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Consider the case of a small, unmanned boat that is performing an autonomous mission. Naturally, such a platform might need to approximate the ocean surface of its surroundings in real-time. Much like obstacle mapping in autonomous (off-road) rovers, an approximation of the ocean surface in a vessel’s surroundings in real-time can be used for improved control and optimized route planning. Unfortunately, such an approximation seems to require either expensive and heavy sensors or external logistics that are mostly not available for small or low-cost vessels. In this paper, we present a real-time method for detecting and tracking ocean waves around a floating object that is based on stereo vision sensors. Based on a large set of experiments, we conclude that the presented method allows reliable, real-time, and cost-effective ocean surface mapping suitable for small autonomous boats.

Tourists are often more vulnerable than residents in sudden disaster situations due to lack of knowledge regarding evacuation routes and safe areas. To establish protocols and the schemes for tourist evacuation to safe areas, it is necessary to gather their likely behavior during an evacuation. Since there are few actual data available we conducted a VR (Virtual Reality) experiment assuming a sudden disaster situation and estimated tourists’ route choice based on the experiment. In the experiment pictures of intersection in the touristic Higashiyama area of Kyoto, Japan, where shown to participants and they could choose the direction they want to proceed until reaching an open space or designated shelter. As a result, we could quantify the impact of road width and, to some degree, network structure. The results reveal the tendency to select wide roads and to proceed straight. If the participants were put under time pressure these tendencies are intensified. Utilizing these results we constructed an evacuation simulation. We estimated the distribution and amount of tourists using data obtained from a mobile phone service provider. We conducted the simulation using VisWalk with various guidance situations and compared those results. The results illustrate potential capacity bottlenecks of designated shelter locations and the importance to provide route guidance and certain points in the network., 24th EURO Working Group on Transportation Meeting, EWGT 2021, 8-10 September 2021, Aveiro, Portugal

Consider a constellation of over a hundred low Earth orbit satellites that aim to capture every point on Earth at least once a day. Clearly, there is a need to download from each satellite a large set of high-quality images on a daily basis. In this paper, we present a laser communication (lasercom) framework that stands as an alternative solution to existing radio-frequency means of satellite communication. By using lasercom, the suggested solution requires no frequency licensing and therefore allows such satellites to communicate with any optical ground station on Earth. Naturally, in order to allow laser communication from a low Earth orbit satellite to a ground station, accurate aiming and tracking are required. This paper presents a free-space optical communication system designed for a set of ground stations and nano-satellites. A related scheduling model is presented, for optimizing the communication between a ground station and a set of lasercom satellites. Finally, we report on SATLLA-2B, the first 300 g pico-satellite with basic free-space optics capabilities, that was launched on January 2022. We conjecture that the true potential of the presented network can be obtained by using a swarm of few hundreds of such lasercom pico-satellites, which can serve as a global communication infrastructure using existing telescope-based observatories as ground stations.

Tourism is one of the largest growing industries worldwide. As the number of tourists is rapidly increasing, so too are tourist safety concerns. The increasing frequency of natural disasters along with the growth of urban areas makes it even more complex to address the resilience of tourists during such events. This article proposes a framework for collecting information about tourist locations and flows within urban areas and how to use this information for more efficient and safe evacuation routing. We define population behavior models that can be obtained from gathering empirical data and categorize them into three groups. We review the different evacuation scenarios (divided into sudden and predictable scenarios) and the types of information needed in each case. Further, we discuss the complexity of monitoring and forecasting tourists’ movements in the long term and for short-term predictions including the available data sources for doing so. The data gathering and tourist behavior are explained with examples from Kyoto, Japan, a major tourist attraction and a location that is prone to disasters. Finally, technological solutions for better guidance during the evacuation process of the population are discussed, including low-tech ones and advanced options such as websites, apps and Bluetooth Low Energy sensors, where the last one is demonstrated by a navigation experiment in a 3D environment.

This paper presents a framework for performing autonomous precise landing of unmanned aerial vehicles (UAVs) on dynamic targets. The main goal of this work is to design the methodology and the controlling algorithms that will allow multi-rotor drones to perform a robust and efficient landing in dynamic conditions of changing wind, dynamic obstacles, and moving targets. Unlike existing GNSS-based vertical landing solutions, the suggested framework does not rely on global positioning and uses adaptive diagonal approaching angle visual landing. The framework was designed to work on existing camera-drone platforms, without any need for additional sensors, and it was implemented using DJI’s API on Android devices. The presented concept of visual sliding landing (VSL) was tested on a wide range of commercial drones, performing hundreds of precise and robust autonomous landings on dynamic targets, including boats, cars, RC-boats, and RC-rovers.

Many systems on Earth and in space require precise orientation when observing the sky, particularly for objects that move at high speeds in space, such as satellites, spaceships, and missiles. These systems often rely on star trackers, which are devices that use star patterns to determine the orientation of the spacecraft. However, traditional star trackers are often expensive and have limitations in their accuracy and robustness. To address these challenges, this research aims to develop a high-performance and cost-effective AI-based Real-Time Star Tracker system as a basic platform for micro/nanosatellites. The system uses existing hardware, such as FPGAs and cameras, which are already part of many avionics systems, to extract line-of-sight (LOS) vectors from sky images. The algorithm implemented in this research is a “lost-in-space” algorithm that uses a self-organizing neural network map (SOM) for star pattern recognition. SOM is an unsupervised machine learning algorithm that is usually used for data visualization, clustering, and dimensionality reduction. Today’s technologies enable star-based navigation, making matching a sky image to the star map an important aspect of navigation. This research addresses the need for reliable, low-cost, and high-performance star trackers, which can accurately recognize star patterns from sky images with a success rate of about 98% in approximately 870 microseconds.

This work presents a concept of intelligent vision-less micro-drones, which are motivated by flying animals such as insects, birds, and bats. The presented micro-drone (named BAT: Blind Autonomous Tiny-drone) can perform bio-inspired complex tasks without the use of cameras. The BAT uses LIDARs and self-emitted optical-flow in order to perform obstacle avoiding and maze-solving. The controlling algorithms were implemented on an onboard micro-controller, allowing the BAT to be fully autonomous. We further present a method for using the information collected by the drone to generate a detailed mapping of the environment. A complete model of the BAT was implemented and tested using several scenarios both in simulation and field experiments, in which it was able to explore and map complex building autonomously even in total darkness.

Automatic Dependent Surveillance-Broadcast (ADS-B) is the main communication system currently being used in Air Traffic Control (ATC) around the world. The ADS-B system is planned to be a key component of the Federal Aviation Administration (FAA) NextGen plan, which will manage the increasingly congested airspace in the coming decades. While the benefits of ADS-B are widely known, its lack of security measures and its vulnerability to cyberattacks such as jamming and spoofing is a great concern for flight safety experts. In this paper, we first summarize the cyberattacks and challenges related to ADS-B’s vulnerabilities. Thereafter, we present theoretical and practical methods for implementing an Internet of Things (IoT)-based system as a possible additional safety layer to mitigate the presented cyber-vulnerabilities. Finally, a set of simulations and field experiments is presented to test the expected performance of the suggested IoT flight safety system. We conjecture that the presented system can be implemented in a wide range of civilian airplanes, leading to an improvement in flight safety in cases of cyberattacks or the absence of reliable ADS-B communication.

Global Navigation Satellite Systems (GNSS) jamming is an acute problem in the world of modern navigation. As more and more applications rely on GNSS for both position and timing, jamming ramifications are becoming more severe. In this paper we suggest a novel framework to cope with these threats. First, a Bayesian jamming detection algorithm is introduced. The algorithm can both detect and track several jammers in a pre-defined region of interest. Then, a jamming coverage map algorithm is offered. Similar to cellular 3G/4G coverage maps, such a map can detect “weak” GNSS reception spots and handle them. Since jamming interference can be a dynamic phenomenon (e.g., a vehicle equipped with a jammer), the coverage map changes with time. Thus, interference patterns can be detected more easily. Utilizing the offered algorithm, both on simulation and field experiments, we have succeeded to localize an arbitrary jammer(s) within the region of interest. Thus, the results validate the viability of the proposed method.

This paper presents a vision-based navigation system designed for indoor localization. The suggested framework works as a standalone 3 D positioning system by fusing a sophisticated optical-flow pedometry with map constrains using an advanced particle filter. The presented method requires no personal calibration and works on standard smartphones with relatively low energy consumption. Field experiments on Android smartphones show that the expected 3 D error is about 1&ndash, 2 m in most real-life scenarios.

Satellite network optimization is essential, particularly since the cost of manufacturing, launching and maintaining each satellite is significant. Moreover, classical communication optimization methods, such as Minimal Spanning Tree, cannot be applied directly in dynamic scenarios where the satellite constellation is constantly changing. Motivated by the rapid growth of the Star-Link constellation that, as of Q4 2021, consists of over 1600 operational LEO satellites with thousands more expected in the coming years, this paper focuses on the problem of constructing an optimal inter-satellite (laser) communication network. More formally, given a large set of LEO satellites, each equipped with a fixed number of laser links, we direct each laser module on each satellite such that the underlying laser network will be optimal with respect to a given objective function and communication demand. In this work, we present a novel heuristic to create an optimal dynamic optical network communication using an Ant Colony algorithm. This method takes into account both the time it takes to establish an optical link (acquisition time) and the bounded number of communication links, as each satellite has a fixed amount of optical communication modules installed. Based on a large number of simulations, we conclude that, although the underlying problem of bounded-degree-spanning-tree is NP-hard (even for static cases), the suggested ant-colony heuristic is able to compute cost-efficient solutions in semi-real-time.

Acquiring real time sensory data using remote swarms of tiny sensors depends on efficient wireless networking. Often, battery longevity of the sensors is a critical design requirement, which makes long-range RF transmission inadequate for this task. A common solution is to use short-range and energy-efficient RF protocols within the swarm, then communicate with the outside world via a capable agent or a networking gateway. In this paper we suggest an alternative networking model, which enables the swarm to operate independently, without the assistance of an intermediate proxy. Specifically, we present a networking model in which the sensors in the swarm transmit data using energy-efficient free space optical links (FSO). We discuss the details of aiming optical transmission interfaces in the directions of distant sinks, such as antenna towers, drones and even LEO-satellites. This FSO-based network architecture poses various data collection problems, which are related to the directional nature of optical transmitters and receivers. Using simulations we address several such problems, and demonstrate the viability of the model for sensory data collection.

This work addresses the problem of performing an accurate 3D mapping of a flexible antenna surface. Consider a high-gain satellite flexible antenna; even a submillimeter change in the antenna surface may lead to a considerable loss in the antenna gain. Using a robotic subreflector, such changes can be compensated for. Yet, in order to perform such tuning, an accurate 3D mapping of the main antenna is required. This paper presents a general method for performing an accurate 3D mapping of marked surfaces such as satellite dish antennas. Motivated by the novel technology for nanosatellites with flexible high-gain antennas, we propose a new accurate mapping framework which requires a small-sized monocamera and known patterns on the antenna surface. The experimental result shows that the presented mapping method can detect changes up to 0.1-millimeter accuracy, while the camera is located 1 meter away from the dish, allowing an RF antenna optimization for Ka and Ku frequencies. Such optimization process can improve the gain of the flexible antennas and allow an adaptive beam shaping. The presented method is currently being implemented on a nanosatellite which is scheduled to be launched at the end of 2018.

The use of multirotor drones has increased dramatically in the last decade. These days, quadcopters and Vertical Takeoff and Landing (VTOL) drones can be found in many applications such as search and rescue, inspection, commercial photography, intelligence, sports, and recreation. One of the major drawbacks of electric multirotor drones is their limited flight time. Commercial drones commonly have about 20–40 min of flight time. The short flight time limits the overall usability of drones in homeland security applications where long-duration performance is required. In this paper, we present a new concept of a “power-line-charging drone”, the idea being to equip existing drones with a robotic mechanism and an onboard charger in order to allow them to land safely on power lines and then charge from the existing 100–250 V AC (50–60 Hz). This research presents several possible conceptual models for power line charging. All suggested solutions were constructed and submitted to a field experiment. Finally, the paper focuses on the optimal solution and presents the performance and possible future development of such power-line-charging drones.

This paper presents a star-tracking algorithm to determine the accurate global orientation of autonomous platforms such as nano satellites, U A V s, and micro-drones using commercial-off-the-shelf ( C O T S ) mobile devices such as smartphones. Such star-tracking is especially challenging because it is based on existing cameras which capture a partial view of the sky and should work continuously and autonomously. The novelty of the proposed framework lies both in the computational efficiency and the ability of the star-tracker algorithm to cope with noisy measurements and outliers using affordable C O T S mobile platforms. The presented algorithm was implemented and tested on several popular platforms including: Android mobile devices, commercial-micro drones, and Raspberry Pi. The expected accuracy of the reported orientation is [0.1&deg, 0.5&deg, ].

GNSS signal classification to LOS and NLOS signals is of great value for conventional ranging-based and shadow matching algorithms. The most common attribute for performing this classification is the signal strength. Alas, such classification is often insufficient, in particular, in urban environments. In this paper, we present a novel approach for LOS/NLOS classification utilizing supervised machine learning algorithms. Provided with a sufficiently large labeled training set, the proposed approach is able to predict with high certainty (>85 percent) the satellites’ visibility status in dense urban regions. This achievement was possible due to the vast raw measurements supplied for the algorithm and using sophisticated feature-selection techniques.

This paper addresses the numeric optimization for NaBH4 powder flow which is commonly used for hydrogen gas production. During the motion process of the powder, a high number of collisions occur between particles constituting the powder. This paper focuses on modeling and finding the parameters that govern these collisions. We use a discrete element method to model the powder and assume that the powder is composed of tiny spheres interacting according to a specific spring damping model. In a series of appropriate physical wedge penetration experiments, force-displacement graphs were measured. In addition, a set of shear tests were conducted from which normal-shear force graphs were extracted. Analytical estimations were formulated for each of the experiments. These graphs were then compared with graphs generated by corresponding simulation tests. Using Genetic Algorithm optimization we obtained a set of governing parameters that best fits the powder behavior. In order to refine our results we have used our analytical formulations to manually search the parameter space for a better fit. Lastly, an angle of repose test validated our model. Keywords: Powder simulated behavior, Discrete element simulation, NaBH4 mechanical parameters, Genetic algorithm as a search method

In typical graph minimization problems, we consider a graph G with fixed weights on the edges of G. The goal is then to find an optimal vertex or set of vertices with respect to some objective function, for example. We introduce a new framework for graph minimization problems, where the weights on the graph edges are not fixed, but rather must be assigned, and the weight is inversely proportional to the cost paid. The goal is to find a valid assignment for which the resulting weighted graph optimizes the objective function.We present algorithms for finding the optimal budget allocation for the center point problem and for the median point problem on trees. Our algorithms run in linear time, both for the case where a candidate vertex is given as part of the input, and for the case where finding a vertex that optimizes the solution is part of the problem. We also present a hardness result for the center point problem on complete metric graphs, followed by an O ( log 2 ? ( n ) ) approximation algorithm in this setting.

This paper presents a set of graph optimization problems related to free-space optical communication networks. Such laser-based wireless networks require a line of sight to enable communication, thus a visibility graph model is used herein. The main objective is to provide connectivity from a communication source point to terminal points through the use of some subset of available intermediate points. To this end, we define a handful of problems that differ mainly in the costs applied to the nodes and/or edges of the graph. These problems should be optimized with respect to cost and performance. The problems at hand are shown to be NP-hard. A generic heuristic based on a genetic algorithm is proposed, followed by a set of simulation experiments that demonstrate the performance of the suggested heuristic method on real-life scenarios. The suggested genetic algorithm is compared with the Euclidean Steiner tree method. Our simulations show that in many settings, especially in dense graphs, the genetic algorithm finds lower-cost solutions than its competitor, while it falls behind in some settings. However, the run-time performance of the genetic algorithm is considerably better in graphs with 1000 nodes or more, being more than twice faster in some settings. We conclude that the suggested heuristic improves run-time performance on large-scale graphs and can handle a wider range of related optimization problems. The simulation results suggest that the 5G urban backbone may benefit significantly from using free-space optical networks.

This work presents a concept of intelligent micro drones which are motivated by flying insects such as bees. The presented micro drones can perform bio inspired complex tasks autonomously using simple sensors which require very low computing power. In particular, we present a set of algorithms for obstacle avoiding, navigation, swarm formation flight and mapping all based on sub-gram sensors which are applicable for on-board processing by micro drones. Using both simulation and field experiment we conclude that many bio-inspired challenges can be performed without the use of high resolution visual sensors. Moreover autonomous micro drones can take part in a wide range of research fields including: mobile sensor networks and swarm algorithms for search & rescue.

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.

Purpose To quantify the pressure exerted on the maternal abdominal wall during ultrasound examination and evaluate its effect on the fetal middle cerebral artery (MCA) peak systolic velocity (PSV). Materials and Method Gravid women with singleton pregnancies in their 2nd-3 rd trimester undergoing fetal sonographic evaluation for various indications were recruited. Each subject underwent transabdominal US measuring fetal distance from the probe, abdominal thickness, amniotic fluid index and biophysical profile. The applied pressure was measured simultaneously using an electronic pressure sensor attached directly to the US probe. For each subject baseline values of the pressure required for proper visualization were obtained. Fetal MCA was then demonstrated using color Doppler US. The PSV was measured at different pressure ranges with each subject used as her own control. Care was taken not to exceed the baseline pressure for each subject. Results 29 women were recruited. 24 subjects (82.7 %) demonstrated a statistically significant positive correlation between the pressure exerted and MCA-PSV (R-0.37, p Conclusion The pressure exerted on the maternal abdominal wall during US examination is an important parameter, producing clinically significant measurable changes in fetal MCA hemodynamics. Further study is needed in order to demonstrate the potential effect of pressure as a parameter influencing the diagnostic accuracy of the MCA-PSV in the setting of fetal anemia.

This paper presents a new framework for long range Free Space Optical communication (FSO). Motivated by the ever-growing demand for communication in 5G networks, the suggested framework tackles the natural FSO link drawbacks, commonly: complicated installation, limited range, size and pricing. The new FSO concept assumes a “best effort” model. It uses a new control and aiming mechanism for the Tx laser beam based on COTS components. This methodology is mostly suitable for urban FSO links but might also be applicable for both short range (IoT) and long range (satellite) communication. Based on simulations and preliminary field results we expect that such FSO links will be widely used in “best-effort” 5G applications allowing an affordable alternative to fiber optics and standard FSO systems.

Real-time voice and video streaming applications require a certain Quality of Service (QoS) level for providing user satisfaction. As Wireless Local Area Networks (WLAN's) are not designed for such applications, assessing the communication's QoS level is a challenging task. Sudden Onset Disasters (SODs) poses even a greater challenge as the QoS level must be assessed without generating traffic or consuming any other network resources that the response forces needs for communicating among themselves. As such, passive measurement is the only viable approach, but it lacks suitable metrics for doing so. In this paper, we briefly review the main QoS parameters and later, we propose two new possible measurements. These measures are an extension of the retry-ratio measurements such as the Frame Retry Ratio (FRR) which reflects the effort-efficiency to communicate through the ratio of the extra effort (retries) to the overall effort. Data Retry-Ratio (DRR) is the ratio of extra data transmitted to the overall transmitted data in a period of time. Airtime Retry-Ratio (ARR) is the ratio of the extra airtime cost to the overall used airtime cost. For exploring the performances of these measures, we have developed and designed a special portable testbed which among other things includes a stratum-1 NTP time server. We used this testbed to explore the relations between active measurements: such as one-way delay, throughput and jitter to passive measurements: one-way throughput, one-way FRR, DDR and ARR. The results of a large field-test reveal that passive measurements can reflect the QoS parameters such as the average throughput and can indicate an asymmetric link similar to one-way delay.

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.

SUMMARYWe introduce a novel probabilistic algorithm (CPRM) for real-time motion planning in the configuration space${\EuScript C}$. Our algorithm differs from a probabilistic road map (PRM) algorithm in the motion between a pair of anchoring points (local planner) which takes place on the boundary of the obstacle subspace${\EuScript O}$. We define a varying potential fieldfon ∂${\EuScript O}$as a Morse function and follow$\vec{\nabla} f$. We then exemplify our algorithm on a redundant worm climbing robot withndegrees of freedom and compare our algorithm running results with those of the PRM.

This paper presents a star tracking algorithm to determine the orientation of aircrafts, spacecrafts and automobiles. Such tracking is especially challenging since it requires robustness and should work continuously and autonomously. A wider view of this problem is utilizing the algorithm on automobiles and aircrafts and predict the device's orientation throughout time from the computed position. The novelty of the proposed framework lies in both the computational efficiency and the ability of the star tracker to cope with noisy measurements and outliers. Both simulator and initial field experiments confirm the benefits of the proposed framework.

As GNSS receivers become common in our daily lives and our reliance on them grows stronger, GNSS attacks also are becoming more common and sophisticated than ever. These attacks can range from accidental interruption to intentional denial of service to advanced spoofing equipment designed to deceive the GNSS receiver and make it think it is located elsewhere. Without proper protection, these real-life threats can cause, for example, temporal closure of airports and loss of autonomous vehicles. Today's solutions for defending against such attacks are limited, expensive or require significant processing power that cannot be implemented in energy-critical applications. This work suggests a low cost, efficient and simple method to cope with the threats and demonstrates its operation in a real-life scenario.

This work presents a generic method for improving the velocity estimation of GNSS devices. The suggested algorithm is based on the famous Kalman Filter which utilizes both GNSS and IMU measurements. The suggested sensor-fusion method contains a built-in classifier for identifying measurements with low confidence. The algorithm was implemented and tested using both simulation and real-world data. The results show significant improvement in the velocity estimation. This improvement can be used for further positioning accuracy improvement of a wide range of COTS GNSS devices.

As communication is the most fundamental resource for a smart city, it cannot allow any disruption with the related services. Therefore, real-time monitoring systems for the QoS state for the communication infrastructure are vital. Such systems that can detect communications failure can be used also for redeploying emergency and ad-hoc communications infrastructures. Sudden-Onset Disasters (SOD) typically generate system-wide infrastructure breakdowns, including communication. Robust, and efficient communication between the first-responders is crucial for the relief efforts to succeed. Deploying a modern and advanced emergency communication infrastructure requires complicated online-mechanisms and raises several technical challenges. Recent major sudden-onset disasters such as the Fukushima nuclear accident in 2011, the Haiti earthquake in 2010 and the flood in New Orleans in 2005, demonstrated the need to improve the existing technology and make it accessible for rapid deployment on a large scale by non-technical personnel. In this paper, we present a communication infrastructure deployment model suited for the first responders' activity at the scene of a sudden-onset disaster. The model relies on a real-time QoS state monitoring system for reliably predicting communication loss by leveraging the retransmission rate of the users as a link state predictor. This online mechanism can be pre-implemented in the smart city communication infrastructure for day-to-day use and in an SOD scenario as a communication redeployment tool that helps the first-response concentrate their efforts on lifesaving actions. The conceptual model was implemented and tested successfully using 802.11 technology thus demonstrating that it can be effectively used as smart city communication solution.

This paper presents a radio paths modeling framework for simulating RF coverage in complex indoor environments. We propose an algorithm which utilizes a geometric visibility graph of a building to traverse all possible bounded radio paths. These paths are needed for the computation of signal strength captured at a given receiver location. We have implemented the suggested algorithm and conducted a set of experiments to evaluate its performance in complex environments. The main conclusion is that the new algorithm is both (i) Accurate: predicts the signal strength inside complex buildings. (ii) Runtime efficient: requires only few seconds to compute all relevant radio paths, even when operating on complex structures containing thousands of walls.

This paper focuses on the centdian problem in a cactus network where a cactus network is a connected undirected graph, and any two simple cycles in the graph have at most one node in common. The cactus network has important applications for wireless sensor networks when a tree topology might not be applicable and for extensions to the ring architecture. The centdian criterion represents a convex combination of two QoS requirements: transport and delay. To the best of our knowledge, no efficient algorithm has yet been developed for constructing a centdian node in a cactus graph, either sequential or distributed. We first investigate the properties of the centdian node in a cycle graph, and then explore the behavior of the centdian node in a cactus graph. Finally, we present new efficient sequential and distributed algorithms for finding all centdian nodes in a cycle graph and a cactus graph.

Given a terrain T and an antenna A located on it, we would like to approximate the Radio Map of A over T, namely, to associate a signal strength for each point p?T as received from A. This work presents a new Radio Map approximation algorithm using an adaptive radial sweep-line technique. The suggested radar-like algorithm (RLA) uses a pipe-line method for computing the signal strength along points on a ray, and an adaptive method for interpolating the signal strength over regions between two consecutive rays. Whenever the difference between two consecutive rays is above a certain threshold, a middle ray is created. Thus, the density of the sampling rays is sensitive to the shape of the terrain. Finally, we report on an experiment which compares the new algorithm with other well-known methods. The main conclusion is that the new RLA is significantly faster than the others, i.e., its running time is 3---15 times faster for the same approximation accuracy.

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.

Efficient algorithms for solving the center problems in weighted cactus networks are presented. In particular, we have proposed the following algorithms for the weighted cactus networks of size n: an O(nlogn) time algorithm to solve the 1-center problem, and an O(nlog3n) time algorithm to solve the weighted continuous 2-center problem. We have also provided improved solutions to the general p-center problems in cactus networks. The developed ideas are then applied to solve the obnoxious 1-center problem in weighted cactus networks.

This article addresses a real-life problem - obtaining communication links between multiple base station sites, by positioning a minimal set of fixed-access relay antenna sites on a given terrain. Reducing the number of relay antenna sites is considered critical due to substantial installation and maintenance costs. Despite the significant cost saved by eliminating even a single antenna site, an inefficient manual approach is employed due to the computational complexity of the problem. From the theoretical point of view we show that this problem is not only NP hard, but also does not have a constant approximation. In this paper we suggest several alternative automated heuristics, relying on terrain preprocessing to find educated potential points for positioning relay stations. A large-scale computer-based experiment consisting of approximately 7,000 different scenarios was conducted. The quality of alternative solutions was compared by isolating and displaying factors that were found to affect the standard deviation of the solutions supplied by the tested heuristics. The results of the simulation based experiments show that the saving potential increases when more base stations are needed to be interconnected. The designs of a human expert were compared to the automatically generated solutions for a small subset of the experiment scenarios. Our studies indicate that for small networks (e.g., connecting up to ten base stations), the results obtained by human experts are adequate although they rarely exceed the quality of automated alternatives. However, the process of obtaining these results in comparison to automated heuristics is longer. In addition, when more base station sites need to be interconnected, the human approach is easily outperformed by our heuristics, both in terms of better results (fewer antennas) and in significant shorter calculation times.

Given a terrain and a point p on or above it, we wish to compute the region R p that is visible from p. We present a generic radar-like algorithm for computing an approximation of R p . The algorithm interpolates the visible region between two consecutive rays (emanating from p) whenever the rays are close enough; that is, whenever the difference between the sets of visible segments along the cross sections in the directions specified by the rays is below some threshold. Thus the density of the sampling by rays is sensitive to the shape of the visible region. We suggest a specific way to measure the resemblance (difference) and to interpolate the visible region between two consecutive rays. We also present an alternative algorithm, which uses circles of increasing radii centered at p instead of rays emanating from p. Both algorithms compute a representation of the (approximated) visible region that is especially suitable for is-visible-from-p queries, i.e., given a query point q on the terrain determine whether q is visible from p. Finally, we report on the experiments that we performed with these algorithms and with their corresponding fixed versions, using a natural error measure. Our main conclusion is that the radar-like algorithm is significantly better than the others.

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.

Existing dish based antennas tend to have geometric morphologic distortion in the surface due to drastic thermal changes common in the space environment. In this paper we present a new concept for a dynamic antenna specially designed for communication satellites. The suggested flexible-robotic antenna is based on a dual-reflector structure, where the subreflector has a complex surface shaping robotic mechanism allowing it to fix most of the morphologic errors in the main reflector. We have implemented a set of searching algorithms allowing the hyper redundant robotic subreflector to adapt its surface to the morphologic distortions in the main reflector. The suggested new antenna was constructed and tested in an RF room in which it was able to fix the loss caused by distortion in the main reflector to the original gain in less than an hour.

In a network of micro sensors, the network capabilities can be greatly enhanced if participant nodes are able to fine-tune their positions. Even when a node is optimally located, it could benefit from subtle maneuvers that optimize the functionality of the node’s directional sensors. In particular, the ability to aim a directional networking interface (e.g., antenna) is essential for low-power networking that is enforced by the tiny form-factor of the nodes. This paper presents a prototype design for a multi-terrain sensor-carrying micro robot, which excels in subtle movements. The robot has an egg-shaped shell, which provides protection, recover-ability and unique maneuvering capabilities in versatile terrains. We demonstrate the advantages of the suggested design in the context of dynamic sensor networks.

GNSS Jammers are devices that generate RF noise in the carrier frequency of the GNSS (e.g.,1.57Ghz for L1 GPS). Jamming behavior can be characterized by a sharp degradation in the SNR of the GNSS satellites. In this paper we suggest a Bayesian particle filter approach for jamming detection and localization using crowd-sourcing of Smart-Phones GNSS data. The presented algorithm computes in real-time a probabilistic map of the jammers' positions. Since a 2D histogram distribution space is generated, multiple jammers can be detected simultaneously. The presented algorithm can cope with various types of jammers — no pre-assumptions are made regarding the propagation pattern of the jammer. Experimental results showed that in less than two minutes and using only 3 smart-phones, our algorithm correctly detected a GNSS jammer within a 2 meters range in a Region-of-Interest (ROI) of 5000 m2.

Video streaming is now responsible for the majority of Internet traffic and is expected to keep growing over the coming years. Dynamic Adaptive Streaming over HTTP (DASH) [1] is an ISO/IEC MPEG multi-quality layer streaming solution that is designed to enable interoperability between servers and clients of different vendors. In the DASH protocol, the client-side player is assumed to have Adaptation Logic (AL). The AL evaluates the various video representation segments available on the server and chooses the most suitable segments balancing between video quality and switching time. Note that dynamic adaptation is necessary due to the fact that the network bandwidth (e.g. cellular network) and the user's buffer are not stable and have a high influence on re-buffering. However, to date, none of the research considers multicast conditions and therefore, there is no AL specifically designed to support multicast at the client side. In this paper, we present the Harmonic Mean Adaptive Logic (HMAL) which is a buffer sensitive adaptation logic that first calculates how many segments exist in the buffer and then estimates the channel bandwidth using the harmonic mean of the previous n samples. The HMAL is designed to support multicast networks by reducing the weights of hight quality segments in the bandwidth estimation and give more weight to lower quality segments. Compared to the multicast versions of well known ALs, the simulation results showed that HMAL has the best bandwidth estimation, the lowest number of re-buffering events, and the highest buffer efficiency.

We present efficient algorithms for several instances of the following facility location problem. Place k obnoxious facilities, with respect to n given demand sites and m given regions, where the goal is to maximize the minimal distance between a demand site and a facility, under the constraint that each of the regions must contain at least one facility. We also present an efficient solution to the following planar problem that arises as a subproblem. Given n transmitters, each of range r, construct a compact data structure that supports coverage queries, i.e., determine whether a query polygonal/rectangular region is fully covered by the transmitters.

The paper focuses on mining patterns that are characterized by a fuzzy lagged relationship between the data objects forming them. Such a regulatory mechanism is quite common in real life settings. It appears in a variety of fields: finance, gene expression, neuroscience, crowds and collective movements are but a limited list of examples. Mining such patterns 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 fuzzy lagged co-cluster is an NP-complete problem. We thus present a polynomial-time Monte-Carlo approximation algorithm for mining fuzzy lagged co-clusters. We prove that for any data matrix, the algorithm mines a fuzzy lagged co-cluster with fixed probability, which encompasses the optimal fuzzy lagged co-cluster by a maximum 2 ratio columns overhead and completely no rows overhead. Moreover, the algorithm handles noise, anti-correlations, missing values and overlapping patterns. The algorithm was extensively evaluated using both artificial and real datasets. The results not only corroborate the ability of the algorithm to efficiently mine relevant and accurate fuzzy lagged co-clusters, but also illustrate the importance of including the fuzziness in the lagged-pattern model., Under consideration for publication in Knowledge and Information Systems. The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-014-0758-7