Petteri Mannersalo

Latency of wireless sensor networks with uncoordinated power saving mechanisms

We consider a wireless sensor network, where nodes switch between an active (on) and a sleeping (off) mode, to save energy. The basic assumptions are that the on/off schedules are completely uncoordinated and that the sensors are distributed according to a Poisson process and their connectivity ranges are larger or equal to their sensing ranges. Moreover, the durations of active and sleeping periods are such that the number of active nodes at any particular time is so low that the network is always disconnected.Is it possible to use such a network for time-critical monitoring of an area? Such a scenario requires indeed to have bounds on the latency, which is the delay elapsed between the time at which an incoming event is sensed by some node of the network and the time at which this information is retrieved by the data collecting sink. A positive answer is provided to this question under some simplifying assumptions discussed in the paper. More precisely, we prove that the messages sent by a sensing node reach the sink with a fixed asymptotic speed, which does not depend on the random location of the nodes, but only on the network parameters (node density, connectivity range, duration of active and sleeping periods). The results are obtained rigorously by using an extension of first passage percolation theory.

A most probable path approach to queueing systems with general Gaussian input

This paper develops a rather general and easy-to-use methodology how to obtain good approximations for queue length distributions in priority and generalized processor sharing systems where the cumulative input traffic streams are modeled as general Gaussian processes with stationary increments. The idea is to identify the most probable path in the threshold-exceeding event, or a heuristic approximation of it, and then to use probability estimates based on this path. The method is particularly useful for long-range dependent traffic and complicated traffic mixes, which are difficult to handle with traditional queueing theory.

Most probable paths and performance formulae for buffers with gaussian input traffic

[Abstract]: In this paper, performance formulae for a queue serving Gaussian traffic are presented. The main technique employed is motivated by a general form of Schilder’s theorem, the large deviation result for Gaussian processes. Most probable paths leading to a given buffer occupancy are identified. Special attention is given to the case where the sample paths of the Gaussian process are smooth. The performance approximations are compared with known analytical results or by means of simulation. The approximations appear to be surprisingly accurate.

GPS schedulers and Gaussian traffic

This article considers Gaussian flows which are fed into a GPS (Generalized Processor Sharing) scheduler. The system is analyzed using a most probable path approach. This method gives quite good approximations for performance measures, like queue length distributions in the full range of queue levels. The approximations are based on the distinction whether it is more probable that an aggregated queue consists of traffic from one class only or whether it is a combination of several classes. The approximate queue length distribution for a specific flow is then calculated either using the Empty Buffer Approximation or the authors’ Rough Full Link Approximation, respectively.

Multifractal products of stochastic processes: construction and some basic properties

In various fields, such as teletraffic and economics, measured time series have been reported to adhere to multifractal scaling. Classical cascading measures possess multifractal scaling, but their increments form a nonstationary process. To overcome this problem, we introduce a construction of random multifractal measures based on iterative multiplication of stationary stochastic processes, a special form of T-martingales. We study the ℒ2-convergence, nondegeneracy, and continuity of the limit process. Establishing a power law for its moments, we obtain a formula for the multifractal spectrum and hint at how to prove the full formalism.

Approximate formulae for Gaussian priority queues

Queueing theory with general Gaussian traffic has no exact results, but reasonably good approximations are easy to obtain. In the context of Differentiated Services, FIFO queues play a minor role. Instead, it is important to understand how priority queues behave when the inputs are Gaussian. This paper focuses on developing practically usable estimates for distributions of Gaussian priority queues. The key idea is to identify the most probable paths along which a big queue arises. Some of the approximations are compared to simulations, and the results are promising.

Modeling the dynamics of caching in content-based publish/subscribe systems

This paper considers cache dimensioning in the context of publish/subscribe (pub/sub) systems. We assume that each broker is equipped with a limited capacity cache and it decides upon a policy for caching and prioritizing messages. By using a request mechanism defined on top of the native pub/sub communication, a client may also request earlier published information. To study the survival time of published messages, a Markovian system model capturing the essential dynamics is defined. The model has a modular generic form which admits a variety of different policies and thus enables the calculation of their performance. For systems without message replication between the caching brokers, the distribution of message survival time is found using matrix analytic methods for solving absorbing Markov chains. For the general problem with messages copied from caches, we propose a heuristic approximation based on estimating the mean rate of copies. The approximate model is evaluated by a discrete event simulator and it is shown that for a wide set of parameters, the approximation provides a good basis for dimensioning the caches in the content-based pub/sub systems.

Cognitive Network Management Framework and Approach for Video Streaming Optimization in Heterogeneous Networks

The future Internet will be highly heterogeneous in supporting a multitude of access technologies and networks with overlapping coverages. Optimization of network operations like management of resources, mobility or Quality of Service in order to ensure smooth network operation and high user satisfaction will be very challenging in the future networks. Cognitive network management can provide a solution of managing such complex systems. This paper studies cognitive network management in the context of optimizing video streaming performance in heterogeneous multi-access networks. The paper proposes a network management framework that relies on cognitive decision techniques in the joint optimization of network and video service performance. The proposed solution is also implemented and validated in part in a testbed environment. The results attest the feasibility of the solution as well as the benefits of cognitive decision techniques over non-learning or non-adaptive approaches.

Queueing systems fed by many exponential on-off sources: an infinite-intersection approach

In queueing theory, an important class of events can be written as ‘infinite intersections’. For instance, in a queue with constant service rate c, busy periods starting at 0 and exceeding L > 0 are determined by the intersection of the events