An L(h, k)-edge labeling of a graph G is the assignment of labels {0, 1, · · · , n} to the edges in such a way that two adjacent edges get labels with a difference at least h and the labels of distance ...
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Synthesis of quaternary quantum circuits involves basic quaternary gates and logic operations in the quaternary quantum domain. In this paper, we propose new projection operations and quaternary logic gates for synthe...
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Multi-Access Edge computing (MEC) has emerged as a promising new paradigm allowing low latency access to services deployed on edge servers to avert network latencies often encountered in accessing cloud services. A ke...
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Multi-Access Edge computing (MEC) has emerged as a promising new paradigm allowing low latency access to services deployed on edge servers to avert network latencies often encountered in accessing cloud services. A key component of the MEC environment is an auto-scaling policy which is used to decide the overall management and scaling of container instances corresponding to individual services deployed on MEC servers to cater to traffic fluctuations. In this work, we propose a Safe Reinforcement Learning (RL)-based auto-scaling policy agent that can efficiently adapt to traffic variations to ensure adherence to service specific latency requirements. We model the MEC environment using a Markov Decision Process (MDP). We demonstrate how latency requirements can be formally expressed in Linear Temporal Logic (LTL). The LTL specification acts as a guide to the policy agent to automatically learn auto-scaling decisions that maximize the probability of satisfying the LTL formula. We introduce a quantitative reward mechanism based on the LTL formula to tailor service specific latency requirements. We prove that our reward mechanism ensures convergence of standard Safe-RL approaches. We present experimental results in practical scenarios on a test-bed setup with real-world benchmark applications to show the effectiveness of our approach in comparison to other state-of-the-art methods in literature. Furthermore, we perform extensive simulated experiments to demonstrate the effectiveness of our approach in large scale scenarios.
For positive integers c, s≥1, let M3(c, s) be the smallest integer such that any set of at least M3(c, s) points in the plane, no three on a line, and colored with c colors, contains a monochromatic triangle with at ...
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Centralized localization dissipates a lot of energy in pooling the proximity information of the individual nodes. In contrast a distributed algorithm usually uses only local information at each node. Most localization...
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Multicasting in wireless local area network is an efficient way to deliver message from a source user to a specified group of destination users simultaneously. In unirate multicasting, all users belonging to a particu...
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In this work, a network selection mechanism for re-configurable intelligent surface (RIS) assisted network has been proposed. The goal of the proposed mechanism is to select appropriate base station (BS) or BS-RIS pai...
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The channel assignment problem with separation is formulated as a vertex coloring problem of a graph G = (V, E) where each vertex represents a base station and two vertices are connected by an edge if their correspond...
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The channel assignment problem with separation is formulated as a vertex coloring problem of a graph G = (V, E) where each vertex represents a base station and two vertices are connected by an edge if their corresponding base stations are interfering to each other. The L(δ1, δ2,,δt) coloring of G is a mapping f: V → {0,1, , λ} such that |f(u) - f(v)| ≥ δi if d(u,v) = i, where d(u,v) denotes the distance between vertices u and v in G and 1 ≤ i ≤ t. Here λ, the largest color assigned to a vertex of G, is known as the span. The same color can be reused in two vertices u and v if d(u,v) ≥ t+1, where t+1 is the reuse distance. The objective is to minimize λ over all such coloring function f. Here (δ1,δ2,,δt) is called the separation vector where δ1,δ2,,δt are positive integers with δ1 ≥ δ2 ≥ ≥ δt. Let λ∗ be the minimum span such that there exists an L(1, 1, , 1) coloring of G. We denote the separation vector (1,1,, 1) as (1t). We deal with the problem of finding the maximum value of δ1 such that there exists an L(δ1,1t-1) coloring with span equal to λ∗. So far bounds on δ1 have been obtained for L(δ1, 1t-1) coloring with span λ∗ for the square and triangular grids. Shashanka et al. [18] posed the problem as open for the honeycomb grid. We give lower and upper bounds of δ1 for L(δ1, 1t-1) coloring with span λ∗ of the honeycomb grid. The bounds are asymptotically tight. We also present color assignment algorithms to achieve the lower bound.
A simple algorithm for automated analysis of granulometric images consisting of touching or overlapping convex objects such as coffee bean, food grain, is presented. The algorithm is based on certain underlying digita...
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