• Energy Research

Wireless sensor networks (WSNs) have enabled an improved awareness in a range of purposes like battlefield surveillance, disaster management, industrial process monitoring and control, machine health monitoring, and medical applications. Accordingly, great numbers of sensor nodes (SNs) are arranged for usages that can function as an individual. Because of minute power of batteries in WSNs, proficient exploitation of this power is a significant aspect. Clustering is a capable method to expand the lifespan of WSNs by diminishing the power utilization. In the research work, the authors have compared resourceful power consumption protocols using different variants of distributed energy-efficient clustering (DEEC), namely developed DEEC (DDEEC), enhanced DEEC (EDEEC), and threshold DEEC (TDEEC). The simulation results demonstrate that out of three variants of DEEC, TDEEC performs better than other two protocols in respect of stability and WSN lifespan.

The energy efficiency of Data Center (DC) operations heavily relies on a DC ambient temperature as well as its IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of smart cloud-based applications. Consequently, the increased demand for computing power will inadvertently increase server waste heat creation in data centers. To improve a DC thermal profile which could undeniably influence energy efficiency and reliability of IT equipment, it is imperative to explore the thermal characteristics analysis of an IT room. This work encompasses the employment of an unsupervised machine learning technique for uncovering weaknesses of a DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for thermal management and cooling improvement that further feeds into DC recommendations. With the aim to identify overheated zones in a DC IT room and corresponding servers, we applied analyzed thermal characteristics of the IT room. Experimental dataset includes measurements of ambient air temperature in the hot aisle of the IT room in ENEA Portici research center hosting the CRESCO6 computing cluster. We use machine learning clustering techniques to identify overheated locations and categorize computing nodes based on surrounding air temperature ranges abstracted from the data. This work employs the principles and approaches replicable for the analysis of thermal characteristics of any DC, thereby fostering transferability. This paper demonstrates how best practices and guidelines could be applied for thermal analysis and profiling of a commercial DC based on real thermal monitoring data.

: This paper presents a novel Energy Efficient Clustering and Routing Algorithm (EECRA) for WSN. It is a clustering-based algorithm that minimizes energy dissipation in wireless sensor networks. The proposed algorithm takes into consideration energy conservation of the nodes through its inherent architecture and load balancing technique. In the proposed algorithm the role of intercluster transmission is not performed by gateways instead a chosen member node of respective cluster is responsible for data forwarding to another cluster or directly to the sink. Our algorithm eases out the load of the gateways by distributing the transmission load among chosen sensor node which acts as a relay node for inter-cluster communication for that round. Grievous simulations show that EECRA is better than PBCA and other algorithms in terms of energy consumption per round and network lifetime. Objective: The objective of this research lies in its inherent architecture and load balancing technique. The sole purpose of this clustering-based algorithm is that it minimizes energy dissipation in wireless sensor networks. Methods: This algorithm is tested with 100 sensor nodes and 10 gateways deployed in the target area of 300m × 300m. The round assumed in this simulation is same as in LEACH. The performance metrics used for comparisons are (a) network lifetime of gateways and (b) energy consumption per round by gateways. Our algorithm gives superior result compared to LBC, EELBCA and PBCA. Results: The simulation was performed on MATLAB version R2012b. The performance of EECRA is compared with some existing algorithms like PBCA, EELBCA and LBCA. The comparative analysis shows that the proposed algorithm outperforms the other existing algorithms in terms of network lifetime and energy consumption. Conclusion: The novelty of this algorithm lies in the fact that the gateways are not responsible for inter-cluster forwarding, instead some sensor nodes are chosen in every cluster based on some cost function and they act as a relay node for data forwarding. Note the algorithm does not address the hot-spot problem. Our next endeavor will be to design an algorithm with consideration of hot-spot problem.

Energy efficiency is a significant challenge in mobile ad hoc networks (MANETs) design where the nodes move randomly with limited energy, leading to acceptable topology modifications. Clustering is a widely applied technique to accomplish energy efficiency in MANET. Therefore, this paper designs a new energy-efficient clustering protocol using an enhanced rain optimization algorithm (EECP-EROA) for MANET. The EROA technique is derived by integrating the Levy flight concept to the ROA to enhance global exploration abilities. In addition, the EECP-EROA technique intends to proficiently select CHs and the nearby nodes linked to the CH to generate clusters. Moreover, the EECP-EROA technique has derived an objective function with different input parameters. To showcase the superior performance of the EECP-EROA technique, a brief set of simulations takes place, and the results are inspected under varying aspects. The experimental values pointed out the betterment of the EECP-EROA technique over the other methods.