Past Location Information Research Articles

Client-Side Network Delay Compensation for Online Shooting Games

In online multiplayer shooting games, a long network delay can adversely impact player performance because it leads to large synchronization errors in the game information, such as location errors, between players. To mitigate synchronization errors between players, we propose a novel client-side delay compensation system for online multiplayer shooting games. The proposed delay compensation system comprises regression-based and reinforcement-based methods, which accurately predict present game information based on past game information from within each player’s device. Specifically, the regression-based method utilizes linear regression to accurately predict a player’s present location based on past location information, whereas the reinforcement-based method utilizes deep reinforcement learning to predict a player’s present commands corresponding to the player’s current location, based on past snapshots of the game fields. The advantage of the proposed system over the existing server-side delay compensation methods, e.g., Lag Compensation, is that they display accurate game information in each player’s device as if the player devices were synchronized with each other; thus, any unfair advantages to the players are prevented. We developed a shooting game simulator to experimentally compare the proposed system with existing ones under constant and fluctuating network delays in loss-free and loss-prone networks. The movement models of different players were considered in the experiments. The results demonstrate that the proposed system simultaneously achieves a lower synchronization error between players and higher shooting accuracy, compared to Lag Compensation. For example, the average decrease in player shooting hit rate under a constant network delay of 100 ms is 0.55, 0.076, and 0.0039 without delay compensation and with the proposed regression-based and reinforcement-based methods, respectively. Under the same network delay, the proposed regression-based and reinforcement-based methods show the improvement of 16 pixels and 14 pixels in terms of the synchronization error compared to Lag Compensation, respectively.

Real-World Spatio–Temporal Behavior Aware D2D Multicast Networks

Studies show that real-world user mobility has certain spatio-temporal correlations. However, most of the existing works on device-to-device multicast (D2MD) networks use randomly generated user locations, thus fail to capture the real-world spatio-temporal behavior of the users. In this work, we propose a realistic performance evaluation of the D2MD network that takes into account the joint spatio-temporal correlations of the users in the network. The proposed performance evaluation will be beneficial in assessing the viability of the D2MD networks before their actual deployment. Further, in order to acquire the required joint spatio-temporal behavior, we propose two novel methods namely observed mobility exploitation (OME) and expected mobility exploitation (EME). In OME, the joint spatio-temporal behavior is extracted from the past location information of the users. On the other hand, in EME, the past location information of a set (i.e., the training set) of users, following an average spatial-temporal behavior similar to the intended users, is utilized to estimate the intended users’ joint spatio-temporal behavior. The realistic performance of two D2MD networks is evaluated for a tractable campus set-up in terms of content delivery time and data offload ratio. It has been shown that for the D2MD networks the data offloading varies spatio-temporally and goes up to 35%. Further, a comparison is drawn between the conventional cellular unicast and multicast networks, and the two D2MD networks.

Workload Allocation Based on User Mobility in Mobile Edge Computing

Mobile Edge Computing (MEC) has become the most possible network architecture to realize the vision of interconnection of all things. By offloading compute-intensive or latency-sensitive applications to nearby small cell base stations (sBSs), the execution latency and device power consumption can be reduced on resource-constrained mobile devices. However, computation delay of Mobile Edge Network (MEN) tasks are neglected while the unloading decision-making is studied in depth. In this paper, we propose a workload allocation scheme which combines the task allocation optimization of mobile edge network with the actual user behavior activities to predict the task allocation of single user. We obtain the next possible location through the user's past location information, and receive the next access server according to the grid matrix. Furthermore, the next time task sequence is calculated on the base of the historical time task sequence, and the server is chosen to preload the task. In the experiments, the results demonstrate a high accuracy of our proposed model.

Efficient security policy enforcement for the mobile environment

In the last decade, mobile communication has enjoyed unprecedented growth all over the world. The recent advances in mobile communication technologies including Global Positioning System (GPS) and Radio Frequency Identification (RFID) have propelled the growth of a number of mobile services. Typically, these require maintaining the mobile objects’ location and profile information and efficiently serving access requests on the past, present and future status of the moving objects. This creates inherent security and privacy challenges. One solution to this is to specify security policies to ensure controlled access. However, this significantly degrades system performance. To alleviate this, Atluri and Guo have proposed an unified index structure, S TPR-tree, to organize both the moving objects and authorizations specified over them. A significant limitation of this approach is that it is unable to store past location information of objects and is therefore not capable of supporting security policies based on tracking of mobile users. In this paper, we propose a new unified index structure, called the S PPF -tree, which maintains past, present and future positions of the moving objects along with authorizations by employing partial persistent storage. Besides demonstrating how the S PPF -tree can be constructed and maintained, we provide algorithms to process queries where either the subject or the object or both are mobile. We provide a comprehensive experimental evaluation to establish the scalability and performance of our approach.