Base Station Energy Efficiency Improvement for Next Generation Mobile Networks

Abstract—As more and more Base Stations (BSs) are being deployed by mobile operators to meet the ever increasing data traffic, solutions have to be found to try and reduce BS energy consumption to make the BSs more energy efficient and to reduce the mobile networks’ operational expenditure (OPEX) and carbon dioxide emissions. In this paper, a BS sleeping technology deployable in heterogeneous networks (HetNets) is proposed. The proposed scheme is validated by using extensive OMNeT++/SimuLTE simulations. From the simulations, it is shown that some lightly loaded micro BSs can be put to sleep in a HetNet when the network traffic is very low without compromising the QoS of the mobile network

Combination Adaptive Traffic Algorithm and Coordinated Sleeping in Wireless Sensor Network

Wireless sensor network (WSN) uses a battery as its primary power source, so that WSN will be limited to battery power for long operations. The WSN should be able to save the energy consumption in order to operate in a long time.WSN has the potential to be the future of wireless communications solutions. WSN are small but has a variety of functions that can help human life. WSN has the wide variety of sensors and can communicate quickly making it easier for people to obtain information accurately and quickly. In this study, we combine adaptive traffic algorithms and coordinated sleeping as powerâ€efficient WSN solution. We compared the performance of our proposed ideas combination adaptive traffic and coordinated sleeping algorithm with nonâ€adaptive scheme. From the simulation results, our proposed idea has goodâ€quality data transmission and more efficient in energy consumption, but it has higher delay than that of nonâ€adaptive scheme.Keywords:WSN,adaptive traffic,coordinated sleeping,beacon order,superframe order

Base station on/off strategies for wireless networks powered with energy harvesting sources

In this paper, we present a procedure for switching on and off base stations (BSs) that are powered with solar panels and have finite batteries. In the scenario under consideration it is considered that the BSs are placed at the same site with fully overlapped coverage areas and using different frequencies. We propose a decision strategy where we assume perfect knowledge of the traffic profile and a second approach where a robust Bayesian strategy is considered in order to account for possible error modeling in the traffic profile information.The research leading to these results has received funding from the European Commission in the framework of the FP7 Network of Excellence in Wireless COMmunications NEWCOM# (Grant agreement no. 318306) and project TUCAN3G (Grant agreement no. ICT-2011-601102), from the Spanish Ministry of Economy and Competitiveness (Ministerio de Economía y Competitividad) through the project TEC2011-29006-C03-02 (GRE3N-LINK-MAC), project TEC2013-41315-R (DISNET), and FPI grant BES-2012-052850, and from the Catalan Government (AGAUR) through the grant 2014 SGR 60.Peer ReviewedPostprint (author's final draft

An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation

The continuous evolution of the Internet of Things (IoT) makes it possible to connect everyday objects to networks in order to monitor physical and environmental conditions, which is made possible due to wireless sensor networks (WSN) that enable the transfer of data. However, it has also brought about many challenges that need to be addressed, such as excess energy consumption. Accordingly, this paper presents and analyzes wireless network energy models using five different communication protocols: Ad Hoc On-Demand Distance Vector (AODV), Multi-Parent Hierarchical (MPH), Dynamic Source Routing (DSR), Low Energy Adaptive Clustering Hierarchy (LEACH) and Zigbee Tree Routing (ZTR). First, a series of metrics are defined to establish a comparison and determine which protocol exhibits the best energy consumption performance. Then, simulations are performed and the results are compared with real scenarios. The energy analysis is conducted with three proposed sleeping algorithms: Modified Sleeping Crown (MSC), Timer Sleeping Algorithm (TSA), and Local Energy Information (LEI). Thereafter, the proposed algorithms are compared by virtue of two widely used wireless technologies, namely Zigbee and WiFi. Indeed, the results suggest that Zigbee has a better energy performance than WiFi, but less redundancy in the topology links, and this study favors the analysis with the simulation of protocols with different nature. The tested scenario is implemented into a university campus to show a real network running

Dependable k-coverage algorithms for sensor networks

Redundant sensing capabilities are often required in sensor network applications due to various reasons, e.g. robustness, fault tolerance, or increased accuracy. At the same time high sensor redundancy offers the possibility of increasing network lifetime by scheduling sleep intervals for some sensors and still providing continuous service with help of the remaining active sensors. In this paper centralized and distributed algorithms are proposed to solve the k-coverage sensing problem and maximize network lifetime. When physically possible, the proposed robust Controlled Greedy Sleep Algorithm provides guaranteed service independently of node and communication errors in the network. The performance of the algorithm is illustrated and compared to results of a random solution by simulation examples