A lightweight dynamic optimization methodology and application metrics estimation model for wireless sensor networks

Munir, Arslan; Gordon‐Ross, Ann; Lysecky, Susan; Lysecky, Roman

doi:10.1016/j.suscom.2013.01.003

Cited by 10 publications

(9 citation statements)

References 9 publications

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“…The results of the proposed technique is compared with three other techniques of optimization. Firstly, Online Greedy Algorithm (OGA) technique which does searching of parameter space in defined order to optimize function [46]. Other two techniques use only one parameters, transmission power or data rate, for 1-Dimension probing keeping other parameter constant along with payload length.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Lightweight Algorithm To Optimize Power Consumption in Sensor Node Through Parametric Tuning of Communication Interface

Patel¹,

Shah²

2019

IJITEE

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Wireless sensor network is widely used to monitor sparsely generated events through a centralized system. The coverage provided by the network to monitor region of interest vary and rely on the system components of sensor node. While monitoring events from cluster head or sink through sensor nodes located at a distant, it experiences increased communication cost due to prone errors which are proportional to losses due to distance and interferences. Biotelemetry application is reviewed for the derivation of the research problem which is worked upon in the present paper. The animals move around in their habitat performing various activities which are to be monitored. The sensing tags are mounted on the animals to study the behaviour of the animals. These animals roam around in their habitat generating variation in the interferences caused for communication. Due to wide variations, the inefficiency of the sensor node in communication results in draining battery rapidly and thereby life of sensor node. So in order to improve lifetime the optimization at the sensor node is very essential to keep network alive. The technique that performs optimization needs to be lightweight in terms of processing required as well as tuning of parameter which are required for model based optimization methods. In the paper, we proposed a technique which is lightweight and can dynamically optimize operating state. It is done by adaptively configuring communication parameters to patch losses and conserve energy to enhance Sensor node lifetime. The optimization technique proposed, does parameter tuning to minimize the communication cost in sensor node through efficient search method. The results are compared with traditionally employed technique with static setting and Online Greedy Optimization Algorithm. NI LabVIEW is used to do simulation of the model and for estimating the effect of parameter reconfiguration upon application of optimization techniques

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Section: Simulation Resultsmentioning

confidence: 99%

A Lightweight Algorithm To Optimize Power Consumption in Sensor Node Through Parametric Tuning of Communication Interface

Patel¹,

Shah²

2019

IJITEE

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“…Munir et al [21] proposed another alternative to overcome the overhead of exhaustive search in their work on dynamic optimization of wireless sensor networks. Their approach was divided into two phases.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we improve on the work carried out by Munir et al [21]. We leverage a similar approach to design space exploration but add two new phases: a set-partitioning phase and an exhaustive search phase.…”

Section: Related Workmentioning

confidence: 99%

Selecting Microarchitecture Configuration of Processors for Internet of Things (IoT)

Kansakar

Munir

2018

IEEE Trans. Emerg. Topics Comput.

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The Internet of Things (IoT) makes use of ubiquitous internet connectivity to form a network of everyday physical objects for purposes of automation, remote data sensing and centralized management/control. IoT objects need to be embedded with processing capabilities to fulfill these services. The design of processing units for IoT objects is constrained by various stringent requirements, such as performance, power, thermal dissipation etc. In order to meet these diverse requirements, a multitude of processor design parameters need to be tuned accordingly. In this paper, we propose a temporally efficient design space exploration methodology which determines power and performance optimized microarchitecture configurations. We also discuss the possible combinations of these microarchitecture configurations to form an effective two-tiered heterogeneous processor for IoT applications. We evaluate our design space exploration methodology using a cycle-accurate simulator (ESESC) and a standard set of PARSEC and SPLASH2 benchmarks. The results show that our methodology determines microarchitecture configurations which are within 2.23%-3.69% of the configurations obtained from fully exhaustive exploration while only exploring 3%-5% of the design space. Our methodology achieves on average 24.16× speedup in design space exploration as compared to fully exhaustive exploration in finding power and performance optimized microarchitecture configurations for processors.Index Terms-Internet of Things (IoT), design space exploration, microarchitecture, tunable processor parameters, cycle-accurate simulator (ESESC), PARSEC and SPLASH2 benchmarksThe authors are with the

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“…In this paper, we improve on the work carried out by Munir et al [10]. We use a similar approach to design space exploration but with an addition of two new phases -a set partition phase and an exhaustive search phase.…”

Section: Related Workmentioning

confidence: 99%

“…Munir et al [10] used a greedy algorithm to overcome the overhead of exhaustive search, in their paper on dynamic optimization of wireless sensor networks. Their algorithm was separated in two phases.…”

Section: Related Workmentioning

confidence: 99%

A Design Space Exploration Methodology for Parameter Optimization in Multicore Processors

Kansakar

Munir

2016

2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

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The need for application-specific design of multicore/manycore processing platforms is evident with computing systems finding use in diverse application domains. In order to tailor multicore/manycore processors for application specific requirements, a multitude of processor design parameters have to be tuned accordingly which involves rigorous and extensive design space exploration over large search spaces. In this paper, we propose an efficient methodology for design space exploration. We evaluate our methodology over two search spaces small and large, using a cycle-accurate simulator (ESESC) and a standard set of PARSEC and SPLASH-2 benchmarks. For the smaller design space, we compare results obtained from our design space exploration methodology with results obtained from fully exhaustive search. The results show that solution quality obtained from our methodology are within 1.35% -3.69% of the results obtained from fully exhaustive search while only exploring 2.74% -3% of the design space. For larger design space, we compare solution quality of different results obtained by varying the number of tunable processor design parameters included in the exhaustive search phase of our methodology. The results show that including more number of tunable parameters in the exhaustive search phase of our methodology greatly improves solution quality.Index Terms-multicore/manycore processors, processor design parameters, design space exploration, cycle-accurate simulator (ESESC), PARSEC and SPLASH-2 benchmarks, parameter optimization ✦ • The authors are with the

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A lightweight dynamic optimization methodology and application metrics estimation model for wireless sensor networks

Cited by 10 publications

References 9 publications

A Lightweight Algorithm To Optimize Power Consumption in Sensor Node Through Parametric Tuning of Communication Interface

A Lightweight Algorithm To Optimize Power Consumption in Sensor Node Through Parametric Tuning of Communication Interface

Selecting Microarchitecture Configuration of Processors for Internet of Things (IoT)

A Design Space Exploration Methodology for Parameter Optimization in Multicore Processors

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