When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Adaptive Consistency Guarantees for Large-Scale Replicated Services

To maintain consistency, designers of replicated services have traditionally been forced to choose from either strong consistency guarantees or none at all. Realizing that a continuum between strong and optimistic consistencies is semantically meaningful for a broad range of network services, previous research has proposed a continuous consistency model for replicated services to support the tradeoff between the guaranteed consistency level, performance and availability. However, to meet changing application needs and to make the model useful for interactive users of large-scale replicated services, the adaptability and the swiftness of inconsistency resolution are important and challenging. This paper presents IDEA (an Infrastructure for DEtection-based Adaptive consistency guarantees) for adaptive consistency guarantees of large-scale, Internet-based replicated services. The main functions enabled by IDEA include quick inconsistency detection and resolution, consistency adaptation and quantified consistency level guarantees. Through experimentation on the Planet-Lab, IDEA is evaluated from two aspects: its adaptive consistency guarantees and its performance for inconsistency resolution. Results show that IDEA is able to provide consistency guarantees adaptive to user’s changing needs, and it achieves low delay for inconsistency resolution and incurs small communication overhead

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have 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 provides 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 due to 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 enlighten them by proposing possible solutions

IDEA: An Infrastructure for Detection-based Adaptive Consistency Control in Replicated Services

In Internet-scale distributed systems, replication-based scheme has been widely deployed to increase the availability and efficiency of services. Hence, consistency maintenance among replicas becomes an important research issue because poor consistency results in poor QoS or even monetary loss. Recent research in this area focuses on enforcing a certain consistency level, instead of perfect consistency, to strike a balance between consistency guarantee and system’s scalability. In this paper, we argue that, besides balancing consistency and scalability, it is equally, if not more, important to achieve adaptability of consistency maintenance. I.e., the system adjusts its consistency level on the fly to suit applications’ ongoing need. This paper then presents the design, implementation, and evaluation of IDEA (an Infrastructure for DEtection-based Adaptive consistency control), which adaptively controls consistency in replicated services by utilizing an inconsistency detection framework that detects inconsistency among nodes in a timely manner. Besides, IDEA achieves high performance of inconsistency resolution in terms of resolution delay. Through two emulated distribution application on Planet-Lab, IDEA is evaluated from two aspects: its adaptive interface and its performance of inconsistency resolution. According the experimentation, IDEA achieves adaptability by adjusting the consistency level according to users’ preference on-demand. As for performance, IDEA achieves low inconsistency resolution delay and communication cost

CVRetrieval: Separating Consistency Retrieval from Consistency Maintenance

In distributed online collaboration applications, such as digital white board and online gaming, it is important to guarantee the consistency among participants’ views to make collaboration meaningful. However, maintaining even a relaxed consistency in a distributed environment with a large number of geographically dispersed participants still involves formidable communication and management cost among them. In this paper, we propose CVRetrieval (Consistency View Retrieval) to solve this scalability problem. Based on the observation that not all participants are equally active or engaged in distributed online collaboration applications, CVRetrieval differentiates the notions of consistency maintenance and consistency retrieval. Here, consistency maintenance implies a protocol that periodically communicates with all participants to maintain a certain consistency level; and consistency retrieval means that passive participants (those with little updating activity) explicitly request a consistent view from the system when the need arises in stead of joining the expensive consistency maintenance protocol all the time. The rationale is that, if a participant does not have updating activities, it is much more cost-effective to satisfy his or her needs on-demand. The evaluation of CVRetrieval is done in two parts. First, we theoretically analyze the scalability of CVRetrieval and compare it to other consistency maintenance protocols. The analytical result shows that CVRetrieval can greatly reduce communication cost and hence make consistency control more scalable. Second, a prototype of CVRetrieval is developed and deployed on the Planet-Lab test-bed to evaluate its performance. The results show that the active participants experience a short response time at some expense of the passive participants that may encounter a longer response time depends on the system setting. Overall, the retrieval performance is still reasonably high

CVRetrieval: Separating Consistency Retrieval from Consistency Maintenance

In distributed online collaboration applications, such as digital white board and online gaming, it is important to guarantee the consistency among participants’ views to make collaboration meaningful. However, maintaining even a relaxed consistency in a distributed environment with a large number of geographically dispersed participants still involves formidable communication and management cost among them. In this paper, we propose CVRetrieval (Consistency View Retrieval) to solve this scalability problem. Based on the observation that not all participants are equally active or engaged in distributed online collaboration applications, CVRetrieval differentiates the notions of consistency maintenance and consistency retrieval. Here, consistency maintenance implies a protocol that periodically communicates with all participants to maintain a certain consistency level; and consistency retrieval means that passive participants (those with little updating activity) explicitly request a consistent view from the system when the need arises in stead of joining the expensive consistency maintenance protocol all the time. The rationale is that, if a participant does not have updating activities, it is much more cost-effective to satisfy his or her needs on-demand. The evaluation of CVRetrieval is done in two parts. First, we theoretically analyze the scalability of CVRetrieval and compare it to other consistency maintenance protocols. The analytical result shows that CVRetrieval can greatly reduce communication cost and hence make consistency control more scalable. Second, a prototype of CVRetrieval is developed and deployed on the Planet-Lab test-bed to evaluate its performance. The results show that the active participants experience a short response time at some expense of the passive participants that may encounter a longer response time depends on the system setting. Overall, the retrieval performance is still reasonably high