Cross-Entropy optimization for sensor selection problems

Naeem, Muhammad; Xue, Song; Lee, D. C.

doi:10.1109/iscit.2009.5341219

Cited by 32 publications

(26 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The concept of sensor selection has been extensively studied in the context of parameter and state estimation. The resulting minimum cardinality combinatorial problem has been tackled by using different tools, from convex relaxations, e.g., [11][12][13], to sub-modularity [14][15][16] and frame theory [17,18]. These tools have their pros and cons.…”

Section: State Of the Artmentioning

confidence: 99%

“…With this in place, the problem we want to solve is how to consistently select minimum rates, relays, and links so to guarantee a certain network performance and connectivity. We can formulate this as minimize r,T ,ν α 1 }r} 0`α2 }T } 0`α3 }ν} 0 (18a) subject to r i P r0, 1s, T ip P r0, 1s, (15), (16), (17),…”

Section: Sensor and Relay Selectionmentioning

confidence: 99%

“…The argument that we use to prove this claim is the same as the one that we have used to prove the connectivity guarantees of Algorithm 1 (where no relay were considered). Consider (17): this constraint has to be true for each active sensor and active relay (the one for which r i " 0 and ν i ą 0), and it reads 0 ď 0 for the not active ones (due to constraints (15)- (16)). Since it has to be true for all active sensors and relays, the sensors have to send out more rate than what they receive (and the difference is given by r iri ), while the relays can send out exactly what they receive.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Consistent sensor, relay, and link selection in wireless sensor networks

2017

View full text Add to dashboard Cite

In wireless sensor networks, where energy is scarce, it is inefficient to have all nodes active because they consume a non-negligible amount of battery. In this paper we consider the problem of jointly selecting sensors, relays and links in a wireless sensor network where the active sensors need to communicate their measurements to one or multiple access points. Information messages are routed stochastically in order to capture the inherent reliability of the broadcast links via multiple hops, where the nodes may be acting as sensors or as relays. We aim at finding optimal sparse solutions where both, the consistency between the selected subset of sensors, relays and links, and the graph connectivity in the selected subnetwork are guaranteed. Furthermore, active nodes should ensure a network performance in a parameter estimation scenario. Two problems are studied: sensor and link selection; and sensor, relay and link selection. To solve such problems, we present tractable optimization formulations and propose two algorithms that satisfy the previous network requirements. We also explore an extension scenario: only link selection. Simulation results show the performance of the algorithms and illustrate how they provide a sparse solution, which not only saves energy but also guarantees the network requirements.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Section: Sensor and Relay Selectionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Consistent sensor, relay, and link selection in wireless sensor networks

2017

View full text Add to dashboard Cite

show abstract

“…A heuristic based on convex relaxation is proposed such that the subset of sensors that minimizes the determinant of the estimator covariance matrix is selected. The work of Naeem et al [2009] further builds on Joshi and Boyd [2009] and proposes an algorithm based on the cross-entropy method for sensor selection. A similar problem of selecting noisy sensors for parameter estimation is presented in Msechu and Giannakis [2012], where the concept of sensor-centric data reduction based on censoring and quantization is proposed.…”

Section: Related Workmentioning

confidence: 99%

Not Every Bit Counts

Hsieh

Chang

Liao

2015

ACM Trans. Sen. Netw.

View full text Add to dashboard Cite

Many applications involving machine-to-machine (M2M) communications are characterized by the large amount of data to transport. To support these M2M applications, we argue in this article that instead of focusing on serving individual machines with better quality, one should focus on solutions that can better serve the data itself. To substantiate, we consider the application of data gathering from a set of machines that communicate directly to an aggregator. Since the aggregator has limited radio resources, the problem arises as to how the resource can be effectively utilized for supporting such an application. We investigate "datacentric" resource allocation that aims to maximize information entropy of data collected through selecting the subset of machines to transmit, determining the amount of resources to allocate, and scheduling the sequence of transmissions. We present two instantiations of the problems when machines can perform distributed source coding or dependent source coding based on the data overheard from neighboring machines and then propose algorithms for solving the joint optimization problems. Evaluation results show that compared to conventional "machine-centric" resource allocation that aims to maximize the aggregate data rates or number of supported machines, "data-centric" resource allocation exhibits significant performance gain in terms of the quality of data that can be collected for the given amount of radio resources.

show abstract

“…Convex optimization techniques have also been proven useful for experimental design [7,Chapter 7.5] with 1 [8], [9] and reweighted 1 norm minimization [10] approaches such as [11], [12], [13], [14]. Earlier work used information theoretic approaches like mutual information maximization [15], [16] and cross entropy optimization [17] or other search heuristics like genetic algorithms [18], tabu search [19] and branch-and-bound methods [20] to solve the sensor placement problems. Several recent works have also considered nonlinear sensor networks [13], tracking applications [21], [22], distributed sensing scenarios [23], [24], correlated noise models [25], estimation of continuous variables [26].…”

Section: Introductionmentioning

confidence: 99%

Sensor Scheduling With Time, Energy, and Communication Constraints

Rusu

Thompson

Robertson

2018

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

In this paper we present new algorithms and analysis for the linear inverse sensor placement and scheduling problems over multiple time instances with power and communications constraints. The proposed algorithms, which deal directly with minimizing the mean squared error (MSE), are based on the convex relaxation approach to address the binary optimization scheduling problems that are formulated in sensor network scenarios. We propose to balance the energy and communications demands of operating a network of sensors over time while we still guarantee a minimum level of estimation accuracy. We measure this accuracy by the MSE for which we provide average case and lower bounds analyses that hold in general, irrespective of the scheduling algorithm used. We show experimentally how the proposed algorithms perform against state-of-the-art methods previously described in the literature.

show abstract

Cross-Entropy optimization for sensor selection problems

Cited by 32 publications

References 15 publications

Consistent sensor, relay, and link selection in wireless sensor networks

Consistent sensor, relay, and link selection in wireless sensor networks

Not Every Bit Counts

Sensor Scheduling With Time, Energy, and Communication Constraints

Contact Info

Product

Resources

About