FAST-DTN: Farther-Aim-Shorter-Try Disruption
Tolerant Network for Building Monitoring
Applications
Manussanun Buranachokphaisan∗ , Hideya Ochiai† , Hiroshi Esaki∗∗ Graduate School of Information Science and Technology, The University of Tokyo

Email: {kaew, hiroshi}@hongo.wide.ad.jp† VLSI Design and Education Center, The University of Tokyo

Email: ochiai@vdec.u-tokyo.ac.jp
Abstract—Wireless sensor network in building monitoring
system (WSN-BMS) generally requires high reliability at scale.
However, the performance of WSN-BMS is always deteriorated
by the unidirectional links and dynamic nature of wirelesslinks in the building. Potential-based Entropy Adaptive Routing(PEAR) protocol uses DTN-based approach to attain reliability
and scalability over intermittently-connected wireless network,
but this approach results high delivery latency such that it is notpreferable in some applications of BMS. In this paper, we pointout the problems leading to large delivery latency and propose
FAST (Farther-Aim-Shorter-Try) forwarding scheme for PEAR
in WSN-BMS. FAST modifies the current next-hop selection
scheme to avoid the unidirectional links and combines DTN-based
approach with the traditional routing scheme, i.e. link quality
metric and retransmission, to improve the delivery latency. We
implemented FAST to PEAR and evaluated its performance on
WiFi-based UTMesh testbed with 16 node-floor scenario and 33
node-multistory scenario. The experiment results show that FAST
outperformed PEAR regarding to delivery latency. It decreased
65.28% and 83.5% of median delivery latency compared to PEAR
in floor and multistory scenario respectively.
Keywords—WSN, DTN, BMS, Retransmission, Routing Metric,
Unidirectional LinkI.

I NTRODUCTION

Wireless sensor network (WSN) has been proposed to use
in building energy management system (BEMS). In BEMS, anumber of sensor are deployed to monitor the environmental data and energy consumption which is used to analyze
consumer behavior as well as manage electrical equipment
in order to reduce energy consumption in the building. Sincemonitoring application generally requires the completeness ofdata, wireless sensor network in building monitoring system(WSN-BMS) must achieve high reliability at scale. However,
WSN always suffers from dynamic nature of wireless links. To
overcome this issue, the approach of delay/disruption tolerant
network (DTN) which promises high delivery ratio even inintermittently-connected links is introduced to WSN-BMS.
DTN [1] describes the characteristics of the network which
is high latency and intermittent connectivity, namely mobile
ad-hoc networks and vehicular ad-hoc networks. Most DTN
routing protocols were designed based on the assumption thatthe network contains the moving nodes, so node mobility orconnectivity pattern is exploited to find the proper next-hop
which generally the node that is close or moves toward sinkor destination (We use sink and destination alternately in thispaper). DTN relies on hop-by-hop reliability with store-andforward mechanism. Nodes store the received messages in thebuffers and periodically exchange information with their neighbors to confirm that the messages are delivered to the next-hop.
ARQ (Automatic Repeat reQuest) with retransmission is notpreferable as DTN expects large delivery latency.
WSN-BMS differs from DTN, so some approaches whichshow the good performance in DTN result the bad performance in WSN-BMS. WSN-BMS is static multi-hop wirelessnetwork. The routing metric of DTN is equivalent to hop
metric in static WSN where nodes select the least hop path
to deliver the messages. Hop metric is not a good metric forsuch network [2], [3] because nodes always choose long links
with high loss rate. Without ARQ, the messages are delayedin the nodes’ buffers when nodes fail to forward the messages.
Moreover, bidirectional communication is necessary for hopby-hop delivery. Howbeit, the unidirectional links often arise
in static WSN [4], [5] and possibly interrupt the informationexchange between a pair of nodes. These problems lead tolarge delivery latency of DTN-based WSN-BMS.
The works in [6] presented that even in static multihop wireless network, the connectivity was intermittent. The
experiment with Potential-based Entropy Adaptive Routing(PEAR) protocol, the DTN-based routing protocol, on 50 nodescale testbed resulted 100% delivery ratio over 10 hops in
the building scenario. This result showed that DTN routing
protocol was able to achieve high reliability and scalability
over the intermittently-connectivity. However, PEAR gave bad
performance on delivery latency. The average and 99th percentile delivery latency was 238 and 700 seconds respectively.
Although BMS is a delay-tolerant application [7], BMS needs
near real-time data to visualize and feedback those data to
consumers [8].
In this paper, we propose FAST (Farther-Aim-Shorter-Try)
forwarding scheme for PEAR to reduce delivery latency in
WSN-BMS. FAST combines DTN approach with the traditional WSN routing protocol by introducing retransmission
scheme and link quality metric to PEAR. The sender challenges sending the messages to the next-hop selected by hop
metric (Farther Aim). When the sender fails in forwarding messages, it has a chance to retransmit the messages once, but tothe alternative next-hop with more reliable link which typicallyis shorter links (Shorter Try). To prevent the failure, FAST
uses Unidirectional link-Aware Next-hop Selection (ULANS)
scheme in the first challenge and Delivery Predictability,
link quality metric for static DTN-based WSN, to find thealternative next-hop in retransmission.
We implemented our proposed scheme to demonstrate theeffect of unidirectional links and the improvement of PEAR
with FAST forwarding scheme. The experiment was carriedout on UTMesh testbed [9] in the building monitoring scenario
with two different deployment. The results show that FAST
gave the best performance among all schemes concerning
delivery latency.
The rest of this paper is structured as follows. Section
II describes the concept of PEAR related to this work andexplains the cause of high delivery latency in PEAR. FAST
forwarding scheme is described in Section III. Section IVshows the experiment and evaluation of proposed scheme. We
give a discussion in Section V. The related works are reviewed
in Section VI. Finally, Section VII concludes this paper.

II.

P OTENTIAL - BASED E NTROPY A DAPTIVE ROUTINGPEAR acquires potential-based routing protocol (PBR) andhop-by-hop delivery with store-and-forward mechanism from
DTN. In PBR, each node holds a potential, a positive scalarvalue representing the distance from each node to the sinkin static scenario. Nodes located farther from the sink havelarger potential. Nodes periodically update their potentials withneighbors by broadcasting 1-hop advertisement (ADV) andselect the lowest potential-neighbor as the next-hop. Nodes
exchange message information to check the status of eachmessage before forwarding similar to hop-by-hop delivery. In
this section, we review only forwarding scheme and hop-byhop delivery of PEAR. More details can be found in [10].

A. Forwarding Scheme
Let N be a set of nodes in the network. Neighbor node ofnode n ∈ N is denoted by nbr(n). Potential of n ∈ N foreach destination d ∈ N is defined by V d (n). The potential at
the destination always ties to zero i.e. V d (d) = 0.
PEAR’s next-hop selection scheme is described as follows,
d
Fmax
(n) =

N H d (n) =max {V d (n) − V d (k)}k∈nbr(n)(1)

d{k|k ∈ nbr(n) ∧ Fmax(n) = V d (n) − V d (k)}(2)

dwhere Fmax(n) is the maximum difference of node n’s pod

tential and its neighbors’ potential and N H (n) is the nexthop of node n for the destination d. First, nodes compare
their potential with neighbors (Eq. 1). Then, nodes choose
the neighbors that give the maximum potential difference tobe their next-hops (Eq. 2). Since the potential indicates the
distance from the destination, the next-hop is the neighborlocated farthest toward the destination in the transmission
range.

B. Hop-by-hop delivery
In store-and-forward mechanism, intermediate nodes copy
and store the received messages in the buffer. Before forwarding the messages, nodes investigate if the next-hop already has
the messages. We call this process as Investigation. A sendersends a request message containing message ID stored in its
buffer to the next-hop. Then, the next-hop replies a response
message consisting of the state of each message, which isalready-received, not-received or delivered, to the sender. After
that, sender sends only not-received messages to the next-hop.
The investigation is illustrated in Fig. 1.

Fig. 1. The illustration of investigation in PEAR when A is a sender and B
is a next-hop of A.

PEAR does not apply ARQ with retransmission in forwarding. Instead of that, nodes periodically investigate and send
the messages in the buffers to make sure that all messages
are delivered to the next hop. Nodes evict the messages in thebuffer only when they receive delivered state from the response
message or when time-to-live of the message reaches zero.
C. Problems
Even though PEAR achieves good performance in DTN,
the different feature between DTN and static WSN causes large
delivery latency when PEAR is implemented to WSN-BMS.
Here, we point out three problems leading to delivery latency.
1) Unidirectional links: Notice that nodes send the messages only when they receive the response message from theirnext-hops. Bidirectional links are necessary in order to succeed
investigation. Nonetheless, the current scheme considers only
the potential in the next-hop selection. In reality, the unidirectional links possibly arises between two connected nodes
when only one of two nodes can directly send the messages
to the other. In this case, nodes cannot succeed investigation,consequently, the messages cannot be forwarded to the nexthop.
2) No ARQ: In traditional routing protocol, if the sender
do not receive an acknowledgement (ACK) within a specifictime, the sender resends the messages instantly until it receives
ACK. On the other hand, PEAR relies on only the periodic
investigation. When the sender does not receive the response
message or fails to forward the data messages, the sender has
to wait for the next investigation period.
3) Hop metric: The current routing scheme is similar to
hop routing metric in static WSN. Choosing the next-hop basedon hop count gives low latency in wired network or networkwith moving node, but this is not true for static WSN. Long
links are likely to be disrupted and cause high message drop
resulting similar to no ARQ problem.
III.

FAST F ORWARDING S CHEME

To solve the issues mentioned in last section, we proposed
FAST (Farther-Aim-Shorter-Try) forwarding scheme for PEAR

to improve delivery latency in static WSN. We first describe
the overview of FAST, then we explain each mechanism indetail.

Forward Predictability:
PF (n, k) = ARRnk × ARRkn(3)

Delivery Predictability:
FAST integrates DTN-based approach and the traditional
WSN routing scheme to decrease delivery latency in DTNbased routing protocol in WSN-BMS. The concept of FAST
is to move the responsibility of forwarding messages to any
neighbors within one investigation period. FAST still conducts
the investigation before forwarding the messages to assure
the delivery, but it applies retransmission to handle the loss
during the investigation. FAST modifies the next-hop selection
to avoid the unidirectional links and prepares more reliablenext-hop for retransmission.
Ideally, the route with lower number of hops gives lower
delivery latency. Thus, FAST attempts to minimize the numberof hops as many as possible with hop metric. The senderchallenge sending the request message to the primary nexthop which is the farthest neighbor with bidirectional links inthe transmission range. When the sender fails in investigation
i.e. the response message is not received, the sender makes areinvestigation once. To prevent repeatedly failure, the senderresends the request message to the alternative next-hop chosen
by link quality metric. Thanks to high quality links, the sender
has higher probability of successful investigation, as well as,
forwarding the messages to another node closer to the sink.
FAST gives only one chance for the reinvestigation, thus thesender waits for next investigation period if it still fails in thereinvestigation.

A. Link Quality Estimation & Information Feedback
Link quality metric helps FAST providing the alternative
path for reinvestigation. FAST seeks the path that nodes havehigh probability to succeed the investigation. In [11], theestimator measures the quality of each link by calculating
the ratio of the number of received ADVs to the number of
expected ADVs to find the delivery probability. FAST also
estimates link by observing the received ADV. However, PEAR
conducts the investigation before sending the data messages,
therefore the estimator does not calculate the probability ofdelivery success directly. The estimator just predicts if nodes
can succeed the investigation.
In this paper, we establish link quality and path quality
metric called Forward Predictability (PF ) and Delivery Predictability (PD ) respectively, where PF , PD ∈ [0, 1]. Forward
predictability is the bidirectional link quality predicting if
nodes can succeed and, as a result, have a chance to send
the messages on each link. Delivery predictability is the multiplication of forward predictability forecasting the probability
that nodes are able to succeed the investigation along the path.
FAST simply estimates link quality by calculating Advertise Reception Ratio (ARR). ARRnk is the ratio of the numberof ADVs received at n to the number of ADVs sent by k ina period of time, where n, k ∈ N and k ∈ nbr(n). Nodes
calculate ARR periodically to track time-varying link quality.
Then, forward predictability between node n and neighbor k
and delivery predictability of node n for destination d arecomputed as follows,

d
PD
(n) =

d
max {PD(k) × PF (n, k)}k∈nbr(n)(4)

dwhere PD(d) = 1.

To calculate PF and PD , nodes share their link quality
information with every neighbor by piggybacking those information with ADV. Each node estimates ARR of every
link locally and embeds its PD and the neighbor sequence
consisting all neighbors’ ID and ARR in ADV. After nodes
receive ADV, they search for their ID in the neighbor sequence
and update PF with received ARR and local estimated ARR(Eq. 3). Then, nodes compute PD and select the maximum
multiple as its PD (Eq. 4).
B. Unidirectional Link Avoidance
FAST detects the unidirectional links from the neighborsequence in ADV. Nodes learn that the link connecting withany neighbor is unidirectional if they cannot find their ID in
the neighbor sequence. FAST adds Link Status (Lnk ) entry in
the neighbor table to classify the link. Link status is either
available(‘A’) or unavailable (‘U ’). It is set to ‘A’ when thelink is bidirectional, while it is assigned to ‘U ’ if the link isunidirectional.
C. Next-hop Selection Scheme
FAST uses two next-hop selection scheme for primarynext-hop and alternative next-hop. The primary next-hop ischosen based on hop metric similar to PEAR. As we explained,
PEAR’s next-hop selection scheme chooses only the lowest
potential node to be the next-hop without being aware of theexistence of the unidirectional links. In order to avoid thisproblem, FAST modifies current next-hop selection and calls
it Unidirectional Link-Aware Next-hop Selection (ULANS).
ULANS is defined as follows,
F d (n) =maxk∈nbr(n)∧Lnk =‘A’

N HUd LAN S (n) ={V d (n) − V d (k)} (5){k|k ∈ nbr(n)∧F d (n) = V d (n) − V d (k)}(6)

ULANS adds one more condition to address the unidirectional
links. When nodes compare the potential with their neighbors,nodes consider only neighbors that bidirectional links exist.
As a result, ULANS forces nodes to choose only the lowest
potential neighbor with bidirectional link as the next-hop.
The alternative next-hop is selected by considering thequality of links along the path from source to sink. FAST
uses delivery predictability to decide the alternative next-hop
as shown in Eq. 7.
d
d
d
N HDP
(n) = {k|k ∈ nbr(n) ∧ PD(n) = PD(k) × PF (n, k)}(7)With this scheme, nodes select the neighbor that gives
the highest delivery predictability among all neighbors forthe reinvestigation. Fig. 2 illustrates the example of paths
established from primary next-hop and alternative next-hop in
FAST.

TABLE I.

T HE DETAIL OF FLOOR AND MULTISTORY SCENARIO .
DetailFloor

Multistory

Location (Floor)
Number of nodes
Source Node ID.
Destination Node ID.
Experiment time/scheme

10th
16
2-16
1
1 hr

3rd -10th
33
14-33
1
1.5 hrs

B. Features of the experimented networks(a) Primary path(b) Alternative path

Fig. 2. The example of primary path and alternative path from node 5 tonode 1.

IV.

E XPERIMENTAL E VALUATION

A. Experiment Setup & Scenario
The experiment was carried out on UTMesh testbed to evaluate our proposed scheme. The UTMesh nodes were operatingwith Linux embedded computer, Armadillo-200, with WiFi
(IEEE802.11) module working in ad-hoc mode. All nodes werepowered by batteries. The source nodes generated and sent
messages to the sink every 30 seconds. ADV broadcast, system
update and next-hop selection period were set to 5 seconds.
Nodes retransmitted the messages in their buffers every 10
seconds.

Fig. 4 shows the topology and connectivity of both floor
and multistory scenario. The thickness of line implies the valueof forward predictability of each link. The thinker line means
higher forward predictability. Since our testbed was operated
with WiFi, some nodes were connected across many floors in
multistory scenario. Considering the links connecting nodes
on the same floor, the shorter links mostly had higher forward
predictability. However, the links connecting nodes deployedon the different floor mainly had low forward predictability.
Even though those links were shorter than the links on the same
floor, the signal strength was attenuated by the roof leading to
low forward predictability.

Apart from the original PEAR, three more selection
schemes were implemented to PEAR in order to study theeffect of unidirectional links on DTN-based routing protocoland evaluate FAST by comparing FAST to PEAR (hop based)routing scheme and link quality based routing scheme. In
the experiment result, PEAR-ULANS denotes the experimentwith only ULANS. PEAR-DP represents the experiment withdelivery predictability based next-hop selection scheme in Eq.
7. Finally, PEAR-FAST is the experiment with our proposed
FAST.
UTMesh nodes were deployed on floor scenario and multistory scenario in Eng. Bldg. 2, The University of Tokyo. The
configuration and detail of each deployment are shown in Fig.
3 and Table. I, respectively. In floor scenario, all nodes except
node 1 were the source nodes, but in multistory scenario, only
nodes deployed on the corridor were the source nodes. Nodeson the stairs just relayed the messages from the sources to thedestination.

(a) Floor scenario
Fig. 4.

(b) Multistory scenario

Topology and connectivity of experimented networks.

C. Performance Metrics
We studied and evaluated the proposed scheme by observing delivery ratio, delivery latency, hop count, copy count andbuffer size. The definition of each metric is given as follows,
1) Delivery Ratio: the ratio of the number of messages
received at the destination to the number of messages sent by
the source.
2) Delivery Latency: the amount of time each message
travels from source to destination.
3) Hop Count: the number of hops that each message
travels from the source to the destination.
4) Copy Count: the number of copies of each message in
the network during the delivery.

(a)

(b)

Fig. 3. The deployment configuration of (a) floor scenario and (b) multistory
scenario.

5) Buffer Size: the number of entries occupied in the bufferof each node.
The delivery latency is the main performance metric interested in this paper. The routing scheme should give lowTABLE II.
Scenario

Floor

Multistory

SchemePEAR
PEAR-ULANSPEAR-DPPEAR-FASTPEAR
PEAR-ULANSPEAR-DPPEAR-FAST(a) Floor scenario
Fig. 5.

Delivery Ratio
(%)
99.33
99.44
99.83
99.61
80.92
96.48
98.97
98.84

OVERALL PERFORMANCE .

Delivery Latency (sec)Median
99%
10.34
160
9.4
100
3.63
80
3.59
70
158.96
2460
93.34
580
40.12
320
26.62
60

Avg. Hop Count

Avg. Copy Count

Avg. Buffer Size

1.64
1.98
3.13
2.25
2.28
3.04
4.75
3.74

1.97
2.61
3.72
3.4
2.95
4.44
6.62
6.38

2.25
2.49
3.18
2.14
50.68
15.39
12.71
6.4

(b) Multistory scenario

Delivery latency vs. Delivery ratiodelivery latency, while preserving high delivery ratio. Hopcount and copy count implies the delivery pattern of the routing
scheme. Buffer size reflects on the congestion and resource
constraint of nodes.
Overall performance of each metric is represented by theaverage value, except delivery latency. We use median and 99th
percentile for delivery latency in order to avoid the outlier.
D. Evaluation Results
The overall performance is shown in Table. II. We analyzed
the effect of unidirectional links and FAST performance asfollows,(a) Floor scenario
Fig. 6.

(b) Multistory scenario

Hop count vs. Delivery latency

were usually long links. Although the hop count was increased,
when we compared the hop count with the delivery latency
as shown in Fig. 6, the messages were delivered faster withthe same hop count. The buffer size was also increased infloor scenario. With short range links, ULANS created andsent more copied messages in the network, so intermediate
nodes received and stored more messages in their buffers.
However, the buffer size was greatly decreased in multistory
scenario. The reason was that PEAR deletes the messages inthe buffer when the state delivered is informed. Seeing that thedelivery latency was greatly decreased in multistory scenario,
the messages were quickly deleted from the buffer.

1) The effect of unidirectional links: Comparing the performance of PEAR and PEAR-ULANS, the unidirectional links
did not influence much on delivery ratio and median delivery
latency in floor scenario (Fig. 5a). On the other hand, theunidirectional links significantly reduced PEAR performancein multistory scenario (Fig. 5b).
We examined unidirectional link selection ratio (UDLSR)
to prove that the unidirectional links were the cause of bad
performance on delivery ratio and delivery latency in PEAR.
UDLSR is the ratio of the number of times that a node
selects the neighbor with unidirectional link as the next-hop
to the number of times next-hop selection process is executed.
Fig. 7 illustrates the distribution of UDLSR of PEAR and
PEAR-ULANS. We can see that some nodes frequently choseneighbors with unidirectional links (40-60% of the experimenttime) in PEAR, while all nodes avoided unidirectional links
and gave low UDLSR in PEAR-ULANS.
PEAR-ULANS decreased 41.28% in the median and
76.43% in 99th percentile delivery latency from PEAR, but
PEAR-ULANS increased hop count and copy count. The
increment of hop count indicated that the unidirectional links

Fig. 7.

The distribution of unidirectional link selection ratio (UDLSR).

2) FAST Performance: Even though ULANS already filtered out the unidirectional links, the rest of long links tended
to be low quality links. PEAR-DP avoided those links andgave the better performance on delivery ratio and delivery
latency. Since high quality links were particularly short links,
PEAR-DP resulted more hop count and copy count comparedto PEAR and PEAR-ULANS, but lower delivery latency withsame number of hops (Fig. 6). PEAR-DP occupied more
buffer entries in floor scenario, but it released buffer fasterin multistory scenario similar to PEAR-ULANS.

PEAR-FAST showed the best performance among all
schemes regarding to the delivery latency, especially in multistory scenario. PEAR-FAST reduced 65.28% and 83.5% inmedian delivery latency in comparison with PEAR on floor
and multistory scenario, respectively. In addition, 99% of
the messages were gathered within one minute, while otherschemes took more than 5 minutes in multistory scenario.
Certainly, PEAR-FAST gave more hop count than PEAR,
but lower than PEAR-DP. The reason for this was the messages
were forwarded across some nodes when nodes succeededin the investigation with primary next-hop. However, FAST
lessened few copy count since FAST created multi-path delivery. The messages were possibly forwarded to both primary
and alternative next-hop at the different transmission time.
For example, when the sender failed in investigation withprimary next-hop, but succeeded in reinvestigation, the senderforwarded the messages to the alternative next-hop at the firstinvestigation time. At the next investigation time, the senderfinished the investigation with primary next-hop, so the sendersent the messages to the primary next-hop. In this case, themessages delivered to both primary and alternative next-hop.
Such situation could happen at every intermediate node, thus
FAST still had large copy count comparing to PEAR and
PEAR-ULANS.
PEAR-FAST resulted the lowest average buffer size in bothscenarios. The effect of delivery latency on the buffer size wasmore dominant than the copy count even in floor scenario.

V.

D ISCUSSION

The combination of DTN approach and traditional routing
scheme in FAST improved PEAR’s performance significantly
on delivery latency and delivery ratio. The improvement ofdelivery latency also benefited to the buffer size. However,there were a trade off between the redundancy and delivery
latency. While PEAR minimized hop count, other schemes
increased the number of hops as well as copied messages. This
redundancy causes congestion in the network and also affects
to energy efficiency of sensor node which is the important
issue of WSN. Thus, we aim to reduce these redundancy infuture works.
Our experiment also confirmed that hop metric is not a
proper metric for static WSN regarding to delivery latency.
PEAR-DP decreased delivery latency approximately 60% in
both scenarios. Nevertheless, only link quality metric is notenough. Without the retransmission, the messages are likely
to lost any time owing to the dynamic of wireless links.
Therefore, the retransmission or retinvestigation in DTN-based
approach is necessary for delivery latency improvement.
The performance of routing scheme depends on the deployment and network scale. We can see that floor and multistory
scenario gave different results in some performance metrics.
The unidirectional links did not influence much in floor
scenario, thus all schemes achieve more than 99% delivery
ratio. In addition, even though PEAR-ULANS and PEARDP delivered messages faster than PEAR, the congestion atintermediate nodes was higher in floor scenario. However,
FAST achieved good performance in both scenarios.

VI.

R ELATED W ORKS

In this section, we review the related works on unidirectional links, link quality metric and retransmission.
Many techniques of detecting and handling the unidirectional links were proposed. However, some were developed
based on Ad-hoc On Demand Distance Vector (AODV) routing [12]–[14] and some exploited the unidirectional links indelivery [15], [16]. Seeing that PBR is different from AODV, in
addition, hop-by-hop delivery requires only bidirectional links,
those techniques cannot apply to PEAR.
Among existing link quality metric [11], [17]–[19], ETX(expected transmission count) is the most widely used in WSN
routing protocol. ETX estimates the number of retranmission
required from source to destination. This metric does notmeet our requirement since we already fix the number ofretransmission. Our estimator works similar to the one in [11],but ours estimates link quality with more precision. The metric
Delivery Predictability has already proposed in [20]. However,it was defined in the context of mobility. Our work describes
this metric in static condition.
Retransmission is the well-known mechanism to guarantee
reliability [21]. All retransmission schemes resend the messages to the same next-hop. With hop metric, the messages arelikely lost on the long links, so the sender has to retransmit
the messages many times before the messages reach thenext-hop. Consequently, retransmission could aggravate thecongestion in the network [22]. The objective of applying
retransmission in this work is different from those researches.
FAST uses hop-by-hop delivery to promise 100% delivery ratio
and retransmission to improve delivery latency. Thus, FAST
limits the number of retransmission and provides alternative
next-hop to avoid repeatedly message lost in retransmission.
VII.

C ONCLUSION

In this paper, we address the issues of DTN-based routing protocol in WSN-BMS. Some DTN approaches are notsuitable for WSN-BMS due to the different features of DTN
and WSN-BMS. Moreover, unawareness of unidirectional
links also leads to high delivery latency. We proposed FAST
forwarding scheme for PEAR to solve these issues. FAST
integrates retransmission and link quality metric from WSN
routing scheme with hop-by-hop delivery in DTN. FAST
relieves the effect of unidirectional links by Unidirectional
Link-Aware Next-hop Selection (ULANS) scheme and enhances investigation success with delivery predictability inretransmission. Our experimental evaluation with 16 nodescale floor scenario and 33 node-scale multistory scenario
proved that FAST can considerably decrease delivery latency,
while achieving high reliability and scalability in comparison
with PEAR.
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