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Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult-to-access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling tradeoffs amongst the various conflicting optimization criteria, such as the network’s energy dissipation, packet-loss rate, coverage, and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming-based scalarization methods, the family of heuristics/metaheuristics-based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

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A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

The effective physical data sharing has been facilitating the functionality of Industrial IoTs, which is believed to be one primary basis for Industry 4.0. These physical data, while providing pivotal information for multiple components of a production system, also bring in severe privacy issues for both workers and manufacturers, thus aggravating the challenges for data sharing. Current designs tend to simplify the behaviors of participants for better theoretical analysis, and they cannot properly handle the challenges in IIoTs where the behaviors are more complicated and correlated. Therefore, this paper proposes a privacy-preserved data sharing framework for IIoTs, where multiple competing data consumers exist in different stages of the system. The framework allows data contributors to share their contents upon requests. The uploaded contents will be perturbed to preserve the sensitive status of contributors. The differential privacy is adopted in the perturbation to guarantee the privacy preservation. Then the data collector will process and relay contents with subsequent data consumers. This data collector will gain both its own data utility and extra profits in data relay. Two algorithms are proposed for data sharing in different scenarios, based on whether the service provider will further process the contents to retain its exclusive utility. This work also provides for both algorithms a comprehensive consideration on privacy, data utility, bandwidth efficiency, payment, and rationality for data sharing. Finally, the evaluation on real-world datasets demonstrates the effectiveness of proposed methods, together with clues for data sharing towards Industry 4.0.

Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs

We present a fast algorithm for the subset convolution problem:given functions f and g defined on the lattice of subsets of ann-element set n, compute their subset convolution f*g, defined for S⊆ N by [ (f * g)(S) = [T ⊆ S] f(T) g(S/T),,]where addition and multiplication is carried out in an arbitrary ring. Via Mobius transform and inversion, our algorithm evaluates the subset convolution in O(n2 2n) additions and multiplications, substanti y improving upon the straightforward O(3n) algorithm. Specifically, if the input functions have aninteger range [-M,-M+1,...,M], their subset convolution over the ordinary sum--product ring can be computed in O(2n log M) time; the notation O suppresses polylogarithmic factors.Furthermore, using a standard embedding technique we can compute the subset convolution over the max--sum or min--sum semiring in O(2n M) time.To demonstrate the applicability of fast subset convolution, wepresent the first O(2k n2 + n m) algorithm for the Steiner tree problem in graphs with n vertices, k terminals, and m edges with bounded integer weights, improving upon the O(3kn + 2k n2 + n m) time bound of the classical Dreyfus-Wagner algorithm. We also discuss extensions to recent O(2n)-time algorithms for covering and partitioning problems (Bjorklund and Husfeldt, FOCS 2006; Koivisto, FOCS 2006).

Fourier meets Möbius: fast subset convolution

In the big data era, much real-world data can be naturally represented as graphs. Consequently, many application domains can be modeled as graph processing. Graph processing, especially the processing of the large-scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large-scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This article surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping, and specific GPU programming. In this article, we summarize the state-of-the-art research on GPU-based graph processing, analyze the existing challenges in detail, and explore the research opportunities for the future.

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Graph Processing on GPUs: A Survey

Rendezvous is a fundamental process in Cognitive Radio Networks, through which a user establishes a link to communicate with a neighbor on a common channel. Most previous solutions use either a central controller or a Common Control Channel (CCC) to simplify the problem, which are inflexible and vulnerable to faults and attacks. Some blind rendezvous algorithms have been proposed that rely on no centralization. Channel Hopping (CH) is a representative technique used in blind rendezvous, with which each user hops among the available channels according to a pre-defined sequence. However, no existing algorithms can work efficiently for both symmetric (both parties have the same set of channels) and asymmetric users. In this paper, we introduce a new notion called Disjoint Relaxed Difference Set (DRDS) and present a linear time constant approximation algorithm for its construction. Then based on the DRDS, we propose a distributed asynchronous algorithm that can achieve and guarantee fast rendezvous for both symmetric and asymmetric users. We also derive a lower bound for any algorithm using the CH technique. This lower bound shows that our proposed DRDS based distributed rendezvous algorithm is nearly optimal. Extensive simulation results corroborate our theoretical analysis.

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Nearly optimal asynchronous blind rendezvous algorithm for Cognitive Radio Networks

Minimum-Latency Aggregation Scheduling (MLAS) is a problem of fundamental importance in wireless sensor networks. There however has been very little effort spent on designing algorithms to achieve sufficiently fast data aggregation under the physical interference model which is a more realistic model than traditional protocol interference model. In particular, a distributed solution to the problem under the physical interference model is challenging because of the need for global-scale information to compute the cumulative interference at any individual node. In this paper, we propose a distributed algorithm that solves the MLAS problem under the physical interference model in networks of arbitrary topology in O(K) time slots, where K is the logarithm of the ratio between the lengths of the longest and shortest links in the network. We also give a centralized algorithm to serve as a benchmark for comparison purposes, which aggregates data from all sources in O(log3n) time slots (where n is the total number of nodes). This is the current best algorithm for the problem in the literature. The distributed algorithm partitions the network into cells according to the value K, thus obviating the need for global information. The centralized algorithm strategically combines our aggregation tree construction algorithm with the non-linear power assignment strategy in [9]. We prove the correctness and efficiency of our algorithms, and conduct empirical studies under realistic settings to validate our analytical results.

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Minimum-latency aggregation scheduling in wireless sensor networks under physical interference model

Given a set of sensor nodes V where each node wants to broadcast a message to all its neighbors that are within a certain broadcasting range, the local broadcasting problem is to schedule all these requests in as few timeslots as possible. In this paper, assuming the more realistic physical interference model and no knowledge of the topology, we present three distributed local broadcasting algorithms where the first one is for the asynchronized model and the other two are for the synchronized model. Under the asynchronized model, nodes may join the execution of the protocol at any time and do not have access to a global clock, for which we give a distributed randomized algorithm with approximation ratio O(log2 n). This improves the state-of-the-art result given in [14] by a logarithmic factor. For the synchronized model where communications among nodes are synchronous and nodes can perform physical carrier sensing, we propose two distributed deterministic local broadcasting algorithms for synchronous and asynchronous node wakeups, respectively. Both algorithms have approximation ratio O(log n).

Distributed local broadcasting algorithms in the physical interference model

The unstructured multi-hop radio network model, with asynchronous wake-up, no collision detection and little knowledge on the network topology, is proposed for capturing the particularly harsh characteristics of initially deployed wireless adhoc and sensor networks. In this paper, assuming such a practical model, we study a fundamental problem of both theoretical and practical interests--the local broadcasting problem. Given a set of nodes V where each node wants to broadcast a message to all its neighbors that are within a certain local broadcasting range R, the problem is to schedule all these requests in the fewest timeslots. By adopting the physical interference mode land without any knowledge on neighborhood, we give a new randomized distributed approximation algorithm for the local broadcasting problem with approximation ratio O (log n) where nis the number of nodes. This distributed approximation algorithm improves the state-of-the-art result in [22] by a logarithmic factor.

https://www.dcoss.org/dcoss12_files/hua%20DCOSS%202012PPT.pdf

Qiang-Sheng Hua

Papers

Graph Processing on GPUs: A Survey

Nearly optimal asynchronous blind rendezvous algorithm for Cognitive Radio Networks

Minimum-latency aggregation scheduling in wireless sensor networks under physical interference model

Distributed local broadcasting algorithms in the physical interference model

An O(log n) Distributed Approximation Algorithm for Local Broadcasting in Unstructured Wireless Networks