Analysis of a mobility management scheme considering battery power conservation in IP-based mobile networks

Mobile hosts (MHs) in all IP-based mobile networks must update their current location to receive incoming packets. The MHs are idle for most of time. Both registration of approximate location information and paging can facilitate efficient power management for the idle MHs. Furthermore, the MHs can be in switch-off state for battery power conservation. Mobile IP, the current standard for IP-based mobility management, needs to be enhanced for use in all IP-based mobile networks. Mobility management in all IP-based mobile networks should consider idle and detached MH states as well as active MH state. A mobility management scheme for all IP-based mobile networks is introduced in this paper. This scheme includes management of communicating, attentive, idle, and detached states. We model MH behavior in the networks when the binding-lifetime-based registrations are utilized as a means of identifying that an MH is switched off. The steady-state probabilities for MH state transitions are derived, and an optimal rate of binding-lifetime-based registrations that results in minimum network cost is derived.     

Adaptive power and rate allocation for service curve assurance in DS-CDMA network

This paper describes schemes for forward and reverse links in a direct sequence code-division multiple-access-based cellular network. The primary objective is to meet the diverse quality-of-service (QoS) needs of mobile hosts (MHs), and the secondary objective is to maximize the system throughput. The QoS needs of the MHs are modeled using the notion of a service curve. Furthermore, a notion of deviation is introduced as a measure of meeting service curve. The scheme proposed in this paper jointly adapts the transmitted power and the number of spreading codes assigned to each MH for receiving/transmitting its data bits. The scheme imposes practical constraints including bounds on the transmitted power for a base station and MHs, a bound on the number of spreading codes that an MH can handle, and minimum signal-to-interference-plus-noise ratio at the receiver. The proposed solutions are evaluated using discrete event simulations. The simulation results characterize the performance of the proposed solutions for several instances of the practical constraints.     

Secured data offloading using reinforcement learning and Markov decision process in mobile edge computing

Mobile Internet services are developing rapidly for several applications based on computational ability such as augmented/virtual reality, vehicular networks, etc. The mobile terminals are enabled using mobile edge computing (MEC) for offloading the task at the edge of the cellular networks, but offloading is still a challenging issue due to the dynamism, and uncertainty of upcoming IoT requests and wireless channel state. Moreover, securing the offloading data enhanced the challenges of computational complexities and required a secure and efficient offloading technique. To tackle the mentioned issues, a reinforcement learning-based Markov decision process offloading model is proposed that optimized energy efficiency, and mobile users' time by considering the constrained computation of IoT devices, moreover guarantees efficient resource sharing among multiple users. An advanced encryption standard is employed in this work to fulfil the requirements of data security. The simulation outputs reveal that the proposed approach surpasses the existing baseline models for offloading overhead and service cost QoS parameters ensuring secure data offloading.     

A web service‐based internet of things framework for mobile resource augmentation

Mobile devices are the primary communication tool in day to day life of the people. Nowadays, the enhancement of the mobile applications namely IoTApps and their exploitation in various domains like healthcare monitoring, home automation, smart farming, smart grid, and smart city are crucial. Though mobile devices are providing seamless user experience anywhere, anytime, and anyplace, their restricted resources such as limited battery capacity, constrained processor speed, inadequate storage, and memory are hindering the development of resource‐intensive mobile applications and internet of things (IoT)‐based mobile applications. To solve this resource constraint problem, a web service‐based IoT framework is proposed by exploiting fuzzy logic methodologies. This framework augments the resources of mobile devices by offloading the resource‐intensive subtasks from mobile devices to the service providing entities like Arduino, Raspberry PI controller, edge cloud, and distant cloud. Based on the recommended framework, an online Repository of Instructional Talk (RIoTalk) is successfully implemented to store and analyze the classroom lectures given by faculty in our study site. Simulation results show that there is a significant reduction in energy consumption, execution time, bandwidth utilization, and latency. The proposed research work significantly increases the resources of mobile devices by offloading the resource‐intensive subtasks from the mobile device to the service provider computing entities thereby providing Quality of Service (QoS) and Quality of Experience (QoE) to mobile users.     

Reliable and efficient multicast protocol for mobile IP networks

To provide a multicasting service, several multicast protocols for mobile hosts (MHs) have been proposed. However, all of these protocols have faults, such as non‐optimal delivery routes and data loss when hosts move to another network, resulting in insecure multicast data transmissions. Thus, this paper presents a new reliable and efficient multicast routing protocol for mobile IP networks. The proposed protocol provides a reliable multicast transmission by compensating the data loss from the previous mobile agent when a MH moves to another network. In addition, an additional function allows for direct connection to the multicast tree according to the status of agents, thereby providing a more efficient and optimal multicast path. The performance of the proposed protocol is confirmed based on simulations under various conditions. Copyright © 2008 John Wiley & Sons, Ltd.     

A genetic‐based decision algorithm for multisite computation offloading in mobile cloud computing

Mobile cloud computing is a promising approach to improve the mobile device's efficiency in terms of energy consumption and execution time. In this context, mobile devices can offload the computation‐intensive parts of their applications to powerful cloud servers. However, they should decide what computation‐intensive parts are appropriate for offloading to be beneficial instead of local execution on the mobile device. Moreover, in the real world, different types of clouds/servers with heterogeneous processing speeds are available that should be considered for offloading. Because making offloading decision in multisite context is an NP‐complete, obtaining an optimal solution is time consuming. Hence, we use a near optimal decision algorithm to find the best‐possible partitioning for offloading to multisite clouds/servers. We use a genetic algorithm and adjust it for multisite offloading problem. Also, genetic operators are modified to reduce the ineffective solutions and hence obtain the best‐possible solutions in a reasonable time. We evaluated the efficiency of the proposed method using graphs of real mobile applications in simulation experiments. The evaluation results demonstrate that our proposal outperforms other counterparts in terms of energy consumption, execution time, and weighted cost model.     

Dynamic hierarchical mobility management strategy for mobile IP networks

One of the major challenges for the wireless network design is the efficient mobility management, which can be addressed globally (macromobility) and locally (micromobility). Mobile Internet protocol (IP) is a commonly accepted standard to address global mobility of mobile hosts (MHs). It requires the MHs to register with the home agents (HAs) whenever their care-of addresses change. However, such registrations may cause excessive signaling traffic and long service delay. To solve this problem, the hierarchical mobile IP (HMIP) protocol was proposed to employ the hierarchy of foreign agents (FAs) and the gateway FAs (GFAs) to localize registration operations. However, the system performance is critically affected by the selection of GFAs and their reliability. In this paper, we introduce a novel dynamic hierarchical mobility management strategy for mobile IP networks, in which different hierarchies are dynamically set up for different users and the signaling burden is evenly distributed among the network. To justify the effectiveness of our proposed scheme, we develop an analytical model to evaluate the signaling cost. Our performance analysis shows that the proposed dynamic hierarchical mobility management strategy can significantly reduce the system signaling cost under various scenarios and the system robustness is greatly enhanced. Our analysis also shows that the new scheme can outperform the Internet Engineering Task Force mobile IP hierarchical registration scheme in terms of the overall signaling cost. The more important contribution is the novel analytical approach in evaluating the performance of mobile IP networks.     

Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications

Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.     

Data gathering and offloading in delay tolerant mobile networks

Mobile sensing emerges as an important application for mobile networks. Smartphones equipped with sensors are used to monitor a diverse range of human activities. One key and challenging procedure of the mobile sensing applications is data gathering, where the sensed data from distributed mobile nodes are captured and uploaded to the cloud or base station for further processing. Yet the mobile sensing application, which usually periodically generates some sensed data, would definitely deteriorate the 3G quality because the network cannot cope with the high demand; and users would be charged at high prices by using the 3G channel, which makes the mobile sensing application infeasible. In this paper, we proposed a hybrid data gathering and offloading algorithm DGO for the mobile sensing applications. Besides the direct uploading through 3G or Wifi offloading, the sensed data could also be forwarded to other peer nodes through short range communications. Nodes collect meta-data such as remaining energy, contact regularity, and expected contact duration to calculate the upload/offload utility and upload priority for data segments. Based on these utility factors, each data segment could decide its own approach at a specific time for uploading. Experimental studies show that DGO is efficient in data gathering and data offloading in mobile sensing applications. Given the low accessibility of Wifi APs, DGO still gains about more than 30 % of data offloading compared with existing algorithms without much extra transmission overhead or delay.     

Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing

The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance.     

A novel data size-aware offloading technique for resource provisioning in mobile cloud computing

Mobile device users are involved in social networking, gaming, learning, and even some office work, so the end users expect mobile devices with high-response computing capacities, storage, and high battery power consumption. The data-intensive applications, such as text search, online gaming, and face recognition usage, have tremendously increased. With such high complex applications, there are many issues in mobile devices, namely, fast battery draining, limited power, low storage capacity, and increased energy consumption. The novelty of this work is to strike a balance between time and energy consumption of mobile devices while using data-intensive applications by finding the optimal offloading decisions. This paper proposes a novel efficient Data Size-Aware Offloading Model (DSAOM) for data-intensive applications and to predict the appropriate resource provider for dynamic resource allocation in mobile cloud computing. Based on the data size, the tasks are separated and gradually allocated to the appropriate resource providers for execution. The task is placed into the appropriate resource provider by considering the availability services in the fog nodes or the cloud. The tasks are split into smaller portions for execution in the neighbor fog nodes. To execute the task in the remote side, the offloading decision is made by using the min-cut algorithm by considering the monetary cost of the mobile device. This proposed system achieves low-latency time 13.2% and low response time 14.1% and minimizes 24% of the energy consumption over the existing model. Finally, according to experimental findings, this framework efficiently lowers energy use and improves performance for data-intensive demanding application activities, and the task offloading strategy is effective for intensive offloading requests.     

An efficient algorithm for dynamic task offloading using cloudlets in mobile cloud computing

It is a visible fact that the growth of mobile devices is enormous. More computations are required to be carried out for various applications in these mobile devices. But the drawback of the mobile devices is less computation power and low available energy. The mobile cloud computing helps in resolving these issues by integrating the mobile devices with cloud technology. Again, the issue is increased in the latency as the task and data to be offloaded to the cloud environment uses WAN. Hence, to decrease the latency, this paper proposes cloudlet‐based dynamic task offloading (CDTO) algorithm where the task can be executed in device environment, cloudlet environment, cloud server environment, and integrated environment. The proposed algorithm, CDTO, is tested in terms of energy consumption and completion time.     

Integrated services packet networks with mobile hosts: Architecture and performance

This paper considers the support of real-time services to mobile users in an Integrated Services Packet Network. In the currently existing architectures, the service guarantees provided to the mobile hosts are mobility dependent, i.e., mobile hosts experience wide variation in the quality of service and often service disruption when hosts move from one location to another. The network performance degrades significantly when mobile hosts are provided with mobility independent service guarantees. In this paper we have proposed a service model for mobile hosts that can support adaptive applications which can withstand service degradation and disruption, as well as applications which require mobility independent service guarantees. We describe an admission control scheme for implementing this service model and evaluate its performance by simulation experiments. Simulation results show that, if sufficient degree of multiplexing of the mobility dependent and independent services are allowed, the network does not suffer any significant performance degradation and in particular our admission control scheme achieves high utilization of network resources.     

A Neural Network-Based Connectivity Management for Mobile Computing Environment

The mobile computing environment experiences wireless problems and suffers from limited bandwidth, which leads to frequent disconnections. This has posed a challenge in maintaining user-to-user connectivity in the mobile computing environment. In this paper, we propose a neural network (NN) based connectivity management for mobile computing environment to maintain the mobile user-to-user connectivity throughout the transaction. Here the connectivity management maintains the status information of mobile hosts at the base station to handle frequent disconnection of mobile hosts (MHs), which occur because of hand-offs and interruptions. The disconnection of an MH because of wireless problems is called interruption, and the disconnection due to MH crossing the cell boundary is called hand-off. The neural networks are trained with respect to the status information to provide an intelligent decision for the connectivity management. The simulation results demonstrate that the proposed technique performs well in terms of percentage acceptance of disconnections and resource utilization (bandwidth and buffer) for the volatile mobile computing environment. It is also observed that the intelligent decision by neural network has improved the performance of the system.     

基于模拟退火机制的车辆用户移动边缘计算任务卸载新方法

在车联网(IOV)环境中,如果将车辆的计算任务都放置在云平台执行,无法满足对于信息处理的实时性,考虑移动边缘计算技术以及任务卸载策略,将用户的计算任务卸载到靠近设备边缘的服务器去执行。但是在密集的环境下,如果所有的任务都卸载到附近的边缘服务器去执行,同样会给边缘服务器带来巨大的负载。该文提出基于模拟退火机制的车辆用户移动边缘计算任务卸载新方法,通过定义用户的任务计算卸载效用,综合考虑时耗和能耗,结合模拟退火机制,根据当前道路的密集程度对系统卸载效用进行优化,改变用户的卸载决策,选择在本地执行或者卸载到边缘服务器上执行,使得在给定的环境下的所有用户都能得到满足低时延高质量的服务。仿真结果表明,该算法在减少用户任务计算时间的同时降低了能量消耗。     

AORS: adaptive mobile data offloading based on attractor selection in heterogeneous wireless networks

The unforeseen mobile data explosion poses a major challenge to the performance of today’s cellular networks, and is in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an attractor-aware offloading ratio selection (AORS) algorithm, which can adaptive select an optimum offloading ratio based on attractor selection for the current networks environment. In the proposed algorithm, the throughput of AP and the cellular load corresponding to the coverage area of the AP, are mapped into the cell activity, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor for each users, the optimal offloading ratio \(\phi \), to adapt to the dynamic network environment. Hence, according to the offloading ratio \(\phi \), the part of the cellular traffic will be transmitted via WiFi networks. Through simulation, we show that the proposed AORS algorithm outperforms the existing ones with 42 % higher heterogeneous network throughput in a dense traffic environment.     

A novel offloading scheduling method for mobile application in mobile edge computing

Wireless Networks - In mobile edge computing (MEC), task offloading can solve the problem of resource constraints on mobile devices effectively, but it is not always optimal to offload all the...     

Location privacy protection of maritime mobile terminals

Mobile Edge Computing (MEC) can support various high-reliability and low-delay applications in Maritime Networks (MNs). However, security risks in computing task offloading exist. In this study, the location privacy leakage risk of Maritime Mobile Terminals (MMTs) is quantified during task offloading and relevant Location Privacy Protection (LPP) schemes of MMT are considered under two kinds of task offloading scenarios. In single-MMT and single-time offloading scenario, a dynamic cache and spatial cloaking-based LPP (DS-CLP) algorithm is proposed; and under the multi-MMTs and multi-time offloading scenario, a pseudonym and alterable silent period-based LPP (PA-SLP) strategy is proposed. Simulation results show that the DS-CLP can save the response time and communication cost compared with traditional algorithms while protecting the MMT location privacy. Meanwhile, extending the alterable silent period, increasing the number of MMTs in the maritime area or improving the pseudonym update probability can enhance the LPP effect of MMTs in PA-SLP. Furthermore, the study results can be effectively applied to MNs with poor communication environments and relatively insufficient computing resources.     

Task offloading based on deep learning for blockchain in mobile edge computing

Blockchain is an advanced technique to realize smart contracts, various transactions, and P2P crypto-currencies in the e-commerce society. However, the traditional blockchain does not consider a mobile environment to design a data offloading of the blockchain such that the blockchain results in high computational cost and huge data propagation delay. In this paper, to remedy the above problem, we propose a scalable blockchain and a task offloading technique based on the neural network of the mobile edge computing scenario. Experimental results show that our approach is very scalable in the mobile scenario.