Distributed coordination for fast iterative optimization in wireless sensor/actuator networks

Balani, Rahul; Chehade, Nabil Hajj; Chakraborty, Supriyo; Srivastava, Mani

doi:10.1109/sahcn.2011.5984934

Cited by 2 publications

(5 citation statements)

References 12 publications

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“…It facilitates data parallel execution of (sub)iterations in the incremental subgradient method [Nedic and Bertsekas 2001] and a randomized local search algorithm [Kansal et al 2006] such that the outcome is equivalent to some random sequence of estimations in each iteration (serializability). A subsequent publication [Balani et al 2011a] demonstrates significant increases in efficiency via similar algorithmic modifications to consensus-based subgradient methods [Johansson et al 2008]. …”

Section: Objective and Proposed Solutionmentioning

confidence: 99%

“…Events are defined as modifications in the external environment, such as users entering or exiting a room or changes in ambient light, to which the controllers must react by altering actuator control inputs. Our prior work [Balani et al 2011a] demonstrates that the former metric is often misleading, as a lower iteration count, although important, does not necessarily translate to lower latency.…”

Section: Incremental Subgradient Algorithmmentioning

confidence: 99%

“…These Shared Variable Repositories (SVRs), although not necessary for the implementation of the Shared Memory Manager, enable neighborhood-based consensus algorithms [Balani et al 2011a] and other spatial operations proposed in Welsh and Mainland [2004], by exposing direct access to these repositories through a SharedVariable interface [Whitehouse et al 2004]. It is important to note that while the SharedMemory interface, used in Figure2(a), indexes each control variable by its global key and returns its latest value irrespective of the controller that updated it, the SharedVariable interface indexes each variable with a (key, node id) tuple to return its value written by the controller with identifier = node id.…”

Section: Shared Variable Repositoriesmentioning

confidence: 99%

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Distributed programming framework for fast iterative optimization in networked cyber-physical systems

Balani

Wanner

Srivastava

2014

ACM Trans. Embed. Comput. Syst.

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Large-scale coordination and control problems in cyber-physical systems are often expressed within the networked optimization model. While significant advances have taken place in optimization techniques, their widespread adoption in practical implementations has been impeded by the complexity of internode coordination and lack of programming support for the same. Currently, application developers build their own elaborate coordination mechanisms for synchronized execution and coherent access to shared resources via distributed and concurrent controller processes. However, they typically tend to be error prone and inefficient due to tight constraints on application development time and cost. This is unacceptable in many CPS applications, as it can result in expensive and often irreversible side-effects in the environment due to inaccurate or delayed reaction of the control system. This article explores the design of a distributed shared memory (DSM) architecture that abstracts the details of internode coordination. It simplifies application design by transparently managing routing, messaging, and discovery of nodes for coherent access to shared resources. Our key contribution is the design of provably correct locality-sensitive synchronization mechanisms that exploit the spatial locality inherent in actuation to drive faster and scalable application execution through opportunistic data parallel operation. As a result, applications encoded in the proposed Hotline Application Programming Framework are error free, and in many scenarios, exhibit faster reactions to environmental events over conventional implementations.Relative to our prior work, this article extends Hotline with a new locality-sensitive coordination mechanism for improved reaction times and two tunable iteration control schemes for lower message costs. Our extensive evaluation demonstrates that realistic performance and cost of applications are highly sensitive to the prevalent deployment, network, and environmental characteristics. This highlights the importance of Hotline, which provides user-configurable options to trivially tune these metrics and thus affords time to the developers for implementing, evaluating, and comparing multiple algorithms. ACM Reference Format:Rahul Balani, Lucas F. Wanner, and Mani B. Srivastava. 2014. Distributed programming framework for fast iterative optimization in networked cyber-physical systems.

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Section: Objective and Proposed Solutionmentioning

confidence: 99%

Section: Incremental Subgradient Algorithmmentioning

confidence: 99%

Section: Shared Variable Repositoriesmentioning

confidence: 99%

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Distributed programming framework for fast iterative optimization in networked cyber-physical systems

Balani

Wanner

Srivastava

2014

ACM Trans. Embed. Comput. Syst.

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“…The first is by estimating the state of the system at all controllers through several iterations of local message exchange and executing the control input after consensus has been achieved on the system state [12]. The second technique is to successively determine control inputs based on localized message exchanges with neighboring controllers until convergence is achieved to the required state [13]. Note that in both these methods, although message exchange in each iteration is local, information is essentially propagated system-wide through multiple such iterations before convergence is achieved.…”

Section: Related Workmentioning

confidence: 99%

“…However, from a communication perspective, a fundamental challenge still remains-the timely and reliable supply of required system state information to each controller. Typically, control decentralization is achieved either by first estimating a synchronous global state at all controllers and then computing the new input [12] or by iteratively computing control inputs based on local information exchange until the solution converges to an optimal control input [13]. Note that although consensus in these techniques is typically achieved by a local exchange of messages with neighboring controllers, both these methods involve several rounds of such message exchange before computing the next control input.…”

Section: Introductionmentioning

confidence: 99%

Sufficiency of Local Feedback for Sensor-Actuator Network-Based Control Systems with Distance Sensitivity Properties

Kulathumani

Lemon

2013

JSAN

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Timely dissemination of required state information poses a significant challenge in the design of distributed sensor/actuator network-based control systems. In this paper, distance sensitivity properties inherent in many sensor-actuator network-based control systems are exploited to establish conditions under which information within a bounded locality of each controller closely approximates optimal control based on knowledge of system-wide state information. By doing so, it is shown that optimal control in extremely large-scale distributed control systems can be achieved in O(1) time using information only within a fixed neighborhood around each controller, the size of which depends on the decay characteristics of the actuator influence matrix

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Distributed coordination for fast iterative optimization in wireless sensor/actuator networks

Cited by 2 publications

References 12 publications

Distributed programming framework for fast iterative optimization in networked cyber-physical systems

Distributed programming framework for fast iterative optimization in networked cyber-physical systems

Sufficiency of Local Feedback for Sensor-Actuator Network-Based Control Systems with Distance Sensitivity Properties

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