Computation offloading at mobile edge computing (MEC) servers can mitigate the resource limitation and reduce the communication latency for mobile devices. Thereby, in this study, we proposed an ...offloading model for a multi-user MEC system with multi-task. In addition, a new caching concept is introduced for the computation tasks, where the application program and related code for the completed tasks are cached at the edge server. Furthermore, an efficient model of task offloading and caching integration is formulated as a nonlinear problem whose goal is to reduce the total overhead of time and energy. However, solving these types of problems is computationally prohibitive, especially for large-scale of mobile users. Thus, an equivalent form of reinforcement learning is created where the state spaces are defined based on all possible solutions and the actions are defined on the basis of movement between the different states. Afterwards, two effective Q-learning and Deep-Q-Network-based algorithms are proposed to derive the near-optimal solution for this problem. Finally, experimental evaluations verify that our proposed model can substantially minimize the mobile devices’ overhead by deploying computation offloading and task caching strategy reasonably.
Mobile edge computing (MEC) is a new paradigm to alleviate resource limitations of mobile IoT networks through computation offloading with low latency. This article presents an efficient and secure ...multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing. It does not only investigate offloading strategy but also considers resource allocation, compression and security issues. Firstly, to guarantee efficient utilization of the shared resource in multi-user scenarios, radio and computation resources are jointly addressed. In addition, JPEG and MPEG4 compression algorithms are used to reduce the transfer overhead. To fulfill security requirements, a security layer is introduced to protect the transmitted data from cyber-attacks. Furthermore, an integrated model of resource allocation, compression, and security is formulated as an integer nonlinear problem with the objective of minimizing the weighted sum of energy under a latency constraint. As this problem is considered as NP-hard, linearization and relaxation approaches are applied to transform the problem into a convex one. Finally, an efficient offloading algorithm is designed with detailed processes to make the computation offloading decision for computation tasks of mobile users. Simulation results show that our model not only saves about 46% of system overhead consumption in comparison with local execution but also scale well for large-scale IoT networks.
This paper studies the electric vehicle (EV) charging scheduling problem under a parking garage scenario, aiming to promote the total utility for the charging operator subject to the time-of-use ...(TOU) pricing. Different from most existing works, we develop a multicharging system incorporating the practical battery charging characteristic, and design an intelligent charging management mechanism to maximize the interests of both the customers and the charging operator. First, to ensure the quality of service for each client, we implement an admission control mechanism to guarantee all admitted EVs' charging requirements being satisfied before their departure. Second, we formulate the charging scheduling process as a deadline constrained causal scheduling problem. Then, we propose an adaptive utility oriented scheduling (AUS) algorithm to optimize the total utility for the charging operator, which can robustly achieve low task declining probability and high profit. The charging operator can also apply the discussed reservation mechanism to mitigate the performance degradation caused by the charging information mismatching with vehicle stochastic arrivals. Finally, we conduct extensive simulations based on realistic EV charging parameters and TOU pricing. Simulation results exhibit the effectiveness of the proposed AUS algorithm in achieving desirable performance compared with other benchmark scheduling schemes.
Multi-scale exposure fusion is an effective image enhancement technique for a high dynamic range (HDR) scene. In this paper, a new multi-scale exposure fusion algorithm is proposed to merge ...differently exposed low dynamic range (LDR) images by using the weighted guided image filter to smooth the Gaussian pyramids of weight maps for all the LDR images. Details in the brightest and darkest regions of the HDR scene are preserved better by the proposed algorithm without relative brightness change in the fused image. In addition, a new weighted structure tensor is introduced to the differently exposed images and it is adopted to design a detail extraction component for the proposed fusion algorithm, such that users are allowed to manipulate fine details in the enhanced image according to their preference. The proposed multi-scale exposure fusion algorithm is also applied to design a simple single image brightening algorithm for both low-light imaging and back-light imaging.
•Dealing with dynamic virtual machine placement in data centers for energy efficiency.•Formulated the problem as a constrained optimization with profile information.•An ant colony system embedded ...with new heuristics to solve the problem.•Significant increase in energy efficiency of data centers.
Data centers are fundamental infrastructure for information technology and cloud services; however, their very high rates of energy consumption are a problem. The placement of Virtual Machines (VMs) to Physical Machines (PMs) in virtualized environments has a significant impact on the energy consumption of a data center. This is an NP-hard problem, for which an optimal solution is not practicable even for a small-scale data center. In this paper, we formulate placement of VMs to PMs in a data center as a constrained combinatorial optimization problem and make use of the information from PM and VM profiles to minimize the total energy consumption of all active PMs. An Ant Colony System (ACS) embedded with new heuristics is presented for an energy-efficient solution to the optimization problem. To demonstrate the effectiveness of the ACS, simulation experiments are conducted on small-, medium- and large-scale data centers. The results from our ACS are compared with two existing ACS methods as well as the widely used First-Fit-Decreasing (FFD) algorithm. Our ACS is shown to outperform the two existing ACS methods and FFD in energy performance for all small-, medium- and large-scale test problems. Our ACS also exhibits good scalability with the increase in the problem size.
Indoor distance measurement technology utilizing Zigbee's Received Signal Strength Indication (RSSI) offers cost-effective and energy-efficient advantages, making it widely adopted for indoor ...distance measurement applications. However, challenges such as multipath effects, signal attenuation, and signal blockage often degrade the accuracy of distance measurements. Addressing these issues, this study proposes a combined filtering approach integrating Kalman filtering, Dixon's Q-test, Gaussian filtering, and mean filtering. Initially, the method evaluates Zigbee's transmission power, channel, and other parameters, analyzing their impact on RSSI values. Subsequently, it fits a signal propagation loss model based on actual measured data to understand the filtering algorithm's effect on distance measurement error. Experimental results demonstrate that the proposed method effectively improves the conversion relationship between RSSI and distance. The average distance measurement error, approximately 0.46 m, substantially outperforms errors derived from raw RSSI data. Consequently, this method offers enhanced distance measurement accuracy, making it particularly suitable for indoor positioning applications.
In addition to wirelength, modern placers need to consider various constraints such as preplaced blocks and density. We propose a high-quality analytical placement algorithm considering wirelength, ...preplaced blocks, and density based on the log-sum-exp wirelength model proposed by Naylor and the multilevel framework. To handle preplaced blocks, we use a two-stage smoothing technique, i.e., Gaussian smoothing followed by level smoothing, to facilitate block spreading during global placement (GP). The density is controlled by white-space reallocation using partitioning and cut-line shifting during GP and cell sliding during detailed placement. We further use the conjugate gradient method with dynamic step-size control to speed up the GP and macro shifting to find better macro positions. Experimental results show that our placer obtains very high-quality results.
Mobile-edge computing (MEC) has emerged as a new computing paradigm with great potential to alleviate resource limitations attributed to mobile device users (MDUs) by offloading intensive ...computations to ubiquitous MEC server. However, most of the current offloading policies allow MDUs to transmit their tasks to the same connected small base stations (sBSs), which invariably increases latency and limits performance gain due to overload. Moreover, the security issue mitigating sensitive communication of information is not adequately addressed. Therefore, in this study, in addition to proposing a joint load balancing and computation offloading (CO) technique for MEC systems, we introduce a new security layer to circumvent potential security issues. First, a load balancing algorithm for efficient redistribution of MDUs among sBSs is proposed. In addition, a new advanced encryption standard (AES) cryptographic technique suffused with electrocardiogram (ECG) signal-based encryption and decryption key is presented as a security layer to safeguard the vulnerability of data during the transmission. Furthermore, an integrated model of load balancing, CO and security is formulated as a problem whose goal is to decrease the time and energy demands of the system. Detailed experimental results prove that our model with and without the additional security layers can save about 68.2% and 72.4% of system consumption compared to the local execution.
Although best known for their phagocytic and immunological functions, macrophages have increasingly been recognised as key players in the development, homeostasis and regeneration of their host ...tissues. Early during development, macrophages infiltrate and colonise all tissues within the body, developing symbiotically with their host tissues and acquiring unique functional adaptations based on the tissue microenvironment. These embryonic resident tissue macrophages (RTMs) are ontogenically distinct from the later adult bone marrow-derived monocytes, and in some tissues are self-maintained independently of general circulation at a steady state. In this article, we briefly discuss the ontogeny, maintenance and unique tissue adaptions of RTMs focusing on microglia, Kupffer cells, Langerhans cells, intestinal macrophages, cardiac macrophages and tumour-associated macrophages, and highlight their role in development, homeostasis and dysfunction.
Recent findings have shown that inflammation indices are associated with prognosis in various malignancies. However, the usefulness of inflammation indices including platelet-to-lymphocyte ratio, ...neutrophil-to-lymphocyte ratio and prognostic nutritional index for gastrointestinal stromal tumors (GISTs) remains controversial.
We retrospectively reviewed 340 primary localized GIST patients who had received surgical resection between 2005 and 2015 to investigate the effect of platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio and prognostic nutritional index on prognosis. 206 patients were selected by propensity score matching to control selection biases.
Kaplan-Meier analysis and the log rank test demonstrated that high prognostic nutritional index (≥43.9) was significantly correlated with better recurrence-free survival (RFS) (P<0.001). Among the three inflammatory indices, only preoperative high prognostic nutritional index was an independent prognostic factor for survival hazard ratio (HR) 0.509; 95% confidence interval (CI) 0.266-0.872; P = 0.031 in multivariate analysis. After propensity score matching, elevated prognostic nutritional index was still a predictor for RFS (HR = 0.498; 95% CI 0.253-0.971; P = 0.042) in the multivariate analyses. In addition, prognostic nutritional index was a significant prognostic factor for GISTs within the National Institutes of Health (NIH) high and very low/low-risk categories. Incorporation prognostic nutritional index into the NIH risk criteria improved the prognostic stratification (c-index, 0.725 vs. 0.686, p = 0.039).
High prognostic nutritional index is a predictor of improved survival for surgically resected GISTs and incorporation prognostic nutritional index into NIH risk criteria improves the predictive accuracy.
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