Kyatsandra G. Nagananda

A Communication-Based Appliance Scheduling Scheme for Consumer-Premise Energy Management Systems

In this paper, a communication-based load scheduling protocol is proposed for in-home appliances connected over a home area network. Specifically, a joint access and scheduling approach for appliances is developed to enable in-home appliances to coordinate power usage so that the total energy demand for the home is kept below a target value. The proposed protocol considers both “schedulable” appliances which have delay flexibility, and “critical” appliances which consume power as they desire. An optimization problem is formulated for the energy management controller to decide the target values for each time slot, by incorporating the variation of electricity prices and distributed wind power uncertainty. We model the evolution of the protocol as a two-dimensional Markov chain, and derive the steady-state distribution, by which the average delay of an appliance is then obtained. Simulation results verify the analysis and show cost saving to customers using the proposed scheme.

A Learning-Based Approach to Caching in Heterogenous Small Cell Networks

A heterogenous network with base stations (BSs), small base stations (SBSs), and users distributed according to independent Poisson point processes is considered. SBS nodes are assumed to possess high storage capacity and to form a distributed caching network. Popular files are stored in local caches of SBSs, so that a user can download the desired files from one of the SBSs in its vicinity. The offloading-loss is captured via a cost function that depends on the random caching strategy proposed here. The popularity profile of cached content is unknown and estimated using instantaneous demands from users within a specified time interval. An estimate of the cost function is obtained from which an optimal random caching strategy is devised. The training time to achieve an ∈ > 0 difference between the achieved and optimal costs is finite provided the user density is greater than a predefined threshold, and scales as N2, where N is the support of the popularity profile. A transfer learning-based approach to improve this estimate is proposed. The training time is reduced when the popularity profile is modeled using a parametric family of distributions; the delay is independent of N and scales linearly with the dimension of the distribution parameter.

Multiuser cognitive radio networks: an information-theoretic perspective

Achievable rate regions and outer bounds are derived for three-user interference channels where the transmitters cooperate in a unidirectional manner via a noncausal message-sharing mechanism. The three-user channel facilitates different ways of message-sharing between the primary and secondary (or cognitive) transmitters. Three natural extensions of unidirectional message-sharing from two users to three users are introduced: (i) Cumulative message sharing; (ii) primary-only message sharing; and (iii) cognitive-only message sharing. To emphasize the notion of interference management, channels are classified based on different rate-splitting strategies at the transmitters. The techniques of superposition coding and Gel’fand–Pinsker’s binning are employed to derive an achievable rate region for each of the cognitive interference channels. The results are specialized to the Gaussian channel, which enables a visual comparison of the achievable rate regions through simulations and help us achieve some additional rate points under extreme assumptions. We also provide key insights into the role of rate-splitting at the transmitters as an aid to better interference management at the receivers.

Caching with Unknown Popularity Profiles in Small Cell Networks

A heterogenous network is considered where the base stations (BSs), small base stations (SBSs) and users are distributed according to independent Poisson point processes (PPPs). We let the SBS nodes to posses high storage capacity and are assumed to form a distributed caching network. Popular data files are stored in the local cache of SBS, so that users can download the desired files from one of the SBS in the vicinity subject to availability. The offloading-loss is captured via a cost function that depends on a random caching strategy proposed in this paper. The cost function depends on the popularity profile, which is, in general, unknown. In this work, the popularity profile is estimated at the BS using the available instantaneous demands from the users in a time interval [0,τ]. This is then used to find an estimate of the cost function from which the optimal random caching strategy is devised. The main results of this work are the following: First it is shown that the waiting time τ to achieve an e > 0 difference between the achieved and optimal costs is finite, provided the user density is greater than a predefined threshold. In this case, τ is shown to scale as N^2, where N is the support of the popularity profile. Secondly, a transfer learning based approach is proposed to obtain an estimate of the popularity profile used to compute the empirical cost function. A condition is derived under which the proposed transfer learning-based approach performs better than the random caching strategy.

A PMU Scheduling Scheme for Transmission of Synchrophasor Data in Electric Power Systems

With the proposition to install a large number of phasor measurement units (PMUs) in the future power grid, it is essential to provide robust communications infrastructure for phasor data across the network. We make progress in this direction by devising a simple time division multiplexing scheme for transmitting phasor data from the PMUs to a central server. Time is divided into frames and the PMUs take turns to transmit to the control center within the time frame. The main contribution of this paper is a scheduling policy based on which PMU transmissions are ordered during a time frame. The scheduling scheme is independent of the approach taken to solve the PMU placement problem, and unlike strategies devised for conventional communications, it is intended for the power network since it is fully governed by the measure of electrical connectedness between buses in the grid. To quantify the performance of the scheduling scheme, we couple it with a fault detection algorithm used to detect changes in the susceptance parameters in the grid. Results demonstrate that scheduling the PMU transmissions leads to an improved performance of the fault detection scheme compared to PMUs transmitting at random.

Secure communications over opportunistic-relay channels

In this paper, we derive information-theoretic performance limits for secure communications over two classes of discrete memoryless relay channels. We consider two different communication scenarios over a four node wireless network comprising a source–destination pair, a relay node and a malicious node eavesdropping on the link between the relay and the destination. In both scenarios, the relay is (1) opportunistic in the sense that, it utilizes the communication opportunity to transmit its own message to the destination; and (2) constrained to secure its communication from the external eavesdropper. We present a novel achievability scheme, namely layered coding, to simultaneously deal with cooperation, cognition and confidentiality. We derive inner bounds on the capacity region for the two communication scenarios, and characterize the rate-penalty for satisfying the security constraints on the messages. Outer bounds are derived using auxiliary random variables which enable single-letter characterization. We also compare the opportunistic-relay models to the classical cognitive radio network setup. Finally, we discuss some of the advantages and drawbacks of our coding strategy in comparison to those in the existing literature, which provides interesting insights into the relative merits of the methods employed in this work for obtaining the capacity bounds.

Analysis of a joint access and scheduling scheme for residential energy management controller

In this paper, we conduct performance analysis for a joint media access and appliance scheduling protocol devised for efficient information-management in smart appliances within a home-area network. This protocol enables appliances in-home to coordinate power usage so that total demand for the home is kept below a target value. In particular, from the system perspective, we model the evolution of the protocol as a two-dimensional Markov chain and compute the steady-state distribution; we then derive the average delay of an appliance by constructing a state transition diagram for this protocol. Simulation results are provided to verify our analysis.

An Approximately Optimal Algorithm for Scheduling Phasor Data Transmissions in Smart Grid Networks

In this paper, we devise a scheduling algorithm for ordering transmission of synchrophasor data from the substation to the control center in as short a time frame as possible, within the real-time hierarchical communications infrastructure in the electric grid. The problem is cast in the framework of the classic job scheduling with precedence constraints. The optimization setup comprises the number of phasor measurement units (PMUs) to be installed on the grid, a weight associated with each PMU, processing time at the control center for the PMUs, and precedence constraints between the PMUs. The solution to the PMU placement problem yields the optimum number of PMUs to be installed on the grid, while the processing times are picked uniformly at random from a predefined set. The weight associated with each PMU and the precedence constraints are both assumed known. The scheduling problem is provably NP-hard, so we resort to approximation algorithms, which provide solutions that are suboptimal yet possessing polynomial time complexity. A lower bound on the optimal schedule is derived using branch and bound techniques, and its performance evaluated using standard IEEE test bus systems. The scheduling policy is power grid-centric, since it takes into account the electrical properties of the network under consideration.

Sequential joint signal detection and signal-to-noise ratio estimation

The sequential analysis of the problem of joint signal detection and signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model is considered. The problem is posed as an optimization setup where the goal is to minimize the number of samples required to achieve the desired (i) type I and type II error probabilities and (ii) mean squared error performance. This optimization problem is reduced to a more tractable formulation by transforming the observed signal and noise sequences to a single sequence of Bernoulli random variables; joint detection and estimation is then performed on the Bernoulli sequence. This transformation renders the problem easily solvable, and results in a computationally simpler sufficient statistic compared to the one based on the (untransformed) observation sequences. Experimental results demonstrate the advantages of the proposed method, making it feasible for applications having strict constraints on data storage and computation.

GPS spoofing attack characterization and detection in smart grids

The problem of global positioning system (GPS) spoofing attacks on smart grids endowed with phasor measurement units (PMUs) is addressed, taking into account the dynamical behavior of the states of the system. First, it is shown how GPS spoofing introduces a timing synchronization error in the phasor readings recorded by the PMUs and alters the measurement matrix of the dynamical model. Then, a generalized likelihood ratio-based hypotheses testing procedure is devised to detect changes in the measurement matrix when the system is subjected to a spoofing attack. Monte Carlo simulations are performed on the 9-bus, 3-machine test grid to demonstrate the implication of the spoofing attack on dynamic state estimation and to analyze the performance of the proposed hypotheses test.