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Methods for node localisation in sensor networks usually rely upon the measurement of received strength, time-of-arrival, and/or angle-of-arrival of an incoming signal. In this paper, we propose a method for achieving higher accuracy by combining redundant measurements taken by different nodes. This method is aimed at compensating for the systematic errors which are dependent on the specific nodes used, as well as their spatial configuration. Utilising a technique for data fusion on the physical layer, the time complexity of the method is constant and independent of the number of participating nodes. Thus, adding more nodes generally increases accuracy but does not require additional time to report measurement results. Our data analysis and simulation models are based on extensive experiments with real ultrasound positioning hardware. The simulations show that the ninety-fifth percentile positioning error can be improved by a factor of three for a network of fifty nodes. [ABSTRACT FROM AUTHOR]

Distance estimation is important for localization and a multitude of other tasks in wireless sensor networks. We propose a new scheme for distance estimation based on the comparison of neighborhood lists. It is inspired by the observation that distant nodes have fewer neighbors in common than close ones. Other than many distance estimation schemes, it relies neither on special hardware nor on unreliable measurements of physical wireless communication properties like RSSI. Additionally the approach benefits from message exchange by other protocols and requires a single additional message exchange for distance estimation. We will show that the approach is universally applicable and works with arbitrary radio hardware. We discuss related work and present the new approach in detail including its mathematical foundations. We demonstrate the performance of our approach by presenting various simulation results. [ABSTRACT FROM AUTHOR]

Localization using the characteristics of the Radio Frequency (RF) in wireless sensor networks is attractive because the method does not require additional measuring devices, and hence satisfies low cost and low power consumption needs. The range information derived from Received Signal Strength (RSS), which attenuates over the distance and node connectivity, is, however, inaccurate and unpredictable in the real world due to problems caused by sensor motes and the environment of the sensor field. In this paper, through an empirical analysis, we present detailed radio signal properties of the 2.4GHz IEEE 802.15.4 radio module. We also provide the methodology of antenna design and mounting to alleviate the antenna orientation and RSS fluctuation problems, which are key factors that make RF-based ranging irregular in an obstacle-free environment. Our work is differentiated from previous work, which concludes with merely revealing the problems, ignoring them by assumptions, or even limiting the feasibility of RF utilization in localization. [ABSTRACT FROM AUTHOR]

While a multithreading approach provides a convenient sensor application developing environment with automatic control flow and stack managment, it is considered to have a larger data memory requirement and energy consumption than an event-driven model. Current threaded sensor operating systems unfortunately do not provide appropriate solutions. This paper presents multithreading optimization techniques for sensor network operating systems. Our work focuses on the three major problems of implementing threads on resource-constraint sensor nodes—memory resources, energy consumption, and scheduling policy. Single kernel stack and the thread stack-size analysis techniques reduce the RAM requirement of thread model. The variable timer saves energy consumption and the event-boosting thread scheduling reflects the characteristics of sensor applications and provides fast response time to threads. The experimental results on a common sensor node show that the multithreaded system could be effectively implemented with reasonable overhead. [ABSTRACT FROM AUTHOR]

This paper presents two lifetime models that describe two of the most common modes of operation of sensor nodes today, trigger-driven and duty-cycle driven. The models use a set of hardware parameters such as power consumption per task, state transition overheads, and communication cost to compute a node's average lifetime for a given event arrival rate. Through comparison of the two models and a case study from a real camera sensor node design we show how the models can be applied to drive architectural decisions, compute energy budgets and duty-cycles, and to preform side-by-side comparison of different platforms. [ABSTRACT FROM AUTHOR]

Event-driven operating systems such as TinyOS are the preferred choice for wireless sensor networks. Alternative designs following a classical multi-threaded approach are also available. A popular implementation of such a multi-threaded sensor network operating system is MANTIS. The event-based TinyOS is more energy efficient than the multi-threaded MANTIS system. However, MANTIS is more capable than TinyOS of supporting time critical tasks as task preemption is supported. Thus, timeliness can be traded for energy efficiency by choosing the appropriate operating system. In this paper we present a MANTIS kernel modification that enables MANTIS to be as power-efficient as TinyOS. Results from an experimental analysis demonstrate that the modified MANTIS can be used to fit both sensor network design goals of energy efficiency and timeliness. [ABSTRACT FROM AUTHOR]

Duty-cycling in wireless sensor networks (WSNs) has both beneficial effects on network lifetime and negative effects on application performance due to the inability of a sensor to perform while it is sleeping. In a typical scenario, the active periods of nodes are randomly initialized, leading to unpredictable and often sub-optimal performance. In this paper, we propose a fully decentralized wake-up scattering algorithm that uniformly spreads wake-up times of nearby sensors. Interestingly, our approach is complementary and dual to existing approaches that aim at synchronizing (instead of scattering) times, and to those that focus on spatial (instead of temporal) coverage. Wake-up scattering is beneficial in several application scenarios, three of which are considered here: responsiveness to one-hop queries from a mobile base station, sensing coverage for event detection, and latency in multi-hop communication. Our evaluation shows that, w.r.t. a random assignment of wake-up times, wake-up scattering brings improvements in all these measures, along with a positive impact on the network lifetime. [ABSTRACT FROM AUTHOR]

This paper introduces Crankshaft, a MAC protocol specifically targeted at dense wireless sensor networks. Crankshaft employs node synchronisation and offset wake-up schedules to combat the main cause of inefficiency in dense networks: overhearing by neighbouring nodes. Further energy savings are gained by using efficient channel polling and contention resolution techniques. Simulations show that Crankshaft achieves high delivery ratios at low power consumption under the common convergecast traffic pattern in dense networks. This performance is achieved by trading broadcast bandwidth for energy efficiency. Finally, tests with a TinyOS implementation demonstrate the real-world feasibility of the protocol. [ABSTRACT FROM AUTHOR]

A theoretical framework is proposed for accurate performance analysis of minimum energy coding schemes in Coded Division Multiple Access (CDMA) wireless sensor networks. Bit error rate and average energy consumption is analyzed for two coding schemes proposed in the literature: Minimum Energy coding (ME), and Modified Minimum Energy coding (MME). Since CDMA wireless systems are strongly limited by multi access interference, the system model includes all the relevant characteristics of the wireless propagation. Furthermore, a detailed model of the energy consumption is described as function of the coding schemes, the radio transmit powers, the characteristics of the transceivers, and the dynamics of the wireless channel. A distributed radio power minimization algorithm is also addressed. Numerical results show that ME and MME coding schemes exhibit similar bit error probabilities, whereas MME outperforms ME only in the case of low data rate and large coding codewords. [ABSTRACT FROM AUTHOR]

In this paper, we present the Deployment Support Network (DSN), a new methodology for developing and testing wireless sensor networks (WSN) in a realistic environment. With an additional wireless backbone network a deployed WSN can be observed, controlled, and reprogrammed completely over the air. The DSN provides visibility and control in a similar way as existing emulation testbeds, but overcomes the limitations of wired infrastructures. As a result, development and experiments can be conducted with realistic in- and outdoor deployments. The paper describes the new concept and methodology. Additionally, an architecture-independent implementation of the toolkit is presented, which has been used in an industrial case-study. [ABSTRACT FROM AUTHOR]

A wireless network system (WSN) has been developed for a team of underwater Collaborative Autonomous Agents (CAAs) that are capable of repairing and locating scale formations in tanks and pipes within inaccessible environments. The design of the hardware is miniaturised and it consists of a stackable 25mm form-factor that includes the appropriate functionality and ISM wireless communications for the application. Sourcing of relevant sensors for the application was based on having the necessary sensing range; being miniature in size and having low power consumption. Once agent functionality was achieved, antennas were placed within the infrastructure of the pipe and CAAs to realise direct and indirect communication for the WSN. [ABSTRACT FROM AUTHOR]

In-network data processing and event detection on resource-constrained devices are widely regarded as distinctive and novel features of wireless sensor networks. The vision is that through cooperation of many sensor nodes the accuracy of event detection can be greatly improved. On the practical side however, little real-world experience exists in how far these goals can be achieved. In this paper, we present the results of a small deployment of sensor nodes attached to a fence with the goal of collaboratively detecting and reporting security relevant incidents, such as a person climbing over the fence. Based on experimental data we discuss in detail the process of in-network event detection both from the conceptual side and by evaluating the results obtained. Reusing the same traces in a simulated network, we also look into the impact of multi-hop event reporting. [ABSTRACT FROM AUTHOR]

The internal link performance inference has become an increasingly important issue in operating and evaluating a sensor network. Since it is usually impractical to directly monitor each node or link in the wireless sensor network, we consider the problem of inferring the internal link loss characteristics from passive end-to-end measurement in this paper. Specifically, the link loss performance inference based on the data aggregation is considered. Under the assumptions that the link losses are mutually independent, we formulate the problem of link loss estimation as a Bayesian inference problem and propose a Markov Chain Monte Carlo algorithm to solve it. Through the simulation, we can safely reach the conclusion that the internal link loss rate can be inferred accurately, comparable to the sampled internal link loss rate, and the simulation also shows that the proposed algorithm scales well according to the sensor network size. [ABSTRACT FROM AUTHOR]

In this paper, we propose and evaluate RIDA, a novel information-driven architecture for distributed data compression in a sensor network, allowing it to conserve energy and bandwidth and potentially enabling high-rate data sampling. The key idea is to determine the data correlation among a group of sensors based on the value of the data itself to significantly improve compression. Hence, this approach moves beyond traditional data compression schemes which rely only on spatial and temporal data correlation. A logical mapping, which assigns indices to nodes based on the data content, enables simple implementation, on nodes, of data transformation without any other information. The logical mapping approach also adapts particularly well to irregular sensor network topologies. We evaluate our architecture with both Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) on publicly available real-world data sets. Our experiments on both simulation and real data show that 30% of energy and 80-95% of the bandwidth can be saved for typical multi-hop data networks. Moreover, the original data can be retrieved after decompression with a low error of about 3%. Furthermore, we also propose a mechanism to detect and classify missing or faulty nodes, showing accuracy and recall of 95% when half of the nodes in the network are missing or faulty. [ABSTRACT FROM AUTHOR]

Data aggregation has been put forward as an essential technique to achieve power efficiency in sensor networks. Data aggregation consists of processing data collected by source nodes at each intermediate node enroute to the sink in order to reduce redundancy and minimize bandwidth usage. The deployment of sensor networks in hostile environments call for security measures such as data encryption and authentication to prevent data tampering by intruders or disclosure by compromised nodes. Aggregation of encrypted and/or integrity-protected data by intermediate nodes that are not necessarily trusted due to potential node compromise is a challenging problem. We propose a secure data aggregation scheme that ensures that sensors participating to the aggregation mechanism do not have access to the content of the data while adding their sensed values thanks to the use of an efficient homomorphic encryption scheme. We provide a layered secure aggregation mechanism and the related key attribution algorithm that limits the impact of security threats such as node compromises. We also evaluate the robustness of the scheme against node failures and show that such failures are efficiently recovered by a small subset of nodes that are at most m hops away from the failure. [ABSTRACT FROM AUTHOR]

Skyline computation is a hot topic in database community due to its promising application in multi-criteria decision making. In sensor network application scenarios, skyline is still useful and important in environment monitoring, industry control, etc. To support energy-efficient skyline monitoring in sensor networks, this paper first presents a naïve approach as baseline, and then proposes an advanced approach that employs hierarchical thresholds at the nodes. The threshold-based approach focuses on minimizing the transmission traffic in the network to save the energy consumption. Finally, we conduct extensive experiments to evaluate the proposed approaches on simulated data sets, and compare the threshold-based approach with the naïve approach. Experimental results show that the proposed threshold-based approach outperforms the naïve approach substantially in energy saving. [ABSTRACT FROM AUTHOR]

Distributed observation and control of mobile objects via static wireless sensors demands timely information in a distance sensitive manner: information about closer objects is required more often and more quickly than that of farther objects. In this paper, we present a wireless sensor network protocol, Trail, that supports distance sensitive tracking of mobile object by in-network subscribers upon demand. Trail achieves a find time that is linear in the distance from the subscriber to the object, via a distributed data structure that is updated only locally when objects move. Trail seeks to minimize the size of the data structure. Moreover, Trail is reliable, fault-tolerant and energy-efficient, despite the network dynamics that are typical of wireless sensor networks. We evaluate the performance of Trail by simulations in a 90-by-90 sensor network and report on 105 node experiments in the context of a pursuer-evader control application. [ABSTRACT FROM AUTHOR]

Camera sensor networks—wireless networks of low-power imaging sensors—have become popular recently for monitoring applications. In this paper, we argue that traditional vision-based techniques for calibrating cameras are not directly suitable for low-power sensors deployed in remote locations.We propose approximate techniques to determine the relative locations and orientations of camera sensors without any use of landmarks or positioning technologies. Our techniques determine the degree and range of overlap for each camera and show this information can be exploited for duty cycling and triggered wakeups. We implement our techniques on a Mote testbed and conduct a detailed experimental evaluation. Our results show that our approximate techniques can estimate the degree and region of overlaps to within 10% of their actual values and this error is tolerable at the application-level for effective duty-cycling and wakeups. [ABSTRACT FROM AUTHOR]

Radio-interferometric ranging is a novel technique that allows for fine-grained node localization in networks of inexpensive COTS nodes. In this paper, we show that the approach can also be applied to precision tracking of mobile sensor nodes. We introduce inTrack, a cooperative tracking system based on radio-interferometry that features high accuracy, long range and low-power operation. The system utilizes a set of nodes placed at known locations to track a mobile sensor. We analyze how target speed and measurement errors affect the accuracy of the computed locations. To demonstrate the feasibility of our approach, we describe our prototype implementation using Berkeley motes. We evaluate the system using data from both simulations and field tests. [ABSTRACT FROM AUTHOR]

Initial deployments of wireless sensor networks (WSNs) were based on a many-to-one communication paradigm, where a single sink collects data from a number of data sources. Recently, however, scenarios with multiple sinks are increasingly being proposed, e.g., to deal with actuator nodes or to support high-level programming abstractions. The resulting many-to-many communication makes the existing solutions for single-sink scenarios inefficient. In this paper, we propose a scheme for routing data efficiently from multiple sources to multiple sinks. We first study the problem from a theoretical standpoint, by mapping it to the multi-commodity network design problem. This allows us to derive an optimal solution that, albeit based on global knowledge and therefore impractical, provides us with a theoretical lower bound to evaluate decentralized solutions against. Then, we propose our own decentralized scheme, based on a periodic adaptation of the message routes aimed at minimizing the number of network links exploited. The resulting protocol is simple and easily implementable on WSN devices. The evaluation of our implementation shows that our protocol generates 50% less overhead than the base scheme without adaptation, a result close to the theoretical optimum we derived. [ABSTRACT FROM AUTHOR]

Many applications in wireless sensor networks (WSNs) benefit significantly from organizing nodes into groups, called clusters, because data aggregation and data filtering applied in each cluster can greatly help to reduce traffic. The size of a cluster is measured by the hop distance from the farthest node to the cluster head. Rather than 1-hop clustering, K-hop clustering is preferred by many energy-constrained applications. However, existing solutions fail to distribute clusters evenly across the sensing field, which may lead to unbalanced energy consumption and network inefficiency. Moreover, they incur high communication overhead. We propose an Evenly Distributed Clustering (EDC) algorithm. Constrained by the maximum cluster size K, EDC distributes clusters uniformly, and minimizes the number of clusters. By introducing a relative synchronization technique, EDC converges fast with low communication overhead. It also helps to improve the successful transmission rate from nodes to their cluster heads. The simulation results indicate that EDC outperforms other existing algorithms. [ABSTRACT FROM AUTHOR]

Many applications in wireless sensor networks rely on data from neighboring nodes. However, the effort for developing efficient solutions for sharing data in the neighborhood is often substantial. Therefore, we present a general-purpose algorithm for this task that makes use of the broadcast nature of radio transmission to reduce the number of packets. We have integrated this algorithm into TinyXXL, a programming language extension for data exchange. This combined system offers seamless support both for data exchange among the components of a single node and for efficient neighborhood data sharing. We show that compared to existing solutions, such as Hood, our approach further reduces the work of the application developer and provides greater efficiency. [ABSTRACT FROM AUTHOR]