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Register a new userPush & Pull: autonomous deployment of mobile sensors for a complete coverage
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Added by
Novella Bartolini
Publication date
2009
fields
Informatics Engineering
and research's language is
English
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Mobile sensor networks are important for several strategic applications devoted to monitoring critical areas. In such hostile scenarios, sensors cannot be deployed manually and are either sent from a safe location or dropped from an aircraft. Mobile devices permit a dynamic deployment reconfiguration that improves the coverage in terms of completeness and uniformity. In this paper we propose a distributed algorithm for the autonomous deployment of mobile sensors called Push&Pull. According to our proposal, movement decisions are made by each sensor on the basis of locally available information and do not require any prior knowledge of the operating conditions or any manual tuning of key parameters. We formally prove that, when a sufficient number of sensors are available, our approach guarantees a complete and uniform coverage. Furthermore, we demonstrate that the algorithm execution always terminates preventing movement oscillations. Numerous simulations show that our algorithm reaches a complete coverage within reasonable time with moderate energy consumption, even when the target area has irregular shapes. Performance comparisons between Push&Pull and one of the most acknowledged algorithms show how the former one can efficiently reach a more uniform and complete coverage under a wide range of working scenarios.
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The use of mobile sensors is of great relevance for a number of strategic applications devoted to monitoring critical areas where sensors can not be deployed manually. In these networks, each sensor adapts its position on the basis of a local evaluation of the coverage efficiency, thus permitting an autonomous deployment. Several algorithms have been proposed to deploy mobile sensors over the area of interest. The applicability of these approaches largely depends on a proper formalization of rigorous rules to coordinate sensor movements, solve local conflicts and manage possible failures of communications and devices. In this paper we introduce P&P, a communication protocol that permits a correct and efficient coordination of sensor movements in agreement with the PUSH&PULL algorithm. We deeply investigate and solve the problems that may occur when coordinating asynchronous local decisions in the presence of an unreliable transmission medium and possibly faulty devices such as in the typical working scenario of mobile sensor networks. Simulation results show the performance of our protocol under a range of operative settings, including conflict situations, irregularly shaped target areas, and node failures.
Edge computing enables Mobile Autonomous Systems (MASs) to execute continuous streams of heavy-duty mission-critical processing tasks, such as real-time obstacle detection and navigation. However, in practical applications, erratic patterns in channel quality, network load, and edge server load can interrupt the task flow execution, which necessarily leads to severe disruption of the systems key operations. Existing work has mostly tackled the problem with reactive approaches, which cannot guarantee task-level reliability. Conversely, in this paper we focus on learning-based predictive edge computing to achieve self-resilient task offloading. By conducting a preliminary experimental evaluation, we show that there is no dominant feature that can predict the edge-MAS system reliability, which calls for an ensemble and selection of weaker features. To tackle the complexity of the problem, we propose SeReMAS, a data-driven optimization framework. We first mathematically formulate a Redundant Task Offloading Problem (RTOP), where a MAS may connect to multiple edge servers for redundancy, and needs to select which server(s) to transmit its computing tasks in order to maximize the probability of task execution while minimizing channel and edge resource utilization. We then create a predictor based on Deep Reinforcement Learning (DRL), which produces the optimum task assignment based on application-, network- and telemetry-based features. We prototype SeReMAS on a testbed composed by a drone, mounting a PixHawk flight controller, a Jetson Nano board, and three 802.11n WiFi interfaces. We extensively evaluate SeReMAS by considering an application where one drone offloads high-resolution images for real-time analysis to three edge servers on the ground. Experimental results show that SeReMAS improves task execution probability by $17%$ with respect to existing reactive-based approaches.
Ajax applications are designed to have high user interactivity and low user-perceived latency. Real-time dynamic web data such as news headlines, stock tickers, and auction updates need to be propagated to the users as soon as possible. However, Ajax still suffers from the limitations of the Webs request/response architecture which prevents servers from pushing real-time dynamic web data. Such applications usually use a pull style to obtain the latest updates, where the client actively requests the changes based on a predefined interval. It is possible to overcome this limitation by adopting a push style of interaction where the server broadcasts data when a change occurs on the server side. Both these options have their own trade-offs. This paper explores the fundamental limits of browser-based applications and analyzes push solutions for Ajax technology. It also shows the results of an empirical study comparing push and pull.
Quantum optimal control involves setting up an objective function that evaluates the quality of an operator representing the realized process w.r.t. the target process. Here we propose a stronger objective function which incorporates not only the target operator but also a set of its orthogonal operators. We find significantly superior convergence of optimization routines with the combined influences of all the operators. We refer to this method as the $textit{push-pull}$ optimization. In particular, we describe adopting the push-pull optimization to a gradient based approach and a variational-principle based approach. We carry out extensive numerical simulations of the push-pull optimization of quantum controls on a pair of Ising coupled qubits. Finally, we demonstrate its experimental application by preparing a long-lived singlet-order in a two-qubit system using NMR techniques.
A classical result of Schubert calculus is an inductive description of Schubert cycles using divided difference (or push-pull) operators in Chow rings. We define convex geometric analogs of push-pull operators and describe their applications to the theory of Newton-Okounkov convex bodies. Convex geometric push-pull operators yield an inductive construction of Newton-Okounkov polytopes of Bott-Samelson varieties. In particular, we construct a Minkowski sum of Feigin-Fourier-Littelmann-Vinberg polytopes using convex geometric push-pull operators in type A.
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