Pub Date : 2015-09-01DOI: 10.1109/ALLERTON.2015.7446990
Andrés Ferragut, F. Paganini
With the advent of renewable sources and Smart-Grid deployments, it is increasingly common to control demands in order to reduce power consumption variability and thus the need for regulation, with load aggregators now exploiting the deferability of some power loads to smooth the consumption profile. In this paper, we analyze the impact of service deferrals and scheduling on power consumption variability using tools from queueing theory. We consider a generic model for a load aggregator that receive job requests, involving a certain amount of energy to be provided and a deadline. We analyze different scheduling policies and examine the impact of service deferrals, quantifying the tradeoff between variance reduction and attained deadlines.
{"title":"Queueing analysis of service deferrals for load management in power systems","authors":"Andrés Ferragut, F. Paganini","doi":"10.1109/ALLERTON.2015.7446990","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7446990","url":null,"abstract":"With the advent of renewable sources and Smart-Grid deployments, it is increasingly common to control demands in order to reduce power consumption variability and thus the need for regulation, with load aggregators now exploiting the deferability of some power loads to smooth the consumption profile. In this paper, we analyze the impact of service deferrals and scheduling on power consumption variability using tools from queueing theory. We consider a generic model for a load aggregator that receive job requests, involving a certain amount of energy to be provided and a deadline. We analyze different scheduling policies and examine the impact of service deferrals, quantifying the tradeoff between variance reduction and attained deadlines.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123329107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-01DOI: 10.1109/ALLERTON.2015.7447141
L. S. Muppirisetty, John Tadrous, A. Eryilmaz, H. Wymeersch
Mobile data traffic has surpassed that of voice to become the main component of the system load of today's wireless networks. Recent studies indicate that the data demand patterns of mobile users are predictable. Moreover, the channel quality of mobile users along their navigation paths is predictable by exploiting their location information. This work aims at fusing the statistically predictable demand and channel patterns in devising proactive caching strategies that alleviate network congestion. Specifically, we establish a fundamental bound on the minimum possible cost achievable by any proactive scheduler under time-invariant demand and channel statistics as a function of their prediction uncertainties, and develop an asymptotically optimal proactive service policy that attains this bound as the prediction window grows. In addition, the established bound yields insights on how the demand and channel statistics affect proactive caching decisions. We reveal some of these insights through numerical investigations.
{"title":"On proactive caching with demand and channel uncertainties","authors":"L. S. Muppirisetty, John Tadrous, A. Eryilmaz, H. Wymeersch","doi":"10.1109/ALLERTON.2015.7447141","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447141","url":null,"abstract":"Mobile data traffic has surpassed that of voice to become the main component of the system load of today's wireless networks. Recent studies indicate that the data demand patterns of mobile users are predictable. Moreover, the channel quality of mobile users along their navigation paths is predictable by exploiting their location information. This work aims at fusing the statistically predictable demand and channel patterns in devising proactive caching strategies that alleviate network congestion. Specifically, we establish a fundamental bound on the minimum possible cost achievable by any proactive scheduler under time-invariant demand and channel statistics as a function of their prediction uncertainties, and develop an asymptotically optimal proactive service policy that attains this bound as the prediction window grows. In addition, the established bound yields insights on how the demand and channel statistics affect proactive caching decisions. We reveal some of these insights through numerical investigations.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126723071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-01DOI: 10.1109/ALLERTON.2015.7447162
Sattar Vakili, Qing Zhao
We study risk-averse multi-armed bandit problems under different risk measures. We consider three risk mitigation models. In the first model, the variations in the reward values obtained at different times are considered as risk and the objective is to minimize the mean-variance of the observed rewards. In the second and the third models, the quantity of interest is the total reward at the end of the time horizon, and the objective is to minimize the mean-variance and maximize the value at risk of the total reward, respectively. We develop risk-averse online learning policies and analyze their regret performance. We also provide tight lower bounds on regret under the model of mean-variance of observations.
{"title":"Mean-variance and value at risk in multi-armed bandit problems","authors":"Sattar Vakili, Qing Zhao","doi":"10.1109/ALLERTON.2015.7447162","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447162","url":null,"abstract":"We study risk-averse multi-armed bandit problems under different risk measures. We consider three risk mitigation models. In the first model, the variations in the reward values obtained at different times are considered as risk and the objective is to minimize the mean-variance of the observed rewards. In the second and the third models, the quantity of interest is the total reward at the end of the time horizon, and the objective is to minimize the mean-variance and maximize the value at risk of the total reward, respectively. We develop risk-averse online learning policies and analyze their regret performance. We also provide tight lower bounds on regret under the model of mean-variance of observations.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115571187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-01DOI: 10.1109/ALLERTON.2015.7447057
Bahar Partov, D. Leith
We consider proportional fair rate allocation in a heterogeneous network with a mix of LTE and 802.11 Radio Access Technologies (RATs) which supports multipath and multi-homed operation (simultaneous connection of a user device to multiple LTE BSs and one 802.11 AP). We show that the utility fair optimisation problem is non-convex but that a global optimum can be found by solving a sequence of convex optimisations in a distributed fashion. The result is a principled approach to offload from LTE to 802.11 and for exploiting LTE/802.11 path diversity to meet user traffic demands.
{"title":"Utility fair RAT selection in multi-homed LTE/802.11 networks","authors":"Bahar Partov, D. Leith","doi":"10.1109/ALLERTON.2015.7447057","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447057","url":null,"abstract":"We consider proportional fair rate allocation in a heterogeneous network with a mix of LTE and 802.11 Radio Access Technologies (RATs) which supports multipath and multi-homed operation (simultaneous connection of a user device to multiple LTE BSs and one 802.11 AP). We show that the utility fair optimisation problem is non-convex but that a global optimum can be found by solving a sequence of convex optimisations in a distributed fashion. The result is a principled approach to offload from LTE to 802.11 and for exploiting LTE/802.11 path diversity to meet user traffic demands.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114123628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-01DOI: 10.1109/ALLERTON.2015.7447013
Bryan Van Scoy, R. Freeman, K. Lynch
In the discrete-time average consensus problem, each agent in a network has a local input and communicates with neighboring agents to calculate the global average of all agent inputs. We analyze diffusion-like algorithms where each agent maintains an internal state which it updates at each time step using its local input together with information it receives from neighboring agents. The agent's estimate of the global average input is then a local function of its internal state. Local memory on each agent can be used to enhance the performance of average consensus estimators in several ways. Agents can use memory to store both internal state variables as well as intermediate diffusion calculations within each time step. We exploit memory to design two types of estimators. First, we design feedback estimators which track constant input signals with zero steady-state error. Such estimators produce estimates that converge exponentially to the global average, and we consider the cost of an estimator to be the largest time constant of the exponential decay of its estimation errors. However, we measure time using normalized units of communicated real variables per agent, so that estimators requiring more communication per time step are potentially costlier even if they converge in fewer time steps. We then show that a certain estimator having two internal state variables and one diffusion calculation per time step achieves the minimal cost over all graphs and all estimators with one or two states no matter how many intermediate diffusion calculations are stored. Second, we design a feedforward estimator which tracks time-varying signals whose frequencies lie below some cut-off frequency. The steady-state error is finite, but can be made arbitrarily small using enough diffusion calculations per time step.
{"title":"Exploiting memory in dynamic average consensus","authors":"Bryan Van Scoy, R. Freeman, K. Lynch","doi":"10.1109/ALLERTON.2015.7447013","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447013","url":null,"abstract":"In the discrete-time average consensus problem, each agent in a network has a local input and communicates with neighboring agents to calculate the global average of all agent inputs. We analyze diffusion-like algorithms where each agent maintains an internal state which it updates at each time step using its local input together with information it receives from neighboring agents. The agent's estimate of the global average input is then a local function of its internal state. Local memory on each agent can be used to enhance the performance of average consensus estimators in several ways. Agents can use memory to store both internal state variables as well as intermediate diffusion calculations within each time step. We exploit memory to design two types of estimators. First, we design feedback estimators which track constant input signals with zero steady-state error. Such estimators produce estimates that converge exponentially to the global average, and we consider the cost of an estimator to be the largest time constant of the exponential decay of its estimation errors. However, we measure time using normalized units of communicated real variables per agent, so that estimators requiring more communication per time step are potentially costlier even if they converge in fewer time steps. We then show that a certain estimator having two internal state variables and one diffusion calculation per time step achieves the minimal cost over all graphs and all estimators with one or two states no matter how many intermediate diffusion calculations are stored. Second, we design a feedforward estimator which tracks time-varying signals whose frequencies lie below some cut-off frequency. The steady-state error is finite, but can be made arbitrarily small using enough diffusion calculations per time step.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126892900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-01DOI: 10.1109/ALLERTON.2015.7446980
J. Hendrickx, J. Tsitsiklis
We consider deterministic anonymous distributed systems with broadcast communications where each node has some initial value, and the goal is to compute a function of all these values. We show that only a very restricted set of functions can be computed if the nodes do not know (and cannot use) the number of their out-neighbors. Our results remain valid even if nodes know the precise structure of the network but do not know where they lie within the structure. They also remain valid if nodes know their out-degree up to an uncertainty of 1. These results are a variation of those obtained by Boldi and Vigna (1997) for a weaker computation model. As a consequence, computing more complex functions in the context of broadcast communications requires the explicit or implicit knowledge or use of either (a) the out-degree of each node, (b) global node identifiers, (c) randomization, or (d) asynchronous updates with specific properties.
{"title":"Fundamental limitations for anonymous distributed systems with broadcast communications","authors":"J. Hendrickx, J. Tsitsiklis","doi":"10.1109/ALLERTON.2015.7446980","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7446980","url":null,"abstract":"We consider deterministic anonymous distributed systems with broadcast communications where each node has some initial value, and the goal is to compute a function of all these values. We show that only a very restricted set of functions can be computed if the nodes do not know (and cannot use) the number of their out-neighbors. Our results remain valid even if nodes know the precise structure of the network but do not know where they lie within the structure. They also remain valid if nodes know their out-degree up to an uncertainty of 1. These results are a variation of those obtained by Boldi and Vigna (1997) for a weaker computation model. As a consequence, computing more complex functions in the context of broadcast communications requires the explicit or implicit knowledge or use of either (a) the out-degree of each node, (b) global node identifiers, (c) randomization, or (d) asynchronous updates with specific properties.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130658904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-01DOI: 10.1109/ALLERTON.2015.7447138
Parisa Hassanzadeh, E. Erkip, J. Llorca, A. Tulino
Mobile network operators are considering caching as one of the strategies to keep up with the increasing demand for high-definition wireless video streaming. By prefetching popular content into memory at wireless access points or end user devices, requests can be served locally, relieving strain on expensive backhaul. In addition, using network coding allows the simultaneous serving of distinct cache misses via common coded multicast transmissions, resulting in significantly larger load reductions compared to those achieved with conventional delivery schemes. However, prior work does not exploit the properties of video and simply treats content as fixed-size files that users would like to fully download. Our work is motivated by the fact that video can be coded in a scalable fashion and that the decoded video quality depends on the number of layers a user is able to receive. Using a Gaussian source model, caching and coded delivery methods are designed to minimize the squared error distortion at end user devices. Our work is general enough to consider heterogeneous cache sizes and video popularity distributions.
{"title":"Distortion-memory tradeoffs in cache-aided wireless video delivery","authors":"Parisa Hassanzadeh, E. Erkip, J. Llorca, A. Tulino","doi":"10.1109/ALLERTON.2015.7447138","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447138","url":null,"abstract":"Mobile network operators are considering caching as one of the strategies to keep up with the increasing demand for high-definition wireless video streaming. By prefetching popular content into memory at wireless access points or end user devices, requests can be served locally, relieving strain on expensive backhaul. In addition, using network coding allows the simultaneous serving of distinct cache misses via common coded multicast transmissions, resulting in significantly larger load reductions compared to those achieved with conventional delivery schemes. However, prior work does not exploit the properties of video and simply treats content as fixed-size files that users would like to fully download. Our work is motivated by the fact that video can be coded in a scalable fashion and that the decoded video quality depends on the number of layers a user is able to receive. Using a Gaussian source model, caching and coded delivery methods are designed to minimize the squared error distortion at end user devices. Our work is general enough to consider heterogeneous cache sizes and video popularity distributions.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128909279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-01DOI: 10.1109/ALLERTON.2015.7447071
Weiran Wang, R. Arora, Karen Livescu, N. Srebro
Deep CCA is a recently proposed deep neural network extension to the traditional canonical correlation analysis (CCA), and has been successful for multi-view representation learning in several domains. However, stochastic optimization of the deep CCA objective is not straightforward, because it does not decouple over training examples. Previous optimizers for deep CCA are either batch-based algorithms or stochastic optimization using large minibatches, which can have high memory consumption. In this paper, we tackle the problem of stochastic optimization for deep CCA with small minibatches, based on an iterative solution to the CCA objective, and show that we can achieve as good performance as previous optimizers and thus alleviate the memory requirement.
{"title":"Stochastic optimization for deep CCA via nonlinear orthogonal iterations","authors":"Weiran Wang, R. Arora, Karen Livescu, N. Srebro","doi":"10.1109/ALLERTON.2015.7447071","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447071","url":null,"abstract":"Deep CCA is a recently proposed deep neural network extension to the traditional canonical correlation analysis (CCA), and has been successful for multi-view representation learning in several domains. However, stochastic optimization of the deep CCA objective is not straightforward, because it does not decouple over training examples. Previous optimizers for deep CCA are either batch-based algorithms or stochastic optimization using large minibatches, which can have high memory consumption. In this paper, we tackle the problem of stochastic optimization for deep CCA with small minibatches, based on an iterative solution to the CCA objective, and show that we can achieve as good performance as previous optimizers and thus alleviate the memory requirement.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115344364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-01DOI: 10.1109/ALLERTON.2015.7447012
Wei Wang, Choon Yik Tang
Betweenness centrality is a fundamental centrality measure that quantifies how important a node or an edge is, within a network, based on how often it lies on the shortest paths between all pairs of nodes. In this paper, we develop a scalable distributed algorithm, which enables every node in a network to estimate its own betweenness and the betweenness of edges incident on it with only local interaction and without any centralized coordination, nor high memory usages. The development is based on exploiting various local properties of shortest paths, and on formulating and solving an unconstrained distributed optimization problem. We also evaluate the algorithm performance via simulation on a number of random geometric graphs, showing that it yields betweenness estimates that are fairly accurate in terms of ordering.
{"title":"Distributed estimation of betweenness centrality","authors":"Wei Wang, Choon Yik Tang","doi":"10.1109/ALLERTON.2015.7447012","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447012","url":null,"abstract":"Betweenness centrality is a fundamental centrality measure that quantifies how important a node or an edge is, within a network, based on how often it lies on the shortest paths between all pairs of nodes. In this paper, we develop a scalable distributed algorithm, which enables every node in a network to estimate its own betweenness and the betweenness of edges incident on it with only local interaction and without any centralized coordination, nor high memory usages. The development is based on exploiting various local properties of shortest paths, and on formulating and solving an unconstrained distributed optimization problem. We also evaluate the algorithm performance via simulation on a number of random geometric graphs, showing that it yields betweenness estimates that are fairly accurate in terms of ordering.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114551404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-01DOI: 10.1109/ALLERTON.2015.7447124
Ramin Soltani, D. Goeckel, D. Towsley, A. Houmansadr
Consider a channel where authorized transmitter Jack sends packets to authorized receiver Steve according to a Poisson process with rate λ packets per second for a time period T. Suppose that covert transmitter Alice wishes to communicate information to covert receiver Bob on the same channel without being detected by a watchful adversary Willie. We consider two scenarios. In the first scenario, we assume that warden Willie cannot look at packet contents but rather can only observe packet timings, and Alice must send information by inserting her own packets into the channel. We show that the number of packets that Alice can covertly transmit to Bob is on the order of the square root of the number of packets that Jack transmits to Steve; conversely, if Alice transmits more than that, she will be detected by Willie with high probability. In the second scenario, we assume that Willie can look at packet contents but that Alice can communicate across an M/M/1 queue to Bob by altering the timings of the packets going from Jack to Steve. First, Alice builds a codebook, with each codeword consisting of a sequence of packet timings to be employed for conveying the information associated with that codeword. However, to successfully employ this codebook, Alice must always have a packet to send at the appropriate time. Hence, leveraging our result from the first scenario, we propose a construction where Alice covertly slows down the packet stream so as to buffer packets to use during a succeeding codeword transmission phase. Using this approach, Alice can covertly and reliably transmit O(λT) covert bits to Bob in time period T over an M/M/1 queue with service rate μ > e · λ.
{"title":"Covert communications on Poisson packet channels","authors":"Ramin Soltani, D. Goeckel, D. Towsley, A. Houmansadr","doi":"10.1109/ALLERTON.2015.7447124","DOIUrl":"https://doi.org/10.1109/ALLERTON.2015.7447124","url":null,"abstract":"Consider a channel where authorized transmitter Jack sends packets to authorized receiver Steve according to a Poisson process with rate λ packets per second for a time period T. Suppose that covert transmitter Alice wishes to communicate information to covert receiver Bob on the same channel without being detected by a watchful adversary Willie. We consider two scenarios. In the first scenario, we assume that warden Willie cannot look at packet contents but rather can only observe packet timings, and Alice must send information by inserting her own packets into the channel. We show that the number of packets that Alice can covertly transmit to Bob is on the order of the square root of the number of packets that Jack transmits to Steve; conversely, if Alice transmits more than that, she will be detected by Willie with high probability. In the second scenario, we assume that Willie can look at packet contents but that Alice can communicate across an M/M/1 queue to Bob by altering the timings of the packets going from Jack to Steve. First, Alice builds a codebook, with each codeword consisting of a sequence of packet timings to be employed for conveying the information associated with that codeword. However, to successfully employ this codebook, Alice must always have a packet to send at the appropriate time. Hence, leveraging our result from the first scenario, we propose a construction where Alice covertly slows down the packet stream so as to buffer packets to use during a succeeding codeword transmission phase. Using this approach, Alice can covertly and reliably transmit O(λT) covert bits to Bob in time period T over an M/M/1 queue with service rate μ > e · λ.","PeriodicalId":112948,"journal":{"name":"2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117266873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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