AI on the Move From : From On-Device to On-Multi-Device

On-Device AI is an emerging paradigm that aims to make devices more intelligent, autonomous and proactive by equipping them with machine and deep learning routines for robust decision making and optimal execution in devices' operations. On-Device intelligence promises the possibility of computing huge amounts of data close to its source, e.g., sensor and multimedia data. By doing so, devices can complement their counterpart cloud services with more sophisticated functionality to provide better applications and services. However, increased computational capabilities of smart devices, wearables and IoT devices along with the emergence of services at the Edge of the network are driving the trend of migrating and distributing computation between devices. Indeed, devices can reduce the burden of executing resource intensive tasks via collaborations in the wild. While several work has shown the benefits of an opportunistic collaboration of a device with others, not much is known regarding how devices can be organized as a group as they move together. In this paper, we contribute by analyzing how dynamic group organization of devices can be utilized to distribute intelligence on the moving Edge. The key insight is that instead of On-Device solutions complementing with cloud, dynamic groups can be formed to complement each other in an On-Multi-Device manner. Thus, we highlight the challenges and opportunities from extending the scope of On-Device AI from an egocentric view to a collaborative, multi-device view.Peer reviewe

Social-aware hybrid mobile offloading

Mobile offloading is a promising technique to aid the constrained resources of a mobile device. By offloading a computational task, a device can save energy and increase the performance of the mobile applications. Unfortunately, in existing offloading systems, the opportunistic moments to offload a task are often sporadic and short-lived. We overcome this problem by proposing a social-aware hybrid offloading system (HyMobi), which increases the spectrum of offloading opportunities. As a mobile device is always co- located to at least one source of network infrastructure throughout of the day, by merging cloudlet, device-to-device and remote cloud offloading, we increase the availability of offloading support. Integrating these systems is not trivial. In order to keep such coupling, a strong social catalyst is required to foster user's participation and collaboration. Thus, we equip our system with an incentive mechanism based on credit and reputation, which exploits users' social aspects to create offload communities. We evaluate our system under controlled and in-the-wild scenarios. With credit, it is possible for a device to create opportunistic moments based on user's present need. As a result, we extended the widely used opportunistic model with a long-term perspective that significantly improves the offloading process and encourages unsupervised offloading adoption in the wild

SCCOF: smart cooperative computation offloading framework for mobile cloud computing services

Virtual reality games and image processing Apps are examples of mobile cloud computing services (MCCS) common on Smartphones (SPs) nowadays, requiring intensive processing and/or wireless networking. The consequences are slow execution and huge battery consumption. Offloading the intensive computations of such Apps to a cloud based server can overcome such consequences. However, such offloading will introduce time delay and communication overheads. This paper proposes to do the offloading to nearby computing resources in a cooperative computation sharing network via short-range wireless connectivity. The proposed SCCOF reduces offloading response time and energy consumption overheads. SCCOF is supported by an intelligent cloud located controller that will form the cooperative resource sharing network on the go when needed, based on available devices in the vicinity, and will use the cloud if necessary. Upon the initiation of the MCCS service via the App, our controller will devise the offloaded VMs as well as the offloading network. A study test scenario was performed to evaluate the performance of SCCOF, resulting in saving of up to 16.2x in execution time and 57.25% energy

Energy Harvesting based Mobile Cloud Network in Latency and QoS Improvement using 5G Systems by Energy Routing Optimization

D2D communication technology enables the User Equipment (UE) in 5G networks to instantly connect with other UEs, with or without partial infrastructure involvement. In a Cloud Assisted energy harvesting system, it has improved user numbers and data transmission between mobile nodes. This research propose energy harvesting for mobile cloud computing in enhancing the QoS and latency of the network. The main aim of this research is to enhance energy optimization using discrete energy efficient offloading algorithm. The routing has been optimized using fuzzy logic cognitive Bellman-Ford routing algorithm. To identify the failing node and find an alternative node to deliver the seamless services, an unique weight-based approach has been presented. The method relies on two working node parameters: execution time and failure rate. Threshold values are specified for the parameters of the chosen master node. By contrasting the values with the threshold values, the alternative node is chosen. The experimental results shows comparative analysis in terms of throughput of 96%, QoS of 96%, latency of 28%, energy consumption of 51%, end-end delay of 41%, average power consumption of 41% and PDR of 85

Intelligent Reward based Data Offloading in Next Generation Vehicular Networks

A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle s data traffic from congested cellular network s licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module