Troubleshooting unresponsive sensor nodes is a significant challenge in remote sensor network deployments. While prior work often targets low-end sensor networks, this article introduces a novel diagnostic tool, called the telediagnostic powertracer, geared for remote high-end sensing systems. Leveraging special properties of high-end systems, this in situ troubleshooting tool uses external power measurements to determine the internal health condition of an unresponsive node and the most likely cause of its failure. We develop our own low-cost power meter with low-bandwidth radio, propose both passive and active sampling schemes to measure the power consumption of the host node, and then report the measurements to a base station, hence allowing remote (i.e., tele-) diagnosis. The tool was deployed and tested in a remote solar-powered sensing system for acoustic and visual environmental monitoring. It was shown to successfully distinguish between several categories of failures that cause unresponsive behavior including energy depletion, antenna damage, radio disconnection, system crashes, and anomalous reboots. It was also able to determine the internal health conditions of an unresponsive node, such as the presence or absence of sensing and data storage activities (for each of multiple applications). The article explores the feasibility of building such a remote diagnostic tool from the standpoint of economy, scale, and diagnostic accuracy. The main novelty lies in its use of power consumption as a side channel, which has more availability than other I/O ports, to diagnose sensing system failures.

Many next generation applications (such as video flows) are likely to have associated minimum data rate requirements in order to ensure satisfactory quality as perceived by end-users. In this paper, we develop a framework to address the problem of maximizing the aggregate utility of traffic flows in a multi-hop wireless network, with constraints imposed both due to self-interference and minimum rate requirements. The parameters that are tuned in order to maximize the utility are (i) transmission powers of individual nodes and (ii) the channels assigned to the different communication links. Our framework is based on using a cross-decomposition technique that takes both inter-flow interference and self-interference into account. The output of our framework is a schedule that dictates what links are to be activated in each slot and the parameters associated with each of those links. If the minimum rate constraint cannot be satisfied for all of the flows, the framework intelligently rejects a sub-set of the flows and recomputes a schedule for the remaining flows. We also design an admission control module that determines if new flows can be admitted without violating the rate requirements of the existing flows in the network. We provide numerical results to demonstrate the efficacy of our framework.

This paper aims at achieving optimal rate allocation for data aggregation in wireless sensor networks. We first formulate this rate allocation problem as a network utility maximization problem. Due to its non-convexity, we take a couple of variable substitutions on the original problem and transform it into an approximate problem, which is convex. We then apply duality theory to decompose this approximate problem into a rate control subproblem and a scheduling subproblem. Based on this decomposition, a distributed algorithm for joint rate control and scheduling is designed, and proved to approach arbitrarily close to the optimum of the approximate problem. Finally, we show that our approximate solution can achieve near-optimal performance through both theoretical analysis and simulations.

The main challenge of designing classification algorithms for sensor networks is the lack of labeled sensory data, due to the high cost of manual labeling in the harsh locales where a sensor network is normally deployed. Moreover, delivering all the sensory data to the sink would cost enormous energy. Therefore, although some classification techniques can deal with limited label information, they cannot be directly applied to sensor networks since they are designed for centralized databases. To address these challenges, we propose a hierarchical aggregate classification (HAC) protocol which can reduce the amount of data sent by each node while achieving accurate classification in the face of insufficient label information. In this protocol, each sensor node locally makes cluster analysis and forwards only its decision to the parent node. The decisions are aggregated along the tree, and eventually the global agreement is achieved at the sink node. In addition, to control the tradeoff between the communication energy and the classification accuracy, we design an extended version of HAC, called the constrained hierarchical aggregate classification (cHAC) protocol. cHAC can achieve more accurate classification results compared with HAC, at the cost of more energy consumption. The advantages of our schemes are demonstrated through the experiments on not only synthetic data but also a real testbed.

Solar-powered sensor nodes have incentive to spend extra energy, especially when the battery is fully charged, because this energy surplus would be wasted otherwise. In this paper, we consider the problem of utilizing such energy surplus to adaptively adjust the redundancy level of erasure codes used in communication, so that the delivery reliability is improved while the network lifetime is still conserved. We formulate the problem as maximizing the end-to-end packet delivery probability under energy constraints. This formulated problem is hard to solve because of the combinatorics involved and the special curvature of its objective function. By exploiting its inherent properties, we propose an effective solution called SolarCode, which has a constant approximation ratio. We evaluate SolarCode in the context of our solar-powered sensor network testbed. Experiments show that SolarCode is successful in utilizing energy surplus and leads to higher data delivery reliability.

Troubleshooting unresponsive sensor nodes is a significant challenge in remote sensor network deployments. This paper introduces the tele-diagnostic powertracer, an in-situ troubleshooting tool that uses external power measurements to determine the internal health condition of an unresponsive host and the most likely cause of its failure. We developed our own low-cost power meter with low-bandwidth radio to report power measurements and findings, hence allowing remote (i.e., tele-) diagnosis. The tool was deployed and tested in a remote solar-powered sensing network for acoustic and visual environmental monitoring. It was shown to successfully distinguish between several categories of failures that cause unresponsive behavior including energy depletion, antenna damage, radio disconnection, system crashes, and anomalous reboots. It was also able to determine the internal health conditions of an unresponsive node, such as the presence or absence of sensing and data storage activities (for each of multiple sensors). The paper explores the feasibility of building such a remote diagnostic tool from the standpoint of economy, scale and diagnostic accuracy. To the authors' knowledge, this is the first paper that presents a remote diagnostic tool that uses power measurements to diagnose sensor system failures.

Identification of a low-level point radioactive source amidst background radiation is achieved by a network of radiation sensors using a two-step approach. Based on measurements from three or more sensors, a geometric difference triangulation method or an N -sensor localization method is used to estimate the location and strength of the source. Then a sequential probability ratio test based on current measurements and estimated parameters is employed to finally decide: (1) the presence of a source with the estimated parameters, or (2) the absence of the source, or (3) the insufficiency of measurements to make a decision. This method achieves specified levels of false alarm and missed detection probabilities, while ensuring a close-to-minimal number of measurements for reaching a decision. This method minimizes the ghost-source problem of current estimation methods, and achieves a lower false alarm rate compared with current detection methods. This method is tested and demonstrated using: (1) simulations, and (2) a test-bed that utilizes the scaling properties of point radioactive sources to emulate high intensity ones that cannot be easily and safely handled in laboratory experiments.

In this paper, we describe oCast, an energy-optimal multicast routing protocol for wireless sensor networks. The general minimum-energy multicast problem is NP-hard. Intermittent connectivity that results from duty-cycling further complicates the problem. Nevertheless, we present both a centralized and distributed algorithm that are provably optimal when the number of destinations is small. This model is motivated by scenarios where sensors report to a small number of base stations or where data needs to be replicated on a small number of other nodes. We further propose an extended version of oCast, called Delay Bounded oCast (DB-oCast), which can discover optimal multicast trees under a predefined delay bound. Finally, we demonstrate the advantages of our schemes through both theoretical analysis and simulations.

We consider the placement of sensors to detect propagative sources where the sensing area of each sensor is anisotropic and arbitrarily-shaped due to the terrain and meteorological conditions. The propagation and detection times are non-negligible due to the propagation of source effects through space at a slow speed. We formulate the problem as placing the minimum number of sensors to ensure a detection time T and the coverage utility C. Both the sensing areas of sensors and the utility function U(ldr) are chosen to capture the environmental factors and the population distribution. We show this problem to be NP-hard, and present heuristic algorithms for 1-coverage and fc-coverage by adopting exiting methods. We evaluate the proposed algorithms in the realistic setting of Port of Memphis where the objective is to protect the population against chemical leaks or attacks. We utilize the SCIPUFF dispersion model to determine the sensing areas by accounting for the terrain and meteorological conditions, and use the real-life population distribution as the utility function. Based on empirical study, we make several important observations.

In this paper, we present a reliable storage service, called SolarStore, that adaptively trades-off storage reliability versus energy consumption in solar-powered sensor networks. SolarStore adopts a predominantly disconnected network model, where long-running data-collection experiments are conducted in the absence of a continuous connection to the outside world. SolarStore (i) replicates data in the network until the next upload opportunity, and (ii) adapts the degree of data replication dynamically depending on solar energy and storage availability. The goal is to maximize the amount of data that can eventually be retrieved from the network subject to energy and storage constraints. Maximization of retrievable data implies minimizing sensing blackouts due to energy depletion as well as minimizing loss due to node damage in harsh environmental conditions. We have deployed an outdoor solar-powered sensor network, on which SolarStore is implemented and tested. An indoor testbed is also set up for performance evaluation under environmental conditions not attained locally. Experiments show that SolarStore is successful in dynamically responding to variations in the environment in a manner that increases retrievable data under different node failure scenarios.

In this paper, we present AdaptSens: a reliable data collection and storage system for solar-powered sensor networks. Unlike battery-operated devices, solar-powered systems have a less predictable energy supply and their ability to harvest energy depends on past spending, thereby creating incentives for adaptive matching of energy supply and demand. Our storage system is novel in its layered architecture and its incremental layer activation mechanism. AdaptSens provides a set of functions, in separate layers, such as sensory data collection, replication (to prevent failure-induced data loss), and storage balancing (to prevent depletion-induced data loss). The mechanism utilizes surplus energy when available by activating more layers, and resorts to progressively more energy-efficient (partial hibernation) modes when energy is scarce. Best reliability is achieved when all layers are active but meaningful intermediate modes allow different degrees of energy conservation. The efficacy of AdaptSens in trading off reliability for energy is tested on both an outdoor system and an indoor testbed. Evaluation results show that AdaptSens minimizes the sum of all data losses when combining the energy, storage and node failure factors.

Many next generation applications (such as video flows) are likely to have associated minimum data rate requirements to ensure satisfactory quality as perceived by end-users. While there have been prior approaches on supporting quality-of-service (QoS) in mesh networks, they have largely ignored the issues that arise due to self-interference, the interference between different link layer transmissions of a single flow along a multi-hop path. In this paper, we develop a framework to address the problem of maximizing the aggregate utility of traffic flows in wireless mesh networks, with constraints imposed both due to self-interference and minimum rate requirements. The output of our framework is a schedule that dictates which links are to be activated simultaneously, and provides specifications of the resources associated with each of those links. Utilizing the proposed framework as a basis, we build an admission control module that intelligently manages the resources among the flows in the network and admits as many new flows as possible without violating the QoS of the existing flows. We provide numerical results to demonstrate the efficacy of our framework.

In recent years, wireless ad hoc networks have become increasingly popular in both military and civilian applications due to their capability of building networks without the need for a pre-existing infrastructure. While a number of studies have been carried out to study the performance of IEEE 802.11 DCF in single-cell wireless LANs, the analysis of IEEE 802.11 DCF in multi-hop wireless networks with consideration of the effects of physical carrier sense, SINR, and collision caused by accumulative interference has been lacking. In this paper, we substantially extend Cali's work and rigorously model, with all these effects considered, channel activities governed by IEEE 802.11 DCF in multi-hop wireless networks. We show that as in single-cell WLANs, the choice of the contention window size can impact the system throughput in multi-hop wireless networks. However, the optimal value of the contention window size is quite different. Moreover, the optimal attempt probability p (the optimal window size) derived for multi-hop wireless networks with respect to maximizing the system throughput is larger (smaller) than that for single-cell WLANs. We also show that, given the minimal SINR threshold beta, the optimal carrier sense range is smaller than the conventional value used (provided that the contention window size is tuned accordingly).

Identification of a low-level point radiation source amidst background radiation is achieved by a network of radiation sensors using a two-step approach. Based on measurements from three sensors, the geometric difference triangulation method is used to estimate the location and strength of the source. Then a sequential probability ratio test based on current measurements and estimated parameters is employed to finally decide: (1) the presence of a source with the estimated parameters, or (2) the absence of the source, or (3) the insufficiency of measurements to make a decision. This method achieves specified levels of false alarm and missed detection probabilities, while ensuring a close-to-minimal number of measurements for reaching a decision. This method minimizes the ghost-source problem of current estimation methods, and achieves a lower false alarm rate compared with current detection methods. This method is tested and demonstrated using: (1) simulations, and (2) a test-bed that utilizes the scaling properties of point radiation sources to emulate high intensity ones that cannot be easily and safely handled in laboratory experiments.

The localization of a radioactive source can be solved in closed-form using 4 ideal sensors and the Apollonius circle in a noise- and error-free environment. When measurement errors and noise such as background radiation are considered, a larger number of sensors is needed to produce accurate results, particularly for extremely low source intensities. In this paper, we present an efficient fusion algorithm that can exploit measurements from n sensors to improve the localization accuracy, and show how the accuracy scales with n. We report testbed results for a 0.911 μCi source to illustrate the effectiveness of our algorithm, in particular performance comparisons with state-of-the-art fusion algorithms based on Mean of Estimates (MoE) and Maximum Likelihood Estimation (MLE). We show that ITP is more accurate than MoE, whereas the choice between ITP and MLE is generally a tradeoff between accuracy and run time efficiency. Higher-intensity radioactive sources are not safe for actual experiments. In this case, we present simulation results based on a validated simulation model. We show that a low-intensity 400 μCi source, similar to the radioactivity of a concealed dirty bomb, can be localized to within 32.5 m using a sensor density of about 1 per 1100 m 2 in a surveillance area.

In this paper, we extend both Bianchi's and Kumar's models and characterize the channel activities governed by IEEE 802.11 DCF in multi-hop wireless networks from the perspective of an individual sender. In particular, we incorporate the effect of PHY/MAC attributes (such as transmit power and physical carrier sense) that need not be considered in WLANs but become extraordinarily important in multi-hop wireless networks, and derive the throughput attained by each sender. With the use of the analytical model derived, we investigate the impact of transmit power and carrier sense threshold on network capacity, and identify a simple operating condition under which the network may attain throughput that is close to its optimal value.

No abstract available.