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Void Hole and Collision Avoidance in Geographic and Opportunistic Routing in UWSNs By

  • January 2017
  • Thesis for: MS(Software Engineering)
  • Advisor: Nadeem Javaid, Mariam Akbar
Authors:
Aasma Khan at COMSATS University Islamabad
Aasma Khan
Nadeem Javaid
Nadeem Javaid
Nadeem Javaid
  • This person is not on ResearchGate, or hasn't claimed this research yet.
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Abstract and Figures

Underwater Wireless Sensor Networks (UWSNs) have been considered as an emerging and promising method for exploring and monitoring deep ocean. The UWSNs face many challenges due to high transmission delays, high deployment cost, movement of nodes, energy constraints, etc. In UWSNs nodes are sparsely and unevenly deployed, that may results in void hole occurrence. Secondly low propagation speed in UWSNs causes high end-to-end delay and energy consumption. In this paper, we propose four schemes: Adaptive Transmission Range in WDFAD-DBR (ATR-WDFADDBR), Cluster Based WDFAD-DBR (CB-WDFAD-DBR), Backward Transmission based WDFAD-DBR (BT-WDFAD-DBR) and Collision Avoidance based WDFADDBR (CA-WDFAD-DBR). The first scheme ATR-WDFAD-DBR scheme adjusts its transmission range when it finds a void node and then continues to forward data towards the sink. CB-WDFAD-DBR is used to minimize end-to-end delay and energy consumption. In BT-WDFAD-DBR fall back recovery mechanism is used to find an alternative route to deliver the data when void hole occurs. In CA-WDFAD-DBR fall along with nomination of forwarder node which has minimum number of neighbor nodes is selected. Simulation results show that our schemes outperform compared with baseline solution in terms of average Packet Delivery Ratio (PDR), energy tax, end-to-end delay and Accumulated Propagation Distance (APD).
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𝒟
((𝐴𝐶𝐻)2)
W ter su f c
W ter de t
h
2
𝑙
10𝑙𝑜𝑔𝐴(𝑙, 𝑓 ) = 𝑐.10𝑙𝑜𝑔𝑙 +𝑙.10𝑙𝑜𝑔𝛼(𝑓)
𝑐
𝑐 𝑐
𝑐 𝛼(𝑓)
𝑁𝑡(𝑓)𝑁𝑠(𝑓)𝑁𝑤(𝑓)𝑁𝑡ℎ(𝑓)
𝑁(𝑓) = 𝑁𝑡(𝑓) + 𝑁𝑠(𝑓) + 𝑁𝑤(𝑓) + 𝑁𝑡ℎ(𝑓).
𝑓 𝑙
𝑆𝑁 𝑅(𝑓, 𝑙) = 𝑇𝑝(𝑓)−𝐴(𝑙, 𝑓 )−𝑁(𝑓) + 𝐷𝑖
𝑇𝑝(𝑓)𝑓 𝐷𝑖
𝑆𝑁 𝑅(𝑓, 𝑙)𝐷𝑡
𝜈= 1448.96 + 4.591𝜏−5.304 ×10−2𝜏2+ 2.374 ×10−2𝜏3
+ 1.340(𝛿−35) + 1.63 ×10−1𝑑+ 1.675 ×10−7𝑑2
−1.025 ×10−2𝜏(𝛿−35) −7.139 ×10−13𝜏 𝑑3
𝜈 𝑚𝑠−1
𝜏 𝛿
0≤𝜏≤30 30 ≤𝛿≤40 0 ≤𝑑≤8000
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑟𝑒𝑞 𝑢𝑒𝑠𝑡 𝑎𝑐𝑘
𝑑𝑎𝑡𝑎𝑝𝑎𝑐𝑘𝑒𝑡
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑟𝑒𝑞 𝑢𝑒𝑠𝑡
𝑎𝑐𝑘
𝑑𝑎𝑡𝑎𝑝𝑎𝑐𝑘𝑒𝑡
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑟𝑒𝑞 𝑢𝑒𝑠𝑡 𝑎𝑐𝑘
𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑡𝑎𝑏𝑙𝑒
𝑃 𝑎𝑐𝑘𝑒𝑡𝑞𝑢𝑒𝑢𝑒
𝑃 𝑎𝑐𝑘𝑒𝑡𝑞𝑢𝑒𝑢𝑒
𝑖
𝑗
𝑖
𝑖
∙
∙
∙
𝑖 𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡 𝑗
𝑝𝑟𝑒𝑣𝑛𝑜𝑑𝑒 𝑑𝑒𝑝𝑡ℎ 𝑐𝑢𝑟𝑟𝑛𝑜𝑑𝑒 𝑑𝑒𝑝𝑡ℎ
𝑑𝑖𝑓𝑓 (𝑆𝑆 𝑅𝑆𝑆𝐼 , 𝑅𝑃 𝑅𝑆𝑆𝐼)
𝑅𝐶(𝑠𝑒𝑛𝑑𝑒𝑟, 𝑟𝑒𝑐𝑖𝑒𝑣𝑒𝑟)
𝑑𝑖𝑓𝑓 (𝑝𝑟𝑒𝑣𝑛𝑜𝑑𝑒 𝑑𝑒𝑝𝑡ℎ, 𝑐𝑢𝑟𝑟𝑛𝑜𝑑𝑒 𝑑𝑒𝑝𝑡ℎ)
(𝑝𝑎𝑐𝑘𝑒𝑡𝑡𝑦𝑝𝑒)
𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑅𝑒𝑞𝑢𝑒𝑠𝑡
𝑖 𝑗
𝐴𝑐𝑘
𝑗 𝑖
𝑝𝑟𝑒𝑣𝑛𝑜𝑑𝑒 𝑑𝑒𝑝𝑡ℎ, 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒, 𝑡𝑐𝑢𝑟𝑟𝑒𝑛𝑡
𝐷𝑎𝑡𝑎𝑃 𝑎𝑐𝑘𝑒𝑡
𝑖 𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟 𝑡𝑎𝑏𝑙𝑒 𝑝𝑟𝑒𝑣𝑛𝑜𝑑𝑒 𝑑𝑒𝑝𝑡ℎ, 𝑡𝑐𝑢𝑟𝑟𝑒𝑛𝑡, 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒
𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡
𝑖 𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡
𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡
𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡
𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡
𝑑𝑒𝑝𝑡ℎ𝑚𝑖𝑛 𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟 𝑡𝑎𝑏𝑙𝑒
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟 𝑡𝑎𝑏𝑙𝑒
𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡
𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡 𝑐𝑢𝑟𝑟𝑒𝑛𝑡𝑛𝑜𝑑𝑒 𝑑𝑒𝑝𝑡ℎ
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑟𝑒𝑞 𝑢𝑒𝑠𝑡𝑠 𝑎𝑐𝑘
`
W t r sur a e
W te de th
S
h
h1
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R di l nk
R ut ng pa h CB-WDF D-DBR
Sate l te
Moni o i g c nte
Rela n d s
An hore nodes
Si k n d s
T a s iss on ran e ad us ment
Cl s er H a s
R u ing pa h A R-W DF D-DBR
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W t r su f c
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S t ll t
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Si k no e
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R ut ng p th w t c l is on
∙
∙
∙
∙
𝑆
𝑛1𝑛2
𝑛3
∙
∙
∙
∙
∙
∙
∙
∙
∙
∙
∙
𝑆
Cluster
CH with max residual energy
𝑆
𝑛3
𝑛4
𝑓𝑖←− 𝑔𝑒𝑡 𝑛𝑒𝑥𝑡 ℎ𝑜𝑝 𝑓 𝑜𝑟𝑤𝑎𝑟𝑑𝑒𝑟(𝑛)
|𝑓𝑖|>0
𝑓𝑜𝑟𝑤𝑎𝑟𝑑 𝑑𝑎𝑡𝑎 𝑝𝑎𝑐𝑘𝑒𝑡()
𝑆1
∙
∙
∙
∙
××𝑘𝑚3
(𝑘𝑚3)× ×
𝜇
∙
∙
𝐸𝑛𝑒𝑟 𝑔𝑦𝑇 𝑎𝑥 =𝐸𝑐𝑜𝑛𝑠
𝜂×𝐷𝑝
𝐸𝑐𝑜𝑛𝑠 𝜂
𝐷𝑝
∙
∙
100 150 200 250 300 350 400 450 500
Node number
0
0.05
0.1
0.15
0.2
0.25
Energy tax (J)
WDFAD-DBR
BT-WDFAD-DBR
CA-WDFAD-DBR
ATR-WDFAD-DBR
CB-WDFAD-DBR
100 150 200 250 300 350 400 450 500
Node number
2
3
4
5
6
7
8
9
10
End-to-end delay (s)
WDFAD-DBR
BT-WDFAD-DBR
CA-WDFAD-DBR
ATR-WDFAD-DBR
CB-WDFAD-DBR
100 150 200 250 300 350 400 450 500
Node number
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
PDR
WDFAD-DBR
BT-WDFAD-DBR
CA-WDFAD-DBR
ATR-WDFAD-DBR
CB-WDFAD-DBR
100 150 200 250 300 350 400 450 500
Node number
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
APD (km)
WDFAD-DBR
BT-WDFAD-DBR
CA-WDFAD-DBR
ATR-WDFAD-DBR
CB-WDFAD-DBR
𝑀𝑖𝑛𝑖𝑚𝑢𝑚Σ𝑟𝑚𝑎𝑥
𝑟=1 𝐸𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛(𝑟)∀𝑟∈𝑟𝑚𝑎𝑥
𝐸𝑡𝑟𝑎𝑛𝑠, 𝐸𝑟 𝑐𝑣 ≥𝐸𝑟𝑒
𝐸𝑡𝑟𝑎𝑛𝑠, 𝐸𝑟 𝑐𝑣 ≤𝐸𝑖𝑛𝑖𝑡
𝑇𝑟𝑛 ≤𝑇𝑟𝑚𝑎𝑥
𝐸𝑟𝑒
𝐸𝑖𝑛𝑖𝑡
𝑇𝑟𝑛
𝑇𝑟𝑚𝑎𝑥
𝐸𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛
Σ𝑟𝑚𝑎𝑥
𝑟=1 𝐸𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛(𝑟) = 𝐸𝑡𝑟𝑎𝑛𝑠 +𝐸𝑟 𝑐𝑣 ∀𝑟∈𝑟𝑚𝑎𝑥.
𝐸𝑡𝑟𝑎𝑛𝑠 =𝑃𝑡𝑟𝑎𝑛𝑠 𝑃 𝑎𝑐𝑘𝑒𝑡 𝑠𝑖𝑧𝑒
𝐷𝑎𝑡𝑎 𝑟𝑎𝑡𝑒
𝐸𝑡𝑟𝑎𝑛𝑠 𝑃𝑡𝑟𝑎𝑛𝑠
𝐸𝑟𝑐𝑣 =𝑃𝑟𝑐𝑣 𝑃 𝑎𝑐𝑘 𝑒𝑡 𝑠𝑖𝑧𝑒
𝐷𝑎𝑡𝑎 𝑟𝑎𝑡𝑒
𝐸𝑟𝑐𝑣 𝑃𝑟𝑐𝑣
𝑃 𝑎𝑐𝑘𝑒𝑡 𝑠𝑖𝑧 𝑒 = 888𝑏𝑖𝑡𝑠 𝐷𝑎𝑡𝑎 𝑟𝑎𝑡𝑒 = 16000𝑏𝑝𝑠 𝑃𝑡𝑟𝑎𝑛𝑠 ={12.5,25, ..., 50}
𝑃𝑟𝑐𝑣 ={0.0395,0.079, ..., 0.158}
0.693 ≤𝐸𝑡𝑥 ≤2.775
0.002 ≤𝐸𝑟𝑥 ≤0.0087
0.695 ≤𝐸𝑡𝑥 +𝐸𝑟𝑥 ≤2.7837
𝑃1(0.693,0.002) = 0.695𝐽
𝑃2(0.693,0.0087) = 0.7017𝐽
𝑃3(2.775,0.0087) = 2.7837𝐽
𝑃4(2.775,0.002) = 2.777𝐽
0123456
Etrans (J)
0
0.2
0.4
0.6
0.8
1
1.2
Ercv (J)
P3(2.775,0.0087)
P1(0.693,0.002)
P2(0.693; 0.0087) Etrans+Ercv= 2.7837
P4(2.775,0.002)
𝑀𝑎𝑥𝑖𝑚𝑢𝑚Σ𝑟𝑚𝑎𝑥
𝑟=1 𝑇 ℎ𝑟(𝑟)∀𝑟∈𝑟𝑚𝑎𝑥
𝐶1:𝐸𝑡𝑥, 𝐸𝑟𝑐𝑣 ≤𝐸𝑖
𝐶2:𝐸𝑡𝑥 ≤𝐸𝑟𝑒
𝐶3:𝑇 𝑋𝑛≤𝑇 𝑋𝑚𝑎𝑥
𝐶4:𝐷𝑖𝑗 ≤𝐷𝑚𝑎𝑥
𝑖𝑗
𝐶5:𝑀𝑖𝑛𝑖𝑚𝑢𝑚Σ𝑟𝑚𝑎𝑥
𝑟=1 𝐵𝑟
𝐹 𝑟𝑤
𝐸𝑖
𝐸𝑡𝑥 𝐸𝑟𝑒
𝑇 𝑋𝑚𝑎𝑥 𝑇 𝑋𝑛
𝑇 𝑋𝑚𝑎𝑥
𝑖 𝑗
𝐵𝐹 𝑟𝑤
𝐵𝐹 𝑟𝑤
𝐵𝑁 𝐹 𝑟𝑤
𝐵 𝐵𝐹 𝑟𝑤 𝐵𝑁 𝐹 𝑟𝑤
200 ≤𝐵𝐹 𝑟𝑤 ≤1000
2000 ≤𝐵𝑁 𝐹 𝑟𝑤 ≤3000
2200 ≤𝐵𝐹 𝑟𝑤 +𝐸𝑁 𝐹 𝑟𝑤 ≤4000
𝑃1(200,2000) = 2200𝐾𝐻𝑧
𝑃2(1000,2000) = 3000𝐾𝐻𝑧
𝑃3(200,3000) = 3200𝐾𝐻𝑧
0 500 1000 1500 2000 2500 3000 3500 4000
BFrw (KHz)
0
500
1000
1500
2000
2500
3000
3500
4000
BNFrw (KHz)
P1(200, 2000)
P2(1000, 2000)
P3(200, 3000)
BFrw+BNFrw = 4000
(𝐴𝐶𝐻)2
ResearchGate has not been able to resolve any citations for this publication.
Optimal Clustering in Underwater Wireless Sensor Networks: Acoustic, EM and FSO Communication Compliant Technique
Article
Full-text available
  • Jul 2017
With a wide scope for exploration and research, underwater wireless sensor network (UWSN) is a fast growing research area in current scenario. UWSNs need energy efficient designing approach because underwater sensor nodes are battery driven. Also the deployed batteries can not be easily recharged by non-conventional energy resources like solar energies. Clustering is an effective technique to design an energy efficient UWSNs. Due to the sparse deployment of nodes and dynamic nature of the channel, the clustering characteristics of UWSNs are different from those of terrestrial wireless sensor networks (TWSNs). In this paper, we focused on optimal clustering for UWSNs which are compliant with any one of the acoustic, free space optical (FSO) and electromagnetic (EM) wave based communication techniques. Besides, we proposed an energy dissipation model of sensor node for FSO and EM wave based communication and compared with contemporary energy dissipation model for acoustic based communication. In particular, the suitability of the above three techniques for underwater communication is investigated and their performance is compared on the basis of energy consumption and optimal clustering.
Hop-by-Hop Dynamic Addressing Based Routing Protocol for Monitoring of long range Underwater Pipeline
Article
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In Underwater Linear Sensor Networks (UW-LSN) routing process, nodes without proper address make it difficult to determine relative sensor details specially the position of the node. In addition, it effects to determine the exact leakage position with minimized delay for long range underwater pipeline monitoring. Several studies have been made to overcome the mentioned issues. However, little attention has been given to minimize communication delay using dynamic addressing schemes. This paper presents the novel solution called Hop-by-Hop Dynamic Addressing based Routing Protocol for Pipeline Monitoring (H2-DARP-PM) to deal with nodes addressing and communication delay. H2-DARP-PM assigns a dynamic hop address to every participating node in an efficient manner. Dynamic addressing mechanism employed by H2-DARP-PM differentiates the heterogeneous types of sensor nodes thereby helping to control the traffic flows between the nodes. The proposed dynamic addressing mechanism provides support in the selection of an appropriate next hop neighbour. Simulation results and analytical model illustrate that H2-DARP-PM addressing support distribution of topology into different ranges of heterogeneous sensors and sinks to mitigate the higher delay issue. One of the distinguishing characteristics of H2-DARP-PM has the capability to operate with a fewer number of sensor nodes deployed for long-range underwater pipeline monitoring.
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Most existing deployment algorithms for event coverage in underwater wireless sensor networks (UWSNs) usually do not consider that network communication has non-uniform characteristics on three-dimensional underwater environments. Such deployment algorithms ignore that the nodes are distributed at different depths and have different probabilities for data acquisition, thereby leading to imbalances in the overall network energy consumption, decreasing the network performance, and resulting in poor and unreliable late network operation. Therefore, in this study, we proposed an uneven cluster deployment algorithm based network layered for event coverage. First, according to the energy consumption requirement of the communication load at different depths of the underwater network, we obtained the expected value of deployment nodes and the distribution density of each layer network after theoretical analysis and deduction. Afterward, the network is divided into multilayers based on uneven clusters, and the heterogeneous communication radius of nodes can improve the network connectivity rate. The recovery strategy is used to balance the energy consumption of nodes in the cluster and can efficiently reconstruct the network topology, which ensures that the network has a high network coverage and connectivity rate in a long period of data acquisition. Simulation results show that the proposed algorithm improves network reliability and prolongs network lifetime by significantly reducing the blind movement of overall network nodes while maintaining a high network coverage and connectivity rate.
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Article
  • May 2017
In this paper, to monitor the fields with square and circular geometries, three energy-efficient routing protocols are proposed for underwater wireless sensor networks (UWSNs). First one is, sparsity-aware energy efficient clustering (SEEC), second one is, circular SEEC (CSEEC), and the third one is, circular depth based SEEC (CDSEEC) routing protocol. All three protocols are proposed to minimize the energy consumption of sparse regions. Whereas, sparsity search algorithm (SSA) is proposed to find sparse regions and density search algorithm (DSA) is used to find dense regions of the network field. Moreover, clustering is performed in dense regions to minimize redundant transmissions of a data packet. While, sinks mobility is exploited to collect data from sensor nodes with an objective of minimum energy consumption. A depth threshold (d th) value is also used to minimize number of hops between source and destination for less energy consumption. Simulation results show that our schemes perform better than their counterpart schemes (DBR, EEDBR) in terms of energy efficiency.
Pressure routing for underwater sensor networks
Article
  • Jan 2010
LRP: Link quality‐aware queue‐based spectral clustering routing protocol for underwater acoustic sensor networks
Article
Recently, underwater acoustic sensor networks (UASNs) have been considered as a promising approach for monitoring and exploring the oceans in lieu of traditional underwater wireline instruments. As a result, a broad range of applications exists ranging from oil industry to aquaculture and includes oceanographic data collection, disaster prevention, offshore exploration, assisted navigation, tactical surveillance, and pollution monitoring. However, the unique characteristics of underwater acoustic communication channels, such as high bit error rate, limited bandwidth, and variable delay, lead to a large number of packet drops, low throughput, and significant waste of energy because of packets retransmission in these applications. Hence, designing an efficient and reliable data communication protocol between sensor nodes and the sink is crucial for successful data transmission in underwater applications. Accordingly, this paper is intended to introduce a novel nature-inspired evolutionary link quality-aware queue-based spectral clustering routing protocol for UASN-based underwater applications. Because of its distributed nature, link quality-aware queue-based spectral clustering routing protocol successfully distributes network data traffic load evenly in harsh underwater environments and avoids hotspot problems that occur near the sink. In addition, because of its double check mechanism for signal to noise ratio and Euclidean distance, it adopts opportunistically and provides reliable dynamic cluster-based routing architecture in the entire network. To sum up, the proposed approach successfully finds the best forwarding relay node for data transmission and avoids path loops and packet losses in both sparse and densely deployed UASNs. Our experimental results obtained in a set of extensive simulation studies verify that the proposed protocol performs better than the existing routing protocols in terms of data delivery ratio, overall network throughput, end-to-end delay, and energy efficiency.
Adaptive Relay Chain Routing With Load Balancing and High Energy Efficiency
Article
The concentration of data traffic toward sink makes sensor nodes nearby have heavier communication burden and more quickly use up their energy, leading to energy hole problem. Sink mobility can realize load balancing data delivery by changing the hotspots around the sink as the sink moves. However, sink mobility also brings about the problem of localization of sink. Frequently broadcasting of mobile sinks' position will generate significant overhead. In this paper, we propose a novel heterogeneous adaptive relay chain routing protocol with a few mobile relay nodes, which is applied to large-scale 1-D long chain network. Mobile relay node is the sink of local subnetwork. The protocol achieves the following performances. First, through scheduled movement of the mobile relay nodes, load balancing is achieved not only among sensor nodes but also among high tier relay nodes in continuous data delivery model. Second, in the context of clock synchronization among nodes, every node decides its operating state by algorithm stored in its own processor. So, there is no need for advertisement of mobile relay nodes' location. Only a few messages for clock synchronization among nodes are needed. Third, by synthetically utilizing node deployment strategy and routing protocol, base station can real-time monitoring residual energy of sensor nodes for timely maintenance, which can extend the protocol to be suitable for event-driven and query-driven data delivery models. Finally, the performances are evaluated via extensive simulations.
WDFAD-DBR: Weighting depth and forwarding area division DBR routing protocol for UASNs
Article
The design of routing protocols for Underwater Acoustic Sensor Networks (UASNs) poses many challenges due to long propagation, high mobility, limited bandwidth, multi-path and Doppler effect. Because of the void-hole caused by the uneven distribution of nodes and sparse deployment, the selection of next hop forwarding nodes only based on the state of current node may result in the failure of forwarding in the local sparse region. In order to reduce the probability of encountering void holes in the sparse networks, in this paper we present weighting depth and forwarding area division DBR routing protocol, called WDFAD-DBR. The novelties of WDFAD-DBR lie in: firstly, next forwarding nodes are selected according to the weighting sum of depth difference of two hops, which considers not only the current depth but also the depth of expected next hop. In this way, the probability of meeting void holes is effectively reduced. Secondly, the mechanisms for forwarding area division and neighbor node prediction are designed to reduce the energy consumption caused by duplicated packets and neighbors' requests, respectively. Thirdly, we make theoretical analyses on routing performance in case of considering channel contending with respect to delivery ratio, energy consumption and average end-to-end delay. Finally we conduct extensive simulations using NS-3 simulator to verify the effectiveness and validity of WDFAD-DBR.