Prepared By Eda Baykan09.02.2005

SupervisorsJun Luo, Jean-Pierre Hubaux, Jacques Panchard

ROUTING TOWARDS A MOBILE SINK

• Motivation• State Of Art• Our Contribution

Energy Balanced & Link Status Aware Routing• Simulation Results• Future Improvement• Conclusion

OUTLINE

• Sensor networks have many applications areas such as military and environmental monitoring Their popularity increases day by day both in academia and industry. However bottleneck of these low cost sensors is their limited batteries. Furthermore once deployed into the observation area, it is very difficult to recharge them. That’s why making network lifetime longer is an important issue that deserves to be explored.

• Many routing protocols have been proposed to deal with energy conservation problem in sensor networks. However most of them focus on scenario where the sink, node that is interested in getting data from other sensor nodes, is static.

• Luo et al. [5] showed that when the sink is mobile, sensor networks have higher network lifetime.

MOTIVATION

• In this project we investigated how network lifetime can further be improved given the mobility of base station.

• Our routing protocol aims to maximize network lifetime in wireless sensor networks where sources are static and the sink is mobile.

• During the design of the protocol it is assumed that the sink moves in a predefined path in a non-predefined manner. What non-predefined manner means is that, the sink can move with different speeds and delays.

• Data is disseminated throughout the network in many to one manner. As the sink moves to new locations, sources continue transmitting.

• Network life time is taken as the first time when one of the nodes runs out of energy.

• In the mobile sink scenario, problem is to locate the sink and send data to it by multihop routing. Since main aim is to maximize lifetime of the network, our design focuses on reducing the effects that make sensors run out of energy.

OBJECTIVE

• Some recent work such as [1], [2] and [3] has investigated the problem of routing to a mobile sink considering lifetime maximization. These papers propose protocols to minimize number of transmissions.

STATE OF ART

SOUTHERN CALIFORNIA PROPOSAL

• Sink does not make any query.The aim of making the sink silent is to reduce the energy consumed during the broadcast of query.

• When source nodes sense unusual events, they send information to the mobile sink.

• Goodness Value :probability density function that indicates how far the sink is from the node who is in the vicinity of the path of the sink.

• Positive & Negative Reinforcements• Period of sink is divided into time domains• When a node has data to be forwarded, the probability of selecting next

node is determined according to its possibility being on a good path to the sink in that time domain.

• Their assumption is that nodes close to the path of the sink can somehow detect the presence of the sink Not realistic

• How nodes learn period of mobile sink ? Not clear • Definition of good path Open to Discussion

Weaknesses of Southern California Protocol

SOUTHERN CALIFORNIA PROPOSAL

Access Point

Mobile Router Data Sources

•Controlled Mobility•Mobile router goes as close as possible to the source node to collect data

This slide is borrowed from Kansal et al. [2]

UCLA PROPOSAL

• Mobile router has no energy limit because of mobility. • Main aim in [2] is to enable data transmission over fewer hops in order to

reduce the number of packets transmitted. Consequently nodes will consume less energy because of less number of transmission.

• Another benefit of decreasing number of hops is the reduction in the probability of error. When less error occurs during the transmission, number of retransmission will decrease consequently .Nodes can save the energy that would be used for retransmission.

• Source nodes wait to send data until mobile router comes as close as possible to them. The disadvantage is latency introduced with this approach. For real time applications this proposal won’t work well.

UCLA PROPOSAL

Weaknesses of UCLA Protocol

• Link Quality

BERKELEY PROPOSAL

• Quality of links can be determined by capturing link connectivity statistics dynamically.

• With this approach lossy and dynamic nature of wireless sensor networks is taken into consideration in the design of the routing protocol.

• Link quality can be estimated by listening to incoming massages, even the ones that are not sent to them.

• For estimating link quality, window mean exponentially moving average is used. Link estimator computes Packets Received in t

(Packets Expected in t, Packets Received in t) Why estimating link qualities?• To determine which nodes are my neighbors

• Neighborhood table manager decides which nodes to keep in the neighbor table according to the frequency algorithm and reception link quality that is

calculated by link estimator • Link quality changes with time and distance

BERKELEY PROPOSAL

BERKELEY ROUTING PROTOCOL

By incorporating link quality into routing decision, [3] deals with thescenario in which long path with less transmission is better,in terms of energy consumption, than shorter path with many transmissions.

Sequence Number

Number of hops to sink

Mac Address of Parent

Reception Link Quality of Parent

Data

Data Message

Neighboorhood Table

Mac Addess of

Neighbor

Number of hops to sink

Reception Link Quality

Send

Link Quality

Child Flag

COMPARISON OF EXISTING APPROACHES

Storage Requirement•BerkeleyConstant•UCLA No limit •S.C depends on # of time domains

Latency•UCLA Nodes stop transmitting when router goes out of range•Berkeley & S.C continues transmitting as the sink moves

Reliability•Berkeley More weight is given to shortest distance than link quality•UCLATransmission over min # of hops•S.CDoes not deal with reliability

COMPARISON OF EXISTING APPROACHES

Energy Cost & Network Lifetime •BerkeleyLink Quality•UCLAMin # of hops•S.CReinforcements

Overhead•BerkeleyNo overhead•UCLAACK Packets•S.CReinforcements

• Our protocol is based on Berkeley approach because it enables reliability, energy efficiency as well as constant storage space.

• Link estimation for selecting neighbors, neighborhood table management policy for keeping table size constant, considering link quality and distance to sink is extended from Berkeley proposal.

• Adaptation of Berkeley Proposal to mobile sink case. When base station moves to a new location, it transmits HELLO message to the

nodes that are in transmission range of it. By this approach static nodes become aware of the new location of the sink.

• Field # of nodes that are in danger to data messages and routing table. By adding this field into messages, our proposal does not consume additional energy for transmission of remaining energy capacity. Main aim of our routing protocol is to make network lifetime longer by preventing choosing paths that have higher number of nodes that are in danger of running out of energy. We tried to achieve energy balancing among nodes.

• Each node has an initial packet processing capacity. Each time it transmits or receives packet, its capacity decreases by 1.Furthermore capacity of nodes that are in the transmission range decrease by 1 too. When the capacity falls below a predetermined threshold, the node becomes in danger node. After a node becomes in danger, while transmitting hello or data messages it increments the number of nodes in danger. With this approach in a distributed manner nodes can become aware of the remaining energy levels of other nodes in the network.

ENERGY BALANCED & LINK STATUS AWARE ROUTING

ENERGY BALANCED & LINK STATUS AWARE ROUTING

Node_id Number of hops to the sink

Number of nodes that are in danger

SequenceNumber

Number ofhops to thesink

Node_id of

Parent

ReceptionQualityOf Parent

Data Number of nodesthat are in danger

Node_id Number of hopsto the sink

Reception link

quality

Send linkquality

ChildFlag

Number ofnodes that are in danger

Data Message

Hello Message

Neighboorhood Table

• The challenging part is how to choose next node from the neighbor table. • For each neighbor i in the table α X(i) + β Y(i) + γ Z(i) is calculated. The

node that makes above equation minimum is selected as next node to forward data.X(i) = shortest distance from node i to sink.(hop count)Y(i) = Node i’s link qualityZ(i) = Number of in danger nodes on shortest path from sink to node iα = 0.5β = 0.2γ=0.3

• We put more weight on distance to sink by following the approach of Berkeley authors. In order to see whether Link Status and Energy Balanced Routing made network lifetime longer, we did high level simulations.

ENERGY BALANCED & LINK STATUS AWARE ROUTING

• High level simulator written in Matlab. • Binary model in order to define neighbors. In other words, for a specified

transmission range, nodes that are in that range are defined as neighbors. Furthermore because of the difficulty of modeling, we ignored that link quality also varies with time. In our simulations, link quality is only distance dependent. However in our protocol link quality is distance and time varying.

• We ignored also MAC effects during the simulation. • Nodes are deployed within a circle of R = 10 units. They are randomly

scattered as a Poisson Process with density 0.1. Each node sends same number of packets in 1 second. Transmission and reception consumes same energy. If node can not send packet successfully, it will stop trying to send that packet after 3 attempts. During transmission process, nodes that are in transmission range of the sender, neighbors, also consume energy although the massage is not destined to them or transmission was not successful.

Simulations

Simulations

Density of nodes

#of nodes Radius of circle

Transmission range

InitialCapacity

Farmer moves with this angle around the circle

Threshold

BerkeleyNetworkLifetime

OurProposalNetworkLifetime

0.1 20 10 4 100 40 30 151 148

0.1 20 10 4 100 30 30 146 150

0.1 30 10 4 100 40 30 172 160

0.1 30 10 4 100 30 30 203 180

• We expected to get higher results for network lifetime for our proposal. However as it can be seen our results are very close to the Berkeley results. Because of computational power limit of the pc’s on which we worked, we simulated for only 20 and 30 nodes. If we were able to do more simulations, maybe we could get more clarifying results.

• Another issue is, we did simulations only for one weight combination. We gave highest weight to the field distance to sink. It is possible that we could get better results if we assigned different weights in each trial.

• During routing decision we are considering candidate next node’s shortest distance to sink and calculating number of nodes that are in danger on the shortest path. Potential weakness of our protocol arises from this approach. Perhaps next node is not using the shortest path during its forwarding. However we are counting the number of nodes that are in danger on the shortest path. What could be done as a future improvement is to count the danger nodes in the path which next node chooses according to

α X(i) + β Y(i) + γ Z(i)

CONCLUSIONS

• [1] P. Baruah, R. Urgaonkar, and B.Krishnamachari, “Learning Enforced Time Domain Routing to Mobile Sinks in Wireless Sensor Fields,” in Proc. of 1st IEEE EmNetS-I, 2004.

• [2] A. Kansal, A. Somasundara, D. Jea, M. Srivastava, and D. Estrin, "Intelligent Fluid Infrastructure for Embedded Networkings," in Proc. of 2nd ACM MobiSys, 2004.  

• [3] A Woo, T. Tong, D. Culler, “Taming the Underlying Challenges of Reliable Multihop

• Routing in Sensor Networks,” in SenSys’03, 2003• [4] K. Kar, M. Kodialam, T. V. Lakshman, and L. Tassiulas, "Routing for

Network Capacity Maximization in Energy-constrained Ad-hoc Networks" in Proc. of 22nd IEEE INFOCOM, 2003

• [5] J. Luo, J. Hubaux, “Joint Mobility and Routing for Lifetime Elongation in Wireless Sensor Networks,” in IEEE INFOCOM, 2005

REFERENCES

Q &A