Ecosystems monitoring is essential to properly understand their development and the effects of events, both climatological and anthropological in nature. The amount of data used in these assessments is increasing at very high rates. This is due to increasing availability of sensing systems and the development of new techniques to analyze sensor data. The Enviro-Net Project encompasses several of such sensor system deployments across five countries in the Americas. These deployments use a few different ground-based sensor systems, installed at different heights monitoring the conditions in tropical dry forests over long periods of time. This paper presents our experience in deploying and maintaining these systems, retrieving and pre-processing the data, and describes the Web portal developed to help with data management, visualization and analysis.
A small clarification on the definition for (wireless) sensor networks may be in order. Mainly within Computing Science (CS) research @cite_14 @cite_20 and in earlier Sensor Web related efforts @cite_9 , this definition is narrower than what is used in this paper. In this more restrictive definition, a (wireless) sensor network is based on nodes (also known as motes'') that have sensing, data storage processing, and communication components plus a power source. These nodes are usually autonomous and operate cooperatively---by communicating amongst themselves---to collect and process data, also being programmable, , able to behave differently according to, for instance, the type of application, power supply conditions, environmental conditions, Although we have used this type of wireless nodes in our deployments, we do not require the capability of offering communication amongst network's components. Instead, we adopt the centralized type of processing architecture as classified by @cite_14 , being more in line with the current Sensor Web approach to networks @cite_47 . It is sufficient for us, for instance, that the connection of sensing elements be done at the level of integrated data products.
@cite_47 @cite_9 @cite_14 @cite_20
2076865372 2034419870 2219395474 1702602994 2150050284 2144740142 1978999584 2170261562 2161118867 2145869511 2080319396 2160450532 2167465093 2135916619 2013227485 2076901266 2112235623 2131007986 1960247298 2139890752
Wireless sensor networks have attracted a lot of attention recently. Such environments may consist of many inexpensive nodes, each capable of collecting, storing, and processing environmental information, and communicating with neighboring nodes through wireless links. For a sensor network to operate successfully, sensors must maintain both sensing coverage and network connectivity. This issue has been studied in [2003] and Zhang and Hou [2004a], both of which reach a similar conclusion that coverage can imply connectivity as long as sensors' communication ranges are no less than twice their sensing ranges. In this article, without relying on this strong assumption, we investigate the issue from a different angle and develop several necessary and sufficient conditions for ensuring coverage and connectivity of a sensor network. Hence, the results significantly generalize the results in [2003] and Zhang and Hou [2004a]. This work is also a significant extension of our earlier work [Huang and Tseng 2003; 2004], which addresses how to determine the level of coverage of a given sensor network but does not consider the network connectivity issue. Our work is the first work allowing an arbitrary relationship between sensing ranges and communication distances of sensor nodes. We develop decentralized solutions for determining, or even adjusting, the levels of coverage and connectivity of a given network. Adjusting levels of coverage and connectivity is necessary when sensors are overly deployed, and we approach this problem by putting sensors to sleep mode and tuning their transmission powers. This results in prolonged network lifetime. Sensor network technology promises a vast increase in automatic data collection capabilities through efficient deployment of tiny sensing devices. The technology will allow users to measure phenomena of interest at unprecedented spatial and temporal densities. However, as with almost every data-driven technology, the many benefits come with a significant challenge in data reliability. If wireless sensor networks are really going to provide data for the scientific community, citizen-driven activism, or organizations which test that companies are upholding environmental laws, then an important question arises: How can a user trust the accuracy of information provided by the sensor networkq Data integrity is vulnerable to both node and system failures. In data collection systems, faults are indicators that sensor nodes are not providing useful information. In data fusion systems the consequences are more dire; the final outcome is easily affected by corrupted sensor measurements, and the problems are no longer visibly obvious. In this article, we investigate a generalized and unified approach for providing information about the data accuracy in sensor networks. Our approach is to allow the sensor nodes to develop a community of trust. We propose a framework where each sensor node maintains reputation metrics which both represent past behavior of other nodes and are used as an inherent aspect in predicting their future behavior. We employ a Bayesian formulation, specifically a beta reputation system, for the algorithm steps of reputation representation, updates, integration and trust evolution. This framework is available as a middleware service on motes and has been ported to two sensor network operating systems, TinyOS and SOS. We evaluate the efficacy of this framework using multiple contexts: (1) a lab-scale test bed of Mica2 motes, (2) Avrora simulations, and (3) real data sets collected from sensor network deployments in James Reserve. Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources that are being used for huge range of applications where the traditional infrastructure-based network is mostly infeasible. The sensor nodes are densely deployed in a hostile environment to monitor, detect, and analyze the physical phenomenon and consume considerable amount of energy while transmitting the information. It is impractical and sometimes impossible to replace the battery and to maintain longer network life time. So, there is a limitation on the lifetime of the battery power and energy conservation is a challenging issue. Appropriate cluster head (CH) election is one such issue, which can reduce the energy consumption dramatically. Low energy adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing protocol, where the CH is elected in rotation basis based on a probabilistic threshold value and only CHs are allowed to send the information to the base station (BS). But in this approach, a super-CH (SCH) is elected among the CHs who can only send the information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy inference engine (Mamdani’s rule) is used to elect the chance to be the SCH. The results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime. The wireless sensor network is an emerging technology that may greatly facilitate human life by providing ubiquitous sensing, computing, and communication capability, through which people can more closely interact with the environment wherever he she goes. To be context-aware, one of the central issues in sensor networks is location tracking, whose goal is to monitor the roaming path of a moving object. While similar to the location-update problem in PCS networks, this problem is more challenging in two senses: (1) there are no central control mechanism and backbone network in such environment, and (2) the wireless communication bandwidth is very limited. In this paper, we propose a novel protocol based on the mobile agent paradigm. Once a new object is detected, a mobile agent will be initiated to track the roaming path of the object. The agent is mobile since it will choose the sensor closest to the object to stay. The agent may invite some nearby slave sensors to cooperatively position the object and inhibit other irrelevant (i.e., farther) sensors from tracking the object.As a result, the communication and sensing overheads are greatly reduced. Our prototyping of the location-tracking mobile agent based on IEEE 802.11b NICs and our experimental experiences are also reported. As a specific area of sensor networks, wireless in-home sensor networks differ from general sensor networks in that the network has nodes with heterogeneous resources and dissimilar mobility attributes. For example, sensor with different radio coverage, energy capacity, and processing capabilities are deployed, and some of the sensors are mobile and others are fixed in position. The architecture and routing protocol for this type of heterogeneous sensor networks must be based on the resources and characteristics of their member nodes. In addition, the sole stress on energy efficiency for performance measurement is not sufficient. System lifetime is more important in this case. We propose a hub-spoke network topology that is adaptively formed according to the resources of its members. A protocol named resource oriented protocol (ROP) was developed to build the network topology. This protocol principally divides the network operation into two phases. In the topology formation phase, nodes report their available resource characteristics, based on which network architecture is optimally built. We stress that due to the existence of nodes with limitless resources, a top-down appointment process can build the architecture with minimum resource consumption of ordinary nodes. In the topology update phase, mobile sensors and isolated sensors are accepted into the network with an optimal balance of resources. To avoid overhead of periodic route updates, we use a reactive strategy to maintain route cache. Simulation results show that the hub-spoke topology built by ROP can achieve much longer system lifetime. Wireless sensor networks involve very large numbers of small, low-power, wireless devices. Given their unattended nature, and their potential applications in harsh environments, we need a monitoring infrastructure that indicates system failures and resource depletion. We describe an architecture for sensor network monitoring, then focus on one aspect of this architecture: continuously computing aggregates (sum, average, count) of network properties (loss rates, energy-levels etc., packet counts). Our contributions are two-fold. First, we propose a novel tree construction algorithm that enables energy-efficient computation of some classes of aggregates. Second, we show through actual implementation and experiments that wireless communication artifacts in even relatively benign environments can significantly impact the computation of these aggregate properties. In some cases, without careful attention to detail, the relative error in the computed aggregates can be as much as 50 . However, by carefully discarding links with heavy packet loss and asymmetry, we can improve accuracy by an order of magnitude. Wireless sensor networks have attracted attention from a diverse set of researchers, due to the unique combination of distributed, resource and data processing constraints. However, until now, the lack of real sensor network deployments have resulted in ad-hoc assumptions on a wide range of issues including topology characteristics and data distribution. As deployments of sensor networks become more widespread [1, 2], many of these assumptions need to be revisited.This paper deals with the fundamental issue of spatio-temporal irregularity in sensor networks We make the case for the existence of such irregular spatio-temporal sampling, and show that it impacts many performance issues in sensor networks. For instance, data aggregation schemes provide inaccurate results, compression efficiency is dramatically reduced, data storage skews storage load among nodes and incurs significantly greater routing overhead. To mitigate the impact of irregularity, we outline a spectrum of solutions. For data aggregation and compression, we propose the use of spatial interpolation of data (first suggested by in [3] and temporal signal segmentation followed by alignment. To reduce the cost of data-centric storage and routing, we propose the use of virtualization, and boundary detection. Wireless sensor networks emerged as a key technology for prolonged, unsupervised monitoring in a wide spectrum of applications, from biological and environmental to civil and military. The sensor networks should operate autonomously for a long period of time under stringent resource and energy constraints. Energy conservation and power-awareness have become a focus of a number of research efforts, as sensor network nodes must operate on batteries or use energy extracted from the environment, such as solar energy or vibrations. Runtime power measurements and characterization of real existing systems are crucial for studies that target power optimizations, including techniques for dynamic adaptation based on the current energy status. This paper introduces an environment for unobtrusive real-time power monitoring that could be used for a number of wireless sensor platforms. We describe our methodology for calibration and validation of the environment and give empirical data for the Telos wireless sensor platform when it runs a subset of representative applications. Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets. Wireless sensor networks are formed by connected sensors that each have the ability to collect, process, and store environmental information as well as communicate with others via inter-sensor wireless communication. These characteristics allow wireless sensor networks to be used in a wide range of applications. In many applications, such as environmental monitoring, battlefield surveillance, nuclear, biological, and chemical (NBC) attack detection, and so on, critical areas and common areas must be distinguished adequately, and it is more practical and efficient to monitor critical areas rather than common areas if the sensor field is large, or the available budget cannot provide enough sensors to fully cover the entire sensor field. This provides the motivation for the problem of deploying the minimum sensors on grid points to construct a connected wireless sensor network able to fully cover critical square grids, termed CRITICAL-SQUARE-GRID COVERAGE. In this paper, we propose an approximation algorithm for CRITICAL-SQUARE-GRID COVERAGE. Simulations show that the proposed algorithm provides a good solution for CRITICAL-SQUARE-GRID COVERAGE. Wireless Sensor Networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pair-wise based, in which discovery is passively achieved when two nodes are pre-scheduled to wake-up at the same time. In contrast, for the first time, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Because proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of the nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pair-wise one. Our testbed experiments using 40 sensor nodes confirm our theoretical analysis, showing one order of magnitude reduction in discovery delay compared with traditional pair-wise methods with only 0.5 ∼8.8 increase in energy consumption. A critical aspect of applications with wireless sensor networks is network lifetime. Power-constrained wireless sensor networks are usable as long as they can communicate sensed data to a processing node. Sensing and communications consume energy, therefore judicious power management and sensor scheduling can effectively extend network lifetime. To cover a set of targets with known locations when ground access in the remote area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The lack of precise sensor placement is compensated by a large sensor population deployed in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node (e.g. cluster head) for processing. In this paper we propose un efficient method to extend the sensor network life time by organizing the sensors into a maximal number of set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while all other nodes are in a low-energy sleep mode. By allowing sensors to participate in multiple sets, our problem formulation increases the network lifetime compared with related work [M. ], that has the additional requirements of sensor sets being disjoint and operating equal time intervals. In this paper we model the solution as the maximum set covers problem and design two heuristics that efficiently compute the sets, using linear programming and a greedy approach. Simulation results are presented to verify our approaches. A wireless network consisting of a large number of small sensors with low-power transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. The communication or message passing process must be designed to conserve the limited energy resources of the sensors. Clustering sensors into groups, so that sensors communicate information only to clusterheads and then the clusterheads communicate the aggregated information to the processing center, may save energy. In this paper, we propose a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters. We then extend this algorithm to generate a hierarchy of clusterheads and observe that the energy savings increase with the number of levels in the hierarchy. Results in stochastic geometry are used to derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the network when all sensors report data through the clusterheads to the processing center. Networks of small, densely distributed wireless sensor nodes are capable of solving a variety of collaborative problems such as monitoring and surveillance. We develop a simple algorithm that detects and tracks a moving target, and alerts sensor nodes along the projected path of the target. The algorithm involves only simple computation and localizes communication only to the nodes in the vicinity of the target and its projected course. The algorithm is evaluated on a small-scale testbed of Berkeley motes using a light source as the moving target. The performance results are presented emphasizing the accuracy of the technique, along with a discussion about our experience in using such a platform for target tracking experiments. One of the main operations in wireless sensor networks is the surveillance of a set of events (targets) that occur in the field. In practice, a node monitors an event accurately when it is located closer to it, while the opposite happens when the node is moving away from the target. This detection accuracy can be represented by a probabilistic distribution. Since the network nodes are usually randomly deployed, some of the events are monitored by a few nodes and others by many nodes. In applications where there is a need of a full coverage and of a minimum allowed detection accuracy, a single node may not be able to sufficiently cover an event by itself. In this case, two or more nodes are needed to collaborate and to cover a single target. Moreover, all the nodes must be connected with a base station that collects the monitoring data. In this paper we describe the problem of the minimum sampling quality, where an event must be sufficiently detected by the maximum possible amount of time. Since the probability of detecting a single target using randomly deployed static nodes is quite low, we present a localized algorithm based on mobile nodes. Our algorithm sacrifices a part of the energy of the nodes by moving them to a new location in order to satisfy the desired detection accuracy. It divides the monitoring process in rounds to extend the network lifetime, while it ensures connectivity with the base station. Furthermore, since the network lifetime is strongly related to the number of rounds, we propose two redeployment schemes that enhance the performance of our approach by balancing the number of sensors between densely covered areas and areas that are poorly covered. Finally, our evaluation results show an over 10 times improvement on the network lifetime compared to the case where the sensors are static. Our approaches, also, outperform a virtual forces algorithm when connectivity with the base station is required. The redeployment schemes present a good balance between network lifetime and convergence time. A critical aspect of applications with wireless sensor networks is network lifetime. Battery-powered sensors are usable as long as they can communicate captured data to a processing node. Sensing and communications consume energy, therefore judicious power management and scheduling can effectively extend operational time. To monitor a set of targets with known locations when ground access in the monitored area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The loss of precise sensor placement would then be compensated by a large sensor population density in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node for processing. In this paper we propose an efficient method to extend the sensor network operational time by organizing the sensors into a maximal number of disjoint set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while nodes from all other sets are in a low-energy sleep mode. In this paper we address the maximum disjoint set covers problem and we design a heuristic that computes the sets. Theoretical analysis and performance evaluation results are presented to verify our approach. SUMMARY The range of applications of wireless sensor networks is so wide that it tends to invade our every day life. In the future, a sensor network will survey our health, our home, the roads we follow, the office or the industry we work in or even the aircrafts we use, in an attempt to enhance our safety. However, the wireless sensor networks themselves are prone to security attacks. The list of security attacks, although already very long, continues to augment impeding the expansion of these networks. The trust management schemes consist of a powerful tool for the detection of unexpected node behaviours (either faulty or malicious). Once misbehaving nodes are detected, their neighbours can use this information to avoid cooperating with them, either for data forwarding, data aggregation or any other cooperative function. A variety of trust models which follow different directions regarding the distribution of measurement functionality, the monitored behaviours and the way measurements are used to calculate define the node’s trustworthiness has been presented in the literature. In this paper, we survey trust models in an attempt to explore the interplay among the implementation requirements, the resource consumption and the achieved security. Our goal is to draw guidelines for the design of deployable trust model designs with respect to the available node and network capabilities and application peculiarities. Copyright © 2010 John Wiley & Sons, Ltd. Wireless sensor networks are deployed for the purpose of monitoring an area of interest. Even when the sensors are properly calibrated at the time of deployment, they develop drift in their readings leading to erroneous network inferences. Based on the assumption that neighbouring sensors have correlated measurements and that the instantiations of drifts in sensors are uncorrelated, the authors present a novel algorithm for detecting and correcting sensor measurement errors. The authors use statistical modelling rather than physical relations to model the spatio-temporal cross-correlations among sensors. This in principle makes the framework presented applicable to most sensing problems. Each sensor in the network trains a support vector regression algorithm on its neighbours' corrected readings to obtain a predicted value for its future measurements. This phase is referred to here as the training phase. In the running phase, the predicted measurements are used by each node, in a recursive decentralised fashion, to self-assess its measurement and to detect and correct its drift and random error using an unscented Kalman filter. No assumptions regarding the linearity of drift or the density (closeness) of sensor deployment are made. The authors also demonstrate using real data obtained from the Intel Berkeley Research Laboratory that the proposed algorithm successfully suppresses drifts developed in sensors and thereby prolongs the effective lifetime of the network. This paper addresses the coverage breach problem in wireless sensor networks with limited bandwidths. In wireless sensor networks, sensor nodes are powered by batteries. To make efficient use of battery energy is critical to sensor network lifetimes. When targets are redundantly covered by multiple sensors, especially in stochastically deployed sensor networks, it is possible to save battery energy by organizing sensors into mutually exclusive subsets and alternatively activating only one subset at any time. Active nodes are responsible for sensing, computing and communicating. While the coverage of each subset is an important metric for sensor organization, the size of each subset also plays an important role in sensor network performance because when active sensors periodically send data to base stations, contention for channel access must be considered. The number of available channels imposes a limit on the cardinality of each subset. Coverage breach happens when a subset of sensors cannot completely cover all the targets. To make efficient use of both energy and bandwidth with a minimum coverage breach is the goal of sensor network design. This paper presents the minimum breach problem using a mathematical model, studies the computational complexity of the problem, and provides two approximate heuristics. Effects of increasing the number of channels and increasing the number of sensors on sensor network coverage are studied through numerical simulations. Overall, the simulation results reveal that when the number of sensors increases, network lifetimes can be improved without loss of network coverage if there is no bandwidth constraint; with bandwidth constraints, network lifetimes may be improved further at the cost of coverage breach. An emerging class of important applications uses ad hoc wireless networks of low-power sensor devices to monitor and send information about a possibly hostile environment to a powerful base station connected to a wired network. To conserve power, intermediate network nodes should aggregate results from individual sensors. However, this opens the risk that a single compromised sensor device can render the network useless, or worse, mislead the operator into trusting a false reading. We present a protocol that provides a secure aggregation mechanism for wireless networks that is resilient to both intruder devices and single device key compromises. Our protocol is designed to work within the computation, memory and power consumption limits of inexpensive sensor devices, but takes advantage of the properties of wireless networking, as well as the power asymmetry between the devices and the base station.