CANDi: context‐aware node discovery for short‐range cooperation

In short-range wireless communications, node discovery is an initial step to initialize communication with other nearby mobile terminals or networks. The existing techniques for node discovery are based on traditional scanning, which is expensive in terms of energy and drains out mobile battery swiftly; hence mobile devices with hybrid character, containing ad hoc and infrastructure access, need mechanisms that keep them connected in an energy efficient way. In this paper, we propose a node discovery mechanism in which context information provides knowledge and guidance to the searching mobile terminal about the nearby mobile terminals or clusters to initialize cooperative communications. The cluster is UWB based. We modify the UWB beacon IEs and embed our own beacon IEs to represent different pieces of information e.g. willingness, battery level, hibernation mode etc. The clusterhead of each cluster collects and packs the information from member devices into a context list, and sends it to the WiMAX base station. The context list is then provided to searching MTs to discover nearby clusters. The simulation results show that our approach saves 50% of energy consumption by utilizing context information compared to normal scanning process.

Due to recent scientific and technological advances in information sys¬tems, it is now possible to perform almost every application on a mobile device. The need to make sense of such devices more intelligent opens an opportunity to design data mining algorithm that are able to autonomous execute in local devices to provide the device with knowledge. The problem behind autonomous mining deals with the proper configuration of the algorithm to produce the most appropriate results. Contextual information together with resource information of the device have a strong impact on both the feasibility of a particu¬lar execution and on the production of the proper patterns. On the other hand, performance of the algorithm expressed in terms of efficacy and efficiency highly depends on the features of the dataset to be analyzed together with values of the parameters of a particular implementation of an algorithm. However, few existing approaches deal with autonomous configuration of data mining algorithms and in any case they do not deal with contextual or resources information. Both issues are of particular significance, in particular for social net¬works application. In fact, the widespread use of social networks and consequently the amount of information shared have made the need of modeling context in social application a priority. Also the resource consumption has a crucial role in such platforms as the users are using social networks mainly on their mobile devices. This PhD thesis addresses the aforementioned open issues, focusing on i) Analyzing the behavior of algorithms, ii) mapping contextual and resources information to find the most appropriate configuration iii) applying the model for the case of a social recommender. Four main contributions are presented: - The EE-Model: is able to predict the behavior of a data mining algorithm in terms of resource consumed and accuracy of the mining model it will obtain. - The SC-Mapper: maps a situation defined by the context and resource state to a data mining configuration. - SOMAR: is a social activity (event and informal ongoings) recommender for mobile devices. - D-SOMAR: is an evolution of SOMAR which incorporates the configurator in order to provide updated recommendations. Finally, the experimental validation of the proposed contributions using synthetic and real datasets allows us to achieve the objectives and answer the research questions proposed for this dissertation.

In this study, a modified form of the total mechanical energy budget equation is developed for 3-D turbulent flows with different hydraulic regimes in open-channel transitions, from which the effect of all turbulence parameters can be evaluated by applying some equalizations and RANS numerical simulations. The total energy equation is also used to extract an explicit relationship for energy loss based on turbulence parameters. The findings are presented in the form of the application of the relationship to calculate the energy loss based on simulation results for subcritical turbulent flow and hydraulic jump in open-channel transitions, which leads to identifying the effect of non-homogeneity and anisotropy of turbulence on flow energy balance. The results show that the work done by viscous shear stresses, the viscous diffusion of turbulence, and the turbulent diffusion have little to no effect on the total mechanical energy budget of open-channel turbulent flows; instead, the effect of the variations of turbulent kinetic energy and the work done by turbulence stresses is more significant. The benefit of using the modified energy equation is demonstrated by the potential for evaluating the details that were not previously considered.

The energy-efficient design of short-distance wireless communication systems requires the inclusion of circuit energy consumption in the system's total energy budget. We consider the design of constant power link adaptation schemes in which circuit energy consumption is included in the total energy budget and in which the system's instantaneous bit error rate (BER) must never exceed a predefined threshold. We show how-in numerous cases of practical importance-the dimensionality of the associated nonlinear optimization problem can be reduced from N + 1 to 1, where N denotes the number of available transmission schemes. The main contribution-a methodology that significantly simplifies the design of such constant power link adaptation schemes-is then illustrated by an example.

Multi-standard mobile devices are allowing users to enjoy higher data rates and ubiquitous connectivity. These advances are achieved on the expense of higher energy consumption requirements due to the continuous connectivity and the multiple active wireless interfaces. In this article, we use one advantage of the multiple interfaces, namely short-range (SR) communications. Mobile terminals (MTs) use SR cooperative networking to take advantage of the good channel quality of SR links to save energy in multi-standard MTs. In this cooperative network, the combined energy of all MTs is treated as a pool of resources, which is used by all MTs in the network. Towards this end, we propose using cooperation between MTs using SR technology to achieve energy savings. We conduct a quantitative numerical analysis to show the energy saving gains that can be achieved. We derive the energy gains in different use cases, considering different combinations of technologies (WiFi-WiMedia, WiMAX-WiFi and WiFi-WiFi) and different channel conditions. We show that up to 80% energy savings can be achieved when using a combination of WiMedia as SR and WiFi as long-range technology, compared to using WiFi without cooperation. We also show that SR cooperation can be used to extend the lifetime of the whole network. In such scenario, terminals, which are running out of battery or low in energy, can still relay their data through other MTs, even if this will result in a slight increase in the total consumption of all MTs. This way, source MTs with low battery level benefit from the good condition of the SR channel, extending the lifetime of their batteries. The increase in the energy consumption of the relays can hence be compensated by some kind of payment, which opens opportunities for new business models involving source MTs, relays and network operators.

In this study, warm and moist air intrusions (WaMAI) over the Arctic Ocean sectors of Barents, Kara, Laptev, East Siberian, Chukchi and Beaufort Seas in recent 40 winters (from 1979 to 2018) are identified from ERA5 reanalysis using both Eulerian and Lagrangian views. The analysis shows that WaMAIs, fuelled by Arctic blockings, causes a relative surface warming and hence a sea ice reduction by exerting positive anomalies of net thermal irradiances and turbulent fluxes to the surface. Over Arctic Ocean sectors with land-locked sea ice in winter, such as Laptev, East Siberian, Chukchi and Beaufort Seas, total surface energy budget is dominated by net thermal irradiance. From a Lagrangian perspective, total water path (TWP) increases linearly with the downstream distance from the sea ice edge over the completely ice-covered sectors, inducing almost linearly increasing net thermal irradiance and total surface energy-budget. However, over the Barents Sea, with an open ocean to the south, total net surface energy-budget is dominated by the surface turbulent flux. With the energy in the warm-and-moist air continuously transported to the surface, net surface turbulent flux gradually decreases with distance, especially within the first 2 degrees north of the ice edge, inducing a decreasing but still positive total surface energy budget. The boundary-layer energy-budget patterns over the Barents Sea can be categorized into three classes: radiation-dominated, turbulence-dominated and turbulence-dominated with cold dome, comprising about 52 %, 40 % and 8 % of all WaMAIs, respectively. Statistically, turbulence-dominated cases with or without cold dome occur along with one order of magnitude larger large-scale subsidence than the radiation-dominated cases. For the turbulence-dominated category, larger turbulent fluxes are exerted to the surface, probably because of stronger wind shear. In radiation-dominated WaMAIs, stratocumulus develops more strongly and triggers intensive cloud-top radiative cooling and related buoyant mixing that extends from cloud top to the surface, inducing a thicker well-mixed layer under the cloud. With the existence of cold dome, fewer liquid water clouds were formed and less or even negative turbulent fluxes could reach the surface.

In this paper we first show that the Wi-Fi Protected Setup (WPS) protocol (used by Wi-Fi Direct, the de facto adhoc communication mechanism for smartphones and mobile devices) is vulnerable to a brute-force or dictionary attack. To defend against these attacks, we propose the idea of using contextual information (i.e., data obtained from mobile device's sensors) to establish a long (128 bits) secure session key between two Wi-Fi Direct enabled devices, instead of using the keypad. Our solution, Session Key Generated from Sensors (SekGens) employs three phases. In the Quantization Phase, the key is iteratively generated based on different sensors' data. In the Reconciliation Phase, the two devices eliminate minor differences in the bits of their keys by using the Cascade reconciliation mechanism. In the Privacy-Amplification-and-Hashing Phase, the two devices omit all bits exposed during the reconciliation phase and apply hashing to the remaining secret bits. SekGens is implemented and evaluated by modifying the Android kernel code responsible for WPS in Google Nexus 5 and Samsung Galaxy S2 smartphones. The results show that SekGens generates keys with low mismatch ratio (less than 3%), at a fast rate (~20 bits/sec), and with high entropy (~92%).

Interactive visual presentation of information can help an analyst gain faster and better insight from data. When combined with situational or context information, visualization on mobile devices is invaluable to in-field responders and investigators. However, several challenges are posed by the form-factor of mobile devices in developing such systems. In this paper, we classify these challenges into two broad categories - issues in general mobile computing and issues specific to visual analysis on mobile devices. Using NetworkVis and Infostar as example systems, we illustrate some of the techniques that we employed to overcome many of the identified challenges. NetworkVis is an OpenVG-based real-time network monitoring and visualization system developed for Windows Mobile devices. Infostar is a flash-based interactive, real-time visualization application intended to provide attendees access to conference information. Linked time-synchronous visualization, stylus/button-based interactivity, vector graphics, overview-context techniques, details-on-demand and statistical information display are some of the highlights of these applications.

This paper presents an exhaustive study on the use of one-relay and two-relay cooperative communication schemes and 2-hop communication scheme for improving the energy efficiency and reliability of ultra-wideband based wireless body area networks (UWB WBANs). Various investigations have been performed to study the impact of the parameters like packet size, hop distance, transmit power and channel error rate on the energy efficiency and reliability. An optimal packet size is obtained for the maximization of energy efficiency for both on-body communication and in-body communication. The analytical and simulation results show enhanced reliability with cooperative communication than direct communication and 2-hop communication, for all values of source to destination distances. The results also depict a threshold behaviour for energy efficiency which separates the hoplength for direct transmission from the hoplength where cooperation and 2-hop communication will be useful. The simulation results reveal that if the channel conditions are poor, when the source to destination distance is larger than the threshold value, 2-hop communication gives higher energy efficiency compared with direct and cooperative communications.

Energy conservation in battery powered mobile devices that perform wireless multimedia streaming has been a significant research problem since last decade. This is because these mobile devices consume a lot of power while receiving, decoding and ultimately, presenting the multimedia content. What makes things worse is the fact that battery technologies have not evolved enough to keep up with the rapid advancement of mobile devices. This survey examines solutions that have been proposed during the last few years, to improve the energy efficiency of wireless multimedia streaming in mobile hand-held devices. We categorize the research work according to different layers of the Internet protocol stack they utilize. Then, we again regroup these studies based on different traffic scheduling and multimedia content adaptation mechanisms. The traffic scheduling category contains those solutions that optimize the wireless receiving energy without changing the actual multimedia content. The second category on the other hand, specifically modifies the content, in order to reduce the energy consumed by the wireless receiver and to decode and view the content. We compare them and provide evidence of the fact that some of these tactics already exist in modern smaprtphones and provide energy savings with real measurements. In addition, we discuss some relevant literature on the complementary problem of energy-aware multimedia delivery from mobile devices and contrast with our target approaches for multimedia transmission to mobile devices.

Context awareness, user friendliness, and interactivity are very important and powerful concepts in building useful teaching/learning environments for the benefit of mankind. Advancements in technology are enabling us to look at new use cases in the methods of teaching, information sharing, knowledge dissemination, and self-learning. Learning environments are not only for teaching things to people., but also for providing more information to them on any subject that they may require from time to time. User friendliness can be enhanced with clever use of sensors to give hands free interactivity with the devices/gadgets. Context aware applications require sensors to get the context information. Mobile devices like smart phones and tablets today come with a hand full of sensors mounted on them which include one or two cameras, a microphone, a touch screen, an accelerometer to sense motion, and in addition have GPS and digital compass to provide location information to applications requiring these sensory inputs. The computational power packed in the processors used in these devices/gadgets including special support for graphics processing, the near general purpose computer like operating system environments, and the quality of cameras available, are all enabling application developers to map some serious computer vision based applications on today's mobile devices. This reality has encouraged us to get into development of Augmented Reality (AR) based interactive learning environments on mobile devices, leading to our development of a software framework for AR applications development and also development of an AR book and an AR board. AR has been called the eighth mass medium, after print, recordings, cinema, radio, television, Internet and mobile phones. There is ample scope for enhancing the learning/teaching experiences of the users with AR based learning environments. AR makes learning more effective and an enjoyable experience by providing more realistic information with the use of 3D graphics and animated models.

Digital video playback on mobile devices is fast becoming widespread and popular. Since mobile devices are typically resource constrained in terms of network bandwidth, battery power and available screen resolution, it is often necessary to formulate special encoding techniques in order to optimize power consumption during video streaming and playback. The existing H.264 standard is popular for video encoding on mobile devices, since it results in a low-bitrate video with visual clarity that is adequate for video playback on mobile devices. However, due to the complexity of the H.264 representation, the video decoding procedure is typically computationally intensive. In this paper, we propose a novel lossy video representation termed as Ligne-Claire (LC) video. LC videos are obtained via graphics overlay of outlines or silhouettes of objects in the video over an approximated texture video. Since the playback of LC video is typically meant for mobile devices, the visual quality of video is adequate for most mobile applications wherein the semantic content of the video can be characterized by object shapes and approximate texture information. Experimental results presented in the paper demonstrate that the proposed lossy LC video encoding scheme results in power savings of 50% or more during video playback compared to standard H.264-encoded videos, of similar video file size. In order to evaluate the visual quality of the LC video, we compare the performance of LC videos with H.264-encoded videos in the context of some typical computer vision tasks. Our results indicate that the performance of the computer vision algorithms on these videos is similar. This fact, coupled with subjective evaluation, and the resulting significant power savings, indicates that the proposed LC representation can be used effectively to encode video for power-constrained mobile devices.

In mobile cloud computing, offloading resource-demanded applications from mobile devices to remote cloud servers can alleviate the resource scarcity of mobile devices, whereas long distance communication may incur high communication latency and energy consumption. As an alternative, fortunately, recent studies show that exploiting the unused resources of the nearby mobile devices for task execution can reduce the energy consumption and communication latency. Nevertheless, it is non-trivial to encourage mobile devices to share their resources or execute tasks for others. To address this issue, we construct an auction model to facilitate the resource trading between the owner of the tasks and the mobile devices participating in task execution. Specifically, the owners of the tasks act as bidders by submitting bids to compete for the resources available at mobile devices. We design a distributed auction mechanism to fairly allocate the tasks, and determine the trading prices of the resources. Moreover, an efficient payment evaluation process is proposed to prevent against the possible dishonest activity of the seller on the payment decision, through the collaboration of the buyers. We prove that the proposed auction mechanism can achieve certain desirable properties, such as computational efficiency, individual rationality, truthfulness guarantee of the bidders, and budget balance. Simulation results validate the performance of the proposed auction mechanism.

China’s information technology development is rapid; the organic combination of science and technology and education has promoted the reform of the education system; the education platform supplemented by mobile devices provides students with a new learning mode; student learning is no longer limited by the traditional education system; and learners can use mobile terminals for online learning, self-control learning steps, arrange learning time, and be able to combine their own deficiencies to strengthen learning content. Therefore, this paper discusses the design of English education teaching system in the Internet of Things technology environment, in order to provide students with a good learning environment and improve students’ English integration strength. This paper analyzes the research status of mobile learning at home and abroad, expounds the construction principles of mobile English learning system, and designs and studies the overall structure and functional modules of the system. The application of the system can replace the traditional English learning method and can enable students to improve their English ability through mobile devices. In view of the problems of poor teaching effect and high energy consumption in the traditional Internet of Things education platform, this paper puts forward a teaching effect evaluation method of Internet of Things education platform based on long-term memory network. By analyzing the current situation of the Internet of Things education platform, including its development and structure, this paper constructs the evaluation model of the combination of long-term memory neural network model and gray model and realizes the evaluation of the teaching effect of the Internet of Things education platform. Finally, through the study of the model, the teaching effect of the Internet of Things education platform is evaluated. The experimental results show that when the method is used to evaluate the teaching effect, the operating energy consumption accounts for 84% of the total energy consumption of the system, which proves the effectiveness of the method.

Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications depends on their smoothness and immersiveness. Modern AR applications applying computationally intensive computer vision algorithms can burden today's mobile devices, and cause high energy consumption and/or poor performance. To tackle this challenge, it is possible to offload part of the computation to nearby devices at the edge. However, this calls for smart task placement strategies in order to efficiently use the resources of the edge infrastructure. In this paper, we introduce Nimbus — a task placement and offloading solution for a multi-tier, edge-cloud infrastructure where deep learning tasks are extracted from the AR application pipeline and offloaded to nearby GPU-powered edge devices. Our aim is to minimize the latency experienced by end-users and the energy costs on mobile devices. Our multifaceted evaluation, based on benchmarked performance of AR tasks, shows the efficacy of our solution. Overall, Nimbus reduces the task latency by <inline-formula><tex-math notation="LaTeX">$\sim 4\times$</tex-math></inline-formula> and the energy consumption by <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 77% for real-time object detection in AR applications. We also benchmark three variants of our offloading algorithm, disclosing the trade-off of centralized versus distributed execution.