Impact of Time Slot Adjustment on a Multi-hop and Multi-channel

Solution for Dynamic WSN Topologies

Honoré Bizagwira

1,3

, Joël Toussaint

2,3

and Michel Misson

2,3

Université Blaise Pascal, Aubière, France

Université d’Auvergne, Aubière, France

LIMOS - CNRS, F-63175 Aubière, France

Keywords:

Wireless Sensor Networks, Adaptive Reception Window, Schedule-based Protocol, Dynamic Topology, Harsh

Environment.

Abstract:

Ensure data delivery and extend the network lifetime are challenges that must be addressed when it comes to

deploy a WSN in outdoor harsh environment where it operates over unstable links. The instability of radio

links may induce a connectivity that is time-varying even when nodes are not moving. For these dynamic

topologies, resources allocated to the nodes must be adjusted to the local trafﬁc conditions. Amongst the

proposed solutions, schedule-based protocols achieve an energy efﬁciency as they allow nodes to sleep during

their inactive periods. As the channel conditions are time-varying, and paths followed by the packets are

changing, inducing an instability in the distribution of the trafﬁc load over the network. This paper is dealing

with the mastering of the size of the reception time slot of the next hop node. Our solution dynamically takes

in consideration the local trafﬁc load to resize the time slots. This adaptive solution is compared with the

ﬁxed-length window scheme, it obviously improves the performance of the WSN in terms of cycle length, idle

listening and collision reduction, and achieves suitable data delivery ratio.

1 INTRODUCTION

Wireless sensor network (WSN) stands as a power-

ful solution for wide area environmental monitoring.

Sensor nodes equipped with data sensing device au-

tonomously organize themselves to deliver sampled

data to a particular node, the sink. As sensor nodes

have limited battery power, the transmission range

and the data rate have to be optimized according to the

deployment conditions. These characteristics impose

many challenges, especially for WSNs with ﬂuctuat-

ing conditions of signal propagation. In this paper,

we focus on convergecast trafﬁc in WSNs having dy-

namic topologies. It is the case for WSNs deployed

in harsh outdoor environment such as over freshwater

areas (lakes for example). For the WSNs we are deal-

ing with, the positions of nodes remain fairly stable

over time, but radio links may be volatile.

Our solution consists in a scheduling-based MAC

protocol where each node is assigned both time slots

and radio frequency for reception. In this proto-

col, each node selects periodically its next-hop node

among the neighbors according to the radio links

characteristics in order to route the trafﬁc. Since we

deal with dynamic topologies, neighborhood of each

node is periodically updated. Instability of links has

an impact on the amount of trafﬁc a particular node

has to route. In the paper we propose and evaluate

a mechanism to adjust the size of the receiving time

slots. We show that, coupled with a multi-channel so-

lution, it has a great impact on the time needed for

gathering data over such a WSN.

2 RELATED WORK

In this section, we investigate meanly the solutions

that have been proposed for dynamic topologies on

one hand and for TDMA-based MAC Protocols for

WSNs on the other hand.

When wireless links are intermittent, routing of

packets may become uncertain and/or induces large

end-to-end delays. WSN nodes are usually densely

deployed in order to ensure the global connectivity

and the coverage of the monitored area. Connectiv-

ity may be lost when some radio links on the path of

packets are temporarily disrupted. This variability of

196

Bizagwira H., Toussaint J. and Misson M.

Impact of Time Slot Adjustment on a Multi-hop and Multi-channel Solution for Dynamic WSN Topologies.

DOI: 10.5220/0006214701960201

In Proceedings of the 6th International Conference on Sensor Networks (SENSORNETS 2017), pages 196-201

ISBN: 421065/17

the topology has a negative impact on the multi-hop

forwarding process and induces unpredictable end-to-

end delays. If this variability of topology happens

when some nodes are moving, the concept of data

mule can be used (Tseng et al., 2013). The goal of the

studies in (Nour Brinis, 2012) is to determine the opti-

mal number of data mules required to extend the cov-

ered network lifetime while meeting a given packet

delivery delay deadline.

In our contribution the hypothesis of having sev-

eral moving nodes is not necessary. Received or lo-

cally produced packets are stored in the node buffer

used with FIFO policy. When the radio link of the

next-hop is temporary broken, packets have to wait

for better propagation conditions or another next-hop

has to be chosen. The amount of trafﬁc reaching

at a given node is depending on (i) the offered load

submitted by the source nodes, (ii) the instantaneous

shape of the WSN topology. Using a static capacity of

packet reception for nodes may lead to congestion ar-

eas and to FIFO overﬂow. This particular point leads

us to specify a dynamic capacity of reception. Our

objective is to adapt the size of the time slot dedicated

to reception of frames, to the local instantaneous load.

The scheduling-based MAC protocol we chose has to

be ﬁtted in order to take into account the impact of

the dynamicity of the WSN topology. The use of a

unique or several frequencies has no signiﬁcant im-

pact on this challenge. The question we are dealing

with here is: how to right-size the length of the re-

ception period without adversely impacting the trafﬁc

ﬂow through the network.

The problem of Time Slot Scheduling has been

deeply investigated for ad-Hoc Wireless Network. In

the WSN domain when TDMA is used, the con-

vergecast strategy requires both adequate coordina-

tion among the nodes and proper sizing of time slots

to avoid high packet collision rates or frequent FIFO

overﬂows near the sink node. Slot assignment and

slot adjustment are the two approaches usually ex-

plored (Pal and Chatterjee, 2014).

In (Cui et al., 2004), a variable length Time Di-

vision Multiple Access scheme is proposed for im-

proving total energy consumption. In (Cui et al.,

2005), delay and energy are taken into account to pro-

pose an algorithm for variable length TDMA sched-

ules. Most of the time, the available contributions are

driven by energy and delay considerations, for WSN

topologies supposed to be rather stable. Slot assign-

ment becomes very complex when the topology is re-

ally time-varying. The scheme allocation as presented

in (Ergen and Varaiya, 2010) seems to be proposed

for topologies having bidirectional and stable radio

links. These hypotheses are usually retained for slot

assignment using a coloring process (Mahfoudh et al.,

2010). Radio link quality measurements we done for

transmission over freshwater (Bizagwira et al., 2014)

justify another approach.

3 PROTOCOL DESCRIPTION

AND PROBLEM

FORMULATION

The protocol stack we propose has to be energeti-

cally efﬁcient for WSNs having an unstable topol-

ogy. This objective leads us (i) to retain a cross lay-

ering approach for Network layer and MAC Layer,

(ii) and to use periodical sleeping periods to spare en-

ergy. The physical layer is inspired from the spectrum

allocation and channel speciﬁcations proposed in the

433 MHz frequency band by DASH7 Alliance (Tuset-

Peiró et al., 2014; Mode, 2013). This choice allows us

to have 8 non-overlapping channels for transmission.

Idle

Time

Sleep period

Reception

Synchronization

Beacon frame

Build-up

network

Single channel

Multi-channel

guard

A cycle

Transmission

Figure 1: Mapping of time slot allocation within a cycle for

both single and multiple channel use.

3.1 Global Segmentation of Time

The operating cycle of the low layers of our proto-

col stack is divided into four periods as shown in Fig-

ure 1:

• Synchronization and Build up Period : For multi-

hop deterministic synchronization of nodes using

beacons.

• Time guard period: Time guard allowing our so-

lution to be adaptive.

• Data gathering period: For multi-hop forwarding

of data frames to the sink.

• Sleeping Period: All the nodes sleep periodically

for energy saving.

Synchronization, neighborhood discovery and

routing strategy used to join the sink are done at

the beginning of each cycle using a beacon cascad-

ing. Synchronization is initiated by the sink that

Impact of Time Slot Adjustment on a Multi-hop and Multi-channel Solution for Dynamic WSN Topologies

197

broadcasts a beacon including the reference clock, the

length of current cycle and other information related

to the reception schedule.

3.2 Main Characteristics of MAC

Protocol

The data gathering period is governed by a TDMA

schedule-based MAC protocol. It is a synchronous

and receiver-based protocol that applies the schedul-

ing decided from information obtained during the last

synchronization and build-up period. The neighbor-

hood has to be discovered again at the beginning of

each cycle. The results (as the quality of the radio

link) is used to choose the best next-hop to reach the

sink. The 8 frequency channels deﬁned by DASH7

provides a multi-channel capability for data transmis-

sion.

The choice of channel scheduling is done dur-

ing the beacon cascading. When a node receives a

beacon, it reads the sender schedule and updates its

neighborhood knowledge table. Then, it selects its

next hop for data transmission before sending, in turn,

its own beacon. The neighborhood knowledge table

allows each node to choose a reception frequency and

time slot, taking into account the local use of the ra-

dio spectrum. As shown in Figure 1, the beginning

of the receiving Time Slot is deliberately chosen to

accelerate the convergecast trafﬁc. The quality of the

path towards the sink is the main factor impacting the

choice of the next hop.

4 ADJUSTMENT OF THE TIME

SLOT DURATION FOR

DYNAMIC TOPOLOGIES

For WSNs using convergecast, the nodes near the

sink have more trafﬁc to store and forward than other

nodes in the network, and nodes located at the pe-

riphery of such networks have no forwarding activity.

Moreover, when a WSN operates using unstable links,

the trafﬁc load of a given node is also time-varying. In

this section, we state the problem of dynamic resizing

of reception time slots of nodes in accordance with

the trafﬁc of their potential child-nodes. The global

objective is to adjust dynamically the time slot size

of each node, to the instantaneous local load. As this

length is adapted to the trafﬁc, (i) the wasted energy

due to the idle listening will be reduced, (ii) the risk of

FIFO overﬂow will be minimized and (iii) the length

of the sleeping period can be maximized.

4.1 Global Presentation of the Master

Process of the Time Slot Duration

Dynamic topologies using unreliable radio links in-

duce a kind of packet routing instability and a variable

risk of packet retransmission. This drawback is miti-

gated by the part played by the FIFO of the nodes. In

a given area the cumulative number of packets stored

in the local FIFOs is a local load indicator. This is the

parameter we retained as the main variable to master

the size of the time slot of nodes. In the closed loop

control system that we propose for the adjustment of

the time slot duration of the node i, our objective is to

empty the FIFOs of its child nodes for each cycle.

4.2 Speciﬁcation of the Algorithm Used

for Resizing the Reception Period

Length

Let us consider node N

a generic node of our WSN.

The Process Variable (PV ) is the number of packets

remaining in the FIFO of the neighboring nodes that

have chosen N

as the next hop. The Set Point (SP)

is corresponding to the state we want to reach for the

content of these FIFOs at the end of the receiving time

slot of N

. SP stands for the number of packets having

to wait for another cycle to be transmitted.

e(t) = PV − SP (1)

represents the error we want to minimize by a

mastering process. S

max

is used to reduce the impact

of proportional component of the mastering process

when the local congestion is growing too fast.

In the Algorithm 1, SP = 1 packet. PV is used

to accumulate the remaining packets in the FIFOs of

nodes having N

as their next-hop. This accumula-

tion is bounded by the threshold S

max

. Let Trx

the length of current reception period of N

. It is re-

sized according to: Trx

←− Trx

+ K

∗ e(t). 5 and

5 packets are empirically chosen, respectively for K

and S

max

. The value of Trx

is constraint within the

interval [Trx

iMin

Trx

iMax

]: interval bounded by the

initial value for the time slot, and a maximum value

allowing us to estimate the length of PA or PB of ﬁg-

ure 1. Let ID

sender

be the address of the node having

chosen N

as next hop, N

pkt(x)

, and L

senders

be respec-

tively the number of packets remaining in the FIFO

of node x and the list of packet sender nodes for the

current cycle.

SENSORNETS 2017 - 6th International Conference on Sensor Networks

198

begin

while reception period not expired do

if data packet received then

records ID

sender

and N

pkt

;

if ID

sender

already exist in L

senders

then

update the N

pkt(ID

sender

)

for

sender

node;

else

append (ID

sender

, N

pkt

) to

senders

;

end

PV ←− 0

foreach neighbor node j in L

senders

PV ←− PV + N

end

if PV > S

max

then

PV ←− S

max

end

e(t) ←− PV − 1

Trx

←− Trx

+ K

∗ e(t)

end

Algorithm 1: The algorithm that is used for resizing the

reception period length.

5 RESULTS

We use NS-3 to simulate our proposal scheme and in-

corporate protocol in several scenarios. In this sec-

tion, we discuss our WSN protocol stack implementa-

tion and give the detail settings. In practice, this WSN

has been implemented on a TI CC430 based com-

ponent to be deployed on the surface of water, i.e.,

over the lac for monitoring the aquatic environment.

All the MAC layer and routing algorithms presented

above have been implemented in our NS-3 model. We

chose to use the Log-Distance Shadowing model ex-

perimental measurement (Bizagwira et al., 2014) as

our propagation model. The choice of parameter val-

ues of the model is justiﬁed by (Tuset-Peiró et al.,

2014; Tuset et al., 2013).

In this section, we evaluate the performance of the

proposed dynamic reception window resizing scheme

and its consequences on the network throughput as

well as energy efﬁciency of the sensor nodes. We

conduct two different simulation scenarios and com-

pare the performance based on three metrics: packet

delivery ratio, average energy consumption, and aver-

age latency. In the ﬁrst scenario, we set a ﬁxed inter-

val for reception period - as is done in SMAC (Sen-

sor MAC) protocol (Chang et al., 2013), for all nodes

in the network. Each node schedules its transmission

time according to the chosen next hop, and ﬁxes the

duration of the reception period. In the second sce-

nario, we apply our dynamic resizing scheme for both

small and large size reception period. Table 1 shows

the parameters used in our simulation. The network

topology consists of 26 nodes, which are spread ran-

domly through the network ﬁeld of 300 m x 300 m as

shown in ﬁgure 2. Each node generates 1 data packet

per cycle except for the sink node (green colored) that

collects the trafﬁc. The cycle duration is 4 seconds.

Figure 2: Network topology.

Table 1: Simulation parameters.

Parameters Value

Prop. model Log-Distance Shadowing

Number of nodes 26

Topology Random

Size 300 m x 300 m

Channel Single, multiple (8 non-

overlapping frequencies)

Cycle duration 4 seconds

Simulation duration 600 seconds

Buffer length 45 packets

Data rate 1 data packet per node and

per cycle

5.1 Fixed-length Reception Period

As we explained before, the sink node triggers the net-

work activity by sending a new beacon as the start sig-

nal of the beginning of a cycle. For this scenario, the

sink includes in the beacon the ﬁxed duration of re-

ception period. The beacon cascading process broad-

casts this value to all others nodes of the WSN.

Impact of Time Slot Adjustment on a Multi-hop and Multi-channel Solution for Dynamic WSN Topologies

199

5.2 Adaptive Length Reception Period

All the nodes are initialized with a same reception

period length of 27 ms. The ﬁgure 3 is an example

showing how the window size adjustment is carried

out over time. Our test utopology is given in the ﬁg-

ure 2.

(a) (b)

Figure 3: The adjustment over time of the reception periods

for the sink neighbor nodes.

The test scenario consists in turning off node B

during a given amount of time (between 150

and

250

cycles). We are looking its impact on the win-

dow size of nodes C and E.

5.3 Discussions

As introduced earlier, we analyze the performance of

our MAC protocol scheme in terms of (i) Packet deliv-

ery ratio (PDR), and (ii) activity duration as deﬁned

in ﬁgure 1 (length of the cycle - sleeping period PD).

It is important to emphasize that - in order to com-

pare both cases, ﬁxed-length and adaptive reception

periods, on the same basis, we set the length value for

the ﬁrst as the maximum allowed value for the second.

Packet Delivery Ratio. The ratio of packets that

are successfully delivered to the sink compared to

the number of packets that have been sent out by the

source nodes.

The results for single channel and multi-channel

are quite similar. For small values (35 and 70 ms),

the window size is not sufﬁcient to allow all trafﬁc,

especially around the sink where there is a bottle-

neck. This induces a large number of packets dropped

due to lack of space in node FIFOs. Concerning the

window size for our adaptive method, we also ob-

serve that for nodes closer to the sink, the max-size

is quickly reached and the size of the reception period

never diminishes.

For large window values, as it is illustrated in the

ﬁgure 4, the major part of the trafﬁc is delivered to the

sink. A 100% delivery of packets is never reached be-

cause some nodes in the network periphery are too

far apart to have a permanent connectivity. So the

packet delivery rate for these nodes is relatively low.

There are also always collisions in congestion areas

and packets never reach the destination despite re-

transmissions.

On the other hand, the results are quite similar be-

cause (i) the mechanism for choosing the start dates

of the reception period ensures that nearby nodes win-

dows can be temporally disjointed, (ii) the CSMA/CA

mechanism is sufﬁciently effective to transmit frames

within the set time, (iii) the topology is sufﬁciently

sparse to have only few collisions.

35 70 105 140 175 210

100

P acket delivery ratio (%)

Mono-canal

35 70 105 140 175 210

Multi-canal

Fixed-length

Adaptive

Maximum reception period length [ms]

Figure 4: Packet delivery ratio.

We note also that the delivery rate in the case of a

ﬁxed window size can sometimes be slightly higher:

this is due to the transient phase at the beginning of

the simulation process. For the adaptive method, sim-

ulation process starts with nodes having a small re-

ception period (27 ms) which does not allow ﬂowing

as much trafﬁc as in the situation where the sizes of

the windows are ﬁxed and larger. This also explains

the small difference that we have, for larger size win-

dows.

The period adjustment process has a transient pe-

riod during which the adaptive algorithm increments

the length of the reception period before reaching

the convenient value. During this time, nodes would

accumulate packets, some are later dropped when a

FIFO overﬂow.

Activity Duration. The activity duration is the time

between the date of the wake up of the ﬁrst node and

the date of the switching to the sleeping mode of the

last node of our topology. As it is illustrated in the

ﬁgure 5, the average activity duration is lower with

the adaptive mechanism than with the ﬁxed-length

scheme. In the ﬁrst case, nodes spend around 50%

of their time in sleeping mode. This is an important

feature, as it would enable reducing the duty-cycle

and makes the protocol more reactive to the topology

changes. We note that the adaptive mechanism of the

window size associated with multi-channel scheme is

highly effective from an energy saving point of view.

SENSORNETS 2017 - 6th International Conference on Sensor Networks

200

35 70 105 140 175 210

500

1000

1500

2000

2500

Average activity duration [ms]

Mono-canal

35 70 105 140 175 210

Multi-canal

Fixed-length

Adaptive

Maximum reception period length [ms]

Figure 5: Average activity duration within working cycle.

Despite the transitional phase of setting up the net-

work that induces FIFO overﬂows and slightly longer

end-to-end delays, the data delivery ratios at the sink

are equivalent and sometimes better.

6 CONCLUSION

In this paper, we proposed an adaptive approach based

on a schedule-based MAC protocol, to efﬁciently uti-

lize the resources of WSNs, especially deployed in

harsh environment and having an unstable topology.

Most of protocols proposed to deal with dynamic

topologies argue on the efﬁciency of the schedule-

based MAC protocol. They ensure an adequate deliv-

ery ratio while optimizing energy consumption is still

a challenging task. Idle listening, overhearing and

collision are the main sources of energy wasting in

WSNs. The presented approach uses the trafﬁc infor-

mation to right-size the reception period and therefore

to minimize the idle listening. Moreover, it employs

multi-channel scheme to enable parallel transmission

within the network and consequently, it reduces both

collision and overhearing.

The results clearly show that it signiﬁcantly im-

proves the performance in terms of average activity

duration while providing, at the same time, a good

packet delivery ratio. It also reduces by a half the la-

tency compared to traditional schemes. This meets

the requirements for our target application where the

nodes will communicate over intermittent radio links.

The global solution we are dealing with is able to

withstand the effect of unstable topologies in an in-

teresting energy-saving manner.

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