School of Electrical Engineering, University of Ulsan, Ulsan 680-749, Republic of Korea
Copyright © 2014 Trung Kien Vu and Sungoh Kwon. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We propose a mobility-assisted on-demand routing algorithm for mobile ad hoc networks in the presence of location errors. Location awareness enables mobile nodes to predict their mobility and enhances routing performance by estimating link duration and selecting reliable routes. However, measured locations intrinsically include errors in measurement. Such errors degrade mobility prediction and have been ignored in previous work. To mitigate the impact of location errors on routing, we propose an on-demand routing algorithm taking into account location errors. To that end, we adopt the Kalman filter to estimate accurate locations and consider route confidence in discovering routes. Via simulations, we compare our algorithm and previous algorithms in various environments. Our proposed mobility prediction is robust to the location errors.
A mobile ad hoc network (MANET)  consists of a set of wireless mobile nodes that dynamically exchange data among themselves without relying on any fixed infrastructure. Because of their easy deployment and extension, MANET application scenarios include emergency and rescue operations, conference settings, car networks, and personal networking. Due to limited transmission ranges and infrastructure-free networks, each node in such networks has the responsibility not only to discover new routes but also to relay messages.
The most challengeable problem of MANETs  is how to adapt the topology changing that affects the performance of the network [3, 4]. Due to changeable topology, routes from sources to destinations may be suddenly broken and nodes have to discover other available routes to deliver data. The ad hoc on-demand distance vector routing algorithm (AODV) was proposed as a reactive routing algorithm to allow mobile nodes to quickly adapt to topology changes and link breaks in mobile ad hoc networks . To find a possible route, the AODV makes a source flood a routing request message over the network and discovers a route based on the principle of the shortest path. The amount of overhead messages for route discovery and route maintenance depends on the longevity of routing paths. The awareness of link and path durations can improve routing performance in such mobile networks [6–8].
In [9, 10], the authors modeled the distribution of path duration and analyzed the relation between path duration and other factors such as relative speed, transmission range, and number of hops. Their analysis shows that routing protocol with higher path duration can improve the network performance. In , the authors also investigate the distribution of path duration and then design a scheme to select a route with the largest expected duration and provide reliable network services in MANETs.
Location information enables nodes to predict mobility and estimate path durations more accurately. In [12–14], the authors proposed schemes to improve routing performance with location awareness. The proposed algorithms in [12, 13] anticipate the link expiration time (LET) based on measured locations and velocities and were applied to routing protocols to reduce overheads in  or to select the most reliable route that has the longest path duration . In , the link duration time is adaptively applied to route maintenance in order to reduce unnecessary overhead. However, lifetime of link may be incorrectly calculated due to location errors that lead to incorrect hello frequency setting.
In practical deployment scenarios, location errors intrinsically occur in measurement , even if locations are measured by the global positioning system (GPS) receiver. Such imperfect location information leads imperfect mobility prediction, which results in performance degradation. However, the previous work assumed error-free location information and developed routing algorithms. In , the impact of location errors on routing performance was provided only by simulations, but there is no effort to improve routing performance in such noisy information environments. Therefore, it is necessary to develop an efficient routing that is robust to location errors.
In this paper, we proposed a mobility-assisted on-demand routing algorithm in the presence of location errors in order to mitigate the impact of location errors on routing performance. To that end, the algorithm adopts the Kalman filter to compensate for the measurement location errors and estimate link durations to reduce overheads and select reliable routes. We also consider the confidence level of route in selecting the best route. Via simulations, we compare our proposed algorithm with previous algorithms.
The rest of this paper is organized as follows. In Section 2, we describe the system model and problem. In Section 3, we propose the Kalman filter based routing algorithm with mobility prediction for location correction and route selection. In Section 4, we provide numerical results to analyze the impact of location errors and the efficient of our proposal in the presence of location errors, and we conclude the paper in Section 5.
2. System Model and Problem
2.1. System Model
In this paper, we consider a mobile wireless network that supports multihop routing. The network is modeled as a set of mobile nodes with transmission range and a set of communication links between nodes and in .
Link is called valid or connected link at time when the distance between nodes and at time is less than or equal to the transmission range ; that is, where and are locations of nodes and , respectively, and stands for a Euclidian distance of vector . Otherwise, link is considered broken or disconnected, because the two nodes are out of their communication range. The link duration of link is defined as the time interval for which the link is valid.
Due to a limited transmission range, packets are delivered from a source to a destination in a multihop manner via a route, which is defined as a set of links. For given source and destination nodes, and , respectively, possible routes at time are denoted as for , which consists of links.
To find a route from a source to a destination and maintain routes, each mobile node employs the AODV routing algorithm, which is one of the reactive routing protocols and frequently adopted in mobile ad hoc networks.
2.2. Overview of AODV
The AODV  routing algorithm consists of two main operations: route discovery and route maintenance. Route discovery is initiated by a source node that has data to send a destination node and does not have an active route in its routing table. To find a valid route to the destination, the source node broadcasts a route request (RREQ) message, including a sequence number, to neighboring nodes. The RREQ message is flooded through the entire network until the message reaches the destination or an intermediate node that has a valid route to the destination. Each node that receives the RREQ message stores a reverse route to the source and then broadcasts the message to their neighboring nodes if the node is not the destination and the RREQ message is not a duplicate. When the RREQ message arrives at a destination node or at an intermediate node that has a valid route to the destination, the node sends a route reply (RREP) message to the neighboring node in a reverse route in a unicast manner. The RREP message contains the number of hops to reach the destination node and the sequence number for the destination. A node receiving the RREP message sends this message to the source via the stored reverse route and then creates or updates a forward route to the destination.
Route maintenance is performed by nodes after route discovery operation, in order to maintain local connectivity and routes. Nodes periodically send a hello message to their neighbors to check if links are connected. If a node does not receive any hello message from its neighbors during a certain time period, referred to as the lifetime of hello message, the node assumes that the link to the neighbor is currently disconnected and reports the link failure to the source corresponding to the link via a route error (RRER) message.
2.3. Location Awareness and Performance Enhancement
In a mobile ad hoc network, the location information of nodes helps to improve routing performance, such as packet delivery rate and overhead by estimating node mobility. In a route discovery operation, the route with the longest lifetime can be selected to reduce the number of transmission failures and the number of overheads to find a new route . To reduce overhead messages, instead of a fixed period for hello message, the adaptive period is proposed using link lifetime to achieve high protocol efficiency in .
To predict mobility, the previous work proposed a location prediction scheme , which is defined as where , , and are the predicted location of node at time , a measured location at time , and a measured velocity at time , respectively. If individual velocities of nodes are not available in (2), the nodes can approximately estimate their velocities using the previously stored location information  as follows. For , the velocity of node at time is approximately expressed as
Based on the mobility prediction, nodes estimate link durations corresponding to adjacent nodes, and destination nodes choose the longest lifetime route among candidates. Since a link between two nodes is connected only if the distance between the two nodes is less than or equal to their transmission range, the estimated link duration between nodes and is defined as where is the estimated distance between nodes and elapsed time from current time . A route consists of ordered links and is disconnected if one of the links is broken. Hence, the route expiration time of a route between nodes and is expressed as for . The most reliable route can be chosen among candidate routes based on (6).
2.4. Location Errors and Estimation Problem
In practice, location errors inevitably exist in measurement. However, in previous work, mobility prediction used perfect location information receiving from the GPS devices or other techniques [16, 17]. The imperfect location information induces erroneous mobility estimate, which results in performance degradation.
For example, let and be the real location and the measured location of node at time . Then, based on measured locations and of nodes and , respectively, after elapsed time from time , the estimated distance between the two nodes is less than the transmission range and the link between two nodes is considered connected, even though node locates out of the transmission range of node ; that is, the communication link between two nodes is disconnected, as shown in Figure 1. Hence, we propose a routing algorithm in the presence of location errors in measurement to mitigate the impact of imperfect location information.
Figure 1: Estimated link duration.
3. Proposed Algorithm
In this section, we proposed an on-demand routing algorithm robust to location errors with mobility prediction. In MANETs, the mobility prediction plays a great role in predicting the link lifetime and the route lifetime, which can reduce overhead messages and improve routing performance . However, as shown in Figure 1, location errors in measurement provide an incorrect mobility prediction, which induces wrong decision for routing. To mitigate the impact of such errors on mobility prediction and routing decision, we adopt two schemes: location error correction and route confidence.
3.1. Location Correction and Mobility Prediction
We employ the discrete Kalman filter, which is a set of recursive mathematical equations and supports the estimation of states in such way that minimizes the variance of estimation errors. The recent updates with previous measured location compensate current location for measurement errors. In this paper, the process errors are ignored and the main focus is the measurement errors. A detail of the discrete Kalman filter is presented in .
From (2), the current or future location depends on the previous location. The location errors are defined as the difference between the actual location and the measurement location. Let be the location errors at node , which is the additive noise; then, the measurement location of node at time
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