodes of the network. This paper achieves the network lifetime. Javaid et al., proposed the routing protocol called the autonomous underwater vehicle-aided efficient data gathering (AUV-AEDG). It performs better packet delivery ratio in UWSNs. The shortest path tree is suitable for sensor nodes which associate with the gateway. It achieves to maximize the network lifetime, greater energy efficiency. However, its limitation is to increase the end to end delay [8]. In [9], authors proposed the routing protocol called nonuniform deployment based on clustering scheme. This protocol used the heterogeneous connectivity than homogeneous con-
nectivity range of nodes. So, it achieves the connection of network. The limitation of this protocol consumes more energy while maximizing the network lifetime. Jiang et al., proposed the routing protocol called guaranteed full connectivity node deployment (GFCND). There are two types of this protocol, one is command node and other is connectivity node. This protocol used the greedy forwarding technique for coverage nodes and connectivity nodes. This protocol ensures the network connectivity and also to achieve network coverage rate in UWSNs. The limitation is no relationship in random node scattering [10]. Xie et al., proposed the routing protocol called vector based forwarding (VBF). In this protocol, determines the routing path from source to destination in upward direction. In this direction nodes are eligible to transmit the data. This protocol prevents the flooding in the network. However, in sparse network the performance of this protocol is poor due to the number of nodes is unavailable. The limitation of this protocol is higher packet drop ratio [11]. In [12], authors proposed the routing protocol which is called joint routing. While, the data transmission is through balanced routing (BR) in UWSNs. The goal minimizes the energy hole problem, prolongs the network lifetime and balances the energy consumption among nodes. However, the load distribution is an imbalance. III. P ROBLEM S TATEMENT Routing hole problem degrades the network performance in terms of lifetime, average end to end path, deviation in energy consumption, etc. This issue is handled up to some extend via dividing the network field [1]. In distributed network field, two modes of data transmission are used to recover from routing hole; default mode and escape mode. In the former, forwarding of data packet is carried towards the initial destination within transmission range. In later, node finds a view angle in the straight from destination, if destination comes in angle, it carries out else transmit back and repeats the same procedure but it reaches the final destination. The aforesaid modes increase the end to end path between the source and destination. The increase in path directly impacts the lifespan of the network. Hence, the minimization of path and reduction in the energy consumption are key issues which require special attention while designing the routing protocol for UWSNs. IV. P ROPOSED S CHEME In our proposed scheme, the sensor nodes are deployed into the three regions. The details of network model are given below: A. Network Model We assume that N number of sensor nodes are randomly deployed in the distributed network field. While, the number of sink is one, located on the water surface and the routing hole represented as H. The distribution is performed based on EG, whereas the gradation is done by in mind the requirement
of DA of in meters (m). To balance the energy among network nodes, homogeneous energy dissipation is considered. The transmission and reception energies are same when the distance between two nodes are same. Moreover, every node is equipped with acoustic modem while sink has both modems radio and acoustic to communicate with underwater nodes and with the offshore data centers. When nodes have the data packet, they forward it through multi-hopping by detecting and avoiding routing at each hop. Further details are given in the following subsections. Region3
Direct Transmissioin
Region2
Sink Back Transmission if minimum energy node
Region1
Sensor Node
H
other grade number of the node then the first node is directly transmitted to sink. However, if energy grade is small then they transmit to backward transmission and find the higher energy node for data transmission as shown in fig. 1. By this process, the nodes dissipate energy evenly, so network lifetime, energy efficiency and other parameters can be increased. C. DA When the node occurs in a void region then the data transmission will have on the DA of the void nodes. Because of this scheme, it is to determine and manage the routing path through void regions. When the data cannot send to the next node due to the void node, then the void node moves to new depth for data transmission. The DA technique is the more effective technique for data forwarding and energy efficiency. In this technique, data transmission using greedy forwarding strategy also used for the improvement of network performance. The data transmission is shown in fig. 2.
Higher Energy Node
V. S IMULATION R ESULTS Transmission Range
Near Destination Node
S2
Routing Hole
H
S1 S
Fig. 1: System Model for EG and Backward Transmission
Region3
Moves to Depth
Region2
Sink Sensor Node Region1
Void Node H Transmission Range
Near Destination Node Void node
Move to depth
H
Routing Hole
S2 S1 S
Fig. 2: System Model for DA B. EG We propose an EG scheme to avoid routing hole during the communication of network nodes. The gradation is performed by fragmenting the node battery into small chunks called in this paper m. Now, if segments are not introduced then in direct transmission more energy is consumed. Basically, the segmentation helps to set out a threshold to control the dissipation of the nodes. For instance, nodes in the direct vicinity of the sink are only allowed to deplete one grade of the battery. The grade number of the first node is greater than the
In this section, we evaluate the performance of our protocols with respect to ELBAR scheme in both cases. There are two cases one is Many to one (M 2O) and other is many to many communication (M 2M ). The proposed schemes to perform better than the existing scheme in average energy consumption and all other parameters because of routing path length decreases. Packet forwarding decision depends on EG and DA. Similarly, we set the different values of the parameter used in the simulation. The total numbers of sensor nodes equal to 1500 which are randomly deployed. The initial energy sensor nodes are set to be Eo=100J, Transmission range =40m, Transmission Power=0.0885W , Reception Power=0.045W respectively. In proposed schemes, considered the comparison of two cases: 1) The converge casting also known as the M 2O routing, the packets are transmitted from different nodes to one destination node. 2) The peer to peer routing explains the many sourcedestination pairs to transmit the data. It is represented by the M 2M . Fig. 3 depicts the comparison of average energy consumption among three different routing protocols. We can observe that the average energy consumption of DA is minimum than other two schemes. It means that the third protocol performs better than other two protocols. However, our first protocol consumes 0.41 due to the EG and another protocol consumes only 0.38 due to the DA. In M 2O communication, the DA achieves up to 12% other than two schemes due to the EG and DA of the void node. Fig. 4 depicts the comparison of average energy consumption among three different routing protocols. We can observe that the average energy consumption of DA is minimum than other two schemes. It means that the third protocol performs better than other two protocols. However, our first protocol consumes 0.49 due to the EG and another protocol consumes
only 0.45 due to the DA. In M 2M communication, the DA achieves up to 19.6% other than two schemes due to the EG and DA of the void node.
ELBAR EG DA
0.5
0.45
0.4 1
0.3
0.25
0.2 20
ELBAR EG DA
0.9
0.35
Deviation of Energy Consumption
Average Energy Consumption (J)
0.6
0.55
observe that the deviation of energy consumption of DA is minimum than other two schemes. It means that the third protocol performs better than other two protocols. However, in our first protocol the deviation energy is 0.59 due to the EG and in other protocol, the deviation is only 0.4 due to the DA. In M 2M communication, the DA achieves up to 36.5% other than two schemes due to the EG and DA of the void node.
25
30
35
40
45
50
Number of Communication Sessions
Fig. 3: M 2O-Average Energy Consumption
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0 20
25
ELBAR EG DA
0.65
0.6
35
40
45
50
Fig. 5: M 2O-Deviation Energy Consumption
0.55
0.5
0.45 1.6
ELBAR EG DA
0.4
Deviation of Energy Consumption
Average Energy Consumption (J)
30
Number of Communication Sessions
0.7
0.35
0.3
0.25
0.2 20
25
30
35
40
45
50
Number of Communication Sessions
Fig. 4: M 2M -Average Energy Consumption The deviation of energy consumption is determined by given formula: v u Pn n u1 X (eu ) 2 σ=t (eu − u=1 ) n u=1 n
1.4
1.2
1
0.8
0.6
0.4
0.2 20
25
30
35
40
45
50
Number of Communication Sessions
Fig. 6: M 2M -Deviation Energy Consumption (1)
where n represents the number of sensor nodes and eu represents the energy consumed by uth node, σ represents the deviation of energy. Fig. 5 depicts the comparison of deviation of energy consumption among three different routing protocols. We can observe that the deviation of an energy consumption of DA is minimum than other two schemes. It means that the third protocol performs better than other two protocols. However, in our first protocol the deviation energy is 0.42 due to the EG and in other protocol, the deviation is only 0.30 due to the DA. In M 2O communication, the DA achieves up to 17% other than two schemes due to the EG and DA of the void node. Fig. 6 depicts the comparison of deviation of energy consumption among three different routing protocols. We can
The average path length is derived by the number of hops among different successfully transmitted data packets. Fig. 7 depicts the average path length among three different routing protocols. We can observe that the routing path of DA is minimum than other two schemes. It means that the third protocol performs better than other two protocols. However, our first protocol the routing path length is 33 due to the EG and backward transmission. While the other protocol shows that the path length is up to 31.5 due to the DA. In M 2O communication, the DA achieves up to 7.35% other than two schemes due to the EG, backward transmission and DA of the void node. Fig. 8 depicts the average path length among three different routing protocols. We can observe that the routing path of DA is minimum than other two schemes. It means that the third protocol performs better than other two protocols. However, in our first protocol the routing path length is 42 due to the EG
and backward transmission. While the other protocol shows that the path length is up to 41.5 due to the DA. In M 2M communication, the DA achieves up to 5.05% other than two schemes due to the EG, backward transmission and DA of the void node.
achieves up to 2.35% other than two schemes due to the EG and DA of the void node. 1000
950
Network Lifetime (s)
900
Average Path Length (hops)
45
ELBAR EG DA
40
850
800
750
700
650
ELBAR EG DA
600
550
35
500 20
25
30
35
40
45
50
Number of Communication Sessions
Fig. 9: M 2O-Network Lifetime 30 20
25
30
35
40
45
50
Number of Communication Sessions
Fig. 7: M 2O-Average Path Length
1000
950
Network Lifetime (s)
900
60
ELBAR EG DA
Average Path Length (hops)
58
56
54
850
800
750
700
650
52
ELBAR EG DA
600 50 550 48 500 20
46
25
30
35
40
45
50
Number of Communication Sessions 44
Fig. 10: M 2M -Network Lifetime
42
40 20
25
30
35
40
45
50
Number of Communication Sessions
Fig. 8: M 2M -Average Path Length Fig. 9 depicts the network lifetime for three different routing protocols. We can observe that the network lifetime increases with the decrease of the number of the communication sessions in many to one transmission. The network lifetime of DA is minimum than other two schemes. It means that the third protocol performs better than other two protocols. However, in our first protocol, the network lifetime is 848 due to the EG and the other protocol shows that the network lifetime is up to 852 due to the DA. In M 2O communication, the DA achieves up to 0.70% other than two schemes due to the EG and DA of the void node. Fig. 10 depicts the network lifetime for three different routing protocols. We can observe that the network lifetime increases with the decrease of the number of the communication sessions in many to one transmission. The network lifetime of DA is minimum than other two schemes. It means that the third protocol performs better than other two protocols. However, in our first protocol, the network lifetime is 830 due to the EG and the other protocol shows that the network lifetime is up to 843 due to the DA. In M 2M communication, the DA
The stretch is determined by the ratio between actual routing path and derived routing path is given by formula: er Stretch = (2) et where stretch is the euclidian stretch, er is the actual routing path and et represents the threshold routing path. Fig. 11 depicts the routing path stretch among three different routing protocols. We can observe that the routing path stretch of DA is minimum than other two schemes due to the decrease of routing path length. It means that the third protocol performs better than other two protocols. However, in our first protocol, the routing path stretch is 1.07 due to the EG and backward transmission. While the other protocol shows that the routing path stretch is up to 1.03 due to the DA. In M 2O communication, the DA achieves up to 6.36% other than two schemes due to the EG, backward transmission and DA of the void node. Fig. 12 depicts the routing path stretch among three different routing protocols. We can observe that the routing path stretch of DA is minimum than other two schemes due to the decrease of routing path length. It means that the third protocol performs better than other two protocols. However, in our first protocol, the routing path stretch is 1.07 due to the EG and
backward transmission. While the other protocol shows that the routing path stretch is up to 1.05 due to the DA. In M 2M communication, the DA achieves up to 4.54% other than two schemes due to the EG, backward transmission and DA of the void node. 2
ELBAR EG DA
1.9
1.8
1.7
Stretch
1.6
1.5
1.4
1.3
1.2
1.1
1 650
700
750
800
850
900
1000
Distance between Ssource and Destination (m)
Fig. 11: M 2O-Euclidean Stretch
2
ELBAR EG DA
1.9
1.8
1.7
Stretch
1.6
1.5
1.4
1.3
1.2
1.1
1 650
700
750
800
850
900
1000
Distance between Source and Destination (m)
Fig. 12: M 2M -Euclidean Stretch VI. C ONCLUSION A ND F UTURE W ORK In this paper, we proposed the two schemes to avoid the routing holes to improve the energy efficiency, network lifetime, path length, deviation of average energy consumption and a euclidean stretch of the UWSNs. In our scheme, the sensor nodes transmit the data packet via EG and DA of void nodes. The simulation results are performing much better than existing scheme. Our schemes can achieve more than 9% and 12% of energy efficiency, 5% and 17% of network lifetime as compared to existing routing scheme. The average routing path length is minimized by 2.9% and 7.35% than existing scheme. In the future, we will work on mathematical analysis and network performance will be improved. R EFERENCES [1] Nguyen, Khanh-Van, Phi Le Nguyen, Quoc Huy Vu, and Tien Van Do. "An energy efficient and load balanced distributed routing scheme for wireless sensor networks with holes." Journal of Systems and Software 123 (2017): 92-105.
[2] Yu, Haitao, Nianmin Yao, Tong Wang, Guangshun Li, Zhenguo Gao, and Guozhen Tan. "WDFAD-DBR: Weighting depth and forwarding area division DBR routing protocol for UASNs." Ad Hoc Networks 37 (2016): 256-282. [3] So, Jungmin, and Heejung Byun. "Load-Balanced Opportunistic Routing for Duty-Cycled Wireless Sensor Networks." IEEE Transactions on Mobile Computing (2016). [4] Zhang, Haibo, and Hong Shen. "Balancing energy consumption to maximize network lifetime in data-gathering sensor networks." IEEE Transactions on Parallel and Distributed Systems 20, no. 10 (2009): 15261539. [5] Xue, Yu, Xiangmao Chang, Shuiming Zhong, and Yi Zhuang. "An efficient energy hole alleviating algorithm for wireless sensor networks." IEEE Transactions on Consumer Electronics 60, no. 3 (2014): 347-355. [6] Jornet, Josep Miquel, Milica Stojanovic, and Michele Zorzi. "Focused beam routing protocol for underwater acoustic networks." In Proceedings of the third ACM international workshop on Underwater Networks, pp. 75-82. ACM, 2008. [7] Wang, Chu-Fu, Jau-Der Shih, Bo-Han Pan, and Tin-Yu Wu. "A network lifetime enhancement method for sink relocation and its analysis in wireless sensor networks." IEEE sensors journal 14, no. 6 (2014): 1932-1943. [8] Javaid, Nadeem, Naveed Ilyas, Ashfaq Ahmad, Nabil Alrajeh, Umar Qasim, Zahoor Ali Khan, Tayyaba Liaqat, and Majid Iqbal Khan. "An Efficient Data-Gathering Routing Protocol for Underwater Wireless Sensor Networks." Sensors 15, no. 11 (2015): 29149-29181. [9] Jiang, Peng, Jun Liu, and Feng Wu. "Node non-uniform deployment based on clustering algorithm for underwater sensor networks." Sensors 15, no. 12 (2015): 29997-30010. [10] Jiang, Peng, Jun Liu, Binfeng Ruan, Lurong Jiang, and Feng Wu. "A new node deployment and location dispatch algorithm for underwater sensor networks." Sensors 16, no. 1 (2016): 82. [11] Xie, Peng, Jun-Hong Cui, and Li Lao. "VBF: vector-based forwarding protocol for underwater sensor networks." In Networking, vol. 3976, pp. 1216-1221. 2006. [12] Bouabdallah, Fatma, and Raouf Boutaba. "Joint Routing and Energy Management in UnderWater Acoustic Sensor Networks." IEEE Transactions on Network and Service Management (2017). [13] Ayaz, Muhammad, and Azween Abdullah. "Hop-by-hop dynamic addressing based (H2-DAB) routing protocol for underwater wireless sensor networks." In Information and Multimedia Technology, 2009. ICIMT’09. International Conference on, pp. 436-441. IEEE, 2009.
