Unmanned aerial vehicle (UAV)-aided large-scale Internet of Things (UAV-LIoT) are widely used but lack a balanced data collection (DC) scheme. To address this, we propose DC- nonorthogonal multiple access (NOMA), a new DC scheme that combines machine learning clustering with NOMA. We introduce an optimization algorithm for peak density clustering and a new LIoT clustering method. Our approach dynamically adjusts cluster size and formulates the energy-time efficiency problem as a tradeoff between energy minimization and data rate maximization. We propose a heuristic algorithm based on NOMA and an intracluster DC protocol. Experimental results show that DC- NOMA achieves balanced DC time, energy efficiency, load balance, and network lifespan extension, outperforming its benchmarks.

In the post-Cloud era, the proliferation of Internet of Things (IoT) has pushed the horizon of Edge computing, which is a new computing paradigm with data processed at the edge of the network. As the important systems of Edge computing, wireless sensor and actuator networks (WSANs) play an important role in collecting and processing the sensing data from the surrounding environment as well as taking actions on the events happening in the environment. In WSANs, in-network data storage and information discovery schemes with high energy efficiency, high load balance and low latency are needed because of the limited resources of the sensor nodes and the real-time requirement of some specific applications, such as putting out a big fire in a forest. In this article, the existing schemes of WSANs on data storage and information discovery are surveyed with detailed analysis on their advancements and shortcomings, and possible solutions are proposed on how to achieve high efficiency, good load balance, and perfect real-time performances at the same time, hoping that it can provide a good reference for the future research of the WSANs-based Edge computing systems.

A novel network paradigm of mobile edge computing, namely TMWSNs (two-tiered mobile wireless sensor networks), has just been proposed by researchers in recent years for its high scalability and robustness. However, only a few works have considered the security of TMWSNs. In fact, the storage nodes, which are located at the upper layer of TMWSNs, are prone to being attacked by the adversaries because they play a key role in bridging both the sensor nodes and the sink, which may lead to the disclosure of all data stored on them as well as some other potentially devastating results. In this paper, we make a comparative study on two typical schemes, EVTopk and VTMSN, which have been proposed recently for securing Top-k queries in TMWSNs, through both theoretical analysis and extensive simulations, aiming at finding out their disadvantages and advancements. We find that both schemes unsatisfactorily raise communication costs. Specifically, the extra communication cost brought about by transmitting the proof information uses up more than 40% of the total communication cost between the sensor nodes and the storage nodes, and 80% of that between the storage nodes and the sink. We discuss the corresponding reasons and present our suggestions, hoping that it will inspire the researchers researching this subject.

A novel network paradigm of edge computing, namely TWSNs (Two-tiered Wireless Sensor Networks), has just been proposed by researchers in recent years for its high scalability and robustness. However, in the TWSNs-based edge computing systems, the storage nodes, which are located at the upper layer of the systems, are prone to be attacked by the adversaries because they play a key role in bridging sensor nodes and Sink, which may lead to the disclosure of all the data stored on them as well as some other potentially devastating results. In this paper, we study the integrity-and-privacy preservation problem for spatial-temporal Top-k queries in the TWSNs-based edge computing systems, and propose a sequence-encryption-based lightweight scheme named SLS-STQ (Sequence-encryption-based Lightweight Scheme for Securing Spatial-temporal Top-k Queries) to solve the problem. In SLS-STQ, three algorithms, namely the report preparation algorithm, the query processing algorithm and the integrity verification algorithm, are designed for the sensor nodes, the storage nodes and Sink respectively. Theoretical analysis shows that SLS-STQ is able to achieve both the integrity validation and the privacy preservation with low computation complexity, and simulation results show that SLS-STQ is much more efficient than the related state-of-the-art schemes.

The n-term Karatsuba algorithm (KA) is an extension of 2-term KA, which can obtain even fewer multiplications than the original one. The existing n-term Karatsuba hybrid GF(2m) multipliers rely on factorization of m or m-1, so that put a confinement to these schemes. In this contribution, we use a new decomposition m=nl+r, such that 0łeq r<n and 0łeq r<l, and propose a novel n-term Karatsuba hybrid GF(2m) multiplier for an arbitrary irreducible trinomial xm+xk+1, m≥2k. Combined with shifted polynomial basis, a new approach (other than Mastrovito approach) is introduced to exploit the spatial correlations among different subexpressions. We investigate the explicit space and time complexities, and discuss related upper and lower bounds. As a main contribution, the flexibilities of n, l and r make our proposal approaching optimal for any given m. The space complexity can achieve to m²/2+O(√11 m3/2/2), which roughly matches the best result of current hybrid multipliers for special trinomials. Meanwhile, its time complexity is slightly higher than the counterparts, but can be improved for a new class of trinomials. In particular, we demonstrate that the hybrid multiplier for xm+xk+1, where k is approaching m2, can achieve a better space-time trade-off than any other trinomials.