This is the presentation for the research paper "Using Request Queues for Enhancing the Performance of Operations in Smart Buildings". It has been presented during the 7th ACM International Workshop on Performance Monitoring, Measurement and Evaluation of Heterogeneous Wireless and Wired Networks (PM2HW2N), in Proc. of MSWiM 2012, in Paphos, Cyprus, October 2012. Its abstract is as follows: Modern houses and buildings are being equipped with embedded wireless sensors and actuators, offering advanced automation possibilities. Embedded technology is becoming mature, forming an enticing option for real-life deployments. Still, embedded wireless computing does not constitute a guaranteed reliable solution since transmission failures occur in the wireless medium while resource-constrained devices have battery limitations and frequent failures. In this paper, we examine the use of request queues as a mechanism to manage the communication with embedded devices. Located in a middleware application framework for smart homes, request queues offer enhanced reliability and fault tolerance, supporting multiple tenants simultaneously.

1. Using Request Queues for
Enhancing the Performance of
Operations in Smart Homes
Andreas Kamilaris and Andreas Pitsillides
Networks Research Laboratory
Dept. of Computer Science, University of Cyprus
7th Performance Monitoring, Measurement and
Evaluation of Heterogeneous Wireless and
Wired Networks (PM2HW2N) Workshop
Paphos, Cyprus
21st October 2012

2. Introduction
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• Modern smart homes tend to being equipped with embedded
sensors, actuators, smart power outlets and meters.
• Transmission failures are a common happening in indoor
environments.
• Resource-constrained home devices have battery limitations and
frequent failures.
• Reduced reliability and performance.
• No guarantees.
• High unpredictability.

3. Motivation
• Better management of the interactions with embedded
devices.
• Reliability and performance need to be ensured.
• Intermediate entities for handling communication with the
smart home environment.
• Request queues a suitable intermediate data structure for
enhancing the performance of pervasive applications that
target smart homes and building automation.
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4. Request Queues
• Request queues are defined as FIFO queues.
• Installed on middleware applications for smart buildings.
• Handle the requests coming from the building's tenants,
targeting the embedded devices of the building.
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5. An Application Framework for Smart Buildings
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• Each home device is associated with its own request queue.

6. Request Queue Operation
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7. Request Queue Analysis
• Incoming requests need to wait in the queue for their turn,
in order to be executed.
• The arrival rate lambda of Web clients at the framework is
modeled by the exponential distribution.
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8. Experimental Setup
• 6LoWPAN-enabled Telosb sensor motes.
• Sensing capabilities exposed as RESTful Web services.
• Transmission power at -25 dBm, message sizes 128 bytes.
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9. Request Queue Analysis
• Request queue retransmission interval α
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10. Request Queue Analysis
• Influence of transmission failures and different arrival rates
on retransmission interval α.
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11. Request Queue Analysis
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• Influence of varied response times on retransmission interval α.
• RTT times and St. Deviation values learned from the device thread.
• Set initially to a larger value, leaving a "safe margin”.
• Fine-tune adaptively during the device operation.

12. Potential Benefits
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• Multi-Client Support
• Multi-hop topology additional delays of around 200 ms.
• Heavy workload increases response times by 18-20% in the singlehop case and 14-17% in the multi-hop topology.

13. Potential Benefits
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• Avoiding Transmission Failures
• In light workload, transmission failures not affect significantly
the response times.
• In heavy workloads, transmission failures cause the response
times to grow almost exponentially.

14. Potential Benefits
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• Estimating Potential Response Times
• Average estimation error is 12.38%, and it increases to
14.60% when the request queue size becomes larger than 2.

15. Potential Benefits
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• Load Balancing for Serving High Traffic
• In low traffic, the improvement of performance is around 4-6%.
• In increased traffic, the improvement reaches 11%.

16. Potential Benefits
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• Handling Priorities
1. Assign a priority to each request (low, normal or high).
2: Each prioritized request is translated into an integer value by the application
framework, according to the following formula: low=1, normal=5 and high=10.
3: The priority heap selects for execution the request with the highest priority number.
4: To avoid starvation of low-priority requests, the priorities of all waiting requests are
increased by 1 at every round, i.e. at a successful execution of some request.

17. Future Work
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• Support for moving objects and dynamic environments
• Detailed system analysis by employing queuing theory.
• Device Utilization.
• Expected number of building tenants/workers and waiting times.
• Estimation of the probability of the request queue to be in certain state.
• Distribution of the length of the busy periods, sojourn and waiting times.
• Distribution of the number of arrivals during the service time etc.
• Task scheduling through priority handling.
• Device-centric Vs Service-centric approach.

18. Conclusions
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• Intermediate request queues a promising mechanism.
• Enhancing pervasive applications with numerous benefits.
• More useful in scenarios with multiple, concurrent users and
increased percentage of transmission failures.
• Support of prioritized requests.
• Serve high traffic through load balancing.
• A dynamic, adaptive system that handles failures in the
embedded environment providing certain reliability.

19. Thanks for your attention!
Contact Details: Andreas Kamilaris (kami@cs.ucy.ac.cy)