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Human mobility prediction techniques are instrumental for many important applications including service management and city planning. Previous work looks at the inherent patterns of a user's historical trajectories to predict his/her future location. Such method suffers when only a small number of historical locations are available, which is a main challenge for mobility prediction on sparse trajectory datasets. In this paper, we propose a multi-task learning based algorithm to predict users’ mobility by learning the mobility behaviors of different users at the same time and exploiting the similarities among them to overcome the sparse issue and improve the prediction performance. Specifically, we use a Bayesian mixture model to describe users’ spatio-temporal mobility patterns, where we introduce a novel von Mises distribution to model the temporal distribution of users’ mobility to better preserve its continuity across time. Then, we use the hierarchical Dirichlet process to perform joint prediction of all users’ mobility. This model allows us to leverage similar mobility patterns among users to improve the prediction performance for users with sparse trajectory data. We conduct extensive evaluations using data collected by a cellular network operator and mobile applications. Our evaluations show that our algorithms achieve prediction accuracy of 53.9% on average and 73.2% for users with high-quality mobility data, with the performance gap over 8.7% on average compared with the state-of-the-art algorithms, demonstrating the effectiveness of our proposed algorithm.

Device-to-device (D2D) communication standardization is underway and considered as one of the key technologies in the 5G ecosystem. Eavesdropping is a well-known security risk for D2D communications. Thus, ensuring information security for both cellular users (CUs) and D2D pairs in an underlay network is quite challenging. In this paper, we look into the problem of physical-layer secure transmission jointly with resource allocation in D2D communications. Existing research works for D2D underlay networks considered perfect channel state information (CSI), which is usually unrealistic in practical networks due to the wireless channels’ dynamic nature. Hence, unlike the previous works, we are considering imperfect CSI that include estimation errors. We leverage social-domain and frame a coalitional game approach, enabling multiple D2D pairs to share CU spectral resource. We assume heterogeneous cellular network based practical scenario considering multiple eavesdroppers, intra-cell interference, and inter-cell interference to evaluate achievable performances in terms of sum-rate and secrecy capacity for both CUs and D2D pairs. Moreover, we prove our proposed algorithm stability, convergence, and computational complexity. The simulation results establish the effectiveness of our proposed approach that not only maximizes the sum rate (i.e., 10 $ \%$ –60 $ \%$ ) but also improves the secrecy capacity (i.e., 20 $ \%$ –67 $ \%$ ) in comparison to the baseline schemes.

With the explosion growth of mobile data demands for proximal services, device-to-device communication is proposed as a vital technology for the next generation cellular network. With the extensive increase in personal mobile devices, content transmission of proximal devices underlaying cellular networks is gaining considerable attention. However, due to the mobility constraint of mobile devices, content transmission underlaying cellular networks greatly affects the overall transmission capacity of these proximal devices. In this paper, we investigate the problem of mobility assisted content transmission and resource allocation by exploiting the contact patterns regulated by these proximal devices’ mobility. We formulate the content transmission and resource allocation with the help of the statistic property of contact rates, and then utilize convex optimization to determine the successful content transmission and resource allocation scheme. We present the optimal resource allocated content transmission algorithm based on the pseudo-polynomial time algorithm using dynamic programming to solve the optimization problem. Real data traces are utilized in our proposed algorithm to find out the natural strategies of mobile homophily. Extensive simulations under the realistic human mobility factors are evaluated to demonstrate the efficiency of our proposed scheme.

To cope with increasing vehicular traffic and extensive application demands on vehicular cellular networks, opportunistic vehicular networks are used to disseminate mobile data by high-capacity device-to-device communication contacts when vehicles come into the communication range of each other, which offloads significant traffic from the cellular network. The current opportunistic vehicular data transmission models often do not take into account the fact that the contact duration is usually very short, due to the high mobility of vehicles, which will limit the amount of data that can be transmitted during one opportunistic contact. In this paper, we consider a mobile data offloading system that integrates cellular network and vehicular opportunistic communications. Based on this proposed system, we establish a mathematical framework to study the problem of contact-aware optimal resource allocation for mobile data offloading by the explicit consideration of the contact duration. Based on theoretical analysis of this problem, we derive an optimal scheme for mobile data offloading to optimally allocate the network resources in terms of opportunistic contacts and offloading helpers’ storage. By carrying out extensive simulation using two realistic urban vehicular traces, we demonstrate the effectiveness of our contact duration aware optimal offloading scheme, in comparison with a wide range of existing schemes.

Heterogeneous cellular networks (HCNs) are emerging as a promising candidate for the fifth-generation (5G) mobile network. With base stations (BSs) of small cells densely deployed, the cost-effective, flexible, and green backhaul solution has become one of the most urgent and critical challenges. With vast amounts of spectrum available, wireless backhaul in the millimeter-wave (mmWave) band is able to provide transmission rates of several gigabits per second. The mmWave backhaul utilizes beamforming to achieve directional transmission, and concurrent transmissions under low interlink interference can be enabled to improve network capacity. To achieve an energy-efficient solution for mmWave backhauling, we first formulate the problem of minimizing the energy consumption via concurrent transmission scheduling and power control into a mixed integer nonlinear program (MINLP). Then, we develop an energy-efficient and practical mmWave backhauling scheme, which consists of the maximum independent set (MIS)-based scheduling algorithm and the power control algorithm. We also theoretically analyze the conditions that our scheme reduces energy consumption, as well as the choice of the interference threshold. Through extensive simulations under various traffic patterns and system parameters, we demonstrate the superior performance of our scheme in terms of energy efficiency and analyze the choice of the interference threshold under different traffic loads, BS distributions, and the maximum transmission power.

With the explosive growth of mobile demand, small cells in millimeter wave (mmWave) bands underlying the macrocell networks have attracted intense interest from both academia and industry. MmWave communications in the 60 GHz band are able to utilize the huge unlicensed bandwidth to provide multiple Gbps transmission rates. In this case, device-to-device (D2D) communications in mmWave bands should be fully exploited due to no interference with the macrocell networks and higher achievable transmission rates. In addition, due to less interference by directional transmission, multiple links including D2D links can be scheduled for concurrent transmissions (spatial reuse). With the popularity of content-based mobile applications, popular content downloading in the small cells needs to be optimized to improve network performance and enhance user experience. In this paper, we develop an efficient scheduling scheme for popular content downloading in mmWave small cells, termed PCDS (popular content downloading scheduling), where both D2D communications in close proximity and concurrent transmissions are exploited to improve transmission efficiency. In PCDS, a transmission path selection algorithm is designed to establish multi-hop transmission paths for users, aiming at better utilization of D2D communications and concurrent transmissions. After transmission path selection, a concurrent transmission scheduling algorithm is designed to maximize the spatial reuse gain. Through extensive simulations under various traffic patterns, we demonstrate PCDS achieves near-optimal performance in terms of delay and throughput, and also superior performance compared with other existing protocols, especially under heavy load., Comment: 12 pages, to appear in IEEE Transactions on Vehicular Technology

With the emerging demands for local area services, mobile device-to-device (D2D) communications is conceived as a vital component for next-generation wireless networks to improve spectral reuse, to bring hop gains, and to enhance system capacity. In mobile D2D networks, energy-efficient probing schemes are vital to prolonging the limited battery life of mobile devices. Current solutions mainly focus on optimizing the probing interval during contacts by assumption of exponential distributed contact. However, studies in the area of network science have shown that the contact interval follows a power-law distribution, which indicates that the current solutions are not optimal while, on the other hand, dramatically increasing the difficulty of analyzing this problem. In this paper, we propose a network science approach for adaptive wakeup schedule based on power-law distributed contacts. Our approach requires nodes to stay asleep when a contact is unlikely to happen and to wake up only when the possibility that it successfully contacts another node is relatively high. By predicting node contacts in the future based on network science, our approach significantly reduces energy consumption without degrading the performance of opportunistic networks. Extensive simulations with real-life human and vehicular traces demonstrate the outstanding performance of our approach. The results show that our scheme saves 30% in energy while keeping the same performance in most scenarios, and it enhances the performance in terms of average delivery ratio and delivery delay by over 15%, compared with the existing best wakeup techniques, without considering network science.

With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures.

With the increase in emerging bandwidth-intensive applications, millimeter-wave (mmWave) communications in 60-GHz band have become a hot topic. There are two unique features that sharply distinguish mmWave wireless personal area networks (WPANs) from other networks using lower carrier frequencies. First, mmWave links use high-gain directional antennas to overcome the high propagation loss. Second, mmWave links are easily blocked by the human body and furniture. In this paper, we develop a blockage robust and efficient directional medium access control (MAC) protocol (BRDMAC), which overcomes the blockage problem by relaying. Relay selection and spatial reuse are jointly optimized to improve network performance. In BRDMAC, relay selection and transmission scheduling algorithms are proposed to compute near-optimal relay selection to maximize spatial reuse and compute near-optimal schedules with respect to the total transmission time, respectively. Finally, extensive simulation results show that BRDMAC performs better in terms of delay and throughput, compared with other existing protocols while attaining a good fairness performance.

Roadside units (RSUs), which enable vehicle-to-infrastructure communications, are deployed along roadsides to handle the growing communication demands as the number of vehicles increases. The current opportunistic RSU-aided content dissemination schemes, however, do not address heterogeneous networks in terms of data items and users. The authors establish a mathematical framework to study the problem of multiple content dissemination under realistic RSU-aided opportunistic network assumptions, where: 1) mobile content items are heterogeneous in terms of size and lifetime; 2) vehicles' interests are different to different data; and 3) the RSU' storage for content dissemination is limited in size. The authors formulate the maximum data dissemination as a submodular function maximization (SFM) problem with multiple linear constraints (MLCs) of limited storage. Then, they propose an efficient heuristic algorithm to solve this NP-hard problem. Finally, they demonstrate the effectiveness of their algorithm through extensive simulations using realistic vehicular traces. The simulation results show that their proposed low-complexity heuristic algorithm performs much better than the existing feasible solutions, and it achieves similar performance to that of the most accurate algorithm currently available, whose computational complexity is unacceptable in practice.

We study packet detection in wireless networks in the presence of a practical analog automatic-gain-control (AGC) amplifier, which is adopted to scale the average power of received signals to an appropriate fixed level. However, when packets are not received, the input of AGC is pure noise, which has a small amplitude and may not be scaled to the desired level, due to the limited gain of AGC. In light of this feature, a generalized likelihood ratio test (GLRT) detector, which is termed AGC-GLRT, is proposed and formulated in this paper for packet detection. The effect of AGC on AGC-GLRT is analyzed, and it is proven that the performance of AGC-GLRT depends on the ratio of output power in hypotheses ${\cal H}_{1}$ and ${\cal H}_{0}$ . A comparison is performed between AGC-GLRT and general GLRT (GEN-GLRT), which considers the output power of AGC in the two hypotheses, as well as some other common detectors. Results show that, in the presence of a practical AGC, AGC-GLRT outperforms GEN-GLRT and the other detectors. Moreover, as the maximum gain of AGC decreases, the ratio of the output power in the two hypotheses increases, and the performance of AGC-GLRT is improved. In addition, it is shown that AGC-GLRT is more sensitive to fading effects than the other detectors. Based on these analysis and comparisons, a general rule on AGC design is achieved to take full advantage of AGC-GLRT.

The newly emerged vehicular communication network is seen as a key technology for solving increasingly serious vehicular traffic congestion and improving road safety. New applications of vehicular networks are also emerging at the same time. Predicting vehicular staying time is vital to both solving the vehicular system problem and building efficient vehicular networking. How much the vehicular staying duration of visits to different areas can be predicted is still an open and unsolved problem. In this paper, we use real vehicular traces in Beijing and Shanghai to explore the limit of predictability of the vehicular visiting time in different areas in large cities, and we analyze the impact of different precision and slot time on predictability. We conclude that using a proper time slot is an efficient way of prediction, and a higher predictability can be achieved if the requirement for precision is reduced. Among all the cases that we studied, we find the predictability to be 76.3% for Beijing and 82.5% for Shanghai in the case with the smallest slot time and reasonable requirements for precision.

To provide communication services in delay-tolerant networks (DTNs) where there may exist no end-to-end paths between mobile node pairs, a variety of relaying and routing algorithms have been proposed under the assumption that the mobile nodes are homogeneously distributed in the network with the same contact rate and delivery cost. However, experimental data have revealed the heterogeneous contact rates between node pairs, and various applications of DTNs have shown that the mobile nodes often belong to different types in terms of energy consumption, communication ability, and other properties. Following the philosophy of exploiting the heterogeneous features of nodes to enhance the routing performance, we design an optimal relaying scheme for DTNs, which takes into account the nodes' heterogeneous contact rates and delivery costs when selecting relays to minimize the delivery cost while satisfying the required message delivery probability. We use the trace-driven simulations to demonstrate the effectiveness of our optimal relaying scheme in various distributions of nodes' delivery costs and mobility environments. Simulation results show that our proposed optimal relaying scheme requires the least delivery cost and achieves the largest maximum delivery probability, compared with the schemes that neglect or do not fully take into account nodes' heterogeneity.

Owing to the uncertainty of transmission opportunities between mobile nodes, the routing in delay-tolerant networks (DTNs) exploits the mechanism of opportunistic forwarding. Energy-efficient algorithms and policies for DTN are crucial to maximizing the message delivery probability while reducing the delivery cost. In this contribution, we investigate the problem of energy-efficient optimal beaconing control in a DTN. We model the message dissemination under variable beaconing rate with a continuous-time Markov model. Based on this model, we then formulate the optimization problem of the optimal beaconing control for epidemic routing and obtain the optimal threshold policy from the solution of this optimization problem. Furthermore, through extensive numerical results, we demonstrate that the proposed optimal threshold policy significantly outperforms the static policy with constant beaconing rate in terms of system energy consumption savings.

Due to the uncertainty of transmission opportunities between mobile nodes, delay tolerant networks (DTNs) exploit the opportunistic forwarding mechanism. This mechanism requires nodes to forward messages in a cooperative and selfish way. However, in the real word, most of the nodes exhibit selfish behaviors, such as individual and social selfishness. In this paper, we are the first to investigate how the selfish behaviors of nodes affect the performance of DTN multicast. We consider two typical multicast relaying schemes, namely, two-hop relaying and epidemic relaying, and study their performance in terms of average message transmission delay and transmission cost. Specifically, we model the message delivery process under selfish behaviors by a 3-D continuous time Markov chain; under this model, we derive closed-form formulas for the message transmission delay and cost. Then, we evaluate the accuracy of the proposed Markov chain model by comparing the theoretical results with the simulation results obtained by simulating the message dissemination under both two-hop and epidemic relaying with different network sizes and mobility models. Our study shows that different selfish behaviors may have different impacts on different performance metrics. In addition, selfish behaviors influence epidemic relaying more than two-hop relaying. Furthermore, our results show that the performance of multicast with selfish nodes depends on the multicast group size.

Due to the uncertainty of transmission opportunities between mobile nodes, the routing of delay-tolerant networks (DTNs) exploits the mechanism of opportunistic forwarding. Efficient algorithms and policies for opportunistic forwarding are crucial for maximizing the message delivery probability while reducing the delivery cost. In this paper, we investigate the problem of energy-efficient opportunistic forwarding for DTNs. First, we model the message dissemination by introducing a continuous-time Markov framework. Based on this framework, we formulate the optimization problem of opportunistic forwarding, with the constraint of energy consumed by the message delivery for both two-hop and epidemic forwarding. Then, based on the solution of the optimization problem, we design different kinds of forwarding policies such as static and dynamic policies. Among these policies, we find that the threshold dynamic policy is optimal for both two-hop and epidemic forwarding. By simulation results, we show the accuracy of our continuous-time Markov analysis model. Furthermore, through extensive numerical results, we demonstrate that the performance of the threshold dynamic policy is the best among the static and continuous dynamic policies, and among the continuous dynamic policies, the negative-power policy provides relatively better performance.