EPS-TRA: Energy efficient Peer Selection and Time switching Ratio Allocation for SWIPT-enabled D2D Communication

This paper considers device-to-device (D2D) network with Simultaneous Wireless Information and Power Transfer (SWIPT) enabled devices to ensure selfsustained communication in situations like disasters. Such direct link networks can ensure connectivity with devices having drained back-up, when trapped in collapsed infrastructure, through mutual sharing of energy on RF link. To guarantee successful execution of SWIPT session for an isolated device in wake of disasters, it is pertinent to select a reliable peer with ultimate aim to maximize link Energy Efficiency (EE). In practice, Energy Harvesting (EH) is not achievable after Information Decoding (ID), however, it has been made possible through splitting the signal in the time domain. Selection of D2D peer for selfsustained communication with an objective to maximize EE through optimum time based splitting of signal has not been extensively studied . In this paper to manifest the aforesaid goal, we worked out a joint problem of peer association and time switching ratio allocation with an objective to maximize the EE for a device contained under collapsed infrastructure. We propose an Energy efficient Peer Selection and Time switching Ratio Allocation (EPS-TRA) algorithm to solve the proposed mixed integer problem. Numerical results validate our proposed approach in acquiring better EE when compared with Uniform Allocation Scheme of time slots for EH & ID. Furthermore, results explain how EE of the link varies with the choice of constrained variables i.e. data rate and harvested energy

Experimental Comparison of Simulation Tools for Efficient Cloud and Mobile Cloud Computing Applications

Cloud computing provides a convenient and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is an emerging technology that integrates cloud computing technology with mobile devices. MCC provides access to cloud services for mobile devices. With the growing popularity of cloud computing, researchers in this area need to conduct real experiments in their studies. Setting up and running these experiments in real cloud environments are costly. However, modeling and simulation tools are suitable solutions that often provide good alternatives for emulating cloud computing environments. Several simulation tools have been developed especially for cloud computing. In this paper, we present the most powerful simulation tools in this research area. These include CloudSim, CloudAnalyst, CloudReports, CloudExp, GreenCloud, and iCanCloud. Also, we perform experiments for some of these tools to show their capabilities

An Edge Computing Based Smart Healthcare Framework for Resource Management

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time

Improving Fog Computing Performance via Fog-2-Fog Collaboration

In the Internet of Things (IoT) era, a large volume of data is continuously emitted from a plethora of connected devices. The current network paradigm, which relies on centralized data centers (aka Cloudcomputing), has become inefficient to respond to IoT latency concern. To address this concern, fog computing allows data processing and storage \close" to IoT devices. However, fog is still not efficient due to spatial and temporal distribution of these devices, which leads to fog nodes' unbalanced loads. This paper proposes a new Fog-2-Fog (F2F) collaboration model that promotes offloading incoming requests among fog nodes, according to their load and processing capabilities, via a novel load balancing known as Fog Resource manAgeMEnt Scheme (FRAMES). A formal mathematical model of F2F and FRAMES has been fomulated, and a set of experiments has been carried out demonstrating the technical doability of F2F collaboration. The performance of the proposed fog load balancing model is compared to other load balancing models

Providing Secure and Reliable Communication for Next Generation Networks in Smart Cities

Finding a framework that provides continuous, reliable, secure and sustainable diversified smart city services proves to be challenging in today’s traditional cloud centralized solutions. This article envisions a Mobile Edge Computing (MEC) solution that enables node collaboration among IoT devices to provide reliable and secure communication between devices and the fog layer on one hand, and the fog layer and the cloud layer on the other hand. The solution assumes that collaboration is determined based on nodes’ resource capabilities and cooperation willingness. Resource capabilities are defined using ontologies, while willingness to cooperate is described using a three-factor node criteria, namely: nature, attitude and awareness. A learning method is adopted to identify candidates for the service composition and delivery process. We show that the system does not require extensive training for services to be delivered correct and accurate. The proposed solution reduces the amount of unnecessary traffic flow to and from the edge, by relying on nodeto-node communication protocols. Communication to the fog andcloud layers is used for more data and computing-extensive applications, hence, ensuring secure communication protocols to the cloud. Preliminary simulations are conducted to showcase the effectiveness of adapting the proposed framework to achieve smart city sustainability through service reliability and security. Results show that the proposed solution outperforms other semicooperative and non-cooperative service composition techniques in terms of efficient service delivery and composition delay, service hit ratio, and suspicious node identification

Cloud-Based Multi-Agent Cooperation for IoT Devices Using Workflow-Nets

Most Internet of Things (IoT)-based service requests require excessive computation which exceeds an IoT device's capabilities. Cloud-based solutions were introduced to outsource most of the computation to the data center. The integration of multi-agent IoT systems with cloud computing technology makes it possible to provide faster, more efficient and real-time solutions. Multi-agent cooperation for distributed systems such as fog-based cloud computing has gained popularity in contemporary research areas such as service composition and IoT robotic systems. Enhanced cloud computing performance gains and fog site load distribution are direct achievements of such cooperation. In this article, we pro- pose a work ow-net based framework for agent cooperation to enable collaboration among fog computing devices and form a cooperative IoT service delivery system. A cooperation operator is used to find the topology and structure of the resulting cooperative set of fog computing agents. The operator shifts the problem defined as a set of work ow-nets into algebraic representations to provide a mechanism for solving the optimization problem mathematically. IoT device resource and collaboration capabilities are properties which are considered in the selection process of the cooperating IoT agents from di_erent fog computing sites. Experimental results in the form of simulation and implementation show that the cooperation process increases the number of achieved tasks and is performed in a timely manner

SDStorage: A Software Defined Storage Experimental Framework

; .; Benkhelifa, E.