Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs

With the growing number of Web services on the internet,there is a challenge to select the best Web service which can offer more quality-of-service(QoS)values at the lowest price.Another challenge is the uncertainty of QoS values over time due to the unpredictable nature of the internet.In this paper,we modify the interval data envelopment analysis(DEA)models[Wang,Greatbanks and Yang(2005)]for QoS-aware Web service selection considering the uncertainty of QoS attributes in the presence of desirable and undesirable factors.We conduct a set of experiments using a synthesized dataset to show the capabilities of the proposed models.The experimental results show that the correlation between the proposed models and the interval DEA models is significant.Also,the proposed models provide almost robust results and represent more stable behavior than the interval DEA models against QoS variations.Finally,we demonstrate the usefulness of the proposed models for QoS-aware Web service composition.Experimental results indicate that the proposed models significantly improve the fitness of the resultant compositions when they filter out unsatisfactory candidate services for each abstract service in the preprocessing phase.These models help users to select the best possible cloud service considering the dynamic internet environment and they help service providers to improve their Web services in the market.

AFAR:adaptive fuzzy ant-based routing for communication networks

We propose a novel approach called adaptive fuzzy ant-based routing (AFAR), where a group of intelligent agents (or ants) builds paths between a pair of nodes, exploring the network concurrently and exchanging obtained information to up-date the routing tables. Routing decisions can be made by the fuzzy logic technique based on local information about the current network state and the knowledge constructed by a previous set of behaviors of other agents. The fuzzy logic technique allows multiple constraints such as path delay and path utilization to be considered in a simple and intuitive way. Simulation tests show that AFAR outperforms OSPF, AntNet and ASR, three of the currently most important state-of-the-art algorithms, in terms of end-to-end delay, packet delivery, and packet drop ratio. AFAR is a promising alternative for routing of data in next generation networks.

6G-Enabled Internet of Things:Vision,Techniques,and Open Issues

There are changes in the development of wireless technology systems every decade.6G(sixth generation)wireless networks improve on previous generations by increasing dependability,accelerating networks,increasing available bandwidth,decreasing latency,and increasing data transmission speed to standardize communication signals.The purpose of this article is to comprehend the current directions in 6G studies and their relationship to the Internet of Things(IoT).Also,this paper discusses the impacts of 6G on IoT,critical requirements and trends for 6G-enabled IoT,new service classes of 6G and IoT technologies,and current 6G-enabled IoT studies selected by the systematic literature review(SLR)method published from 2018 to 2021.In addition,we present a technical taxonomy for the classification of 6G-enabled IoT,which includes self-organization systems,energy efficiency,channel assessment,and security.Also,according to the articles reviewed,we consider the evaluation factors in this domain,including data transmission,delay,energy consumption,and bandwidth.Finally,we focus on open issues and future research challenges in 6G-enabled IoT.To mention important future challenges and directions,we can point to migration,data storage,data resource,data security,data sharing,data offloading,availability,scalability,portability,user experience,reliability,authentication,and authorization.

COCM: Class Based Optimized Congestion Management Protocol for Healthcare Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes which can be used in many new emerging applications like healthcare. One of the major challenges in healthcare environments is to manage congestion, because in applications, such as medical emergencies or patients remote monitoring, transmitted data is important and critical. So it is essential in the first place to avoid congestion as much as possible and in cases when congestion avoidance is not possible, to control the congestion. In this paper, a class based congestion management protocol has been proposed for healthcare applications. We distinguish between sensitive, non-sensitive and control traffics, and service the input traffics based on their priority and quality of service requirements (QoS). The proposed protocol which is called COCM avoids congestion in the first step using multipath routing. The proposed AQM algorithm uses separate virtual queue's condition on a single physical queue to accept or drop the incoming packets. In cases where input traffic rate increases and congestion cannot be avoided, it mitigates congestion by using an optimized congestion control algorithm. This paper deals with parameters like end to end delay, packet loss, energy consumption, lifetime and fairness which are important in healthcare applications. The performance of COCM was evaluated using the OPNET simulator. Simulation results indicated that COCM achieves its goals.