2019 IEEE MOBILE COMPUTING

A Secure AMR Fixed Codebook Steganographic Scheme Based on Pulse Distribution Model

Abstract: Adaptive multi-rate (AMR), a popular audio compression standard, is widely used in mobile communication and mobile Internet applications and has become a novel carrier for hiding information. To improve the statistical security, this paper presents a steganographic scheme in the AMR fixed codebook (FCB) domain based on the pulse distribution model (PDMAFS), which is obtained from the distribution characteristics of the FCB value in the cover audio. The pulse positions in stego audio are controlled by message encoding and random masking to make the statistical distribution of the FCB parameters close to that of the cover audio. The experimental results show that the statistical security of the proposed scheme is better than that of the existing schemes. Furthermore, the hiding capacity is maintained compared with the existing schemes. The average hiding capacity can reach 2.06 kbps at an audio compression rate of 12.2 kbps and the auditory concealment is good. To the best of our knowledge, this is the first secure AMR FCB steganographic scheme that improves the statistical security based on the distribution model of the cover audio. This scheme can be extended to other audio compression codecs under the principle of Algebraic Code Excited Linear Prediction (ACELP), such as G.723.1 and G.729.

A Stackel berg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowd sensing

Abstract: Mobile crowd sensing has shown a great potential to address large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowd sensing platform if they can receive a satisfactory reward. In this paper, to recruit effectively and efficiently sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowd sensing service provider. We apply a two-stage Stackel berg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowd sensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackel berg game with incomplete information to analyze the interaction between the crowd sensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackel berg equilibrium is analytically validated by identifying the best response strategies of the mobile users. Numerical results corroborate the fact that the network effects significantly stimulate higher mobile participation level and greater revenue of the crowd sensing service provider. In addition, the social structure information helps the crowd sensing service provider to achieve greater revenue gain.

Backup Battery Analysis and Allocation against Power Outage for Cellular Base Stations

Abstract: Base stations have been widely deployed to satisfy the service coverage and explosive demand increase in today’s cellular networks. Their reliability and availability heavily depend on the electrical power supply. Battery groups are installed as backup power in most of the base stations in case of power outages due to severe weathers or human-driven accidents, particularly in remote areas. The limited numbers and capacities of batteries, however, can hardly sustain a long power outage without a well-designed allocation strategy. As a result, the service interruption occurs along with an increasing maintenance cost. Meanwhile, a deep discharge of a battery in such case can also accelerate the battery degradation and eventually contribute to a higher battery replacement cost. In this paper, we closely examine the base station features and backup battery features from a 1.5-year dataset of a major cellular service provider, including 4,206 base stations distributed across 8,400 square kilometers and more than 1.5 billion records on base stations and battery statuses. Through exploiting the correlations between the battery working conditions and battery statuses, we build up a deep learning based model to estimate the remaining lifetime of backup batteries. We then develop BatAlloc, a battery allocation framework to address the mismatch between the battery supporting ability and diverse power outage incidents. We present an effective solution that minimizes both the service interruption time and the overall cost. Our real world trace-driven experiments show that BatAlloc cuts down the average service interruption time from 4.7 hours to nearly zero with only 85% of the overall cost compared to the current practical allocation.

Counting Human Objects Using Backscattered Radio Frequency Signals

Abstract: In this paper, we propose a system called R# to estimate the number of human objects using passive RFID tags but without attaching anything to human objects. The idea is based on our observation that the more human objects are present, the higher the variation in the RSS values of the tag backscattered RF signals. Thus, based on the received RF signals, the reader can estimate the number of human objects. R# includes an RFID reader and some (say 20) passive tags, which are deployed in the region that we want to monitor the number of human objects, such as the region in front of a supermarket shelf. The RFID reader periodically emits RF signals to identify all tags and the tags simply respond with their IDs via EPCglobal Class 1 Generation 2 protocol. We implemented R# using commercial Impinj H47 passive RFID tags and Impinj reader model R420. We conducted experiments in a simulated picking aisle area of the supermarket environment. The experimental results show that R# can achieve high estimation accuracy (more than 90% with up to ten human objects).

Energy-Efficient Dynamic Computation Offloading and Cooperative Task Scheduling in Mobile Cloud Computing

Abstract: Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance com- putation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resource- constrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for Application completion time remains a challenge. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requires- ment and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three sub algorithms of computation offloading selection, clock frequency control and transmission power allocation. Next, we show that computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, we provide experimental results in a real test bed and demonstrate that the eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting the transmission power for wireless channel conditions in cloud computing.

Follow-Me Cloud When Cloud Services Follow Mobile Users

Abstract: The trend towards the codification of the 3GPP LTE mobile network architecture and the emergence of federated cloud infrastructures call for alternative service delivery strategies for improved user experience and efficient resource utilization. We propose Follow-Me Cloud (FMC), a design tailored to this environment, but with a broader applicability, which allows mobile users to always be connected via the optimal data anchor and mobility gateways, while cloud-based services follow them and are delivered via the optimal service point inside the cloud infrastructure. FMC applies a Markov-Decision-Process-based algorithm for cost-effective, performance-optimized service migration decisions, while two alternative schemes to ensure service continuity and disruption-free operation are proposed, based on either Software Defined Networking technologies or the Locator/Identifier  Separation  Protocol. Numerical results from our analytic model for FMC, as well as test bed experiments with the two alternative FMC implementations we have developed, demonstrate quantitatively and qualitatively the advantages it can bring about.

Food Net Toward an Optimized Food Delivery Network Based on Spatial Crowd sourcing

Abstract: This paper builds a Food Delivery Network (Food Net in short) using spatial crowd sourcing (SC). It investigates the participation of urban taxis to support on demand take-out food delivery. Unlike existing SC-enabled service sharing systems (e.g., ridesharing), the delivery of food in Food Net is more time-sensitive and the optimization problem is more complex regarding high-efficiency, huge-number of delivery needs. In particular, two on demand food delivery problems under different situations are studied in our work: (1) for O-OTOD, the food is opportunistically delivered by taxis when carrying passengers, and the optimization goal is to minimize the number of selected taxis to maintain a relatively high incentive to the participated drivers; (2) for D-OTOD, taxis dedicatedly deliver food without taking passengers, and the aim is to minimize the number of selected taxis (i.e., to raise the reward for each participant) and the total traveling distance to reduce the cost. A two-stage approach, including the construction algorithm and the Adaptive Large Neighborhood Search (ALNS) algorithm based on simulated annealing, is proposed to solve the problem. We have conducted extensive experiments based on the real-world datasets, including city-wide restaurant data, cell tower data, and the large-scale taxi trajectory data with 10,000 taxis in the city of Chengdu, China. Experimental results demonstrate that our proposed algorithms are more effective and efficient than baselines, fulfilling the food delivery service using a smaller number of taxis within the given time.

Signal Processing of Multimodal Mobile Life logging Data Towards Detecting Stress in Real-World Driving

Abstract: Stress is a negative emotion that is part of everyday life. However, frequent episodes or prolonged periods of stress can be detrimental to long-term health. Nevertheless, developing self-awareness is an important aspect of fostering effective ways to self-regulate these experiences. Mobile life logging systems provide an ideal platform to support self-regulation of stress by raising awareness of negative emotional states via continuous recording of psychophy siological and behavioral data. However, obtaining meaningful information from large volumes of raw data represents a significant challenge because these data must be accurately quantified and processed before stress can be detected. This work describes a set of algorithms designed to process multiple streams of life logging data for stress detection in the context of real world driving. Two data collection exercises have been performed where multimodal data, including raw cardiovascular activity and driving information, were collected from twenty-one people during daily commuter journeys. Our approach enabled us to 1) pre-process raw physiological data to calculate valid measures of heart rate variability, a significant marker of stress, 2) identify/correct artefacts in the raw physiological data and 3) provide a comparison between several classifiers for detecting stress. Results were positive and ensemble classification models provided a maximum accuracy of 86.9% for binary detection of stress in the real-world.