Nirmalya Roy

A game theory based pricing strategy for job allocation in mobile grids

Summary form only given. This article realizes the vision of mobile grid computing by proposing a fair pricing strategy and an optimal, static job allocation scheme. Mobile devices has not yet been integrated into grid computing platforms mainly due to their inherent limitations in processing and storage capacity, power and bandwidth shortages. However, millions of laptops, PDAs and other mobile devices remain unused most of the time and this huge resource repository can be potentially utilized in the grid environment. Here, we propose a game theoretic pricing model, to address load balancing issues in mobile grids. In particular, by drawing upon the Nash bargaining solution (NBS), we show that we can obtain an unified framework for addressing such issues as network efficiency, fairness, utility maximization, and pricing. The advantage of this framework is that we have a precise mathematical characterization of the solutions and their properties. Our current endeavor characterizes a two-player alternating-offer bargaining game between the wireless access point (WAP) server and the mobile devices to determine the pricing strategy. This pricing strategy is then made use of to effectively distribute jobs to the mobile devices. Our job allocation scheme maximizes the revenue of the grid user, and yet is comparable to the overall system response time of other load balancing schemes.

A pricing strategy for job allocation in mobile grids using a non-cooperative bargaining theory framework

Due to their inherent limitations in computational and battery power, storage and available bandwidth, mobile devices have not yet been widely integrated into grid computing platforms. However, millions of laptops, PDAs and other portable devices remain unused most of the time, and this huge repository of resources can be potentially utilized, leading to what is called a mobile grid environment. In this paper, we propose a game theoretic pricing strategy for efficient job allocation in mobile grids. By drawing upon the Nash bargaining solution, we show how to derive a unified framework for addressing such issues as network efficiency, fairness, utility maximization, and pricing. In particular, we characterize a two-player, non-cooperative, alternating-offer bargaining game between the Wireless Access Point Server and the mobile devices to determine a fair pricing strategy which is then used to effectively allocate jobs to the mobile devices with a goal to maximize the revenue for the grid users. Simulation results show that the proposed job allocation strategy is comparable to other task allocation schemes in terms of the overall system response time.

An energy-efficient quality adaptive framework for multi-modal sensor context recognition

In pervasive computing environments, understanding the context of an entity is essential for adapting the application behavior to changing situations. In our view, context is a high-level representation of a user or entitys state and can capture location, activities, social relationships, capabilities, etc. Inherently, however, these high-level context metrics are difficult to capture using uni-modal sensors only, and must therefore be inferred with the help of multi-modal sensors. However a key challenge in supporting context-aware pervasive computing environments, is how to determine in an energy-efficient manner multiple (potentially competing) high-level context metrics simultaneously using low-level sensor data streams about the environment and the entities present therein. In this paper, we first highlight the intricacies of determining multiple context metrics as compared to a single context, and then develop a novel framework and practical implementation for this problem. The proposed framework captures the tradeoff between the accuracy of estimating multiple context metrics and the overhead incurred in acquiring the necessary sensor data stream. In particular, we develop a multi-context search heuristic algorithm that computes the optimal set of sensors contributing to the multi-context determination as well as the associated parameters of the sensing tasks. Our goal is to satisfy the application requirements for a specified accuracy at a minimum cost. We compare the performance of our heuristic based framework with a brute-forced approach for multi-context determination. Experimental results with SunSPOT sensors demonstrate the potential impact of the proposed framework.

Mobility-Aware Efficient Job Scheduling in Mobile Grids

In this paper, we present a node mobility prediction framework based on a generic mobile grid architecture. We show how this framework can be used to formulate a cost effective job scheduling scheme based on a predetermined pricing strategy at the wireless access point. The proposed scheme is for distributing grid computing jobs to the mobile nodes and considers the bandwidth constraints along with any internal job (e.g., call processing) arrival rate at the nodes. The simulation results point to the efficacy of our algorithm.

Enhancing availability of grid computational services to ubiquitous computing applications

The grid is an integrated infrastructure that can play the dual roles of a coordinated resource consumer as well as a donator in distributed computing environments. The enormous growth in the use of mobile and embedded devices in ubiquitous computing environment and their interaction with human beings produces a huge amount of data that need to be processed efficiently anytime anywhere. However, such devices often have limited resources in terms of CPU, storage, battery power, and communication bandwidth. Thus, there is a need to transfer ubiquitous computing application services to more powerful computational resources. In this paper, we investigate the use of the grid as a candidate for provisioning computational services to applications in ubiquitous computing environments. In particular, we present a competitive model that describes the possible interaction between the competing resources in the grid infrastructure as service providers and ubiquitous applications as subscribers. The competition takes place in terms of quality of service (QoS) and cost offered by different grid service providers (GSPs). We also investigate the job allocation of different GSPs by exploiting the noncooperativeness among the strategies. We present the equilibrium behavior of our model facing global competition under stochastic demand and estimate guaranteed QoS assurance level by efficiently satisfying the requirement of ubiquitous application. We have also performed extensive experiments over distributed parallel computing cluster (DPCC) and studied overall job execution performance of different GSPs under a wide range of QoS parameters using different strategies. Our model and performance evaluation results can serve as a valuable reference for designing appropriate strategies in a practical grid environment.

Context-aware resource management in multi-inhabitant smart homes: A framework based on Nash H-learning

Abstract A smart home aims at building intelligent automation with a goal to provide its inhabitants with maximum possible comfort, minimum resource consumption and thus reduced cost of home maintenance. ‘Context Awareness’ is perhaps the most salient feature of such an intelligent environment. An inhabitant’s mobility and activities play a significant role in defining his/her contexts in and around the home. Although there exists an optimal algorithm for location and activity tracking of a single inhabitant, the correlation and dependence between multiple inhabitants’ contexts within the same environment make the location and activity tracking more challenging. In this paper, we first prove that the optimal location prediction across multiple inhabitants in smart homes is an NP-hard problem. Next, to capture the correlation and interactions between different inhabitants’ movements (and hence activities), we develop a novel framework based on a game theoretic, Nash H -learning approach that attempts to minimize the joint location uncertainty of inhabitants. Our framework achieves a Nash equilibrium such that no inhabitant is given preference over others. This results in more accurate prediction of contexts and more adaptive control of automated devices, thus leading to a mobility-aware resource (say, energy) management scheme in multi-inhabitant smart homes. Experimental results demonstrate that the proposed framework is capable of adaptively controlling a smart environment, significantly reduces energy consumption and enhances the comfort of the inhabitants.

A Middleware Framework for Ambiguous Context Mediation in Smart Healthcare Application

Ubiquitous healthcare applications envision future computing and networking environments as being filled with sensors that can determine various types of contexts of its inhabitants, such as location, activity and vital signs. While such information is useful in providing context-sensitive services to the inhabitants to promote intelligent independent living, however in reality, both sensed and interpreted contexts may often be ambiguous. Thus, a challenge facing the development of realistic and deployable context-aware services is the ability to handle ambiguous contexts to prevent hazardous situations. In this paper, we propose a framework which supports efficient context-aware data fusion for healthcare applications that assume contexts could be ambiguous. Our framework provides a systematic approach to derive context fragments, and deal with context ambiguity in a probabilistic manner. We also incorporate the ability to represent contexts within the applications, and the ability to easily compose rules to mediate ambiguous contexts. Through simulation and analysis, we demonstrate the effectiveness of our proposed framework for monitoring elderly people in the smart home environment.

Enhancing Availability of Grid Computational Services to Ubiquitous Computing Applications

The grid is an integrated infrastructure that can play the dual roles of a coordinated resource consumer as well as a donator in distributed computing environments. The enormous growth in the use of mobile and embedded devices in ubiquitous computing environment and their interaction with human beings produces a huge amount of data that need to be processed efficiently anytime anywhere. However, such devices often have limited resources in terms of CPU, storage, battery power, and communication bandwidth. Thus, there is a need to transfer ubiquitous computing application services to more powerful computational resources. In this paper, we investigate the use of the grid as a candidate for provisioning computational services to applications in ubiquitous computing environments. In particular, we present a competitive model that describes the possible interaction between the competing resources in the grid infrastructure as service providers and ubiquitous applications as subscribers. The competition takes place in terms of quality of service (QoS) and cost offered by different grid service providers (GSPs). We also investigate the job allocation of different GSPs by exploiting the noncooperativeness among the strategies. We present the equilibrium behavior of our model facing global competition under stochastic demand and estimate guaranteed QoS assurance level by efficiently satisfying the requirement of ubiquitous application. We have also performed extensive experiments over distributed parallel computing cluster (DPCC) and studied overall job execution performance of different GSPs under a wide range of QoS parameters using different strategies. Our model and performance evaluation results can serve as a valuable reference for designing appropriate strategies in a practical grid environment.

Automated Functional and Behavioral Health Assessment of Older Adults with Dementia

Dementia is a clinical syndrome of cognitive deficits that involves both memory and functional impairments. While disruptions in cognition is a striking feature of dementia, it is also closely coupled with changes in functional and behavioral health of older adults. In this paper, we investigate the challenges of improving the automatic assessment of dementia, by better exploiting the emerging physiological sensors in conjunction with ambient sensors in a real field environment with IRB approval. We hypothesize that the cognitive health of older individuals can be estimated by tracking their daily activities and mental arousal states. We employ signal processing on wearable sensor data streams (e.g., Electrodermal Activity (EDA), Photoplethysmogram (PPG), accelerometer (ACC)) and machine learning algorithms to assess cognitive impairments and its correlation with functional health decline. To validate our approach, we quantify the score of machine learning, survey and observation based Activities of Daily Living (ADLs) and signal processing based mental arousal state, respectively for functional and behavioral health measures among 17 older adults living in a continuing care retirement community in Baltimore. We compare clinically observed scores with technology guided automated scores using both machine learning and statistical techniques.

CACE: Exploiting Behavioral Interactions for Improved Activity Recognition in Multi-inhabitant Smart Homes

We propose CACE (Constraints And Correlations mining Engine) which investigates the challenges of improving the recognition of complex daily activities in multi-inhabitant smart homes, by better exploiting the spatiotemporal relationships across the activities of different individuals. We first propose and develop a loosely-coupled Hierarchical Dynamic Bayesian Network (HDBN), which both (a) captures the hierarchical inference of complex (macro-activity) contexts from lower-layer microactivity context (postural and improved oral gestural context), and (b) embeds the various types of behavioral correlations and constraints (at both micro-and macro-activity contexts) across the individuals. While this model is rich in terms of accuracy, it is computationally prohibitive, due to the explosive increase in the number of jointly-defined states. To tackle this challenge, we employ data mining to learn behaviorally-driven context correlations in the form of association rules, we then use such rules to prune the state space dramatically. To evaluate our framework, we build a customized smart home system and collected naturalistic multi-inhabitant smart home activities data. The system performance is illustrated with results from real-time system deployment experiences in a smart home environment reveals a radical (max 16 fold) reduction in the computational overhead compared to traditional hybrid classification approaches, as well as an improved activity recognition accuracy of max 95%.