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The accuracy of sensor location estimation influences directly the quality and reliability of services provided by a wireless sensor network (WSN). However, current localization methods may require additional hardware, like global positioning system (GPS), or suffer from inaccuracy like detecting radio signals. It is not proper to add extra hardware in tiny sensors, so the aim is to improve the accuracy of localization algorithms.

The original signal propagation‐based localization algorithm adopts a static attenuation factor model and cannot adjust its modeling parameters in accordance with the local environment. In this paper an adaptive localization algorithm for WSNs that can dynamically adjust ranging function to calculate the distance between two sensors is presented. By adjusting the ranging function dynamically, the location of a sensor node can be estimated more accurately.

The NCTUNs simulator is used to verify the accuracy and analyze the performance of the algorithm. Simulation results show that the algorithm can indeed achieve more accurate localization using just a small number of reference nodes in a WSN.

There is a need to have accurate location information of reference nodes.

This is an effective low‐cost solution for the localization of sensor nodes.

An adaptive localization algorithm that can dynamically adjust ranging function to calculate the distance between two sensors for sensor network deployment and providing location services is described.

Recent wireless communication and electronics technology has enabled the development of low‐cost, low‐power, and multi‐functional sensor nodes. However, the fact that sensor nodes are severely energy‐constrained has been an issue and many energy‐efficient routing protocols have been proposed to resolve it. Cluster‐based routing protocol is one of them. To achieve longer lifetime, some cluster‐based routing protocols use information on GPS‐based location of each sensor node. However, because of high cost, not all sensor nodes can be GPS‐enabled. The purpose of this paper is to propose a simple dynamic clustering approach to achieve energy efficiency for wireless sensor networks (WSN).

Instead of using location information of each sensor node, this approach utilizes information of remaining energy of each sensor node and changes in the number of cluster head nodes dependent on the number of sensor nodes alive. Performance results are presented and compared with some related protocols.

The simulations described in the paper show that both residual energy of each sensor node and changing cluster head nodes depending on the number of sensor nodes alive are very critical factors to obtain performance enhancement in terms of lifetime and data transmission. Especially, in some special environment, the proposal has better performance than GPS‐enabled protocol.

The paper is of value in proposing a simple dynamic clustering approach to achieve energy efficiency for WSN.

Presence of innumerable sensors, complex deduction of contexts from sensor data, and reusability of contextual information impose the requirement of middleware for context aware computing. Smart applications, hosted in myriad devices (e.g. PDA, mobile, PCs), acquire different contexts from the middleware and act intelligently based on the available contexts in a context‐aware computing environment. As the system grows larger, scalable delivery of contexts from the middleware to numerous context‐aware applications will be inevitable. However, pure unicast based or pure broadcast‐based dissemination cannot provide high scalability as well as low‐average latency. The purpose of this paper is to present a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems.

The proposed scheme is based on hybrid data dissemination technique where the most frequently requested data (e.g. HOT contexts) are delivered through multicast and the rest (e.g. COLD contexts) are delivered through unicast to reduce network traffic. The paper dynamically prioritizes and classifies the HOT and COLD context data depending on the number of requests and longest waiting time. Moreover, the division of bandwidth between the delivery of HOT and COLD contexts reduces average latency. Polling traffic is decreased by incorporating leasing mechanism. Extensive simulation is conducted to evaluate the proposed scheme.

The mechanism dynamically prioritizes and classifies the hot and cold context data depending on the request rate and longest waiting time. The solution addresses the push popularity problem that occurs in the passive as the passive clients access data without sending explicit requests. The leasing mechanism is incorporated to reduce the periodical requests (polling) for better performance.

The paper is of value in presenting a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems and also in presenting implementation details of a prototype that is developed using Jini framework and Java reliable multicast service (JRMS) library.

The purpose of this paper is to describe a speech and character combined recognition engine (SCCRE) developed for working on personal digital assistants (PDAs) or on mobile devices. Also, the architecture of a distributed recognition system for providing a more convenient user interface is discussed.

In SCCRE, feature extraction for speech and for character is carried out separately, but the recognition is performed in an engine. The client recognition engine essentially employs a continuous hidden Markov model (CHMM) structure and this CHMM structure consists of variable parameter topology in order to minimize the number of model parameters and to reduce recognition time. This model also adopts the proposed successive state and mixture splitting (SSMS) method for generating context independent model. SSMS optimizes the number of mixtures through splitting in mixture domain and the number of states through splitting in time domain.

The recognition results show that the developed engine can reduce the total number of Gaussian up to 40 per cent compared with the fixed parameter models at the same recognition performance when applied to speech recognition for mobile devices. It shows that SSMS can reduce the size of memory for models to 65 per cent and that for processing to 82 per cent. Moreover, the recognition time decreases 17 per cent with the SMS model while maintaining the recognition rate.

The proposed system will be very useful for many on‐line multimodal interfaces such as PDAs and mobile applications.

Recently the advances in wireless communication technology and the popularity of portable computers have rendered mobile computing environments from which mobile users with battery‐operated palmtops can access the information via wireless channels, without space and time restriction. In mobile computing environments, mobile users cache the data items to use the bandwidth efficiently and improve the response time of mobile transactions. If the data items cached in mobile users are updated at the server, the server broadcasts an invalidation report for maintaining the cache consistency of mobile users. However, this method has an obstacle that does not guarantee the serializable execution of mobile transactions. The purpose of this paper is to propose the four types of reports for mobile transaction (FTR‐MT) method for ensuring the serializable execution of mobile transactions.

The paper describes the FTR‐MT method, which is composed of four types of algorithms, e.g. group report composition algorithm, immediate commit decision algorithm, cache consistency algorithm, and disconnection cache consistency algorithm. FTR‐MT method for improving the response time of mobile transactions makes a commit decision by using the four types of reports.

With the FTR‐MT method, mobile users can make a commit decision by using the four types of reports. The response time of mobile transactions can be reduced. Furthermore, the FTR‐MT method can improve the cache efficiency in the case that the disconnection of mobile users is longer than the broadcast interval of the window report.

This paper proposes a new method for guaranteeing the serializable execution of mobile transactions, called FTR‐MT, using four types of reports. Also, it can prevent the entire cache dropping, even though the disconnection of a mobile host is longer than the broadcast interval of a window report. Through the analytical model, this method is felt to be superior to other methods, in terms of the average response time and the commit rate of mobile transactions, and bandwidth usage.

Context‐aware mobile computing extends the horizons of the conventional computing model to a ubiquitous computing environment that serves users at anytime, anywhere. To achieve this, mobile applications need to adapt their behaviors to the changing context. The purpose of this paper is to present a generalized adaptive middleware infrastructure for context‐aware computing.

Owing to the vague nature of context and uncertainty in context aggregation for making adaptation decisions, the paper proposes a fuzzy‐based service adaptation model (FSAM) to improve the generality and effectiveness of service adaptation using fuzzy theory.

By the means of fuzzification of the context and measuring the fitness degree between the current context and the predefined optimal context, FSAM selects the most suitable policy to adopt for the most appropriate service. The paper evaluates the middleware together with the FSAM inference engine by using a Campus Assistant application.

The paper is of value in presenting a generalized adaptive middleware infrastructure for context‐aware computing and also comparing the performance of the fuzzy‐based solution with a conventional threshold‐based approach for context‐aware adaptation.

IEEE 802.15.4 is a new standard uniquely designed for low‐rate wireless sensor networks (WSNs). It targets low data rate, low power consumption and low‐cost wireless networking, and offers device level wireless connectivity. The purpose of this paper is to propose a traffic adaptive power control algorithm for beacon relayed distributed WSNs.

A general coordinated sleeping algorithm and the traffic adaptive algorithm are combined in an IEEE 802.15.4 MAC protocol to achieve high‐energy efficiency and high performance at the same time.

By observing the sporadic traffic and beacon relaying characteristics of WSNs, the paper proposes a traffic‐adaptive IEEE 802.15.4 MAC with a coordinated sleeping algorithm. Based on various performance studies, it was found that the proposed algorithm can significantly improve power consumptions in wireless sensor networks.

The paper is of value in proposing a traffic adaptive power control algorithm showing highly efficient power consumptions in low‐traffic conditions as well as with an acceptable degree of adaptation to high‐traffic conditions. In delay performance, it shows longer delay performance compared with other schemes because of the beacon relay procedure while the proposed algorithm reduces the power consumptions dramatically.

The intent of this essay is to offer a range of resources on the psychology of disability to the serious researcher in the field. The researcher may be an upper‐college level or graduate student. This individual may be researching for academic purposes, or for personal reasons associated with the disability of himself or herself, or a loved one. Although the individual with a disability can be psychologically or physically disabled, or both, this essay will focus more heavily on the physically disabled.