1. Prepared By Eda Baykan 09.02.2005 Supervisors Jun Luo, Jean-Pierre Hubaux, Jacques Panchard ROUTING TOWARDS A MOBILE SINK

2. Motivation State Of Art Our Contribution Energy Balanced & Link Status Aware Routing Simulation Results Future Improvement Conclusion OUTLINE

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. Link Quality BERKELEY PROPOSAL

10. 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

11. BERKELEY ROUTING PROTOCOL By incorporating link quality into routing decision, [3] deals with the scenario 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

12. COMPARISON OF EXISTING APPROACHES Storage Requirement Berkeley  Constant 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 UCLA  Transmission over min # of hops S.C  Does not deal with reliability

13. COMPARISON OF EXISTING APPROACHES Energy Cost & Network Lifetime Berkeley  Link Quality UCLA  Min # of hops S.C  Reinforcements Overhead Berkeley  No overhead UCLA  ACK Packets S.C  Reinforcements

14. 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

15. Node_idNumber of hops to the sink Number of nodes that are in danger Sequence Number Number of hops to the sink Node_id of Parent Reception Quality Of Parent DataNumber of nodes that are in danger Node_idNumber of hops to the sink Reception link quality Send link quality Child Flag Number of nodes that are in danger Data Message Hello Message Neighboorhood Table

16. 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 quality Z(i) = Number of in danger nodes on shortest path from sink to node i α = 0.5 β = 0.2 γ=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

17. 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

24. Density of nodes #of nodesRadius of circle Transmission range Initial Capacity Farmer moves with this angle around the circle Thres hold Berkeley Network Lifetime Our Proposal Network Lifetime 0.1201041004030151148 0.12010410030 146150 0.1301041004030172160 0.13010410030 203180

25. 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

26. [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

27. Q &A