Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Actors: The Ideal Abstraction for Programming Kernel-Based Concurrency

GPU and multicore hardware architectures are commonly used in many different application areas to accelerate problem solutions relative to single CPU architectures. The typical approach to accessing these hardware architectures requires embedding logic into the programming language used to construct the application; the two primary forms of embedding are: calls to API routines to access the concurrent functionality, or pragmas providing concurrency hints to a language compiler such that particular blocks of code are targeted to the concurrent functionality. The former approach is verbose and semantically bankrupt, while the success of the latter approach is restricted to simple, static uses of the functionality. Actor-based applications are constructed from independent, encapsulated actors that interact through strongly-typed channels. This paper presents a first attempt at using actors to program kernels targeted at such concurrent hardware. Besides the glove-like fit of a kernel to the actor abstraction, quantitative code analysis shows that actor-based kernels are always significantly simpler than API-based coding, and generally simpler than pragma-based coding. Additionally, performance measurements show that the overheads of actor-based kernels are commensurate to API-based kernels, and range from equivalent to vastly improved for pragma-based annotations, both for sample and real-world applications

Personalised mobile services supporting the implementation of clinical guidelines

Telemonitoring is emerging as a compelling application of Body Area Networks (BANs). We describe two health BAN systems developed respectively by a European team and an Australian team and discuss some issues encountered relating to formalization of clinical knowledge to support real-time analysis and interpretation of BAN data. Our example application is an evidence-based telemonitoring and teletreatment application for home-based rehabilitation. The application is intended to support implementation of a clinical guideline for cardiac rehabilitation following myocardial infarction. In addition to this the proposal is to establish the patient’s individual baseline risk profile and, by real-time analysis of BAN data, continually re-assess the current risk level in order to give timely personalised feedback. Static and dynamic risk factors are derived from literature. Many sources express evidence probabilistically, suggesting a requirement for reasoning with uncertainty; elsewhere evidence requires qualitative reasoning: both familiar modes of reasoning in KBSs. However even at this knowledge acquisition stage some issues arise concerning how best to apply the clinical evidence. Furthermore, in cases where insufficient clinical evidence is currently available, telemonitoring can yield large collections of clinical data with the potential for data mining in order to furnish more statistically powerful and accurate clinical evidence

Enhancement of Surgical Training Practice with the Spring Tensor Heuristic Model

The enhancement of surgical simulation tools is an important research study, to assist in the assessment and feedback of medical training practice. In this research, the Spring Tensor Model (STEM) has been used for laparoscopic end-effector navigation through obstacles and high-risk areas. The modelling of the surgical trainer as part of the laparoscopic simulator seeks to emulate the physical environment as a virtualised representation in the integrated infrastructure. Combining sensor network framework paradigms to a surgical knowledge-based construct demonstrates how STEMcan enhance medical practice. The architectural hybridisation of the training framework has enabled the adaptation of STEM modelling techniques for a simulated laparoscopic training methodology. The primary benefit of the architecture is that this integration strategy has resulted in a seamless transition of the heuristic framework to be applied to surgical training

Enhancement of surgical training practice with the spring tensor heuristic model

The enhancement of surgical simulation tools is an important research study, to assist in the assessment and feedback of medical training practice. In this research, the Spring Tensor Model (STEM) has been used for laparoscopic end-effector navigation through obstacles and high-risk areas. The modelling of the surgical trainer as part of the laparoscopic simulator seeks to emulate the physical environment as a virtualised representation in the integrated infrastructure. Combining sensor network framework paradigms to a surgical knowledge-based construct demonstrates how STEM can enhance medical practice. The architectural hybridisation of the training framework has enabled the adaptation of STEM modelling techniques for a simulated laparoscopic training methodology. The primary benefit of the architecture is that this integration strategy has resulted in a seamless transition of the heuristic framework to be applied to surgical training

Business Case and Technology Analysis for 5G Low Latency Applications

A large number of new consumer and industrial applications are likely to change the classic operator's business models and provide a wide range of new markets to enter. This article analyses the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analysed. Conclusions show that operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.Comment: 18 pages, 5 figure

Improving the Unreliability of Competence Information:an Argumentation to Apply Information Fusion in Learning Networks

Miao, Y., Sloep, P. B., Hummel, H., & Koper, R. (2009). Improving the Unreliability of Competence Information: an Argumentation to Apply Information Fusion in Learning Networks [Special issue]. International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), 19(4/5/6), 366-380.Automated competence tracking and management is crucial for an effective and efficient lifelong competence development in learning networks. In this paper, we systematically analyze the problem of unreliability of competence information in learning networks. In tracking the development of competences in learning networks, a large amount of competence information can be gathered from diverse sources and diverse types of sources. Individual information is more or less credible. This paper investigates information fusion technologies that may be applied to address the problem and that show promise as candidate solutions for achieving an improved estimate of competences by fusing information coming from multiple sources and diverse types of sources.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org