Mobility Prediction Scheme Research Articles

Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph

Predicting vehicle mobility is crucial in domains such as ride-hailing, where the balance between offer and demand is paramount. Since city road networks can be easily represented as graphs, recent works have exploited graph neural networks (GNNs) to produce more accurate predictions on real traffic data. However, a better understanding of the characteristics and limitations of this approach is needed. In this work, we compare several GNN aggregated mobility prediction schemes to a selection of other approaches in a very restricted and controlled simulation scenario. The city graph employed represents roads as directed edges and road intersections as nodes. Individual vehicle mobility is modeled as transitions between nodes in the graph. A time series of aggregated mobility is computed by counting vehicles in each node at any given time. Three main approaches are employed to construct the aggregated mobility predictors. First, the behavior of the moving individuals is assumed to follow a Markov chain (MC) model whose transition matrix is inferred via a least squares estimation procedure; the recurrent application of this MC provides the aggregated mobility prediction values. Second, a multilayer perceptron (MLP) is trained so that—given the node occupation at a given time—it can recursively provide predictions for the next values of the time series. Third, we train a GNN (according to the city graph) with the time series data via a supervised learning formulation that computes—through an embedding construction for each node in the graph—the aggregated mobility predictions. Some mobility patterns are simulated in the city to generate different time series for testing purposes. The proposed schemes are comparatively assessed compared to different baseline prediction procedures. The comparison illustrates several limitations of the GNN approaches in the selected scenario and uncovers future lines of investigation.

Human Behavior-Based Mobility Models in Mobile Wireless Networks: a Literature Survey

Recent advancement in processing power, mobile wireless networks enabled technology are leading to more researches and studies, investigating mobility prediction in wireless network environments. User mobility behavior knowledge is a key element to understand the network traffic behavior in today mobile applications, including context-aware advertising and city wide sensing applications. We extend our previous work that was dedicated to mobility prediction schemes, in order to cover a correlated research topic: study of the user mobility behavior.

Swarm-Based and Energy-Aware Unmanned Aerial Vehicle System for Video Delivery of Mobile Objects

Floods are the most common disaster in the world and cause deaths, damages to houses, buildings, and possessions, as well as disruption to communications. In such dynamic scenarios, it is needed to search and track mobile objects transported by the water flows, such as humans, animals, vehicles, and debris. The use of Unmanned Aerial Vehicles (UAVs) is essential in disaster scenarios to help first responders determine correct procedures in terms of searching, tracking, and rescuing the victims, as well as in defining the actions to minimize the risks in a sustainable and timely manner. However, the tracking of mobile objects and the delivery of real-time video sequences from UAVs with energy constraints toward first responders is still a hard task. Therefore, it is necessary to orchestrate a swarm of UAVs for searching and tracking objects while reacting fast to frequent changes in the topology and trajectory, as well as distributing real-time video flows with quality level support to the ground team. This article proposes an energy-aware swarm-based and mobility prediction scheme for UAVs, called SUAV. SUAV provides a unique UAV-based system for rescue scenarios with mobile objects and with support for route and path planning, searching and tracking procedures, mobility prediction and multi-hop communication management, video delivery with Quality of Service (QoS)/Quality of Experience (QoE) support, and energy-efficiency. Simulation results show the efficiency of SUAV in transmitting real-time video flows with QoS/QoE support while minimizing the energy consumption in flooding scenarios with mobile targets.

A Distributed Mode Reassignment Scheme for Mobile Multihop Wireless Network of FDD Nodes

The use of frequency division duplex (FDD) communication in a multihop wireless network partitions the nodes in two operating modes. Since the FDD nodes of the same mode cannot communicate with each other, it can limit the node connectivity and also lead to network partitioning. In this article, a novel, distributed, low-complexity mode reassignment (MR) scheme is designed for maintaining connectivity among the mobile FDD nodes in a decentralized, multihop wireless (airborne) network. Since it is not practical to know the complete topology of such a network in real time, the proposed scheme is fully distributed, relies only on the local node information, and applied only on those nodes whose connectivity drops below a threshold. Besides maintaining a desired value of connectivity degree for each node while it is moving around, the proposed MR scheme also minimizes the impact of mode change on the existing links and ongoing data transmissions. A Kalman filter based node mobility prediction and link failure prediction scheme is designed to select the best candidate nodes for mode change. The optimality and error control bounds of the proposed MR scheme show that it consistently achieves optimal connectivity for most nodes, while requiring a low number of nodes undergoing the mode change during each MR round.

Efficient Caching and Mobility Adaptive Popular Data Access in Mobile Ad Hoc Networks

Background: Caching technique is widely used to cope up with the resource constraints and to improve the data accessibility in MANETs. The key constraints are reducing the data access time, overhead and energy cost. Methods: This work proposes a Popularity-driven Caching and Mobility adaptive query Searching (PCMS) to achieve the constraints. The PCMS consists of Cache Node selection, popularity and data size driven replacement (POP) algorithm and mobility adaptive searching for cache data. With the aim of reducing the access delay of popular data, the PCMS selects a node that is closer to the destination in a route to cache the frequently requested data item and serves the future requests. The PCMS takes into account the data popularity and size to decide caching and replacement policy and thus significantly improves the performance of caching. The mobility adaptive searching for cache data looks for an item in Query Directory (QD) nodes and redirects the request to the concerned caching node. It updates the cache information at QD by adhering to the mobility prediction scheme. Findings: The PCMS considers the data popularity and size as key factors to decide caching and replacement policy for the better utilization of cache memory. The PCMS has successfully reduced the network traffic and improves the Cache Hit Ratio by adhering the mobility prediction based update scheme. Applications/Improvements: The proposed system achieves appreciably better performance in simulation than the existing caching systems by utilizing minimum battery power, bandwidth and access delay.

HYBRID MOBILITY PREDICTION OF 802.11 INFRASTRUCTURE NODES BY LOCATION TRACKING AND DATA MINING

In an IEEE 802.11 Infrastructure network, as the mobile node is moving from one access point to another, the resource allocation and smooth hand off may be a problem. If some reliable prediction is done on mobile node’s next move, then resources can be allocated optimally as the mobile node moves around. This would increase the performance throughput of wireless network. We plan to investigate on a hybrid mobility prediction scheme that uses location tracking and data mining to predict the future path of the mobile node. We also propose a secure version of the same scheme. Through simulation and analysis, we present the prediction accuracy of our proposal.

A Destination and Mobility Path Prediction Scheme for Mobile Networks

Mobile multimedia services are gaining great momentum among subscribers of mobile networks (MNs). An understanding of the network traffic behavior is essential in the evolution of today's MNs and, thus, leads to more efficient planning and management of the network's scarce bandwidth resources. The communication efficiency can be largely improved (i.e., optimizing the allocation of the network's limited resources and sustaining a desirable quality of service) if the network anticipates the needs of its users on the move and, thus, performs reservation of radio resources at cells along the path to the destination. In this vein, we propose a mobility prediction scheme for MNs; more specifically, we first apply probability and Dempster–Shafer processes for predicting the likelihood of the next destination, for an arbitrary user in an MN, based on the user's habits (e.g., frequently visited locations). Then, at each road junction, we apply the second-order Markov chain process for predicting the likelihood of the next road segment transition, given the path from the trip origin to that specific road junction and the direction to the destination. We evaluate our proposed scheme using real-life mobility traces; the simulation results show that the proposed scheme outperforms traditional schemes.

Support Vector Machine Based Mobility Prediction Scheme in Heterogeneous Wireless Networks

To improve the intelligence of the mobile-aware applications in the heterogeneous wireless networks (HetNets), it is essential to establish an advanced mechanism to anticipate the change of the user location in every subnet for HetNets. This paper proposes a multiclass support vector machine based mobility prediction (Multi-SVMMP) scheme to estimate the future location of mobile users according to the movement history information of each user in HetNets. In the location prediction process, the regular and random user movement patterns are treated differently, which can reflect the user movements more realistically than the existing movement models in HetNets. And different forms of multiclass support vector machines are embedded in the two mobility patterns according to the different characteristics of the two mobility patterns. Moreover, the introduction of target region (TR) cuts down the energy consumption efficiently without impacting the prediction accuracy. As reported in the simulations, our Multi-SVMMP can overcome the difficulties found in the traditional methods and obtain a higher prediction accuracy and user adaptability while reducing the cost of prediction resources.

SVM-based spectrum mobility prediction scheme in mobile cognitive radio networks.

Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements.

An enhanced clustering algorithm for mobile ad hoc networks to improve the throughput

Mobile ad hoc networks (MANETs) consist of a number of wireless hosts that forms network without a fixed infrastructure. Clustering plays a vital role for MANETs in managing the resources. Clustering involves two processes such as cluster head election and cluster maintenance. Since cluster head ensures the network performance, it is important to elect the desired cluster heads. In this paper, an enhanced weight-based clustering algorithm using a mobility prediction scheme (EWCAMP) for finding the desired cluster head is proposed. In the cluster maintaining phase the proposed algorithm defines the admission criteria for newly arriving nodes, which helps the nodes in joining the correct cluster. The goals of the proposed algorithm are reducing the number of clusters and re-affiliation of nodes. We have evaluated the performance of the proposed algorithm using network simulator (NS2). The simulation results demonstrate the superior performance of the proposed algorithm.

Behavior-based mobility prediction for seamless handoffs in mobile wireless networks

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms.

Security in service-oriented vehicular networks

Service-oriented vehicular networks support diverse infrastructure-based commercial services including Internet access, real-time traffic concerns, video streaming, and content distribution. The success of service delivery in vehicular networks depends on the underlying communication system to enable the user devices to connect to a large number of communicating peers and even to the Internet. This poses many new research challenges, especially in the aspects of security, user privacy, and billing. In this article we first identify the key requirements of authentication, privacy preservation, and billing for service delivery in vehicular networks. We then review the existing industrial and academic efforts on service- oriented vehicular networks. We also point out two security challenges, minimizing vehicleto- infrastructure authentication latency and distributed public key revocation, which are considered among the most challenging design objectives in service-oriented vehicular networks. A novel fast vehicle-to-infrastructure authentication based on a vehicle mobility prediction scheme and an infrastructure-based short-time certificate management scheme are then proposed to address these two challenges.

A Mobility-Prediction-Based Relay Deployment Framework for Conserving Power in MANETs

There has been a growing interest in designing mobile systems consisting of special relay nodes whose mobility can be controlled by the underlying network. In this paper, we consider the design of a heterogeneous mobile ad hoc network (MANET) consisting of two kinds of mobile nodes-traditional nodes with limited energy and a few controllable mobile relay nodes with relatively abundant energy resources. We propose a novel relay deployment framework that utilizes mobility prediction and works in tandem with the underlying MANET routing protocol to optimally define the movement of the relay nodes. We present two instances of the relay deployment problem, together with the solutions, to achieve different goals. Instance 1, termed Min-Total, aims to minimize the total energy consumed across all the traditional nodes during data transmission, while instance 2, termed Min-Max, aims to minimize the maximum energy consumed by a traditional node during data transmission. Our solutions also enable the prioritization of individual nodes in the network based on residual energy profiles and contextual significance. We perform an extensive simulation study to understand the trade-offs involved in deploying an increasing fraction of such relay nodes in the network. We also investigate the performance of the proposed framework under different mobility prediction schemes. Results indicate that even when the relay nodes constitute a small fraction of the total nodes in the network, the proposed framework results in significant energy savings. Further, we observed that while both the schemes have their potential advantages, the differences between the two optimization schemes are clearly highlighted in a sparse network.

Link quality prediction in mesh networks

Wireless self-organizing networks such as mesh networks strive hard to get rid of mobility and radio propagation effects. Links – the basic elements ensuring connectivity in wireless networks – are impacted first from them. But what happens if one could mitigate these effects by forecasting the links’ future states? In this paper, we propose XCoPred (using Cross-Correlation to Predict), a pattern matching based scheme to predict link quality variations. XCoPred does not require the use of any external hardware, it relies simply on Signal to Noise Ratio (SNR) measurements (that can be obtained from any wireless interface) as a quality measure. The nodes monitor and store the links’ SNR values to their neighbors in order to obtain a time series of SNR measurements. When a prediction on the future state of a link is required, the node looks for similar SNR patterns to the current situation in the past (time series) using a cross-correlation function. The matches found are then used as a base for the prediction. Clearly, XCoPred takes advantage of the occurrence and recurrence of patterns observed in SNR measures reflecting the joint effect of human motion and radio propagation. XCoPred focuses only on the scale of links and as such is complementary to mobility prediction schemes, which target prediction at a broader scale. We first prove the occurrence of SNR patterns resulted by the joint effect of human motion and radio propagation. Then we evaluate XCoPred in an indoor mesh network showing, that XCoPred is able to recognize mobility patterns in up to 85% of the cases correctly and the average prediction error on mid-term predictions (i.e., assessing the future link quality more than 1 min ahead) is less than half the error we get using linear prediction. Eventually, we propose and evaluate an enhanced handoff management scheme for 802.11 mesh networks showing the usefulness of XCoPred as a cross-layer input.

Prediction Schemes to Enhance the Routing Process in Geographical GPSR Ad Hoc Protocol

Geographical routing protocols have received a serious attention due to more advantages they have in comparison to the conventional routing protocols. They require information about the physical position of nodes needed to be available. Commonly, each node determines its own position through the use of Global Positioning System (GPS) or some other type of positioning service. Greedy Perimeter Stateless Routing (GPSR) protocol, which is one of geographical routing protocols, limits the forwarding decision of the packet based on the node′s own position, the destination′s position and the position of the forwarding node′s neighbors. Location information has some inaccuracy depending on the localization system and the environment exists in. This paper aims to study the impact of mobility metrics (beacon interval, and node speed) on introducing location information error in GPSR protocol using different mobility models. The effect of these metrics is identified in GPSR as Neighbor Break Link (NBL) problem. Based on simulation analysis, mobility prediction schemes are proposed to migrate the observed problem.

A cluster based mobility prediction scheme for ad hoc networks

The ad hoc networks are completely autonomous wireless networks where all the users are mobile. These networks do not work on any infrastructure and the mobiles communicate either directly or via other nodes of the network by establishing routes. These routes are prone to frequent ruptures because of nodes mobility. If the future movement of the mobile can be predicted in a precise way, the resources reservation can be made before be asked, which enables the network to provide a better QoS. In this aim, we propose a virtual dynamic topology, which on one hand, will organize the network as well as possible and decreases the impact of mobility, and on the other hand, is oriented user mobility prediction. Our prediction scheme uses the evidence theory of Dempster–Shafer in order to predict the future position of the mobile by basing itself on relevant criteria. These ones are related to mobility and network operation optimisation. The proposed scheme is flexible and can be extended to a general framework. To show the relevance of our scheme, we combine it with a routing protocol. Then, we implemented the prediction-oriented topology and the prediction scheme which performs on it. We implemented also a mobility prediction based routing protocol. Simulations are made according to a set of elaborate scenarios.

Performance Analysis of Call Admission Control in WCDMA Systems with Mobility based Interference Margin Reservation

This study presents performance of call admission control and resource reservation schemes based on the mobility of the users in WCDMA cellular systems. In order to guarantee the handoff dropping probability, the mobility of the user is predicted based on a realistic mobility model. The mobility prediction scheme used in this study is used to estimate the set of candidate cells into which the mobile may move in the near future and calculates the likeliness value for each candidate cell. It also estimates the time slot at which the mobile may enter into the candidate cells based on the distance between the current location of the mobile and the candidate cell center distance. Based on the mobility prediction, resource is reserved in terms of Interference Guard margin (IGM) to guarantee some target handoff dropping probability. The admission threshold is adaptively controlled to achieve a better balance between guaranteeing handoff-dropping probability and maximizing resource utilization. Simulation results show that the call admission control scheme with mobility based resource reservation scheme outperforms well when compared with the fixed reservation scheme.

The effect of mobility-induced location errors on geographic routing in mobile ad hoc sensor networks: analysis and improvement using mobility prediction

Geographic routing has been introduced in mobile ad hoc networks and sensor networks. Under ideal settings, it has been proven 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. We study the effect of inaccurate location information caused by node 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 percent improvement in packet delivery and 37 percent reduction in network resource wastage, on average without incurring any additional communication or intense computation.

Aggregate History of User Mobility Pattern for QoS Provisioning in Multimedia Wireless Networks

Multimedia traffic is expected to be included in the next generation of wireless networks. As in wireline networks, the wireless network must also be capable of providing guaranteed quality-of-service (QoS) over the lifetime of mobile connections. In this paper, a bandwidth reservation scheme incorporating a user mobility prediction is proposed to manage the QoS of the networks. The mobility prediction scheme is developed based on the aggregate history of mobile users. Based on the mobility prediction, bandwidth is reserved to guarantee the uninterrupted handoff process. Simulation results demonstrate that the proposed scheme can guarantee the required QoS requirements in terms of handoff call dropping probability and new call blocking probability while maintaining efficient bandwidth utilization.

Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks

This paper presents call admission control and bandwidth reservation schemes in wireless cellular networks that have been developed based on assumptions more realistic than existing proposals. In order to guarantee the handoff dropping probability, we propose to statistically predict user mobility based on the mobility history of users. Our mobility prediction scheme is motivated by computational learning theory, which has shown that prediction is synonymous with data compression. We derive our mobility prediction scheme from data compression techniques that are both theoretically optimal and good in practice. In order to utilize resource more efficiently, we predict not only the cell to which the mobile will handoff but also when the handoff will occur. Based on the mobility prediction, bandwidth is reserved to guarantee some target handoff dropping probability. We also adaptively control the admission threshold to achieve a better balance between guaranteeing handoff dropping probability and maximizing resource utilization. Simulation results show that the proposed schemes meet our design goals and outperform the static-reservation and cell-reservation schemes.