无线传感器网络的Trickle自我调节算法

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"Trickle: 一种无线传感器网络中的自我调节代码传播和维护算法，由Philip Levis等人提出。该算法结合了流行病/八卦、可扩展多播和无线广播领域的技术，旨在有效地在无线传感器网络中传播和更新代码，同时避免过度的网络拥堵。" 在无线传感器网络中，代码的传播和维护是至关重要的，因为这些网络通常由大量小型设备组成，这些设备可能需要定期接收和更新软件或固件以保持正常运行。传统的传播算法如泛洪算法（Flooding）虽然简单，但在大规模网络中可能导致大量的重复数据传输，消耗宝贵的网络资源。 Trickle算法借鉴了八卦（Gossip）算法的思想，通过一种礼貌的八卦策略来优化代码传播。在网络中的每个节点（ mote）周期性地向其邻居广播代码摘要，但如果节点最近接收到的摘要与自身的相同，它会选择沉默，不发送重复信息。当一个节点接收到比自身更旧的代码摘要时，它会广播更新包，这样确保了只有最新的代码信息在网络中传播。 Trickle算法的核心在于控制每个节点的发送速率，使得每个节点仅接收少量的数据包，但足以保持代码的最新状态。这种机制能够防止网络被大量的更新包淹没，从而实现对网络流量的有效管理。通过智能地调整发送速率，Trickle算法能够在网络规模变化很大的情况下，仍然保持高效和稳定。此外，Trickle算法还具有自适应性，它可以根据网络的当前状态动态调整其参数，以适应不同的网络条件和负载。这种自调节能力使得Trickle算法在无线传感器网络的动态环境中表现出色，无论网络规模如何变化，都能保证代码的快速、可靠传播，同时减少不必要的通信开销。 Trickle算法提供了一种平衡效率和资源消耗的解决方案，对于无线传感器网络中的代码管理和更新具有重要的应用价值。通过有效地控制数据传播，它减少了网络拥堵，提高了网络的整体性能，使得大规模的无线传感器网络能够更有效地运作。

Figure 2: The TinyOS mica2

To generate network topologies, we used TOSSIM’s

empirical model, based on data gathered from TinyOS

motes [7]. Figure 1 shows an experiment illustrating the

model’s packet loss rates over distance (in feet). As link

directions are sampled independently, intermediate dis-

tances such as twenty feet commonly exhibit link asym-

metry. Physical topologies are fed into the loss distribu-

tion function, producing a loss topology. In our studies,

link error rates were constant for the duration of a simu-

lation, but packet loss rates could be affected by dynamic

interactions such as collisions at a receiver.

In addition to standard bit-level simulations, we used a

modiﬁed version of TOSSIM that supports packet-level

simulations. This version simulates loss due to packet

corruption from bit errors, but does not model collisions.

By comparing the results of the full bit-level simulation

and this simpler packet-level simulation, we can ascer-

tain when packet collisions – failures of the underlying

MAC – are the cause of protocol behavior. In this paper,

we refer to the full TOSSIM simulation as TOSSIM-bit,

and the packet level simulation as TOSSIM-packet.

2.3 TinyOS motes

In our empirical experiments, we used TinyOS mica2

motes, with a 916MHz radio.

These motes provide 128KB

of program memory, 4KB of RAM, and a 7MHz 8-bit

microcontroller for a processor. The radio transmits at

19.2 Kbit, which after encoding and media access, is

approximately forty TinyOS packets/second, each with

a thirty-six byte data payload. For propagation experi-

ments, we instrumented mica2 motes with a special hard-

ware device that bridges their UART to TCP; other com-

puters can connect to the mote with a TCP socket to read

and write data to the mote. We used this to obtain mil-

lisecond granularity timestamps on network events. Fig-

ure 2 shows a picture of one of the mica2 motes used in

our experiments.

We performed two empirical studies. One involved

placing varying number of motes on a table, with the

transmission strength set very low to create a small multi-

hop network. The other was a nineteen mote network

There is also a 433 MHz variety.

in an ofﬁce area, approximately 160’ by 40’. Section 5

presents the latter experiment in greater depth.

3. TRICKLE OVERVIEW

In the next three sections, we introduce and evaluate

Trickle. In this section, we describe the basic algorithm

primitive colloquially, as well as its conceptual basis. In

Section 4, we describe the algorithm more formally, and

evaluate the scalability of Trickle’s maintenance cost, start-

ing with an ideal case – a lossless and perfectly synchro-

nized single-hop network. Incrementally, we remove each

of these three constraints, quantifying scalability in sim-

ulation and validating the simulation results with an em-

pirical study. In Section 5, we show how, by adjust-

ing the length of time intervals, Trickle’s maintenance

algorithm can be easily adapted to also rapidly propa-

gate code while imposing a minimal overhead. Trickle

assumes that motes can succinctly describe their code

with metadata, and by comparing two different pieces of

metadata can determine which mote needs an update.

Trickle’s basic primitive is simple: every so often, a

mote transmits code metadata if it has not heard a few

other motes transmit the same thing. This allows Trickle

to scale to thousand-fold variations in network density,

quickly propagate updates, distribute transmission load

evenly, be robust to transient disconnections, handle net-

work repopulations, and impose a maintenance overhead

on the order of a few packets per hour per mote.

Trickle sends all messages to the local broadcast ad-

dress. There are two possible results to a Trickle broad-

cast: either every mote that hears the message is up to

date, or a recipient detects the need for an update. Detec-

tion can be the result of either an out-of-date mote hear-

ing someone has new code, or an updated mote hearing

someone has old code. As long as every mote communi-

cates somehow – either receives or transmits – the need

for an update will be detected.

For example, if mote A broadcasts that it has code φ

but B has code φ

x+1

, then B knows that A needs an

update. Similarly, if B broadcasts that it has φ

x+1

, A

knows that it needs an update. If B broadcasts updates,

then all of its neighbors can receive them without having

to advertise their need. Some of these recipients might

not even have heard A’s transmission.

In this example, it does not matter who ﬁrst transmits,

A or B; either case will detect the inconsistency. All that

matters is that some motes communicate with one an-

other at some nonzero rate; we will informally call this

the “communication rate.” As long as the network is con-

nected and there is some minimum communication rate

for each mote, everyone will stay up to date.

The fact that communication can be either transmis-

sion or reception enables Trickle to operate in sparse as

well as dense networks. A single, disconnected mote

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