Sensor networks are ubiquitously used for detection and tracking and, as a result, covering is one of the main tasks of such networks. We study the problem of maximizing the coverage lifetime of a barrier by mobile sensors with limited battery power, where the coverage lifetime is the time until there is a breakdown in coverage due to the death of a sensor. Sensors are first deployed and then coverage commences. Energy is consumed in proportion to the distance traveled for mobility, while for coverage, energy is consumed in direct proportion to the radius of the sensor raised to a constant exponent. We study two variants which are distinguished by whether the sensing radii are given as part of the input or can be optimized: the fixed radii problem and the variable radii problem. We design parametric search algorithms for both problems for the case where the final order of the sensors is predetermined (e.g., sensors cannot swap locations and the initial order must be preserved) and for the case where sensors are initially located at barrier endpoints. In contrast, we show that the variable radii problem is strongly NP-hard and provide hardness of approximation results for fixed radii for the case where all the sensors are initially colocated at an internal point of the barrier.

This paper complements the large body of social sensing literature by developing means for augmenting sensing data with inference results that "fill-in" missing pieces. Unlike trend-extrapolation methods, we focus on prediction in disaster scenarios where disruptive trend changes occur. A set of prediction heuristics (and a standard trend extrapolation algorithm) are compared that use either predominantly-spatial or predominantly-temporal correlations for data extrapolation purposes. The evaluation shows that none of them do well consistently. This is because monitored system state, in the aftermath of disasters, alternates between periods of relative calm and periods of disruptive change (e.g., aftershocks). A good prediction algorithm, therefore, needs to intelligently combine time-based data extrapolation during periods of calm, and spatial data extrapolation during periods of change. The paper develops such an algorithm. The algorithm is tested using data collected during the New York City crisis in the aftermath of Hurricane Sandy in November 2012. Results show that consistently good predictions are achieved. The work is unique in addressing the bi-modal nature of damage propagation in complex systems subjected to stress, and offers a simple solution to the problem.

In social swarming applications, participants equipped with 3G and WiFi-capable smartphones are tasked to provide reports (possibly voluminous ones that include full-motion video) about their immediate environment to a central coordinator. In this paper, we consider the problem of timely delivery of these reports: Each report has an associated deadline, and the goal of the system is to retrieve as many reports as possible (or retrieve the most valuable reports), while satisfying each report's deadline. Reporters can use their cellular interface to upload their reports but can also ask neighbors (using their faster WiFi interface) to help upload parts of their reports. Under an assumption that WiFi transmission delays are negligible, we first show that there exists a polynomial time optimal solution using an earliest-deadline-first (EDF) strategy for achieving the goals described above. In practice, WiFi delays are not negligible; in this case, it turns out that the scheduling problem is strongly NP-hard. We formulate two heuristic algorithms, and show, through simulations and experiments on an Android-based implementation, that these heuristics perform 2-4× better than without peer-assistance, and within 60% of an upper-bound on the optimal.

Motivated by an availability gap for visual media, where images and videos are uploaded from mobile devices well after they are generated, we explore the selective, timely retrieval of media content from a collection of mobile devices. We envision this capability being driven by similarity-based queries posed to a cloud search front-end, which in turn dynamically retrieves media objects from mobile devices that best match the respective queries within a given time limit. Building upon a crowd-sensing framework, we have designed and implemented a system called MediaScope that provides this capability. MediaScope is an extensible framework that supports nearest-neighbor and other geometric queries on the feature space (e.g. clusters, spanners), and contains novel retrieval algorithms that attempt to maximize the retrieval of relevant information. From experiments on a prototype, MediaScope is shown to achieve near-optimal query completeness and low to moderate overhead on mobile devices.

Motivated by an availability gap for visual media, where images and videos are uploaded from mobile devices well after they are generated, we explore the selective, timely retrieval of media content from a collection of mobile devices.

In social swarming applications, participants equipped with 3G and WiFi-capable smart phones are tasked to provide reports (possibly voluminous ones that include full-motion video) about their immediate environment to a central coordinator. In this paper, we consider the problem of timely delivery of these reports: each report has an associated deadline and the goal of the system is to retrieve as many reports as possible (or retrieve the most valuable reports), while satisfying each report's deadline. Reporters can use their cellular interface to upload their reports, but can also ask neighbors (using their faster WiFi interface) to help upload parts of their reports. Under an assumption that WiFi transmission delays are negligible, we first show that there exists a polynomial time optimal solution using an earliest-deadline-first (EDF) strategy for achieving the goals described above. In practice, WiFi delays are not negligible: in this case, it turns out that the scheduling problem is strongly NP-hard. We formulate two heuristic algorithms, and show, through simulations with real-world measurements, that these heuristics perform 2-4× better than without peer-assistance, and within 60% of an upper-bound on the optimal.

As sensor mobility becomes more and more universal, Wireless Sensor Network (WSN) configurations that utilize such mobility will become the norm. We consider the problem of maximizing the lifetime of a wireless connection between a transmitter and a receiver using mobile relays. Initially, all relays are positioned arbitrarily on the line between the transmitter and the receiver and have arbitrary battery capacities. Energy is consumed in proportion to the distance traveled for mobility and in proportion to an exponential function of the distance over which information is sent for communication. Relays can move to different locations as long as they have the energy to do so. The objective is to find positions and thus transmission ranges for the nodes that maximize the lifetime of the network. We study two models. The first is more restrictive, and corresponds to the case where relays are allowed to be set once at time zero (single deployment), while the second model corresponds to the case where relays can be adjusted multiple times (multiple deployments). We show how to compute an optimal solution for the case of no movement cost for both models. We consider a discrete version of the single deployment model, in which relays must be deployed on grid points. We provide two algorithms for this case: a dynamic programming algorithm and a binary search algorithm on potential lifetimes. We prove that both algorithms are FPTASs for the non-discrete problem, if batteries are not too small. Based on these algorithms and on additional ideas we develop a number of heuristics for the multiple deployments model. We evaluate them using simulations and compare them with the lower bound of relays not moving at all and the upper bound of cost-free movement. Our simulations - across a range of mobility and transmission costs, sensible starting locations and battery capacities - demonstrate the benefit of moving over remaining at initial locations even for single deployment.

With the advent of smartphone technology, it has become possible to conceive of entirely new classes of applications. Social swarming, in which users armed with smartphones are directed by a central director to report on events in the physical world, has several real-world applications: search and rescue, coordinated fire-fighting, and the DARPA balloon hunt challenge. In this paper, we focus on the following problem: how does the director optimize the selection of reporters to deliver credible corroborating information about an event. We first propose a model, based on common notions of believability, about the credibility of information. We then cast the problem posed above as a discrete optimization problem, prove hardness results, introduce optimal centralized solutions, and design an approximate solution amenable to decentralized implementation whose performance is about 20 percent off, on average, from the optimal (on real-world data sets derived from Google News) while being three orders of magnitude more computationally efficient. More interesting, a time-averaged version of the problem is amenable to a novel stochastic utility optimization formulation, and can be solved optimally, while in some cases yielding decentralized solutions. To our knowledge, we are the first to propose and explore the problem of extracting credible information from a network of smartphones.

With the advent of smartphone technology, it has become possible to conceive of entirely new classes of applications. Social swarming, in which users armed with smartphones are directed by a central director to report on events in the physical world, has several real-world applications. In this paper, we focus on the following problem: how does the director optimize the selection of reporters to deliver credible corroborating information about an event? We first propose a model, based on common intuitions of believability, about the credibility of information. We then cast the problem as a discrete optimization problem, and introduce optimal centralized solutions and an approximate solution amenable to decentralized implementation whose performance is about 20% off on average from the optimal while being 3 orders of magnitude more computationally efficient. More interesting, a time-averaged version of the problem is amenable to a novel stochastic utility optimization formulation, and can be solved optimally, while in some cases yielding decentralized solutions.