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Choosing the appropriate system configuration that meets the multi-objective of profit-making and environmental protection is always a critical decision for both entrepreneurs and the government. The choice is complicated primarily if several possible alternatives exist. In this paper, a decision framework was developed to select from four configurations, the configuration that best fits decision-makers' multi-objective of profit-making and environmental protection. It was found that, overall, the grid-connected PV-battery hybrid configuration, for now, offers the best fit to the desired profitability and CO2mitigation effort with a similarity index of 96.10%. Whereas for now, the standalone PV-batt configuration is only about 29.93% close to this desired objective. It is inferred, therefore, that the complete exclusion of conventional energy from the concept of sustainable energy, for now, leads to an unequivocal increase in installation costs, preventing investments in green technology, particularly in developing countries.

In wireless sensor networks (WSNs), a large number of tiny, inexpensive and computable sensor nodes are usually deployed randomly to monitor one or more physical phenomena. The sensor nodes collect and process the sensed data and send the data to the sink wirelessly. Energy consumption is however a serious problem affecting WSNs lifetime. Radio communication is often the major cause of energy consumption in wireless sensor nodes. Thus, applying data compression before transmission can significantly help in reducing the total power consumption of a sensor node. In this paper, we propose an efficient and robust adaptive data compression scheme (ADCS). The proposed scheme independently compresses each block of source data losslessly or lossily on local nodes based on the given application. Simulation results show the merits of the proposed compression scheme in comparison with other recently proposed compression algorithms for WSNs including S-LZW, LEC, MPDC, Two-modal GPC and LTC.