12254807
PIKE: peer intermediaries for key establishment in sensor networks
The establishment of shared cryptographic keys between communicating neighbor nodes in sensor networks is a challenging problem due to the unsuitability of asymmetric key cryptography for these resource-constrained platforms. A range of symmetric-key distribution protocols exist, but these protocols do not scale effectively to large sensor networks. For a given level of security, each protocol incurs a linearly increasing overhead in either communication cost per node or memory per node. We describe peer intermediaries for key establishment (PIKE), a class of key-establishment protocols that involves using one or more sensor nodes as a trusted intermediary to facilitate key establishment. We show that, unlike existing key-establishment protocols, both the communication and memory overheads of PIKE protocols scale sub-linearly (O(/spl radic/n)) with the number of nodes in the network yet achieving higher security against node compromise than other protocols.
4540744
Random key predistribution schemes for sensor networks
Key establishment in sensor networks is a challenging problem because asymmetric key cryptosystems are unsuitable for use in resource constrained sensor nodes, and also because the nodes could be physically compromised by an adversary. We present three new mechanisms for key establishment using the framework of pre-distributing a random set of keys to each node. First, in the q-composite keys scheme, we trade off the unlikeliness of a large-scale network attack in order to significantly strengthen random key predistribution's strength against smaller-scale attacks. Second, in the multipath-reinforcement scheme, we show how to strengthen the security between any two nodes by leveraging the security of other links. Finally, we present the random-pairwise keys scheme, which perfectly preserves the secrecy of the rest of the network when any node is captured, and also enables node-to-node authentication and quorum-based revocation.
239370
SIA: secure information aggregation in sensor networks
Sensor networks promise viable solutions to many monitoring problems. However, the practical deployment of sensor networks faces many challenges imposed by real-world demands. Sensor nodes often have limited computation and communication resources and battery power. Moreover, in many applications sensors are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the sensor's cryptographic keys.One of the basic and indispensable functionalities of sensor networks is the ability to answer queries over the data acquired by the sensors. The resource constraints and security issues make designing mechanisms for information aggregation in large sensor networks particularly challenging.In this paper, we propose a novel framework for secure information aggregation in large sensor networks. In our framework certain nodes in the sensor network, called aggregators, help aggregating information requested by a query, which substantially reduces the communication overhead. By constructing efficient random sampling mechanisms and interactive proofs, we enable the user to verify that the answer given by the aggregator is a good approximation of the true value even when the aggregator and a fraction of the sensor nodes are corrupted. In particular, we present efficient protocols for secure computation of the median and the average of the measurements, for the estimation of the network size, and for finding the minimum and maximum sensor reading. Our protocols require only sublinear communication between the aggregator and the user. To the best of our knowledge, this paper is the first on secure information aggregation in sensor networks that can handle a malicious aggregator and sensor nodes.
987821
Using Clustering Information for Sensor Network Localization
Sensor network localization continues to be an important research challenge. The goal of localization is to assign geographic coordinates to each node in the sensor network. Localization schemes for sensor network systems should work with inexpensive off-the-shelf hardware, scale to large networks, and also achieve good accuracy in the presence of irregularities and obstacles in the deployment area. 
 
We present a novel approach for localization that can satisfy all of these desired properties. Recent developments in sensor network clustering algorithms have resulted in distributed algorithms that produce highly regular clusters. We propose to make use of this regularity to inform our localization algorithm. The main advantages of our approach are that our protocol requires only three randomly-placed nodes that know their geographic coordinates, and does not require any ranging or positioning equipment (i.e., no signal strength measurement, ultrasound ranging, or directional antennas are needed). So far, only the DV-Hop localization mechanism worked with the same assumptions [1]. We show that our proposed approach may outperform DV-Hop in certain scenarios, in particular when there exist large obstacles in the deployment field, or when the deployment area is free of obstacles but the number of anchors is limited.
182657
Key infection: smart trust for smart dust
Future distributed systems may include large self-organizing networks of locally communicating sensor nodes, any small number of which may be subvened by an adversary. Providing security for these sensor networks is important, but the problem is complicated by the fact that managing cryptographic key material is hard: low-cost nodes are neither tamper-proof nor capable of performing public key cryptography efficiently. We show how the key distribution problem can be dealt with in environments with a partially present, passive adversary: a node wishing to communicate securely with other nodes simply generates a symmetric key and sends it in the clear to its neighbours. Despite the apparent insecurity of this primitive, we can use mechanisms for key updating, multipath secrecy amplification and multihop key propagation to build up extremely resilient trust networks where at most a fixed proportion of communications links can be eavesdropped. We discuss applications in which this assumption is sensible. Many systems must perforce cope with principals who are authenticated weakly, if at all; the resulting issues have often been left in the 'too hard' tray. One particular interest of sensor networks is that they present a sufficiently compact and tractable version of this problem. We can perform quantitative analyses and simulations of alternative strategies, some of which we present here. We also hope that This work may start to challenge the common belief that authentication is substantially about bootstrapping trust. We argue that, in distributed systems where the opponent can subvert any small proportion of nodes, it is more economic to invest in resilience than in bootstrapping.
6124330
ACE: An Emergent Algorithm for Highly Uniform Cluster Formation
The efficient subdivision of a sensor network into uniform, mostly non-overlapping clusters of physically close nodes is an important building block in the design of efficient upper layer network functions such as routing, broadcast, data aggregation, and query processing.
10310314
Sensitivity Analysis in Markov Networks
This paper explores the topic of sensitivity analysis in Markov networks, by tackling questions similar to those arising in the context of Bayesian networks: the tuning of parameters to satisfy query constraints, and the bounding of query changes when perturbing network parameters. Even though the distribution induced by a Markov network corresponds to ratios of multi-linear functions, whereas the distribution induced by a Bayesian network corresponds to multi-linear functions, the results we obtain for Markov networks are as effective computationally as those obtained for Bayesian networks. This similarity is due to the fact that conditional probabilities have the same functional form in both Bayesian and Markov networks, which turns out to be the more influential factor. The major difference we found, however, is in how changes in parameter values should be quantified, as such parameters are interpreted differently in Bayesian networks and Markov networks.
17538498
Reasoning about Bayesian Network Classifiers
Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDS), which are then used to reason about the properties of these classifiers. Specifically, we present an algorithm for converting any naive Bayes classifier into an ODD, and we show theoretically and experimentally that this algorithm can give us an ODD that is tractable in size cvcn given an intractable number of instances. Since ODDS are tractable representations of classifiers, our algorithm allows us to efficiently test the equivalence of two naive Bayes classifiers and characterize discrepancies between them. We also show a number of additional results including a count of distinct classifiers that can be induced by changing some CPT in a naive Bayes classifier, and the range of allowable changes to a CPT which keeps the current classifier unchanged.
11373384
Joint Call Admission Control for Next Generation Wireless Network
Next generation wireless network (NGWN) will be heterogeneous in nature where different radio access technologies (RATs) such as UMTS, WiMax, WLAN, etc, coexist. This coexistence necessitates joint radio resource management (JRRM). This paper focuses on joint call admission control (JCAC) for NGWN. Factors that determine a user's preference for a particular RAT are discussed. We propose a JCAC algorithm which considers the user's preference in making admission decision. A specific case where the user prefers to be served by the RAT which has the least service cost is modeled and evaluated using Markov decision process. The results obtained shows that overall service cost in heterogeneous network can be significantly reduced by using the proposed joint call admission control
8638055
Function-Based Analysis of an Electronic Commerce Website
Traditionally, studies with the technology acceptance model (TAM) have treated software systems as holistic units. A survey is conducted to understand the model better by dropping from system-wide measurement to function-based measurement. The first part of the survey replicates Gefen and Straub’s (2000) study where only intention is measured at the function level, with consistent results. System-wide perceived ease of use (PEOU) has more effect on an intrinsic task (intention to search for books) than on an extrinsic task (intention to purchase books). In the second part of the survey, all factors are measured at the function level. The result shows that the function-level models are more predictive than the system-wide model. Specifically, each PEOU is a significant predictor for each intention in both models. This study shows the value of using function-based analysis and suggests that function-based analysis is particularly useful and timely for the study of integrated products offering relatively distinct functions.
8031191
Dynamic dictionary matching and compressed suffix trees
Recent breakthrough in compressed indexing data structures has reduced the space for indexing a text (or a collection of texts) of length <i>n</i> from <i>O</i>(<i>n</i> log <i>n</i>) bits to <i>O</i>(<i>n</i>) bits, while allowing very efficient pattern matching [10, 13]. Yet the compressed nature of such indices also makes them difficult to update dynamically. This paper presents the first <i>O</i>(<i>n</i>)-bit representation of a suffix tree for a dynamic collection of texts whose total length is <i>n</i>, which supports insertion and deletion of a text <i>T</i> in <i>O</i>(|<i>T</i> | log<sup>2</sup> <i>n</i>) time, as well as all suffix tree traversal operations, including forward and backward suffix links. This work can be regarded as a generalization of the compressed representation of static texts. Our new suffix tree representation serves as a core part in a compact solution for the dynamic dictionary matching problem, i.e., providing an <i>O</i>(<i>d</i>)-bit data structure for a dynamic collection of patterns of total length <i>d</i> that can support the dictionary matching query efficiently. When compared with the <i>O</i>(<i>d</i> log <i>d</i>)-bit suffix tree based solution of Amir <i>et al.</i>, the compact solution increases the query time by roughly a factor of log <i>d</i> only. In the study of the above results, we also derive the first <i>O</i>(<i>n</i>)-bit representation for maintaining <i>n</i> pairs of balanced parentheses in <i>O</i>(log <i>n</i>/log log <i>n</i>) time per operation, matching the time complexity of the previous <i>O</i>(<i>n</i> log <i>n</i>)-bit solution.