Workload allocation in mobile edge computing empowered internet of things

In the past few years, a tremendous number of smart devices and objects, such as smart phones, wearable devices, industrial and utility components, are equipped with sensors to sense the real-time physical information from the environment. Hence, Internet of Things (IoT) is introduced, where various smart devices are connected with each other via the internet and empowered with data analytics. Owing to the high volume and fast velocity of data streams generated by IoT devices, the cloud that can provision flexible and efficient computing resources is employed as a smart brain to process and store the big data generated from IoT devices. However, since the remote cloud is far from IoT users which send application requests and await the results generated by the data processing in the remote cloud, the response time of the requests may be too long, especially unbearable for delay sensitive IoT applications. Therefore, edge computing resources (e.g., cloudlets and fog nodes) which are close to IoT devices and IoT users can be employed to alleviate the traffic load in the core network and minimize the response time for IoT users. In edge computing, the communications latency critically affects the response time of IoT user requests. Owing to the dynamic distribution of IoT users (i.e., UEs), drone base station (DBS), which can be flexibly deployed for hotspot areas, can potentially improve the wireless latency of IoT users by mitigating the heavy traffic loads of macro BSs. Drone-based communications poses two major challenges: 1) the DBS should be deployed in suitable areas with heavy traffic demands to serve more UEs; 2) the traffic loads in the network should be allocated among macro BSs and DBSs to avoid instigating traffic congestions. Therefore, a TrAffic Load baLancing (TALL) scheme in such drone-assisted fog network is proposed to minimize the wireless latency of IoT users. In the scheme, the problem is decomposed into two sub-problems, two algorithms are designed to optimize the DBS placement and user association, respectively. Extensive simulations have been set up to validate the performance of the proposed scheme. Meanwhile, various IoT applications can be run in cloudlets to reduce the response time between IoT users (e.g., user equipments in mobile networks) and cloudlets. Considering the spatial and temporal dynamics of each application\u27s workloads among cloudlets, the workload allocation among cloudlets for each IoT application affects the response time of the application\u27s requests. To solve this problem, an Application awaRE workload Allocation (AREA) scheme for edge computing based IoT is designed to minimize the response time of IoT application requests by determining the destination cloudlets for each IoT user\u27s different types of requests and the amount of computing resources allocated for each application in each cloudlet. In this scheme, both the network delay and computing delay are taken into account, i.e., IoT users\u27 requests are more likely assigned to closer and lightly loaded cloudlets. The performance of the proposed scheme has been validated by extensive simulations. In addition, the latency of data flows in IoT devices consist of both the communications latency and computing latency. When some BSs and fog nodes are lightly loaded, other overloaded BSs and fog nodes may incur congestion. Thus, a workload balancing scheme in a fog network is proposed to minimize the latency of IoT data in the communications and processing procedures by associating IoT devices to suitable BSs. Furthermore, the convergence and the optimality of the proposed workload balancing scheme has been proved. Through extensive simulations, the performance of the proposed load balancing scheme is validated

Internet of Drones (IoD): Threats, Vulnerability, and Security Perspectives

The development of the Internet of Drones (IoD) becomes vital because of a proliferation of drone-based civilian or military applications. The IoD based technological revolution upgrades the current Internet environment into a more pervasive and ubiquitous world. IoD is capable of enhancing the state-of-the-art for drones while leveraging services from the existing cellular networks. Irrespective to a vast domain and range of applications, IoD is vulnerable to malicious attacks over open-air radio space. Due to increasing threats and attacks, there has been a lot of attention on deploying security measures for IoD networks. In this paper, critical threats and vulnerabilities of IoD are presented. Moreover, taxonomy is created to classify attacks based on the threats and vulnerabilities associated with the networking of drone and their incorporation in the existing cellular setups. In addition, this article summarizes the challenges and research directions to be followed for the security of IoD.Comment: 13 pages, 3 Figures, 1 Table, The 3rd International Symposium on Mobile Internet Security (MobiSec'18), Auguest 29-September 1, 2018, Cebu, Philippines, Article No. 37, pp. 1-1

Unmanned Aerial Vehicles (UAVs) for Integrated Access and Backhaul (IAB) Communications in Wireless Cellular Networks

An integrated access and backhaul (IAB) network architecture can enable flexible and fast deployment of next-generation cellular networks. However, mutual interference between access and backhaul links, small inter-site distance and spatial dynamics of user distribution pose major challenges in the practical deployment of IAB networks. To tackle these problems, we leverage the flying capabilities of unmanned aerial vehicles (UAVs) as hovering IAB-nodes and propose an interference management algorithm to maximize the overall sum rate of the IAB network. In particular, we jointly optimize the user and base station associations, the downlink power allocations for access and backhaul transmissions, and the spatial configurations of UAVs. We consider two spatial configuration modes of UAVs: distributed UAVs and drone antenna array (DAA), and show how they are intertwined with the spatial distribution of ground users. Our numerical results show that the proposed algorithm achieves an average of 2.9× and 6.7× gains in the received downlink signal-to-interference-plus-noise ratio (SINR) and overall network sum rate, respectively. Finally, the numerical results reveal that UAVs cannot only be used for coverage improvement but also for capacity boosting in IAB cellular networks

FLEXNET: Flexible Networks for IoT based services

Internet of Things is becoming one of the main triggers in designing and deploying new services aiming at fulfilling the wide demand imposed by end-users. Usually, concrete solutions addressing the optimization of the wireless segment are found in the literature. However, it is much less frequent to find end-to-end solutions to be easily adopted by the corresponding stakeholders. It is in this context that FLEXNET brings an integrated solution, relying on cutting-edge technologies, dealing with a wide set of technical requirements imposed by the different applications and services.This work was supported by FLEXNET Project: "Flexible IoT Networks for Value Creators" (Celtic 2016/3), in the Eureka Celtic-Next Cluster

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS

On distributed mobile edge computing

Mobile Cloud Computing (MCC) has been proposed to offload the workloads of mobile applications from mobile devices to the cloud in order to not only reduce energy consumption of mobile devices but also accelerate the execution of mobile applications. Owing to the long End-to-End (E2E) delay between mobile devices and the cloud, offloading the workloads of many interactive mobile applications to the cloud may not be suitable. That is, these mobile applications require a huge amount of computing resources to process their workloads as well as a low E2E delay between mobile devices and computing resources, which cannot be satisfied by the current MCC technology. In order to reduce the E2E delay, a novel cloudlet network architecture is proposed to bring the computing and storage resources from the remote cloud to the mobile edge. In the cloudlet network, each mobile user is associated with a specific Avatar (i.e., a dedicated Virtual Machine (VM) providing computing and storage resources to its mobile user) in the nearby cloudlet via its associated Base Station (BS). Thus, mobile users can offload their workloads to their Avatars with low E2E delay (i.e., one wireless hop). However, mobile users may roam among BSs in the mobile network, and so the E2E delay between mobile users and their Avatars may become worse if the Avatars remain in their original cloudlets. Thus, Avatar handoff is proposed to migrate an Avatar from one cloudlet into another to reduce the E2E delay between the Avatar and its mobile user. The LatEncy aware Avatar handDoff (LEAD) algorithm is designed to determine the location of each mobile user\u27s Avatar in each time slot in order to minimize the average E2E delay among all the mobile users and their Avatars. The performance of LEAD is demonstrated via extensive simulations. The cloudlet network architecture not only facilitates mobile users in offloading their computational tasks but also empowers Internet of Things (IoT). Popular IoT resources are proposed to be cached in nearby brokers, which are considered as application layer middleware nodes hosted by cloudlets in the cloudlet network, to reduce the energy consumption of servers. In addition, an Energy Aware and latency guaranteed dynamic reSourcE caching (EASE) strategy is proposed to enable each broker to cache suitable popular resources such that the energy consumption from the servers is minimized and the average delay of delivering the contents of the resources to the corresponding clients is guaranteed. The performance of EASE is demonstrated via extensive simulations. The future work comprises two parts. First, caching popular IoT resources in nearby brokers may incur unbalanced traffic loads among brokers, thus increasing the average delay of delivering the contents of the resources. Thus, how to balance the traffic loads among brokers to speed up IoT content delivery process requires further investigation. Second, drone assisted mobile access network architecture will be briefly investigated to accelerate communications between mobile users and their Avatars