1,015 research outputs found
MoMo: a group mobility model for future generation mobile wireless networks
Existing group mobility models were not designed to meet the requirements for
accurate simulation of current and future short distance wireless networks
scenarios, that need, in particular, accurate, up-to-date informa- tion on the
position of each node in the network, combined with a simple and flexible
approach to group mobility modeling. A new model for group mobility in wireless
networks, named MoMo, is proposed in this paper, based on the combination of a
memory-based individual mobility model with a flexible group behavior model.
MoMo is capable of accurately describing all mobility scenarios, from
individual mobility, in which nodes move inde- pendently one from the other, to
tight group mobility, where mobility patterns of different nodes are strictly
correlated. A new set of intrinsic properties for a mobility model is proposed
and adopted in the analysis and comparison of MoMo with existing models. Next,
MoMo is compared with existing group mobility models in a typical 5G network
scenario, in which a set of mobile nodes cooperate in the realization of a
distributed MIMO link. Results show that MoMo leads to accurate, robust and
flexible modeling of mobility of groups of nodes in discrete event simulators,
making it suitable for the performance evaluation of networking protocols and
resource allocation algorithms in the wide range of network scenarios expected
to characterize 5G networks.Comment: 25 pages, 17 figure
Non-convex resource allocation in communication networks
The continuously growing number of applications competing for resources
in current communication networks highlights the necessity for efficient resource allocation mechanisms to maximize user satisfaction. Optimization
Theory can provide the necessary tools to develop such mechanisms that will
allocate network resources optimally and fairly among users. However, the
resource allocation problem in current networks has characteristics that turn
the respective optimization problem into a non-convex one. First, current
networks very often consist of a number of wireless links, whose capacity is
not constant but follows Shannon capacity formula, which is a non-convex
function. Second, the majority of the traffic in current networks is generated
by multimedia applications, which are non-concave functions of rate. Third,
current resource allocation methods follow the (bandwidth) proportional
fairness policy, which when applied to networks shared by both concave
and non-concave utilities leads to unfair resource allocations. These characteristics make current convex optimization frameworks inefficient in several
aspects. This work aims to develop a non-convex optimization framework
that will be able to allocate resources efficiently for non-convex resource allocation formulations. Towards this goal, a necessary and sufficient condition
for the convergence of any primal-dual optimization algorithm to the optimal solution is proven. The wide applicability of this condition makes this a fundamental contribution for Optimization Theory in general. A number
of optimization formulations are proposed, cases where this condition is not
met are analysed and efficient alternative heuristics are provided to handle
these cases. Furthermore, a novel multi-sigmoidal utility shape is proposed
to model user satisfaction for multi-tiered multimedia applications more accurately. The advantages of such non-convex utilities and their effect in the
optimization process are thoroughly examined. Alternative allocation policies are also investigated with respect to their ability to allocate resources
fairly and deal with the non-convexity of the resource allocation problem. Specifically, the advantages of using Utility Proportional Fairness as an allocation policy are examined with respect to the development of distributed
algorithms, their convergence to the optimal solution and their ability to
adapt to the Quality of Service requirements of each application
Rate-Distortion Classification for Self-Tuning IoT Networks
Many future wireless sensor networks and the Internet of Things are expected
to follow a software defined paradigm, where protocol parameters and behaviors
will be dynamically tuned as a function of the signal statistics. New protocols
will be then injected as a software as certain events occur. For instance, new
data compressors could be (re)programmed on-the-fly as the monitored signal
type or its statistical properties change. We consider a lossy compression
scenario, where the application tolerates some distortion of the gathered
signal in return for improved energy efficiency. To reap the full benefits of
this paradigm, we discuss an automatic sensor profiling approach where the
signal class, and in particular the corresponding rate-distortion curve, is
automatically assessed using machine learning tools (namely, support vector
machines and neural networks). We show that this curve can be reliably
estimated on-the-fly through the computation of a small number (from ten to
twenty) of statistical features on time windows of a few hundreds samples
A Channel Assignment and Routing Algorithm for Energy Harvesting Multi-Radio Wireless Mesh Networks
Wireless mesh networks are being deployed all around the world both to provide ubiquitous connection to the Internet and to carry data generated by several services (video surveillance, smart grids, earthquake early warning systems, etc.). In those cases where fixed power connections are not available, mesh nodes operate by harvesting ambient energy (e.g., solar or wind power) and hence they can count on a limited and time-varying amount of power to accomplish their functions. Since we consider mesh nodes equipped with multiple radios, power savings and network performance can be maximized by properly routing flows, assigning channels to radios and identifying nodes/radios that can be turned off. Thus, the problem we address is a joint channel assignment and routing problem with additional constraints on the node power consumption, which is NP-complete. In this paper, we propose a heuristic, named minimum power channel assignment and routing algorithm (MP-CARA), which is guaranteed to return a local optimum for this problem. Based on a theoretical analysis that we present in the paper, which gives an upper bound on the outage probability as a function of the constraint on power consumption, we can guarantee that the probability that a node runs out of power with MP-CARA falls below a desired threshold. The performance of MP-CARA is assessed by means of an extensive simulation study aiming to compare the solutions returned by MP-CARA to those found by other heuristics proposed in the literature.Publicad
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