ELS: Energy-Aware Some-for-Some Location Service for Ad Hoc Mobile Networks Abdelouahid Derhab1 , Nadjib Badache2 , Karim Tari2 , and Sihem Sami2 1

Basic software laboratory, CERIST, Rue des 3 fr`eres Aissou, Ben-Aknoun, BP 143 Algiers, 16030 Algeria 2 LSI, USTHB, BP 32 El-Alia Bab-Ezzouar, 16111, Algiers, Algeria

Abstract. In this paper, we propose a new location service for Ad hoc mobile networks. The network area is divided into non-overlapping zones. Using a hash function, a node identifier is mapped to a set of zones, in which the location information of the node are stored. We also propose a location information distribution scheme that achieves low rate of outdated location information. Using cross-layer design, the service can tolerate servers mobility and failure, and last for a long time period. Simulation Results show that the proposed location service experiences low overhead and high location information availability and accuracy.

1

Introduction

A mobile ad hoc network is a collection of mobile nodes forming a temporary network without any form of centralized administration or predefined infrastructure. Each node acts both as a host and a router. Due to mobility, the network topology changes frequently, which makes the design of a scalable and robust routing protocol with low message overhead one of the challenging tasks in such a network. In recent years, location-awareness is increasingly becoming an important feature of routing protocols and applications. Position-based routing protocols [3,11,9,7,13,18] use the geographic position of nodes available from positioning systems such as GPS [8] or other type of positioning service [1,4,2] to forward data packets. In contrast with topology-based category, they do not need to keep global states for routing data packets. To enable position-based routing, a node must be able to discover the location of the node whom it wants to communicate with. Thus, they have the advantage to scale to a larger number of nodes. Location information are provided by a so-called location service. The use of location services extends to other location-aware applications, e.g., location tracking and navigation, geocasting, or a tour guide that can provide locationdependent information to tourists (such as map, traffic, and site information). The effort needed to search for tourism information can be significantly reduced with the help of positioning. The role of a location service is to map the ID of a node to its geographical position. Each location service performs two basic operations: the location update and the location query. The location update is responsible for distributing X. Cheng, W. Li, and T. Znati (Eds.): WASA 2006, LNCS 4138, pp. 240–251, 2006. c Springer-Verlag Berlin Heidelberg 2006 

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information about the current location of a given node D to a set of nodes called location servers. If a node S wants to know the location of node D, it sends a location query message to one of the location servers of node D. When designing a location service, the following basic questions need to be considered: (1) When should a node update its location information? (2) to whom should a node send its position information? and (3) how should a node find the appropriate servers to query for a location. Ad hoc networks characteristics impose different challenges on designing location services. First, dynamic topology leads that there is no static relation between a node and its location. To distribute location information to a set of servers, the location service utilizes a routing protocol, but the routing protocol requires the location information of these servers in first place, which results in a functional deadlock. Second, the location service must incur low overhead in order that the servers do not drain out their batteries quickly. Third, network partitioning would make the location servers unreachable to some nodes, and hence it considerably decreases the availability and the accuracy of location information. According to Mauve classification [15], the location services are classified according to how many nodes host the service (i.e., some or all nodes). Furthermore, each location server may maintain the position of some specific nodes or all nodes of the network. Thus, the four possible combinations are as follows: some-for-some, some-for-all, all-for-some, and all-for-all. In this paper, our original contributions are the following. First, we propose a location information distribution scheme that achieves a lower cost than the quorum scheme. The proposed scheme is more likely to provide high location information availability and accuracy. Second, we combine the flat hashing-based approach and the proposed scheme to construct an efficient some-for-some location service. Third, we propose a cross-layer framework that helps the service to estimate when a server will run out of battery power or leave its zone. In doing so, the server will seamlessly replicate its stored location information on an alternative node before it disappears from its actual zone, and hence the service lifetime is increased. Fourth, according to application requirements, position information are provided with different levels of accuracy. The rest of the paper is organized as follows: In Section 2, we present an overview of the system framework. A detailed description of the location service is presented in Section 3. Section 4 presents simulation results. Finally, Section 5 concludes the paper.

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System Framework

The architecture of the proposed system framework shown in Fig. 1 consists of four layers: the application layer, the middleware layer, the routing layer and the mac layer. As part of the framework, the routing and the mac layers provide an estimation of the residual node lifetime and the available bandwidth respectively. When a location-aware application such as the tour guide wants to obtain the position of a certain node D, it contacts the location service. This latter will

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check if such a position is available in its own location table. If so, it will directly respond to the application. Otherwise, it will demand the position-based routing protocol to find one of the location servers of D. The forwarding decision at each node is based on the destinations position and the position of the neighboring nodes. Typically, the packet is forwarded to a neighbor that is closer to the destination than the forwarding node itself, thus making progress toward the destination. In order to inform all neighbors within transmission range about its own position, a node transmits beacons at regular intervals. The location servers should respond with the current location of D. On the other hand, when a node S wants to communicate with a node D, it first contacts the routing protocol, which in turn contacts the location service. The location servers of D should respond with an approximative location information, i.e., the geographic area where D is located. In the cross-layer information, the routing layer estimates the residual battery lifetime by recording the different packets that pass through it. It can be used to determine the time at which the location information table will be replicated onto another node, in order to maintain location information availability for a long time period. The available bandwidth estimation provided by the mac layer is used to derive an upper-bound on the replication time.

3 3.1

Energy-Aware Some-for-Some Location Service Basic Idea

The quorum based location services [10,12,6,14] can be configured to operate as a some-for-some approach. Update and query operations are performed on a subset of servers called a write quorum and a read quorum respectively. Using a such construction, we can avoid flooding the network, thus avoid wasting mobile nodes battery power. Furthermore, the load of each location server can be reduced by evenly distributing the load among the servers. These subsets are designed such that each read quorum for a node intersects the write quorum for any other node. An important aspect of quorum-based

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position services is the following trade-off: the larger the quorum sets, the higher the cost for position updates and queries, but also the larger the number of nodes in the intersection of two quorums, which improves resilience against unreachable location servers. Ad hoc networks are subject to frequent network partitioning, link and node failures. In such environments, quorum systems may suffer from low availability in the face of node failures or unreachable nodes. Update and query operations may be performed at non-intersecting quorums, which may disable the quorum system, since the intersection property of that system is not guaranteed. To alleviate the problem of query failures, a set of heuristics is used in selecting servers for update and queries, by maintaining a list of servers that are unreachable [10]. The fundamental property of the quorum system states that ”each read quorum must overlap with any write quorum”. A location information can be written to or read from any randomly chosen quorum. Thus, the quorum systems are only efficient in an environment where the relation between a datum and a node is not considered. The location information are shared private, i.e., they are updated by a unique node (single-writer), and queried by the other nodes (multiple-reader). The existence of one-to-one relation between nodes and their positions, allows us to relax the intersection property and propose a new construction that maps each node i to a fixed subset of nodes, F Si . To illustrate the advantage of the proposed construction, let us consider an example of a quorum system A = {{1, 2, 3}, {1, 4, 5}, {2, 4, 6}, {3, 5, 6}}. In this example, only one member is in the intersection of any pair of quorums. The absence of a single node makes the system inefficient. On the other hand, if we apply the proposed construction to the system A, and consider that the fixed subsets are the quorums of A, (i) we can get the same cost since update and query operations are performed on subsets with the same size, and (ii) the correct information is more likely to be provided since the probability that all nodes of a specific subset are unreachable is lower than that of the quorum construction. The proposed construction is simple and more resilient to node failure and network partitioning than the quorum systems. However, such static membership may result in low data availability and accuracy, because nodes drain out of power very quickly. Thus, the disadvantages of quorum system still remains. To deal with the static feature of the proposed construction, we define the fixed subset F S as a set geographic zones. In each zone, a node is dynamically selected as a location server. 3.2

Area Partitioning

The area covered by the ad hoc network is partitioned into G square zones of equal-sizes. All of these zones have well-known identifiers (IDs) distributed over the range [0, · · · , G − 1]. We assume that there exists a static function f that maps a node’s ID into a specific zone. Formally, f (node ID) → zone ID. This many-to-one mapping is also known by all nodes in the network. Figure 2 shows the partitioning scheme. The respective zones’ identifier are shown in the upperleft corner of each zone. Each node is assigned a fixed subset consisting of α

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Algorithm 1. The fixed subset construction for node A 1: 2: 3: 4: 5: 6:

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zones, where α is a system parameter upper-bounded by G. The construction of the fixed subset for a node A is presented in Algorithm 1, where UA denotes the set of zones’ identifier. An example of a function f is: f (ID) = (ID%G). For k = 0, nodes that return the same value of f () are assigned to the same fixed subset. 3.3

Location Server Selection

In the flat-based location services [19,17,5], a well-known hash function is used to map each node’s identifier to a home region. All nodes in the home region maintain the location information for the node and reply to queries for that node. A major disadvantage of this design is the single fixed home region. Absence of a server from the region makes the service carries out complex actions like storing the location information in the neighboring regions. Unlike the other flat-based location services, a unique node from the zone is selected as a location server. We propose the stability of a node as a metric for the location server selection criterion. The metric predicts the time period during which the node will remain in its zone. It is based on the residual battery power and the current positions of the node. A node disappears from its zone either: (i) if it moves out of its current zone, or (ii) or it drains out of its energy power. Each node i es-

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timates its stability as follows: First, it calculates the rate of its battery power depletion Ri for every time period T , then it estimates , as shown in equation 1, the residual time before it runs out of energy power such that Ei denotes i’s residual battery power. Ei (s) (1) T powi = max (Ri ) Second, it calculates the remaining time before it moves out of its current zone, T mobi . min (dN (i), dS(i), dE(i), dW (i)) (2) T mobi = V maxi Where dN (i), dS(i), dE(i), and dw(i) are the distances that separate node i from the north, south, east, west sides of its current zone, and V maxi is the node i’s maximum velocity. The respective distances are depicted in Fig. 2. Equation 2 derives a conservative estimate on T mobi . It assumes that the node is moving toward the nearest side. Finally, the stability of node i ξi is given by the following equation. ξi = min (T powi , T mobi ) (3) Initially, the node with the highest value of stability will be selected as the location server of its zone. Ties are broken by comparing node identifiers. The fixed subsets construction, the flat-based approach, and the location server selection procedure leads to the creation of G servers. We assume that G < n. Each server node stores the location information of ( α×n G ) nodes. As α (G ) < 1, the proposed location service can be classified as a some-for-some approach. 3.4

Service Operations

Nodes perform two types of operations: update and query. Each operation has its corresponding response, ack for update and reply for query. Each operation is transformed into a message. Each location server holds a location table, which records for each stored node the following fields: (1) its id, (2) the zone id where that node is in, (3) its exact location, and (4) its tiemstamp(i.e. the latest update time known by the location server). Location Update. When a node i moves out of its current zone and into a new one, it sends an update message toward the center of each zone ∈ F Si . The update packet contains the following information: < src id , seq, src region, target region, new region, new position, new timestamp >.

The pair (src id, seq) denotes the source node and the sequence number of the packet. It uniquely identifies the update packet. src region is the id of the source node’s zone and target region is the server node’s zone. new region and new position is the node’s new region and new position respectively. The update packet includes the id of its new zone and the new timestamp. the new timestamp is obtained by increasing the current timestamp by one.

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When the packet reaches a node in the target zone, two cases can occur. If that node has a cached route to the location server, the packet is immediately routed toward the server. Otherwise, a route discovery packet is broadcasted in the zone to find a route to the location server. Upon receiving the update packet, the location server sends back an ack packet to the source node. Location Query. According to users and applications requirements, query packets can be sent with two levels of accuracy: high, and low: – High accuracy: The exact position of the requested node is needed by the location-aware applications. – Low accuracy: It is required by position-based routing protocols, that aims to deliver data packets to destination nodes, and not to know the exact location of those nodes. A source node S wishing to obtain the position of a node D, sends a query packet toward the center of each zone ∈ F SD . The query packet contains the following information: < src id , seq, src region, target region, queried id , accuracy >. queried id is the id of the node whose location is being queried. accuracy indicates the accuracy level required. A node inside a target zone which receives the query packet, either it uses a cached route to the location server or it broadcasts a route discovery packet to all nodes within the zone so that a route toward the location server is established, and then it routes the packet to the server. Upon receiving the query packet, the location server will first check the accuracy field of the packet. If the accuracy required is low, it sends back to S a reply packet containing D’s zone along with the timestamp. Node S chooses among the reply packets that of the largest timestamp (i.e., the most recent location information). If the accuracy field is high, the location server will send the query packet to the target node D. D sends back a reply packet containing D’s location to node S. Any further query packets with the same source node and sequence number will be discarded by node D. 3.5

Unreachable Zones

If a node i fails to contact the server of a certain zone, it considers that the zone is unreachable. A zone is called unreachable if either it is empty or the location server in that zone is unreachable due to network partitioning. To avoid unnecessary message overhead, each node keeps a set, called the Unreachable zone list (UZL). This latter includes the zones’ ID which are unreachable. When a given zone is declared as unreachable, it will be added to Unreachable zone list for a period of time T . During that period, update and query packets are sent to F Si − U ZLi . After the expiration of T , the zone in question will be removed from that set. 3.6

Handling Mobility and Failure of Location Servers

When a location server becomes no longer available, queries that arrive between the time that the server is unavailable and the next updates from nodes whose

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locations are stored at the server will fail. To address this issue, the location server node that is about to cross the boundary of its zone or it is about to run out of battery power, has to replicate the location information table onto a new node before it leaves the zone or it dies. The service replication is triggered when the following constraint is verified: (ξi − ΔT )
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In this section, we study the performance of the proposed service using GloMoSim simulator [21]. The entire region is a square with 2000 m. All nodes have a transmission range of 250 m. They move according the waypoint mobility model. In this model, a node randomly selects a location and moves toward it with a constant speed uniformly distributed between zero and a maximum speed Vmax = 7 m/s , then it stays stationary during a pause time of 1 second before moving to a new random location. Initially, each mobile node has a battery capacity of 400 joules. We have implemented three location services, which are (1) the quorum-based location service with unreachable node list and hybrid construction [10] that uses AODV routing protocol [16], (2) DLM [20], and (3) our proposed energy-aware location service (ELS). We have also implemented GPSR [9] with perimeter as a routing protocol. Nodes periodically initiate queries with different levels to random destinations. The following metrics are evaluated for the location service protocols: 1. Update cost: The total number of hops traversed by update packets. 2. Query cost: The total number of hops traversed by query packets. 3. Availability: It indicates the ability to access location information when needed. If Ns denotes the number of successful attempts to access a location information, and Na is the total number of attempts. The availability Ns is defined to be: N . a 4. Accuracy: If Nq denotes the number of query operations. and No the number of outdated values returned by those queries. The accuracy is defined to be: (Nq −No ) . Nq 5. Service lifetime: The time until all the location servers run out of power.

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All of our simulations are conducted without any data traffic, which discard the factors affecting node lifetime, and hence allows us to better judge the performance of services. To study the performance of the service for network scalability, we have varied the total number of nodes in the network. Figure 3(a) and figure 3(b) show the location query cost and the location update cost respectively. Hybrid quorum has the best results. However, these results are misleading because as topology changes frequently, more nodes will be added to the set of unreachable nodes and hence, query and update packets will be sent to a fewer number of nodes. The quorum construction fails to efficiently spread the location information in the network. In contrast to hybrid quorum, the targets of query and update packets in DLM and ELS do not consist of moving nodes but of regions with fixed positions making the scheme more robust to node mobility. ELS performs better than DLM since packets are routed directly toward the target zones. In DLM packets must follow the defined hierarchical order, which results that packets take long routes in terms of number of hops. Figure 4(a) and figure 4(b) show the availability and accuracy of the three location services. Availability and accuracy increase as density increase in all services, because the network gets more connected, and hence nodes are likely to successfully deliver their query and update packets to the target location servers. ELS shows the best rates of availability and accuracy. This is due to the fact that packets are sent toward location servers locating in different zones. A node can obtain location information of a queried node following the response of one server. Second, as we will see in Fig. 5, ELS lasts for a long time period because the servers replicates their location tables onto a new node before they leave their zones or run out of power. So, the location information is more likely to be available and be correct. In contrast to ELS, DLM must access a chain of location servers in a certain order. If any single server in this chain is unreachable, the query operation fails. Moreover, a server in DLM does not hand over the location information to another server before it leaves its region, and does not handle the depletion of node’s power, which increase the probability of query failures. Figure 5 shows the service lifetime experienced by the location services. Hybrid quorum has the best results. However, these results are misleading since as we have seen in Fig. 3, hybrid quorum does not efficiently spread location information in the network, and as topology changes, more nodes will be added to the set of unreachable nodes resulting in less messages generation. ELS performs better than DLM because the location servers replicate their location tables onto a new elected location server before they disappear from their zones, which considerably increases the lifetime of ELS.

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In this paper, we have presented the Energy-aware Some-for-some Location Service (ELS). ELS combines a simple location information distribution scheme and a flat-based hash function to select zones where location information will be stored. ELS is suitable for different application requirements. Updates and query

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packets are sent toward fixed zones, which permits to avoid tracking the location servers when they change their zones or die. A new server is elected when the current server is about to leave the zone or run out of power. This technique significantly increases the service lifetime. The simulation results show the effectiveness of the proposed scheme. The proposed location service experiences high location information availability and accuracy, high service lifetime and it incurs low cost.

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