Task Offloading and Dispatching for MEC with Selfish Mobile Devices and Access Points

Aims: This research aims to secure and support mobile devices and IoTs enabled in WLAN and 5G Multi-Access Edge Computing (MEC) infrastructures. Background: Currently, wireless network access gains increasing potential in today's networks. At the same time, such ongoing wireless network access also raises the risk of network attacks. The 5G technology is expected to empower people inter-communications by integrating wireless networking technologies in all kinds of networks. Additionally, the emerging 5G technologies put forward many new requirements for RF characteristics and techniques regarding bandwidth and power issues, in addition to supporting high connectivity capacity for the emerging IP-based IoT applications. Objective: The objective is to provide a hybrid architecture of the proposed framework for a 5G network with cloudlet computing. The purpose that we are focusing on it is to support and monitor wireless networks under attack. Methods: The solution is done through a hybrid architecture. In this architecture, the cloudlet and Wi-Fi access points (AP) are integrated for providing an integrated Wi-Fi-enabled cloudlet. On the other hand, we have specified a solution for detecting unauthorized APs by using authentication and authorization techniques for securing communications among endpoint devices such as IoT devices or mobile devices, cloudlet, and the main cloud. A framework of this architecture has been developed to face the issue of securing IoT devices and mobile wireless devices. Results: The traffic sniffing and traffic filtering of the endpoints are done. Therefore, the required actions that define the organization's policy are applied through the permitted access list. In addition to a wireless network location, the management components use more than one authorized attribute value to generate the authorized list, such as SSID, MAC, BSSID …etc. Finally, Traffic analysis provides the user to get reports, statistics, and analysis charts of secure endpoints activities. Conclusion: We are focusing on one of the critical security issues of wireless networks. Namely, the issue of unauthorized access becomes more critical due to getting access to a network without subscriber permission. Moreover, the proposed solution allows managing access control permissions to allow or block access to resources for the user of a mobile device.

Joint optimization of phase shift and task offloading for RIS-assisted multi-access edge computing in beyond 6G communication

Multi-access Edge Computing (MEC) technology has emerged to overcome traditional cloud computing limitations, challenged by the new 5G services with heavy and heterogeneous requirements on both latency and bandwidth. In this work, we tackle the problem of clustering access points in MEC environments, introducing a set of clustering models to be deployed at the pre-provisioning phase. We go through extensive simulations on real-world traffic demands to evaluate the performance of the proposed solutions. In addition, we show how MEC hosts capacity violation can be decreased when integrating access points clustering into the orchestration model, by investigating on solution accuracy when applied on held-out users traffic demands. The obtained results show that our approach outperforms two state-of-the-art algorithms, reducing both memory usage and execution time, by 46% and 50%, respectively, in comparison to a baseline algorithm. It surpasses the two methods in gaining control over MEC hosts capacity usage for different maximum achieved occupancy levels on MEC hosts.

With the popularity of mobile devices such as smartphones and tablets, the improvement of service of quality is an important issue facing great challenges. The improvement of user service of quality is mainly reflected in reducing the energy consumption of mobile devices and the delay of task execution. Multi-access edge computing sinks computing and storage capabilities from the remote cloud to the edge network, which can effectively reduce the high latency caused by the transmission of tasks between the mobile device and the remote cloud and the high energy consumption of tasks performed locally. Most of the previous work was limited to service of quality optimization through dynamic service layout, while ignoring the critical impact of access network selection on network congestion. This article studies the task offloading model of multiple tasks and services with several MEC servers, and jointly optimizes the MEC's access network selection and service placement issues. Considering the delay and energy consumption caused by task offloading and execution, this article designs an effect function on delay and energy consumption, and aims to minimize this function to solve the MEC problem. Since this problem is NP-hard, this article designs a new optimization algorithm based on particle swarm optimization to solve this problem. Extensive simulation experiments show that the proposed optimization algorithm realize better performance than other algorithms. The algorithm has achieved good results in terms of time delay and energy consumption, which effectively reduces the system cost.

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

Mobility-aware task offloading in MEC with task migration and result caching

Computation offloading plays a critical role in reducing task completion time for mobile devices. The advantages of computation offloading to cloud resources in Mobile Cloud Computing have been widely considered. In this paper, we have investigated different scenarios for offloading to less distant Multi-Access Edge Computing (MEC) servers for multiple users with a range of mobile devices and computational tasks. We present detailed simulation data for how offloading can be beneficial in a MEC network with varying quantitative mobile user demand, heterogeneity in mobile device on-board and MEC processor speeds, computational task complexity, communication speeds, link access delays and mobile device user numbers. Unlike previous work where simulations considered only limited communication speeds for offloading, we have extended the range of link speeds and included two types of communication delay. We find that more computationally complex applications are offloaded preferentially (especially with the higher server:mobile device processor speed ratios) while low link speeds and any delays caused by network delays or excessive user numbers degrade any advantages in reduced task completion times offered by offloading. Additionally, significant savings in energy usage by mobile devices are guaranteed except at very low link speeds.

Mobile Network Operators (MNO) can reduce their Capital and Operational Expenditure (CAPEX) and (OPEX) with the help of tower sharing approach by utilizing the physical infrastructure equipped by a third party tower provider to expand their network coverage. Moreover, Computing Resource Providers (CRP) are also setting up their micro-datacenters at tower stations to provide the Multi-access Edge Computing (MEC) services by cooperating with tower providers. Since both the communication and computing services contribute to the task offloading in MEC, the resource allocation has become a challenging problem. In this paper, we formulate the joint uplink, downlink, and computing resources allocation problem in which the objectives of both MNOs and CRP are to minimize their OPEX. The task offloading is modeled as a network of queues where the end-to-end latency is calculated based on the performance of the queue network. Then, the formulated problem is transformed into a Generalized Nash Equilibrium Problem (GNEP) to capture the conflicting interests in the resource allocation among MNOs and CRP. To solve the formulated GNEP efficiently, two decentralized algorithms are proposed by introducing the penalty parameters to the coupling constraints. In addition, the convergence and performance of the algorithms on different parameters are analyzed.

Multi-access edge computing (MEC) plays a key role in fifth-generation (5G) networks in bringing cloud functionalities at the edge of the radio access network, in close proximity to mobile users. In this paper we focus on mobile-edge computation offloading, a way to transfer heavy demanding, and latency-critical applications from mobile handsets to close-located MEC servers, in order to reduce latency and/or energy consumption. Our goal is to provide an optimal strategy to associate mobile users to access points (AP) and MEC hosts, while contextually optimizing the allocation of radio and computational resources to each user, with the objective of minimizing the overall user transmit power under latency constraints incorporating both communication and computation times. The overall problem is a mixed-binary problem. To overcome its inherent computational complexity, we propose two alternative strategies: i) a method based on successive convex approximation (SCA) techniques, proven to converge to local optimal solutions; ii) an approach hinging on matching theory, based on formulating the assignment problem as a matching game.

Recent demands on affordable, portable wireless communication and computation devices have resulted in exponential growth of wireless networks ranging from Wireless Local Area Networks (WLAN) and Wireless Wide Area Networks (WWAN) to Ad-Hoc and Sensor networks. The major goal of wireless communication is to allow users to communicate together and to have access to global network anytime anywhere. This has led to wide acceptance of infrastructure based cellular networks (WWANs) where mobile stations communicate with a centralized controller, often referred as Access Point (AP) that is connected to the wired networks. On the other hand, WLANs have appeared as dominant popular technologies in many venues including a local area such as an academic campus or an airport terminal. These wireless networks mostly rely on IEEE 802.11 Wi-Fi (Wireless Fidelity) technology and its various derived versions (i.e. 802.11a,b,g). IEEE 802.11 standard supports two operational modes: The infrastructure-based Wireless Local Area Networks (WLANs) and an infrastructure-less Ad-Hoc Networks. A WLAN (Conti, 2003) typically imposes the existence of an AP and normally is connected to the wired networks to provide internet access for mobile devices. Obviously, only one hop link is needed to communicate between mobile devices and AP. In contrast, there is no AP or infrastructure in Ad-Hoc networks. Any two stations can communicate directly when they are in the range of reception of each other. To this end, the stations may use multi-hop routing to deliver their packets to destinations. The ad-hoc protocols (Conti, 2003; Mohapatra & Krishnamurthy, 2005) are self-configured for address and routing in the face of mobility and the network topology may change in each configuration. The multi-hop wireless ad-hoc networks, or multi-hop wireless networks enable wireless networking in the environments where the wired or cellular connections are impossible, inadequate, or cost effective (e.g. battle field, disaster recovery, etc.). The popularity of internet over the last decades has resulted in rapid advancement of demanding applications. The Transmission Control Protocol/Internet Protocol (TCP/IP) (Stevens, 1994) is a well-known de facto protocol in developing today’s internet. Basically, TCP provides a connection-oriented and reliable end-to-end data delivery between two hosts in traditional wired networks. Since TCP is well tuned and due to its wide acceptance in internet, it is desirable to extend and adopt its functionality to wireless networks. On the

With the rapid advancement of the information age, an increasing number of people have started using Wi-Fi tethering, which can turn their mobile phones into mobile access points (APs) to meet their networking needs anywhere. However, the existing energy-saving mechanisms in IEEE 802.11 mainly aim at stations (STAs) and rarely consider APs. To solve the problem of mobile APs’ high energy consumption, we propose an energy-saving protocol for mobile APs, called GreenAP. On the one hand, the protocol is compatible with the original IEEE 802.11 standard. On the other hand, the adaptive strategy in the protocol ensures that the energy consumption of the AP is reduced without affecting the user experience. The energy-saving AP protocol is implemented in NS-2. The experimental results show that GreenAP-3 can enable APs’ sleep duration up to 74.7% when the traffic intensity is low, and the energy consumption can be reduced by 64.6% with small packet delay. In the case of high traffic intensity, the protocol can ensure less packet delay by adaptively adjusting APs’ sleep time, which guarantees that the user experience is not affected under any circumstances.

Sensor network with mobile access (SENMA) is an architecture in which randomly deployed low-power sensors are orchestrated by a few powerful mobile access points. This paper considers SENMA from energy-efficiency and information-theoretic perspectives. By allowing sensors to propagate data directly to mobile access points over multiaccess channels and relieving sensors from energy-consuming network functions, SENMA has the potential of offering orders of magnitude of improvement in energy efficiency over the multihop ad hoc architecture, as demonstrated by our analysis on scalability. Optimization configurations of SENMA such as the altitude, the trajectory, and the coverage of access points are considered next, using the sum-rate as the performance metric. Optimal strategies for single and multiple access points are determined. For multiple access points, the possibility of and the gain due to cooperation (i.e., joint decoding of signals received at different access points) are investigated.

The rapid development of 5G technology and edge computing prompts emerging edge applications with heterogeneous requirements for network services with respect to latency, reliability and throughput. Although content distribution networks (CDN) and edge computing proposals such as multiaccess edge computing (MEC) improve quality of service by deploying services close to end users at the edge, the session efficiency of service still is constrained by host name resolution which also leads to quality degradation of service in the case of user mobility. In this article, we proposed a novel network architecture, Named Service Edge Network (NSEN), on the basis of DNS protocol. We enabled NSEN functionality in the MEC reference architecture and presented high-efficiency session models to satisfy the requirements of various edge applications and enhance the mobility support in distributed MEC environment. The simulation and experiment results demonstrate the NSENenabled MEC architecture can improve session efficiency of MEC services in 5G cellular networks in terms of latency and throughput.

Multi access edge computing (MEC) is an extension of 5G network on the edge side. Its network characteristics of high bandwidth and low delay provide necessary guarantee for the extreme user experience of VR (virtual reality). At the same time, the distributed architecture of 5g MEC network and the fast data exchange capability between nodes provide the hardware foundation for VR application marginalization deployment and equipment mobility access. Considering the cost and experience of VR, this paper first analyzes the advantages and disadvantages of VR based on traditional hardware architecture, and then describes the benefits of MEC on VR cost and experience. Finally, taking VR live video scene as an example, the key technologies of VR based on MEC are described.

Location-based service provisioning is of great interests to wireless Internet service providers (WISPs) to deliver attractive value-added services, such as service advertisements, product marketing, to the special network users according to their geographic coordinates, along with Internet access benefits. It is usually the case that the basic location information is derived from direct interactions between the infrastructure network and the wireless mobile devices. Unfortunately, the Internet access service easily becomes the target of free-riders and attackers by exploiting the location authentication protocols, and collusively forging their location claims. We propose a location authentication and authorization protocol, LBAC (location-based network access control), to securely authenticate the location claims of mobile wireless users, and to securely distribute the shared keys for data encryption purposes. In LBAC, location areas are defined by the shared points of multiple wireless access points. The fact that a mobile node is located at certain places is authenticated by the mobile node collecting all the key information from the corresponding access points. Using Diffie-Hellman algorithm, LBAC authenticates location claims, and derives the keys for each mobile node and access point pair. LBAC eliminates the dependence on global positioning system (GPS) or ultrasonic devices in order to localize the mobile devices. We enumerate possible attacks to the system and analyze their countermeasures. The computational, communicational and the memory requirement are also evaluated