Browsing by Subject "Distributed processing"

Now showing 1 - 4 of 4

Open Access
Communication efficient channel estimation over distributed networks
(IEEE, 2014) Sayın, Muhammed O.; Vanlı, N. Denizcan; Göze, T.; Kozat, Süleyman Serdar
We study diffusion based channel estimation in distributed architectures suitable for various communication applications such as cognitive radios. Although the demand for distributed processing is steadily growing, these architectures require a substantial amount of communication among their nodes (or processing elements) causing significant energy consumption and increase in carbon footprint. Due to growing awareness of telecommunication industry's impact on the environment, the need to mitigate this problem is indisputable. To this end, we introduce algorithms significantly reducing the communication load between distributed nodes, which is the main cause in energy consumption, while providing outstanding performance. In this framework, after each node produces its local estimate of the communication channel, a single bit or a couple of bits of information is generated using certain random projections. This newly generated data is diffused and then used in neighboring nodes to recover the original full information, i.e., the channel estimate of the desired communication channel. We provide the complete state-space description of these algorithms and demonstrate the substantial gains through our experiments.
Open Access
Distributed adaptive filtering with reduced communication load
(IEEE, 2016) Utlu, İhsan; Kozat, Süleyman Serdar
We propose novel algorithms for distributed processing in applications constrained by available communication resources, using diffusion strategies that achieve up to three orders-of-magnitude reduction in communication load on the network, while delivering equal performance with respect to the state of the art. After computation of local estimates, the information is diffused among processing elements (or nodes) non-uniformly in time by conditioning the information transfer on level-crossings of the diffused parameter, resulting in a greatly reduced communication requirement. We provide the mean stability analysis of our algorithms, and illustrate the gain in communication efficiency compared to other reducedcommunication distributed estimation schemes.
Open Access
SLIM: A scalable location-sensitive information monitoring service
(IEEE, 2013) Bamba, B.; Wu, K.-L.; Gedik, Buğra; Liu L.
Location-sensitive information monitoring services are a centerpiece of the technology for disseminating content-rich information from massive data streams to mobile users. The key challenges for such monitoring services are characterized by the combination of spatial and non-spatial attributes being monitored and the wide spectrum of update rates. A typical example of such services is "alert me when the gas price at a gas station within 5 miles of my current location drops to $4 per gallon". Such a service needs to monitor the gas price changes in conjunction with the highly dynamic nature of location information. Scalability of such location sensitive and content rich information monitoring services in the presence of different update rates and monitoring thresholds poses a big technical challenge. In this paper, we present SLIM, a scalable location sensitive information monitoring service framework with two unique features. First, we make intelligent use of the correlation between spatial and non-spatial attributes involved in the information monitoring service requests to devise a highly scalable distributed spatial trigger evaluation engine. Second, we introduce single and multi-dimensional safe value containment techniques to efficiently perform selective distributed processing of spatial triggers to reduce the amount of unnecessary trigger evaluations. Through extensive experiments, we show that SLIM offers high scalability for location-sensitive, content-rich information monitoring services in terms of the number of information sources being monitored, number of users and monitoring requests. © 2013 IEEE.
Open Access
Stochastic subgradient algorithms for strongly convex optimization over distributed networks
(IEEE Computer Society, 2017) Sayin, M. O.; Vanli, N. D.; Kozat, S. S.; Başar, T.
We study diffusion and consensus based optimization of a sum of unknown convex objective functions over distributed networks. The only access to these functions is through stochastic gradient oracles, each of which is only available at a different node; and a limited number of gradient oracle calls is allowed at each node. In this framework, we introduce a convex optimization algorithm based on stochastic subgradient descent (SSD) updates. We use a carefully designed time-dependent weighted averaging of the SSD iterates, which yields a convergence rate of O N ffiffiffi N p (1s)T after T gradient updates for each node on a network of N nodes, where 0 ≤ σ < 1 denotes the second largest singular value of the communication matrix. This rate of convergence matches the performance lower bound up to constant terms. Similar to the SSD algorithm, the computational complexity of the proposed algorithm also scales linearly with the dimensionality of the data. Furthermore, the communication load of the proposed method is the same as the communication load of the SSD algorithm. Thus, the proposed algorithm is highly efficient in terms of complexity and communication load. We illustrate the merits of the algorithm with respect to the state-of-art methods over benchmark real life data sets. © 2017 IEEE.