Chi Harold Liu

Mobile Cloud Computing: A Survey, State of Art and Future Directions

In the recent years, cloud computing frameworks such as Amazon Web Services, Google AppEngine and Windows Azure have become increasingly popular among IT organizations and developers. Simultaneously, we have seen a phenomenal increase in the usage and deployment of smartphone platforms and applications worldwide. This paper discusses the current state of the art in the merger of these two popular technologies, that we refer to as Mobile Cloud Computing (MCC). We illustrate the applicability of MCC in various domains including mobile learning, commerce, health/wellness and social medias. We further identify research gaps covering critical aspects of how MCC can be realized and effectively utilized at scale. These include improved resource allocation in the MCC environment through efficient task distribution and offloading, security and privacy.

A Survey on Internet of Things From Industrial Market Perspective

The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as radio frequency identifications, sensors, and actuators, as well as other instruments and smart appliances that are becoming an integral component of the Internet. Over the last few years, we have seen a plethora of IoT solutions making their way into the industry marketplace. Context-aware communications and computing have played a critical role throughout the last few years of ubiquitous computing and are expected to play a significant role in the IoT paradigm as well. In this paper, we examine a variety of popular and innovative IoT solutions in terms of context-aware technology perspectives. More importantly, we evaluate these IoT solutions using a framework that we built around well-known context-aware computing theories. This survey is intended to serve as a guideline and a conceptual framework for context-aware product development and research in the IoT paradigm. It also provides a systematic exploration of existing IoT products in the marketplace and highlights a number of potentially significant research directions and trends.

The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as Radio frequency identifications, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organizations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. Based on the application domain, we classify and discuss these solutions under five different categories: 1) smart wearable; 2) smart home; 3) smart city; 4) smart environment; and 5) smart enterprise. This survey is intended to serve as a guideline and a conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating “things” or Internet Connected Objects (ICOs) that will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection, and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM considers user preferences and a broad range of sensor characteristics such as reliability, accuracy, location, battery life, and many more. This paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This paper also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.

A Survey of Incentive Mechanisms for Participatory Sensing

Participatory sensing is now becoming more popular and has shown its great potential in various applications. It was originally proposed to recruit ordinary citizens to collect and share massive amounts of sensory data using their portable smart devices. By attracting participants and paying rewards as a return, incentive mechanisms play an important role to guarantee a stable scale of participants and to improve the accuracy/coverage/timeliness of the sensing results. Along this direction, a considerable amount of research activities have been conducted recently, ranging from experimental studies to theoretical solutions and practical applications, aiming at providing more comprehensive incentive procedures and/or protecting benefits of different system stakeholders. To this end, this paper surveys the literature over the period of 2004-2014 from the state of the art of theoretical frameworks, applications and system implementations, and experimental studies of the incentive strategies used in participatory sensing by providing up-to-date research in the literature. We also point out future directions of incentive strategies used in participatory sensing.

QoI-Aware Multitask-Oriented Dynamic Participant Selection With Budget Constraints

By using increasingly popular smartphones, participatory sensing systems can collect comprehensive sensory data to retrieve context-aware information for different applications (or sensing tasks). However, new challenges arise when selecting the most appropriate participants when considering their different incentive requirements, associated sensing capabilities, and uncontrollable mobility, to best satisfy the quality-of-information (QoI) requirements of multiple concurrent tasks with different budget constraints. This paper proposes a multitask-oriented participant selection strategy called “DPS,” which is used to tackle the aforementioned challenges, where three key design elements are proposed. First is the QoI satisfaction metric, where the required QoI metrics of the collected data are quantified in terms of data granularity and quantity. Second is the multitask-orientated QoI optimization problem for participant selection, where task budgets are treated as the constraint, and the goal is to select a minimum subset of participants to best provide the QoI satisfaction metrics for all tasks. The optimization problem is then converted to a nonlinear knapsack problem and is solved by our proposed dynamic participant selection (DPS) strategy. Third is how to compute the expected amount of collected data by all (candidate) participants, where a probability-based movement model is proposed to facilitate such computation. Real and extensive trace-based simulations show that, given the same budget, the proposed participant selection strategy can achieve far better QoI satisfactions for all tasks than selecting participants randomly or through the reversed-auction-based approaches.

Context-Awareness for Mobile Sensing: A Survey and Future Directions

The evolution of smartphones together with increasing computational power has empowered developers to create innovative context-aware applications for recognizing user-related social and cognitive activities in any situation and at any location. The existence and awareness of the context provide the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze, and share local sensory knowledge in the purpose for a large-scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects and also assist individuals. However, many open challenges remain, which are mostly arisen because the middleware services provided in mobile devices have limited resources in terms of power, memory, and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved and, at the same time, better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlightens them by proposing possible solutions.

Energy-Efficient Relay Selection for Cooperative Relaying in Wireless Multimedia Networks

In existing wireless networks, supporting multimedia services are becoming more popular and important. In general, wireless multimedia networks should require energy efficiency and reliable transmission while keeping satisfactory quality of services. In this respect, cooperative communications have been considered as an efficient approach to address these demands by offering significant diversity gains over single-antenna systems without increasing requirements on radio resources. In this paper, we propose a power-allocation method to optimize the decode-and-forward (DF) cooperative transmission for source and relay nodes as a means to reduce the total power consumption, while maintaining the required quality of services, and investigate fundamental characteristics of cooperative transmission in terms of power efficiency. Moreover, for a network with multiple cooperative nodes, we also propose an energy-efficient relay selection rule to offer fairness at each node and implement it into a practical routing protocol. Our performance analysis is supplemented by simulation results to illustrate significant energy savings of the proposed methods.

Efficient network management for context-aware participatory sensing

Participatory sensing is becoming more popular with the help of sensor-embedded smartphones to retrieve context-aware information for users. However, new challenges arise for the maintenance of the energy supply, the support of the quality-of-information (QoI) requirements, and the generation of maximum revenue for network operator, but with sparsely research exposure. This paper proposes a novel efficient network management framework to tackle the above challenges, where four key design elements are introduced. First is the QoI satisfaction index, where the QoI benefit the queries receive is quantified in relation to the level they require. Second is the credit satisfaction index, where the credits are used by the network operator to motivate the user participation, and this index is to quantify its degree of satisfaction. Third is the Gur Game-based distributed energy control, where the above two indexes are used as inputs to the mathematical framework of the Gur Game for distributed decision-making. Fourth is the dynamic pricing scheme, based on a constrained optimization problem to allocate credits to the participants while minimizing the necessary adaptation of the pricing scheme from the network operator. We finally evaluate the proposed scheme under an event occurrence detection scenario, where the proposed scheme successfully guarantees less than 7% detection outage, saves 80% of the energy reserve if compared with the lower bound solution, and achieves the suboptimum with only 4% gap if compared with optimal solution.

Toward QoI and Energy-Efficiency in Internet-of-Things Sensory Environments

Considering physical sensors with certain sensing capabilities in an Internet-of-Things (IoTs) sensory environment, in this paper, we propose an efficient energy management framework to control the duty cycles of these sensors under quality-of-information (QoI) expectations in a multitask-oriented environment. Contrary to past research efforts, our proposal is transparent and compatible both with the underlying low-layer protocols and diverse applications, and preserving energy-efficiency in the long run without sacrificing the QoI levels attained. In particular, we first introduce the novel concept of QoI-aware sensor-to-task relevancy to explicitly consider the sensing capabilities offered by a sensor to the IoT sensory environments, and QoI requirements required by a task. Second, we propose a novel concept of the critical covering set of any given task in selecting the sensors to service a task over time. Third, energy management decision is made dynamically at runtime, to reach the optimum for long-term application arrivals and departures under the constraint of their service delay. We show a case study to utilize sensors to perform environmental monitoring with a complete set of performance analysis. We further consider the signal propagation and processing latency into the proposal, and provide a thorough analysis on its impact on average measured delay probability.