Shohreh Ahvar

CACEV: A Cost and Carbon Emission-Efficient Virtual Machine Placement Method for Green Distributed Clouds.

Distributed clouds have recently attracted many cloud providers and researchers as a topic of intensive interest. High energy costs and carbon emissions are two significant problems in distributed clouds. Due to the geographic distribution of data centers (DCs), there are a variety of resources, energy prices and carbon emission rates to consider in a distributed cloud, which makes the placement of virtual machines (VMs) for cost and carbon efficiency even more critical than in centralized clouds. Most previous work in this field investigated either optimizing cost without considering the amount of produced carbon or vice versa. This paper presents a cost and carbon emission-efficient VM placement method (CACEV) in distributed clouds. CACEV considers geographically varying energy prices and carbon emission rates as well as optimizing both network and server resources at the same time. By combining prediction-based A* algorithm with Fuzzy Sets technique, CACEV makes an intelligent decision to optimize cost and carbon emission for providers. Simulation results show the applicability and performance of CACEV.

An Energy-Aware Routing Protocol for Query-Based Applications in Wireless Sensor Networks

Wireless sensor network (WSN) typically has energy consumption restriction. Designing energy-aware routing protocol can significantly reduce energy consumption in WSNs. Energy-aware routing protocols can be classified into two categories, energy savers and energy balancers. Energy saving protocols are used to minimize the overall energy consumed by a WSN, while energy balancing protocols attempt to efficiently distribute the consumption of energy throughout the network. In general terms, energy saving protocols are not necessarily good at balancing energy consumption and energy balancing protocols are not always good at reducing energy consumption. In this paper, we propose an energy-aware routing protocol (ERP) for query-based applications in WSNs, which offers a good trade-off between traditional energy balancing and energy saving objectives and supports a soft real time packet delivery. This is achieved by means of fuzzy sets and learning automata techniques along with zonal broadcasting to decrease total energy consumption.

RER: a real time energy efficient routing protocol for query-based applications in wireless sensor networks

Energy-aware routing protocols can be classified into two categories, energy savers and energy balancers. Energy saver protocols are used to minimize the overall energy consumed by a wireless sensor network (WSN), while energy balancer protocols efficiently distribute the consumption of energy throughout the network. Energy saver protocols are not necessarily good at balancing energy consumption and energy balancer protocols are not always efficient at reducing energy consumption. On the other hand, although delay is another important factor for WSNs energy-aware protocols do not take care of it. This paper proposes a real time energy efficient routing protocol for query-based applications in WSNs, which offers an efficient trade-off between traditional energy balancing and energy saving objectives while supporting a soft real time packet delivery. This is achieved by means of fuzzy sets and learning automata techniques along with zonal broadcasting to decrease total energy consumption.

Improving EEQR algorithm in Wireless Sensor Networks

Routing in Wireless Sensor Networks (WSNs) is a very challenging task due to the large number of nodes, their mobility and lack of proper infrastructure. Since the sensors are battery powered devices, energy efficiency is considered as one of the main factors in designing routing protocols in WSNs. EEQR is an energy efficient query-based routing protocol. It is able to operate as both energy saver and energy balancer. In addition, it is intended for contexts in which geographic routing criteria are not applicable. This paper improves EEQR by proposing a new way of saving path between event detector node and query source. It also proposes a new method to resolve looped routes. Finally, the improved EEQR (IEEQR) is compared with EEQR, Rumor and DRumor routing protocols. Results show that this paper achieves significant improvement in term of energy saving.

CCVP: Cost-efficient centrality-based VNF placement and chaining algorithm for network service provisioning

Network services have been significantly increased in todays enterprise networks. The time and cost of deploying these services are recently considered as critical challenges for enterprise networks. Network Functions Virtualization (NFV) is a promising solution to offer cost-efficient, scalable and more rapid deployment of such services. It allows the implementation of fine-grained services as a chain of Virtual Network Functions (VNFs). These chains need to be placed in the network. The chain placement is critical since it effects on both quality of service (QoS) and the provider cost. This paper formulates the problem of VNF placement and chaining as an Integer Linear Program (ILP) and proposes a Cost-efficient Centrality-based VNF Placement and chaining algorithm (CCVP). The objective is to find the optimal number of VNFs along with their locations in such a manner that the provider cost is minimized. Apart from cost minimization, the support for large-scale environments with a large number of servers and end-users is an important feature of the proposed algorithm. Finaly, the algorithm behavior is analyzed through simulations.

Ontology-based model for trusted critical site supervision in FUSE-IT

Smart buildings combine ICT and IoT technologies (e.g., smart appliances, sensors, actuators and smart meters) in order to provide supervision functions for Building Management Systems (BMS), which are capable to monitor and control both their physical elements (e.g., energy systems, network and storage facilities) and conceptual elements (e.g., building users, usage scenarios, security and trust and intrusion). To develop such systems, there is a need for a common information base which is expressive and flexible enough to describe building elements, their characteristics and interrelationships, as well as the constraints that apply to them. Despite the plethora of existing BMS models, there is still a lack of a common model showing a large compatibility and interoperability with existing BMS. Therefore, we introduce in this paper FUSE-IT ontology which provides a unified view of smart buildings by merging IoT/BMS ontologies such as Semantic Sensor Network (SSN), Smart Appliances REFerence (SAREF), Smart Energy Aware Systems (SEAS), among others. The obtained model is the basis of smart BMS we are aimed to implement within the FUSE-IT project to ensure global physical and cyber security, trust and safety in critical sites.

Improving learning automata-based routing in Wireless Sensor Networks

Recent research in the field of Wireless Sensor Networks (WSNs) has demonstrated the advantages of using learning automata theory to steer the routing decisions made by the sensors in the network. These advantages include aspects such as energy saving, energy balancing, increased lifetime, the selection of relatively short paths, as well as combinations of these and other goals. In this paper, we propose a very simple yet effective technique, which can be easily combined with a learning automaton to dramatically improve the performance of the routing process obtained with the latter. As a proof-of-concept, we focus on a typical learning automata-based routing process, which aims at finding a good trade off between the energy consumed and the number of hops along the paths chosen. In order to assess the performance of this routing process, we apply it on a WSN scenario where a station S gathers data from the sensors. In this typical WSN setting, we show that our combined technique can significantly improve the decisions made with the automata; and more importantly, even though the proof-of-concept particularizes somehow the automata and their behavior, the technique described in this paper is general in scope, and therefore can be applied under different routing methods and settings using learning automata.