Towards Design of an Efficient Sensing Data Acquisition scheme for UAVs-assisted Wireless Sensor Networks

This paper investigates sensing data acquisition issues from large-scale hazardous environments using UAVs-assisted WSNs. Most of the existing schemes suffer from low scalability, high latency, low throughput, and low service time of the deployed network. To overcome these issues, we considered a clustered WSN architecture in which multiple UAVs are dispatched with assigned path knowledge for sensing data acquisition from each cluster heads (CHs) of the network. This paper first presents a non-cooperative Game Theory (GT)-based CHs selection algorithm and load balanced cluster formation scheme. Next, to provide timely delivery of sensing information using UAVs, hybrid meta-heuristic based optimal path planning algorithm is proposed by combing the best features of Dolphin Echolocation and Crow Search meta-heuristic techniques. In this research work, a novel objective function is formulated for both load-balanced CHs selection and for optimal the path planning problem. Results analyses demonstrate that the proposed scheme significantly performs better than the state-of-art schemes.

LBFA: A Load-Balanced and Fragmentation-Aware Resource Allocation Algorithm in Space-Division Multiplexing Elastic Optical Networks

We consider a space-division multiplexing elastic optical network (SDM-EON) that supports super-channels (SChs). A Sch comprises a set of contiguous frequency slots on multiple cores in a multi-core fiber. The problem of finding a lightpath using SChs involves routing, modulation, spectrum and core assignment (RMSCA). To minimize the request blocking probability (RBP), two critical issues must be addressed. First, routing and modulation assignment (RMA) should not cause hotspots, or overutilized links. Second, spectrum and core assignment (SCA) should aim at minimizing fragmentation, or small frequency slot blocks that can hardly be utilized by future requests. In this paper, a pre-computation method is first proposed for better load balancing in RMA. Then an efficient fragmentation-aware SCA is proposed based on a new fragmentation metric that measures both the spectral and spatial fragmentation. With the enhanced RMA and SCA, a joint load-balanced and fragmentation-aware algorithm called LBFA is designed to solve the RMSCA problem. As compared with the existing algorithms, simulation results show that our LBFA provides significant reduction in RBP.

Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling

Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.