Due to advances in wireless communication technologies, wireless transmissions gradually replace traditional wired data transmissions. In recent years, vehicles on the move can also enjoy the convenience of wireless communication technologies by assisting each other in message exchange and form an interconnecting network, namely Vehicular Ad Hoc Networks (VANETs). In a VANET, each vehicle is capable of communicating with nearby vehicles and accessing information provided by the network. There are two basic communication models in VANETs, V2V and V2I. Vehicles equipped with wireless transceiver can communicate with other vehicles (V2V) or roadside units (RSUs) (V2I). RSUs acting as gateways are entry points to the Internet for vehicles. Naturally, vehicles tend to choose nearby RSUs as serving gateways. However, due to uneven density distribution and high mobility nature of vehicles, load imbalance of RSUs can happen. In this paper, we study the RSU load-balancing problem and propose two solutions. In the first solution, the whole network is divided into sub-regions based on RSUs’ locations. A RSU provides Internet access for vehicles in its sub-region and the boundaries between sub-regions change dynamically to adopt to load migration. In the second solution, vehicles choose their serving RSUs distributedly by taking their future trajectories and RSUs’ loading information into considerations. From simulation results, the proposed methods can improve packet delivery ratio, packet delay, and load balance among RSUs.

Traffic congestion has become one of the major society issues in many urban areas around the world. It greatly increases the commuting time and fuel consumption. Researchers have proposed many solutions in different aspects to alleviate this issue. In recent years, many works adopt Vehicular Ad Hoc Networks (VANETs) to relieve the problem. Vehicles with communication ability can receive traffic information from infrastructures or other vehicles. Furthermore, drivers can select its driving path by avoiding congestion areas based on the real-time information. However, to speed up the procedures of information collections, many existing works suggest proactive messages to acquire traffic information from other vehicles, which incurs heavy communication overheads. In this work, we propose a real-time traffic collection mechanism, which adopt both proactive and passive schemes. Vehicles passively collect traffic information in most of the time, and proactively broadcast the traffic data only in certain situations in order to speed up urgent information spreading. Thus, vehicles can obtain real-time traffic information in a low-cost way. In addition, we propose a routing planning scheme, which considers travel time, reliability, expected traffic flow as well as other factors, to select reliable and fast routes for vehicles. The simulation results show that, comparing with previous works, the proposed schemes can find better driving paths for vehicles with lower communication cost.

Vehicular Ad Hoc Networks (VANETs) have attracted a lot of attention during the last decade. VANETs can not only improve driving safety, but also convenience, and support most future Intelligent Transportation System (ITS). Due to the highly dynamic network topology of VANETs, many geographic routing protocols have been proposed and use real-time traffic information as an important metric to select a reliable forwarding path. However, most of the existing works do not describe how to gather real-time traffic. They either assume this information is already available, or can query an existing traffic center. Few studies have noticed this issue but the proposed solutions only consider a small region. In this paper, we propose a Comprehensive Real-Time Traffic Map (CRT Map) to collect wide-ranging real-time traffic information with low overhead. In the design of a CRT Map, the concept of Crowdsensing is adopted. Vehicles cooperatively gather traffic information and share it with each other to construct an overview of the whole road network traffic. In addition, we design a CRT Map Based Routing (CBR), which takes into account the connectivity of consecutive roads in routing decisions. Simulation results show that the CBR can achieve a lower end-to-end delay and a higher packet delivery ratio.

Data collection is one of the fundamental functions in wireless sensor networks (WSNs). Different from a typical WSN with only a single and stationary sink, this paper considers a scenario with multiple mobile sinks. The motivation is for a WSN to support future applications, such as Internet of Things (IoT). In this case, a WSN requires abilities to deliver sensing results to multiple users roaming in the network. There are two difficulties in this problem: sink mobility and multiple sinks. Since sinks are mobile, data delivery paths need to be updated frequently, which causes huge maintenance cost. To resolve this problem, we propose a hop-count-based architecture together with an efficient mobility management scheme. On the other side, sensing results from a large number of sensors are delivered to multiple sinks, which causes lots of packet transmissions. To reduce transmission cost, the concepts of multicast and data aggregation are integrated and applied. We first prove that a distributed multicast decision is a NP-hard problem and then propose a heuristic solution. Afterward, we further merge data aggregation into multicast and propose a distributed many-to-many aggregation scheme. Simulations are constructed to evaluate the efficiency of the proposed schemes. The results show that both the proposed multicast scheme and many-to-many aggregation scheme can efficiently reduce transmission cost and energy when delivering data to multiple mobile sinks.

Internet of things (IoT) has been considered as one of the most promising technologies over the next decade. One of the basic requirements of IoT is the global communication connectivity between smart objects. LTE-A has been considered as the main communication channel for connecting devices. For this reason, the machine-type communication (MTC) has been defined in the 3GPP LTE-A specification. With the rapid growth of the IoT devices, MTC in LTE-A faces many challenges. The primary design of a LTE-A network is to support the human-type communication (HTC). However, MTC and HTC have different characteristics, such as packet size, traffic arrival rate, and delay tolerance. How to accommodate a large amount of MTC traffic without affecting the grade of service of the HTC is challenging. More specifically, before accessing the LTE-A network, a random access (RA) procedure needs to be performed by a device to synchronize in the uplink of an eNB. Since the number of MTC devices is expected to be much greater than the HTC devices, without suitable access control, the success rate of the RA requests from the HTC devices can be significantly degraded. Therefore, this research aims to design an appropriate RA procedure to relieve this problem. The authors propose an Adaptive RACH Resource Allocation (ARRA) which integrates several control schemes, including a Resource Allocation scheme, an Access Class Barring scheme, and a Priority Device Setting scheme. Simulation results show that the proposed ARRA is able to achieve a higher access success rate and a lower latency for HTC devices while providing a different quality of service to different types of MTC devices.

In this paper, we propose a cyber-physical signage interacting framework for the interaction between digital signage and mobile users using smart handheld devices, such as smartphones and tablets. The proposed framework can provide diverse multimedia/feedback services to mobile users interacting with digital signage through face detection, classification, and recognition techniques based on mobile cloud computing. Mobile users only need to click and drag the interested service over the face of advertising celebrities/endorsers displayed on digital signage and can obtain supplementary multimedia information or feedback related comments. Using the intuitive way of gesture-based operations, mobile users can directly interact with digital signage through their handheld devices. Our framework reveals an innovative human-machine interface for signage interacting between digital signage and mobile users. In addition, we integrate our framework with a face cache mechanism that can make the interaction delay as small as possible for popular signage. Furthermore, an Android-based signage interacting system is implemented to verify the feasibility and superiority of our framework. Experimental results show that our approach outperforms the existing methods and can significantly reduce the average consumption time of obtaining interested multimedia contents from digital signage.

Data collection is one of the fundamental functions in Wireless Sensor Networks (WSNs). Different from traditional data collection mechanisms which only consider a single and stationary sink, this paper studies this problem in a scenario with multiple mobile sinks. The motivation is for a WSN to support future applications, such as Internet of Things (IoT). In this case, a WSN is required to be able to deliver sensing results to multiple users moving around the network. There are two difficulties in this problem: sink mobility and multiple sinks. Since sinks are mobile, data delivery paths need to be updated frequently, which causes huge maintenance cost. To resolve this problem, we propose a hop-count based data collection architecture together with an efficient mobility management scheme. On the other side, sensing results from a large number of sensors are sent to multiple sinks, which causes lots of packet transmissions. To resolve this problem, we combine the idea of multicast and data aggregation. We first prove that the optimal multicast decision is a NP-hard problem and then propose a distributed heuristic solution. In addition, we further integrate data aggregation into multicast and propose a distributed many-to-many aggregation mechanism. Simulations are constructed to show the efficiency of the proposed schemes. The results show that both our multicast method and many-to-many aggregation method can efficiently reduce communication cost when delivering data to multiple mobile sinks.

With the increasing demand for multimedia transmissions, Multimedia Broadcast/Multicast Service (MBMS) has been standardized by 3GPP to confront the new challenges. In the MBMS system, a group of adjacent base stations (BSs) can form a multimedia broadcast single-frequency network (MBFSN) area, hereinafter referred to as MBSA, and are synchronized to collaboratively transmit multimedia data. By forming a MBSA, the MBMS users can avoid excessive inter-cell handovers as well as have better radio efficiency at the cell-edge. This paper studies the MBSA planning (MBSAP) problem under the constraints that each BS can supply only a limited number of video programs. With the information of the probability of user handover and popularity of video programs, MBSAP seeks to maximize the total system rewards by simultaneously: (1) arranging a list of video programs served by each BS, and (2) organizing BSs into a set of MBSAs. In this paper, the MBSAP problem is formulated as an Integer Linear Programming (ILP) problem. We also propose a genetic algorithm (GA) based algorithm and a Fusion-and-Refinement (FUSE) algorithm due to the high computational complexity of ILP. Simulation results show that the performance of GA and FUSE are competitive to that of ILP.

Locations of devices or objects are important information in many applications. In addition to the global positioning system (GPS) system, positioning can also be done using some wireless networking infrastructures. Several location estimation models, such as angle of arrival (AoA); time of arrival (ToA); received signal strength (RSS); phase of arrival (PoA); and assisted GPS (A-GPS), are widely used in cellular networks and wireless sensor networks. Much work has been dedicated to positioning and location tracking in the area of wireless ad hoc and sensor networks. This chapter introduces some fundamental distance estimation models, discusses some positioning and location tracking algorithms and reviews some experimental systems. To position an object or a device, the basic step is to use a reference point to determine the distance and angle between the device and the reference point. The chapter shows how to use multiple reference points jointly to estimate the location of a device.

This paper considers storage management in an isolated WSN, under the constraint that the storage space per node is limited. We formulate the memory spaces of these sensor nodes as a distributed storage system. Assuming that there is a sink in the WSN that will be visited by mobile mules intentionally (e.g., pre-arranged buses) or occasionally (e.g., non-pre-arranged taxis), we address three issues: (1) how to buffer sensory data to reduce data loss due to shortage of storage spaces, (2) if dropping of data is inevitable, how to avoid higher priority data from being dropped, and (3) how to keep higher priority data closer to the sink, such that the mobile mules can download more important data first when the downloading time is limited. We propose a Distributed Storage Management Strategy (DSMS) based on a novel shuffling mechanism similar to heap sort. It allows nodes to exchange sensory data with neighbors based on only local information. To the best of our knowledge, this is the first work addressing distributed and prioritized storing strategies for isolated WSNs.

The IEEE 802.11p/1609 standards specify a suite of communication protocols for vehicular communication networks. A new operational mode, called theWAVE (Wireless Access in Vehicular Environments) mode, is defined to enable communication among high-speed vehicles or between a vehicle and a roadside infrastructure network. In the WAVE mode, a mobile/stationary station uses a combined FDMA/TDMA channel access scheme to switch its channel between the control channel and a service channel every 50 ms. The primary purpose of this design is to detect new services announced by other stations. However, if a station would like to use all of its service channel bandwidth to transmit a large volume of data to another station, switching back to the control channel every 50 ms without doing anything useful only unnecessarily reduces the maximum possible transmission throughput by a half. To overcome this problem, in this paper we propose an improved channel access scheme to allow a station to stay on a service channel as long as it requires before returning to the control channel. Our simulation results show that the proposed scheme significantly improves the service channel utilization under various network conditions.

This paper proposes a new application framework for group tour guiding services based on RFIDs and wireless sensor networks. We consider a sensing field mixed with multiple independent tourist groups, each with a leader and several members. Members of a group will follow the moving path of their leader, but may occasionally roam around randomly on their own interests. Sensor nodes have to track leaders' locations and maintain guiding paths from members to leaders. A member may inquire where his/her leader is, and a leader may "recall" his/her members. We propose a feasible solution to such an application by using existing technologies and off-the-shelf components. A group guiding protocol is presented. The design enables reliable group guiding at low cost and low traffic load. Our prototyping system is reported and system performance is discussed.

Primary liver lymphoma is extremely rare. The diagnosis depends on the physician's Suspicions and histological examination. We report the case of a man aged 38 years who suffered from abdominal discomfort and hypercalcemia. Sonography showed a huge, solid liver tumor, and magnetic resonance imaging showed the tumor had characteristics of hypointensity on T1-weighted and hyperintensity on T2-weighted imaging. Primary liver lymphoma was diagnosed by histological examination from biopsy. We report this rare type of liver tumor and review the clinical presentation and treatment of the disease.

An inherent concern for a wireless sensor network (WSN) is the unbalanced energy consumption problem, where sensors closer to the sink are more likely to exhaust their energy faster than other nodes. To mitigate this problem, this paper considers including some resource-rich mobile nodes, called mobile data-pumps, to conduct data relaying from static sensors to the sink. The network thus becomes a two-tier network, with the original static sensors at the low tier and data-pumps at both low and high tiers. We propose a novel distributed navigation protocol that does not rely on any location information of sensor nodes to relocate data-pumps to meet both goals of connectivity and load balance. The main idea is a concept called virtual Voronoi cells, which can help data-pumps to locally balance their loads using the underlaying low-tier topology and thus significantly balance energy consumption of sensors. Simulation results are presented to verify the effectiveness of our result.

While wireless sensor networks (WSNs) are typically targeted at large-scale deployment, due to many practical or inevitable reasons, a WSN may not always remain connected. In this paper, we consider the possibility that a WSN may be spatially separated into multiple subnetworks. Data gathering, which is a fundamental mission of WSN, thus may rely on a mobile mule (“mule” for short) to conduct data gathering by visiting each subnetwork. This leads to the problem of minimizing the path length traversed by the mobile mule. We show that minimizing the path length, which may reflect the data gathering latency and the energy consumption of the mule is a generalization of the traveling salesman problem and is NP-complete. Some heuristics based on geometrical properties of node deployment are proposed. Our simulation results show that these heuristics perform very close to optimal solutions in most practical cases.

Following the trend of miniature intelligent sensing, wearing small, integrated wireless sensor nodes, such as one with accelerometers and compasses, to capture human body motions may have many applications in medical care and computer animation. In this paper, we demonstrate the use of intelligent sensors to capture human motions for home rehabilitation. We design a game to help a patient to conduct his/her rehabilitation program. For each exercise, the patient is instructed to wear sensors on specified movable body parts. The system will then estimate the quality of the movements and give scores as if it is advised by a therapist. In this way, patients will no longer feel painful and boring as that in traditional rehabilitation, which is typically done in hospitals.

One important goal of surveillance systems is to collect information about the behavior and position of interested targets in the sensing environment. These systems can be applied to many applications, such as fire emergency, surveillance system, and smart home. Recently, surveillance systems combining wireless sensor networks with video cameras have become more and more popular. In traditional video surveillance systems, the system performance and cost is proportional to the number of deployed video camera. In this paper, we propose a real time video surveillance system consisting of many low cost sensors and a few wireless video cameras. The system allows a group of cooperating sensor devices to detect and track mobile objects and to report their positions to the sink node in the wireless sensor network. Then, the sink node uses the IP cameras deployed in the sensing area to record these events and display the present situations. We also propose a camera control scheme to initialize the coverage distribution of cameras and support the inter-task handoff operations between cameras. We have implemented the proposed system with 16 sensor nodes and two IP cameras, and evaluated the system performance. The result shows that our surveillance system is adaptable to variant environments and provides real time information of the monitored environment.

In this paper I develop and empirically analyze a continuous-time, linear-quadratic, representative consumer model in which the consumer has time-nonseparable preferences of several forms. Within this framework I show how time aggregation and time nonseparabilities in preferences over consumption streams can interact. I show that the behavior of both seasonally adjusted and unadjusted consumption data is consistent with a model of time-nonseparable preferences in which the consumption goods are durable and in which individuals develop habit over the flow of services from the good. The presence of time nonseparabilities in preferences is important because the data does not support a version of the model that focuses solely upon time aggregation and ignores time nonseparabilities in preferences by making preferences time additive. Many helpful comments and suggestions were made by Edward Allen, Toni Braun, John Cochrane, George Constantinides, Martin Eichenbaum, Ayman Hindy, Chi-fu Huang, Narayana Kocherlakota, Robert Lucas, Robin Lumsdaine, Masao Ogaki, Chris Phelan, Paul Romer, Karl Snow, Grace Tsiang, Kei-Mu Yi and and participants of workshops at Chicago, Carnegie-Mellon, Duke, Harvard, M.I.T., Minnesota, Northwestern, Princeton, Queen's, Rochester, Stanford, Toronto, Western Ontario, Wharton and Yale. I would especially like to thank three referees for their very detailed and helpful comments and suggestions and Lars Peter Hansen for his many suggestions and his constant encouragement. I gratefully acknowledge financial support from the Greentree Foundation, the National Science Foundation, the Sloan Foundation and the Social Science and Humanities Research Council of Canada. This paper is a revised version of my University of Chicago Ph.D. dissertation. Any errors are of course my own.