In this paper, we study sensing data collection of IoT devices in a sparse IoT-sensor network, using an energy-constrained Unmanned Aerial Vehicle (UAV), where the sensory data is stored in IoT devices while the IoT devices may or may not be within the transmission range of each other. We formulate two novel data collection problems to fully or partially collect data stored from IoT devices using the UAV, by finding a closed tour for the UAV that consists of hovering locations and the sojourn duration at each of the hovering locations such that the accumulative volume of data collected within the tour is maximized, subject to the energy capacity on the UAV, where the UAV consumes energy on both hovering for data collection and flying from one hovering location to another hovering location. To this end, we first propose a novel data collection framework that enables the UAV to collect sensory data from multiple IoT devices simultaneously if these IoT devices are within the coverage range of the UAV, through adopting the orthogonal frequency division multiple access (OFDMA) technique. We then formulate two data collection maximization problems to deal with full or partial data collection from IoT devices at each hovering location, and show that both defined problems are NP-hard. We instead devise approximation and heuristic algorithms for the problems. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrated that the proposed algorithms are promising.

It is known that Boolean functions used in stream and block ciphers should have good cryptographic properties to resist algebraic attacks. Up until now, there have been several constructions of Boolean functions achieving optimum algebraic immunity. However, most of their nonlinearities are very low. Carlet and Feng studied a class of Boolean functions with optimum algebraic immunity and deduced the lower bound of its nonlinearity, which is good, but not very high. Moreover, the main practical problem with this construction is that it cannot be implemented efficiently. In this paper, we put forward a new method to construct cryptographically significant Boolean functions by using primitive polynomials, and construct three infinite classes of Boolean functions with good cryptographic properties: balancedness, optimum algebraic degree, optimum algebraic immunity, and a high nonlinearity.

Along with Network Function Virtualization (NFV), Mobile Edge Computing (MEC) is becoming a new computing paradigm that enables accommodating innovative applications and services with stringent response delay and resource requirements, including autonomous vehicles and augmented reality. Provisioning reliable network services for users is the top priority of most network service providers, as unreliable services or severe service failures can result in tremendous losses of users, particularly for their mission-critical applications. In this paper, we study reliability-aware VNF instances provisioning in an MEC, where different users request different network services with different reliability requirements through paying their requested services with the aim to maximize the network throughput. To this end, we first formulate a novel reliability-aware VNF instance placement problem by provisioning primary and secondary VNF instances at different cloudlets in MEC for each user while meeting the specified reliability requirement of the user request. We then show that the problem is NP-hard and formulate an Integer Linear Programming (ILP) solution. Due to the NP-hardness of the problem, we instead devise an approximation algorithm with a logarithmic approximation ratio for the problem. Moreover, we also consider two special cases of the problem. For one special case where each request only requests one primary and one secondary VNF instances, the problem is still NP-hard, and we devise a constant approximation algorithm for it. For another special case where different VNFs have the same amounts of computing resource demands, we show that it is polynomial-time solvable by developing a dynamic programming solution for it. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and the empirical results of the algorithms outperform their analytical counterparts as theoretical estimations usually are very conservative.