Survey of Resources Allocation Techniques with a Quality of Service (QoS) Aware in a Fog Computing Environment

The tremendous advancement in distributed computing and Internet of Things(IoT)applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing.Thus,suitable and effective applications could be performed to satisfy the applications’latency requirement.Resource allocation techniques are essential aspects of fog networks which prevent unbalanced load distribution.Effective resource management techniques can improve the quality of service metrics.Due to the limited and heterogeneous resources available within the fog infrastructure,the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT net-work.There has been limited research on resource management strategies in fog networks in recent years,and a limited systematic review has been done to compile these studies.This article focuses on current developments in resource allocation strategies for fog-IoT networks.A systematic review of resource allocation techniques with the key objective of enhancing QoS is provided.Steps involved in conducting this systematic literature review include developing research goals,accessing studies,categorizing and critically analysing the studies.The resource management approaches engaged in this article are load balancing and task offloading techniques.For the load balancing approach,a brief survey of recent work done according to their sub-categories,including stochastic,probabilistic/statistic,graph theory and hybrid techniques is provided whereas for task offloading,the survey is performed according to the destination of task offloading.Efficient load balancing and task-offloading approaches contribute significantly to resource management,and tremendous effort has been put into this critical topic.Thus,this survey presents an overview of these extents and a comparative analysis.Finally,the study discusses ongoing research issues and potential future directions for developing effective management resource allocation techniques.

An Optimized Offloaded Task Execution for Smart Cities Applications

Wireless nodes are one of the main components in different applications that are offered in a smart city.These wireless nodes are responsible to execute multiple tasks with different priority levels.As the wireless nodes have limited processing capacity,they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity.Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications.This execution delay is reduced by placing fog computing nodes near these application nodes.A fog node has limited processing capacity and is sometimes unable to execute all the requested tasks.In this work,an optimal task offloading scheme that comprises two algorithms is proposed for the fog nodes to optimally execute the time-sensitive offloaded tasks.The first algorithm describes the task processing criteria for local computation of tasks at the fog nodes and remote computation at the cloud server.The second algorithm allows fog nodes to optimally scrutinize the most sensitive tasks within their task capacity.The results show that the proposed task execution scheme significantly reduces the execution time and most of the time-sensitive tasks are executed.

Efficient UAV Communications: Recent Trends and Challenges

Unmanned Ariel Vehicles(UAVs)are flying objects whose trajectory can be remotely controlled.UAVs have lot of potential applications in the areas of wireless communications,internet of things,security,traffic management,monitoring,and smart surveying.By enabling reliable communication between UAVs and ground nodes,emergency notifications can be efficiently and quickly disseminated to a wider area.UAVs can gather data from remote areas,industrial units,and emergency scenarios without human involvement.UAVs can support ubiquitous connectivity,green communications,and intelligent wireless resource management.To efficiently use UAVs for all these applications,important challenges need to be investigated.In this paper,we first present a detailed classification of UAVs based on factors such as their size,communication range,weight,and flight altitude.We also explain the hardware system configuration and uses of these UAVs.We present a brief overview of recent work done related to three major challenges in UAVs.These challenges include trajectory control,energy efficiency and resource allocation.We also present three open challenges and future opportunities for efficient UAV communications.These include use of learning algorithms for resource allocation and energy efficiency in UAVs,intelligent surfaces-based communications for enhanced reliability in UAVs,and security algorithms to combat malicious attacks against UAVs.

Ambient BackCom in beyond 5G NOMA networks: A multi-cell resource allocation framework

The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom), though opening new possibilities for massive connectivity, poses several challenges in dense wireless networks. One such challenge is the performance degradation of ambient BackCom in multi-cell NOMA networks under the effect of inter-cell interference. Driven by providing an efficient solution to the issue, this article proposes a new resource allocation framework that uses a duality theory approach. Specifically, the sum rate of the multi-cell network with backscatter tags and NOMA user equipment is maximized by formulating a joint optimization problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the original problem is first divided into two subproblems, and then the closed form solution is derived. A comparison with the Orthogonal Multiple Access (OMA) ambient BackCom and pure NOMA transmission has been provided. Simulation results of the proposed NOMA ambient BackCom indicate a significant improvement over the OMA ambient BackCom and pure NOMA in terms of sum-rate gains.

Space Division Multiple Access for Cellular V2X Communications

Vehicular communication is the backbone of future Intelligent Transportation Systems(ITS).It offers a network-based solution for vehicle safety,cooperative awareness,and traffic management applications.For safety applications,Basic Safety Messages(BSM)containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic map.BSMs are shared using mode 4 of Cellular V2X(C-V2X)communications in which resources are allocated in an ad hoc manner.However,the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network,thus reducing the reliability of safety applications.Moreover,as vehicles choose the transmission resources in a distributed manner in mode 4 of CV2X,the packet collision problem is further aggravated.This paper presents a novel solution in the form of a Space Division Multiple Access(SDMA)protocol that intelligently schedules BSM transmissions using vehicle position data to reduce concurrent transmissions from hidden node interferers.The proposed protocol works by dividing road segments into clusters and subclusters.Several sub-frames are allocated to a cluster and these sub-frames are reused after a certain distance.Within a cluster,sub-channels are allocated to sub-clusters.We implement the proposed SDMA protocol and evaluate its performance in a highway vehicular network.Simulation results show that the proposed SDMA protocol outperforms standard Sensing-Based Semi Persistent Scheduling(SB-SPS)in terms of safety range and packet delay.

Transmit Power Optimization for Relay-Aided Multi-Carrier D2D Communication

In this paper, we consider the power optimization problem in Orthogonal Frequency Division Multiplexing （OFDM）-based relay-enhanced device-to-device （D2D） communication. In a single cell transmission scenario, dual- hop communication is assumed in which each D2D user re-uses the spectrum of just one Cellular User （CU）. In this work, we formulate a joint optimization scheme under a Decode-and-Forward （DF） relaying protocol to maximize the sum throughput of D2D and cellular networks via power allocation over different sub-carriers. The problem is thus transformed into a standard convex optimization, subject to individual power constraints at different transmitting nodes. We exploit the duality theory to decompose the problem into several sub-problems and use Karush-Kuhn- Tucker （KKT） conditions to solve each sub-problem. We provide simulation results to validate the performance of our proposed scheme.