130150
Insider Attacker Detection in Wireless Sensor Networks
Though destructive to network functions, insider attackers are not detectable with only the classic cryptography-based techniques. Many mission-critic sensor network applications demand an effective, light, flexible algorithm for internal adversary identification with only localized information available. The insider attacker detection scheme proposed in this paper meets all the requirements by exploring the spatial correlation existent among the networking behaviors of sensors in close proximity. Our work is exploratory in that the proposed algorithm considers multiple attributes simultaneously in node behavior evaluation, with no requirement on a prior knowledge about normal/malicious sensor activities. Moreover, it is application-friendly, which employs original measurements from sensors and can be employed to monitor many aspects of sensor networking behaviors. Our algorithm is purely localized, fitting well to the large-scale sensor networks. Simulation results indicate that internal adversaries can be identified with a high accuracy and a low false alarm rate when as many as 25% sensors are misbehaving.
12323265
SeGrid: A Secure Grid Infrastructure for Sensor Networks
In this paper, we propose SeGrid, a secure grid infrastructure for large scale sensor networks. The basic idea relies on the availability of a low-cost public cryptsystem (e.g. Blom’s key management scheme [4]) that can be used for shared key computation between the source and the destination, as long as the public shares are known to each other. In SeGrid, each sensor resides in a grid computed from its physical location. Within a grid, one or a few number of sensors, with one of them the grid head, are active at any instant of time and all other sensors fall asleep for energy conservation. We intend to compute a shared key for two grids instead of two nodes, such that the grid heads can securely communicate with each other. The public shares of a grid are stored at designated locations based on our public share management protocol such that the closer two grids, the shorter distance to obtain each other’s public shares. We instantiate SeGrid based on Blom’s key management scheme [4] to illustrate the computation of a grid key. To our best knowledge, this is the first work that simultaneously considers both key management and network lifetime extension, which explores along the dimension of network density.
285048
Location-centric storage for safety warning based on roadway sensor networks
We propose a novel vision for roadway safety warning based on sensor networks, aiming at providing user-friendly zero-delay safety warnings to motorists. Our idea leverages the advanced sensing, networking and storage technologies. Roadway sensors detect events and store event records at multiple designated locations along the against traffic direction, such that the passing-by drivers can be alerted to potential dangers or traffic delays through the wireless communication between roadway sensors and the vehicle. We design a location-centric storage (LCS) protocol, which manages the propagation and storage of event records based on the time needed to clear the road. In LCS, the density of the sensors storing an event record decreases logarithmically with respect to the distance to the event location. Thus, the closer to the event position, the more number of warnings a driver may obtain. LCS is further tailored for the case of ''highway'' sensor networks when all sensors are deployed along a straight line mimicking a highway, and the more complex case when two roads intersect at some place. We conduct both theoretic analysis and simulation study to verify the performance of LCS when applied to roadway sensor networks for safety warning. The results indicate that LCS is fair to all sensors. We conclude that roadway safety warning based on sensor networks is a promising idea for realizing ITS's ''Zero Fatality, Zero Delay'' roadway safety philosophy.
11279692
TPSS: A Time-Based Positioning Scheme for Sensor Networks with Short Range Beacons
Location discovery is a challenging problem in sensor networks. However, many sensor network applications require the availability of the physical sensor positions. In this paper, we present TPSS, a time-based positioning scheme for sensor networks when a number of short-range beacons are randomly and uniformly deployed. By measuring the Time Difference of Arrivals (TDoAs) of signals from nearby beacons, nodes can use TPSS to effectively estimate their locations based on the range differences through trilateration. TPSS requires no long-range beacons to cover the entire network, an essential difference compared to TPS [2] and iTPS [15]. Features of TPSS include high scalability, low communication and computation overheads, no requirement for time synchronization, etc. Simulation results indicate that TPSS is an effective and efficient self-positioning scheme for sensor networks with short range beacons.
17825087
Feature Matrix Models for Information Extraction
To extract useful information which really contribute to the target issue from huge amounts of data or text description is an important task towards a number of research fields (e.g., genomics study and text mining). In this paper, a general feature matrix model (FMM) is proposed aiming to provide partial answer to this task. Specifically, one instantiation of FMM is used for identifying feature genes based on microarray data, and the other for extracting information according to case description.
27655175
A Convex-Hull Based Algorithm to Connect the Maximal Independent Set in Unit-Disk Graphs
In this paper we propose and analyze a localized convex-hull based algorithm to connect a maximal independent set. The cardinality of the resultant connected dominating set is at most 76 · opt +19, where opt is the size of a minimum connected dominating set. To our knowledge, this is a dramatic improvement compared to the best published results in the same context [1,6] . Our algorithm plays an important rule in efficiently constructing a virtual backbone for ad hoc and sensor networks.
1291381
Multiobjective Optimization Based on Coevolutionary Algorithm
With the intrinsic properties of multiobjective optimization problems in mind, multiobjective coevolutionary algorithm (MOCEA) is proposed. In MOCEA, a Pareto crossover operator, and 3 coevolutionary operators are designed for maintaining the population diversity and increasing the convergence rate. Moreover, a crowding distance is designed to reduce the size of the nondominated set. Experimental results demonstrate that MOCEA can find better solutions at a low computational cost. At the same time, the solutions found by MOCEA scatter uniformly over the entire Pareto front.
33837332
An object-oriented approach on Web information representation and derivation
A framework on Web information representation, extraction and derivation utilising the object-oriented approach is proposed. We define a structure which is similar to a class in object-oriented design and programming, and name it as an extended class (eclass). An eclass contains data attributes, member functions/methods, inference rules and presentations, and therefore, facilities information derivation and Web presentation. As an object-oriented approach, the framework also supports encapsulation, polymorphism, composition and inheritance. It can be implemented as an extension of an existing object-oriented programming language. An eclass can be extremely effective in content-dependent entity description. It describes a value under its context, consequently, the search engine not only searches the value, but also understands the meaning of the value in the context. We argue that information in any form or complexity can always be represented by instances of eclasses.
39140575
GOPT-Resolution in Web Information Derivation
11140869
Effective keyword search in relational databases
With the amount of available text data in relational databases growing rapidly, the need for ordinary users to search such information is dramatically increasing. Even though the major RDBMSs have provided full-text search capabilities, they still require users to have knowledge of the database schemas and use a structured query language to search information. This search model is complicated for most ordinary users. Inspired by the big success of information retrieval (IR) style keyword search on the web, keyword search in relational databases has recently emerged as a new research topic. The differences between text databases and relational databases result in three new challenges: (1) Answers needed by users are not limited to individual tuples, but results assembled from joining tuples from multiple tables are used to form answers in the form of tuple trees. (2) A single score for each answer (i.e. a tuple tree) is needed to estimate its relevance to a given query. These scores are used to rank the most relevant answers as high as possible. (3) Relational databases have much richer structures than text databases. Existing IR strategies to rank relational outputs are not adequate. In this paper, we propose a novel IR ranking strategy for effective keyword search. We are the first that conducts comprehensive experiments on search effectiveness using a real world database and a set of keyword queries collected by a major search company. Experimental results show that our strategy is significantly better than existing strategies. Our approach can be used both at the application level and be incorporated into a RDBMS to support keyword-based search in relational databases.
18981738
Dynamic Range Query in Spatial Network Environments
Moving range queries over mobile objects are important in many location management applications. There have been quite a few research works in this area. However, all existing solutions assume an open space environment, which are either not applicable to spatial network environment or require non-trivial extensions. In this paper, we consider a new class of query called Dynamic Range Query. A dynamic range query is a moving range query in a network environment, which retrieves the moving objects within a specified network distance of the moving query point. As this query point moves in the network, the footprint (or shape) of the query range changes accordingly to reflect the new relevant query area. Our execution strategy leverages computing power of the moving objects to reduce server load and communication costs. This scheme is particularly desirable for many practical applications such as vehicles in a street environment, where mobile energy is not an issue. We describe the design details and present our simulation study. The performance results indicate that our solution is almost two magnitudes better than a query index method in terms of server load, and requires similar number of messages when compared to a query-blind optimal scheme.