Bagging and Boosting Negatively Correlated Neural Networks

In this paper, we propose two cooperative ensemble learning algorithms, i.e., NegBagg and NegBoost, for designing neural network (NN) ensembles. The proposed algorithms incrementally train different individual NNs in an ensemble using the negative correlation learning algorithm. Bagging and boosting algorithms are used in NegBagg and NegBoost, respectively, to create different training sets for different NNs in the ensemble. The idea behind using negative correlation learning in conjunction with the bagging/boosting algorithm is to facilitate interaction and cooperation among NNs during their training. Both NegBagg and NegBoost use a constructive approach to automatically determine the number of hidden neurons for NNs. NegBoost also uses the constructive approach to automatically determine the number of NNs for the ensemble. The two algorithms have been tested on a number of benchmark problems in machine learning and NNs, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, satellite, soybean, and waveform problems. The experimental results show that Neg- Bagg and NegBoost require a small number of training epochs to produce compact NN ensembles with good generalization.othe

A Comprehensive Framework of Usability Issues Related to the Wearable Devices

Wearable devices have the potential to be used for monitoring, augmenting, assisting, delivering content, and tracking in both individual and organizational contexts. Despite this potential, previous studies indicate that the abandonment rate is quite high relative to the usage rate due to usability factors. This chapter provides a comprehensive systematic literature review on the usability issues related to wearable devices, as well as recommendations for overcoming the identified problems. It also investigates and presents a survey of the existing usability evaluation methods used to identify and evaluate the usability of wearable devices, including their strengths and limitations. As such, we present a categorization framework that gives an overview of the overall usability issues that act as the barriers to user adoption and a summary of which types of usability issues are associated with which type of device category. The chapter has the potential to inform and assist researchers, practitioners, and application developers as they work toward developing, implementing, and evaluating wearable devices and their associated interfaces, and this, in turn, may assist with sustained engagement among users.Post-print / Final draf

IAA: interference aware anticipatory algorithm for scheduling and routing periodic realtime streams in wireless sensor networks

Abstract—This paper provides a polynomial time heuristic for the real-time communication scheduling problem in multi-hop wireless sensor networks. Wireless networks add a new dimension to the real-time communication problem because of interference: a transmission cannot be scheduled on a radio link if another transmission is scheduled on any interfering link. The problem being NP-hard in nature, we propose a novel heuristic that comes into two parts: (1) a scheduler that uses a topological analysis of the network to anticipate the effects of radio interference in order to improve scheduling prioritization, (2) an iterative route update scheme that pushes apart interfering streams and spreads them out over the network to reduce interference and improve schedulability while meeting the deadline requirements. The whole algorithm runs in polynomial time of O(N 3 d), where N and d are the number of streams and maximum deadline respectively. We use a simulation-based study to demonstrate that this algorithm produces near-optimal schedules for approximately 10 packet streams in a 100 node network, where the optimal schedule can be computed. We also show that the overall algorithm is able to schedule as much as 47 % more steams than simple heuristics that takes only deadline or interference into account. Of this improvement, 4 % − 26 % contribution comes from the iterative route update scheme. I

Addressing Burstiness for Reliable Communication and Latency Bound Generation

As wireless sensor networks mature, they are increasingly being used in real-time applications. Many of these applications require reliable transmission within latency bounds. Achieving this goal is very difficult because of link burstiness and interference. Based on significant empirical evidence of 21 days and over 3,600,000 packets transmission per link, we propose a scheduling algorithm that produces latency bounds of the real-time periodic streams and accounts for both link bursts and interference. The solution is achieved through the definition of a new metric Bmax that characterizes links by their maximum burst length, and by choosing a novel least-burst-route that minimizes the sum of worst case burst lengths over all links in the route. A testbed evaluation consisting of 48 nodes spread across a floor of a building shows that we obtain 100 % reliable packet delivery within derived latency bounds. We also demonstrate how performance deteriorates and discuss its implications for wireless networks with insufficient high quality links