Adaptive Mediation for Data Exchange in IoT Systems

International audienceMessaging and communication is a critical aspect of next generation Internet-of-Things (IoT) systems where interactions among devices, software systems/services and end-users is the expected mode of operation. Given the diverse and changing communication needs of entities, the data exchange interactions may assume different protocols (MQTT, CoAP, HTTP) and interaction paradigms (point to point, multicast, unicast). In this paper, we address the issue of supporting adaptive communications in IoT systems through a mediation-based architecture for data exchange. Here, components called mediators support protocol translation to bridge the heterogeneity gap. Aiming to provide a placement of mediators to nodes, we introduce an integer linear programming solution that takes as input: a set of Edge nodes, IoT devices, and networking semantics. Our proposed solution achieves adaptive placement resulting in timely interactions between IoT devices for larger topologies of IoT spaces

The filter-placement problem and its application to content de-duplication

In many information networks, data items such as updates in social networks, news flowing through interconnected RSS feeds and blogs, measurements in sensor networks, route updates in ad-hoc networks, etc. propagate in an uncoordinated manner: nodes often relay information they receive to neighbors, independent of whether or not these neighbors received such information from other sources. This uncoordinated data dissemination may result in significant, yet unnecessary communication and processing overheads, ultimately reducing the utility of information networks. To alleviate the negative impacts of this information multiplicity phenomenon, we propose that a subset of nodes (selected at key positions in the network) carry out additional information de-duplication functionality namely, the removal (or significant reduction) of the duplicative data items relayed through them. We refer to such nodes as filters. We formally define the Filter Placement problem as a combinatorial optimization problem, and study its computational complexity for different types of graphs. We also present polynomial-time approximation algorithms for the problem. Our experimental results, which we obtained through extensive simulations on synthetic and real-world information flow networks, suggest that in many settings a relatively small number of filters is fairly effective in removing a large fraction of duplicative information.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798, 1017529

Operator Placement for Snapshot Multi-Predicate Queries in Wireless Sensor Networks

Abstract—This work aims at minimize the cost of answering snapshot multi-predicate queries in high-communication-cost networks. High-communication-cost (HCC) networks is a family of networks where communicating data is very demanding in resources, for example in wireless sensor networks transmitting data drains the battery life of sensors involved. The important class of multi-predicate queries in horizontally or vertically distributed databases is addressed. We show that minimizing the communication cost for multi-predicate queries is NP-hard and we propose a dynamic programming algorithm to compute the optimal solution for small problem instances. We also propose a low complexity, approximate, heuristic algorithm for solving larger problem instances efficiently and running it on nodes with low computational power (e.g. sensors). Finally, we present a variant of the Fermat point problem where distances between points are minimal paths in a weighted graph, and propose a solution. An extensive experimental evaluation compares the proposedalgorithms tothebest knowntechniqueusedtoevaluate queries in wireless sensor networks and shows improvement of 10 % up to 95%. The low complexity heuristic algorithm is also shown to be scalable and robust to different query characteristics and network size. I

Centrality measures and analyzing dot-product graphs

In this thesis we investigate two topics in data mining on graphs; in the first part we investigate the notion of centrality in graphs, in the second part we look at reconstructing graphs from aggregate information. In many graph related problems the goal is to rank nodes based on an importance score. This score is in general referred to as node centrality. In Part I. we start by giving a novel and more efficient algorithm for computing betweenness centrality. In many applications not an individual node but rather a set of nodes is chosen to perform some task. We generalize the notion of centrality to groups of nodes. While group centrality was first formally defined by Everett and Borgatti (1999), we are the first to pose it as a combinatorial optimization problem; find a group of k nodes with largest centrality. We give an algorithm for solving this optimization problem for a general notion of centrality that subsumes various instantiations of centrality that find paths in the graph. We prove that this problem is NP-hard for specific centrality definitions and we provide a universal algorithm for this problem that can be modified to optimize the specific measures. We also investigate the problem of increasing node centrality by adding or deleting edges in the graph. We conclude this part by solving the optimization problem for two specific applications; one for minimizing redundancy in information propagation networks and one for optimizing the expected number of interceptions of a group in a random navigational network. In the second part of the thesis we investigate what we can infer about a bipartite graph if only some aggregate information -- the number of common neighbors among each pair of nodes -- is given. First, we observe that the given data is equivalent to the dot-product of the adjacency vectors of each node. Based on this knowledge we develop an algorithm that is based on SVD-decomposition, that is capable of almost perfectly reconstructing graphs from such neighborhood data. We investigate two versions of this problem, in the versions the dot-product of nodes with themselves, e.g. the node degrees, are either known or hidden