Vijay Arya

User-sensitive scheduling of home appliances

Demand response (DR) programs encourage end-use customers to alter their power consumption in response to DR events such as change in real-time electricity prices. Facilitating household participation in DR programs is essential as the residential sector accounts for a sizable portion of the total energy consumed. However, manually tracking energy prices and deciding on how to schedule home appliances can be a challenge for residential consumers who are accustomed to fixed price electricity taris. In this work, we present Yupik, a system that helps users respond to real-time electricity prices while being sensitive to their context and lifestyle. Yupik combines sensing, analytics, and optimization to generate appliance usage schedules that may be used by households to minimize their energy bill as well as potential lifestyle disruptions. Yupik uses jPlugs, appliance level energy metering devices, to continuously monitor the power usage by various home appliances. The consumption patterns as well as data from external sources are analyzed using data mining algorithms to infer users preferred usage profile. Using the preferred profile as a reference, Yupiks optimization engine generates multiple usage plans that attempt to minimize energy and inconvenience costs. Some of Yupiks capabilities are demonstrated with the help of preliminary data collected from a home that was instrumented with jPlugs to monitor the power usage of a few devices.

nPlug: a smart plug for alleviating peak loads

The Indian electricity sector, despite having the worlds fifth largest installed capacity, suffers from a 12.9% peaking shortage. This shortage could be alleviated, if a large number of deferrable loads, particularly the high powered ones, could be moved from on-peak to off-peak times. However, conventional DSM strategies may not be suitable for India as the local conditions usually favor only inexpensive solutions with minimal dependence on the pre-existing infrastructure. In this work, we present nPlug, a smart plug that sits between the wall socket and deferrable loads such as water heaters, washing machines, and electric vehicles. nPlugs combine real-time sensing and analytics to infer peak periods as well as supply-demand imbalance and reschedule attached appliances in a decentralized manner to alleviate peaks whenever possible. They do not require any manual intervention by the end consumer nor any enhancements to the appliances or existing infrastructure. Some of nPlugs capabilities are demonstrated using experiments on a combination of synthetic and real data collected from plug-level energy monitors. Our results indicate that nPlug can be an effective and inexpensive technology to address the peaking shortage.

On managing quality of experience of multiple video streams in wireless networks

Managing the Quality-of-Experience (QoE) of video streaming for wireless clients is becoming increasingly important due to the rapid growth of video traffic on wireless networks. The inherent variability of the wireless channel as well as the Variable Bit Rate (VBR) of the compressed video streams make QoE management a challenging problem. In this paper, we investigate scheduling algorithms to transmit multiple video streams from a base station to mobile clients. We present an epoch-by-epoch framework to fairly allocate wireless transmission slots to streaming videos. In each epoch, our scheme reduces the vulnerability to stalling by allocating slots to videos in a way that maximizes the minimum “playout lead” across all videos. We show that the problem of allocating slots fairly is NP-complete even for a constant number of videos. We then present a fast lead-aware greedy scheduling algorithm. Our greedy algorithm is optimal when the channel quality of a user remains unchanged within an epoch. Our experimental results, based on public MPEG-4 video traces and wireless channel traces that we collected from a WiMAX test-bed, show that the lead-aware greedy approach results in a fair distribution of stalls across the clients when compared to other algorithms, while still maintaining similar or fewer average number of stalls per client.

Phase identification in smart grids

Electricity is distributed throughout the electrical power network in 3-phase voltage. This power reaches households as a single-phase voltage, generally 115vac or 240vac. This is achieved by allocating households with either phases A, B, or C of the final 3-phase power distributed to the street through a low voltage transformer. A present problem confronting the electrical power industry is identification of which particular phase a household is connected to. This information is often not tracked and the mechanisms for identifying phase require either manual intervention or costly signal injection technologies. Phase information is important as it is a foundation for the larger problem of balancing phase loads. Unbalanced phases lead to significant energy losses and sharply reduced asset lifetimes. In this paper we propose a new approach to compute household phase. Our techniques are novel as they are purely based upon a time series of electrical power measurements taken at the household and at the distributing transformer. Our methods involve the use of integer programming and solutions can be retrieved using branch and bound search algorithms implemented by MIP solvers such as CPLEX. Furthermore, as the number of measurements increase, continuous relaxations of integer programs may also be used to retrieve household phase efficiently. Simulation results using a combination of synthetic and real smart meter datasets demonstrate the performance of our techniques and the number of measurements needed to uniquely identify household phase.

Multicast inference of temporal loss characteristics

Multicast-based inference has been proposed as a method of estimating average loss rates of internal network links, using end-to-end loss measurements of probes sent over a multicast tree. We show that, in addition to loss rates, temporal characteristics of losses can also be estimated. Knowledge of temporal loss characteristics has applications for services such as voip which are sensitive to loss bursts, as well as for bottleneck detection. Under the assumption of mutually independent, but otherwise general, link loss processes, we show that probabilities of arbitrary loss patterns, mean loss-run length, and even the loss-run distribution, can be recovered for each link. Alternative estimators are presented which trade-off efficiency of data use against implementation complexity. A second contribution is a novel method of reducing the computational complexity of estimation, which can also be used by existing minc estimators. We analyse estimator performance using a combination of theory and simulation.

On Managing Quality of Experience of Multiple Video Streams in Wireless Networks

Managing the Quality-of-Experience (QoE) of video streaming for wireless clients is becoming increasingly important due to the rapid growth of video traffic on wireless networks. The inherent variability of the wireless channel as well as the Variable Bit Rate (VBR) of the compressed video streams make QoE management a challenging problem. In this paper, we investigate scheduling algorithms to transmit multiple video streams from a base station to mobile clients. We present an epoch-by-epoch framework to fairly allocate wireless transmission slots to streaming videos. In each epoch, our scheme reduces the vulnerability to stalling by allocating slots to videos in a way that maximizes the minimum “playout lead” across all videos. We show that the problem of allocating slots fairly is NP-complete even for a constant number of videos. We then present a fast lead-aware greedy scheduling algorithm. Our greedy algorithm is optimal when the channel quality of a user remains unchanged within an epoch. Our experimental results, based on public MPEG-4 video traces and wireless channel traces that we collected from a WiMAX test-bed, show that the lead-aware greedy approach results in a fair distribution of stalls across the clients when compared to other algorithms, while still maintaining similar or fewer average number of stalls per client.

Inferring connectivity model from meter measurements in distribution networks

We present a novel analytics approach to infer the underlying interconnection between various metered entities in a radial distribution network. Our approach uses a time series of power measurements collected from different meters in the distribution grid and infers the underlying network between these meters. The collected measurements are used to set up a system of linear equations based upon the principle of conservation of energy. The equations are analyzed to estimate a tree network that optimally fits the time series of meter measurements. We study experimentally the number of measurements needed to infer the true underlying connectivity with the help of both synthetic and real smart meter measurements in the noiseless setting.

Temporal Delay Tomography

Multicast-based network tomography enables inference of average loss rates and delay distributions of internal network links from end-to-end measurements of multicast probes. Recent work showed that this method, based on correlating observations of multicast receivers, also supports the inference of temporal loss characteristics of network links. In this paper, we show that temporal characteristics can, in fact, be estimated even for link delay processes. Knowledge of temporal delay characteristics has applications for delay sensitive services such as VoIP as well as for characterizing the queueing behavior of bottleneck links. By assuming mutually independent, but arbitrary link delay processes, we develop estimators which can infer, in addition to delay distributions, the probabilities of arbitrary patterns of delay, means and full distributions of delay-run periods at chosen delay levels, for each link in the multicast tree. By applying the recently proposed principle of subtree-partitioning, the estimator is made scalable to multicast trees of large degree. Estimation error and convergence rates are evaluated using simulations.

Voltage-based clustering to identify connectivity relationships in distribution networks

We present a novel technique to infer connectivity relationships corresponding a set of measurement or monitoring locations in a distribution grid. Our approach uses a time series of voltage measurements from each location and partitions these into clusters based on whether the locations belong to same or different sub-circuits in the grid. For instance, customer meters connected to the same phase may be clustered into one group. The methods allow distributors to verify the connectivity model of their distribution network and also improve its accuracy. Furthermore, the methods support incremental meter deployment and can be applied to any subset of measurement locations. We present initial experimental results with the help of real voltage time series measurements collected from a microgrid.

CPS-Net: In-network aggregation for synchrophasor applications

Synchrophasors are sensors that sample power grids and publish these measurements over a network to a number of grid applications such as voltage monitoring, state estimation, visualization, etc. The sampled data is QoS sensitive and must be delivered reliably with minimal delays to the target applications. However, during network overloads or grid emergencies when the volume of data transmitted is high, it is important to gracefully degrade performance and data stream delivery in an application-specific manner.