Time synchronization is critical in sensor networks at many layers of their design. It enables better duty-cycling of the radio, accurate and secure localization, beamforming, and other collaborative signal processing tasks. These benefits make time-synchronization protocols a prime target of malicious adversaries who want to disrupt the normal operation of a sensor network. In this article, we analyze attacks on existing time synchronization protocols for wireless sensor networks and we propose a secure time synchronization toolbox to counter these attacks. This toolbox includes protocols for secure pairwise and group synchronization of nodes that either lie in the neighborhood of each other or are separated by multiple hops. We provide an in-depth analysis of the security and the energy overhead of the proposed protocols. The efficiency of these protocols has been tested through an experimental study on Mica2 motes.

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 network? 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.

CSMA/CA protocols rely on the random deferment of packet transmissions. Like most other protocols, CSMA/CA was designed with the assumption that the nodes would play by the rules. This can be dangerous, since the nodes themselves control their random deferment. Indeed, with the higher programmability of the network adapters, the temptation to tamper with the software or firmware is likely to grow; by doing so, a user could obtain a much larger share of the available bandwidth at the expense of other users. We use a game-theoretic approach to investigate the problem of the selfish behavior of nodes in CSMA/CA networks, specifically geared towards the most widely accepted protocol in this class of protocols, IEEE 802.11. We characterize two families of Nash equilibria in a single stage game, one of which always results in a network collapse. We argue that this result provides an incentive for cheaters to cooperate with each other. Explicit cooperation among nodes is clearly impractical. By applying the model of dynamic games borrowed from game theory, we derive the conditions for the stable and optimal functioning of a population of cheaters. We use this insight to develop a simple, localized and distributed protocol that successfully guides multiple selfish nodes to a Pareto-optimal Nash equilibrium.

Radio duty cycling has received significant attention in sensor networking literature, particularly in the form of protocols for medium access control and topology management. While many protocols have claimed to achieve significant duty-cycling benefits in theory and simulation, these benefits have often not translated to practice. The dominant factor that prevents the optimal usage of the radio in real deployment settings is time uncertainty between sensor nodes. This paper proposes an uncertainty-driven approach to duty-cycling where a model of long-term clock drift is used to minimize the duty-cycling overhead. First, we use long-term empirical measurements to evaluate and analyze in-depth the interplay between three key parameters that influence long-term synchronization - synchronization rate, history of past synchronization beacons and the estimation scheme. Second, we use this measurement-based study to design a rate-adaptive, energy-efficient long-term time synchronization algorithm that can adapt to changing clock drift and environmental conditions while achieving application-specific precision with very high probability. Finally, we integrate our uncertainty-driven time synchronization scheme with a MAC layer protocol, BMAC, and empirically demonstrate one to two orders of magnitude reduction in the transmit energy consumption at a node with negligible impact on the packet loss rate.

Recently, researchers have proposed a number of ranging and positioning techniques for wireless networks. However, they all studied these techniques in non-adversarial settings. Distance estimation and positioning techniques are, nevertheless, highly vulnerable to attacks from dishonest nodes and malicious attackers. Internal attackers can report false position and distance information in order to cheat on their locations and external attackers can modify the measured positions and distances of wireless nodes. In this work, we demonstrate two attacks on ultrasonic ranging systems: the wormhole attack, by which the attackers reducethe distance measured between two honest nodes, and the pulsedelay attack, by which the attackers enlarge the measured distance. With these attacks, we show that the attackers can arbitrarily modify distances measured with ultrasonic ranging, despite the authentication and integrity protection of the messages used in the ranging protocol. Based on this difference, B estimates its distance to A.

Time synchronization is critical to sensor networks at many layers of its design and enables better duty-cycling of the radio, accurate localization, beamforming and other collaborative signal processing. While there has been significant work in sensor network synchronization, measurement based studies have been restricted to very short-term (few minutes) datasets and have focused on obtaining accurate instantaneous synchronization. Long-term synchronization has typically been handled by periodic re-synchronization schemes with beacon intervals of a few minutes based on the assumption that long-term drift is too hard to model and predict. Thus, none of this work exploits the temporally correlated behavior of the clock drift. Yet, there are incredible energy gains to be achieved from better modeling and prediction of long-term drift that can provide bounds on long-term synchronization error across a sensor network. Better synchronization can lead to significantly lower duty-cycles of the radio, simplify signal processing and can enable an order of magnitude greater lifetime than current techniques.We measure, evaluate and analyze in-depth the long-term behavior of synchronization skew and drift on typical Mica sensor nodes and develop an efficient long-term time synchronization protocol. We use four real time data sets gathered over periods of 12-30 hours in different environmental conditions to study the interplay between three key parameters that influence long-term synchronization - synchronization rate, history of past synchronization beacons and the estimation scheme. We use this measurement-based study to design an online adaptive time-synchronization algorithm that can adapt to changing clock drift and environmental conditions while achieving application-specified precision with very high probability. We find that our algorithm achieves between one and two orders of magnitude improvement in energy efficiency over currently available time-synchronization approaches.

Actuation ability introduces a fundamentally new design dimension in wireless ad-hoc sensor networks, allowing the network to adaptively reconfigure and repair itself in response to unpredictable run-time dynamics. One of the key network resources in these systems is energy and several uncontrollable factors lead to situations where a certain segment of the network becomes energy constrained before the remaining network. The performance gets limited due to the constrained sections. We argue that in this scenario, instead of rendering the complete network useless, the remaining energy resources should be reorganized to form a new functional topology in the network. We present methods for the network to be aware of its own integrity and use actuation to improve performance when needed. This capability of the system is referred to as "self aware actuation". In this paper, we consider a network where nodes (or a subset of the nodes) have traction ability. The network uses mobility to repair the coverage loss in the area being monitored by it. We present a completely distributed energy aware algorithm (referred to as CO-Fi) for coordinated coverage fidelity maintenance in sensor networks. The energy overheads of mobility are incorporated in the algorithm, thus leaving no hidden costs. Our preliminary analysis shows that CO-Fi can significantly help improve the usable lifetime of these networks.

As embedded systems are being networked, often wirelessly, an increasingly larger share of their total energy budget is due to the communication. This necessitates the development of power management techniques that address communication subsystems, such as radios, as opposed to computation subsystems, such as embedded processors, to which most of the research effort thus far has been devoted. In this paper, we present techniques for energy efficient packet scheduling and fair queuing in wireless communication systems. Our techniques are based on an extensive slack management approach that dynamically adapts the output rate of the system in accordance with the input packet arrival rate. We use a recently proposed radio power management technique, dynamic modulation scaling (DMS), as a control knob to enable energy-latency trade-offs during wireless packet transmission. We first analyze a single input stream scenario, and describe a rate adaptation technique that results in significantly lower energy consumption (reductions of up to 10 ×), while still bounding the resulting packet delays. By appropriately setting the various parameters of our algorithm, the system can be made to traverse the energy-latency-fidelity trade-off space. We extend our techniques to a multiple input stream scenario, and present E 2 WFQ , an energy efficient version of the weighted fair queuing (WFQ) algorithm for fair packet scheduling. Simulation results show that large energy savings can be obtained through the use of E 2 WFQ , with only a small, bounded increase in worst case packet latency. Further, our results demonstrate that E 2 WFQ does not adversely affect the throughput allocation (and hence, fairness) of WFQ.

With the growing interest in wireless sensor networks, techniques for their systematic analysis design and optimization are essential. Despite numerous research efforts in optimizing hardware, algorithms and protocols for these networks, it remains largely unexplored how these innovations can be all tied together to design a sensor network for a specific practical application. We propose a methodology that starts from four independent quality of service (QoS) parameters and allows the user to completely and unambiguously describe the desired performance, without having to deal with the details of individual devices or protocols. By making appropriate choices in terms of device capabilities and run-time techniques, a design can be positioned in this four-dimensional QoS space. Furthermore, we describe a technique to explore the associated tradeoffs at design time, using both analytical expressions and simulations. To illustrate the benefits of our approach, a design example is worked out, which shows a five fold improvement in network operational lifetime by adapting the event reporting delay.

Wireless ad-hoc sensor networks have caught the fancy of many researchers through the world in a very small span of time. Although notable progress has been made in several key areas, several researchers falter in the way they look at sensor networks i.e. just another kind of wireless ad hoc networks, albeit one composed of energy constrained nodes. A key aspect that makes sensor networks different from traditional networks is their strong link to the physical world. We develop this perspective by studying an interesting class of sensor network applications called aggregation applications.We argue that when a user queries for aggregates like maximum, average etc., he is interested in knowing the statistics of the underlying physical process. In general, this is not equivalent to the statistics gathered from a bunch of nodes. We introduce a distinction between aggregates calculated over a region and aggregates calculated over a discrete set of nodes. We classify them as spatial and nodal aggregates respectively. Till now, researchers have followed the approach of doing nodal aggregation in response to every user query in sensor networks. For a continuous physical process and a random deployment of sensor nodes, which are a norm for sensor networks, the conventional approach of doing nodal aggregation produces inaccurate results.We verify our claim by studying a specific aggregation function namely the average over a region (spatial average) in detail. We use a voronoi-based approach to propose an algorithm for calculating spatial average in sensor networks. The algorithm can work in two modes -- centralized and distributed. The energy and accuracy tradeoff associated with each of the two modes is analyzed in detail. The efficacy of the proposed algorithm is tested over a wide variety of physical processes including real precipitation data of South America acquired over a span of past 50 years. We will show that our algorithm gives a 2-8 times performance gain as compared to the conventional approach of doing nodal aggregation. We further explore the robustness of our algorithm to link failures and channel impairments.Although not 100% accurate, our approach is a simple, efficient and a practical solution for calculating the spatial average. We have implemented our algorithm on Berkeley motes. Our first prototype implementation occupies less than 12K flash ROM and consumes less than 1.6K run-time memory. Our algorithm can be easily integrated with available frameworks for doing aggregation in sensor networks. For a minimal cost, our algorithm can be used to provide a spatial aggregation service on a network of motes.

Wireless ad-hoc sensor networks have emerged as an interesting and important research area in the last few years. The applications envisioned for such networks require collaborative execution of a distributed task amongst a large set of sensor nodes. This is realized by exchanging messages that are time-stamped using the local clocks on the nodes. Therefore, time synchronization becomes an indispensable piece of infrastructure in such systems. For years, protocols such as NTP have kept the clocks of networked systems in perfect synchrony. However, this new class of networks has a large density of nodes and very limited energy resource at every node; this leads to scalability requirements while limiting the resources that can be used to achieve them. A new approach to time synchronization is needed for sensor networks.In this paper, we present Timing-sync Protocol for Sensor Networks (TPSN) that aims at providing network-wide time synchronization in a sensor network. The algorithm works in two steps. In the first step, a hierarchical structure is established in the network and then a pair wise synchronization is performed along the edges of this structure to establish a global timescale throughout the network. Eventually all nodes in the network synchronize their clocks to a reference node. We implement our algorithm on Berkeley motes and show that it can synchronize a pair of neighboring motes to an average accuracy of less than 20ms. We argue that TPSN roughly gives a 2x better performance as compared to Reference Broadcast Synchronization (RBS) and verify this by implementing RBS on motes. We also show the performance of TPSN over small multihop networks of motes and use simulations to verify its accuracy over large-scale networks. We show that the synchronization accuracy does not degrade significantly with the increase in number of nodes being deployed, making TPSN completely scalable.

As embedded systems are being networked, often wirelessly, an increasingly larger share of their total energy budget is due to the communication. This necessitates the development of power management techniques that address communication subsystems, such as radios, as opposed to computation subsystems, such as embedded processors, to which most of the research effort thus far has been devoted. In this paper, we present E2WFQ, an energy efficient version of the Weighted Fair Queuing (WFQ) algorithm for packet scheduling in communication systems. We employ a recently proposed radio power management technique, Dynamic Modulation Scaling (DMS), as a control knob to enable energy-latency tradeoffs during wireless packet scheduling. The use of E2WFQ results in an energy aware packet scheduler, which exploits the statistics of the input arrival pattern as well as the variability in packet lengths. Simulation results show that large savings in energy consumption can be obtained through the use of our scheduling scheme, compared to conventional WFQ, with only a small, bounded increase in worst case packet latency.

In wireless sensor networks, energy efficiency is crucial to achieve satisfactory network lifetime. In order to reduce the energy consumption of a node significantly, its radio needs to be turned off. Yet, some nodes have to participate in multi-hop packet forwarding. We tackle this issue by exploiting two degrees of freedom in topology management: the path setup latency and the network density. First, we propose a new technique called Sparse Topology and Energy Management (STEM), which aggressively puts nodes to sleep. It provides a method to wake up nodes only when they need to forward data, where latency is traded off for energy savings. Second, STEM integrates efficiently with existing approaches that leverage the fact that nearby nodes can be equivalent for traffic forwarding. In this case, an increased network density results in more energy savings. We analyze a hybrid scheme, which takes advantage of both setup latency and network density to increase the nodes' lifetime. Our results show improvements of nearly two orders of magnitude compared to sensor networks without topology management.

In wireless sensor networks, energy efficiency is crucial to achieving satisfactory network lifetime. To reduce the energy consumption significantly, a node should turn off its radio most of the time, except when it has to participate in data forwarding. We propose a new technique, called sparse topology and energy management (STEM), which efficiently wakes up nodes from a deep sleep state without the need for an ultra low-power radio. The designer can trade the energy efficiency of this sleep state for the latency associated with waking up the node. In addition, we integrate STEM with approaches that also leverage excess network density. We show that our hybrid wakeup scheme results in energy savings of over two orders of magnitude compared to sensor networks without topology management. Furthermore, the network designer is offered full flexibility in exploiting the energy-latency-density design space by selecting the appropriate parameter settings of our protocol.