Performing a building-level positioning using WLAM and cellular information is a well-known methodology which was suggested and implemented by many researches. In this paper we present a general framework for accurate indoor positioning and navigation which improves the expected accuracy to a sub-meter error rate. The main algorithm is based on a modified particle filter which combines RF finger-printing, odometry, visual landmarks and map constrains. The accuracy improvement is achieved by using a low resolution camera to track dominant landmarks such as lights. The use of “glowing-markers” allows one to accurately map relatively complex indoor buildings with a compact representation. The suggested method [BmS15] was implemented and tested on android based mobile devices. Our tests indicate a robust sub-meter 3D positioning at 10 - 30Hz with a fairly low energy consumption.

Receiver modules in Global Navigation Satellite Systems (GNSS) are capable of providing positioning and velocity estimations that are sufficiently accurate for the purpose of road navigation. However, even in optimal open-sky conditions, GNSS-based positioning carries an average error of 2-4 m. This imposes an effective limitation on GNSS-based vehicle lane detection, a desired functionality for various navigation and safety applications. In this paper, we present a novel framework for lane-level accuracy using GNSS devices and 3-D shadow matching. The suggested framework is based on detection and analysis of rapid changes in navigation satellites' signal strength, which are caused by momentary blockages due to utility and light poles. A method for detecting such momentary changes between line of sight and non line of sight is presented, followed by a geometric algorithm that improves location accuracy of commercial GNSS devices. We have tested the framework's applicability using both simulations and field experiments. We provide the results of these tests and discuss receiver-side sampling rate requirements for high-performance lane-level positioning.

Mobile Ad-Hoc Networks (MANET) are known for their rapid deployment and self-organizing capabilities. Those qualities are making MANET a candidate communication infrastructure for rescue forces in emergency events. However, existing Wi-Fi MANET implementations are exhibiting unsatisfactory performance, and the dynamic multi-hop topology of the network makes it difficult to identify the bottlenecks. This paper1 suggests a performance monitoring model for Wi-Fi MANET, incorporating concepts of a Geographic Information and Monitoring System (GIMS), that passively monitors the MANET deployment, thus enabling to optimize and fine-tune the network. Specifically, our monitoring model addresses the known Wi-Fi problems of hidden node and exposed node that are intensified in MANET. We provide a theoretical solution, deriving from the field of conflict graphs, which assists to identify and locate such situations. Experimental results from a real-life testbed that emulates such problems confirm that the suggested approach can effectively detects cases of hidden and exposed nodes in MANET.

Monitoring and analyzing wireless networks for network structure and behavior is a complex task. Such monitoring often requires creating extra traffic, dedicated hardware and a prior knowledge of the network components and structure. In this paper we present a novel approach for monitoring large and complex wireless networks, fast deployed which operate seamlessly and in real time. The suggested framework uses few passive sniffers in order to sample the WiFi communication in the "air" per packet and have an extended cover range due to overhearing abilities. This monitoring system requires no prior knowledge of the network structure. We have designed, implemented and deployed such a passive monitoring system and used it to monitor the campus WLAN network (Wi-Fi). Experimental results show that the suggested framework is highly applicable for unmanaged and partly managed wireless networks such as Ad-hoc, first responders, self deployed and any highly dynamic network.

Video on the Internet has become an integral part of the user content consuming behavior. The use of High Definition (HD) video content increases the present bandwidth limitations of Internet infrastructure providers. Moreover, the rapid raise in Over the Top (OTT) Internet video bandwidth consumption presents major implications on current Internet Service Providers' (ISP) business models. In order to supply the ever-growing demand for IP-based video a major technology is used: Multi Layer Cache (MLC) for fully stored video content. The low level of the MLC hierarchy is the Access node. In this paper, we explore the influence of a memory device in the access and choose the best memory device to fulfill the HD requirements.

Video has become an integral part of the Internet user content. The use of High Definition (HD) video content increases the bandwidth requirements of the Internet infrastructure. Moreover, rapid increase in the Over-the-Top (OTT) Internet video bandwidth consumption has major impact on the business models of the Internet Service Providers (ISP). To support the ever-growing demand for IP-based video, two major technologies are used: Peer-to-Peer (P2P) for live video and Multi-Layer Cache (MLC) for fully-stored video content. In this paper we focus on partial streaming, which is a `semi-live' form of video delivery of live events such as sports, concerts, and news in the Video-on-Demand (VOD) format. The VOD supports user control features such as Start Over, Pause, Rewind, and Forward. We suggest a new framework for optimizing partial streaming using MLC, which allows storing partial video content in the time-based chunks of data (that is, data packets) while forwarding these data chunks to the users at various levels of ISP networks. The suggested framework may be helpful for solving the OTT bottleneck caused be video streaming.

This work presents a new radio paths computation frame-work especially designed for complex indoor RF field prediction. We propose a new algorithm utilizing a geometric visibility graph of a building to traverse all possible bounded radio paths. These paths are needed to compute the signal strength as received at given receiver location. We have implemented the suggested algorithm and performed a set of experiments testing the radio paths over complex buildings. The main conclusion is that the new algorithm is both (i) Accurate: predicts the signal strength within complex buildings, (ii) Runtime efficient: can compute all relevant radio paths even on relatively complex structures comprised of thousands of walls in a matter of seconds.

The paper focuses on mining clusters that are characterized by a lagged relationship between the data objects. We call such clusters lagged co-clusters. A lagged co-cluster of a matrix is a sub matrix determined by a subset of rows and their corresponding lag over a subset of columns. Extracting such subsets (not necessarily successive) may reveal an underlying governing regulatory mechanism. Such a regulatory mechanism is quite common in real life settings. It appears in a variety of fields: meteorology, seismic activity, stock market behavior, neuronal brain activity, river flow and navigation, are but a limited list of examples. Mining such lagged co-clusters not only helps in understanding the relationship between objects in the domain, but assists in forecasting their future behavior. For most interesting variants of this problem, finding an optimal lagged co-cluster is an NP-complete problem. We present a polynomial-time Monte-Carlo algorithm for finding a set of lagged co-clusters whose error does not exceed a pre-specified value, which handles noise, anti-correlations, missing values, and overlapping patterns. Moreover, we prove that the list includes, with fixed probability, a lagged co-cluster which is optimal in its dimensions. The algorithm was extensively evaluated using various environments. First, artificial data, enabling the evaluation of specific, isolated properties of the algorithm. Secondly, real-world data, using river flow and topographic data, enabling the evaluation of the algorithm to efficiently mine relevant and coherent lagged co-clusters in environments that are temporal, i.e., time reading data, and non-temporal, respectively.

The mounting evidence, that cellular radiation may adversely affect the health of its users, results in growing concern among the general public. This concern only grows as cellular technologies become an essential part of modern life (mobile e-mail, social networking, etc.). Radiating antennas in the proximity of the user, such as antennas of mobile phones are of special interest for this matter. In this paper we study the performance of a recently proposed architecture for wireless networks, aiming at minimal emission from mobile stations, without any additional radiation sources. The new architecture, dubbed Green Cellular, abandons the classical transceiver base station design and suggests the augmentation of transceiver base stations with receive only devices. These devices, dubbed Green Antennas, are not aiming at coverage extension but rather at minimizing the emission from mobile stations. We employ indoor and outdoor propagation simulation tools and field experiments to study the expected impact of the Green Cellular architecture on emission from mobile stations. Our results reveal a significant, up to 50dB, decrease in emission power and respective exposure to radiation.

Attribute data and relationship data are two principal types of data, representing the intrinsic and extrinsic properties of entities. While attribute data have been the main source of data for cluster analysis, relationship data such as social networks or metabolic networks are becoming increasingly available. It is also common to observe both data types carry complementary information such as in market segmentation and community identification, which calls for a joint cluster analysis of both data types so as to achieve better results. In this article, we introduce the novel Connected k -Center ( CkC ) problem, a clustering model taking into account attribute data as well as relationship data. We analyze the complexity of the problem and prove its NP-hardness. Therefore, we analyze the approximability of the problem and also present a constant factor approximation algorithm. For the special case of the CkC problem where the relationship data form a tree structure, we propose a dynamic programming method giving an optimal solution in polynomial time. We further present NetScan, a heuristic algorithm that is efficient and effective for large real databases. Our extensive experimental evaluation on real datasets demonstrates the meaningfulness and accuracy of the NetScan results.

We present the first constant‐factor approximation algorithm for a nontrivial instance of the optimal guarding (coverage) problem in polygons. In particular, we give an $O(1)$‐approximation algorithm for placing the fewest point guards on a 1.5D terrain, so that every point of the terrain is seen by at least one guard. While polylogarithmic‐factor approximations follow from set cover results, our new results exploit the geometric structure of terrains to obtain a substantially improved approximation algorithm.

Attribute data and relationship data are two principle types of data, representing the intrinsic and extrinsic properties of entities. While attribute data has been the main source of data for cluster analysis, relationship data such as social networks or metabolic networks are becoming increasingly available. It is also common to observe both data types carry orthogonal information such as in market segmentation and community identification, which calls for a joint cluster analysis of both data types so as to achieve more accurate results. For this purpose, we introduce the novel Connected k-Center problem, taking into account attribute data as well as relationship data. We analyze the complexity of this problem and prove its NP-completeness. We also present a constant factor approximation algorithm, based on which we further design NetScan, a heuristic algorithm that is efficient for large, real databases. Our experimental evaluation demonstrates the meaningfulness and accuracy of the NetScan results.

We study the problem of computing the visibility graph defined by a set P of n points inside a polygon Q: two points p,q ε P are joined by an edge if the segment ‾pq ⊂ Q. Efficient output-sensitive algorithms are known for the case in which P is the set of all vertices of Q. We examine the general case in which P is an arbitrary set of points, interior or on the boundary of Q and study a variety of algorithmic questions. We give an output-sensitive algorithm, which is nearly optimal, when Q is a simple polygon. We introduce a notion of "fat" or "robust" visibility, and give a nearly optimal algorithm for computing visibility graphs according to it, in polygons Q that may have holes. Other results include an algorithm to detect if there are any visible pairs among P, and algorithms for output-sensitive computation of visibility graphs with distance restrictions, invisibility graphs, and rectangle visibility graphs.

The terrain surface simplification problem has been studied extensively, as it has important applications in geographic information systems and computer graphics. The goal is to obtain a new surface that is combinatorially as simple as possible, while maintaining a prescribed degree of similarity with the original input surface. Generally, the approximation error is measured with respect to distance (e.g., Hausdorff) from the original or with respect to visual similarity. In this paper, we propose a new method of simplifying terrain surfaces, designed specifically to maximize a new measure of quality based on preserving inter-point visibility relationships. Our work is motivated by various problems of terrain analysis that rely on inter-point visibility relationships, such as optimal antenna placement.We have implemented our new method and give experimental evidence of its effectiveness in simplifying terrains according to our quality measure. We experimentally compare its performance with that of other leading simplification methods.

We study several natural proximity and facility location problems that arise for a set ${\cal P}$ of $n$ points and a set $\R$ of $m$ disjoint rectangular obstacles in the plane, where distances are measured according to the $L_1$ shortest path (geodesic) metric. In particular, we compute, in time $O(mn\log(m+n))$, a data structure of size $O(mn)$ that supports $O(\log(m+n))$-time farthest point queries; we avoid computing the more complicated farthest neighbor Voronoi diagram, whose combinatorial complexity we show to be $\Theta(mn)$. We study the center point problem, finding in $O(mn\log(m+n))$ time a center point (and the set of center points) that minimize the maximum distance to sites of ${\cal P}$; this result improves the best previous bound by a factor of roughly $m$. In addition, we give algorithms for approximating the diameter, $D$, and radius, $r$, of ${\cal P}$, including methods to (i) compute a pair of points $a,b \in {\cal P}$, such that $d(a,b) \ge (1-\eps)D$, in $O(n\log n + \frac{1}{\eps}(n+m) \log m)$ time; and (ii) compute a point $c'$, such that $\max \{d(p, c') \ | \ p \in {\cal P}\} \le (1+\eps)r$, in $O(n\log(m+n) + (m/\eps)\log(m+1/\eps))$ time. Finally, we show that for all the problems above it is enough to consider only a subset of ${\cal P}$. This subset is likely to be much smaller than ${\cal P}$, it is computable in $O(n \log n)$ time, and using it results in significantly decreased runtime in practice.