The distance vector hop algorithm is commonly used for sensor node localization. However, its high localization accuracy error and stability issues make it unsuitable for many applications. To overcome these concerns, this article proposes a new function and binary distance vector hop (FBDV-Hop) algorithm with binary controllers and function correction methods while considering the application requirement. In FBDV-Hop, binary controllers are designed to analyze the optimization effect of the module fully and make it adaptable to diverse scenarios. The correction strategies were based on average hop distance measurement, estimated distance, equation composition method, and localization after supplementary correction, which were divided into four modules in accordance with the module error sources of different design correction functions. The simulation experiment was designed to analyze the principle of the role of each module in depth and achieve the optimal optimization effect. The experimental results show that the localization error optimization rate under the FBDV-Hop algorithm was more than 70%, and the optimization rate, stability, effectiveness, and adaptability of the algorithm were considerably greater than the baseline algorithms.

Modern data center network often possesses multiple end-to-end parallel paths, which undertake the crucial task of transmitting vast heterogeneous data traffic generated by a wide variety of applications. To fully utilize the offered super high bisection network bandwidth thus benefiting application performance, many data center load balancing schemes are proposed to improve path utilization for avoiding network congestion hot-spot. However, these schemes are naturally agnostic to data center traffic pattern and the diverse requirements on flow-transmission, leading to the sub-optimal network transmission performance. To address this issue, this paper presents a new data center load balancing scheme, called PCLB, which selectively generates Clone Packets by considering both flow-transmission phases and path states, thereby helping different types of flows choose more appropriate paths for speeding up their data transmission. Experimental results of numerous NS2 simulations show that PCLB significantly reduces the average and tail flow completion time for delay-sensitive flows, while the performance of throughput-oriented flows can be always maintained at high level.

With an increasing amount of data produced by pervasive and ubiquitous smart devices, many Internet of Things (IoT) applications adopt the cloud platform to store and process data. To protect data security and privacy, attribute-based encryption (ABE) has been widely used in cloud-assisted IoT systems. However, most ABE schemes usually require a central authority to distribute decryption keys for all users, which may raise security and efficiency concerns; in addition, the exposure of decryption keys would severely damage the data privacy. In this article, we introduce a novel notion of decentralized attribute-based puncturable encryption (DABPE). DABPE allows data owner to generate public and secret keys by himself, without relying on any central authority. When outsourcing data to the cloud, the data owner can encrypt data with an access policy; moreover, the data owner could issue particular keys for different data users and only those users whose keys satisfy the access policy can access the data. To achieve a flexible forward security, the data owner and data user can update their keys with some tags such that the data specified by the tags would not be revealed even if the keys are disclosed. We design a concrete DABPE scheme and prove its security in the standard model, and also conduct extensive experiments to show the efficiency of the proposed scheme.

Nowadays, the deluge of heterogeneous data generated by various cloud-based industrial applications often has to be delivered to the data center for analysis and storage. To speed up data processing thus facilitating application performance, the modern data center network offers rich parallel paths and super high bisection bandwidth for data communications between servers, expecting to provide good transmission performance for the heterogeneous data traffic caused by cloud-based industrial applications. Due to high path diversities, however, balancing the heterogeneous traffic load across multiple parallel paths for fully utilizing the offered super high bisection bandwidth is full of challenges (i.e., how to achieve high path utilization without incurring adverse impact). Although prior studies demonstrate that the flowlet-based solutions are promising to fill the bill, we argue that their rerouting operations are still inappropriate in timing and manner. This article presents HG, a load balancing scheme adopting hybrid switching granularity to make traffic rerouting. HG embeds the flow-fragment-based and flowcell-based path switching into the flowlet-based path switching, and employs state-weighted path measurement to choose paths for newly appeared flow fragments, flowcells, and flowlets. The results of numerous NS2 tests show that, compared with the state-of-the-art data center load balancing schemes, HG significantly reduces the average and tail-flow completion times for delay-sensitive flows, and the throughput of throughput-oriented flows is always maintained at high level.

To solve the problems of slow convergence and limited prediction ability of the Differential Evolution (DE) algorithm, an enhanced DE algorithm with the trustworthiness of a dynamic-selection framework (denoted by DEDF) is proposed. DEDF develops a trusted framework containing five mutation strategies to realize the dynamic selection of mutation strategies. On the basis of the framework, the mutation factor, crossover factor, and local exit strategy are improved to balance the algorithm’s local search and global search ability. A series of tests on the function set CEC2017 has been performed, and the findings show that compared with other benchmark algorithms, the DEDF has advantages in convergence speed and accuracy. The proposed algorithm DEDF can be effectively leveraged to address multi-objective optimization issues in IIOT.

Abstract The generalized $k$-connectivity of a graph $G$, denoted by $\kappa _k(G)$, is a generalization of the traditional connectivity and can serve for measuring the capability of a network $G$ to connect any $k$ vertices in $G$. It is well known that the generalized $k$-connectivity is an important indicator for measuring the fault tolerance and reliability of interconnection networks. The $n$-dimensional folded hypercube $FQ_n$, which is an important variation of hypercubes, can be obtained from the $n$-dimensional hypercube $Q_n$ by adding an edge between any pair of vertices with complementary addresses. In this paper, we show that $\kappa _3(FQ_n)=n$ for $n\ge 2$, that is, for any three vertices in $FQ_n$, there exist $n$ internally disjoint trees connecting them.

Cloud computing has become an increasingly popular option for users to store and share data. Encryption prior to outsourcing data to the cloud is the best way to protect data security and privacy; however, it hinders sharing of the data that was encrypted. In addition, users in many real-world organizations (e.g., enterprises) have multiple level structures and a higher-level user should have the privilege to decide which data can be shared with a lower-level user. Most solutions in the literature suffer from inefficiency or inflexibility in tackling this problem. In this article, we propose a fine-grained hierarchical data sharing (FHDS) scheme in clouds. With FHDS, the data owner can encrypt data with his public key, and then selectively share encrypted data with users in a hierarchy; if necessary, the users can disseminate the owner's data to their subordinates in the lower levels by generating access keys. In particular, the higher-level users could puncture the keys with some tags such that the part of the owner's data which is labeled by the punctured tags will not be accessible to the lower-level users. The proposed scheme is provable secure under our security model and performance analyses show the efficiency of the scheme.

The high computational capability provided by a data center makes it possible to solve complex manufacturing issues and carry out large-scale collaborative cloud manufacturing. Accurately, real-time estimation of the power required by a data center can help resource providers predict the total power consumption and improve resource utilization. To enhance the accuracy of server power models, we propose a real-time energy consumption prediction method called IECL that combines the support vector machine, random forest, and grid search algorithms. The random forest algorithm is used to screen the input parameters of the model, while the grid search method is used to optimize the hyperparameters. The error confidence interval is also leveraged to describe the uncertainty in the energy consumption by the server. Our experimental results suggest that the average absolute error for different workloads is less than 1.4% with benchmark models.

Virtualized networked datacenters (VNDCs) are gaining considerable attention for stochastic task execution under real-time constraints. However, the problem of efficiently minimizing the high energy consumption while ensuring high quality of service (QoS) in VNDCs has not been fully addressed. Although many solutions have been proposed to address this challenge, they are not efficient and only consider one or two of the energy consuming resources of VNDCs. To this end, an adaptive energy-aware algorithm,MCEC, that efficiently reduces the energy consumption of VNDCs while ensuring high QoS is proposed. Different from the existing approaches, the MCEC algorithm considers energy consumed by computing resources, virtual machine (VM) reconfiguration, communication resources and storage media resources while meeting user QoS requirements defined in the service level agreement (SLA). To validate the effectiveness of our algorithm, we carried out extensive experiments and compared the performance of our algorithm with existing baseline algorithms. The results of the experiments show that our algorithm substantially outperforms the baseline algorithms with respect to reducing energy consumption while respecting the service level agreement.

Existing WiFi recognition schemes are capable of discovering patterns of indoor semantics, such as human activity, identity, indoor environment, and so on. We note that channel state information (CSI) presents an opportunity for hackers to learn indoor privacy, however, currently there is a lack of security research on CSI. In this article, we are the first to discuss and define the security problem of CSI signals, which is further extended to the problems of nontargeted protection and targeted protection. To solve them, we present two types of adversarial autoencoder networks (AAENs). Through replacing the original signals with the generated adversarial ones, the protected semantic features are modified, and the significant features of the other semantics required to be recognized are reserved. Intensive evaluations demonstrate that with the proposed AAENs, the recognition accuracy of the protected semantic can be significantly decreased, while still maintaining the other semantics to be identified correctly.

Resource optimization algorithm based on clustering and improved differential evolution strategy, as a new global optimized algorithm, has wide applications in language translation, language processing, document understanding, cloud computing, and edge computing due to high efficiency. With the development of deep learning technology and the rise of big data, the resource optimization algorithm encounters a series of challenges, such as the workload imbalance and low resource utilization. To address the preceding problems, this study proposes a novel resource optimization algorithm based on clustering and an improved differential evolution strategy (Multi-objective Task Scheduling Strategy (MTSS)). Three indexes, namely task completion time, execution cost, and workload, of virtual machines are selected and used to build the fitness function of the MTSS algorithm. At the same time, the preprocessing state is set up to cluster according to the resource and task characteristics to reduce the magnitude of their matching scale. Moreover, to solve the workload imbalance among different resource sets, local resource tasks are reallocated using the Q-value method in the MTSS strategy to achieve workload balance of global resources and improve the resource utilization rate. Experiments are carried out to evaluate the effectiveness of the proposed algorithm. Results show that the proposed algorithm outperforms other algorithms in terms of task completion time, execution cost, and workload balancing.

Crowd sensing is an efficient way to collect heterogeneous information in the complicated infrastructures for fingerprinting based indoor localization. However, the information related to the dynamic trajectory are difficult to fuse due to the reliability issues from different devices and user moving habits. In this paper, we proposed a crowd sensing based indoor localization system with heterogeneous information fusion, which is called Wimage. Wimage can efficiently fuse multiple information sources related to location information, e.g., visual image, WiFi and geomagnetic data, even if the targets are moving with different and variable speeds. Then we design image-base subregion matching algorithm to locate the initial position and segmented weighted K-nearest neighbor algorithm to attain the matched trajectories in the database. A dynamic temporal warping algorithm is proposed for further calibrating the estimations. The experimental results indicate that with the helps from different kinds of information, the root mean square error is only below 0. 4m, which is highly accurate for locating a target in a large scale of indoor environment.

Summary With the increasing scale of tasks in cloud computing, the problem of high energy consumption becomes increasingly serious. To deal with the problem, we propose a cloud computing energy consumption model, which takes into account the execution and transmission cost of the processor. Then, based on this model, we put forward a task scheduling optimization algorithm named modified particle swarm optimization (M‐PSO) to handle the local optimum and slow convergence problem. Different from the PSO, M‐PSO can dynamically adjust the inertia weight coefficient to improve the speed of convergence according to the number of iterations. Finally, the performance of the proposed algorithm is evaluated through the CloudSim toolkit, and the experimental results show that the M‐PSO can efficiently reduce total cost compared with other algorithms.

Summary Cloud computing has gained more and more attention from industrial and academic circle since it offers pay‐as‐you‐go model, and business applications based on the cloud are also increasing. These applications meet the requirement of users while at the same time triggering the problem of high energy consumption in data centers. To deal with the problem, we propose a new algorithm named EEOM (Energy Efficiency Optimization of VM Migrations). Under considering CPU and memory factors, the key three steps for EEOM algorithm, including trigger time, VM selection, and host location, are optimized. EEOM algorithm takes use of the virtualization technology and migrates some VMs on the lightly loaded host and heavily loaded host to other hosts. The idle hosts are switched to low‐power mode or shut down so as to save energy consumption. The experimental results show that, as compared with Double Threshold (DT) algorithm, the EEOM algorithm saves 7% energy consumption and reduces 13% SLA violations.

Real-time tracking of subway riding will provide great convenience to millions of commuters in metropolitan areas. Traditional approaches using timetables need continuous attentions from the subway riders and are limited to the poor accuracy of estimating the travel time. Recent approaches using mobile devices rely on GSM and WiFi, which are not always available underground. In this work, we present SubTrack, utilizing sensors on mobile devices to provide automatic tracking of subway riding in real time. The real-time automatic tracking covers three major aspects of a passenger: detection of entering a station, tracking the passenger's position, and estimating the arrival time of subway stops. In particular, SubTrack employs the cell ID to first detect a passenger entering a station and exploits inertial sensors on the passenger's mobile device to track the train ride. Our algorithm takes the advantages of the unique vibrations in acceleration and typical moving patterns of the train to estimate the train's velocity and the corresponding position, and further predict the arrival time in real time. Our extensive experiments in two cities in China and USA respectively demonstrate that our system can accurately track the position of subway riders, predict the arrival time and push the arrival notification in a timely manner.

Abstract Because of the surging demands of multimedia services, quality‐of‐experience (QoE) is becoming an important metric to evaluate network quality from users' perspective. In this paper, resource optimisation to achieve optimal tradeoff between QoE and energy consumption in bidirectional orthogonal frequency‐division multiple‐access relaying networks is addressed so as to provide satisfactory multimedia delivery quality and support green communications. We first formulate a QoE‐energy efficiency tradeoff optimisation where QoE requirements and relaying traffic balance are considered and prove that QoE‐energy efficiency is quasiconcave on QoE, which suggests the existence of a unique global optimal tradeoff point. We then propose an optimisation framework to achieve the optimal tradeoff efficiently. With the framework, we develop resource allocation approaches for two specific relaying strategies, that is, two‐phase decode‐and‐forward relaying with dynamic XOR network coding and compute‐and‐forward relaying with physical network coding via structured codes. Numerical results validate theoretical findings and demonstrate the effectiveness of the proposed optimisation solution for achieving the tradeoff between QoE and energy consumption. Copyright © 2015 John Wiley & Sons, Ltd.

With the proliferation of context-aware services and human-computer interaction scenarios, the novel techniques of motion detection have attracted growing interests. There are many products which use camera, infrared sensors, RF, etc. With more Wi-Fi devices in homes and the ability to measure the PHY layer information, Wi-Fi based motion detection has attracted more attention than before. In this poster, we present the design of RoMD, a robust motion detection system which uses existing Wi-Fi devices. Changing environment and human movement would cause significant pattern changes of the CSI amplitude over time. The basic idea of RoMD is to eliminate the “bad antennas” and interference of CSI measurements so that the features we extract would not be affected by changing environments. To achieve this goal, we implement an efficient data processing structure to accurately extract the feature and identify the human motion. Then, we simply evaluate the scheme and demonstrate feasibility. And finally, we introduce the work in the future.

The authors consider a cognitive radio network overlaying on top of a legacy primary network in which a secondary user is allowed to access primary channel by overhearing feedback signals over the primary channels. Each channel is assumed to be a two state Makovian process. Aiming at maximising the expected accumulated discounted network throughput, the considered sequential decision‐making problem can be cast into a restless multi‐armed bandit (RMAB) problem which is well‐known to be PSPACE‐hard, and thus a natural alternative approach is to seek a simple myopic policy. This study presents a theoretical study on the optimality of the proposed myopic policy for the special RMAB problem by considering four different cases: negatively correlated homogeneous channels, heterogeneous channels, positively correlated heterogeneous channels and negatively correlated heterogeneous channels. More specifically, the authors establish the closed‐form conditions to guarantee the optimality of the myopic policy for the four cases, respectively, which, combined with the case of positively correlated homogeneous channels, constitute a complete paradigm for the optimality of the myopic policy.

In this paper, we consider the probing order and stopping problem arising from the identification of spectrum holes in multi-channel cognitive radio networks, in which a secondary user (SU) seeks to maximize the probability of finding an available channel while minimizing the related probing cost within a long time horizon. This problem can be casted into a restless multi-armed bandit problem, which is proved to be PSPACE-hard. The key point of this problem is the trade-off between exploitation, in which the SU stops probing once an available channel is identified, and exploration, in which the SU continues to probe new channels even after identifying an available channel in order to learn the system state to reduce probing cost in the future. To strike a desirable balance between the two conflicting objectives, we develop a heuristic channel probing policy, termed the v-step lookahead policy, in which the SU makes its decision based on the prediction of system state within the future v steps, with v being a tunable parameter. We conduct an analytical study on the structure of the proposed v-step lookahead policy and demonstrate how the policy can be implemented with linear complexity with respect to the number of channels in the system via a detailed analysis on the 1-step lookahead policy. Numerical experiments between the v-step lookahead policy and myopic probing policy on two representative network scenarios demonstrate the effectiveness of the proposed v-step lookahead policy.

In this paper, we investigate the joint optimization of power allocation and subcarrier assignment in the OFDM bidirectional systems where two users exchange data assisted by multiple decode-and-forward (DF) relays. We assume the availability of imperfect channel state information (CSI) that contains a probabilistic uncertainty component and consider its worst effect when performing resource allocation. Moreover, we adopt dynamic network coding to exploit channel diversity. The objective of the resource allocation problem is to maximize the weighted sum rates of the two users subject to quality-of-service (QoS) requirements and individual power constraints. We formulate the problem as a mixed integer programming problem and solve it efficiently in an asymptotic manner by using the dual method. Simulation results show that the proposed scheme is robust to imperfect CSI and provides better performance compared to the scheme with static network coding.

ABSTRACT The capacity of wireless networks can be improved by the use of multi‐channel multi‐interface (MCMI), multi‐packet reception (MPR), and directional antenna (DA). MCMI can provide the concurrent transmission in different channels for each node with multiple interfaces; MPR offers an increased number of concurrent transmissions on the same channel; DA can be more effective than omni‐DA by reducing interference and increasing spatial reuse. This paper explores the capacity of wireless networks that integrate MCMI, MPR, and DA technologies. Unlike some previous research, which only employed one or two of the aforementioned technologies to improve the capacity of networks, this research captures the capacity bound of the networks with all the aforementioned technologies in arbitrary and random wireless networks. The research shows that such three‐technology networks can achieve at most capacity gain in arbitrary networks and capacity gain in random networks compared with MCMI wireless networks without DA and MPR. The paper also explored and analyzed the impact on the network capacity gain with different , θ, and k‐MPR ability. Copyright © 2012 John Wiley & Sons, Ltd.

Multi-channel multi-interface (MCMI) medium access can provide high-throughput services for wireless ad hoc networks. However, the multi-channel hidden terminal problem and the rendezvous problem must be addressed. This study aims to design a low-cost integrated solution to address the two problems, so as to fully exploit the available wireless interfaces and channels to enhance the network performance, especially the network aggregated throughput. The authors propose a new MCMI synchronisation-based media access control (SMAC) protocol with the operation of two interfaces. Unlike other studies, the authors propose a novel interface classification using a synchronisation interface (SynIF) and a normal interface (NorIF). A synchronisation mechanism and a new channel coordination procedure are operated on SynIF, and data packets can be transmitted on SynIF and NorIF concurrently. All control messages occur only in a variable-length channel coordination window (CCW) on a common control channel. The maximum length of CCW and network throughput are analysed mathematically and the performance of the protocol is validated in ns-2 simulator through comparisons with few other MCMI-MAC protocols. In IEEE 802.11-based multi-interface ad hoc networks, when using MCMI-SMAC, the results show that the network aggregated throughput is enhanced significantly and the protocol overhead is very low.

AODV protocol is a comparatively mature on-demand routing protocol in mobile ad hoc networks. However, the traditional AODV protocol seems less than satisfactory in terms of delivery reliability. This paper presents an AODV with reliable delivery (AODV-RD), a link failure fore-warning mechanism, metric of alternate node in order to better select, and also repairing action after primary route breaks basis of AODV-BR. Performance comparison of AODV-RD with AODV-BR and traditional AODV using ns-2 simulations shows that AODV-RD significantly increases packet delivery ratio (PDR). AODV-RD has a much shorter end-to-end delay than AODV-BR. It both optimizes the network performance and guarantees the communication quality.