This paper presents the first study on scheduling for cooperative data dissemination in a hybrid infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication environment. We formulate the novel problem of cooperative data scheduling (CDS). Each vehicle informs the road-side unit (RSU) the list of its current neighboring vehicles and the identifiers of the retrieved and newly requested data. The RSU then selects sender and receiver vehicles and corresponding data for V2V communication, while it simultaneously broadcasts a data item to vehicles that are instructed to tune into the I2V channel. The goal is to maximize the number of vehicles that retrieve their requested data. We prove that CDS is NP-hard by constructing a polynomial-time reduction from the Maximum Weighted Independent Set (MWIS) problem. Scheduling decisions are made by transforming CDS to MWIS and using a greedy method to approximately solve MWIS. We build a simulation model based on realistic traffic and communication characteristics and demonstrate the superiority and scalability of the proposed solution. The proposed model and solution, which are based on the centralized scheduler at the RSU, represent the first known vehicular ad hoc network (VANET) implementation of software defined network (SDN) concept.Index Terms-Cooperative data dissemination, scheduling, software defined network, vehicular ad hoc networks.

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In this paper, by applying motion detection and machine learning technologies, we have designed and developed an activity tracking and monitoring system, called SmartMind, to help Alzheimer's Disease (AD) patients to live independently within their living rooms while providing emergency assistances and supports when necessary. Allowing AD patients to handle their daily activities not only can release the burdens on their families and caregivers, it is also highly important to help them regain confidence towards a healthy life. The daily activities of a patient captured from SmartMind can also serve as an important indicator to describe his/her normal living habit (NLH). By checking NLH, the patient's current health status can be estimated on a daily basis. In the testing experiments of SmartMind, we have demonstrated the accuracy of SmartMind in activity detection and investigated its A preliminary version of the paper is appearred in Multimed Tools Appl performance when different machine learning algorithms were adopted for posture detection. The performance results indicate that both support vector machine (SVM) and naive bayes (NB) can achieve an accuracy of higher than 97 % while the random forrests (RF) only gives an accuracy of around 73 %.

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This article examines a new problem of k-anonymity with respect to a reference dataset in privacyaware location data publishing: given a user dataset and a sensitive event dataset, we want to generalize the user dataset such that by joining it with the event dataset through location, each event is covered by at least k users. Existing k-anonymity algorithms generalize every k user locations to the same vague value, regardless of the events. Therefore, they tend to overprotect against the privacy compromise and make the published data less useful. In this article, we propose a new generalization paradigm called local enlargement, as opposed to conventional hierarchy-or partition-based generalization. Local enlargement guarantees that user locations are enlarged just enough to cover all events k times, and thus maximize the usefulness of the published data. We develop an O(H n )-approximate algorithm under the local enlargement paradigm, where n is the maximum number of events a user could possibly cover and H n is the Harmonic number of n. With strong pruning techniques and mathematical analysis, we show that it runs efficiently and that the generalized user locations are up to several orders of magnitude smaller than those by the existing algorithms. In addition, it is robust enough to protect against various privacy attacks.

Abstract. Received Signal Strength (RSS) is one of the most useful information used for location estimation in Wireless LAN (WLAN). Most of the proposed WLAN positioning systems obtain RSS from either the Access Point or from the Mobile Device, but there are few researches that make use of the RSS obtained from both Access Points and Mobile Devices to perform location estimation. In this paper, we propose a new WLAN positioning system which makes use of the RSS collected from both the Access Points and Mobile Device. Our experimental result shows that the performance of our system is enhanced more than 23%, as compares to the traditional fingerprint-based WLAN positioning system which uses either RSS information obtained at Access Points or Mobile Device exclusively.

On-demand broadcast is an attractive data dissemination method for mobile and wireless computing. In this paper, we propose a new online preemptive scheduling algorithm, called PRDS that incorporates urgency, data size and number of pending requests for real-time on-demand broadcast system. Furthermore, we use pyramid preemption to optimize performance and reduce overhead. A series of simulation experiments have been performed to evaluate the real-time performance of our algorithm as compared with other previously proposed methods. The experimental results show that our algorithm substantially outperforms other algorithms over a wide range of workloads and parameter settings.

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