CCVNet: A Modified Content-Centric Approach to Enable Multiple Types of Applications in Vehicular Networks

Wireless Personal Communications - Intelligent transportation systems and connected vehicles, by utilization of new facilities and technologies, have a significant role in improving the quality of...     

Degradation kinetics of anthocyanins in freeze-dried microencapsulates from lowbush blueberries (Vaccinium angustifolium Aiton) and prediction of shelf-life

The addition of anthocyanins in food products is limited by their instability. In this study, an extract containing anthocyanins from lowbush blueberries was encapsulated by freeze-drying in three maltodextrin matrices with different dextrose equivalents, for potential use as a colorant and functional ingredient. The resultant encapsulates differed in physicochemical properties. Kinetic and thermodynamic parameters for anthocyanin degradation in accelerated shelf life studies at 70, 80, and 90°C were determined using the Arrhenius model. The half-life (t_1/2) varied from 0.96 h (freeze-dried extract) to 108 days (encapsulated extract). Our results demonstrate that freeze-dried berry encapsulates effectively retard anthocyanin degradation during storage.     

CR-CEA: A collision- and energy-aware routing method for cognitive radio wireless sensor networks

Currently, working in the overcrowded shared unlicensed spectrum band, leads to a reduction in the quality of communications in wireless networks. This makes a considerable increase in packet loss caused by collisions that necessitates packets retransmissions. In the case of wireless sensor networks (WSN), a large amount of energy of sensor nodes will be wasted by these retransmissions. Cognitive radio technology makes it possible for sensor nodes, to opportunistically use licensed bands with better propagation characteristics and less congestion. In this paper a routing method for cognitive radio wireless sensor networks (CR-CEA) is presented, that is based on a cross-layer design that jointly considers route and spectrum selection. The CR-CEA method has two main phases: next hop selection and channel selection. The routing is performed hop-by-hop with local information and decisions, which are more compatible with sensor networks. Primary user activity and prevention from interference with them, is considered in all spectrum decisions. It uniformly distributes frequency channels between adjacent nodes, which lead to a local reduction in collision probability. This clearly affects energy consumption in all sensor nodes. In CR-CEA, route selection is energy-aware and a learning-based technique is used to reduce the packet delay in terms of hop-count. The simulation results reveal that by applying cognitive radio technology to WSNs and selecting a proper operating channel, we can consciously decrease collision probability. This saves energy of sensor nodes and improves the network lifetime.     

Task offloading in an optimized power-performance manner

The cloud computing systems, such as the Internet of Things (IoT), are usually introduced with a three-layer architecture (IoT-Fog-Cloud) for the task offloading that is a solution to compensate for resource constraints in these systems. Offloading at the right location is the most significant challenge in this field. It is more appropriate to offload tasks to fog than to cloud based on power and performance metrics, but its resources are more limited than the resources of the cloud. This paper tries to optimize these factors in the fog by specifying the number of usable servers in the fog. For this purpose, we model a fog computing system using the queueing theory. Furthermore, binary search and reinforcement learning algorithms are proposed to determine the minimum number of servers with the lowest power consumption. We evaluate the cost of the fog in different scenarios. By solving the model, we find that the proposed dispatching policy is very flexible and outperformed the known policies by up to 31% and in no case is it worse than either of them, and the overall offloading cost increases when fog rejects tasks with a high probability. Our offloading method is more effective than running all fog servers simultaneously, based on simulation results. It is evident from the similarities between the simulation results and those derived from the analytical method that the model and results are valid.     

A token-based incentive mechanism for video streaming applications in peer-to-peer networks

Free-riding is one of the main challenges of Peer-to-Peer (P2P) streaming systems which results in reduction in video streaming quality. Therefore, providing an incentive mechanism for stimulating cooperation is one of the essential requirements to maintain video Quality of Experience (QoE) in such systems. Among the existing mechanisms, payment-based schemes are most suitable for streaming applications due to their low overhead. However, to date, no dynamic payment mechanism has been proposed which can take the stochastic dynamics of the video streaming ecosystem (e.g., the request arrival, demand submission, bandwidth availability, etc.) into account. In this paper, we propose a dynamic token-based payment mechanism in which each peer earns tokens by admitting other peers’ requests and spends tokens for submitting its demands to the others. This system allows the peers to dynamically adjust their income level in adaptation to changes in the system state. We propose a Constrained Markov Decision Process (CMDP) formulation in which the goal of each peer is to obtain a request admission policy which minimizes the expected cumulative cost of consumed bandwidth, while satisfying a long-term constraint on the Mean Opinion Score (MOS) of the users as the measure of QoE. The proposed admission policy is adaptive to the request arrival process, bandwidth state and the token bucket length of each peer. To make up for the lack of design-time knowledge of the system’s statistics, each individual peer is equipped with a model-free algorithm to learn its optimal admission policy over the course of real-time interaction with the system. Simulation results are presented to compare the performance of the proposed algorithm against baseline schemes such as: random, token-threshold, bandwidth-threshold and myopic algorithms.     

Assessing the environmental footprints of the manganese recovery process from low-grade ore to synthesize manganese ferrite

Clean Technologies and Environmental Policy - Increasing demand for manganese and rapid depletion of high-grade manganese ores grow attention to other resources. However, environmental impacts and...