Wireless Rechargeable Sensor Networks (WRSNs) are emerging systems to build data acquisition systems to monitor critical areas. It has better features than Wireless Sensor Networks (WSNs) since in WRSNs, sensors are rechargeable. Wireless Rechargeable Sensor Networks allow sensors with sensing ability to collect and supply data to the operation center (sink). The Wireless Rechargeable Sensor Network appears as a more advanced solution because it can overcome the challenges and disadvantages such as battery limitation. In practical Wireless Rechargeable Sensor Networks, there is a budgetary constraint for the different expenditure categories, and therefore the number of used mobile chargers needs to be minimized. We investigate the problem of scheduling the minimum number of mobile chargers to replenish energy all sensors in a WRSN such that the network can be operated without limitations, and the total number of used chargers is minimized, referred to as the Periodic Energy Replenishment with Minimum Mobile Charger (PERMMC) problem. We design a new algorithm, namely the Mobile Charger Scheduling Algorithm (MCSA) to address the PERMMC problem. Finally, we conduct extensive simulations and provide analyses to demonstrate the performance of MCSA.

This article addresses polynomial computation with unipolar stochastic logic by exploiting correlation between the bit-streams. The AND-OR, double-NAND, OR-AND and double-NOR circuits are presented for polynomials with all positive coefficients whose sum is less than or equal to one by mathematically analyzing the joint probability distribution of coefficient bit-streams. The NAND-AND expansion is also developed for polynomials with alternatively positive and negative coefficients whose absolute values are decreasing by applying the same idea. Unlike the original methods with multiple uncorrelated random number sources (RNSs) for coefficient bit-stream generation, the presented methods only require a single RNS. Since the RNSs take up huge hardware resource in stochastic circuits, the proposed RNS-sharing techniques for polynomial computation result in a significant reduction of hardware complexity. For the factorization technique in the general polynomials, this paper enhances the original stochastic designs for the second-order polynomial and further presents the simple correlation-dependent circuits. Results show that the proposed architectures are superior to the previous ones by reducing the total number of RNSs.

Network function virtualization (NFV) is a novel network architecture yielding many optimization choices to Internet service providers due to its flexibility. Based on this technique, a network service is constructed from a chain of virtual network functions (VNFs). Although many studies deeply investigate the NFV and propose approaches to address the problem of deploying services in physical networks under different perspectives, they do not consider the importance of services. This issue becomes more critical once the number of network services ever-increasing while the network resources are limited. Here, each service is assigned a weight representing its importance. From this aspect, maximizing the total weight of services accepted to deploy over the physical network is a critical problem and poses many challenges, particularly for networks with limited resources. Therefore, we call this issue the VNF placement for weighted services (VPWS) problem. We then propose three algorithms termed first-fit server algorithm (FSA), best-fit server algorithm (BSA), and service segment-based algorithm (SSBA), respectively, to address the VPWS problem. Eventually, we perform simulations to evaluate the proposed algorithms and factors that affect the VNF placement. The experiment indicates that the total weight yielded by the SSBA outperforms those of the FSA and BSA.