Showing papers on "Sensor node published in 2008"

Reputation-based framework for high integrity sensor networks

Abstract: 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.

DBR: depth-based routing for underwater sensor networks

Abstract: Providing scalable and efficient routing services in underwater sensor networks (UWSNs) is very challenging due to the unique characteristics of UWSNs. Firstly, UWSNs often employ acoustic channels for communications because radio signals do not work well in water. Compared with radio-frequency channels, acoustic channels feature much lower bandwidths and several orders of magnitudes longer propagation delays. Secondly, UWSNs usually have very dynamic topology as sensors move passively with water currents. Some routing protocols have been proposed to address the challenging problem in UWSNs. However, most of them assume that the full-dimensional location information of all sensor nodes in a network is known in prior through a localization process, which is yet another challenging issue to be solved in UWSNs. In this paper, we propose a depth-based routing (DBR) protocol. DBR does not require full-dimensional location information of sensor nodes. Instead, it needs only local depth information, which can be easily obtained with an inexpensive depth sensor that can be equipped in every underwater sensor node. A key advantage of our protocol is that it can handle network dynamics efficiently without the assistance of a localization service. Moreover, our routing protocol can take advantage of a multiple-sink underwater sensor network architecture without introducing extra cost. We conduct extensive simulations. The results show that DBR can achieve very high packet delivery ratios (at least 95%) for dense networks with only small communication cost.

CHEF: Cluster Head Election mechanism using Fuzzy logic in Wireless Sensor Networks

Abstract: In designing the wireless sensor networks, the energy is the most important consideration because the lifetime of the sensor node is limited by the battery of it. To overcome this demerit many research have been done. The clustering is the one of the representative approaches. In the clustering, the cluster heads gather data from nodes, aggregate it and send the information to the base station. In this way, the sensor nodes can reduce communication overheads that may be generated if each sensor node reports sensed data to the base station independently. LEACH is one of the most famous clustering mechanisms. It elects a cluster head based on probability model. This approach may reduce the network lifetime because LEACH does not consider the distribution of sensor nodes and the energy remains of each node. However, using the location and the energy information in the clustering can generate big overheads. In this paper we introduce CHEF - cluster head election mechanism using fuzzy logic. By using fuzzy logic, collecting and calculating overheads can be reduced and finally the lifetime of the sensor networks can be prolonged. To prove efficiency of CHEF, we simulated CHEF compared with LEACH using the matlab. Our simulation results show that CHEF is about 22.7% more efficient than LEACH.

Security in wireless sensor networks

Abstract: Recent advances in electronics and wireless communication technologies have enabled the development of large-scale wireless sensor networks that consist of many low-power, low-cost, and small-size sensor nodes. Sensor networks hold the promise of facilitating large-scale and real-time data processing in complex environments. Security is critical for many sensor network applications, such as military target tracking and security monitoring. To provide security and privacy to small sensor nodes is challenging, due to the limited capabilities of sensor nodes in terms of computation, communication, memory/storage, and energy supply. In this article we survey the state of the art in research on sensor network security.

Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection

Abstract: Activity recognition from an on-body sensor network enables context-aware applications in wearable computing. A guaranteed classification accuracy is desirable while optimizing power consumption to ensure the system's wearability. In this paper, we investigate the benefits of dynamic sensor selection in order to use efficiently available energy while achieving a desired activity recognition accuracy. For this purpose we introduce and characterize an activity recognition method with an underlying run-time sensor selection scheme. The system relies on a meta-classifier that fuses the information of classifiers operating on individual sensors. Sensors are selected according to their contribution to classification accuracy as assessed during system training. We test this system by recognizing manipulative activities of assembly-line workers in a car production environment. Results show that the system's lifetime can be significantly extended while keeping high recognition accuracies. We discuss how this approach can be implemented in a dynamic sensor network by using the context-recognition framework Titan that we are developing for dynamic and heterogeneous sensor networks.

Energy Efficient Medium Access Protocol for Wireless Medical Body Area Sensor Networks

Abstract: This paper presents a novel energy-efficient MAC Protocol designed specifically for wireless body area sensor networks (WBASN) focused towards pervasive healthcare applications. Wireless body area networks consist of wireless sensor nodes attached to the human body to monitor vital signs such as body temperature, activity or heart-rate. The network adopts a master-slave architecture, where the body-worn slave node periodically sends sensor readings to a central master node. Unlike traditional peer-to-peer wireless sensor networks, the nodes in this biomedical WBASN are not deployed in an ad hoc fashion. Joining a network is centrally managed and all communications are single-hop. To reduce energy consumption, all the sensor nodes are in standby or sleep mode until the centrally assigned time slot. Once a node has joined a network, there is no possibility of collision within a cluster as all communication is initiated by the central node and is addressed uniquely to a slave node. To avoid collisions with nearby transmitters, a clear channel assessment algorithm based on standard listen-before-transmit (LBT) is used. To handle time slot overlaps, the novel concept of a wakeup fallback time is introduced. Using single-hop communication and centrally controlled sleep/wakeup times leads to significant energy reductions for this application compared to more ldquoflexiblerdquo network MAC protocols such as 802.11 or Zigbee. As duty cycle is reduced, the overall power consumption approaches the standby power. The protocol is implemented in hardware as part of the Sensiumtrade system-on-chip WBASN ASIC, in a 0.13- mum CMOS process.

Y-MAC: An Energy-Efficient Multi-channel MAC Protocol for Dense Wireless Sensor Networks

Abstract: As the use of wireless sensor networks (WSNs) becomes widespread, node density tends to increase. This poses a new challenge for Medium Access Control (MAC) protocol design. Although traditional MAC protocols achieve low-power operation, they use only a single channel which limits their performance. Several multi-channel MAC protocols for WSNs have been recently proposed. One of the key observations is that these protocols are less energy efficient than single-channel MAC protocols under light traffic conditions. In this paper, we propose an energy efficient multi-channel MAC protocol, Y-MAC, for WSNs. Our goal is to achieve both high performance and energy efficiency under diverse traffic conditions. In contrast to most of previous multi-channel MAC protocols for WSNs, we implemented Y-MAC on a real sensor node platform and conducted extensive experiments to evaluate its performance. Experimental results show that Y-MAC is energy efficient and maintains high performance under high-traffic conditions.

Monte Carlo localization for mobile wireless sensor networks

Abstract: Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors' radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy.

Design and Implementation of Scalable Wireless Sensor Network for Structural Monitoring

Abstract: An integrated hardware and software system for a scalable wireless sensor network WSN is designed and developed for structural health monitoring. An accelerometer sensor node is designed, developed, and calibrated to meet the requirements for structural vibration monitoring and modal identification. The nodes have four channels of accelerometers in two directions and a microcontroller for processing and wireless communication in a multihop network. Software components have been implemented within the TinyOS oper- ating system to provide a flexible software platform and scalable performance for structural health monitoring applications. These components include a protocol for reliable command dissemination through the network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. The prototype WSN was deployed on a long-span bridge with 64 nodes. The data acquired from the testbed were used to examine the scalability of the network and the data quality. Robust and scalable performance was demonstrated even with a large number of hops required for communication. The results showed that the WSN provides spatially dense and accurate ambient vibration data for identifying vibration modes of a bridge.

Mitigating DoS attacks against broadcast authentication in wireless sensor networks

Abstract: Broadcast authentication is a critical security service in wireless sensor networks. There are two general approaches for broadcast authentication in wireless sensor networks: digital signatures and μTESLA-based techniques. However, both signature-based and μTESLA-based broadcast authentication are vulnerable to Denial of Services (DoS) attacks: An attacker can inject bogus broadcast packets to force sensor nodes to perform expensive signature verifications (in case of signature-based broadcast authentication) or packet forwarding (in case of μTESLA-based broadcast authentication), thus exhausting their limited battery power. This paper presents an efficient mechanism called message-specific puzzle to mitigate such DoS attacks. In addition to signature-based or μTESLA-based broadcast authentication, this approach adds a weak authenticator in each broadcast packet, which can be efficiently verified by a regular sensor node, but takes a computationally powerful attacker a substantial amount of time to forge. Upon receiving a broadcast packet, each sensor node first verifies the weak authenticator, and performs the expensive signature verification (in signature-based broadcast authentication) or packet forwarding (in μTESLA-based broadcast authentication) only when the weak authenticator is valid. A weak authenticator cannot be precomputed without a non-reusable (or short-lived) key disclosed only in a valid packet. Even if an attacker has intensive computational resources to forge one or more weak authenticators, it is difficult to reuse these forged weak authenticators. Thus, this weak authentication mechanism substantially increases the difficulty of launching successful DoS attacks against signature-based or μTESLA-based broadcast authentication. A limitation of this approach is that it requires a powerful sender and introduces sender-side delay. This article also reports an implementation of the proposed techniques on TinyOS, as well as initial experimental evaluation in a network of MICAz motes.

A Simple Algorithm for Data Compression in Wireless Sensor Networks

Abstract: Power saving is a critical issue in wireless sensor networks (WSNs) since sensor nodes are powered by batteries which cannot be generally changed or recharged. As radio communication is often the main cause of energy consumption, extension of sensor node lifetime is generally achieved by reducing transmissions/receptions of data, for instance through data compression. Exploiting the natural correlation that exists in data typically collected by WSNs and the principles of entropy compression, in this Letter we propose a simple and efficient data compression algorithm particularly suited to be used on available commercial nodes of a WSN, where energy, memory and computational resources are very limited. Some experimental results and comparisons with, to the best of our knowledge, the only lossless compression algorithm previously proposed in the literature to be embedded in sensor nodes and with two well- known compression algorithms are shown and discussed.

Wireless Sensor Networks and Applications

Abstract: Contributing Authors Preface SECTION I. Network Design and Network Modelling Chapter 1. A Taxonomy-based Approach to Design Large-scale Sensor Networks (Aravind Iyer, Sunil S. Kulkarni, Vivek Mhatre and Catherine P. Rosenberg) Chapter 2. Algorithms for Robotic Deployment of WSN in Adaptive Sampling Applications (Dan O. Popa and Frank L. Lewis) Chapter 3. A Scalable Graph Model and Coordination Algorithms for Mobile Sensor Networks (Jindong Tan) SECTION II. Network Management Chapter 4. Medium Access Control Protocols for Wireless Sensor Networks (Ali Abu-el Humos, Mihaela Cardei, Bassem Alhalabi and Sam Hsu) Chapter 5. Topology Control for Wireless Sensor Networks (Yu Wang) Chapter 6. Boundary Detection for Sensor Networks (Ren-Shiou Liu, Lifeng Sang and Prasun Sinha) Chapter 7. TPSS: A Time-based Positioning Scheme for Sensor Networks with Short Range Beacons (Fang Liu, Xiuzhen Cheng, Dong Hua and Dechang Chen) Chapter 8. Wakeup Strategies in Wireless Sensor Networks (Curt Schurgers) Chapter 9. Time-Synchronization Challenges and Techniques (Weilian Su) Chapter 10. Location Service, Information Dissemination and Object Tracking in Wireless Sensor Networks by Using Quorum Methods (Dan-Dan Liu and Xiao-Hua Jia) Chapter 11. Maximizing the Lifetime of an Always-On Wireless Sensor Network Application: A Case Study (Santosh Kumar, Anish Arora and Ten H. Lai) SECTION III. Data Management Chapter 12. Data Management in Sensor Networks (Jinbao Li, Zhipeng Cai and Jianzhong Li) Chapter 13. Time-Synchronization Challenges and Techniques (Kai-Wei Fan, Sha Liu and Prasun Sinha) Chapter 14. Performance Comparison of Clustering Schemes in Sensor Networks (Yadi Ma and Maggie Cheng) Chapter 15. Reliable and Efficient Information Forwarding and Traffic Engineering in Wireless Sensor Networks (Fernand S. Cohen, Joshua Goldberg and Jaudelice C. de Oliveira) Chapter 16. Modeling Data Gathering in Wireless Sensor Networks (Bhaskar Krishnamachari) SECTION IV. Security Chapter 17. A Survey on Sensor Network Security (Xiaojiang Du and Yang Xiao) Chapter 18. A Passive Approach to Unauthorized Sensor Node Identication (Cherita Corbett, John Copeland and Raheem Beyah)

Distributed EM Algorithm for Gaussian Mixtures in Sensor Networks

Abstract: This paper presents a distributed expectation-maximization (EM) algorithm over sensor networks. In the E-step of this algorithm, each sensor node independently calculates local sufficient statistics by using local observations. A consensus filter is used to diffuse local sufficient statistics to neighbors and estimate global sufficient statistics in each node. By using this consensus filter, each node can gradually diffuse its local information over the entire network and asymptotically the estimate of global sufficient statistics is obtained. In the M-step of this algorithm, each sensor node uses the estimated global sufficient statistics to update model parameters of the Gaussian mixtures, which can maximize the log-likelihood in the same way as in the standard EM algorithm. Because the consensus filter only requires that each node communicate with its neighbors, the distributed EM algorithm is scalable and robust. It is also shown that the distributed EM algorithm is a stochastic approximation to the standard EM algorithm. Thus, it converges to a local maximum of the log-likelihood. Several simulations of sensor networks are given to verify the proposed algorithm.

Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks

Abstract: Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsity of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network.

An improved DV-Hop localization algorithm for wireless sensor networks

Abstract: Aiming at the positioning problem of wireless sensor network node location, an improved DV-hop positioning algorithm is proposed in this paper, together with its basic principle and realization issues. The proposed method can improve location accuracy without increasing hardware cost for sensor node. Simulation results show that it has good positioning accuracy and coverage. The influences of anchor nodes on the DV-hop algorithm are also explored in the paper.

Malicious node detection in wireless sensor networks using weighted trust evaluation

Abstract: Deployed in a hostile environment, individual nodes of a wireless sensor network (WSN) could be easily compromised by the adversary due to the constraints such as limited battery lifetime, memory space and computing capability. It is critical to detect and isolate the compromised nodes in order to avoid being misled by the falsified information injected by the adversary through compromised nodes. However, it is challenging to secure the flat topology networks efficiently because of the poor scalability and high communication overhead. On top of a hierarchical WSN architecture, in this paper we proposed a novel scheme based on weighted-trust evaluation to detect malicious nodes. The hierarchical network can reduce the communication overhead between sensor nodes by utilizing clustered topology. Through intensive simulation, we verified the correctness and efficiency of our detection scheme.

Radio-Triggered Wake-ups with Addressing Capabilities for extremely low power sensor network applications

Abstract: Sensor network applications are generally characterized by long idle durations and intermittent communication patterns. The traffic loads are typically so low that overall the idle duration energy consumption dominates. Low duty cycle MAC protocols are used to reduce the idle duration energy consumption. However, lowering down the duty cycle in favour of energy consumption results in increased latency, which makes it undesirable for many applications. In this paper, we propose radio triggered wake-up with addressing capabilities (RTWAC) that allows suppressing the idle duration current consumption. Our solution consists of an external low-cost hardware wakeup circuit attached to the microcontroller of a sensor node. The sensor node stays in the sleep mode with its normal communication radio turned off. In order to communicate with a sensor node, a special kind of out-of-band modulated wakeup signal is transmitted. The modulated signal contains data that enables to distinguish between differently addressed nodes in order to avoid undesired node wake-ups. Furthermore, we augment this solution to a MAC protocol running on the normal radio on the sensor node in an advantageous way to achieve high energy gains and low latency for data communication.

Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection

Abstract: This paper presents a dynamic model of wireless sensor networks (WSNs) and its application to sensor node fault detection. Recurrent neural networks (NNs) are used to model a sensor node, the node's dynamics, and interconnections with other sensor network nodes. An NN modeling approach is used for sensor node identification and fault detection in WSNs. The input to the NN is chosen to include previous output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of a backpropagation-type NN. The input to the NN and the topology of the network are based on a general nonlinear sensor model. A simulation example, including a comparison to the Kalman filter method, has demonstrated the effectiveness of the proposed scheme.

LiveNet: Using Passive Monitoring to Reconstruct Sensor Network Dynamics

Abstract: We describe LiveNet, a set of tools and analysis methods for reconstructing the complex behavior of a deployed sensor network LiveNet is based on the use of multiple passive packet sniffers co-located with the network, which collect packet traces that are merged to form a global picture of the network's operation The merged trace can be used to reconstruct critical aspects of the network's operation that cannot be observed from a single vantage point or with simple application-level instrumentation We address several challenges: merging multiple sniffer traces, determining sniffer coverage, and inference of missing information for routing path reconstruction We perform a detailed validation of LiveNet's accuracy and coverage using a 184-node sensor network testbed, and present results from a real-world deployment involving physiological monitoring of patients during a disaster drill Our results show that LiveNet is able to accurately reconstruct network topology, determine bandwidth usage and routing paths, identify hot-spot nodes, and disambiguate sources of packet loss observed at the application level

PowerTOSSIM z: realistic energy modelling for wireless sensor network environments

Abstract: Energy continues to be the key constraint in wireless sensor networks. We review existing methods for estimating software power consumption and battery modelling, as applied to embedded systems such as Wireless Sensor Networks. We consider current developments in hardware and software technology, in particular the availability of high-fidelity simulators. Once such simulator, TOSSIM for TinyOS 1.x, models power consumption via a plugin, PowerTOSSIM. We complete the port of PowerTOSSIM to TinyOS 2.0 for the latest model of sensor node, the MICAz. Finally, we extend the simulator to model non-linear battery effects.

A Software-Defined Radio System for Backscatter Sensor Networks

Abstract: Backscatter radio is proposed for sensor networks. In that way, the transmitter for each sensor is simplified to a transistor connected to an antenna and therefore, the cost for each sensor's communicator becomes negligible, while energy used for wireless communication per sensor is minimized. A software-defined transceiver is built to transmit a carrier, receive the reflections from various sensors and extract their transmitted messages. This work presents a thorough model of the backscatter radio link, the system architecture and a set of data extraction techniques for each sensor's information, testing in practice a sensor communicating through backscatter at a range of approximately 15 meters indoors, with 5 milliwatt transmission power at 10 bits per second. This work highlights the idiosyncrasies of the backscatter channel and provides a new communication perspective in the fertile area of scalable sensor networks, especially when low bit-rate, ultra-low cost sensors are required.

Energy-efficient connected-coverage in wireless sensor networks

Abstract: This paper addresses the target coverage problem inWireless Sensor Networks (WSNs). Communication and sensing consume energy, therefore, efficient power management can extend network lifetime. In this paper, we consider a large number of sensors randomly deployed to monitor a number of targets. Each target may be redundantly covered by multiple sensors. To conserve energy, we organise sensors in sets activated sucessively. In this paper, we introduce the Connected Set Covers (CSC) problem that has as objective finding a maximum number of set covers such that each sensor node to be activated is connected to the Base Station (BS). A sensor can participate in multiple sensor sets, but the total energy spent in all sets is constrained by the initial energy reserves. We show that the CSC problem is NP-complete and we propose three solutions: an Integer Programming (IP)-based solution, a greedy approach and a distributed and localised heuristic. Simulation results that validate our approaches are also presented.

Mobility Metric based LEACH-Mobile Protocol

Abstract: Cluster based protocols like LEACH were found best suited for routing in wireless sensor networks. In mobility centric environments some improvements were suggested in the basic scheme. LEACH-Mobile is one such protocol. The basic LEACH protocol is improved in the mobile scenario by ensuring whether a sensor node is able to communicate with its cluster head. Since all the nodes, including cluster head is moving it will be better to elect a node as cluster head which is having less mobility related to its neighbours. In this paper, LEACH-Mobile protocol has been enhanced based on a mobility metric "remoteness" for cluster head election. This ensures high success rate in data transfer between the cluster head and the collector nodes even though nodes are moving. We have simulated and compared our LEACH-mobile-enhanced protocol with LEACH-mobile. Results show that inclusion of neighbouring node information improves the routing protocol.

Improving the Data Delivery Latency in Sensor Networks with Controlled Mobility

Abstract: Unlike traditional multihop forwarding among homogeneous static sensor nodes, use of mobile devices for data collection in wireless sensor networks has recently been gathering more attention. It is known that the use of mobility significantly reduces the energy consumption at each sensor, elongating the functional lifetime of the network, in exchange for increased data delivery latency. However, in previous work, mobility and communication capabilities are often underutilized, resulting in suboptimal solutions incurring unnecessarily large latency. In this paper, we focus on the problem of finding an optimal path of a mobile device, which we call "data mule," to achieve the smallest data delivery latency in the case of minimum energy consumption at each sensor, i.e., each sensor only sends its data directly to the data mule. We formally define the path selection problem and show the problem is $\mathcal{NP}$-hard. Then we present an approximation algorithm and analyze its approximation factor. Numerical experiments demonstrate that our approximation algorithm successfully finds the paths that result in 10%-50% shorter latency compared to previously proposed methods, suggesting that controlled mobility can be exploited much more effectively.

SAKE: Software Attestation for Key Establishment in Sensor Networks

Abstract: This paper presents a protocol called SAKE (Software Attestation for Key Establishment), for establishing a shared key between any two neighboring nodes of a sensor network. SAKE guarantees the secrecy and authenticity of the key that is established, without requiring any prior authentic or secret information in either node. In other words, the attacker can read and modify the entire memory contents of both nodes before SAKE executes. Further, to the best of our knowledge, SAKE is the only protocol that can perform key re-establishment after sensor nodes are compromised, because the presence of the attacker's code in the memory of either protocol participant does not compromise the security of SAKE. Also, the attacker can perform any active or passive attack using an arbitrary number of malicious, colluding nodes. SAKE does not require any hardware modification to the sensor nodes, human mediation, or secure side channels. However, we do assume the setting of a computationally-limited attacker that does not introduce its own computationally powerful nodes into the sensor network. SAKE is based on ICE (Indisputable Code Execution), a primitive we introduce in previous work to dynamically establish a trusted execution environment on a remote, untrusted sensor node.

Sensor Position Determination with Flying Anchors in Three-Dimensional Wireless Sensor Networks

Abstract: This paper presents a range-free position determination (localization) mechanism for sensors in a three-dimensional wireless sensor network based on the use of flying anchors. In the scheme, each anchor is equipped with a Global Positioning System (GPS) receiver and broadcasts its location information as it flies through the sensing space. Each sensor node in the sensing area then estimates its own location by applying basic geometry principles to the location information it receives from the flying anchors. The scheme eliminates the requirement for specific positioning hardware, avoids the need for any interaction between the individual sensor nodes, and is independent of network densities and topologies. The performance of the localization scheme is evaluated in a series of simulations performed using ns-2 software and is compared to that of the Centroid and Constraint range-free mechanisms. The simulation results demonstrate that the localization scheme outperforms both Centroid and Constraint in terms of a higher location accuracy, a reduced localization time, and a lower beacon overhead. In addition, the localization scheme is implemented on the Tmote Sky for validating the feasibility in the real environment.

Passive diagnosis for wireless sensor networks

Abstract: Network diagnosis, an essential research topic for traditional networking systems, has not received much attention for wireless sensor networks. Existing sensor debugging tools like sympathy or EmStar rely heavily on an add-in protocol that generates and reports a large amount of status information from individual sen-sor nodes, introducing network overhead to a resource constrained and usually traffic sensitive sensor network. We report in this study our initial attempt at providing a light-weight network diag-nosis mechanism for sensor networks. We propose PAD, a prob-abilistic diagnosis approach for inferring the root causes of ab-normal phenomena. PAD employs a packet marking algorithm for efficiently constructing and dynamically maintaining the inference model. Our approach does not incur additional traffic overhead for collecting desired information. Instead, we introduce a prob-abilistic inference model which encodes internal dependencies among different network elements, for online diagnosis of an operational sensor network system. Such a model is capable of additively reasoning root causes based on passively observed symptoms. We implement the PAD design in our sea monitoring sensor network test-bed and validate its effectiveness. We further evaluate the efficiency and scalability of this design through ex-tensive trace-driven simulations.

Wireless Sensor Node hardware: A review

Abstract: Wireless sensor networks (WSN) are becoming increasingly popular, due to the benefits they bring to many applications as well as the increasing availability and maturity of the underlying technology. The fundamental building blocks of these networks are the sensor nodes themselves, the sensors attached to these nodes, and the software running on the nodes. A basic sensor node platform consists of a CPU, a radio and a power supply. For the last 10 years a number of research institutions and companies have been designing and producing nodes with these three components as a minimum. We review how these sensor nodes have evolved over this time and we also categorize the features of various platforms so as to enable an application developer to quickly determine which node is appropriate for their particular network or which features are desirable for inclusion on a custom built sensor platform.

CODA: A Continuous Object Detection and Tracking Algorithm for Wireless Ad Hoc Sensor Networks

Abstract: Wireless sensor networks make possible many new applications in a wide range of application domains. One of the primary applications of such networks is the detection and tracking of continuously moving objects, such as wild fires, biochemical materials, and so forth. This study supports such applications by developing a continuous object detection and tracking algorithm, designated as CODA, based on a hybrid static/dynamic clustering technique. The CODA mechanism enables each sensor node to detect and track the moving boundaries of objects in the sensing field. The numerical results obtained using a Qualnet simulator confirm the effectiveness and robustness of the proposed approach.

Emergent properties: detection of the node-capture attack in mobile wireless sensor networks

Abstract: One of the most vexing problems in wireless sensor network security is the node capture attack. An adversary can capture a node from the network as the first step for further different types of attacks. For example, the adversary can collect all the cryptographic material stored in the node. Also, the node can be reprogrammed and re-deployed in the network in order to perform malicious activities. To the best of our knowledge no distributed solution has been proposed to detect a node capture in a mobile wireless sensor network. In this paper we propose an efficient and distributed solution to this problem leveraging emergent properties of mobile wireless sensor networks. In particular, we introduce two solutions: SDD, that does not require explicit information exchange between the nodes during the local detection, and CCD, a more sophisticated protocol that uses local node cooperation in addition to mobility to greatly improve performance. We also introduce a benchmark to compare these solutions with. Experimental results demonstrate the feasibility of our proposal. For instance, while the benchmark requires about 9,000 seconds to detect node captures, CDD requires less than 2,000 seconds. These results support our intuition that node mobility, in conjunction with a limited amount of local cooperation, can be used to detect emergent global properties.