Minimizing Cost of K-Replica in Hierarchical Data Grid Environment

A channel scheduling problem for a given graph G(V,E) is to select a subset E′ of edges of E and assign channel to each one in E′ under the restriction that all edges of E′ are interference free. We introduce the notion of two sides approximation for the channel scheduling problem. A pair of parameters (f,g) controls the accuracy of approximation in this paper. A (f,g)-approximation satisfies \(\sum_{e\in E'}W(e)\ge{\sum_{e\in Opt}W(e)\over f}\) and \(\sum_{e\in E-E'}W(e)\le g\sum_{e\in E- Opt^*}W(e)\), where Opt * is the set of edges assigned channels in an optimal solution, and W(e) is the weight of edge e. An f-approximation satisfies \(\sum_{e\in E'}W(e)\ge{\sum_{e\in Opt}W(e)\over f}\). We show that a simple greedy algorithm can obtain an (O(1), O(1))-approximation for the single channel scheduling problem. In many cases, the greedy algorithms gives much more accurate result than the worst ratio. Furthermore, we develop an \(|E|^{O({1\over \epsilon})}\) time (1 − ε, O(1))-approximation algorithm for the single channel scheduling problem. We also show that a simple greedy algorithm can obtain an O(1)-approximation for the multi-channel scheduling problem which satisfies \(\sum_{e\in E'}W(e)\ge{\sum_{e\in Opt}W(e)\over \Omega(1)}\). We also develop a \(|E|^{O({d\over \epsilon})}\) time (1 − ε)-approximation algorithm the multi-channel scheduling problem, where d is the number of channels. This improves the existing approximation scheme for multi-channel scheduling problem with \(|E|^{O({d\over \epsilon^2})}\) time by Cheng et al. We also develop a polynomial time constant factor greedy approximation algorithm for the multi-channel scheduling that allows variate radii of interference among those nodes.

Efficient operation of wireless networks and switches requires using simple (and in some cases distributed) scheduling algorithms. In general, simple greedy algorithms (known as Greedy Maximal Scheduling - GMS) are guaranteed to achieve only a fraction of the maximum possible throughput (e.g., 50% throughput in switches). However, it was recently shown that in networks in which the Local Pooling conditions are satisfied, GMS achieves 100% throughput. Moreover, in networks in which the σ- Local Pooling conditions hold, GMS achieves σ% throughput. In this paper, we focus on identifying the specific network topologies that satisfy these conditions. In particular, we provide the first characterization of all the network graphs in which Local Pooling holds under primary interference constraints (in these networks GMS achieves 100% throughput). This leads to a polynomial time algorithm for identifying Local Pooling-satisfying graphs. Moreover, by using similar graph theoretical methods, we show that in all bipartite graphs (i.e., input-queued switches) of size up to 7×n, GMS is guaranteed to achieve 66% throughput, thereby improving upon the previously known 50% lower bound. Finally, we study the performance of GMS in interference graphs and show that in certain specific topologies its performance could be very bad. Overall, the paper demonstrates that using graph theoretical techniques can significantly contribute to our understanding of greedy scheduling algorithms.

Relay stations can be deployed in a wireless network to extend its coverage and improve its capacity. In this paper, we study a scheduling problem in OFDMA-based wireless relay networks with consideration for multi-user diversity, channel diversity and spatial reuse. First, we present a Mixed Integer Linear Programming (MILP) formulation to provide optimum solutions. It has been shown by previous research that performance of a wireless scheduling algorithm is usually related to the interference degree delta, which is the maximum number of links that interfere with a common link but do not interfere with each other. Therefore, we then show that the interference degree delta is at most 4 for any 2-hop relay network and 14 for any general h-hop (h >; 1) relay network. Furthermore, we present a simple greedy algorithm for the scheduling problem and show it has an approximation ratio of 1/(1+δ), which leads to an approximation ratio of 1/5 for the 2-hop case and 1/15} for the general case. In addition, we present three heuristic algorithms, namely, the weighted degree greedy algorithm, the Maximum Weighted Independent Set (MWIS) algorithm and the Linear Programming (LP) rounding algorithm, to solve the scheduling problem. Extensive simulation results have showed that the LP rounding algorithm performs best and always provides close-to-optimum solutions. The performance of the simple greedy algorithm is comparable to that of the other algorithms.

Influence maximization problem is to find a small set of influential nodes in a social network, whose activation of information propagation can be maximized using one of the propagation models such as independent cascade model. This paper proposes a new method to solve this problem based on discretized linear automaton algorithm and simple greedy algorithm. Every allowable node in the social network is regarded as an action and the goal of the learning automaton is to select an optimal subset of actions. To speed up the convergence, firstly, the problem space is reduced by excluding the nodes lacking influence; then, the original learning process is divided into two parts: the sampling process and the learning process. Besides, a new scheme for determining the best parameters is presented, which is more applicable to practical problems. The obtained results show that the new proposed algorithm is efficient in real-life social networks. The results are close to the ones obtained by the greedy algorithm in terms of accuracy, and the new method greatly improves the speed of selecting the optimal subset.

The well‐known lower bound on the independence number of a graph due to Caro (Technical Report, Tel‐Aviv University, 1979) and Wei (Technical Memorandum, TM 81 ‐ 11217 ‐ 9, Bell Laboratories, 1981) can be established as a performance guarantee of two natural and simple greedy algorithms or of a simple randomized algorithm. We study possible generalizations and improvements of these approaches using vertex weights and discuss conditions on so‐called potential functions pG: V(G)→ℕ0 defined on the vertex set of a graph G for which suitably modified versions of the greedy algorithms applied to G yield independent sets I with . We provide examples of such potentials, which lead to bounds improving the bound due to Caro and Wei. Furthermore, suitably adapting the randomized algorithm we give a short proof of Thiele's lower bound on the independence number of a hypergraph (T. Thiele, J Graph Theory 30 (1999), 213–221).

This dissertation presents novel learning and optimization frameworks for sparse sensor scheduling in large-scale complex networks. In the first part, efficient algorithms for sensor scheduling in dynamical networks are introduced, with guaranteed performance. In the second part, the applications of these frameworks are explored, focusing on experimental design for nonlinear biological models and visual feature selection for robotic navigation. The first framework addresses sensor scheduling in discrete-time, linear time-invariant systems, proposing a novel learning-based rounding method to transform weighted sensor schedules into sparse, unweighted schedules, while preserving similar observability performance. The second framework extends this approach to linear time-varying systems, employing a myopic strategy within a Kalman filtering framework to address non-submodular sensor scheduling in large-scale Discrete-Time Linear Time-Varying (DT-LTV) systems. A simple greedy algorithm is utilized, providing approximation bounds based on submodularity and curvature concepts. The third framework explores a non-myopic approach for DT-LTV systems through an online sparse sensor scheduling methodology, leveraging randomized algorithms to approximate fully-sensed systems with a constant average number of active sensors at each time step. Finally, a non-myopic approach within a Kalman filtering framework is considered for DT-LTV dynamics, incorporating both process and measurement noise. This approach utilizes recent advances in online learning and regret minimization to offer an adaptive sensor scheduling framework, effectively addressing submodular objectives. In the second part of this dissertation, the focus shifts to the application of these frameworks, specifically addressing the challenge of selecting an optimal subset of observations to improve parameter estimation, a common problem in machine learning and signal processing. For nonlinear measurement models, we go beyond standard linear Taylor approximations by adopting a quadratic approximation for greater accuracy in highly nonlinear cases. An efficient greedy selection method with theoretical utility bounds is proposed to optimally select experiments, specifically demonstrated using the Michaelis-Menten model in a simple enzyme reaction system. In the final section, a task-oriented framework is developed to improve visual-inertial navigation in robots, addressing constraints such as time and energy. The framework selects visual landmarks using a mean squared error based, non-submodular objective and a simplified dynamic model. To solve the NP-hard landmark selection problem, three efficient algorithms are proposed: a simple greedy method, a randomized variant for faster results, and a Taylor series-based approximation. The greedy and randomized variant approaches provide performance guarantees with respect to the optimal solution. Simulation results conducted for each proposed framework validate the effectiveness and superiority of these methods compared to existing techniques. Additionally, potential future research directions are discussed in the conclusion.--Author's abstract

Optimizing the use of grid resources is critical for users to effectively exploit a Data Grid (DG). Data replication is considered as a major technique for increasing access performance and data availability in DG systems. Current works on data replications in Grid systems focuses on infrastructure for replication mechanism for creating or deleting replicas. One of the challenges in data replication is determining Optimal Number of Replicas (ONR) with Quality of Service (QoS) assurance, as well as their Optimal Location of Replicas (OLR) in DG. In this paper, we propose an algorithm that finds ONR of an object over DG systems, such that the overall communication and storage cost is minimized. This algorithm ensures that QoS required from the users are satisfied. In addition, we proposed a sketch of the proof for our algorithm and its integrity.

Massive multiple-input multiple-output (MIMO) technology is expected to achieve significant gains in both signal-to-noise-plus-interference ratio (SINR) and throughput for 5G cellular wireless networks. Efficient and highly accurate channel state information (CSI) acquisition at the base stations (BS) is essential to achieve the potential benefits of massive MIMO systems. However, the inadequate number of orthogonal pilot sequences used for CSI estimation leads to erroneous channel estimation as it causes interference between pilot sequences. This phenomenon is coined pilot contamination and severely limits the system performance. Therefore, we address in this paper the pilot contamination problem in massive MIMO Cloud-Radio Access Networks systems. Leveraging on a real telecom operator data delivered by Call Detail Records (CDR), our objective is to maximize the average uplink achievable rate by reducing the effect of pilot contamination in massive MIMO Cloud-Radio Access Networks (C-RAN). As such a problem is a non-linear integer problem that has no solution in polynomial time, we develop a two-stage solution to solve it. First, a coalition game is proposed where Remote Radio Heads (RRHs) gather into clusters with random user-pilot allocation. Then, two greedy heuristics are applied to match each user in the cluster with a given pilot. The goal is to further improve the average uplink rate achieved in the first stage. Simulation results show that our heuristic solutions for pilot contamination mitigation outperform the traditional pilot allocation solution and a state-of-the-art pilot allocation scheme based on large-scale fading in terms of average uplink achievable rate and SINR.

We study mechanisms for the combinatorial auction (CA) problem, in which m objects are sold to rational agents and the goal is to maximize social welfare. Of particular interest is the special case of s -CAs, where agents are interested in sets of size at most s , for which a simple greedy algorithm obtains an s + 1 approximation, but no polynomial time deterministic truthful mechanism is known to attain an approximation ratio better than O ( m /√log m ). We view this not only as an extreme gap between the power of greedy auctions and truthful greedy auctions, but also as exemplifying the gap between the known power of truthful and non-truthful polynomial time deterministic algorithms. We associate the notion of greediness with a broad class of algorithms, known as priority algorithms, which encapsulate many natural auction methods. This motivates us to ask: how well can a truthful greedy algorithm approximate the optimal social welfare for CA problems? We show that no truthful greedy priority algorithm can obtain an approximation to the CA problem that is sublinear in m , even for s -CAs with s ≥ 2. Our inapproximations are independent of any time constraints on the mechanism and are purely a consequence of the greedy-type restriction. We conclude that any truthful combinatorial auction mechanism with a non-trivial approximation factor must fall outside the scope of many natural auction methods.

The 5G Authentication and Key Management for Applications (AKMA) protocol is a 5G standard proposed by 3GPP in order to standardize the authentication procedure of mobile users towards applications based on the authentication of the user to the mobile network. As pointed out by several authors, the 5G-AKMA protocol inherently poses severe security issues, including privacy, unlinkability, ephemeral secret leakage and stolen device attacks. Also, the protocol does not offer perfect forward secrecy. In addition, the network operator is able to record all applications to which the user is subscribed and any outsider eavesdropping the communication channel is able to link requests to different applications coming from the same user. While the state of the shows that various protocols are proposed to solve the 5G-AKMA security issues, they are either vulnerable to severe attacks or are computationally extensive. In this paper, we provide a new version of the protocol able to solve these privacy issues in an effective manner. In addition, we also extend the protocol such that it can be used for communications in multiaccess edge computing (MEC) applications, taking into account handover procedures from one MEC server to another. The proposed protocol has been thoroughly compared to existing ones, revealing its efficiency in terms of communication, computation storage, and energy costs. The comparative analysis shows that the proposed 5G-AKMA reduces computational cost by 92%, communication cost by 74%, storage cost by 38%, and energy consumption cost by 58%. The security verification has been conducted using informal and formal methods (Real-Or-Random (ROR) and Scyther Validation tools) to ensure the protocol’s security. Additionally, we conduct a comparative analysis under an unknown attack scenario. Furthermore, the simulation is carried out using NS3.

A coin set is a strictly increasing list of positive integers that always begins with 1. A coin set is called greedy when the simple greedy change-making algorithm always produces the fewest number of coins in change. Here, the greedy change-making algorithm repeatedly selects the largest denomination coin less than the remaining amount until it has assembled the correct change. Pearson has provided an efficient algorithm for determining whether a coin set is greedy. We study a stricter property on coin sets, called total greediness, which requires that all initial subsequences of the coin set also be greedy, and a simple property makes it easy to test if a coin set is totally greedy. We begin to explore the theory of greedy obstructions– those coin sets that cannot be extended to greedy coin sets by the addition of coins in larger denominations.

Optimizing strategies for post-disaster reconstruction of school systems

A central question in information theory is to determine the maximum success probability that can be achieved in sending a fixed number of messages over a noisy channel. This was first studied in the pioneering work of Shannon who established a simple expression characterizing this quantity in the limit of multiple independent uses of the channel. Here we consider the general setting with only one use of the channel. We observe that the maximum success probability can be expressed as the maximum value of a submodular function. Using this connection, we establish the following results: 1. There is a simple greedy polynomial-time algorithm that computes a code achieving a (1-1/e)-approximation of the maximum success probability. Moreover, for this problem it is NP-hard to obtain an approximation ratio strictly better than (1-1/e). 2. Shared quantum entanglement between the sender and the receiver can increase the success probability by a factor of at most 1/(1-1/e). In addition, this factor is tight if one allows an arbitrary non-signaling box between the sender and the receiver. 3. We give tight bounds on the one-shot performance of the meta-converse of Polyanskiy-Poor-Verdu.

Designing a key management scheme for secure group communication is a challenging task. There are several tree based and non-tree based group key management schemes. In the existing non-tree based group key management schemes, the rekeying and storage costs are mostly in linear with the number of members in the group. Also, in some of the efficient tree-based group key management schemes, the rekeying and storage cost is a function of the number of group users. We propose a rekey efficient centralized group key management scheme in which communication, storage and computation costs are constant. In the proposed scheme, each user has to store only two keys. We compare the proposed scheme with some of the existing tree and non-tree based group key management schemes and show that the proposed scheme is rekey and storage efficient.

The information-centric Internet of things (IC-IoT) is different from the traditional Internet of things (IoT) in that the device-to-device pattern is generalized to a device-to-network pattern. Furthermore, in an IC-IoT environment, there is a demand for protecting the security of all data generated from IC-IoT devices. A cryptography scheme named attribute-based encryption (ABE) represents a smart method of providing the fine-grained access control that can sufficiently protect data security. The most attractive advantage of ABE is its expressive access policy, which makes the access control of data flexible and manageable. However, there is a serious problem caused by such an access policy; it incurs a greater ciphertext redundancy and computational overhead. This implies that the current ABE scheme is hard to implement in the thin client devices of IC-IoT. In this paper, we propose a universalized policy-compacting method via sharing public parts of the policy. Compared with the original policy, the compacted policy applies a more compact ciphertext and requires less computation, communication, and storage cost. However, the policy-compacting problem is proved to be a non-deterministic polynomial complete (NPC) problem. Thus, a greedy algorithm is provided to obtain an approximate minimum compacted policy scale. Finally, we propose a compact ciphertext-policy attribute-based encryption (CCP-ABE) scheme with the policy-compacting method. A security proof and performance evaluation show that the proposed CCP-ABE scheme provides a comprehensive performance improvement.