Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by application components, network conditions, computational load, amount of data transfer, and delays in communication. We formulate the cost function involving all aforementioned factors, obtain the cost for all possible combinations of component offloading policies, select the optimal policies over an exhaustive dataset, and train a deep learning network as an alternative for the extensive computations involved. Simulation results show that our proposed model is promising in terms of accuracy and energy consumption of UEs. INDEX TERMS Computational offloading, deep learning, energy efficient offloading, mobile edge computing, user equipment.

Chronic Kidney Disease (CKD) implies that the human kidneys are harmed and unable to blood filter in the manner which they should. The disease is designated ''chronic'' in light of the fact that harm to human kidneys happen gradually over a significant time. This harm can make wastes to build up in your body. Many techniques and models have been developed to diagnos the CKD in early-stage. Among all techniques, Machine Learning (ML) techniques play a significant role in the early forecasting of different kinds ailments. ML techniques have been used for achieving analytical results which is one of the instruments utilize in medical analysis and prediction. In this paper, we employ experiential analysis of ML techniques for classifying the kidney patient dataset as CKD or NOTCKD. Seven ML techniques together with NBTree, J48, Support Vector Machine, Logistic Regression, Multi-layer Perceptron, Naïve Bayes, and Composite Hypercube on Iterated Random Projection (CHIRP) are utilized and assessed using distinctive evaluation measures such as mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), recall, precision, F-measure and accuracy.The experimental outcomes accomplished of MAE are 0.0419 for NB, 0.035 for LR, 0.265 for MLP, 0.0229 for J48, 0.015 for SVM, 0.0158 for NBTree and 0.0025 for CHIRP. Moreover, experimental results using accuracy revealed 95.75% for NB, 96.50% for LR, 97.25% for MLP, 97.75% for J48, 98.25% for SVM, 98.75% for NBTree, and 99.75% for CHIRP. The overall outcomes show that CHIRP performs well in terms of diminishing error rates and improving accuracy.

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In homogeneous cellular networks, fractional power control (FPC) is employed to partially compensate the path-loss and, hence, improve uplink (U L) signal-to-interference ratio (SIR). However, this scheme is less effective in heterogeneous cellular networks (HetNets) because: (i) except the typical user, all other users with variable U L transmit power (UTP) act as interferers, (ii) FPC leads to high UTP by edge users and, hence, more interference, and (iii) small base stations (SBSs)' densification further increases network interferences. Leveraging FPC in HetNets, we propose nonuniform SBS deployment (NU-SBS D) to reduce interference and, thus, increase network performance. According to our NU-SBS D model, SBS deployment (SBS D) near macro base station (MBS) is avoided, whereas MBS coverage edge area is enriched with ultra-dense SBS D. NU-SBS D model leads to: (i) better SIR reception of MBS coverage edge users, (ii) fewer SBS D requirement, and (iii) better SBS coverage in the MBS coverage edge area. Moreover, to make a model more proactive, we also consider reverse frequency allocation (RFA) to further abate both U L and downlink (D L) interferences. The coverage probability expressions are derived for both uniform SBS deployment (U-SBS D) and NU-SBS D while using RFA and FPC. Through simulation and numerical results, we characterize coverage probability for different values of SIR threshold, path loss compensation factor, SBS density, users density, and the distance between the typical user and the associated base station. The proposed NU-SBS D model along with RFA leads to reduced network interference as compared with U-SBS D and, thus, leverages FPC in HetNets.

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Vehicular ad hoc networks play a pivotal role in the enrichment of transportation systems by making them intelligent and capable of avoiding road accidents. For transmission of warning messages, direction-based greedy protocols select the next hop based on the current location of relay nodes towards the destination node, which is an efficient approach for uni-directional traffic. However, such protocols experience performance degradation by neglecting the movement directions of nodes in bi-directional traffic where topological changes occur dynamically. This paper pioneers the use of movement direction and relative positions of source and destination nodes to cater to the dynamic nature of bi-directional highway environments for efficient and robust routing of warning messages. A novel routing protocol, namely, Direction Aware Best Forwarder Selection (DABFS), is presented in this paper. DABFS takes into account directions and relative positions of nodes, besides the distance parameter, to determine a node's movement direction using Hamming distance and forwards warning messages through neighbor and best route discovery. Analytical and simulation results demonstrate that DABFS offers improved throughput and reduced packet loss rate and end-to-end delay, as compared with eminent routing protocols.

Providing congestion control in the Internet, while ensuring fairness among myriad of heterogeneous flows is a challenging task. The conventional wisdom is to rely on end-user applications cooperatively deploying congestion control mechanisms to achieve high network utilization and some degree of fairness among flows. However, as the Internet has evolved to encompass all of society, such a cooperative behavior from enduser applications is not always granted. Applications may simply act selfishly to be more competitive through bandwidth abuse. Bandwidth starvation may also arise unintentionally depending on the nature of traffic sources. The ensuing impact can be severe fairness hazard and even congestion collapse. Router-based queue management schemes driven by fairness objectives thus become an inescapable necessity for fairly sharing network resources.Given a significant volume of literature relating to fairnessdriven queue management schemes, there has remained a need for a broader and coherent survey. This paper presents a systematic and comprehensive review of eminent fairness-driven queue management schemes from the inception of the concept and the preliminary work to the most recent work. We present a new taxonomy of categorizing fairness-driven queue management schemes. We discuss design approaches and key attributes of these schemes and provide their comparison and analysis. Based on the outcomes of this survey, we discuss a number of open issues and provide generic design guidelines and future directions for the research in this field. Fairness-Driven Queue Management: A Survey and Taxonomy 2 c packets/s Scheduling algorithm Dequeueend B packets Buffer k l 1 l Enqueueend Output link Input links Queue management algorithm mechanisms at all, either deliberately or by accident, and generate misbehaving traffic [9]. Furthermore, there are applications that selfishly improve throughput by splitting a single TCP connection into multiple connections [10], [3]. All such applications and the resulting traffic aggregates are potentially dangerous as they inflict unfairness in the network, and can eventually cause congestion collapse [9], [11].In response to these problems, there has been a long history, dating back to [12], of realizing that routers play a significant role in fair bandwidth allocation. The technical report by Floyd and Fall [13] (later published as [3]) is, however, the first one to extensively demonstrate the danger of unfairness due to unresponsive flows. Floyd and Fall [13] argue that the incentives for cooperative behavior can only come from the network itself, and therefore, routers inevitably need to deploy mechanisms to provide an incentive structure for applications to use end-to-end congestion control. The report also proposes queue management based techniques for identifying and restricting unresponsive flows. Since then, a number of fairness-driven queue management schemes have been proposed to shield responsive flows and to regulate unresponsive and aggressive flows.This paper provid...