Minimization of network power consumption with redundancy elimination

International audienceRecently, energy-aware routing (EAR) has gained an increasing popularity in the networking research community. The idea is that traffic demands are redirected over a subset of the network links, allowing other links to sleep to save energy. In this paper, we propose GreenRE – a new EAR model with the support of data redundancy elimination (RE). This technique, enabled within routers, can virtually increase the capacity of network links. Based on real experiments on Orange Labs platform, we show that performing RE increases the energy consumption for routers. Therefore, it is important to determine which routers should enable RE and which links to put into sleep mode so that the power consumption of the network is minimized. We model the problem as Mixed Integer Linear Program and propose greedy heuristic algorithms for large networks. Simulations on several network topologies show that the GreenRE model can gain further 37% of energy savings compared to the classical EAR model

Energy management in communication networks: a journey through modelling and optimization glasses

The widespread proliferation of Internet and wireless applications has produced a significant increase of ICT energy footprint. As a response, in the last five years, significant efforts have been undertaken to include energy-awareness into network management. Several green networking frameworks have been proposed by carefully managing the network routing and the power state of network devices. Even though approaches proposed differ based on network technologies and sleep modes of nodes and interfaces, they all aim at tailoring the active network resources to the varying traffic needs in order to minimize energy consumption. From a modeling point of view, this has several commonalities with classical network design and routing problems, even if with different objectives and in a dynamic context. With most researchers focused on addressing the complex and crucial technological aspects of green networking schemes, there has been so far little attention on understanding the modeling similarities and differences of proposed solutions. This paper fills the gap surveying the literature with optimization modeling glasses, following a tutorial approach that guides through the different components of the models with a unified symbolism. A detailed classification of the previous work based on the modeling issues included is also proposed

Survey on Data-Centric based Routing Protocols for Wireless Sensor Networks

The great concern for energy that grew with the technological advances in the field of networks and especially in sensor network has triggered various approaches and protocols that relate to sensor networks. In this context, the routing protocols were of great interest. The aim of the present paper is to discuss routing protocols for sensor networks. This paper will focus mainly on the discussion of the data-centric approach (COUGAR, rumor, SPIN, flooding and Gossiping), while shedding light on the other approaches occasionally. The functions of the nodes will be discussed as well. The methodology selected for this paper is based on a close description and discussion of the protocol. As a conclusion, open research questions and limitations are proposed to the reader at the end of this paper

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

Redundancy Elimination with Coverage Preserving algorithm in Wireless Sensor Network

In Wireless Sensor Network, the sensor nodes are deployed using random or deterministic deployment methods. Many applications prefer random deployment for deploying the sensor nodes. Random deployment is the main cause of redundancy. Detection and elimination of redundant sensor nodes while preserving coverage is very important issue after the sensor nodes are deployed randomly in the region of interest. The redundancy elimination with coverage preserving algorithm is proposed in this paper and the results are presented. The proposed algorithm determines redundant sensor nodes and also the sensor nodes which provide the least coverage of region of interest. If two sensor nodes cover same area or if the Euclidian distance between two nodes is less than 25% of sensing range of a sensor node, the sensor which is not located at optimal position will be deactivated, so that, it reduces the number of optimal nodes required to cover complete region of interest. This in turn increases the lifetime of the network. The simulation results illustrate that the proposed algorithm preserves 100% coverage or region of interest by removing redundant nodes and also the nodes which provide the least coverage of region of interest. It also reduces the number of optimal nodes required to provide 100% coverage of region of interest

TailoredRE: A Personalized Cloud-based Traffic Redundancy Elimination for Smartphones

The exceptional rise in usages of mobile devices such as smartphones and tablets has contributed to a massive increase in wireless network trac both Cellular (3G/4G/LTE) and WiFi. The unprecedented growth in wireless network trac not only strain the battery of the mobile devices but also bogs down the last-hop wireless access links. Interestingly, a signicant part of this data trac exhibits high level of redundancy in them due to repeated access of popular contents in the web. Hence, a good amount of research both in academia and in industries has studied, analyzed and designed diverse systems that attempt to eliminate redundancy in the network trac. Several of the existing Trac Redundancy Elimination (TRE) solutions either does not improve last-hop wireless access links or involves inecient use of compute resources from resource-constrained mobile devices. In this research, we propose TailoredRE, a personalized cloud-based trac redundancy elimination system. The main objective of TailoredRE is to tailor TRE mechanism such that TRE is performed against selected applications rather than application agnostically, thus improving eciency by avoiding caching of unnecessary data chunks. In our system, we leverage the rich resources of the cloud to conduct TRE by ooading most of the operational cost from the smartphones or mobile devices to its clones (proxies) available in the cloud. We cluster the multiple individual user clones in the cloud based on the factors of connectedness among users such as usage of similar applications, common interests in specic web contents etc., to improve the eciency of caching in the cloud. This thesis encompasses motivation, system design along with detailed analysis of the results obtained through simulation and real implementation of TailoredRE system