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

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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 + 4.591𝜏5.304 ×102𝜏2+ 2.374 ×102𝜏3
+ 1.340(𝛿35) + 1.63 ×101𝑑+ 1.675 ×107𝑑2
1.025 ×102𝜏(𝛿35) 7.139 ×1013𝜏 𝑑3
𝜈 𝑚𝑠1
𝜏 𝛿
0𝜏30 30 𝛿40 0 𝑑8000
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
`
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
`
W t r su f c
W t r d pt
n6
7
n
h
h1 `
R d o l nk
Routi g p th
1 h p b ck a d t a s i si n
S t ll t
M n t r ng c nt r
R l y n d s
A c o ed no es
Si k no e
S1
R ut ng p th w t c l is on
𝑆
𝑛1𝑛2
𝑛3
𝑆
Cluster
CH with max residual energy
𝑆
𝑛3
𝑛4
𝑓𝑖𝑔𝑒𝑡 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)𝑟𝑟𝑚𝑎𝑥
𝐸𝑡𝑟𝑎𝑛𝑠, 𝐸𝑟 𝑐𝑣 𝐸𝑟𝑒
𝐸𝑡𝑟𝑎𝑛𝑠, 𝐸𝑟 𝑐𝑣 𝐸𝑖𝑛𝑖𝑡
𝑇𝑟𝑛 𝑇𝑟𝑚𝑎𝑥
𝐸𝑟𝑒
𝐸𝑖𝑛𝑖𝑡
𝑇𝑟𝑛
𝑇𝑟𝑚𝑎𝑥
𝐸𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛
Σ𝑟𝑚𝑎𝑥
𝑟=1 𝐸𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛(𝑟) = 𝐸𝑡𝑟𝑎𝑛𝑠 +𝐸𝑟 𝑐𝑣 𝑟𝑟𝑚𝑎𝑥.
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)𝑟𝑟𝑚𝑎𝑥
𝐶1:𝐸𝑡𝑥, 𝐸𝑟𝑐𝑣 𝐸𝑖
𝐶2:𝐸𝑡𝑥 𝐸𝑟𝑒
𝐶3:𝑇 𝑋𝑛𝑇 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
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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.
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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.