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Underwater wireless sensor networks (UWSNs) are promising, emerging technologies for the applications in oceanic research. UWSN contains high number of sensor nodes and autonomous underwater vehicles that are deployed to perform the data transmission in the sea. In UWSN networks, the sensors are placed in the buoyant which are highly vulnerable to selfish behavioural attack. In this paper, the major challenges in finding secure and optimal route navigation in UWSN are identified and in order to address them, Entropy based ACO algorithm (E-ACO) is proposed for secure route selection. Moreover, the Selfish Node Recovery (SNR) using the Grasshopper Optimisation Algorithm (GOA) is used to minimize the packet loss in the UWSN. The performance of the proposed E-ACO method is compared with existing routing methods such as Secure Authentication with Protected Data Aggregation (SAPDA), Secure Energy Efficient and Cooperative Routing (SEECR), Fault Resilient Routing using Moth Flame Optimization (FRR-MFO) and Improved ACO (IACO) method. The packet delivery ratio of the proposed E-ACO with 500 nodes is 0.89 which is higher than other existing methods such as SAPDA, SEECR, FRR-MFO and IACO. [ABSTRACT FROM AUTHOR]

The change detection (CD) of heterogeneous remote sensing images is an important but challenging task. The difficulty is to obtain the change information by directly comparing the different statistical characteristics of the images acquired by different sensors. This paper proposes an unsupervised method for heterogeneous image CD based on an image domain transfer network. First, an attention mechanism is added to the Cycle-generative adversarial networks (Cycle-GANs) to obtain a more consistent feature expression by transferring bi-temporal heterogeneous images to the common domain. The Euclidean distance of the corresponding pixels is calculated in the common domain to form a difference map, and a threshold algorithm is applied to get a rough change map. Finally, the proposed adaptive Discrete Cosine Transform (DCT) algorithm reduces the noise introduced by false detection, and the final change map is obtained. The proposed method is verified on three real heterogeneous CD datasets and compared with the current state-of-the-art methods. The results show that the proposed method is accurate and robust for performing heterogeneous CD tasks. [ABSTRACT FROM AUTHOR]

LoRa has been recognized as one of the most promising low-power wide-area (LPWA) techniques. Since LoRa devices are usually powered by batteries, energy efficiency (EE) is an essential consideration. In this paper, we investigate the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. Specifically, our objective is to maximize the corresponding EE by jointly exploiting user scheduling, spreading factor (SF) assignment, and transmit power allocations. To solve them efficiently, we first propose a suboptimal algorithm, including the low-complexity user scheduling scheme based on matching theory and the heuristic SF assignment approach for LoRa users scheduled on the same channel. Then, to deal with the power allocation, an optimal algorithm is proposed to maximize the SEE. To maximize the MEE of LoRa users assigned to the same channel, an iterative power allocation algorithm based on the generalized fractional programming and sequential convex programming is proposed. Numerical results show that the proposed user scheduling algorithm achieves near-optimal EE performance, and the proposed power allocation algorithms outperform the benchmarks. [ABSTRACT FROM AUTHOR]

At a high level, data centres are large IT facilities hosting physical machines (servers) that often run a large number of virtual machines (VMs)—but at a lower level, data centres are an intricate collection of interconnected and virtualised computers, connected services, complex service-level agreements. While data centre managers know that reassigning VMs to the servers that would best serve them and also minimise some cost for the company can potentially save a lot of money—the search space is large and constrained, and the decision complicated as they involve different dimensions. This paper consists of a comparative study of heuristics and exact algorithms for the multi-objective machine reassignment problem. Given the common intuition that the problem is too complicated for exact resolutions, all previous works have focused on various (meta)heuristics such as First-Fit, GRASP, NSGA-II or PLS. In this paper, we show that the state-of-art solution to the single objective formulation of the problem (CBLNS) and the classical multi-objective solutions fail to bridge the gap between the number, quality and variety of solutions. Hybrid metaheuristics, on the other hand, have proven to be more effective and efficient to address the problem—but as there has never been any study of an exact resolution, it was difficult to qualify their results. In this paper, we present the most relevant techniques used to address the problem, and we compare them to an exact resolution (ϵ -Constraints). We show that the problem is indeed large and constrained (we ran our algorithm for 30 days on a powerful node of a supercomputer and did not get the final solution for most instances of our problem) but that a metaheuristic (GeNePi) obtains acceptable results: more (+ 188%) solutions than the exact resolution and a little more than half (52%) the hypervolume (measure of quality of the solution set). [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) can achieve high throughput by using the same time and frequency resources for multiple users. NOMA distinguishes multiple users in power domain by computationally-heavy successive interference cancellation (SIC) method. Recently, outsourcing baseband computations to graphics processing units (GPUs) have become an attractive solution for some wireless communication applications, particularly for the ones include parallel processing. Although SIC is a sequential operation, when user clustering is deployed, multiple SIC operations are required and GPU based computation becomes a natural solution to alleviate the high computation demand of SIC receivers. In this work, we implemented GPU based SIC implementation for uplink NOMA systems with user clustering and our results reveal a significant speedup when compared to that of using central processing unit based computations. [ABSTRACT FROM AUTHOR]

The use of the evolutionary heuristic simulated evolution for the optimization of the multi-dimensional vector bin packing problem, which is encountered in several industrial applications, is described. These applications range from production planning and steel fabrication to assignment of virtual machines (VMs) onto physical hosts at cloud-based data centers. The dimensions of VMs can include demands of CPU, memory, bandwidth, disk space etc. The generalized goodness functions that aid traversing the search space in an intelligent manner are designed to cater to the multidimensional nature of items (VMs). The efficiency of heuristics is tested by considering phase transition in the generation of difficult test cases. The quality of the heuristics is judged by determining how close the solution is to the estimated lower bound. A new implementation of a tighter lower bound is proposed. Experiments show that superior quality results are obtained by employing the proposed strategy. [ABSTRACT FROM AUTHOR]

A progressive paradigm shift from centralized to distributed network architectures has been consolidated since the 4G communication standard, calling for novel decision-making mechanisms with distributed control to operate at the network edge. This situation implies that each base station (BS) must manage resources independently to meet the quality of service (QoS) of existing human-type communication devices (HTC), as well as the emerging machine type communication (MTC) devices from the internet of things (IoT). In this paper, we address the BS assignment problem, whose aim is to determine the most appropriate serving BS to each mobile device. This problem is formulated as an optimization problem for maximizing the system throughput and imposing constraints on the air interface and backhaul resources. The assignment problem is challenging to solve, so we present a simple yet valid reformulation of the original problem while using dual decomposition theory. Subsequently, we propose a distributed price-based BS assignment algorithm that performs at each BS the assignment process, where a novel pricing update scheme is presented. The simulation results show that our proposed solution outperforms traditional maximum signal to interference plus noise ratio (Max-SINR) and minimum path-loss (Min-PL) approaches in terms of system throughput. [ABSTRACT FROM AUTHOR]

In this paper, we study the submodular load balancing problem with submodular penalties. The objective of this problem is to balance the load among sets, while some elements can be rejected by paying some penalties. Officially, given an element set V, we want to find a subset R of rejected elements, and assign other elements to one of m sets A 1 , A 2 , ⋯ , A m . The objective is to minimize the sum of the maximum load among A 1 , A 2 , ⋯ , A m and the rejection penalty of R, where the load and rejection penalty are determined by different submodular functions. We study the submodular load balancing problem with submodular penalties under two settings: heterogenous setting (load functions are not identical) and homogenous setting (load functions are identical). Moreover, we design a Lovász rounding algorithm achieving a worst-case guarantee of m + 1 under the heterogenous setting and a min { m , ⌈ n m ⌉ + 1 } = O (n) -approximation combinatorial algorithm under the homogenous setting. [ABSTRACT FROM AUTHOR]

Resource allocation for machine-type communication (MTC) devices is one of the keys challenges in the 5G network as it affects the lifetime of battery powered devices and also the quality of service of the applications. MTC devices are battery restrained and cannot afford a lot of power consumption due to spectrum usage. In this paper, we propose a novel resource allocation algorithm termed threshold controlled access (TCA) protocol. We propose a novel technique of uplink resource allocation in which the devices make a decision of resource allocation blocks based on their battery status and related application's power profile that eventually leads to required quality of service (QoS) metric. The first phase of the TCA algorithm selects the number of carriers to be allocated to a certain device for the better lifetime of low power MTC devices. In the second phase, the efficient solution is implemented through inducing a threshold value. A certain value of the threshold is selected through a mapping based on a QoS metric. The threshold enhances the selection of subcarriers for less powered devices, such as small e-health sensors. The algorithm is simulated for the physical layer of the 5G network. Simulation results show that the proposed algorithm is less complex and achieves better performance when compared to existing solutions in the literature. [ABSTRACT FROM AUTHOR]