Self-organizing TDMA for multihop networks

Laura Marie Feeney∗†

∗Swedish Institute of Computer Science, Kista, Sweden†Uppsala University, Uppsala, Sweden

I. OVERVIEW

We present a preliminary study of self-organizing TDMA

for battery-constrained multihop wireless networks, such as

sensor networks. Compared with existing TDMA approaches,

this work emphasizes operation in the context of battery saving

techniques that allow the radio to spend most of its time

powered down, relying on some rendezvous mechanism to

ensure that sender and receiver are simultaneously powered

on and able to communicate. While such techniques reduce

battery consumption, they also significantly limit the amount

of information – even local information – that nodes can obtain

from channel sensing or packet exchange. We therefore focus

on self-organizing TDMA in the case of very limited state

information. This work is also relevant to the practical case of

independent, co-located sensor networks, which need to adapt

to each other’s presence without explicit coordination.

II. MOTIVATION AND RELATED WORK

The need to minimize the radio duty cycle makes TDMA

transmission scheduling attractive, especially in conjunction

with radio scheduling. Unfortunately, implementing TDMA in

multihop wireless networks poses real challenges: CSMA is

used precisely because it is a natural fit for a dynamic, dis-

tributed, asynchronous environment. Even for centralized algo-

rithms with complete channel information, TDMA scheduling

problems are computationally hard.

Nevertheless, there has been interesting progress in the

development of distributed TDMA slot allocation, with a

number of proposed solutions based on distributed graph

coloring, e.g.[1]. To the best of our knowledge, however, none

of these solutions have been studied in the context of other

battery saving mechanisms.

Existing solutions also vary in their synchronization require-

ments and tolerances, although recent success in providing

clock synchronization in sensor networks makes this less

of a limitation than previously. However, synchronized slot

allocations may be problematic in the presence of independent

co-located networks, each defining its own slot boundaries.

This is particularly an issue for sensor and body area networks,

which are intended for ubiquitous use: It may not be possible

for all of the sensor networks in an area to be isolated at the

PHY/MAC layer, even though they are isolated cryptograph-

ically and therefore unable to mutually synchronize or even

exchange data [2].

In earlier work [3], we examined the interaction between

power saving protocols that use unsynchronized radio sched-

ules and channel access. We showed that the rendezvous

between sender and receiver radios creates timing patterns

in transmission opportunities that can affect the efficiency of

CSMA channel access by separating (or not) incompatible

transmissions. Specifically, we studied the impact of variation

in radio wakeup schedules on the network capacity and showed

that although there is a considerable (50%) difference between

the best and worst schedules, even the best schedules obtain

only around 75-80% of the baseline capacity of the network.

We also speculated on the relationship between this ap-

proach and an (optimal) TDMA schedule. Here, we extend

this idea to examine the possibility of self-organizing TDMA

without explicit synchronization, focusing on the case where

nodes have extremely limited information about the channel

state – that is, when the radio is mostly powered down.

III. SIMULATION MODEL

For now, we are mostly interested in the high level behavior

of a self-organizing solution, rather than specific protocol

design and channel modeling. In this exploratory mode, we

simulate a large number of scenarios under an abstract model.

We use a simple MATLAB/Octave simulation: Channel

state is represented by a binary matrix and the outcome of

any attempt to transmit can be quickly computed with a matrix

multiply and logical operations. This model obviously depends

on substantial simplifications: connectivity is defined by the

unit disk graph, interference is non-cumulative and matched to

the connectivity graph, and the channel access does not take

into account retransmissions or acknowledgments. However,

our tool has the advantage of being very fast and allowing

us to run large numbers of simulations without committing to

modeling specific protocol behaviors. We use it as follows:

Rather than compute the length of TDMA schedule for a

given load, the model uses a period of fixed length and com-

putes how many flows (from an offered load of random source-

destination pairs) can be supported with a throughput of one

frame per period (not considering latency). This means that the

channel has to accommodate the transmission associated with

each hop of each flow once in each period and the simulation

only needs to model a single, representative period.

To minimize preference for flows with fewer hops, the

flows making up the offered load are selected from source-

destination pairs whose path length is equal to the mean path

length for all connected pairs. Routes are fixed and randomly

selected from among equal length routes for each pair.

For transmission scheduling, it is assumed that the sender

and receiver at each hop know about their own transmissions,

but have no information about their neighbors’ activity. This

2011 19th IEEE International Conference on Network Protocols

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Fig. 1. Number of successful, evicted, and inadmissible flows in a sparsenetwork (45-55% connectivity). The error bars show max-min variation over10 runs of the same topology/flow scenario.

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Fig. 2. Number of attempted transmissions with high, low, or zero probabilityof success (left bar and axis). Number of attempted repairs (right bar and axis).Same topologies as Fig.1, mean over all runs.

models the effect of battery saving mechanisms based on LPL

or rendezvous (or even out-of-band “wakeup radios”), all of

which limit the amount of information nodes obtain about their

neighbors. The sender and receiver are assumed to exchange a

small amount of data to coordinate the transmission, but this

is not explicitly modeled.The sender selects a transmit time at random, considering

only the pair’s own transmissions and without “slot” synchro-

nization wrt other transmissions. The transmission fails if the

sender cannot access the channel because one of its neighbors

is transmitting or if the receiver does not ack because one of

its neighbors is transmitting. If the sender does not eventually

find a successful transmit time, the transmission – and hence

the flow to which it belongs – is not admissible.Of course, a successful transmission may create interference

within a previously existing flow, i.e. a hidden terminal. Once

the new flow has been established and its transmissions occur

regularly, it will create persistent loss at the hidden terminal.

Because the successful sender-receiver pair cannot detect the

situation, it is the affected pair that will need to reschedule

their transmission.

IV. EXPERIMENT

The experiments measure the overall flow admissibility, as

well as internal metrics of success probability for transmission

and repair. Figure 1 shows the proportion of successful,

inadmissible, and evicted (unrepairable) flows. Figure 2 shows

internal metrics for the number and success probability of

transmit and repair attempts.The network is a 50 node network randomly deployed on

a 7x7 area, with a topology that is 45-55% connected. The

offered load consisted of 40 flows. For each transmission, up to

10 attempts were made to discover a suitable transmission time

and up to two attempts were made to repair each flow affected

by hidden terminals. The packet length was 35 units, relative to

a period granularity of 2000 units. Each of 10 topology/offered

load combinations was simulated 10 times.The results suggest that in these relatively sparse networks,

most repairs are successful and very few flows are evicted,

even when only two repair attempts are permitted. In most

cases, failing flows are never admitted to the network at

all: Examining internal metrics suggests that in many cases,

there are no valid transmission times available, given the

current channel state (p = 0 above). This suggests that local

repair is insufficient and that even viable flows should be

“repaired” to try to use the channel more efficiently (recall

that transmissions are not “slot synchronized” wrt each other).

V. CONCLUSION

This abstract has presented preliminary results of an ex-

ploratory study of self-organizing TDMA in the context of

battery saving mechanisms that limit nodes’ state information.

This limitation results in (nearly) random selection of transmit

times and uncoordinated repairs to reschedule transmissions

affected by newly scheduled transmissions. Using a fast,

but simplified, mechanism independent simulation model, we

evaluated the proportion of successful and unsuccessful flows,

transmissions, and repairs. Overall, these preliminary results

suggest that individual repairs have a good probability of

success, but more efficient channel access will require more

preemptive repair/adjustment of successful flows. Further an-

alytic work is required to better understand the risk of repair

feedback loops and we are looking at Markov models for this.

ACKNOWLEDGMENTS

Anders Ahlen and Christian Rohner of Uppsala University

provided valuable input to this work.

REFERENCES

[1] I. Rhee, A. Warrier, J. Min, and L. Xu, “Drand: : distributed randomizedtdma scheduling for wireless ad-hoc networks,” in 7th ACM Int’l Sympon Mobile Ad Hoc Networking and Computing (MobiHoc06), May 2006.

[2] L. M. Feeney, “Exploring semantic interference in heterogenous sensornetworks,” in 1st ACM Workshop on Heterogeneous Sensor and ActorNetworks, in conjunction with the 9th ACM Int’l Symp on Mobile AdHoc Networking and Computing (MobiHoc2008), May 2008.

[3] L. M. Feeney, C. Rohner, and B. Ahlgren, “The impact of wakeup sched-ule distribution in asynchronous power save protocols on the performanceof multihop wireless networks,” in IEEE Wireless Communications andNetworking Conf (WCNC’07), Mar. 2007.

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