Mobile Agents in Mobile Data Access Systems

Jiao, Yu; Hurson, Ali R.

doi:10.1007/3-540-36124-3_9

Cited by 10 publications

(10 citation statements)

References 3 publications

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“…Based on the experimental results obtained from our mobile agent-based information retrieval prototype [9], we chose to model the server response time as a normal distribution with a mean value μ = 2.5 s and a variance σ = 0.2.…”

Section: Simulation Methodologymentioning

confidence: 99%

Energy-efficient wireless information retrieval

Jiao

Hurson

2007

Journal of Computer and System Sciences

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In this paper, we propose an adaptive application-driven power management (AADPM) protocol for wireless information retrieval applications within the IEEE 802.11b infrastructure WLAN environment. Our goal is to minimize energy consumption while achieving low round trip time delay. We discuss the protocol and evaluate its effectiveness using the network simulator NS2. We also draw horizontal comparisons among a variety of PM methods reported in the literature. Experimental results show that, compared to the power save mode supported by 802.11b, AADPM reduces the network interface card energy consumption by 52% while only introducing 3% RTT delay.

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Section: Simulation Methodologymentioning

confidence: 99%

Energy-efficient wireless information retrieval

Jiao

Hurson

2007

Journal of Computer and System Sciences

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“…The two experimental scenarios were considered to evaluate and compare performance by running join statements without any attention if the none join predicate will be used at the first or not. Then watch the system how it will convert the join statement to use none join predicate as a driving table (Jiao and Hurson, 2002).…”

Section: Methodsmentioning

confidence: 99%

Increasing Database Performance through Optimizing Structure Query Language Join Statement

Muslih¹,

Saleh²

2010

Journal of Computer Science

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Problem statement: A join statement is a select statement with more than table in the FROM clause. A join predicate is a predicate in the WHERE clause that combines the columns of two of the tables in the join. Any database gives you the ability to join various tables together through different types of joins, resulting large number of rows to process. Query language can be used to join these tables and as it is well known query language should be declarative, so we can write alternative formulas to perform join statements. Different formulas provide variation in performance. Approach: This research presented a transparent middle layer between application interface front end and database back end. Results: The responsibilities of this layer were catching the SQL commands sent by application before reaching the database then examining these commands to see if they join more than one table, after that rewriting the SQL command taking into consideration the order of executing join predicates and none join predicates. This research focused on rewriting the SQL commands without application modification. Conclusion: Rewriting stage is the most complex stage because the system will restructure the SQL command with new syntax taking two things in its consideration, the first one was rewriting the command with better performance syntax after getting the help from recommendation dictionary, the second one was resulting the same data (output) as previous old command

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“…Based on the experimental results obtained from our mobile agent-based information retrieval prototype [6], we chose to mode the server response time as a normal distribution with a mean value µ = 2.5 s and a variance = 0.2. The user-think times obtained from the UC-Berkley-home-IP trace are often tens of seconds long and therefore, the energy consumed during the think time will completely dominate the overall NIC energy consumption.…”

Section: Figure 3 Cdfs Of the Request Size And R Esponse Sizementioning

confidence: 99%

“…The mobile agent-based design model addresses the problems of the mobile devices' limited resources by migrating tasks to more powerful servers on the network, and it resolves the intermittent connectivity issue by supporting disconnected operations. The advantages of agent-based information retrieval systems are discussed in detail in [6]. Because of these advantages, many mobile agent-based information retrieval systems have been developed.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Power Management for Mobile Agent-Based Information Retrieval

Jiao

Hurson

19th International Conference on Advanced Information Networking and Applications (AINA'05) Volume 1 (AINA Papers)

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Mobile agent technology has been used in wireless distributed information retrieval for many years. However, the energy-efficiency issue in such systems remains unexplored. In this paper, we proposed an adaptive application-driven power management (AADPM) protocol for mobile agent-based applications within the IEEE 802.11b infrastructure WLAN environment. Our goal is to minimize energy while achieving low round trip time (RTT) delay. We discuss the protocol and evaluate its effectiveness using the network simulator NS2. We also draw horizontal comparisons among a variety of PM methods reported in the literature. Experimental results show that, compared to the power save mode supported by 802.11b, AADPM reduces the network interface card energy consumption by 65% while only introducing 3% RTT delay.

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Mobile Agents in Mobile Data Access Systems

Cited by 10 publications

References 3 publications

Energy-efficient wireless information retrieval

Energy-efficient wireless information retrieval

Increasing Database Performance through Optimizing Structure Query Language Join Statement

Adaptive Power Management for Mobile Agent-Based Information Retrieval

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