Keep Your Nice Friends Close, but Your Rich Friends Closer -- Computation Offloading Using NFC

The increasing complexity of smartphone applications and services necessitate high battery consumption but the growth of smartphones' battery capacity is not keeping pace with these increasing power demands. To overcome this problem, researchers gave birth to the Mobile Cloud Computing (MCC) research area. In this paper we advance on previous ideas, by proposing and implementing the first known Near Field Communication (NFC)-based computation offloading framework. This research is motivated by the advantages of NFC's short distance communication, with its better security, and its low battery consumption. We design a new NFC communication protocol that overcomes the limitations of the default protocol; removing the need for constant user interaction, the one-way communication restraint, and the limit on low data size transfer. We present experimental results of the energy consumption and the time duration of two computationally intensive representative applications: (i) RSA key generation and encryption, and (ii) gaming/puzzles. We show that when the helper device is more powerful than the device offloading the computations, the execution time of the tasks is reduced. Finally, we show that devices that offload application parts considerably reduce their energy consumption due to the low-power NFC interface and the benefits of offloading.Comment: 9 pages, 4 tables, 13 figure

On the traffic offloading in Wi-Fi supported heterogeneous wireless networks

Heterogeneous small cell networks (HetSNet) comprise several low power, low cost (SBSa), (D2D) enabled links wireless-fidelity (Wi-Fi) access points (APs) to support the existing macrocell infrastructure, decrease over the air signaling and energy consumption, and increase network capacity, data rate and coverage. This paper presents an active user dependent path loss (PL) based traffic offloading (TO) strategy for HetSNets and a comparative study on two techniques to offload the traffic from macrocell to (SBSs) for indoor environments: PL and signal-to-interference ratio (SIR) based strategies. To quantify the improvements, the PL based strategy against the SIR based strategy is compared while considering various macrocell and (SBS) coverage areas and traffic–types. On the other hand, offloading in a dense urban setting may result in overcrowding the (SBSs). Therefore, hybrid traffic–type driven offloading technologies such as (WiFi) and (D2D) were proposed to en route the delay tolerant applications through (WiFi) (APs) and (D2D) links. It is necessary to illustrate the impact of daily user traffic profile, (SBSs) access schemes and traffic–type while deciding how much of the traffic should be offloaded to (SBSs). In this context, (AUPF) is introduced to account for the population of active small cells which depends on the variable traffic load due to the active users

Cooperation Strategies for Enhanced Connectivity at Home

WHILE AT HOME , USERS MAY EXPERIENCE A POOR I NTERNET SERVICE while being connected to their 802.11 Access Points (APs). The AP is just one component of the Internet Gateway (GW) that generally includes a backhaul connection (ADSL, fiber,etc..) and a router providing a LAN. The root cause of performance degradation may be poor/congested wireless channel between the user and the GW or congested/bandwidth limited backhaul connection. The latter is a serious issue for DSL users that are located far from the central office because the greater the distance the lesser the achievable physical datarate. Furthermore, the GW is one of the few devices in the home that is left always on, resulting in energy waste and electromagnetic pollution increase. This thesis proposes two strategies to enhance Internet connectivity at home by (i) creating a wireless resource sharing scheme through the federation and the coordination of neighboring GWs in order to achieve energy efficiency while avoiding congestion, (ii) exploiting different king of connectivities, i.e., the wired plus the cellular (3G/4G) connections, through the aggregation of the available bandwidth across multiple access technologies. In order to achieve the aforementioned strategies we study and develop: • A viable interference estimation technique for 802.11 BSSes that can be implemented on commodity hardware at the MAC layer, without requiring active measurements, changes in the 802.11 standard, cooperation from the wireless stations (WSs). We extend previous theoretical results on the saturation throughput in order to quantify the impact in term of throughput loss of any kind of interferer. We im- plement and extensively evaluate our estimation technique with a real testbed and with different kind of interferer, achieving always good accuracy. • Two available bandwidth estimation algorithms for 802.11 BSSes that rely only on passive measurements and that account for different kind of interferers on the ISM band. This algorithms can be implemented on commodity hardware, as they require only software modifications. The first algorithm applies to intra-GW while the second one applies to inter-GW available bandwidth estimation. Indeed, we use the first algorithm to compute the metric for assessing the Wi-Fi load of a GW and the second one to compute the metric to decide whether accept incoming WSs from neighboring GWs or not. Note that in the latter case it is assumed that one or more WSs with known traffic profile are requested to relocate from one GW to another one. We evaluate both algorithms with simulation as well as with a real test-bed for different traffic patterns, achieving high precision. • A fully distributed and decentralized inter-access point protocol for federated GWs that allows to dynamically manage the associations of the wireless stations (WSs) in the federated network in order to achieve energy efficiency and offloading con- gested GWs, i.e, we keep a minimum number of GWs ON while avoiding to create congestion and real-time throughput loss. We evaluate this protocol in a federated scenario, using both simulation and a real test-bed, achieving up to 65% of energy saving in the simulated setting. We compare the energy saving achieved by our protocol against a centralized optimal scheme, obtaining close to optimal results. • An application level solution that accelerates slow ADSL connections with the parallel use of cellular (3G/4G) connections. We study the feasibility and the potential performance of this scheme at scale using both extensive throughput measurement of the cellular network and trace driven analysis. We validate our solution by implementing a real test bed and evaluating it “in the wild, at several residential locations of a major European city. We test two applications: Video-on-Demand (VoD) and picture upload, obtaining remarkable throughput increase for both applications at all locations. Our implementation features a multipath scheduler which we compare to other scheduling policies as well as to transport level solution like MTCP, obtaining always better results

Evidence-Aware Mobile Computational Offloading

Non Peer reviewe

Mobile Big Data Analytics in Healthcare

Mobile and ubiquitous devices are everywhere around us generating considerable amount of data. The concept of mobile computing and analytics is expanding due to the fact that we are using mobile devices day in and out without even realizing it. These mobile devices use Wi-Fi, Bluetooth or mobile data to be intermittently connected to the world, generating, sending and receiving data on the move. Latest mobile applications incorporating graphics, video and audio are main causes of loading the mobile devices by consuming battery, memory and processing power. Mobile Big data analytics includes for instance, big health data, big location data, big social media data, and big heterogeneous data. Healthcare is undoubtedly one of the most data-intensive industries nowadays and the challenge is not only in acquiring, storing, processing and accessing data, but also in engendering useful insights out of it. These insights generated from health data may reduce health monitoring cost, enrich disease diagnosis, therapy, and care and even lead to human lives saving. The challenge in mobile data and Big data analytics is how to meet the growing performance demands of these activities while minimizing mobile resource consumption. This thesis proposes a scalable architecture for mobile big data analytics implementing three new algorithms (i.e. Mobile resources optimization, Mobile analytics customization and Mobile offloading), for the effective usage of resources in performing mobile data analytics. Mobile resources optimization algorithm monitors the resources and switches off unused network connections and application services whenever resources are limited. However, analytics customization algorithm attempts to save energy by customizing the analytics process while implementing some data-aware techniques. Finally, mobile offloading algorithm decides on the fly whether to process data locally or delegate it to a Cloud back-end server. The ultimate goal of this research is to provide healthcare decision makers with the advancements in mobile Big data analytics and support them in handling large and heterogeneous health datasets effectively on the move

Fog-supported delay-constrained energy-saving live migration of VMs over multiPath TCP/IP 5G connections

The incoming era of the fifth-generation fog computing-supported radio access networks (shortly, 5G FOGRANs) aims at exploiting computing/networking resource virtualization, in order to augment the limited resources of wireless devices through the seamless live migration of virtual machines (VMs) toward nearby fog data centers. For this purpose, the bandwidths of the multiple wireless network interface cards of the wireless devices may be aggregated under the control of the emerging MultiPathTCP (MPTCP) protocol. However, due to the fading and mobility-induced phenomena, the energy consumptions of the current state-of-the-art VM migration techniques may still offset their expected benefits. Motivated by these considerations, in this paper, we analytically characterize and implement in software and numerically test the optimal minimum-energy settable-complexity bandwidth manager (SCBM) for the live migration of VMs over 5G FOGRAN MPTCP connections. The key features of the proposed SCBM are that: 1) its implementation complexity is settable on-line on the basis of the target energy consumption versus implementation complexity tradeoff; 2) it minimizes the network energy consumed by the wireless device for sustaining the migration process under hard constraints on the tolerated migration times and downtimes; and 3) by leveraging a suitably designed adaptive mechanism, it is capable to quickly react to (possibly, unpredicted) fading and/or mobility-induced abrupt changes of the wireless environment without requiring forecasting. The actual effectiveness of the proposed SCBM is supported by extensive energy versus delay performance comparisons that cover: 1) a number of heterogeneous 3G/4G/WiFi FOGRAN scenarios; 2) synthetic and real-world workloads; and, 3) MPTCP and wireless connections

EFFORT: Energy efficient framework for offload communication in mobile cloud computing

There is an abundant expansion in the race of technology, specifically in the production of data, because of the smart devices, such as mobile phones, smart cards, sensors, and Internet of Things (IoT). Smart phones and devices have undergone an enormous evolution in a way that they can be used. More and more new applications, such as face recognition, augmented reality, online interactive gaming, and natural language processing are emerging and attracting the users. Such applications are generally data intensive or compute intensive, which demands high resource and energy consumption. Mobile devices are known for the resource scarcity, having limited computational power and battery life. The tension between compute/data intensive application and resource constrained mobile devices hinders the successful adaption of emerging paradigms. In the said perspective, the objective of this paper is to study the role of computation offloading in mobile cloud computing to supplement mobile platforms ability in executing complex applications. This paper proposes a systematic approach (EFFORT) for offload communication in the cloud. The proposed approach provides a promising solution to partially solve energy consumption issue for communication-intensive applications in a smartphone. The experimental study shows that our proposed approach outperforms its counterparts in terms of energy consumption and fast processing of smartphone devices. The battery consumption was reduced to 19% and the data usage was reduced to 16%