The effect of mobility-induced location errors on geographic routing in mobile ad hoc sensor networks: analysis and improvement using mobility prediction
IEEE Transactions on Mobile Computing
Geographic routing has been introduced in mobile ad hoc networks and sensor networks. Under ideal settings, i t has been proved to provide drastic performance improvement over strictly address-centric routing schemes. While geographic routing has been shown to be correct and efficient when location information is accurate, its performance in the face of location errors is not well understood. In this paper, we study the effect of inaccurate location information caused by node mobility under a
... mobility under a rich set of scenarios and mobility models. We identify two main problems, named LLNK and LOOP, that are caused by mobility-induced location errors. Based on analysis via ns -2 simulations, we propose two mobility prediction schemes ---neighbor location prediction (NLP) and destination location prediction (DLP) to mitigate these problems. Simulation results show noticeable improvement under all mobility models used in our study. Under the settings we examine, our schemes achieve up to 27% improvement in packet delivery and 37% reduction in network resource wastage on average without incurring any additional communication or intense computation. Keywords -Location error , Mobility prediction, Mobile Ad hoc networks, Sensor networks Studying the impact of mobility is not only of relevance for mobile ad hoc networks, but also for sensor networks with mobile nodes (e.g. MSN  ). Further, it is important to investigate the impact of realistic mobility patterns. Most previous studies on geographic routing have used the random waypoint mobility model that ignores movement correlation among nodes. In this study we provide the first study to (1) understand the effect of inaccurate location information caused by node mobility on geographic routing protocols under various mobility models, and (2) provide remedies for the identified problems using mobility prediction schemes. We examine the following three main factors that greatly affect t he performance of geographic routing protocols: (a) The freshness of location information: It is not possible to avoid the time gap between the measurement of a location and the time when this information is actually used for a routing decision, in both proactive and reactive routing protocols. This is because of the latency involved in the delivery of location information, and also because the time interval between location updates is generally longer than the inter-packet arrival times. (b) The speed of mobile nodes in the network: Each mobile node can move at a different speed, and the maximum node speed is another critical factor deciding the level of inaccuracy. (c) The mobility pattern of mobile nodes: If the node movement exhibits a different pattern, the effect of node mobility on the geographic routing protocol will be different. Four different mobility models  are adopted in our work: Random waypoint (RWP), Freeway (FWY), Manhattan (MH) and Reference Point Group Mobility (RPGM). Based on the simulation results, two major problem types are identified and discussed in this paper: Lost Link (LLNK) problem and loop in packet delivery (LOOP) problem. The LLNK problem is related to the link connection problem with neighboring nodes, and the LOOP problem is related to the inaccurate location information of destination nodes caused by their mobility. We present two mobility prediction (MP) schemes to address these problems: neighbor location prediction (NLP) and destination location prediction (DLP). We find that the performance of geographic routing is significantly increased with MP without any added communication overhead. We evaluate our proposed schemes through ns-2 simulations of the greedy perimeter stateless routing protocol, GPSR [2, 11] , using the IMPORTANT  mobility tool. The rest of the paper is organized as follows. In section 2, we provide background information regarding GPSR and the mobility models used in our work. In section 3, we discuss the effect of node mobility on geographic routing based on simulation results. In section 4, we identify two mobility-induced problems. In section 5, we introduce mobility prediction schemes and discuss related issues. In section 6, we present results showing performance improvement with mobility prediction. We present concluding comments in section 7.