15147736
Exploiting medium access diversity in rate adaptive wireless LANs
Recent years have seen the growing popularity of multi-rate wireless network devices (e.g., 802.11a cards) that can exploit variations in channel conditions and improve overall network throughput. Concurrently, rate adaptation schemes have been developed that selectively increase data transmissions on a link when it offers good channel quality. In this paper, we propose a Medium Access Diversity (MAD) scheme that leverages the benefits of rate adaptation schemes by aggressively exploiting multiuser diversity. The basic mechanism of MAD is to obtain instantaneous channel condition information from multiple receivers and selectively transmit data to a receiver that improves the overall throughput of the network, while maintaining temporal fairness among multiple data flows. We identify and address the challenges in the design and implementation of MAD's three phases: channel probing, data transmission, and receiver scheduling. We also use analytical models to examine the tradeoff between network performance improvement and overhead of channel probing, and derive an asymptotic performance bound for the receiver scheduling algorithms used by MAD. Results from the analysis and the extensive simulations demonstrate that, on average, MAD can improve the overall throughput of IEEE 802.11 wireless LANs by 50% as compared with the best existing rate adaptation scheme.
557343
MAYA: Integrating hybrid network modeling to the physical world
The flourish of large-scale network applications across the Internet and or MANET has raised a challenge to network modeling environments that support experimentation and analysis of close interactions between real applications and network dynamics. To facilitate such experimentations, this paper presents MAYA, a multiparadigm network modeling framework including discrete event models, analytical models and physical network interfaces, together with its illustrative implementation using QualNet, fluid flow TCP model and physical network interface. MAYA framework allows users to interface simulated networks directly with physical networks, while attaining real-time constraints even for large-scale networks by incorporating above multiparadigm network modeling techniques. It also gives user the flexibility to emulate applications on nodes in both real and simulated networks. Experiments are conducted to validate the interoperation of QualNet and fluid flow model, to examine the performance of MAYA as well as to evaluate the optimization techniques, namely interleaved execution of fluid flow model and causality-preserve realtime synchronization relaxation. Experimental results indicate that MAYA is a scalable and extensible solution to modeling of close interactions between real application and network dynamics.
17829598
Maya: a Multi-Paradigm Network Modeling Framework
This paper presents Maya, a multi-paradigm, scalableand extensible network modeling framework for emulatingdistributed applications. A novel three-tier architecture isproposed to integrate three disparate modeling paradigms,namely, discrete event models, analytical models and physicalnetwork interfaces into one unified framework of Maya.As the first effort to integrate all three paradigms intoone framework, this paper discusses the implementationsof Maya using Qualnet, fluid flow based TCP model andphysical network interface. It addresses the performanceissues involved in attaining the real time constraints imposedby distributed applications and demonstrates the effectivenessof using analytical models in Maya. Furthermore, it identifies the negative impact on real time performance through the computation intensive ordinary differential equation (ODE) solver in the fluid flow model. A new approach to interleaved executions of the fluid flow model is proposed to hide ODE solver turnaround time. As a result, the percentage of packets missing their deadlines has been reduced from more than 6% to less than 0.2%.
1326222
Scalable simulation of large-scale wireless networks with bounded inaccuracies
Discrete event network simulators have emerged as popular tools for verification and performance evaluation for various wireless networks. Nevertheless, the desire to model such networks at high fidelity implies high computational costs, prohibiting most researchers from simulating wireless networks with thousands of nodes. There have been attempts on performance optimizations for large-scale wireless network simulation, but they have not appropriately modeled accumulation of weak interference, thereby suffering inaccuracies which may be magnified by upper layer protocols. This paper presents analysis of the effects of common optimization techniques for large-scale wireless network simulation on the overall network performance and also proposes modifications and novel techniques that introduce only limited inaccuracies or no additional inaccuracy at all. The study quantifies the effects of those optimizations on the simulation results for given thresholds and network parameters, and also identifies thresholds tolerable to most network studies. The experimental results show that these optimizations can improve the runtime performance of an already efficient wireless network simulator substantially, by a factor of up to 55 for wireless networks with 3200 nodes without compromising accuracy of the simulation results.
7810682
Diverging Keys in Wireless Sensor Networks
Currently, the most popular ways of dealing with the key distribution problem in sensor networks are random predistribution schemes. For relaxed, realistic assumptions about the attacker, the key infection protocol [1] is also available. In this paper, by accepting the relaxed assumptions from [1], we propose a scheme which makes pairwise keys “drift” or diverge, which enhances security and can be used as a key distribution method. The most notable feature of this scheme is that, under some assumptions about the sensor nodes, it incurs no communication overhead at all.
17064783
Implementing database operations using SIMD instructions
Modern CPUs have instructions that allow basic operations to be performed on several data elements in parallel. These instructions are called SIMD instructions, since they apply a single instruction to multiple data elements. SIMD technology was initially built into commodity processors in order to accelerate the performance of multimedia applications. SIMD instructions provide new opportunities for database engine design and implementation. We study various kinds of operations in a database context, and show how the inner loop of the operations can be accelerated using SIMD instructions. The use of SIMD instructions has two immediate performance benefits: It allows a degree of parallelism, so that many operands can be processed at once. It also often leads to the elimination of conditional branch instructions, reducing branch mispredictions.We consider the most important database operations, including sequential scans, aggregation, index operations, and joins. We present techniques for implementing these using SIMD instructions. We show that there are significant benefits in redesigning traditional query processing algorithms so that they can make better use of SIMD technology. Our study shows that using a SIMD parallelism of four, the CPU time for the new algorithms is from 10% to more than four times less than for the traditional algorithms. Superlinear speedups are obtained as a result of the elimination of branch misprediction effects.
3767276
On the Security of Wireless Sensor Networks
Wireless Sensor Networks are extremely vulnerable against any kind of internal or external attacks, due to several factors such as resource-constrained nodes and lack of tamper-resistant packages. As a result, security must be an important factor to have in mind when designing the infrastructure and protocols of sensor networks. In this paper we survey the “state-of-the-art” security issues in sensor networks and highlight the open areas of research.
34424162
Minimizing TTP's involvement in signature validation
A digital signature applied on a message could serve as irrefutable cryptographic evidence to prove its origin and integrity. However, evidence solely based on digital signatures may not enforce strong non-repudiation. Additional mechanisms are needed to make digital signatures as valid non-repudiation evidence in the settlement of possible disputes. Most of existing mechanisms for maintaining the validity of digital signatures rely on the supporting services from trusted third parties, e.g., time-stamping and certificate revocation. Obviously, this is less efficient for on-line transactions. In this paper, we propose two new schemes for validating digital signatures as non-repudiation evidence that minimize the trusted third party's involvement.
14371353
Algorithm Selectors for Providing Location Estimation Services within a Cellular Radio Network
Mobile location estimation is becoming an important value-added service for mobile phone operators. Many mobile location estimation algorithms based on the cellular radio networks have been proposed but there exists no general solution since each algorithm has its own advantage depending on specific terrain and environmental factors. In this paper, we propose and investigate three algorithm selectors, one with a LDA classifier and the other two with Bayes classifiers using either a Naive Bayes probabilistic model or a Bayes probabilistic model, to select the best mobile location estimation algorithms from our previous work in order to combine their merits, and provide a more accurate estimation for location services. We have tested these three algorithm selectors with real data taken in Hong Kong. Experiment results have shown that they are all useful in particular, and the one with a Bayes probabilistic model outperforms all other existing location algorithms among different kinds of terrains in terms of average errors