Security and privacy issues in wireless medical sensor networks (WMSNs) have attracted lots of attention in both academia and industry due to the sensitiveness of medical system. In the past decade... Show More

Security and privacy issues in wireless medical sensor networks (WMSNs) have attracted lots of attention in both academia and industry due to the sensitiveness of medical system. In the past decade, extensive research has been carried out on these security issues, but no single study exists that addresses them adequately, especially for some important security properties, such as user anonymity and forward secrecy. As a step towards this direction, in this paper, the authors propose a lightweight three-factor anonymous authentication scheme with forward secrecy for personalized healthcare applications using only the lightweight cryptographic primitives. The proposed scheme adopts pseudonym identity technique to protect users' real identities and employs one-way hash chain technique to ensure forward secrecy. Analysis and comparison results demonstrate that the proposed scheme can not only reduce execution time by 34% as compared with the most effective related schemes, but also achieve more security and functional features.

Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique.... Show More

Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique. While temporal association rule-based recommendation algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting the recommendation performance. Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed. The proposed time-aware CF algorithm considers evolving features of users' historical feedback. And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences. The proposed temporal association rule-based recommendation algorithm considers the time context of users' historical behaviors when mining effective temporal association rules. Experimental results on real datasets show the feasibility and performance improvement of the proposed hybrid recommender system compared to other baseline approaches.

Smart transportation system is a cross-field research topic that involves both the organizations that manage the large-scaled system and individual end-users who enjoy these services. Recent... Show More

Smart transportation system is a cross-field research topic that involves both the organizations that manage the large-scaled system and individual end-users who enjoy these services. Recent advancement of machine learning-based algorithms has either enabled or improved a wide range of applications due to its strength in making accurate predictions for complex problems with a minimal amount of domain knowledge and great ability of generalization. These nice properties imply potential to be explored for building smart transportation system. This paper studies how deep reinforcement learning (DRL) can be used to optimize the operating policy in modern bike sharing systems. As a case study, the authors demonstrate the potential power of the modern DRL by showing a policy-gradient-based reinforcement learning approach to the rebalancing problem in a bike sharing system, which can simultaneously improve both the user experience and reduce the operational expense.

With the development of internet of things (IoT) technology, servitization of IoT device functions has become a trend. The cooperation between IoT devices can be equivalent to web service... Show More

With the development of internet of things (IoT) technology, servitization of IoT device functions has become a trend. The cooperation between IoT devices can be equivalent to web service composition. However, current service composition approaches applied in the internet cannot work well in IoT environments due to weak adaptability, low accuracy, and poor time performance. This paper, based on service dependency graph, proposes a top-k QoS-optimal service composition approach suitable for IoT. It aims to construct the relationship between services by applying the service dependency model and to reduce the traversal space through effective filtering strategies. On the basis of a composition path traversal sequence, the generated service composition can be represented directly to avoid backtracking search. Meanwhile, the redundant services can be removed from the service composition with the help of dynamic programming. Experiments show that the approach can obtain the top-k QoS-optimal service composition and better time performance.

Mobile and smart devices provide a platform for firms/brands to communicate directly with past, present, or potential consumers (via online pop-ups, sponsored ads, ads on social media messengers... Show More

Mobile and smart devices provide a platform for firms/brands to communicate directly with past, present, or potential consumers (via online pop-ups, sponsored ads, ads on social media messengers, timelines, walls, etc.). Existing research on human-mobile interaction and end-user mobile management only highlights the positive fronts of repetitive exposures to mobile ads, ignoring the negative. The present study examines the effects of mobile ad wearout on irritation, intrusiveness, engagement, and loyalty via social media outlets. Survey data were solicited from consumers in Jordan, and the partial least squares structural equation modeling (PLS-SEM) technique was applied. Results show that mobile ad wearout is a strong determinant for increased consumer irritation and perceived intrusiveness. Intrusiveness resulted in lower levels of consumer engagement and higher levels of consumer loyalty. Irritation resulted in lower levels of consumer engagement; no changes were observed in consumer loyalty. This study shows managers how mobile ad wearout causes irritation and intrusiveness, which diminishes consumer engagement and loyalty. In essence, managers can gain insights on the positive and negative outcomes of mobile ad wearout. Implications for theory and practice are discussed.