Sensor networks have been deployed in many real-world applications for tracking and monitoring of dynamic targets over large geographical areas. We consider a scenario where a number of ground and/or airborne sensors are used to jointly track a ground target with certain constraints on its motion dynamics, such as those defined by roadways. The state estimates from these sensors are sent over a lossy communications network to a remote fusion center for fusion. We propose both centralized and distributed projection-based methods to incorporate known constraints into the estimation and fusion process. In addition to presenting simple closed-form projected fusion solutions, we also show how to utilize knowledge of the constraints into the fusion process by two distance-based weighting methods. We also explore the effect of link loss and sensor measurement bias on tracking accuracy performance of constrained fusion using illustrative tracking examples. Performance evaluation results demonstrate the potential of adopting weighted average fusers in improving the overall tracking performance in the presence of link loss and measurement bias.

We abstract the accuracy performance of any fuser into a fusion quality measure that is within the range [0, 1] and monotonically decreases with increasing errors. Although there are many possible ways to map the actual error to this quality measure, we adopt a mapping function consisting of both concave and convex regions and its parameters can be tuned based on system design requirements. The effect of communication loss over the links from the sensors, where estimates are generated, to the fuser is then considered, and based on the variations of the quality measure with loss, we define the overall fuser utility that characterizes the resilience of a fuser with increasingly adverse communication constraints. Tracking examples are shown to demonstrate the comparative quality and utility performances of several closed-form fusers.

We consider a scenario where a cognitive radio sensor performs spectrum sensing and target tracking alternately. Both tasks may impose certain performance guarantee requirements that necessitate considerations of how to schedule these tasks over time. In addition, multiple such cognitive radio sensors can perform collaborative spectrum sensing and target tracking, where a fusion center serves to collect the sensor estimates and their covariances to generate fused target state estimates. We study the performance tradeoffs of these two tasks from variable scheduling intervals. The effect of sensing errors on the target tracking performance due to induced loss from perceived channel unavailability is investigated and conclusions are drawn based on the observed performance tradeoffs.

The support for high data rate applications with the cognitive radio technology necessitates wideband spectrum sensing. However, it is costly to apply long-term wideband sensing and is especially difficult in the presence of uncertainty, such as high noise, interference, outliers, and channel fading. In this work, we propose scheduling of sequential compressed spectrum sensing which jointly exploits compressed sensing (CS) and sequential periodic detection techniques to achieve more accurate and timely wideband sensing. Instead of invoking CS to reconstruct the signal in each period, our proposed scheme performs backward grouped-compressed-data sequential probability ratio test (backward GCD-SPRT) using compressed data samples in sequential detection, while CS recovery is only pursued when needed. This method on one hand significantly reduces the CS recovery overhead, and on the other takes advantage of sequential detection to improve the sensing quality. Furthermore, we propose (a) an in-depth sensing scheme to accelerate sensing decision-making when a change in channel status is suspected, (b) a block-sparse CS reconstruction algorithm to exploit the block sparsity properties of wide spectrum, and (c) a set of schemes to fuse results from the recovered spectrum signals to further improve the overall sensing accuracy. Extensive performance evaluation results show that our proposed schemes can significantly outperform peer schemes under sufficiently low SNR settings.

The cognitive radio (CR) technology holds promise to significantly increase spectrum availability and wireless network capacity. With more spectrum bands opened up for CR use, it is critical yet challenging to perform efficient wideband sensing. We propose an integrated sequential wideband sensing scheduling framework that concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and lower-cost spectrum sensing. First, to ensure more timely detection without incurring high overhead involved in periodic recovery of CS signals, we propose smart scheduling of a CS-based sequential wideband detection scheme to effectively detect the PU activities in the wideband of interest. Second, to further help users under severe channel conditions identify the occupied sub-channels, we develop two collaborative strategies, namely, joint reconstruction of the signals among neighboring users and wideband sensing-map fusion. Third, to achieve robust wideband sensing, we propose the use of anomaly detection in our framework. Extensive simulations demonstrate that our approach outperforms peer schemes significantly in terms of sensing delay, accuracy and overhead.

File transfers between the decentralized storage sites over dedicated wide-area connections are becoming increasingly important in high-performance computing and big data scenarios. Designing such scientific workflows for large file transfers is extremely challenging as they depend on the file, I/O, host, and local- and wide-area network subsystems, and their interactions. To gain insights into file-transfer rate profiles, we develop polynomial, bagging, and boosting regression models for Lustre and XFS file transfer measurements, which are collected using XDD over a suite of 10 Gbps connections with 0-366 ms round trip times (RTTs). In addition to overall trends and analytics, these regressions also provide file-transfer rate estimates for RTTs and number of parallel flows at which measurements might not have been collected. They show that bagging and boosting techniques provide closer data fits than the polynomial regression. We develop probabilistic bounds on the generalization error of these methods, which combined with the cross-validation error establish that former two are more accurate estimators than the polynomial regression. In addition, we present a method to efficiently determine the number of parallel flows to achieve a peak file-transfer rate using fewer than full sweep measurements; in our measurements, the peak is achieved in 96% of cases with 15-25% of measurements of a full sweep.

In this paper, we consider a scenario where sensors are deployed over a large geographical area for tracking a target with circular nonlinear constraints on its motion dynamics. The sensor state estimates are sent over long-haul networks to a remote fusion center for fusion. We are interested in different ways to incorporate the constraints into the estimation and fusion process in the presence of communication loss. In particular, we consider closed-form projection-based solutions, including rules for fusing the estimates and for incorporating the constraints, which jointly can guarantee timely fusion often required in realtime systems. We test the performance of these methods in the long-haul tracking environment using a simple example.

ide-area data transfers in high-performance computing infrastructures are increasingly being carried over dynamically provisioned dedicated network connections that provide high capacities with no competing traffic. We present extensive TCP throughput measurements and time traces over a suite of physical and emulated 10 Gbps connections with 0-366 ms round-trip times (RTTs). Contrary to the general expectation, they show significant statistical and temporal variations, in addition to the overall dependencies on the congestion control mechanism, buffer size, and the number of parallel streams. We analyze several throughput profiles that have highly desirable concave regions wherein the throughput decreases slowly with RTTs, in stark contrast to the convex profiles predicted by various TCP analytical models. We present a generic throughput model that abstracts the ramp-up and sustainment phases of TCP flows, which provides insights into qualitative trends observed in measurements across TCP variants: (i) slow-start followed by well-sustained throughput leads to concave regions; (ii) large buffers and multiple parallel streams expand the concave regions in addition to improving the throughput; and (iii) stable throughput dynamics, indicated by a smoother Poincare map and smaller Lyapunov exponents, lead to wider concave regions. These measurements and analytical results together enable us to select a TCP variant and its parameters for a given connection to achieve high throughput with statistical guarantees.

Dedicated wide-area network connections are increasingly employed in high-performance computing and big data scenarios. One might expect the performance and dynamics of data transfers over such connections to be easy to analyze due to the lack of competing traffic. However, non-linear transport dynamics and end-system complexities (e.g., multi-core hosts and distributed filesystems) can in fact make analysis surprisingly challenging. We present extensive measurements of memory-tomemory and disk-to-disk file transfers over 10 Gbps physical and emulated connections with 0-366 ms round trip times (RTTs). For memory-to-memory transfers, profiles of both TCP and UDT throughput as a function of RTT show concave and convex regions; large buffer sizes and more parallel flows lead to wider concave regions, which are highly desirable. TCP and UDT both also display complex throughput dynamics, as indicated by their Poincarέmaps and Lyapunov exponents. For diskto-disk transfers, we determine that high throughput can be achieved via a combination of parallel I/O threads, parallel network threads, and direct I/O mode. Our measurements also show that Lustre filesystems can be mounted over long-haul connections using LNet routers, although challenges remain in jointly optimizing file I/O and transport method parameters to achieve peak throughput.

The network infrastructures have been rapidly upgraded in many high-performance networks (HPNs). However, such infrastructure investment has not led to corresponding performance improvement in big data transfer, especially at the application layer, largely due to the complexity of optimizing transport control on end hosts. We design and implement ProbData, a PRofiling Optimization Based DAta Transfer Advisor, to help users determine the most effective data transfer method with the most appropriate control parameter values to achieve the best data transfer performance. ProbData employs a profiling optimization-based approach to exploit the optimal operational zone of various data transfer methods in support of big data transfer in extreme-scale scientific applications. We present a theoretical framework of the optimized profiling approach employed in ProbData as well as its detailed design and implementation. The advising procedure and performance benefits of ProbData are illustrated and evaluated by proof-of-concept experiments in real-life networks.

The transfer of big data is increasingly supported by dedicated channels in high-performance networks, where transport protocols play an important role in maximizing application-level throughput and link utilization. The performance of transport protocols largely depend on their control parameter settings, but it is prohibitively time consuming to conduct an exhaustive search in a large parameter space to find the best set of parameter values. We propose FastProf, a stochastic approximation-based transport profiler, to quickly determine the optimal operational zone of a given data transfer protocol/method over dedicated channels. We implement and test the proposed method using both emulations based on real-life performance measurements and experiments over physical connections with short (2ms) and long (380ms) delays. Both the emulation and experimental results show that FastProf significantly reduces the profiling overhead while achieving a comparable level of end-to-end throughput performance with the exhaustive search-based approach.

Wide-area data transfers in high-performance computing and big data scenarios are increasingly being carried over dedicated network connections that provide high capacities at low loss rates. UDP-based transport protocols are expected to be particularly well-suited for such transfers but their performance is relatively unexplored over a wide range of connection lengths, compared to TCP over shared connections. We present extensive throughput measurements of UDP-based Data Transfer (UDT) over a suite of physical and emulated 10 Gbps connections. In sharp contrast to current UDT analytical models, these measurements indicate much more complex throughput dynamics that are sensitive to the connection modality, protocol parameters, and round-trip times. Lyapunov exponents estimated from the Poincaré maps of UDT traces clearly indicate regions of instability and complex dynamics. We propose a simple model based on the ramp-up and sustainment regimes of a generic transport protocol, which qualitatively illustrates the dominant monotonicity and concavity properties of throughput profiles and relates them to Lyapunov exponents. These measurements and analytical results together enable us to comprehensively evaluate UDT performance and select parameters to achieve high throughput, and they also provide guidelines for designing effective transport protocols for dedicated connections.

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as target tracking. In this paper, we study the scenario where sensors take measurements of one or more dynamic targets and send state estimates of the targets to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels reduce the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. In addition, the errors in target-sensor data association can also degrade the estimation performance. To mitigate the effect of imperfect communications on state estimation and fusion, we consider retransmission and retrodiction. The system adopts certain retransmission-based transport protocols so that lost messages can be recovered over time. Moreover, retrodiction/smoothing techniques are applied so that the chances of incurring excess delay due to retransmission are greatly reduced. We analyze the extent to which retransmission and retrodiction can improve the performance of delay-sensitive target tracking tasks under variable communication loss and delay conditions. Simulation results of a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.

Cognitive radio (CR) techniques promise to significantly increase the available spectrum thus wireless bandwidth. With the increase of spectrum allowed for CR, it is critical and challenging to perform efficient wideband sensing. We propose an integrated sequential wideband sensing framework which concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and lower cost spectrum sensing. First, to ensure more timely spectrum detection while avoiding the high overhead involved in periodic recovery of CS signals, we design a CS-based sequential wideband detection scheme to effectively detect the PU activities in the wideband of interest. Second, to further identify the sub-channels occupied, we exploit joint sparsity of the signals among neighboring users to achieve efficient cooperative wideband sensing. Our performance evaluations demonstrate that our proposed scheme can outperform other peer schemes significantly in terms of the detection delay, detection accuracy, sensing overhead and sensing accuracy.

In long-haul sensor networks, remote sensors are deployed to cover a large geographical area, such as a continent or the entire globe. Related applications can be found in military surveillance, air traffic control, greenhouse gas emission monitoring, and global cyber attack detection, among others. In this paper, we consider target monitoring and tracking using a long-haul sensor network, wherein the state and covariance estimates are sent from the sensors to a fusion center that generates a fused state estimate. Long-haul communications over submarine fibers and satellite links are subject to long latencies and/or high loss rates, which lead to lost or out-of-order messages. These in turn may significantly degrade the fusion performance: Fusing fewer state estimates may compromise the accuracy of the fused state, whereas waiting for all estimates to arrive may compromise its timeliness. We propose an online selective linear fusion method to fuse the state estimates based on projected information contribution from the pending data. Using both prediction and retrodiction techniques, our scheme enables the fusion center to opportunistically make decisions on when to fuse the estimates, thereby achieving a balance between accuracy and timeliness of the fused state. Simulation results of a target tracking application show that our scheme yields accurate and timely fused estimates under variable communications delay and loss conditions.

Spectrum sensing is a crucial element of dynamic spectrum access (DSA) as it enables cognitive radios (CRs) to opportunistically access the under-utilized spectrum. Existing efforts on sensing have not adequately addressed sensing scheduling over time for better detection performance. In this work, we consider sequential periodic sensing of an in-band channel. We focus primarily on finding the appropriate sensing frequency during an SU's active data transmission on a licensed channel. Detection schemes addressing channel state change and anomalous data are designed specifically to facilitate short-term sensing adaptation to the variations in sensed data. In addition, long-term adaptation is also considered so that the evolving sensing environment can be reflected in the sensing schedule as well. Simulation results demonstrate that our design guarantees better conformity to the spectrum access policies by significantly reducing the delay in change detection while ensuring better sensing accuracy.

Spectrum sensing enables cognitive radios (CRs) to opportunistically access the under-utilized spectrum. Existing efforts on sensing have not adequately addressed sensing scheduling over time for better detection performance. In this work, we consider sequential periodic sensing of an in-band channel. We focus primarily on finding the appropriate sensing frequency during an SU's active data transmission on a licensed channel. Change and outlier detection schemes are designed specifically to facilitate short-term sensing adaptation to the variations in sensed data. Simulation results demonstrate that our design guarantees better conformity to the spectrum access policies by significantly reducing the delay in change detection while ensuring better sensing accuracy.

Long-haul sensor networks are deployed in a wide range of applications from national security to environmental monitoring. We consider target tracking over a long-haul sensor network, wherein state and covariance estimates are sent from sensors to a fusion center that generates a fused state. Fusion serves as a viable means to improve the estimation performance to meet the system requirement on accuracy and delay. Communications over the long-haul links, such as submarine fibers and satellite links, is subject to long latencies and high loss rates that lead to many lost or out-of-order messages and may significantly degrade the fusion performance. We propose an online selective fuser to combine the received state estimates based on estimated information contribution from the pending data. By concurrently using prediction and retrodiction, the fuser opportunistically makes timely decisions to achieve a balance between accuracy and timeliness of the fused estimate. Simulation results show that our method effectively maintains high levels of fusion performance under various communication delay and loss conditions.

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks. We consider a class of such networks where sensors take measurements of one or more dynamic targets and send state estimates of the target(s) to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels render insufficient the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. The system can adopt certain retransmission-based transport protocols so that lost messages can be recovered over time. However, excess delay may be incurred that can potentially violate the deadline for reporting the estimate. For many applications, though, retrodiction/smoothing techniques can be applied so that the chances of incurring such excess delay are greatly reduced. In this work, we analyze the extent to which retrodiction, along with message retransmission, can improve the performance of delay-sensitive state estimation tasks. Results of numerical and simulation studies of an illustrative example and a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.