关键词： Mobile edge computing   resource allocation   energy efficient   ADMM  

摘要： Mobile edge computing (MEC) is a new promising technique to provide cloud-computing capabilities at the edge of cellular networks in close proximity to mobile users. In this paper, we consider joint optimization of the communication and computation resources in a multiuser, multiserver MEC system. The objective of this optimization problem is to minimize the total energy consumption of mobile devices under the time-sharing constraint. Given the fact that no coordination is involved between mobile devices, we propose a light-weight and decentralized algorithm based on the alternating direction method of multipliers (ADMM) framework. Experimental results demonstrate that the proposed algorithm performs well in terms of convergence and outperforms the conventional centralized approach.

关键词： Mobile crowdsensing   mobile edge computing   MCS functional architecture   MEC reference architecture  

摘要： Mobile crowdsensing (MCS) is a human-driven Internet of Things service empowering citizens to observe the phenomena of individual, community, or even societal value by sharing sensor data about their environment while on the move. Typical MCS service implementations utilize cloud-based centralized architectures, which consume a lot of computational resources and generate significant network traffic, both in mobile networks and toward cloud-based MCS services. Mobile edge computing (MEC) is a natural choice to distribute MCS solutions by moving computation to network edge, since an MEC-based architecture enables significant performance improvements due to the partitioning of problem space based on location, where real-time data processing and aggregation is performed close to data sources. This in turn reduces the associated traffic in mobile core and will facilitate MCS deployments of massive scale. This paper proposes an edge computing architecture adequate for massive scale MCS services by placing key MCS features within the reference MEC architecture. In addition to improved performance, the proposed architecture decreases privacy threats and permits citizens to control the flow of contributed sensor data. It is adequate for both data analytics and real-time MCS scenarios, in line with the 5G vision to integrate a huge number of devices and enable innovative applications requiring low network latency. Our analysis of service overhead introduced by distributed architecture and service reconfiguration at network edge performed on real user traces shows that this overhead is controllable and small compared with the aforementioned benefits. When enhanced by interoperability concepts, the proposed architecture creates an environment for the establishment of an MCS marketplace for bartering and trading of both raw sensor data and aggregated/processed information.

关键词： Heterogeneous networks   matching theory   mobile edge computing   resource allocation  

摘要： In this paper, we propose an optimization framework of computation offloading and resource allocation for mobile-edge computing with multiple servers. Concretely, we aim to minimize the system wide computation overhead by jointly optimizing the individual computation decisions, transmit power of the users, and computation resource at the servers. The crux of the problem lies in the combinatorial nature of multi-user offloading decisions, the complexity of the optimization objective, and the existence of inter cell interference. To overcome these difficulties, we adopt a suboptimal approach by splitting the original problem into two parts: 1) computation offloading decision and 2) joint resource allocation. To enable distributed computation offloading, two matching algorithms are investigated. Moreover, the transmit power of offloading users is found using a bisection method with approximate inter-cell interference, and the computation resources allocated to offloading users is achieved via the duality approach. Simulation results validate that the proposed framework can significantly improve the percentage of offloading users and reduce the system overhead with respect to the existing schemes. Our results also show that the proposed framework performs close to the centralized heuristic algorithm with a small optimality gap.

关键词： Internet of Thing   low power wide area network   mobile edge computing   remote software update   trusted connection  

摘要： The Internet of Things (IoT) leads to intelligent services by collecting information from tiny sensor devices. In recent years, storage-less sensing devices have been used to implement IoT services. They depend on delivered software from a network server to operate service functions, and IoT services are based on collected user information. Therefore, it is important to maintain trusted connections during software delivery or data transmission. If a network connection is untrustworthy, stable data transmission cannot be achieved. Untrustworthy data connections cause many problems in IoT services. Therefore, this paper proposes a software update method in trusted connection of IoT networking. The proposed method employs a low-power wide area network (LPWAN) as a long-range IoT networking technology and uses a mobile edge cloud to improve computing efficiency in an access network that consists of IoT devices with insufficient resources. In the proposed method, the mobile edge cloud is integrated into a gateway and processes sensing data and remote software updates of LPWAN. IoT devices can receive software functions from the mobile edge cloud. The proposed method analyzes statistical information about connections in an access network and determines the LPWAN trusted connections. Then, software updates can be performed over the trusted connection. Using trusted connections leads to an increased packet delivery rate and reduced transmission energy consumption. The proposed method is compared with currently available systems through computer simulation and the proposed method's efficiency is validated.

关键词： Cloudlets   deep learning   Internet of Things (IoT)   mobile edge computing   mobile healthcare   preventive healthcare   traffic classification   traffic prediction   survey   fog computing   cloud computing   multimedia applications   smart cities  

摘要： Smart city advancements are driving massive transformations of healthcare, the largest global industry. The drivers include increasing demands for ubiquitous, preventive, and personalized healthcare, to be provided to the public at reduced risks and costs. Mobile cloud computing could potentially meet the future healthcare demands by enabling anytime, anywhere capture and analyses of patients' data. However, network latency, bandwidth, and reliability are among the many challenges hindering the realization of next-generation healthcare. This paper proposes a ubiquitous healthcare framework, UbeHealth, that leverages edge computing, deep learning, big data, high-performance computing (HPC), and the Internet of Things (IoT) to address the aforementioned challenges. The framework enables an enhanced network quality of service using its three main components and four layers. Deep learning, big data, and HPC are used to predict network traffic, which in turn are used by the Cloudlet and network layers to optimize data rates, data caching, and routing decisions. Application protocols of the traffic flows are classified, enabling the network layer to meet applications' communication requirements better and to detect malicious traffic and anomalous data. Clustering is used to identify the different kinds of data originating from the same application protocols. A proof of concept UbeHealth system has been developed based on the framework. A detailed literature review is used to capture the design requirements for the proposed system. The system is described in detail including the algorithmic implementation of the three components and four layers. Three widely used data sets are used to evaluate the UbeHealth system.

关键词： Mobile Edge Computing   Multimedia   Internet of Things   Survey  

摘要： Recently, to provide a low-latency mobile computing platform, Mobile Edge Computing (MEC) is proposed. In this paper, we first summarized the feature capabilities of MEC, such as content distribution and caching, computational offloading and multimedia Internet of Things (IoT). Then, to understand recent research efforts of multimedia MEC, we briefly highlight the research efforts in terms of above three capabilities: how to achieve edge caching in video distribution, how to schedule computational offloading to the cloud and how the communication quality degradation affects to the user experience of multimedia IoT. Finally, we addressed the emerging research issues of multimedia MEC to improve reliability and robustness of multimedia MEC.

关键词： Mobile edge computing   Internet of Thing   service provisioning   service composition  

摘要： The increasing number of Internet of Thing (IoT) devices and services makes it convenient for people to sense the real world and makes optimal decisions or complete complex tasks with them. However, the latency brought by unstable wireless networks and computation failures caused by constrained resources limit the development of IoT. A popular approach to solve this problem is to establish an IoT service provision system based on a mobile edge computing (MEC) model. In the MEC model, plenty of edge servers are placed with access points via wireless networks. With the help of cached services on edge servers, the latency can be reduced, and the computation can be offloaded. The cache services must be carefully selected so that many requests can by satisfied without overloading resources in edge servers. This paper proposes an optimized service cache policy by taking advantage of the composability of services to improve the performance of service provision systems. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of these IoT services.

关键词： Mobile edge computing   MCPTT   virtualized 5G   network function virtualization   IOPS   CUPS  

摘要： There is a growing interest in adapting mission critical public-safety communications from traditional private radio technologies toward 5G. The need for resiliency and strong delay requirements has resulted in mobile edge computing (MEC) emerging as a key technology to improve the deployment of public safety applications over the mobile network. This paper presents a non-standalone 5G ETSI MEC-based architecture for mission-critical push-to-talk (MCPTT) services. The proposal suggests a hierarchical distributed MCPTT architecture that allocates the user plane at the edge, keeping the control plane (CP) centralized for synchronization and assistance purposes. The MEC architecture enables the deployment of low latency services, due to the proximity of functional servers to the end-user, releases user equipment from heavy workloads, and benefits from horizontal scalability to provide dynamic allocation of network resources at specific locations. The proposed architecture is also beneficial in an isolated E-UTRAN operation situation, where in the case of backhaul connection loss, local MCPTT services can be straightforwardly deployed within an isolated group of eNodeBs.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) enables battery-powered mobile nodes to acquire information technology services at the network edge. These nodes desire to enjoy their service under power saving. The sampling rate invariant detection (SRID) is the first downclocking WiFi technique that can achieve this objective. With SRID, a node detects one packet arrival at a downclocked rate. Upon a successful detection, the node reverts to a full-clocked rate to receive the packet immediately. To ensure that a node acquires its service immediately, the detection performance (namely, the miss-detection probability and the false-alarmprobability) of SRID is of importance. This paper is the first one to theoretically study the crucial impact of SRID attributes (e.g., tolerance threshold, correlation threshold, and energy ratio threshold) on the packet detection performance. Extensive Monte Carlo experiments show that our theoretical model is very accurate. This study can help system developers set reasonable system parameters for WiFi downclocking.

关键词： Mobile edge computing   offloading   energy saving   Krill herd algorithm  

摘要： Due to advances in Internet of Things (IoT) technologies, mobile devices have become an inseparable part of human *** limited executing capabilities of mobile devices along with constrained energy remain barriers in front of this expectation. To address these challenges, Mobile Edge Computing (MEC) is considered a promising computing model to offer computing ability to mobile users in fifth-generation (5G) networks. In this paper, we jointly create an optimization problem to minimize the combination of energy cost and packet congestion. By adopting a Promoted-by-probability (PBP) scheme, we efficiently control packet congestion of different priority packets transmitted to MEC. An improved krill herd metaheuristic optimization algorithm is presented to obtain optimal results for minimizing the total overhead of MEC in terms of energy consumption and queuing congestion. The evaluation study demonstrates that our proposal performs efficiently in terms of energy consumption and execution delay.