The widespread acceptance of cloud based services in the healthcare sector has resulted in cost effective and convenient exchange of Personal Health Records (PHRs) among several participating entities of the e-Health systems. Nevertheless, storing the confidential health information to cloud servers is susceptible to revelation or theft and calls for the development of methodologies that ensure the privacy of the PHRs. Therefore, we propose a methodology called SeSPHR for secure sharing of the PHRs in the cloud. The SeSPHR scheme ensures patient-centric control on the PHRs and preserves the confidentiality of the PHRs. The patients store the encrypted PHRs on the un-trusted cloud servers and selectively grant access to different types of users on different portions of the PHRs. A semi-trusted proxy called Setup and Re-encryption Server (SRS) is introduced to set up the public/private key pairs and to produce the re-encryption keys. Moreover, the methodology is secure against insider threats and also enforces a forward and backward access control. Furthermore, we formally analyze and verify the working of SeSPHR methodology through the High Level Petri Nets (HLPN). Performance evaluation regarding time consumption indicates that the SeSPHR methodology has potential to be employed for securely shar-ing the PHRs in the cloud.

Power consumption cost constitutes significant portion of a data center's total operational cost. Variations in parameters, such as power demand, electricity price, and renewable power generation effects the data center's power consumption cost. Therefore, in this paper, a smart power management system based on a robust energy cost optimization algorithm is proposed for the data center. The designed robust optimization algorithm coordinates data center workload, battery bank, diesel generators, renewable power, and trade electricity price in real-time and day-ahead power market to reduce expected energy consumption cost. The uncertain parameters, such as data center workload and renewable power are computed using forecasting algorithms. The optimization algorithm is used to limit unbalance power purchase from real-time power market due to uncertainty of real-time electricity price. The algorithm formulation is obtained using mixed-integer linear programming. Moreover, a model to calculate prices to be used in service level agreements with data centers' clients for on-demand cloud services is designed considering operational cost of the data center. Simulations are performed on actual data center workload, weather parameters, and electricity price. The results have shown that the proposed methodology is an effective tool to minimize data center operational cost.

Enormous energy consumption of data centers has a major impact on power systems by significantly increasing the electrical load. Due to the increase in electrical load, power systems are facing demand and supply miss-management problems. Therefore, power systems require efficient and intelligent ancillary services to maintain robustness, reliability, and stability. Data centers can provide the computational capabilities to manage power systems; however, data centers consume a tremendous amount of energy, and energy price accounts for a significant portion of their operational cost. Power system jobs will make this situation even more critical for data centers. In our work, we seek an Ancillary Services Model (ASM) to service data centers and power systems. In ASM, we find an optimal job scheduling technique for executing power systems' jobs on data centers in terms of low power consumption, reduced makespan, and fewer preempted jobs. The power systems' jobs include Optimal Power Flow (OPF) calculation, transmission line importance index, and bus importance index. Moreover, a Service Level Agreement (SLA) between data centers and power systems is shown to provide mutual benefits.

We present a new keywords extraction method that applies the semantic similarity among the frequent words on the web page along with the distribution of POS tags. We apply hierarchical clustering to cluster the semantically similar words that have more coverage of the content of the web page. Our method shows better performance than CL-Rank and other existing methodologies.

Online Social Networks (OSNs) have not only significantly reformed the social interaction pattern but have also emerged as an effective platform for recommendation of services and products. The upswing in use of the OSNs has also witnessed growth in unwanted activities on social media. On the one hand, the spammers on social media can be a high risk towards the security of legitimate users and on the other hand some of the legitimate users, such as bloggers can pollute the results of recommendation systems that work alongside the OSNs. The polluted results of recommendation systems can be precarious to the masses that track recommendations. Therefore, it is necessary to segregate such type of users from the genuine experts. We propose a framework that separates the spammers and unsolicited bloggers from the genuine experts of a specific domain. The proposed approach employs modified Hyperlink Induced Topic Search (HITS) to separate the unsolicited bloggers from the experts on Twitter on the basis of tweets. The approach considers domain specific keywords in the tweets and several tweet characteristics to identify the unsolicited bloggers. Experimental results demonstrate the effectiveness of the proposed methodology as compared to several state-of-the-art approaches and classifiers.

In recent years, recommendation systems have seen significant evolution in the field of knowledge engineering. Most of the existing recommendation systems based their models on collaborative filtering approaches that make them simple to implement. However, performance of most of the existing collaborative filtering-based recommendation system suffers due to the challenges, such as: (a) cold start, (b) data sparseness, and (c) scalability. Moreover, recommendation problem is often characterized by the presence of many conflicting objectives or decision variables, such as users' preferences and venue closeness. In this paper, we proposed MobiContext, a hybrid cloud-based bi-objective recommendation framework (BORF) for mobile social networks. The MobiContext utilizes multi-objective optimization techniques to generate personalized recommendations. To address the issues pertaining to cold start and data sparseness, the BORF performs data preprocessing by using the Hub-Average (HA) inference model. Moreover, the Weighted Sum Approach (WSA) is implemented for scalar optimization and an evolutionary algorithm (NSGA-II) is applied for vector optimization to provide optimal suggestions to the users about a venue. The results of comprehensive experiments on a large-scale real dataset confirm the accuracy of the proposed recommendation framework.

To respond to emergencies in a fast and an effective manner, it is of critical importance to have efficient evacuation plans that lead to minimum road congestions. Although emergency evacuation systems have been studied in the past, the existing approaches, mostly based on multi-objective optimizations, are not scalable enough when involve numerous time varying parameters, such as traffic volume, safety status, and weather conditions. In this paper, we propose a scalable emergency evacuation service, termed the MacroServ that recommends the evacuees with the most preferred routes towards safe locations during a disaster. Unlike many existing approaches that model systems with static network characteristics, our approach considers real-time road conditions to compute the maximum flow capacity of routes in the transportation network. The evacuees are directed towards those routes that are safe and have least congestion resulting in decreased evacuation time. We utilized probability distributions to model the real-life stochastic behaviors of evacuees during emergency scenarios. The results indicate that recommendation of appropriate routes during emergency scenarios play a critical role in quicker and safe evacuation of the population.

The evolution of mobile social networks and the availability of online check-in services, such as Foursquare and Gowalla, have initiated a new wave of research in the area of venue recommendation systems. Such systems recommend places to users closely related to their preferences. Although venue recommendation systems have been studied in recent literature, the existing approaches, mostly based on collaborative filtering, suffer from various issues, such as: 1) data sparseness, 2) cold start, and 3) scalability. Moreover, many existing schemes are limited in functionality, as the generated recommendations do not consider group of “friends” type situations. Furthermore, the traditional systems do not take into account the effect of real-time physical factors (e.g., distance from venue, traffic, and weather conditions) on recommendations. To address the aforementioned issues, this paper proposes a novel cloud-based recommendation framework OmniSuggest that utilizes: 1) Ant colony algorithms, 2) social filtering, and 3) hub and authority scores, to generate optimal venue recommendations. Unlike existing work, our approach suggests venues at a finer granularity for an individual or a “group” of friends with similar interest. Comprehensive experiments are conducted with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than many state of the art schemes.