Blockchain technology enables the Internet of Things (IoT) to build trust among various stakeholders. Offloading the computation-intensive IoT data processing and blockchain mining computations as tasks to mobile edge computing (MEC) and nearby devices in a device-to-device (D2D) network is a promising solution for resource constraint IoT devices. In this article, to decide on the offloading plan, we consider enhancing the users’ Quality of Experience (QoE) parameters reflecting the token-based cost of delay and energy consumption. For this purpose, we formulate the problem as a mixed integer nonlinear bilevel programming (MINLBP), which is NP-hard. Therefore, we propose a suboptimal solution with the help of two-sided matching theory games in two phases: 1) we decide to offload IoT data processing tasks using a many-to-one and 2) make a decision for mining tasks elaborating a many-to-many matching game. We also present algorithms based on the deferred acceptance (DA) mechanism to guarantee the stability of the matching results. The simulation results show that the proposed method can perform better in terms of delay and energy cost compared to the existing offloading approaches. Furthermore, it improves the utility of the users deciding to offload their tasks.

This article investigates the performance of multicriteria-based load-balancing scheme among gateways in fog-assisted Internet of Things (IoT). We employ a queueing model of the IoT system to calculate the latency of data streams from the IoT devices to the applications. However, a gateway node may easily become congested since all the traffic to IP networks are directed toward the gateway. A congested point can affect system performance and may cause reliability problems in the system. Thus, we employ multiple gateways to alleviate the performance degradation of the network, along with a multicriteria decision-making (MCDM)-based load-balancing policy among the gateways to achieve a global load fairness. The proposed model is evaluated in single-hop IPv6 over low-power wireless personal area networks (6LoWPANs). The evaluation results show the effectiveness of the proposed load-balancing model in providing fast and reliable responses to user queries.

Internet of Things (IoT) offers a variety of solutions to control industrial environments. The new generation of IoT consists of millions of machines generating huge traffic volumes; this challenges the network in achieving the Quality-of-Service (QoS) and avoiding overload. Diverse classes of applications in IoT are subject to specific QoS treatments. In addition, traffic should be distributed among IoT servers based on their available capacity. In this article, we propose a novel framework based on software-defined networking (SDN) to fulfill the QoS requirements of various IoT services and to balance traffic between IoT servers simultaneously. At first, the problem is formulated as an integer linear programming (ILP) model that is NP-hard. Then, a predictive and proactive heuristic mechanism based on time-series analysis and fuzzy logic is proposed. Afterward, the proposed framework is implemented in a real testbed, which consists of the Open vSwitch, Floodlight controller, and Kaa servers. To evaluate the performance, various experiments are conducted under different scenarios. The results indicate the improved IoT QoS parameters, including throughput and delay, and illustrate the nonoccurrence of overload on IoT servers in heavy traffic. Furthermore, the results show improved performance compared to similar methods.

Wide area measurement system (WAMS) infrastructure is one of the most significant elements of the smart grid that measures, collects, and analyzes data in the power system. Fundamental components of the WAMS are phasor measurement units (PMUs), phasor data concentrators (PDCs), and its relevant applications. The PDC collects data from several different PMUs, integrates data and sends it to the control center. In order to achieve a stable implementation of WAMS, the communication structure should be reliable and meets the quality of service (QoS) requirements for various applications in the network. In this paper, we propose a novel WAMS communication infrastructure by utilizing the software-defined networking technology, which can enhance the reliability of the corresponding networks. In the proposed model, in order to meet the applications QoS needs, the traffic flows are categorized into different class of services, and then by applying a QoS mechanism, along with a content-aware queuing algorithm, the maximum capacity for the critical traffic is obtained. This provides a low latency for critical WAMS applications. The proposed model has been implemented in the Mininet environment using the Ryu controller. The implementation results indicate that the proposed infrastructure reduces end-to-end delay and packets loss and utilizes the network resources optimally.

This paper presents a constrained application protocol enabled multithreaded Internet of Things (IoT) gateway that allows reliable and quick access to big data from IoT domains. We consider a proxy caching policy to accelerate the content delivery rate of sensor data while reducing the energy consumption of corresponding nodes. We propose a selective caching policy in which resource validation requests are sent based on user requests, besides updates from IoT nodes. We also describe an analytical model to evaluate the performance of multithreaded IoT gateway under various traffic conditions to meet the quality of service constraints. The proposed model is evaluated over single hop IPv6 over low-power wireless personal area networks/IEEE 802.15.4 cluster. The performance evaluation clearly shows the effectiveness of our model in providing fast response and reduced energy consumption of the resources.

In recent years, Electric Vehicles (EV) received many adaptation and improvements which make them suitable for real life. The popularity of them demands more electricity and consequently smart charge scheduling algorithms to reduce driving cost and peak loads in charge stations. To address this challenge, we propose a smart charging scheduler based on mathematical optimization to determine the optimal charging volume from charging stations in a given route. The addressed problem formulated as a Mixed Integer Linear Program (MILP) to choose the optimal charging points between an origin and destination. This problem takes predicted time slot price signals and vehicles information to calculate the optimal solution. To deal with NP-hardness of MILP formulation, the proposed model solved using a heuristic simulated annealing (SA) algorithm. In the end, we evaluate the benefit of such scheduling using the Southern California Edison average hourly price. The results show that this schedule can contribute to EV owners and charge stations interests.

HTTP adaptive streaming (HAS) is quickly becoming the dominant video delivery technique for adaptive streaming over the Internet. Still considered as its primary challenges are determining the optimal rate adaptation and improving both the quality of experience (QoE) and QoE-fairness. Recent studies have shown that techniques providing a comprehensive and central view of the network resources can lead to greater gains in performance. By leveraging software defined networking (SDN), the current study proposes an SDN-based approach to maximize QoE metrics and QoE-fairness in AVC-based HTTP adaptive streaming. The proposed approach determines both the optimal adaptation and data paths for delivering the requested video files from HTTP-media servers to DASH clients. In fact, the proposed approach, which includes a set of application modules, is centrally executed by an SND controller in a time slot fashion. We formulate the problem as a mixed integer linear programming (MILP) optimization model in such a way that it applies defined policies, e.g. setting priorities for clients in obtaining video quality. We conduct experiments by emulating the proposed framework in Mininet using Floodlight as the SDN controller. In terms of improving QoE-fairness and QoE metrics, the effectiveness of the proposed approach is validated by a comparison with different approaches.

Internet of Energy (IoE) is a subset of the Internet of Things which covers all aspects of electrical energy systems and provides secure connectivity and interoperability between power grid and Internet. In this paper, we present a fog-based IoE architecture for transactive energy (TE) management systems. The proposed design consists of three different layers. In the first tier, home gateways are employed which collect customers energy consumption data and provide necessary interface between customers and power grid. In the second layer, there are some local fog nodes located at the network edge and provide services with low latency. From the TE system point of view, the fog node act as retail energy market server which provides energy services to the end users. In the third layer, cloud servers are utilized to provide permanent and reliable data storage and high computing power. The proposed architecture supports different communication protocols such as hypertext transfer protocol, constrained application protocol, and OpenADR. We calculate the required bandwidth and delay performance of both fog- and cloud-based models. We present an optimal day ahead energy consumption schedule and an intercustomer energy trading mechanism for exchanging energy between end users. The performance of the proposed architecture is evaluated in terms of different power grid and communication network metrics. Results confirm the superiority of the proposed architecture.

The advanced metering infrastructure (AMI) is one of the main services of smart grid (SG), which collects data from smart meters (SMs) and sends them to utility company meter data management systems (MDMSs) via a communication network. In the next generation AMI, both the number of SMs and the meter sampling frequency will dramatically increase, thus creating a huge traffic load which should be efficiently routed and balanced across the communication network and MDMSs. This paper initially formulates the global load-balanced routing problem in the AMI communication network as an integer linear programming model, which is NP-hard. Then, to overcome this drawback, it is decomposed into two subproblems and a novel software defined network-based AMI communication network is proposed called OpenAMI. This paper also extends the OpenAMI for the cloud computing environment in which some virtual MDMSs are available. OpenAMI is implemented on a real test bed, which includes Open vSwitch, Floodlight controller, and OpenStack, and its performance is evaluated by extensive experiments and scenarios. Based on the results, OpenAMI achieves low end-to-end delay and a high delivery ratio by balancing the load on the entire AMI network.

Organizing sensor nodes into a clustered architecture is an effective method for load balancing and prolonging the network lifetime. However, a serious drawback of the clustering approach is the imposed energy overhead caused by the “global” clustering operations in every round of the global round-based policy (GRBP). To mitigate this problem, this paper proposes a hierarchical clustering-task scheduling policy (HCSP), which triggers node-driven clustering as opposed to GRBP's time-driven clustering. Based on HCSP, each cluster is reconfigured only once at each local super round. Therefore, the cluster reconfiguration frequency varies on-demand and may differ from one cluster to another throughout the network lifetime. However, in order to refresh the entire network structure, global clustering is performed at the end of every global hyper round. Accordingly, HCSP aims to achieve a more flexible, energy-efficient, and scalable clustering-task scheduling than that of GRBP. This policy mitigates the clustering overhead, which is the worst disadvantage of clustering approaches. Energy consumption calculations and extensive simulations show the effectiveness of HCSP in saving energy and in prolonging the network lifetime.

Prolonging the network life cycle is an essential requirement for many types of Wireless Sensor Network (WSN) applications. Dynamic clustering of sensors into groups is a popular strategy to maximize the network lifetime and increase scalability. In this strategy, to achieve the sensor nodes' load balancing, with the aim of prolonging lifetime, network operations are split into rounds, i.e., fixed time intervals. Clusters are configured for the current round and reconfigured for the next round so that the costly role of the cluster head is rotated among the network nodes, i.e., Round-Based Policy (RBP). This load balancing approach potentially extends the network lifetime. However, the imposed overhead, due to the clustering in every round, wastes network energy resources. This paper proposes a distributed energy-efficient scheme to cluster a WSN, i.e., Dynamic Hyper Round Policy (DHRP), which schedules clustering-task to extend the network lifetime and reduce energy consumption. Although DHRP is applicable to any data gathering protocols that value energy efficiency, a Simple Energy-efficient Data Collecting (SEDC) protocol is also presented to evaluate the usefulness of DHRP and calculate the end-to-end energy consumption. Experimental results demonstrate that SEDC with DHRP is more effective than two well-known clustering protocols, HEED and M-LEACH, for prolonging the network lifetime and achieving energy conservation.

VoIP is becoming a low-priced and efficient replacement for PSTN in communication industries. With a widely growing adoption rate, session initiation protocol (SIP) is an application layer signaling protocol, standardized by the IETF, for creating, modifying, and terminating VoIP sessions. Generally speaking, SIP routes a call request to its destination by using SIP proxies. With the increasing use of SIP, traditional configurations pose certain drawbacks, such as ineffective routing, un-optimized management of proxy resources (including CPU and memory), and overload conditions. This paper presents OpenSIP to upgrade the SIP network framework with emerging technologies, such as software-defined networking (SDN) and network function virtualization (NFV). SDN provides for management that decouples the data and control planes along with a software-based centralized control that results in effective routing and resource management. Moreover, NFV assists SDN by virtualizing various network devices and functions. However, current SDN elements limit the inspected fields to layer 2-4 headers, whereas SIP routing information resides in the layer-7 header. A benefit of OpenSIP is that it enforces policies on SIP networking that are agnostic to higher layers with the aid of a deep packet inspection engine. Among the benefits of OpenSIP is programmability, cost reduction, unified management, routing, as well as efficient load balancing. This paper implements OpenSIP on a real testbed which includes Open vSwitch and the Floodlight controller. The results show that the proposed architecture has a low overhead and satisfactory performance and, in addition, can take advantage of a flexible scale-out design during application deployment.

Demand side management (DSM) is an important application of the future smart grid. DSM programs allow consumers to participate in the operation of the electric grid by reducing or shifting their electricity usage during peak periods. Therefore, in this paper, we propose a two-tier cloud-based DSM to control the residential load of customers equipped with local power generation and storage facilities as auxiliary sources of energy. We consider a power system consisting of multiple regions and equipped with a number of microgrids. In each region, an edge cloud is utilized to find the optimal power consumption schedule for customer appliances in that region. We propose a two-level optimization algorithm with a linear multilevel cost function. At the edge cloud, the power consumption level of local storage and the amount of power being demanded from both local storage facilities and power grid are scheduled by using a bi-level optimization approach. The core cloud then gathers information of the total demand from consumers in different Regions and finds the optimal power consumption schedule for each microgrid in the power system. Simulation results show that the proposed model reduces consumption cost for the customers and improves the power grid in terms of peak load and peak-to-average load ratio.

Network functions virtualization provides opportunities to design, deploy, and manage networking services. It utilizes cloud computing virtualization services that run on high-volume servers, switches, and storage hardware to virtualize network functions. Virtualization techniques can be used in IP multimedia subsystem (IMS) cloud computing to develop different networking functions (e.g., load balancing and call admission control). IMS network signaling happens through session initiation protocol (SIP). An open issue is the control of overload that occurs when an SIP server lacks sufficient CPU and memory resources to process all messages. This paper proposes a virtual load balanced call admission controller (VLB-CAC) for the cloud-hosted SIP servers. VLB-CAC determines the optimal “call admission rates” and “signaling paths” for admitted calls along with the optimal allocation of CPU and memory resources of the SIP servers. This optimal solution is derived through a new linear programming model. This model requires some critical information of SIP servers as input. Further, VLB-CAC is equipped with an autoscaler to overcome resource limitations. The proposed scheme is implemented in smart applications on virtual infrastructure (SAVI) which serves as a virtual testbed. An assessment of the numerical and experimental results demonstrates the efficiency of the proposed work.

The Session Initiation Protocol (SIP) is an application-layer control protocol for creating, modifying and terminating multimedia sessions. An open issue is the control of overload that occurs when a SIP server lacks sufficient CPU and memory resources to process all messages. We prove that the problem of overload control in SIP network with a set of n servers and limited resources is in the form of NP-hard. This paper proposes a Load-Balanced Call Admission Controller (LB-CAC), based on a heuristic mathematical model to determine an optimal resource allocation in such a way that maximizes call admission rates regarding the limited resources of the SIP servers. LB-CAC determines the optimal "call admission rates" and "signaling paths" for admitted calls along optimal allocation of CPU and memory resources of the SIP servers through a new linear programming model. This happens by acquiring some critical information of SIP servers. An assessment of the numerical and experimental results demonstrates the efficiency of the proposed method.

Today wireless sensor networks (WSNs) emerge as a revolution in all aspects of our life. WSNs have special specifications of themselves that made them different from other networks. Fault tolerance is one of the most significant of many challenges in these networks. In this paper, we present a cluster-based fault tolerant protocol that utilizes from energy efficient technique for clustering. We propose a novel mechanism for error detection. Our scheme detects faults and errors very accurately. Also, we propose a novel fault recovery technique to recover cluster-heads efficiently. Yet our scheme is accurate, saves energy of nodes too. That is very important for such networks.

We have proposed (¿, ¿)-based flow regulation to reduce delay and backlog bounds in SoC architectures, where ¿ bounds the traffic burstiness and ¿ the traffic rate. The regulation is conducted per-flow for its peak rate and traffic burstiness. In this paper, we optimize these regulation parameters in networks on chips where many flows may have conflicting regulation requirements. We formulate an optimization problem for minimizing total buffers under performance constraints. We solve the problem with the interior point method. Our case study results exhibit 48% reduction of total buffers and 16% reduction of total latency for the proposed problem. The optimization solution has low run-time complexity, enabling quick exploration of large design space.

Nowadays, wireless sensor networks are widely used in a variety of applications such as in military, vehicle tracking, disaster management and environmental monitoring. Accurate estimation of the sensor position can be crucial in many of these applications. In this research, we propose a localization algorithm based on probabilistic fuzzy logic systems (PFLS) for range-free localization. The algorithm utilizes received signal strength (RSS) from the anchor nodes embedded with variant degrees of environmental noise. The proposed system is compared with another algorithm based on Fuzzy Logic Systems (FLS) against variant amount of noise. Simulation results demonstrate that FLS can be much more accurate than PFLS method if the environment is noise-free. However, as the environmental noise increases, the PFLS reaches better performance.

Energy efficiency and long network lifetime has been of a great interest in wireless sensor networks. In addition to delivering data to the base station, a routing protocol must be energy efficient. An efficient routing technique is known as hierarchical routing based on clustering. In this paper, wireless sensor network is considered with a mobile base station. After clustering the nodes and selecting the cluster heads, the best location of the base station is determined based on the most efficient energy consumption for data delivery of cluster heads. In other words, the location of the base station for the next round is determined so that the minimum energy cost is imposed for data communication, in which we have prevented from data overflow with sending information through single hoping from all the CHs to the base station. Simulation results are provided to prove the efficiency of this technique.

Recent developments in biosensor and wireless technology have led to rapid progress in wearable real time health monitoring. Unlike in wired networks, wireless networks are more subject to packet loss and congestion. In this paper, we propose a congestion control and service prioritization protocol for real time monitoring of patients' vital signs using wireless biomedical sensor networks. The proposed system is able to discriminate between different physiological signals and assign them different priorities. Thus, it would be possible to provide better quality of service for transmitting highly important vital signs. Congestion control is performed by considering both the congestion situation in the parent node and the priority of the child nodes in assigning network bandwidth to signals from different patients. Given the dynamic nature of a patient's health condition, the proposed system can detect an anomaly in the received vital signs from a patient and hence assign more priority to patients in need. Simulation results confirm the superior performance of the proposed protocol. To our knowledge, this is the first attempt at a special-purpose congestion control protocol specifically designed for wireless biomedical sensor networks.

For network-on-chip (NoC) designs, optimizing buffers is an essential task since buffers are a major source of cost and power consumption. This paper proposes flow regulation and has defined a regulation spectrum as a means for system-on-chip architects to control delay and backlog bounds. The regulation is performed per flow for its peak rate and burstiness. However, many flows may have conflicting regulation requirements due to interferences with each other. Based on the regulation spectrum, this paper optimizes the regulation parameters aiming for buffer optimization. Three timing-constrained buffer optimization problems are formulated, namely, buffer size minimization, buffer variance minimization, and multiobjective optimization, which has both buffer size and variance as minimization objectives. Minimizing buffer variance is also important because it affects the modularity of routers and network interfaces. A realistic case study exhibits 62.8% reduction of total buffers, 84.3% reduction of total latency, and 94.4% reduction on the sum of variances of buffers. Likewise, the experimental results demonstrate similar improvements in the case of synthetic traffic patterns. The optimization algorithm has low run-time complexity, enabling quick exploration of large design spaces. This paper concludes that optimal flow regulation can be a highly valuable instrument for buffer optimization in NoC designs.

The quality of services is one of the most important issues of today's internet. The programs and the different internet users' quality of services are different from one another. Varieties of methods have been presented in providing the internet's quality of services. The Proportional Differentiated services method is one of the newest ways of providing quality of services. In this essay, a proper fuzzy model for providing quality of services in the relative differentiated services is presented. The presented method is based on the JOBS method which is one of the newest methods of services differentiation. The suggested model is implemented and analyzed in ns2 simulation environment. The final results of this simulation reveal the superiority of the suggested fuzzy method compared with the non-fuzzy one.

We consider two flow control schemes for best effort traffic in on-chip architectures, which can be deemed as the solutions to the boundary extremes of a class of utility maximization problem. At one extreme, we consider the so-called rate-sum flow control scheme, which aims at improving the performance of the underlying system by roughly maximizing throughput while satisfying capacity constraints. At the other extreme, we deem the max-min flow control, whose concern is to maintain max-min fairness in rate allocation by fairly sacrificing the throughput. We then elaborate our argument through a weighting mechanism in order to achieve a balance between the orthogonal goals of performance and fairness. Moreover, we investigate the implementation facets of the presented flow control schemes in on-chip architectures. Finally, we validate the proposed flow control schemes and the subsequent arguments through extensive simulation experiments.

Many traffic engineering and network design tasks require a network-wide description as an input for their performance evaluation. Therefore, a traffic matrix represents the demands for these tasks and is applicable to them. Traffic matrix can be measured directly or estimated from an indirect data. Generating synthetic traffic matrix method, is a better way to achieve traffic matrix indirectly. We have previously proposed the conqueror traffic based synthetic traffic matrix and we have indicated that our conqueror traffic based matrix performed better in all cases where conqueror traffic existed. In this paper by measuring the maximum link-utilization, we consider how well the conqueror traffic based synthetic matrix optimizes the specific traffic engineering task; that is optimizing network routing to minimize congestion. First, we optimize the routing with the conqueror traffic based synthetic matrix to minimize congestion. Then, we test the performance of the resulting routing on the real traffic matrix. Simulation results indicate that OSPF optimization which we use in this paper to optimize the weights based on the conqueror traffic based synthetic matrix, optimize the routing better and result in a better performance on real traffic matrix.

Wireless sensor networks (WSN) consist of some nodes that have limited processing power, memory and energy source. These constraints cause the algorithms that presented in this field focus on these constraints. Data aggregation is any process in which information is gathered and expressed in a summary form. Data aggregation has been put forward as an essential paradigm for wireless routing in sensor networks. The idea is to combine the data coming from different sources, eliminating redundancy, minimizing the number of transmissions and thus saving energy. For this purpose, sensor nodes must form aggregation tree, then forward sensed data to the root of this tree. Data is aggregated in intermediate nodes and the results are sent toward the root. In this paper we propose an energy aware algorithm for construction the aggregation tree. The proposed algorithm considers both the energy and distance parameters to construct the tree. Simulation results show that the proposed algorithm has better performance in terms of energy efficiency and number of failed nodes which increases the network lifetime.

Different network applications need different Quality of Service (QoS) requirements such as packet delay, packet loss, bandwidth and availability. It is important to develop a network architecture which is able to guaranty quality of service requirements for high priority traffic. In Wireless Multimedia Sensor Networks (WMSNs), a sensor node may have different kinds of sensor which gather different types of data, with differing levels of importance. We argue that the sensor networks should be willing to spend more resources in disseminating packets that carry more important information. Some applications of WMSNs need to send real time traffic toward the sink node. This real time traffic requires low latency and high reliability so that immediate remedial and defensive actions can be taken, where necessary. Similar to wired networks, service differentiation in wireless sensor networks is also very important. In this paper we propose a differentiated service model for WMSNs. The proposed model can provide requested quality of service for high priority real time classes. In the proposed model, we distinguish high priority real time traffic from the low priority non-real time traffic, and input traffic streams are then serviced based on their priorities. Simulation results confirm the efficiency of the proposed model.

The research community has recently witnessed the emergence of multi-processor system on chip (MPSoC) platforms consisting of a large set of embedded processors. Particularly, Interconnect networks methodology based on Network-on-Chip (NoC) in MP-SoC design is imminent to achieve high performance potential. More importantly, many well established schemes of networking and distributed systems inspire NoC design methodologies. Employing end-to-end congestion control is becoming more imminent in the design process of NoCs. This paper presents a centralized congestion scheme in the presence of both elastic and streaming flow traffic mixture. In this paper, we model the desired Best Effort (BE) source rates as the solution to a utility maximization problem which is constrained with link capacities while preserving Guaranteed Service (GS) traffics services requirements at the desired level. We proposed an iterative algorithm as the solution to the maximization problem which has the benefit of low complexity and fast convergence. The proposed algorithm may be implemented by a centralized controller with low computation and communication overhead.

New applications made possible by the rapid improvements and miniaturization in hardware has motivated recent developments in wireless multimedia sensor networks (WMSNs). As multimedia applications produce high volumes of data which require high transmission rates, multimedia traffic is usually high speed. This may cause congestion in the sensor nodes, leading to impairments in the quality of service (QoS) of multimedia applications. Thus, to meet the QoS requirements of multimedia applications, a reliable and fair transport protocol is mandatory. An important function of the transport layer in WMSNs is congestion control. In this paper, we present a new queue based congestion control protocol with priority support (QCCP-PS), using the queue length as an indication of congestion degree. The rate assignment to each traffic source is based on its priority index as well as its current congestion degree. Simulation results show that the proposed QCCP-PS protocol can detect congestion better than previous mechanisms. Furthermore it has a good achieved priority close to the ideal and near-zero packet loss probability, which make it an efficient congestion control protocol for multimedia traffic in WMSNs. As congestion wastes the scarce energy due to a large number of retransmissions and packet drops, the proposed QCCP-PS protocol can save energy at each node, given the reduced number of retransmissions and packet losses.

QoS provisioning over wireless fading channel is challenging. We develop a novel scheduler design in cellular packet-switched wireless networks which provides QoS for users in all three aspects of QoS i.e. throughput, delay and packet loss simultaneously. We establish a three-dimensional space with specific basis vectors for QoS and find the efficient point of system performance in that space. Then we develop a generalized measure, the QoS Deviation, which is the Euclidean distance between flow QoS work point and the efficient QoS point in the 3D space. Based on this measure, a scheduling approach, namely BQDF is outlined and will be extended to AQDC scheduler for wireless channels which makes it possible to tune the tradeoff between QoS provisioning and optimizing throughput in an adaptive manner depending on current cell QoS Deviation level (CDL). Finally, we introduce a QoS Deviation-based CAC policy for the proposed system.

Clustering belongs to the set of mathematical problems which aim at classification of data or objects into related sets or classes. Many different pattern clustering approaches based on the pattern membership model could be used to classify objects within various classes. Different models of Crisp, Hierarchical, Overlapping and Fuzzy clustering algorithms have been developed which serve different purposes. The main deficiency that most of the algorithms face is that the number of clusters for reaching the optimal arrangement is not automatically calculated and needs user intervention. In this paper we propose a fuzzy clustering technique (FACT) which determines the number of appropriate clusters based on the pattern essence. Different experiments for algorithm evaluation were performed which show a much better performance compared with the typical widely used K-means clustering algorithm

In this paper we propose a fuzzy logic implementation of random early detection (RED) mechanism. The main objective of the proposed fuzzy controller is to reduce the loss probability of the RED mechanism without any change in channel utilization. Using fuzzy logic capabilities, we try to dynamically tune the loss probability of RED gateway. To achieve this goal, we use a two-input-single-output fuzzy controller. The inputs of fuzzy controllers are: 1) the error signal e1 which represents the difference between average queue size and a target point. 2) The error signal e2, which is calculated as the difference between the estimated value of incoming data rate and the target link capacity. The output of fuzzy controller is used to dynamically tune the max p parameter of RED gateway. All simulation results show that the proposed fuzzy mechanism has better performance than traditional RED and adaptive RED (ARED) mechanisms.

Today's Internet can only support "best effort" traffic while with fast emerging multimedia services, it will become clear that the future Internet architecture will have to support various applications with different quality of service (QoS) requirements. The Differentiated Services (DiffServ) model allows the support of QoS Differentiated Services on an IP backbone network. The DiffServ model provides service classification by means of the Differentiated Service (DS) field in the IP header and the per hop behavior (PHB) observable behavior at the node. In this paper we propose a fuzzy based DiffServ model to implement the Differentiated Services in the Internet. In the proposed model we use a fuzzy meter which is based on a dynamic rate leaky bucket (LB) mechanism. Furthermore we use random early discard (RED) mechanism to provide low loss probability for Assured Services. Simulation results show that the proposed fuzzy DiffServ model has better performance than the conventional DiffServ model with leaky bucket meter.