Clustering is one of the most fundamental tools in artificial intelligence, machine learning, and data mining. In this paper, we follow one of the recent mainstream topics of clustering, Sum of Radii (SoR), which naturally arises as a balance between the folklore k-center and k-median. SoR aims to determine a set of k balls, each centered at a point in a given dataset, such that their union covers the entire dataset while minimizing the sum of radii of the k balls. We propose a general technical framework to overcome the challenge posed by varying radii in SoR, which yields fixed-parameter tractable (fpt) algorithms with respect to k (i.e., whose running time is f(k) ploy(n) for some f). Our framework is versatile and obtains fpt approximation algorithms with constant approximation ratios for SoR as well as its variants in general metrics, such as Fair SoR and Matroid SoR, which significantly improve the previous results.

Existing literature on k-Maximum Inner-Product Search has made it a common belief that tree based methods are less effective in terms of index construction time and query performance compared to locality sensitive hashing based, similarity graph based and quantization based methods. However, in this paper we partially roll over the existing assessments about tree based k- Maximum Inner-Product Search methods by our newly proposed tree structure named LRUS-CoverTree. The experimental results show that the new k- Maximum Inner-Product Search algorithm based on LRUS-CoverTree outperforms the state-of-the-art locality sensitive hashing based methods, and achieves comparable performance with similarity graph based and quantization based methods in terms of query time and accuracy. What's more important, the desirable query performance is attained with significantly lower index construction time compared to all the other methods. Besides the experimental evaluations, substantial theoretical results about the LRUS-CoverTree and the new k-Maximum Inner-Product Search algorithm are provided, including construction time and search time complexity, size and height of the tree, and so on. Furthermore, several new effective upper bounds on the inner-product value are provided to support the efficient branch-and-bound algorithm on LRUS-CoverTree. In summary, our novel tree structure and new algorithm significantly improve upon existing tree based methods, and it is hoped that this contribution can lead to a reconsideration of tree based k-Maximum Inner-Product Search methods.

Ground-based cloud image sequence prediction provides valuable insights into cloud motion and meteorological conditions, which are essential for photovoltaic power generation systems. Most existing models are, however, recurrent-based, which is problematic in providing satisfactory forecasting results with rapid speed because these recurrent-based models do not support parallel inference and usually suffer from slow inference speed. A novel recurrent-free deep learning-based framework, called multiscale spatiotemporal attention network (MSTANet) to address the issues is proposed in this study. The MSTANet leverages a multiscale spatiotemporal attention (MSTA) module to extract the multiscale, nonlinear spatiotemporal dependencies from cloud image sequences and uses a multiscale temporal attention (MTA) module to reinforce the temporal dependencies by capturing the high- and low-frequency spatiotemporal fluctuations of clouds. A gated aggregation unit (GAU) to mitigate the ghosting effects that are prevalent in spatiotemporal prediction tasks is introduced to filter the useful context information by integrating the historical information with the updated predictions. Additionally, a multiorder differential divergence regularization term is introduced into the loss function to improve the model’s performance by encouraging MSTANet to focus on the evolving trends of the neighborhood of clouds. Experimental results show that the proposed MSTANet outperforms the state-of-the-art (SOTA) prediction methods. It reduces 46% parameters and mean-squared-error (MSE) by 4.31% on the Moving Mnist dataset and reduces 22% parameters with a 1.82% performance improvement on the Folsom dataset compared to the baseline temporal attention unit (TAU). The codes are available athttps://github.com/Csorasky/MSTANet.

Acoustic sensing for heartbeat monitoring has become a prevailing research topic in wireless sensing. Existing acoustic sensing systems have two limitations---limited sensing range, and heartbeat monitoring for a single user only, hindering the large-scale deployment of applications. In this paper, we present DF-Sense, a Dual Forming based multi-user acoustic Sensing system for heartbeat monitoring in home settings. Specifically, we design a novel sensing signal-to-noise ratio (SSNR) enhancement model, namely Dualforming, based on the constructive superposition across multiple subcarriers and microphones, and further build the quantitative relationship between critical factors and SSNR enhancement to optimize sensing performance. To enable Dualforming, we propose a novel MUltiple Subtle SIgnal Classification (MUS2IC) method to identify multiple subjects with subtle motions. We implement DF-Sense using commercial acoustic devices and conduct extensive experiments in a home setting. Results show that DF-Sense achieves high precision measurement of instantaneous heart rate within the range of 10 m, which is sufficient for most daily space requirements, and is able to monitor heartbeat for up to 6 subjects in a 2-D space simultaneously.

Abstract As an important tool of multiway/tensor data analysis tool, Tucker decomposition has been applied widely in various fields. But traditional sequential Tucker algorithms have been outdated because tensor data is growing rapidly in term of size. To address this problem, in this article, we focus on parallel Tucker decomposition of dense tensors on distributed‐memory systems. The proposed method uses hierarchical SVD to accelerate the SVD step in traditional sequential algorithms, which usually takes up most computation time. The data distribution strategy is designed to follow the implementation of hierarchical SVD. We also find that compared with the state‐of‐the‐art method, the proposed method has lower communication cost in large‐scale parallel cases under the assumption of the α–β model.

Summary Data caching is an effective method to reduce traffic and improve the quality of service in network. Traditionally, users' requests are offloaded to the cloud for centralized computing. However, due to security and privacy, these tasks are executed in the nearest server, so that the data and service needed by the task are also essential. After the task is completed, in case the next arriving request needs the same data, resulting in transmission cost, the data need to be stored for a period of time, because we know nothing about the coming request information under an online request stream. In this article, we study data caching problem by extending single data item to multiple data items among servers. About the homogeneous model and the submodular model with constraint, we propose a data caching strategy minimizing the total transfer and caching costs of the system. Moreover, we also solve the semiheterogeneous model by the anticipatory caching (AC) algorithm in Reference 21. Meanwhile we find it is more efficient for our three models in this article to improve the performance.

Generating an accurate machine learning (ML) model is of great importance for the Internet of Vehicles (IoV). However, obtaining such a model is challenging due to the fact that subgroups of in-network vehicles receive data from different resources. A worthwhile investment then would be identifying those groups before inferring models. Similarity metrics are widely used to distinguish different groups. However, the efficiency of most existing similarity measurements is at the cost of increased computational complexity and decreased accuracy, making them unsuitable for IoV’s stringent conditions. To address this issue, we propose a computationally efficient method to measure the similarity of different vehicles, where a simplified version of Earth mover’s distance (EMD) is adopted. This distance metric is then embedded into a distributed clustering algorithm to learn the global pattern for vehicular systems. Our algorithm’s overall performance is measured using an asynchronous message delay simulator. Compared to the best algorithm of the state of the art, our proposed algorithm converges slightly slower (by less than 1%) but improves the clustering accuracy by as much as 20% with synthetic data. Additionally, real-world data collected from vehicles validates the efficiency of our proposed algorithm.

In this paper, we study a data caching problem in the cloud environment, where multiple frequently co-utilised data items could be packed as a single item being transferred to serve a sequence of data requests dynamically with reduced cost. To this end, we propose an online algorithm with respect to a homogeneous cost model, calledPackCache, that can leverage the FP-Tree technique to mine those frequently co-utilised data items for packing whereby the incoming requests could be cost-effectively served online by exploiting the concept of anticipatory caching. We show the algorithm is$2/\alpha$competitive, reaching the lower bound of the competitive ratio for any deterministic online algorithm on the studied caching problem, and also time and space efficient to serve the requests. Finally, we evaluate the performance of the algorithm via experimental studies to show its actual cost-effectiveness and scalability.

In this paper, we explore using the data-centric approach to tackle the Multiple Sequence Alignment (MSA) construction problem. Unlike the algorithm-centric approach, which reduces the construction problem to a combinatorial optimization problem based on an abstract mathematical model, the data-centric approach explores using classification models trained from existing benchmark data to guide the construction. We identified two simple classifications to help us choose a better alignment tool and determine whether and how much to carry out realignment. We show that shallow machine-learning algorithms suffice to train sensitive models for these classifications. Based on these models, we implemented a new multiple sequence alignment pipeline, called MLProbs. Compared with 10 other popular alignment tools over four benchmark databases (namely, BAliBASE, OXBench, OXBench-X and SABMark), MLProbs consistently gives the highest TC score. More importantly, MLProbs shows non-trivial improvement for protein families with low similarity; in particular, when evaluated against the 1,356 protein families with similarity ≤ 50%, MLProbs achieves a TC score of 56.93, while the next best three tools are in the range of [55.41, 55.91] (increased by more than 1.8%). We also compared the performance of MLProbs and other MSA tools in two real-life applications - Phylogenetic Tree Construction Analysis and Protein Secondary Structure Prediction - and MLProbs also had the best performance. In our study, we used only shallow machine-learning algorithms to train our models. It would be interesting to study whether deep-learning methods can help make further improvements, so we suggest some possible research directions in the conclusion section.

In this paper, we studied a data caching problem in edge-based CDNs to facilitate the content delivery to serve a sequence of requests, off-line and online, with minimum costs as a goal based on a semi-homo cost model. To this end, we first designed an O(mn \log(mn)) time and space optimal proactive off-line algorithm,called pro-caching, by reducing the problem to a simple shortest path problem in a directed weighted network graph, and then extended the idea of anticipatory caching to develop an 2-competitive reactive online algorithm, called re-caching, for this problem and showed its tightness by proving that no deterministic online algorithm can do better than 2-o(1) in its worst case. Finally, to combine the advantages of both algorithms, we also presented a hybrid algorithm, called hy-caching, to fully utilize the power and benefits of edge-based CDNs while reducing their service costs. Our results improve the previous results not only in the cost model being used but also in the time complexity, competitive ratio, and the quality of the solutions. We provably achieve these results with our deep insights into the problem and the careful analysis, together with an empirical evaluation.

With the fast growth of mobile edge computing (MEC), the deep neural network (DNN) has gained more opportunities in application to various mobile services. Given the tremendous number of learning parameters and large model size, the DNN model is often trained in cloud center and then dispatched to end devices for inference via edge network. Therefore, maximizing the cost-efficiency of learned model dispatch in the edge network would be a critical problem for the model serving in various application contexts. To reach this goal, in this article we focus mainly on reducing the total model dispatch cost in the edge network while maintaining the efficiency of the model inference. We first study this problem in its off-line form as a baseline where a sequence of$n$requests can be pre-defined in advance and exploit dynamic programming techniques to obtain a fast optimal algorithm in time complexity of$O(m^{2}n)$under a semi-homogeneous cost model in a$m$-sized network. Then, we design and implement a 2.5-competitive algorithm for its online case with a provable lower bound of 2 for any deterministic online algorithm. We verify our results through careful algorithmic analysis and validate their actual performance via a trace-based study based on a public open international mobile network dataset.

BACKGROUND: Multi-Agent Simulation is an essential technique for exploring complex systems. In research of contagious diseases, it is widely exploited to analyze their spread mechanisms, especially for preventing COVID-19. Nowadays, transmission dynamics and interventions of COVID-19 have been elaborately established by this method, but its computation performance is seldomly concerned. As it usually suffers from inadequate CPU utilization and poor data locality, optimizing the performance is challenging and important for real-time analyzing its spreading. RESULTS: This paper explores approaches to optimize multi-agent simulation for COVID-19 disease. The focus of this work is on the algorithm and data structure designs for improving performance, as well as its parallelization strategies. We propose two successive methods to optimize the computation. We construct a case-focused iteration algorithm to improve data locality, and propose a fast data-mapping scheme called hierarchical hash table to accelerate hash operations. As a result, The case-focused method degrades [Formula: see text] cache references and achieves [Formula: see text] speedup. Hierarchical hash table can further boost computation speed by 47%. And parallel implementation with 20 threads on CPU achieves [Formula: see text] speedup consequently. CONCLUSIONS: In this work, we propose optimizations for multi-agent simulation of COVID-19 transmission from aspects of algorithm and data structure. Benefit from improvement of locality and multi-thread implementation, our methods can significantly accelerate the simulation computation. It is promising in supporting real-time prevention of COVID-19 and other infectious diseases in the future.

Data caching in the cloud is an efficient way to improve the QoS of diverse data applications. However, this benefit is not freely available, given monetary cost to manage the caches in the cloud. In this paper, we study the data caching problem in the cloud that is driven by the monetary cost reduction, instead of the hit rate under limited capacity as in traditional cases. In particular, given a stream of requestsRto a shared data item, we present a shortest-path based optimal algorithm that can minimize the total transfer and caching costs within O(mn) time for off-line case, here m represents the number of nodes in the network, while n is the length of the request stream. The cost model in this computation is semi-homo, which indicates that all pairs of nodes have the same transfer cost, but each cache server node has its own caching cost rate. Our off-line algorithm improves the previous results not only in reducing the time complexity from O(m2n) to O(mn), but also in relaxing the cost model to be semi-homogeneous, rendering the algorithm more practical in reality. Furthermore, we also study this problem in its online form, and by extending the anticipatory caching idea, we propose a 2-competitive online algorithm based on the same cost model and show its tightness by giving a lower bound of the competitive ratio as 2 - o(1) for any deterministic online algorithm. We provably achieve these results with our deep insights into the problem and careful analysis of the solution algorithms, together with a trace-based study to evaluate their performance in reality.

In the past decades, Dynamic Internet of Things (D-IoT) networks have played a conspicuously more important role in many real-life areas, including disaster relief, environment monitoring, public safety, and so on, to rapidly collect information from the environment and help people to make the decision. Meanwhile, due to the widespread implementation of dynamic IoT networks, there exists an enormous demand on designing suitable models and efficient algorithms for fundamental operations in dynamic IoT networks, to achieve the high throughput and reliable low-latency communication demands in 6G networks. In this article, we first present a general dynamic model to comprehensively depict most of the dynamic phenomena in IoT networks. Then, based on the proposed dynamic model, a distributed scheduling algorithm is proposed to competitively optimize the Age-of-Information (AoI) problem in the context of a D-IoT network. We say our scheduling algorithm is competitive: the throughput of the base station approximates the optimal solution with constant competitive ratio; and, the latency for a packet received by the base station is only constant times larger than the optimal latency. Rigorous theoretical analysis and extensive simulations are presented to verify the high throughput and reliable low-latency communications in our proposed algorithm.

Dynamicity is one of the most challenging, yet, key aspects of wireless networks. It can come in many guises, such as churn (node insertion/deletion) and node mobility. Although the study of dynamic networks has been popular in distributed computing domain, previous works considered only partial factors causing dynamicity. In this work, we propose a dynamic model that is comprehensive to include crucial dynamic factors on nodes and links. Our model defines dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a localized dynamic model suits distributed algorithm studies better than a global one. The proposed dynamic model makes use of the more realistic SINR model to describe wireless interference, instead of the oversimplified graph-based models adopted by most existing research. Under the proposed dynamic model, we develop an efficient distributed algorithm accomplishing local broadcast services in the abstract MAC layer that was first presented by Kuhnet al.[24]. Our solution paves the way for many new fast algorithms to solve high-level problems in dynamic networks, such as consensus, single-message broadcast, and multiple-message broadcast. Extensive simulation studies indicate that our algorithm exhibits good performance in realistic environments with dynamic network behaviors.

In this paper, we present the first algorithm to precisely implement the abstract MAC (absMAC) layer under the physical SINR model in dynamic networks. The absMac layer, first presented by (Kuhn et al., 2009), provides reliable local broadcast communications, with timing guarantees stated in terms of a collection of abstract delay functions, based on which high-level algorithms can be designed, independent of specific channel behaviors. The implementation of absMAC requires the design of a distributed algorithm for the local broadcast communication primitives over a particular communication model that defines concrete channel behaviors, and the objective is to minimize the bounds of the abstract delay functions. Halldórsson et al. (2015) showed that under the standard SINR model (synchronous communications without physical carrier sensing or location information), there exist no efficient exact implementations. In this work, we demonstrate that physical carrier sensing, a commonly seen function performed by wireless devices, can help get efficient exact implementation algorithms. Specifically, we propose an algorithm that precisely implements the absMAC layer under the SINR model in dynamic networks. The algorithm provides asymptotically optimal bounds for both acknowledgement and progress functions defined in the absMAC layer. Our algorithm leads to many new faster algorithms for solving high-level problems under the SINR model in dynamic networks. We demonstrate this by exemplifying problems of Consensus, Multi-Message Broadcast, and Single-Message Broadcast. It deserves to point out that our implementation algorithm is designed based on an optimal algorithm for a General Local Broadcast (GLB) problem, which takes the number of distinct messages into consideration for the first time. The GLB algorithm can handle many communication scenarios apart from those defined in the absMAC layer. Simulation results show that our proposed algorithms perform well in reality.

The byzantine model is widely used to depict a variety of node faults in networks. Previous studies on byzantine-resilient protocols in wireless networks assume reliable communications and do not consider the jamming behavior of byzantine nodes. Such jamming, however, is a very critical and realistic behavior to be considered in modern wireless networks. In this paper, for the first time, we integrate the jamming behavior of byzantine nodes into the network setting. We show that, in this much more comprehensive and harsh model, efficient distributed communication protocols can be still devised with elaborate protocol design. In particular, we developed an algorithm that can accomplish the basic multiple-message dissemination task close to the optimal solution in terms of running time. Empirical results validate the byzantine-resilience and efficiency of our algorithm.

Network function virtualization (NFV) is a promising virtualization technology that has the potential to significantly reduce the expenses and improve service agility. The NFV makes it possible for Internet service providers (ISPs) to employ various virtual network functions (VNFs) without installing new equipments. One of the most attractive approaches in the NFV technology is the so-called joint placement and allocation of virtual network functions (JPA-VNFs), which considers the balance between VNF investment with Quality of Services (QoS). We introduce a novel capability function to measure the potential of locating VNF instances for each server in the proposed OJPA-HS model. This model allows the servers in the network to be heterogeneous, at the same time combines and generalizes many classical JPA-VNF models. Despite its NP-hardness, we present a provable best-possible deterministic online algorithm based on dynamic programming (DP). To conquer the high complexity of DP, we propose two additional randomized heuristics, Las Vegas (LV) and Monte Carlo (MC) randomized algorithms, which perform even as good as DP with much smaller complexity. Besides, MC is a promising heuristic in practice as it has the advantage to deal with the big data environment. Extensive numerical experiments are constructed for the proposed algorithms in this article.

In smart city services, information systems can provide efficient and effective support during an emergency, and an emergency management system can make use of any available infrastructure network, such as the Internet of Things. However, ordinary communication infrastructures can be prone to disruptions or even failures during emergencies. Hence, it is necessary to present a fallback system in case of such failures. In this article, we propose such a fallback design for emergency management that relies on short-range multihop wireless communications. Specifically, we model the crowd by a multihop ad hoc network consisting of nodes (i.e., civilians with smartphones or wearable devices) that are capable of short-range communications, and address the problem of how to “diffuse” the crowd in an efficient and distributed fashion. The problem is subdivided into crowd density computation and crowd diffusion. We treat the area as a grid that is divided into square cells. Crowd density computation is to compute the density of each cell, for which we present efficient distributed algorithms that compute the density of each grid cell exactly. With the computed densities, crowd diffusion is to design a load-balancing strategy (to direct local movements of individual civilians) such that in a short time the nodes/civilians will become evenly distributed over the entire area. We present a distributed diffusion algorithm that has good performance. We conduct extensive simulations to evaluate the proposed algorithms, and the results corroborate our theoretical analyses.

This paper investigates distributed Dominating Set (DS) and Connected Dominating Set (CDS) construction in dynamic wireless networks under the SINR interference model. Specifically, we present a new model for dynamic networks that admits both churns (due to node arrivals/departures) and node mobility. Under this dynamic model, we propose efficient algorithms to construct a DS and a CDS with constant approximation ratios w.r.t. the corresponding minimum ones in O(log n) time with a high probability guarantee. To the best of our knowledge, these algorithms are the first known ones for DS and CDS construction in dynamic networks assuming the SINR interference model. We believe our dynamic network model can greatly facilitate distributed algorithm studies in mobile and dynamic wireless networks.

In this paper, we present the first algorithm for exactly implementing the abstract MAC (absMAC) layer in the physical SINR model. The absMac layer, first presented by Kuhn et al. in [15], provides reliable local broadcast communication, with timing guarantees stated in terms of a collection of abstract delay functions, such that high-level algorithms can be designed in terms of these functions, independent of specific channel behavior. The implementation of absMAC layer is to design a distributed algorithm for the local broadcast communication primitives over a particular communication model that defines concrete channel behaviors, and the objective is minimizing the bounds of the abstract delay functions. Halldórsson et al. [10] have shown that in the standard SINR model (synchronous communication, without physical carrier sensing or location information), there cannot be efficient exact implementations. In this work, we show that physical carrier sensing, a commonly seen function performed by wireless devices, can help get efficient exact implementation algorithms. Specifically, we propose an algorithm that exactly implements the absMAC layer. The algorithm provides asymptotically optimal bounds for both acknowledgement and progress functions defined in the absMAC layer. Our algorithm can lead to many new faster algorithms for solving high-level problems in the SINR model. We demonstrate this by giving algorithms for problems of Consensus, Multi-Message Broadcast and Single-Message Broadcast. It deserves to point out that our implementation algorithm is designed based on an optimal algorithm for a General Local Broadcast (GLB) problem, which takes the number of distinct messages into consideration for the first time. The GLB algorithm can handle much more communication scenarios apart from those defined in the absMAC layer. Simulation results show that our proposed algorithms perform well in reality.

Dynamicity is one of the critical characteristics and a major challenge in designing communication protocols in wireless networks. Most of the previous works had focused on the internal node changes (e.g., mobility, arrival, or departure) and not considered the effect of external environmental change. However, the external environmental change, in general, is a more complex phenomenon that can impede nodes from successful communication, implying the protocols of the previous dynamic models do not work well in practice. In this paper, we give an algorithm for distributed broadcasting in a more general model with fully dynamic wireless networks, called FD-Broadcast. Specifically, we present a fully dynamic model which allows node mobility and churns (due to node arrivals/departure) and external environmental change. In contrast to the previous works on dynamic networks, our model defines the full dynamicity in terms of localized topological changes of each node and can tolerate some external environmental change. The external environment changes are captured by the random jamming method. We show that FD-Broadcast can achieve broadcasting in$O(D_{S})$rounds with a high probability guarantee under the assumption of constant dynamic rate in the SINR model, where$D_{S}$is the dynamic diameter, a parameter proposed to depict the complexity of dynamic broadcasting. Moreover, the lower bound of dynamic broadcasting is proved to be$\Omega(D_{S})$, thus, FD-Broadcast is asymptotically optimal with high probability.

In this paper, we propose the optional preference model for the facility location game with two heterogeneous facilities on a line. Agents in this new model are allowed to have optional preference, which gives more flexibility for agents to report. Aiming at minimizing maximum cost or sum cost of agents, we propose different deterministic strategy-proof mechanisms without monetary transfers. Depending on which facility the agent with optional preference cares for, we consider two variants of the optional preference model: Min (caring for the closer one) and Max (caring for the further one). For the Min variant, we propose a 2-approximation mechanism for the maximum cost objective, as well as a lower bound of 4/3, and a (n/2+1)-approximation mechanism for the sum cost objective, as well as a lower bound of 2. For Max variant, we propose an optimal mechanism for the maximum cost objective and a 2-approximation mechanism for the sum cost objective.

Current large-scale computing services, such as online social networking and web searching, make the wired links in the data centers with an Ethernet infrastructure oversubscribed. Therefore, researchers consider to augment the data centers with wireless communication, called a hybrid data center network (HDCN), to improve the communication flexibility and network capacity. In this paper, we investigate how to use the wireless communication in hybrid DCNs from a cross-layer view. In the network layer, we propose a routing protocol to minimize the number of hops for data flows, and a congestion control protocol to reduce the congestion and deal with sporadic link failure. In the physical layer, we study the channel and power allocation problem with the SINR and QoS constraints in hybrid DCNs. In single channel scenarios, we prove the problem to be a geometric programming problem. In multi-channel scenarios, we prove the problem to be NP-hard and propose a Greedy based Online Channel and Power Allocation (GOCPA) algorithm. Our proposed protocols in network and physical layers collaborate to manage the wireless communication in hybrid DCNs. Extensive simulations show that our protocols can significantly increase the network throughput, decrease the latency, and moreover increase the robustness of the networks.

A recent study in Science indicated that the confidence of a decision maker played an essential role in group decision making problems. In order to make use of the information of each individual's confidence of the current decision problem, a new hybrid weighted aggregation method to solve a group decision making peoblem is proposed in this paper. Specifically, the hybrid weight of each expert is generated by a convex combination of his/her subjective experience-based weight and objective problem-domain-based weight. The experience-based weight is derived from the expert's historical experiences and the problem-domain-based weight is characterized by the confidence degree and consensus degree of each expert's opinions in the current decision making process. Based on the hybrid weighted aggregation method, all the experts' opinions which are expressed in the form of fuzzy preference relations are consequently aggregated to obtain a collective group opinion. Some valuable properities of the proposed method are discussed. A nurse manager hiring problem in a hospital is employed to illustrate that the proposed method provides a rational and valid solution for the group decision making problem when the experts are not willing to change their initial preferences, or the cost of change is high due to time limitation.

The conventional methods of improving the performance of wireless sensor network focus on proposed algorithm mechanism to increase the packet delivery ratio, reduce the delay and so on. In this paper, firstly we propose an adaptive sleep scheduled scheme to reduce the energy consumption of the whole network. Based on this, we introduce weight w to measure the importance of data. We assume that the faster important the data is transmitted to sink node, the better the performance of whole network is. Applied the scheduling policy, system sleep optimum is impossible. The simulation shows that the performance has increased compared with full connect network.

The 2D Online Bin Packing is a fundamental problem in Computer Science and the determination of its asymptotic competitive ratio has research attention. In a long series of papers, the lower bound of this ratio has been improved from 1.808, 1.856 to 1.907 and its upper bound reduced from 3.25, 3.0625, 2.8596, 2.7834 to 2.66013. In this article, we rewrite the upper bound record to 2.5545. Our idea for the improvement is as follows. In 2002, Seiden and van Stee [Seiden and van Stee 2003] proposed an elegant algorithm called H ⊗ C , comprised of the Harmonic algorithm H and the Improved Harmonic algorithm C , for the two-dimensional online bin packing problem and proved that the algorithm has an asymptotic competitive ratio of at most 2.66013. Since the best known online algorithm for one-dimensional bin packing is the Super Harmonic algorithm [Seiden 2002], a natural question to ask is: could a better upper bound be achieved by using the Super Harmonic algorithm instead of the Improved Harmonic algorithm? However, as mentioned in Seiden and van Stee [2003], the previous analysis framework does not work. In this article, we give a positive answer for this question. A new upper bound of 2.5545 is obtained for 2-dimensional online bin packing. The main idea is to develop new weighting functions for the Super Harmonic algorithm and propose new techniques to bound the total weight in a rectangular bin.

Given a mobile telephone network, whose geographical coverage area is divided into cells, phone calls are serviced by assigning frequencies to them, so that no two calls emanating from the same or neighboring cells are assigned the same frequency. Assuming an online arrival of calls and the calls will not terminate, the problem is to minimize the span of frequencies used.