28 research outputs found
STEPS - an approach for human mobility modeling
In this paper we introduce Spatio-TEmporal Parametric Stepping (STEPS) - a simple parametric mobility model which can cover a large spectrum of human mobility patterns. STEPS makes abstraction of spatio-temporal preferences in human mobility by using a power law to rule the nodes movement. Nodes in STEPS have preferential attachment to favorite locations where they spend most of their time. Via simulations, we show that STEPS is able, not only to express the peer to peer properties such as inter-ontact/contact time and to reflect accurately realistic routing performance, but also to express the structural properties of the underlying interaction graph such as small-world phenomenon. Moreover, STEPS is easy to implement, exible to configure and also theoretically tractable
Tracking Topology Dynamicity for Link Prediction in Intermittently Connected Wireless Networks
Through several studies, it has been highlighted that mobility patterns in
mobile networks are driven by human behaviors. This effect has been
particularly observed in intermittently connected networks like DTN (Delay
Tolerant Networks). Given that common social intentions generate similar human
behavior, it is relevant to exploit this knowledge in the network protocols
design, e.g. to identify the closeness degree between two nodes. In this paper,
we propose a temporal link prediction technique for DTN which quantifies the
behavior similarity between each pair of nodes and makes use of it to predict
future links. We attest that the tensor-based technique is effective for
temporal link prediction applied to the intermittently connected networks. The
validity of this method is proved when the prediction is made in a distributed
way (i.e. with local information) and its performance is compared to well-known
link prediction metrics proposed in the literature.Comment: Published in the proceedings of the 8th International Wireless
Communications and Mobile Computing Conference (IWCMC), Limassol, Cyprus,
201
Energy-Aware Clustering in the Internet of Things by Using the Genetic Algorithm
Internet of things (IoT) uses a lot of key technologies to collect different types of data around the world to make an intelligent and integrated whole. This concept can be as simple as a connection between a smartphone and a smart TV, or can be complex communications between the urban infrastructure and traffic monitoring systems. One of the most challenging issues in the IoT environment is how to make it scalable and energy-efficient with regard to its growing dimensions. Object clustering is a mechanism that increases scalability and provides energy efficiency by minimizing communication energy consumption. Since IoT is a large scale dynamic environment, clustering of its objects is a NP-Complete problem. This paper formulates energy-aware clustering of things as an optimization problem targeting an optimum point in which, the total consumed energy and communication cost are minimal. Then. it employs the Genetic Algorithm (GA) to solve this optimization problem by extracting the optimal number of clusters as well as the members of each cluster. In this paper, a multi objective GA for clustering that has not premature convergence problem is used. In addition, for fast GA execution multiple implementation, considerations has been measured. Moreover, the consumed energy for received and sent data, node to node and node to BS distance have been considered as effective parameters in energy consumption formulation. Numerical simulation results show the efficiency of this method in terms of the consumed energy, network lifetime, the number of dead nodes and load balancing
Quantifying Spatiotemporal Stability by means of Entropy: Approach and Motivations
Several studies demonstrate that there are critical differences between real
wireless networks and simulation models. This finding has permitted to extract
spatial and temporal properties for links and to provide efficient methods as
biased link sampling to guarantee efficient routing structure. Other works have
focused on computing metrics to improve routing, specially the reuse of the
measure of entropy. From there, rises the idea of formulating a new measure of
entropy that gives an overview of the spatiotemporal stability of a link. This
measure will rely on spatial and temporal properties of links and fed with the
efficiency of biased link sampling.Comment: 10 pages, Telecom SudParis Research Repor