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Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs

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Abstract

Current state of the art in the field of UAV activation relies solely on human operators for the design and adaptation of the drones flying routes. Furthermore, this is being done today on an individual level (one vehicle per operators), with some exceptions of a handful of new systems, that are comprised of a small number of self-organizing swarms, manually guided by a human operator. Drones-based monitoring is of great importance in variety of civilian domains, such as road safety, homeland security, and even environmental control. In its military aspect, efficiently detecting evading targets by a fleet of unmanned drones has an ever increasing impact on the ability of modern armies to engage in warfare. The latter is true both traditional symmetric conflicts among armies as well as asymmetric ones. Be it a speeding driver, a polluting trailer or a covert convoy, the basic challenge remains the same — how can its detection probability be maximized using as little number of drones as possible. In this work we propose a novel approach for the optimization of large scale swarms of reconnaissance drones — capable of producing on-demand optimal coverage strategies for any given search scenario. Given an estimation cost of the threat’s potential damages, as well as types of monitoring drones available and their comparative performance, our proposed method generates an analytically provable strategy, stating the optimal number and types of drones to be deployed, in order to cost-efficiently monitor a pre-defined region for targets maneuvering using a given roads networks. We demonstrate our model using a unique dataset of the Israeli transportation network, on which different deployment schemes for drones deployment are evaluated.

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The field of multi agents and multi robotics has become increasingly popular during the last two decades. The motivation behind multi agents based systems is that many tasks can be rather efficiently completed by using multi- ple simple autonomous agents (robots, software agents, etc.) instead of a single sophisticated one. Such systems are usually also more adaptive, scalable and ro- bust than those based on a single, highly capable, unit. However, when examining such systems, one may be concerned of the price tag attached to the decentral- ized nature of swarm based approaches. Meaning, while we simplify designs and control mechanisms in order to save costs and computation resources, how far do our systems drift from optimality ? This work examines this issue by con- structing an optimal algorithm for the Dynamic Cooperative Cleanersproblem (presented and analyzed in (2)). The performance of the SWEEP protocol of (2) is compared to this of the optimal algorithm. The results of this comparison show that as the problem gets harder, the performance of the SWEEP protocol gets closer to those of the optimal algorithm. The work also presents insightful results concerning optimal swarms in symmetric environments.
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A national transportation planning model is needed for several reasons, including heavy investments in transportation infrastructures and the need to perform formal cost-benefit analyses of all medium- and large-scale transport projects. The paper describes the formulation and development of the planning model. The model is unique for the data collected and is used for analyzing nationwide travel. After careful legal review regarding privacy laws, cellular phone (CP) data were obtained for sixteen 1-week samples of 10,000 phones. In total, data for 1.04 million person-days were obtained. Data records included the unique CP identification, the antenna serving the CP, and a time stamp (date, hour, minute, second). At the minimum, a record was written each time a moving CP changed its connecting antenna. To ensure privacy, neither information nor identification of the cellular phone owner was recorded. The paper describes the structure of the planning model. The CP survey data were used only in the models for constructing person trip tables. To the authors' knowledge, this is the first time that data obtained by wireless location technology (WLT) in large quantities have been used in transportation planning. The paper discusses the advantages and limitations of using WLT and presents directions for further research.
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This article discusses the important role that Signals Intelligence (Sigint) has played, and continues to play, in the war against international terrorism. It sets out what is known or can be authoritatively established about the role that Sigint played in the events leading up to the terrorist attacks on September 11, 2001, especially the performance of America's Sigint organization, the National Security Agency (NSA). The article also analyzes what the potential future role of Sigint may be in the war on terrorism given the ever changing nature of terrorist operations, the growing number of technological impediments to effective Sigint collection against terrorist targets, and shifting geostrategic considerations on the part of the nations engaged in the fight against the international terrorists.
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Thispaper presents a complete algorithm DCR (distributedcoverage of rectilinear environments) which gives robotsthis ability. DCR is applicable to teams of square robotsoperating innite rectilinear environments and executesindependently on each robot in the team, directingthe individual robots so as to cooperatively cover theirshared environment relying only on intrinsic contactsensing to detect boundaries. DCR exploits the structureof this environment along with reliable position...
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“Kinematic bases,” the first paper of this series, discussed the geometric and kinematic factors involved in search—the positions, motions, and contacts of observers and targets. Probability was introduced only in assuming specific relative positions for the observer and target. The present paper discusses the uncertainties inherent in the act of detection under various specific conditions of contact. In the course of the discussion a body of methods for applying probability to problems of detection is developed. It must be emphasized, however, that these methods are conditioned by the particular situation in the case of visual detection because the different elementary acts of looking or “glimpses” are essentially independent trials. The reason for the distinction follows.
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The aim of this paper is to understand the interrelations among relations within concrete social groups. Social structure is sought, not ideal types, although the latter are relevant to interrelations among relations. From a detailed social network, patterns of global relations can be extracted, within which classes of equivalently positioned individuals are delineated. The global patterns are derived algebraically through a ‘functorial’ mapping of the original pattern. Such a mapping (essentially a generalized homomorphism) allows systematically for concatenation of effects through the network. The notion of functorial mapping is of central importance in the ‘theory of categories,’ a branch of modern algebra with numerous applications to algebra, topology, logic. The paper contains analyses of two social networks, exemplifying this approach.
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Network planning and traffic flow optimization requires the acquirement and analysis of large quantities of data such as the network topology, its traffic flow data, vehicle fleet composition, emission measurements etc. Data acquirement is an expensive process that involves household surveys and automatic as well as semi-automatic measurements performed all over the network. For example, in order to accurately estimate the effect of a certain network change on the total emissions produced by vehicles in the network, assessment of the vehicle fleet composition for each origin-destination pair is required. As a result, problems that optimize non-local merit functions becomes highly difficult to solve. One such problem is finding the optimal deployment of traffic monitoring units. In this paper we suggest a new traffic assignment model that is based on the concept of Shortest Path Betweenness Centrality measure borrowed from the domain of complex network analysis. We show how Betweenness can be augmented in order to solve the traffic assignment problem given an arbitrary travel cost definition. The proposed traffic assignment model is evaluated using a high resolution Israeli transportation dataset derived from the analysis of cellular phones data. The group variant of the augmented Betweenness Centrality is then used to optimize the locations of traffic monitoring units, hence reducing the cost and increasing the effectiveness of traffic monitoring.
Dear Sir, The frequent opportunities I have had of receiving pleasure from your writings and conversation, have induced me to prefer offering to the Royal Society through your medium, this Paper on Life Contingencies, which forms part of a continuation of my original paper on the same subject, published among the valuable papers of the Society, as by passing through your hands it may receive the advantage of your judgment.
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One of the most striking features of the post-9/11 era has been the convergence of military and intelligence operations. Nothing illustrates the trend better than the CIA‟s emergence as a veritable combatant command in the conflict with al Qaeda, though it manifests as well through the expansion of clandestine special forces activities, joint CIA-special forces operations, and cyber activities that defy conventional categorization. All of which obviously is important from a policy perspective. Less obviously, it also has significant legal implications.I do not refer to questions such as who lawfully may be targeted or what computer network operations amount to “armed attack,” though those are of course important matters. Rather, I am concerned here with America‟s domestic legal architecture for military and intelligence operations. That architecture is a half-baked affair consisting of a somewhat haphazard blend of decision-making rules, congressional notification requirements, and standing authorizations and constraints relevant to particular agencies. Convergence has a disruptive impact on key elements in that framework, especially those that rely on categorical distinctions that convergence confounds (like the notion of crisp delineations among collection, covert action, and military activity).My first aim in this article is to map that impact as thoroughly as can be done through the public record, drawing attention to and disaggregating issues that have bedeviled government lawyers behind closed doors for some time. My second aim is normative, as I suggest a modest set of changes to the existing legal framework meant to improve democratic accountability and compliance with the rule of law in such operations, while preserving the benefits convergence generates.
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New sensor technologies enable synthesis of disaggregated vehicle information from multiple locations. This paper proposes a reliable facility location model to optimize traffic surveillance benefit from synthesized sensor pairs (e.g., for travel time estimation) in addition to individual sensor flow coverage (e.g., for traffic volume statistics), while considering probabilistic sensor failures. Customized greedy and Lagrangian relaxation algorithms are proposed to solve this problem, and their performance is discussed. Numerical results show that the proposed algorithms solve the problem efficiently. We also discuss managerial insights on how optimal sensor deployment and surveillance benefits vary with surveillance objective and system parameters (such as sensor failure probabilities).
Chapter
The fact that civilian intelligence agencies may be authorised under their own domestic law to conduct lethal strikes is generally well known. What has generally received little discussion are the international law issues surrounding such acts. However, the reported—but not confirmed—use by the United States Central Intelligence Agency of armed drones to conduct attacks in Yemen and Pakistan has prompted vigorous legal debate. This chapter provides an in-depth discussion of the international law concerning the resort to the use of force by States, and the regulation of that use of force, in complex factual situations like the border region between Pakistan and Afghanistan. Particular emphasis is given to discussing how the law concerning the regulation of the use of force applies to civilian intelligence agencies. KeywordsArmed drone-Remotely piloted aircraft-Civilian intelligence agency/agencies; international law; armed conflict-National self-defence-Use of force
Conference Paper
Many don't realize that the history of UAVs started nearly a century ago, and that the modern era of UAVs goes back nearly four decades. It often goes unnoticed that UAV development and employment is being pursued by more than 50 countries world-wide. This paper examines the history of unmanned aerial vehicles including the initial concepts and employment and the role of UAVs in warfare, providing examples from several conflicts throughout the world. An analysis of the reasons for the underutilization of UAV capabilities, the reasons why UAV missions have been successful, and basis for continuing to fund UAV development and employment is given. Finally, current UAV trends are discussed.
Conference Paper
We have developed a novel control mechanism that deploys a large number of inexpensive robots as a distributed remote sensing array, called a distributed robotic macrosensor (DRM). This DRM has the capability to track targets of both a discrete (e.g., a vehicle) and diffuse (e.g., a chemical plume) nature. Attack resistance is an inherent property of the DRM as well. A relatively simple virtual spring mesh abstraction is used to provide fully distributed control that is both flexible and fault-tolerant. We describe the algorithms for spring mesh formation and control, discrete target tracking, and diffuse target tracking. We also present simulation results demonstrating the efficacy and robustness of DRMs
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In this paper, we discuss the threat of malware targeted at extracting information about the relationships in a real-world social network as well as characteristic information about the individuals in the network, a type of attack which we dub Stealing Reality. We explain how Stealing Reality attacks differ from traditional types of attacks against individuals’ privacy and discuss why their impact is significantly more dangerous than that of other attacks such as identity theft. We then analyze this new form of attack and show what an optimal attack strategy would look like. Surprisingly, it differs significantly from many conventional network attacks in that it involves extremely slow spreading patterns. We point out that besides yielding the best outcome for the attackers, such an attack may also deceive existing monitoring tools because of its low traffic volumes and the fact that it imitates natural end-user communication patterns.
Conference Paper
In this paper we discuss the problem of collaborative monitoring of applications that are suspected of being malicious. New operating systems for mobile devices allow their users to download millions of new applications created by a great number of individual programmers and companies, some of which may be malicious or flawed. The importance of defense mechanisms against an epidemic spread of malicious applications in mobile networks was recently demonstrated by Wang et. al. In many cases, in order to detect that an application is malicious, monitoring its operation in a real environment for a significant period of time is required. Mobile devices have limited computation and power resources and thus can monitor only a limited number of applications that the user downloads. In this paper we propose an efficient collaborative application monitoring algorithm called "TPP" - Time-To-Live Probabilistic Flooding, harnessing the collective resources of many mobile devices. Mobile devices activating this algorithm periodically monitor mobile applications, derive conclusion concerning their maliciousness, and report their conclusions to a small number of other mobile devices. Each mobile device that receives a message (conclusion) propagates it to one additional mobile device. Each message has a predefined TTL. The algorithm's performance is analyzed and its time and messages complexity are shown to be significantly lower compared to existing state of the art information propagation algorithms. The algorithm was also implemented and tested in a simulated environment.
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Information on link flows in a vehicular traffic network is critical for developing long-term planning and/or short-term operational management strategies. In the literature, most studies to develop such strategies typically assume the availability of measured link traffic information on all network links, either through manual survey or advanced traffic sensor technologies. In practical applications, the assumption of installed sensors on all links is generally unrealistic due to budgetary constraints. It motivates the need to estimate flows on all links of a traffic network based on the measurement of link flows on a subset of links with suitably equipped sensors. This study, addressed from a budgetary planning perspective, seeks to identify the smallest subset of links in a network on which to locate sensors that enables the accurate estimation of traffic flows on all links of the network under steady-state conditions. Here, steady-state implies that the path flows are static. A “basis link” method is proposed to determine the locations of vehicle sensors, by using the link-path incidence matrix to express the network structure and then identifying its “basis” in a matrix algebra context. The theoretical background and mathematical properties of the proposed method are elaborated. The approach is useful for deploying long-term planning and link-based applications in traffic networks.
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There has been substantial interest in development and application of methodology for estimating origin–destination (O–D) trip matrices from traffic counts. Generally, the quality of an estimated O–D matrix depends much on the reliability of the input data, and the number and locations of traffic counting points in the road network. The former has been investigated extensively, while the latter has received very limited attention. This paper addresses the problem of how to determine the optimal number and locations of traffic counting points in a road network for a given prior O–D distribution pattern. Four location rules: O–D covering rule, maximal flow fraction rule, maximal flow-intercepting rule and link independence rule are proposed, and integer linear programming models and heuristic algorithms are developed to determine the counting links satisfying these rules. The models and algorithms are illustrated with numerical examples.
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This study addresses two objectives: (1) to develop a formal method of optimally locating a dense network of air pollution monitoring stations; and (2) to derive an exposure assessment model based on these monitoring data and related land use, population, and biophysical information. Previous studies have located monitors in an ad hoc fashion, favouring the placement of monitors in traffic “hot spots” or in areas deemed subjectively to be of interest. We apply our methodology in locating 100 nitrogen dioxide monitors in Toronto, Canada. Locations identified by the method represent land use, transportation infrastructure and the distribution of at-risk populations. Our exposure assessments derived from the monitoring program produce reasonable estimates at the intra-urban scale. The method for optimally locating monitors may have widespread applicability for the design of pollution monitoring networks, particularly for measuring traffic pollutants with fine-scale spatial variability.
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Betweenness centrality based on shortest paths is a standard measure of control utilized in numerous studies and implemented in all relevant software tools for network analysis. In this paper, a number of variants are reviewed, placed into context, and shown to be computable with simple variants of the algorithm commonly used for the standard case.
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The placement of sensors to support security monitoring is obviously critical as it will directly impact the efficacy of allocated resources and system performance. It is critical to be able to observe and monitor the greatest total area possible. In addition, it is necessary to be able to spatially track the movement of people and activities in support of security. It is shown that important aspects of the security sensor placement problem can be modeled using the maximal covering location problem (MCLP) and/or the backup coverage location problem (BCLP) combined with visibility analysis. Thus, an approach is detailed for supporting security monitoring. The approach is applied in the context of video sensor placement in an urban area, illustrating the various tradeoffs that can be identified using optimization-based techniques.
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In many applications we are required to increase the deployment of a distributed monitoring system on an evolving network. In this paper we present a new method for finding candidate locations for additional deployment in the network. This method is based on the Group Betweenness Centrality (GBC) measure that is used to estimate the influence of a group of nodes over the information flow in the network. The new method assists in finding the location of k additional monitors in the evolving network, such that the portion of additional traffic covered is at least (1−1/e) of the optimal.