Data availability is one of the most important performance factors in cloud storage systems. To enhance data availability, replication is a common approach to handle the machine failures. However, previously proposed replication schemes cannot effectively handle both correlated and non-correlated machine failures, especially while increasing the data availability with limited resources. The schemes for correlated machine failures must create a constant number of replicas for each data object, which often neglects diverse data popularities and does not utilize the resource to maximize the expected data availability. Also, the previous schemes neglect the consistency maintenance cost and the storage cost caused by replication. It is critical for cloud providers to maximize data availability (hence minimize SLA violations) while minimizing costs caused by replication in order to maximize the revenue. In this paper, we build a nonlinear integer programming model to maximize data availability in both types of failures, and therefore minimize the cost caused by replication. Based on the model's solution for the replication degree of each data object, we propose a low-cost multi-failure (correlated and non-correlated machine failures) resilient replication scheme (MRR). MRR can effectively handle both correlated and non-correlated machine failures, considers data popularities to enhance data availability, and also tries to minimize consistency maintenance and storage cost. Extensive numerical results from trace parameters and experiments from real-world Amazon S3 demonstrate that MRR achieves high data availability, low data loss probability and low consistency maintenance and storage costs when compared to previous replication schemes.

Information-aware connected and automated vehicles (CAVs) have drawn great attention in recent years due to their potentially significant positive impacts on roadway safety and operational efficiency. In this paper, we conduct an in-depth review of three basic and key interrelated aspects of a CAV: sensing and communication technologies; human factors; and information-aware controller design. First, the different vehicular sensing and communication technologies and their protocol stacks, to provide reliable information to the information-aware CAV controller, are thoroughly discussed. Diverse human factors, such as user comfort, preferences, and reliability, to design the CAV systems for mass adaptation are also discussed. Then, the different layers of a CAV controller (route planning, driving mode execution, and driving model selection) considering human factors and information through connectivity are reviewed. In addition, the critical challenges for the sensing and communication technologies, human factors, and information-aware controller are identified to support the design of a safe and efficient CAV system while considering user acceptance and comfort. Finally, the promising future research directions of these three aspects are discussed to overcome existing challenges to realize a safe and operationally efficient CAV.

The ability to move the tongue from side to side inside the mouth and chew or apply pressure on food is an essential skill for feeding therapy and development. Those abilities are usually called Tongue Lateralization. To put it simply, it is how we "scavenge" those left-over food particles inside our teeth, cheeks, the gums, and lips. It is also essential for toddlers to develop speech skills as well as eating and chewing skills. These skills include awareness, strength, coordination, movement, and endurance of the lips, cheeks, tongue, and jaw. However, many children find difficulty in performing these tasks, which often prevent their growth and skills development. Currently, many exercises and tools can help children with these problems. However, most of them have low quality, easy to break, and not user-friendly. For example, parents may give the tools to their child to practice, but they would not know if their children are making progress. Not only that, in some cases, it is difficult for a therapist to keep track whether the patients are actually exercising or doing it properly since they cannot be with them 24/7. Therefore, we develop a tool by integrating sensors and Wi-Fi- enabled microcontroller into a therapeutic tool and develop an algorithm to overcome the sensory issues which are related to the practice times so that it can give better feedback on the patients' progress. By the help of this tool, parents and therapists can monitor the patients' progress in real time and assess whether these exercises are effective.

Large-scale data stores are an increasingly important component of cloud datacenter services. However, cloud storage system usually experiences data loss, hindering data durability. Three-way random replication is commonly used to lead better data durability in cloud storage systems. However, three-way random replication cannot effectively handle correlated machine failures to prevent data loss. Although Copyset Replication and Tiered Replication can reduce data loss in correlated and independent failures, and enhance data durability, they fail to leverage different data popularities to substantially reduce the storage cost and bandwidth cost caused by replication. To address these issues, we present a popularity-aware multi-failure resilient and cost-effective replication (PMCR) scheme for high data durability in cloud storage. PMCR splits the cloud storage system into primary tier and backup tier, and classifies data into hot data, warm data and cold data based on data popularities. To handle both correlated and independent failures, PMCR stores the three replicas of the same data into one Copyset formed by two servers in the primary tier and one server in the backup tier. For the third replicas of warm data and cold data in the backup tier, PMCR uses the compression methods to reduce storage cost and bandwidth cost. Extensive numerical results based on trace parameters and experimental results from real-world Amazon S3 show that PMCR achieves high data durability, low probability of data loss, and low storage cost and bandwidth cost compared to previous replication schemes.

Mobile crowdsensing serves as a critical building block for emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy, and storage) and may sacrifice the Quality-of-Service (QoS) of applications. Prior work has demonstrated that there is significant redundancy in the content of the sensed data. By judiciously reducing redundant data, data size and load can be significantly reduced, thereby reducing resource cost and facilitating the timely delivery of unique, probably critical information and enhancing QoS. This article presents a survey of existing works on mobile crowdsensing strategies with an emphasis on reducing resource cost and achieving high QoS. We start by introducing the motivation for this survey and present the necessary background of crowdsensing and IoT. We then present various mobile crowdsensing strategies and discuss their strengths and limitations. Finally, we discuss future research directions for mobile crowdsensing for IoT. The survey addresses a broad range of techniques, methods, models, systems, and applications related to mobile crowdsensing and IoT. Our goal is not only to analyze and compare the strategies proposed in prior works, but also to discuss their applicability toward the IoT and provide guidance on future research directions for mobile crowdsensing.

As a popular routing protocol in wireless sensor networks (WSNs), greedy routing has received great attention. The previous works characterize its data deliverability in WSNs by the probability of all nodes successfully sending their data to the base station. Their analysis, however, neither provides the information of the quantitative relation between successful data delivery ratio and transmission power of sensor nodes nor considers the impact of the network congestion or link collision on the data deliverability. To address these problems, in this paper, we characterize the data deliverability of greedy routing by the ratio of successful data transmissions from sensors to the base station. We introduce n-guaranteed delivery which means that the ratio of successful data deliveries is not less than n, and study the relationship between the transmission power of sensors and the probability of achieving n-guaranteed delivery. Furthermore, with considering the effect of network congestion, link collision, and holes (e.g., those caused by physical obstacles such as a lake), we provide a more precise and full characterization for the deliverability of greedy routing. Extensive simulation and real-world experimental results show the correctness and tightness of the upper bound of the smallest transmission power for achieving n-guaranteed delivery.

In cloud systems, efficient resource provisioning is needed to maximize the resource utilization while reducing the Service Level Objective (SLO) violation rate, which is important to cloud providers for high profit. Several methods have been proposed to provide efficient provisioning. However, the previous methods do not consider leveraging the complementary of jobs' requirements on different resource types and job size concurrently to increase the resource utilization. Also, by simply packing complementary jobs without considering job size in the job packing, it can decrease the resource utilization. Therefore, in this paper, we consider both jobs' demands on different resource types (in the spatial space) and jobs' execution time (in the temporal space); we pack the complementary jobs (whose demands on multiple resource types are complementary to each other) belonging to the same type and assign them to a Virtual Machine (VM) to increase the resource utilization. Moreover, the previous methods do not provide efficient resource allocation for heterogeneous jobs in current cloud systems and do not offer different SLO degrees for different job types to achieve higher resource utilization and lower SLO violation rate. Therefore, we propose a Customized Cooperative Resource Provisioning (CCRP) scheme for the heterogeneous jobs in clouds. CCRP uses the hybrid resource allocation and provides SLO availability customization for different job types. To test the performance of CCRP, we compared CCRP with existing methods under various scenarios. Extensive experimental results based on a real cluster and Amazon EC2 show that CCRP achieves 50% higher or more resource utilization and 50% lower or less SLO violation rate compared to the previous resource provisioning strategies.

Internet usage over mobile devices is on the rise. The bandwidth demand for mobile Internet access is also increasing with the number of mobile users. To answer users' demand, carrier aggregation is proposed in LTE-A system. In carrier aggregation, the best available one or more component carriers of each band are assigned to each user to provide efficient services. Several works have been reported in the literature on mandatory and periodic component carrier assignment methods. Although the previous works, especially periodic component carrier assignment methods, have significantly improved performance of LTE and LTE- A systems, many limitations still exist. One limitation of previous works is that data transfer is interrupted during periodic component carrier assignment operation which can decrease performance of the system. Therefore, in this paper, selective periodic component carrier assignment technique which allows continues data transfer during periodic carrier assignment operations is proposed. Results show that the proposed technique increases throughput rate up to 25% and decreases average delay time up to 35%.

Large multimedia files are accessed by a high number of mobile users over the Internet. Therefore, the bandwidth demand for mobile Internet access is increasing exponentially. To answer users' demand, carrier aggregation is proposed in LTE-A. In carrier aggregation, the best available one or more component carriers of each band are assigned as primary and secondary component carriers to each user for efficient services. The previous works have significantly improved the performance of LTE-A by using efficient component carrier assignment methods. The previous works, however, have two limitations. First, they have not investigated which of joint or partial secondary component carrier assignment techniques shows better performance. Second, overall system performance is analyzed in order to evaluate methods by ignoring system behavior such as packet drops and delay metrics during carrier reassignment operations. Therefore, in this paper, firstly, packet drops and delay which are experienced by users during the carrier reassignment process are investigated. Secondly, joint and partial secondary component carrier assignment techniques are compared. Results show that the partial carrier assignment technique increases efficiency of resource usage, throughput rate up to 15% and decreases average delay time up to 12%.

The number of mobile users is rapidly increasing. Therefore, the bandwidth demand of mobile users significantly growing. To answer users' demand, Carrier Aggregation is proposed in LTE systems. In Carrier Aggregation, communication between users and base stations are achieved by multi bands which have different coverage areas and mobile users can simultaneously connect one or multi carrier components in each band. Because of mobility of users and quality of carriers, the best available Carrier Components of each band should be assigned to each user in order to provide desired service to users. Several works have been proposed in the literature to address Carrier Components assignment methods in LTE systems by using Channel Quality Indicator, quality of service and service types. Although the previous works on carrier assignment methods significantly increase the performance of LTE system, continuously increasing bandwidth demand of users forces the operators to manage resource allocation more intelligently. Therefore, we have proposed a novel Carrier Component assignment method which considers user profiles and channel quality indicator to increase quality of services and experiences getting by mobile users. Results show that the proposed method uses system resources efficiently and can improve performance of LTE systems. Our method will help service providers build efficient carrier components assignment methods through considering user profile and performance metrics, such as band usage, throughput and delay.

Simplicity of usage, flexibility of data access, ease of maintenance, time and energy efficiency, and pay as you go policy have increased the usage of cloud computing over traditional computing. Cloud computing should be able to meet the performance expectations of different classes of customers. On the contrary, inefficient scheduling algorithms decrease the quality of service experienced by users. To improve quality of service, there are several proposed scheduling algorithms in the literature. However, these scheduling algorithms are limited because they do not consider different types of customers. Our objective is to satisfy performance expectations of customers by proposing an efficient Dynamic Dedicated Server Scheduling (DDSS) while considering different types of customers. Results show that the customer drop rate and throughput can be significantly improved by DDSS. Our proposed scheduling and related analysis will help cloud service providers build efficient cloud computing service architectures through considering different types of priority class performances such as, drop rate, throughput, and utilization.

Simplicity of usage, flexibility of data access, ease of maintenance, time and energy efficiency, and pay as you go policy have increased the usage of cloud computing over traditional computing. Cloud computing should be able to meet the performance expectations of different classes of customers. However, inefficient scheduling algorithms decrease the quality of service experienced by users. To improve quality of service, several scheduling algorithms have been proposed in the literature. However, these scheduling algorithms do not consider heterogeneous servers or different priority classes of customers. Our objective is to satisfy performance expectations of customers by proposing a Heterogeneous Dynamic Dedicated Server Scheduling (h-DDSS) while considering heterogeneous servers and different priority classes of customers. Results show that h-DDSS architecture can provide improved customer throughput and drop rate over homogeneous DDSS in cloud computing. Our proposed scheduling algorithm and related analysis will help cloud service providers build efficient cloud service architectures which are adaptable to homogeneous and heterogeneous environments by considering different types of priority class performances, such as, drop rate, throughput, and utilization.

To facilitate higher bandwidth for multimedia traffic, modern routers support simultaneous multi-band communication, leading to less interference, higher capacity and better reliability. However, there is lack of quantitative evaluation to judge whether multi-band is better than single band router in realistic scenarios. Our objective is to propose a scheduling algorithm for multi-band routers and compare single band and multi-band system with different allocation policies. We have used different scheduling algorithms for multi-band routers which transmit different classes of traffic through different frequency bands, thereby achieving improved performance. By comparing multi-band and single band mobile router performances, we have found out that one of them is not always better than the other although multi-band is expected to have better performance.

Recent trends in Internet usage have seen large amount of multimedia data due to increasingly large number of mobile users. To facilitate higher bandwidth, modern mobile routers are capable of supporting simultaneous multi-band, leading to less interference, higher capacity. However, there exists no previous work that attempts to maximize utilization through sharing of traffic among different frequency bands. In this paper, we have proposed a novel scheduling algorithm for multi-band mobile routers which transmits different classes of traffic through different frequency bands to achieve improved performance. We have developed an analytical model to perform queuing analysis of the proposed multi-band system and derived various performance metrics, validated by extensive simulations. Our results show that the proposed architecture can ensure maximum possible utilization through sharing of capacities among the bands. Our proposed scheduling algorithm and related analytical model can help network engineers build next generation mobile routers with higher throughput and utilization ensuring least packet loss for different classes of traffic.