<span lang="EN-GB">In the past years trend of microgrids is increasing very fast to reduce peak-hour costs. However, in these systems, third parties are still involved in selling surplus energy. This results in increased cost of energy and there are many operational and security barriers in such systems. These issues can be solved by the decentralized distributed system of microgrids where a consumer can locally sell their surplus energy to another consumer. To deploy such a system, one must consider security barriers for the transaction of energy. This paper proposes a solution to these problems by devising a scheme as a marketplace where users interact with each other to buy and sell energy at better rates and get energy-generating resources on lease so that users do not have to worry about capital investment. Agreement between owner of resources and consumer is recorded on blockchain based smart contracts. In this paper, a survey is performed for existing well known, decentralized energy solutions. This paper also proposes an extra layer of security to leverage a shielded execution environment so that information of energy generated, utilized, and shared cannot be changed by consumers and third parties even if the system is compromised.</span>

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This letter addresses an intelligent reflecting surface (IRS) to the uplink nonorthogonal multiple access (NOMA) served by a multiantenna receiver for more efficient data collection from massive devices. For rate fairness, we formulate a problem of maximizing the minimum rate of the devices by optimizing receive beamforming (BF), IRS reflection, and transmit power allocation (PA) of the devices jointly. We first design a block coordinate descent (BCD) algorithm optimizing receive BF, IRS reflection, and PA iteratively. We then reformulate the problem as a nonlinear optimization (NLO) problem with a smooth objective function of the IRS phase and PA vectors by incorporating the optimal receive BF into the objective and using an approximate minimum. To handle massive IRS elements and devices efficiently, we solve the NLO problem with the limitedmemory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm using the gradient derived in a closed form. The results show that the L-BFGS-B optimizing the IRS phase and PA vectors concurrently reduces the computational complexity of the BCD algorithm significantly at a slight performance gain.