Due to global climate change as well as economic concern of network operators, energy consumption of the infrastructure of cellular networks, or “Green Cellular Networking,” has become a popular research topic. While energy saving can be achieved by adopting renewable energy resources or improving design of certain hardware (e.g., power amplifier) to make it more energy-efficient, the cost of purchasing, replacing, and installing new equipment (including manpower, transportation, disruption to normal operation, as well as associated energy and direct cost) is often prohibitive. By comparison, approaches that work on the operating protocols of the system do not require changes to current network architecture, making them far less costly and easier for testing and implementation. In this survey, we first present facts and figures that highlight the importance of green mobile networking and then review existing green cellular networking research with particular focus on techniques that incorporate the concept of the “sleep mode” in base stations. It takes advantage of changing traffic patterns on daily or weekly basis and selectively switches some lightly loaded base stations to low energy consumption modes. As base stations are responsible for the large amount of energy consumed in cellular networks, these approaches have the potential to save a significant amount of energy, as shown in various studies. However, it is noticed that certain simplifying assumptions made in the published papers introduce inaccuracies. This review will discuss these assumptions, particularly, an assumption that ignores the effect of traffic-load-dependent factors on energy consumption. We show here that considering this effect may lead to noticeably lower benefit than in models that ignore this effect. Finally, potential future research directions are discussed.

This paper proposes a distributed mechanism for energy trading among microgrids in a competitive market. We consider multiple interconnected microgrids in a region where, at a given time, some microgrids have superfluous energy for sale or to keep in storage facilities, whereas some other microgrids wish to buy additional energy to meet local demands and/or storage requirements. Under our approach, sellers lead the competition by independently deciding the amount of energy for sale subject to a tradeoff between the attained satisfaction from the received revenue and that from the stored energy. Buyers follow the sellers' actions by independently submitting a unit price bid to the sellers. Correspondingly, the energy is allocated to the buyers in proportion to their bids, whereas the revenue is allocated to the sellers in proportion to their sales. We study the economic benefits of such an energy trading mechanism by analyzing its hierarchical decision-making scheme as a multileader-multifollower Stackelberg game. We show that distributing the energy based on a well-defined utility function converges to a unique equilibrium solution for maximizing the payoff of all participating microgrids. This game-theoretic study provides an incentive for energy trading among microgrids in future power grids.

A comprehensive energy model for wireless sensor networks is provided by considering seven key energy consumption sources some of which are ignored by currently available models. We demonstrate the importance of using such a comprehensive model by comparing it to other existing energy models in terms of the lifetime of a sensor node. We use our model to evaluate energy consumption and node lifetime for a sensor network with fixed configuration and we validate this evaluation by simulation. We show that existing energy models over-estimate life expectancy of a sensor node by 30-58% and also yield an "optimised" number of clusters which is too large. We further make the following two observations: 1) the optimal number of clusters increases with the increase of free space fading energy, 2) for sensor networks with 100 sensors over an area of 104-105 (m2), finding the optimal number of clusters becomes less important when free space fading energy is very low (less than 1670 pJ/bit/m2), while for larger networks, on the other hand, cluster optimization is still important even if free space fading energy is low. Guidelines for efficient and reliable sensor network design as well as extension to a sensor network with rotating cluster heads are provided.

Wireless communication between sensors allows the formation of flexible sensor networks, which can be deployed rapidly over wide or inaccessible areas. However, the need to gather data from all sensors in the network imposes constraints on the distances between sensors. This survey describes the state of the art in techniques for determining the minimum density and optimal locations of relay nodes and ordinary sensors to ensure connectivity, subject to various degrees of uncertainty in the locations of the nodes.

We consider an elastic optical network and study the static routing and spectrum assignment (RSA) problem aiming to minimize the maximum number of frequency slots required to accommodate all lightpath demands. We introduce a novel node-arc integer linear programming (ILP) model, which jointly decides optimal routes and assigned spectra for lightpaths between all source-destination pairs. To reduce the total number of variables, and therefore lower the computational complexity, our new node-arc model extends previous work by representing the spectrum assigned to each lightpath by two variables (i.e., the starting and ending boundary frequency slot indexes). To achieve scalability, we also develop an efficient spectrum-window-based greedy heuristic algorithm, and further propose three multiiteration-based algorithms that consider the effect of demand-serving sequences. Analytical and numerical results show that comparing with an existing approach, our solution to the RSA problem based on the new node-arc ILP model achieves a significant computational complexity improvement. Further numerical results demonstrate that the proposed multiiteration algorithms obtain solutions closer to optima than existing algorithms.

We propose a new active queue management (AQM) scheme to improve the performance of the well-known random early detection (RED) AQM. The new AQM is based on the proportional derivative (PD) control principle, and we call it PD-RED. In PD-RED we introduce minimal changes to RED. We demonstrate the improvement in performance of PD-RED over adaptive RED AQM by simulations.