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In recent decades, cluster-based schemes have emerged as viable solutions for energy conservation problem in wireless sensor networks (WSN). However, most of the existing solutions incur imbalanced energy consumption and high network overheads. In addition, existing cluster-based solutions do not optimize route discovery based on the restricted resources of sensor nodes. Moreover, most of the cluster-based solutions perform periodical re-clustering for load balancing, which results in shortening network lifetime. This research paper presents a Dynamic Energy-aware Fault Tolerant Routing (DEFTR) protocol that exploits uniform-sized network partitioning based on network size and utilizes a multi-facet routing mechanism, which takes into consideration the residual energy, and position and link quality of neighbors. Furthermore, DEFTR not only offers reliable and energy efficient data routing but also supports fault tolerance. Simulation results demonstrate that DEFTR improved the network lifetime by 20.9% and throughput by 35%, also it reduced the delay by 29% and transmission cost by 46% in comparison to the existing work. [ABSTRACT FROM AUTHOR]

A Mobile Ad hoc Network (MANET) is an infrastructure-less collection of nodes that are powered by portable batteries. Consumption of energy is the major constraint in a wireless network. This paper presents a new algorithm called Energy-Aware Span Routing Protocol (EASRP) that uses energy-saving approaches such as Span and the Adaptive Fidelity Energy Conservation Algorithm (AFECA). Energy consumption is further optimized by using a hardware circuit called the Remote Activated Switch (RAS) to wake up sleeping nodes. These energy-saving approaches are well-established in reactive protocols. However, there are certain issues to be addressed when using EASRP in a hybrid protocol, especially a proactive protocol. Simulation results for the EASRP protocol show an increase in energy efficiency of 12.2% and 17.45% compared with EAZRP and ZRP, respectively. The EASRP protocol also proves to be effective in by producing a better packet delivery ratio for low- and high-density networks as measured by the NS-2 simulation tool. [ABSTRACT FROM AUTHOR]

In cognitive radio networks, it is well known that the cooperative spectrum sensing can overcome damaging effects of fading and shadowing. However, it also increases the amount of energy consumption which is a critical factor in low powered wireless communications. In this paper based on bi-threshold energy detection, we maximize the network throughput such that the energy consumption is below a predefined value and also sufficient protection of primary users against interference is guaranteed. Convex optimization analysis is presented to jointly obtain the optimal values of sensing time and detection thresholds. Simulation results show that the proposed method is very flexible such that a good tradeoff between achievable throughput and energy efficiency can be established, while it often outperforms the conventional sensing method significantly. [ABSTRACT FROM AUTHOR]

The necessity and significance of improving the energy efficiency of cloud implementations have increased due to the rapid growth and proliferation of cloud computing services around the world. Virtual machines (VMs) comprise the backend of most, if not all, cloud computing services. Several VMs are often consolidated on a physical machine to efficiently utilize its resources. In this paper, we take into account the cooling and network structure of the datacenter host ing the physical machines when consolidating the VMs so that fewer racks and routers are employed, without compromising the service-level agreements; consequently, idle routing and cooling equipment can be turned off in order to reduce the energy consumption. Our experimental results on four benchmarks show that the proposed techniques improve energy consumption of servers, network equipment, and cooling systems by 2.5%, 18.8%, and 28.2% respectively compared to state of the art, resulting in a total of 14.7% energy improvement on average in the entire datacenter. [ABSTRACT FROM AUTHOR]