The development of real-time locating systems (RTLS) has become an important add-on to many existing location aware systems. While GPS has solved most of the outdoor RTLS problems, it fails to repeat this success indoors. A number of technologies have been used to address the indoor tracking problem. The ability to accurately track the location of people indoors has many applications ranging from medical, military and logistical to entertainment. However, current systems cannot provide continuous real-time tracking of a moving target or lose capability when coverage is poor. The deployment of a real-time location determination system however is fraught with problems. To date there has been little research into comparing commercial systems on the market with regard to informing IT departments as to their performance in various aspects which are important to tracking devices and people in relatively confined areas. This article attempts to provide such a useful comparison by providing a review of the practicalities of installing certain location-sensing systems. We also comment on the accuracies achieved and problems encountered using the position-sensing systems.

This article introduces a micro-simulation of urban traffic flows within a large scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic simulations come from a population census and dedicated road surveys which only partly cover shopping, leisure or recreational trips. To account for the latter, the presented traffic modelling framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of communities in a country-wide mobile phone network to account for socially related journeys. These data-sets were used to calibrate a variant of a radiation model of spatial choice, which we introduced in order to drive individuals' decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a comprehensive set of urban facilities and a list of typical activity sequences of city dwellers collected within a national travel survey, the developed micro-simulation reproduces not only the journey statistics such as peak travel periods but also the traffic volumes at main road segments with surprising accuracy.

In a world of ever-growing customer data, businesses are required to have a clear line of sight into what their customers think about the business, its products, people and how it treats them. Insight into these critical areas for a business will aid in the development of a robust customer experience strategy and in turn drive loyalty and recommendations to others by their customers. It is key for business to access and mine their customer data to drive a modern customer experience. This article investigates the use of a text mining approach to aid sentiment analysis in the pursuit of understanding what customers are saying about products, services and interactions with a business. This is commonly known as Voice of the Customer (VOC) data and it is key to unlocking customer sentiment. The authors analyse the relationship between unstructured customer sentiment in the form of verbatim feedback and structured data in the form of user review ratings or satisfaction ratings to explore the question of whether customers say what they really think when given the opportunity to provide free text feedback as opposed to how they rate a product on a scale of one to five. Using various Sentiment Analysis approaches, the authors assign a sentiment score to a piece of verbatim feedback and then categorise it as positive, negative, or neutral. Using this normalised sentiment score, they compare it to the corresponding rating score and investigate the potential business insights. The results obtained indicate that a business cannot rely solely on a standalone single metric as a source of truth regarding customer experience. There is a significant difference between the customer ratings score and the sentiment of their corresponding review of the product. The authors propose that it is imperative that a business supplements their customer feedback scores with a robust sentiment analysis strategy.

Abstract. Knowledge of the location of people and things has always been a valuable commodity. The explosion of new devices and techniques has brought people and item tracking out of the experimental stage and into the wider world. Using Wi-Fi signals is an attractive and reasonably affordable option to deal with the currently unsolved problem of widespread tracking in an indoor environment. HABITS (History Aware Based Indoor Tracking System) models human movement patterns by applying a discrete Bayesian filter to predict the areas that will, or will not, be visited in the future. We outline here the operation of the HABITS Real-Time Location System (RTLS) and discuss the implementation in relation to indoor Wi-Fi tracking with a large wireless network. Testing of HABITS shows that it gives comparable levels of accuracy to those achieved by doubling the number of access points. These probabilistic predictions may be used as an additional input into building automation systems for intelligent control of heating and lighting. It is twice as accurate as existing systems in dealing with signal black spots and it can predict the final destination of a person within the test environment almost 80% of the time.

SUMMARYLocation awareness is becoming an important capability for mobile computing; however, it has not been possible until now to provide cheap pervasive positioning systems. Wide area coverage is most famously achieved by using global positioning systems (GPS). A constellation of low-orbit satellites cover the earth's surface. Unfortunately GPS does not work indoors and has limited success in big cities because of the 'urban canyon' effect. PlaceLab is a research project that attempts to solve the ubiquity issues surrounding 802.11-based location estimation. PlaceLab, like RADAR, uses a device's 802.11 interface; however, it does not require the area to be pre-calibrated. It predicts location via the known positions of the access points detected by the device. Commonly used systems have a number of drawbacks, including cost, accuracy and the ability to work indoors. PlaceLab is a piece of open source software developed by Intel Research that can pinpoint a user within a Wi-Fi network. We set out here to investigate whether PlaceLab can be used as a means of establishing a user's position. This type of investigation could, if successful, pave the way for the development of other location-based applications. This report documents the efforts to answer the above question. PlaceLab was found to work, but only in ideal locations where factors such as the number of fl oors and the lack of available APs did not affect its use. It was concluded that these factors prevent the system from being effective as a means of establishing a user's position in most locations on campus.