Wireless Communication Issues in Sensor Networks

Alec Woo UC Berkeley October 2 nd , 2003

Theme

 Explore underlying communication issues and their effects on high-level protocol design   Single hop  Zhao and Govindan, SenSys 2003  Extra: SCALE (Cerpa et al. CENS TR 2003) Network-level protocol  multi-hop routing for data collection. (Woo et al., SenSys 2003)

Motivation

 Why do we care about all these details?

   we will experience it!

if we want to implement our protocols actual deployment decision

Roadmap

  Single Hop packet loss characteristics  Core dimensions  Environment, distance, transmit power, temporal correlation, data rate, packet size Services for High Level Protocols/Applications    Link estimation Neighborhood management Reliable Multihop Routing

Zhao’s Study

    Hardware  Mica, RFM 433MHz MAC  TinyOS Mac (CSMA) Encoding  Manchester (1:2)   4b/6b (1:1.5) SECDED (1:3) Environment  Indoor, Open Structure, Habitat Environment

Indoor is the Harshest

    Linear topology over a hallway (0.5/0.25m spacing) 40% of the links have quality < 70% Lower transmit power  yields smaller tail distribution SECDEC  significantly helps to lower the heavy tail

Packet Loss and Distance

 Gray/Transitional Area   ranges from 20% to 50% of the communication range Habitat has smaller communication range?

  Other evidence (Cerpa et al., Woo et al.) RFM: BAD RADIO??

ChipCon Radio (Cerpa et al.)

Mica On Ceiling   Higher transmit power doesn’t eliminate transitional region  Range in (a) and (b) are the same?

Indoor RFM result is worst than that in Zhao’s work  cannot even see the effective region

Can better coding help?

  SECDED is effective if start symbol is detected but does not increase “communication range”  Bit error rate (BER) is higher in transitional region Missing start symbol is fatal  Better coding for start symbol?

Loss Variation (Cerpa et al.)

  Variation over distance and over time  binomial approximation for variation over time?

Zhao shows that SECDED helps decrease the variation over distance (but very large SD here)

Packet Loss vs. Workload

 Packet loss increases as network load increases  But what is the network load?

 How many nodes are in range?

 Not sure!   Is 0.5packets/s already in saturation?

Difficult to observe is it hidden node terminal

Packet Loss vs. RSSI

 Low packet loss => good RSSI  But not vice versa  Too high a threshold limits number of links  Network partition??

Other Findings

   Correlation of Packet Loss   correlation at the gray (transitional) region for indoor Habitat: much less  Independent losses are reasonable 50%-80% of the retransmissions are wasted  Neighbor = hear a node once Asymmetric links are common  > 10% of link pairs have link quality difference > 50%  Cerpa et al.  Moving a little bit doesn’t help  Swap the two nodes, asymmetrical link swaps too  i.e. not due to the environment

Packet Size (Cerpa et al.)

 Loss over distance is relatively the same for different packet size (25 bytes and 150 bytes) at different transmit power

Take Away

     Who to blame?

 Radio?

  Similar results found over RFM and ChipCon radio Hardware calibration! Yeah!   Base-band radio  Multi-path will remain unless spread-spectrum radio is used  But 802.11 is also not ideal (Decouto et al. Mobicom 03) What is the effective communication range?

 What does it mean when you deploy a network What defines a neighbor?

Why study high density sensor network?

Break?

Roadmap

  Single Hop packet loss characteristics  Core dimensions  Environment, distance, transmit power, temporal correlation, data rate, packet size Services for High Level Protocols/Applications    Link estimation Neighborhood management Reliable multihop routing for data collection

Link Quality Estimation

 Estimate rate of successful reception from neighboring nodes   RSSI may not work well Neighbors exchange estimations to derive bi directional link quality  2 Techniques: Passive vs. Active  Key decision factor: broadcast medium  Passive: snoop on neighbor packets

What is a good Estimator?

 For a given error bound and agility,    yields the most stable estimation with the smallest memory footprint and the simplest algorithm  Agility and stability are at odds with each other

Agility and Error Bound

  Simulation worst case: 10% error ~ 100 packet time Assuming IID Binomial model, by the central limit theorem  Worst case (p = 0.5) requires  10% error with 90% confidence requires ~100 packet opportunities to learn   For example: at 30sec/packet   50 minutes for 100 packets forwarding traffic helps to reduce this time but potentially a long delay Major disadvantage

Infer Packet Loss

  Packet sequence number for inferring packet loss  Issue: cannot infer loss until hearing the next packet  E.g. dead node or mobility Can infer losses based on time  Assume minimum data rate is known  Likely to be true in periodic data collection

WMEWMA Estimator

    Compute an average success rate over time, T, and smoothes with an exponentially weighted moving average (EWMA) Average calculation  Packet Received over T divided by  Max of  Number of packets expected over T  Number of packets sent over T suggested by sequence number Tuning parameters:  T and history size of EWMA Performance   Yields agile and stable estimations Uses constant memory, and is very simple

Neighbor Table

    Maintain link estimation statistics and routing information of each neighbor How large should this table be?

 O(cell density) * meta-data for each neighbor Issue:   Density can be high but memory is limited At high density, many links are poor or asymmetric Question:  Can we use constant memory to maintain a set of good neighbors regardless of cell density?

Neighborhood Management

   Question: when table becomes full,   should we add new neighbor?

If so, evict which old neighbor?

Neighbor Goodness  Basic one is link quality but it is unknown  Signal strength is a hint  Similar to  frequency estimation of data streams, or  Rely on frequency of packet reception  Assume periodic data packets or beacons classical cache policy

Management Algorithm

  When we hear a node, if   In table: increment a counter for this node Not in table  Insert if table is not full  down-sample if table is full  If successful, insert only if some nodes can be evicted Eviction: (FREQUENCY)   Decrement counter for each table entry Nodes with counter = 0 can be evicted  Otherwise, all nodes stay in the table

Key Results

 FREQUENCY algorithm can effectively   utilize 50% to 70% of the table space to maintain a set of good neighbors while being adaptive to neighborhood changes  Routing simulation:  Neighbor goodness is augmented to avoid maintaining sibling nodes based on routing cost difference

Reliable Routing

 3 core components for Routing    Routing protocol Neighbor table management Link estimation  Example  Tree based routing for data collection    Reliable end-to-end packet delivery with minimum number of transmissions (link retransmissions) Advocate stability Simple

Design Issues  Shortest path alone yields poor end-to-end success rate  Multi-hop over bad links has exponential loss effect  Two approaches   SP over some link quality threshold Minimum expected number of transmissions as routing cost  Route damping  New route is not evaluated on every route updates  Link failure detection using consecutive packet loss leads to instability  link quality characterization is better  Queuing Policy   Two queues Fair allocation between forwarding and originating traffic  Cycles  Detection vs. loop-free

SP with Threshold

 High threshold (e.g. 70%) fails to form a tree  Works fine in simulation!

 link quality degrades when there is traffic   High threshold leads to network partition Echo the observation made in Zhao’s work  Lower threshold (e.g. 40%) is also problematic  Tree prunes and rebuilds over time when traffic is high

MT

 No predefine threshold is necessary  Captures both reliability and energy cost  Routing cost builds upon individual estimations along the path  Cost = hops + number of expected link retransmissions  if link quality = 100%, MT reduces to normal SP routing

Methodology

 Graph analysis  Network simulation  Assuming packet loss are independent, following Binomial model  Empirical evaluation  On site connectivity vs. distance study   Find minimum transmit power Transitional/gray area starts at average node distance

Findings (I)

 Hop distribution and success rate  longer majority hop-count yields higher success  Hop distribution and distance  Evidence of long links, potentially reliable  Retransmissions are not too effective   MT yields ~80% success rate   packets delivered only experience 1 retransmission along the path A maximum of 2 retransmission per hop can Needs a maximum of 3 per hop to achieve over 90% end-to-end success rate

Findings (II)

 Link failure detection with consecutive packet loss leads to instability  Stability and Congestion  link quality fluctuates at congestion period  creates global instability  BS can hear half the number of neighbors in the network even with a low power setting  MT metrics build upon link estimations are stable  No cycles are detected

Discussions (I)

    Passive snooping   What are the assumptions for this to work?

Estimation takes too long   Can we infer from BER before FEC? (tricky)?

But missing start symbol is the major cause!

Neighborhood management argument  Do you buy it?

Stability  Do we care?

Congestion   How to avoid it?

Scheduled communication?

Discussions (II)

   Can we define a hop?

 One hop neighbor?

 What is the averaged hop distance?

Deployment   What’s the expected hop-count?

What distance or transmit power should we use?

Overhead  Anecdotal setting of route update rate  Can it be adaptive?

Discussion (III)

   Power   No address on power management How does it work with scheduled communication which avoids overhearing?

 Potentially run over low-power listening  What’s used in Great Duck Island DSDV (Yarvis et al. ICPP Workshop 2002)  Different kinds of link estimation and routing cost  Do we need to prevent cycle like DSDV in a relatively static network?

N-to-N Routing?