"Cold Start" in participatory sensing applications refers to the initial stage in service deployment, during which service adoption remains sparse and, hence, the collected data does not offer adequate coverage. Predictive models, learned from data, offer a way to generalize from sparse observations, but the models themselves need to be statistically reliable to offer a reliable service. To achieve service reliability, this paper offers a modeling approach, where simpler models are used initially, gradually transitioning to more elaborate models, when enough data is collected. A key challenge and contribution of the work is to time model transitions correctly to provide theoretical guarantees on modeling error. Our technique takes a holistic approach in bounding modeling error as opposed to prior statistical approaches that bound the error of a single model component at a time. This technique is tested in the context of a vehicular participatory sensing application.

In this paper, we present the "Slow Start Problem" in participatory sensing applications where a service is provided based on data collected by participants. The slow start problem refers to the initial stage in participatory sensing service deployment, during which service adoption remains sparse and, hence, the collected data does not offer adequate coverage. Predictive models, learned from data, offer a way to generalize from sparse observations, but the models themselves need to be statistically reliable to offer a reliable service. To achieve service reliability, this paper offers a modeling approach, where simpler models are used initially, gradually transitioning to more elaborate models, when enough data is collected. A key challenge and contribution of the work is to time model transitions correctly to provide theoretical guarantees on modeling error. Our technique takes a holistic approach in bounding modeling error as opposed to prior statistical approaches that bound the error of a single model component at a time. This technique is tested in the context of a vehicular participatory sensing application, called GreenGPS, where participant data is used to build models that predict fuel consumption of vehicles on different routes for the purposes of choosing the most fuel-efficient route for each vehicle (as opposed to the shortest or fastest). We show that our approach significantly reduces prediction error in the initial stages of deployment.

Participatory sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context ofGreenGPS; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the participatory sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed participatory sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in[1], however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5 over the fastest, 11.2 percent over the shortest, and 8.4 percent over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.

Intelligent transportation systems serve as important technologies to improve traffic safety, mobility, cost and environmental sustainability. Towards that end, a variety of applications and driver advisory tools have been developed. To work efficiently, many require knowledge of not only street maps but also elements affecting traffic flow. The most obvious elements are traffic lights and stop signs, which we shall henceforth call traffic regulators. Unfortunately, information on traffic regulators is not widely available in public databases such as Open Street Map (OSM). Prior work described crowd-sourcing solutions to predict regulator type and locations. In this paper, we improve the prediction by offering a combination of map-based modeling and crowd-sensing solutions. The modeling component reverse engineers rules for placement of traffic regulators, allowing it to predict their locations and type based on map information. Where available, crowd-sourced vehicular GPS traces are incorporated into the prediction function to improve the results. The approach is evaluated across multiple cities and is shown to outperform both crowd-sourcing alone and map-based modeling alone. It achieves a prediction accuracy level above 97% in detecting the existence and determining the type of traffic regulators in the cities considered.

In this paper, we describe the design, implementation and evaluation of Proteus, a power-proportional cache cluster which eliminates the delay penalty during server provisioning dynamics. To speed up data center services, a cache cluster is used in front of the database tier, providing fast in-cache data access. Since the number of cache servers is large, building power-proportional cache clusters can lead to considerable monetary savings. Dynamic server provisioning, one common methodology for realizing power proportionality in data centers, calls for agile load balancing schemes and smart in-cache data migration algorithms when applied to cache clusters. Otherwise, it induces unacceptable delay spikes due to data re-allocation among cache servers. Proteus addresses both challenges by using a specifically designed virtual nodes placement algorithm and an amortized data migration policy. We implement Proteus, and evaluate it on a 40-server cluster using real Wikipedia data and workload traces. The results show that, with Proteus, the load distribution is much more evenly balanced compared to the case of applying unmodified consistent hashing. At the same time, Proteus induces almost no extra delay during provisioning transitions, which is a significant advantage over other state-of-the-art solutions.

We propose PhotoNet, a picture delivery service for camera sensor networks. PhotoNet is motivated by the needs of disaster-response applications, where a group of survivors and first responders may survey damage and send images to a rescue center in the absence of a functional communication infrastructure. The protocol runs on mobile devices, handling opportunistic forwarding (when they come in contact) and in-network storage. It assigns priorities to images for forwarding and replacement depending on the degree of similarity (or dissimilarity) among them, such that scarce resources are assigned to delivery of most ``deserving'' content first. Prioritization aims at reducing semantic redundancy such as that between pictures of the same scene at the same location taken from slightly different angles. This is in contrast to redundancy among identical objects and among time series data. PhotoNet delivers more diverse pictures in terms of event coverage suppressing logically redundant content belonging to the same event. We show that, in resource constrained networks, reducing semantic redundancy can significantly improve the utility of the service.

This paper computes end-to-end delay bounds for prioritized data flows in disruption-tolerant networks (DTNs). DTNs suffer intermittent connectivity among nodes due to node mobility. When deployed in mission-critical applications, such as disaster response, an interesting question becomes to quantify end-to-end packet delays under assumptions on node mobility. In this paper, we answer this question for the special case of DTNs with recurrent mobility patterns. A recurrent pattern refers to one where nodes revisit the same locations repeatedly. We devise a suitable model for recurrent DTNs that captures their timing and mobility properties. We then apply the recently proposed delay composition algebra to the resulting network model in order to determine an upper bound on end-to-end communication delays of network flows. Evaluation results show that the upper bound is moderate in its pessimism and can be used for deployment planning purposes.

In new applications of mobile ad hoc networks (MANETs), nodes may decide not to cooperate in routing protocols in order to save their limited resources while still using the network to relay their own traffic. Exhibiting such a selfish behavior by even a few nodes may degrade network performance and other cooperating nodes may find themselves unfairly loaded. To cope with such a situation, we propose a stable path, low overhead, truthful, and cost efficient (SLTC) routing protocol which stimulates nodes to cooperate and act truthfully by utilizing the game theoretic notion of mechanism design. To the best of our knowledge, SLTC is the first protocol attaining the message complexity of O(nd), where n is the number of nodes, and d is the network diameter. In addition, SLTC considers stability of the paths in order to deal with the mobility of the nodes in a better way. We evaluated SLTC through simulation in terms of packet delivery ratio, end-to-end delay, traffic overhead and energy consumption. SLTC can achieve more than 80% packet delivery ratio. Compared to ad hoc-VCG which is one of the best known methods in this area, the packet delivery ratio is increased by a factor of 9 along with a reduction in the end-to-end delay, the traffic overhead, and the energy consumption respectively by a factor of 22, 7, and 3.

Overlay multicast has gained much attention in recent years as an alternative method to network layer multicast, especially for mobile ad hoc networks(MANETs). In this paper, we propose a new overlay multicast protocol to achieve simplicity of deployment, rapid adaptation of overlay structure when nodes move, and reduced delay. In our algorithm, to join or leave a multicast group, it is only sufficient for a member node to inform its first upstream member node. This updates the tree structure more rapidly when nodes move. In addition, join and leave delays are reduced and this makes the protocol suitable for multimedia multicasting in MANETs. Simulation results compared to that of ODMRP and ODOMP show that when the number of member nodes increases, our protocol outperforms ODOMP and when speed of nodes is high, it outperforms both ODOMP and ODMRP in terms of delivery ratio.