Dong Zhao

COUPON: A Cooperative Framework for Building Sensing Maps in Mobile Opportunistic Networks

Human-carried or vehicle-mounted sensors can be exploited to collect data ubiquitously for building various sensing maps. Most of existing mobile sensing applications consider users reporting and accessing sensing data through the Internet. However, this approach cannot be applied in the scenarios with poor network coverage or expensive network access. Existing data forwarding schemes for mobile opportunistic networks are not sufficient for sensing applications as spatial-temporal correlation among sensory data has not been explored. In order to build sensing maps satisfying specific sensing quality with low delay and energy consumption, we design COUPON, a novel cooperative sensing and data forwarding framework. We first notice that cooperative sensing scheme can eliminate sampling redundancy and hence save energy. Then we design two cooperative forwarding schemes by leveraging data fusion: Epidemic Routing with Fusion (ERF) and Binary Spray-and-Wait with Fusion (BSWF). Different from previous work assuming that all packets are propagated independently, we consider that packets are spatial-temporal correlated in the forwarding process, and derive the dissemination law of correlated packets. Both the theoretic analysis and simulation results show that our cooperative forwarding schemes can achieve better tradeoff between delivery delay and transmission overhead. We also evaluate our proposed framework and schemes with real mobile traces. Extensive simulations demonstrate that the cooperative sensing scheme can reduce the number of samplings by 93 percent compared with the non-cooperative scheme; ERF can reduce the transmission overhead by 78 percent compared with Epidemic Routing (ER); BSWF can increase the delivery ratio by 16 percent, and reduce the delivery delay and transmission overhead by 5 and 32 percent respectively, compared with Binary Spray-and-Wait (BSW).

Budget-feasible online incentive mechanisms for crowdsourcing tasks truthfully

Mobile crowd sensing (MCS) is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous novel applications. To achieve good service quality for an MCS application, incentive mechanisms are necessary to attract more user participation. Most existing mechanisms apply only for the offline scenario where all users report their strategic types in advance. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arriving, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of services (assumed to be a nonnegative monotone submodular function) provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty, and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.

Energy-efficient opportunistic coverage for people-centric urban sensing

Human-carried or vehicle-mounted sensors can be exploited to collect data ubiquitously for urban sensing. In this work, we study a new coverage problem, opportunistic coverage, to characterize the sensing quality of such people-centric sensing systems. Compared with the traditional static coverage and dynamic coverage in sensor networks, opportunistic coverage has some unique characteristics caused by the requirements of urban sensing applications and human mobility features such as spatio-temporal correlation, hotspots effects and randomness. In order to achieve good trade-off between energy consumption and coverage quality, we propose an offline node selection mechanism and an online adaptive sampling mechanism. The former can select the minimum number of nodes to achieve coverage requirements, based on the history trajectories of the given set of nodes, and the latter can help each selected node to decide whether to perform the sampling task at some time adaptively. Based on a real human mobility dataset and a taxi mobility dataset, extensive simulation results evaluate that our proposed models and mechanisms are effective and efficient in terms of energy consumption and coverage quality.

On Networking of Internet of Things: Explorations and Challenges

Internet of Things (IoT), as the trend of future networks, begins to be used in many aspects of daily life. It is of great significance to recognize the networking problem behind developing IoT. In this paper, we first analyze and point out the key problem of IoT from the perspective of networking: how to interconnect large-scale heterogeneous network elements and exchange data efficiently. Combining our on-going works, we present some research progresses on three main aspects: 1) the basic model of IoT architecture; 2) the internetworking model; and 3) the sensor-networking mode. Finally, we discuss two remaining challenges in this area.

Urban Resolution: New Metric for Measuring the Quality of Urban Sensing

The rising popularity of smartphones and vehicles equipped with onboard sensors sheds lights on building a city-scale sensing system for urban surveillance. This paper proposes a novel metric, urban resolution, to measure the quality of urban sensing. Urban resolution describes how sensitivity the urban sensing system could achieve for environment monitoring applications. Then, we study the relationship between resolution r and number of sensing nodes s, and reveal the linear growth relationship between √r and √s . Furthermore, by employing a commonly used human/vehicle mobility model, SLAW, we find that the distribution model of urban sensing nodes is able to be described by a truncated Pareto distribution, and derive the complementary cumulative distribution function (CCDF) of urban resolution. The CCDF reveals the radio of the sub-regions which satisfy the required sensing quality to the whole region. Our findings provide valuable insights to infer the urban sensing quality according to the scale of urban sensing system or determine how many smartphone/vehicles needed for participating in urban sensing applications. Finally, based on five real datasets-three human/vehicle trajectory datasets and two environment monitoring datasets, we examine the metric of urban resolution and evaluate the main results in this paper.

On Opportunistic Coverage for Urban Sensing

Opportunistic sensing is a new paradigm which exploits human-carried or vehicle-mounted sensors to collect data ubiquitously for large-scale urban sensing. Existing work lacks an in-depth investigation on the sensing quality of such sensing systems, which faces two basic problems: 1) how to measure the sensing quality? and 2) how many humans or vehicles are necessary to satisfy the sensing quality requirement of the whole urban area? To solve the first problem, we propose a metric called Inter-Cover Time (ICT) to characterize the opportunity with which a sub region is covered, which reflects the sensing quality directly. According to the empirical measurement studies on real mobility traces of thousands of taxis collected in Beijing and Shanghai, we find that the aggregated ICT Distribution (ICTD) closely resembles a truncated power-law distribution regardless of the size of sub regions and the number of vehicles. We also analyze the reasons behind this particular pattern by the evaluation on four known mobility models. To solve the second problem, we further propose a metric called opportunistic coverage ratio based on the ICTD to characterize the relationship between the sensing quality of an urban area and vehicle number. Our results provide fundamental guidelines on the measurement of sensing quality and network planning for opportunistic urban sensing applications.

Opportunistic coverage for urban vehicular sensing

Opportunistic vehicular sensing is a new paradigm which exploits variety of sensors embedded in vehicles or smartphones to collect data ubiquitously for large-scale urban sensing. Existing work lacks in-depth investigations on the coverage problem in such sensing systems: (1) how to define and measure the coverage? (2) what is the relationship between the coverage quality and the number of vehicles? and (3) how to select the minimum number of vehicles to achieve the specific coverage quality? First, we propose a metric called Inter-Cover Time (ICT) to characterize the coverage opportunities. According to the empirical measurement studies on real mobility traces of thousands of taxis, we find that the aggregated ICT Distribution (ICTD) follows a truncated power-law distribution. We also analyze the reasons behind this particular pattern by evaluating four known mobility models. Second, we propose a metric called opportunistic coverage ratio, and derive it as a function of the aggregated ICTD. We also analyze the changes of opportunistic coverage ratios on different days of a week. Finally, we present a vehicle selection algorithm to address the third problem. In addition, we present a framework of recruiting vehicles, serving as fundamental guidelines on the coverage measurement and network planning for urban vehicular sensing applications.

Mobile sensor scheduling for timely sweep coverage

Mobile sensors are a viable choice for providing monitoring service on a set of Points of Interest (PoIs) in a large sensing field. In some applications, each PoI should be covered periodically (sweep coverage), and the collected data should be delivered to the sink node timely (timely transmission), namely both the sensing and transmission delay constraints for each PoI should be satisfied, which we call as timely sweep coverage. We investigate how to optimize the movement path of one mobile sensor to satisfy the two delay constraints for each PoI, so that the required movement velocity for the mobile sensor is minimized. We consider two cases: 1) all PoIs are placed along a straight line (linear case), and 2) all PoIs are arbitrarily placed on a plane (general 2-D case). Under the linear case, the optimal algorithm is presented. Under the general 2-D case, we prove the problem is NP-hard, and two algorithms, STSP and ITSP, are presented. We prove that the approximation ratio of STSP depends on the ratio between the sensing and transmission delay constraints. The ITSP can improve the solution much especially when the two delay constraints differ greatly for each PoI. Extensive simulation results are provided to evaluate our algorithms.

Frugal Online Incentive Mechanisms for Mobile Crowd Sensing

Mobile crowd sensing has emerged as a novel data collection paradigm by leveraging pervasive mobile sensing devices to enable various applications. To obtain good quality of service, incentive mechanisms are indispensable for attracting enough users. Most of the existing mechanisms focus on the <italic>offline</italic> scenario in which all users submit profiles in advance. However, the <italic>online</italic> scenario often appears in the real world wherein users arrive one by one in random order. In this paper, we investigate the frugal online incentive problem based on an online auction model, where users report their strategic profiles to the crowdsourcer in an online mode, and the crowdsourcer selects users before a deadline to complete a specific number of tasks while minimizing the total payment. We design two online mechanisms, namely, <italic>Frugal-OMZ</italic> and <italic>Frugal-OMG</italic>, satisfying <italic>computational efficiency</italic>, <italic>individual rationality</italic>, <italic>truthfulness</italic>, <italic>consumer sovereignty</italic>, and <italic>constant frugality</italic> under the <italic>zero arrival–departure interval</italic> model and the <italic>general interval</italic> model, respectively. Extensive simulations verify the desirable properties of our mechanisms.

COUPON: Cooperatively Building Sensing Maps in Mobile Opportunistic Networks

With the popularity and advancements of smartphones, mobile users can sense the city using variety of sensors opportunistically, and forward the sensory data to the monitoring center for building sensing maps through intermittent connections with short-range communications. In order to build sensing maps satisfying specific sensing quality with low delay and energy consumption, we design COUPON, a novel cooperative sensing and data forwarding framework. We first notice that cooperative sensing scheme can eliminate sampling redundancy and hence save energy. Then we design two cooperative forwarding schemes by leveraging data fusion: Epidemic Routing with Fusion (ERF) and Binary Spray-and-Wait with Fusion (BSWF). Different from previous work assuming that all packets are propagated individually, we consider that packets are spatial-temporal correlated in the forwarding process, and derive the dissemination law of correlated packets. We theoretically prove that our cooperative forwarding schemes can achieve better tradeoff between delivery delay and transmission overhead. We evaluate our proposed framework and schemes with real mobile traces. Extensive simulations demonstrate that the cooperative sensing scheme can reduce the number of samplings by 93% compared with the non-cooperative scheme, ERF can reduce the transmission overhead by 78% compared with Epidemic Routing (ER), BSWF can increase the delivery ratio by 16%, and reduce the delivery delay and transmission overhead by 5% and 32% respectively, compared with Binary Spray-and-Wait (BSW).