The binary exponential backoff (BEB) mechanism is applied to the packet retransmission in lots of wireless network protocols including IEEE 802.11 and 802.15.4. In distributed dynamic network environments, the fixed contention window (CW) updating factor of BEB mechanism can’t adapt to the variety of network size properly, resulting in serious collisions. To solve this problem, this paper proposes a backoff algorithm based on self-adaptive contention window update factor for IEEE 802.11 DCF. In WLANs, this proposed backoff algorithm can greatly enhance the throughput by setting the optimal CW updating factor according to the theoretical analysis. When the number of active nodes varies, an intelligent scheme can adaptively adjust the CW updating factor to achieve the maximal throughput during run time. As a result, it effectively reduces the number of collisions, improves the channel utilization and retains the advantages of the binary exponential back-off algorithm, such as simplicity and zero cost. In IEEE 802.11 distributed coordination function (DCF) protocol, the numerical analysis of physical layer parameters show that the new backoff algorithm performance is much better than BEB, MIMD and MMS algorithm.

This paper presents the similarity based adaptive step size glowworm swarm optimization algorithm (SBASS-GSO), an improved version of glowworm swarm optimization algorithm (GSO). The standard GSO algorithm lacks unified metric standard to different problems in the selection of neighbor set, which makes the algorithm converge slowly because of improper selection. Because the step size s is fixed, the oscillation phenomenon may occur in local search space, which leads to inferior search accuracy In SBASS-GSO algorithm, we change neighborhood definition base on the similarity not on the distance. The neighborhood is selected by computing average similarity, which provides priori knowledge for the adaptive size s. The dynamic size s is useful for removing oscillation phenomenon and improving the convergence speed. Experimental results demonstrate the efficacy of the proposed glowworm algorithm in capturing multiple optima of a series of complex test functions, such as Zakharov and Sphere functions. We also provide some comparisons of SBASS-GSO with GSO and verify the superiority in the precision and convergence speed.

In this paper, we propose a novel vector coevolving particle swarm optimization algorithm (VCPSO). In VCPSO, the full dimension of each particle is first randomly partitioned into several sub-dimensions. Then, we randomly assign either one of our newly designed scalar operators or learning operators to update the values in each sub-dimension. The scalar operators are designed to enhance the population diversity and avoid premature convergence. In addition, the learning operators are designed to enhance the global and local search ability. The proposed algorithm is compared with several other classical swarm optimizers on thirty-three benchmark functions. Comprehensive experimental results show that VCPSO displays a better or comparable performance compared to the other algorithms in terms of solution accuracy and statistical results.

Nowadays automotive industry has been working on the connectivity between automobile and smartphones, e.g., Ford’s SmartDeviceLink, MirrorLink, etc. However, as the interoperability between the smartphone and automotive system increase, the security concern of the increased attack surface bothers the automotive industry as well as the security community. In this paper, we thoroughly study the attack vectors against the novel connection framework between automobile and smartphones, and propose a generic security model to implement a dependable connection to eliminate the summarized attack vectors. Finally, we present how our proposed model can be integrated into existing automotive framework, and discuss the security benefits of our model. Copyright

With the popularity of mobile phones with Android platform, Android platform-based individual privacy information protection has been paid more attention to. In consideration of individual privacy information problem after mobile phones are lost, this paper tried to use SMS for remote control of mobile phones and providing comprehensive individual information protection method for users and completed a mobile terminal system with self-protection characteristics. This system is free from the support of the server and it can provide individual information protection for users by the most basic SMS function, which is an innovation of the system. Moreover, the protection mechanism of the redundancy process, trusted number mechanism and SIM card detection mechanism are the innovations of this system. Through functional tests and performance tests, the system could satisfy user functional and non-functional requirements, with stable operation and high task execution efficiency

This paper is concerned with the consensus of multiple Euler-Lagrange systems with pulse-width modulated sampled-data control. Different from traditional sampled-data strategies, a pulse-modulated sampled-data strategy is developed to realize the consensus of multiple Euler-lagrange systems, in which a pulse function that can be distinct at different sampling instants is proposed to modulate the sampling interval. In addition, a new definition of average sampling interval, which is parallel to the average dwell time in switching control or average impulsive interval in impulsive control, is proposed to characterize the number of the updating of the sampling controller during some certain interval. The proposed average sampling interval makes our sampled-data strategy more suitable for a wide range of sampling signals. By utilizing the comparison principle, a sufficient criterion is obtained to guarantee the consensus of multiple Euler-Lagrange systems. The sufficient criterion is heavily dependent on the actual control duration time, the upper and lower bounds of the pulse function and the communication graph. Finally, a simulation example is presented to verify the applicability of the proposed results.

Many complex systems can be modeled as temporal networks with time-evolving connections. The influence of their characteristics on epidemic spreading is analyzed in a susceptible-infected-susceptible epidemic model illustrated by the discrete-time Markov chain approach. We develop the analytical epidemic thresholds in terms of the spectral radius of weighted adjacency matrix by averaging temporal networks, e.g., periodic, nonperiodic Markovian networks, and a special nonperiodic non-Markovian network (the link activation network) in time. We discuss the impacts of statistical characteristics, e.g., bursts and duration heterogeneity, as well as time-reversed characteristic on epidemic thresholds. We confirm the tightness of the proposed epidemic thresholds with numerical simulations on seven artificial and empirical temporal networks and show that the epidemic threshold of our theory is more precise than those of previous studies.

The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). MapReduce pioneered this paradigm change and rapidly became the primary big data processing system for its simplicity, scalability, and fine-grain fault tolerance. However, compared with DBMSs, MapReduce also arouses controversy in processing efficiency, low-level abstraction, and rigid dataflow. Inspired by MapReduce, nowadays the big data systems are blooming. Some of them follow MapReduce's idea, but with more flexible models for general-purpose usage. Some absorb the advantages of DBMSs with higher abstraction. There are also specific systems for certain applications, such as machine learning and stream data processing. To explore new research opportunities and assist users in selecting suitable processing systems for specific applications, this survey paper will give a high-level overview of the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category. As the pioneer, the original MapReduce system, as well as its active variants and extensions on dataflow, data access, parameter tuning, communication, and energy optimizations will be discussed at first. System benchmarks and open issues for big data processing will also be studied in this survey.

The family of conventional half-duplex (HD) wireless systems relied on transmitting and receiving in different time slots or frequency subbands. Hence, the wireless research community aspires to conceive full-duplex (FD) operation for supporting concurrent transmission and reception in a single time/frequency channel, which would improve the attainable spectral efficiency by a factor of two. The main challenge encountered in implementing an FD wireless device is the large power difference between the self-interference (SI) imposed by the device’s own transmissions and the signal of interest received from a remote source. In this survey, we present a comprehensive list of the potential FD techniques and highlight their pros and cons. We classify the SI cancellation techniques into three categories, namely passive suppression, analog cancellation and digital cancellation, with the advantages and disadvantages of each technique compared. Specifically, we analyze the main impairments (e.g., phase noise, power amplifier nonlinearity, as well as in-phase and quadrature-phase (I/Q) imbalance, etc.) that degrading the SI cancellation. We then discuss the FD-based media access control (MAC)-layer protocol design for the sake of addressing some of the critical issues, such as the problem of hidden terminals, the resultant end-to-end delay and the high packet loss ratio (PLR) due to network congestion. After elaborating on a variety of physical/MAC-layer techniques, we discuss potential solutions conceived for meeting the challenges imposed by the aforementioned techniques. Furthermore, we also discuss a range of critical issues related to the implementation, performance enhancement and optimization of FD systems, including important topics such as hybrid FD/HD scheme, optimal relay selection and optimal power allocation, etc. Finally, a variety of new directions and open problems associated with FD technology are pointed out. Our hope is that this treatise will stimul- te future research efforts in the emerging field of FD communications.

In order to enhance the attainable transmission rates to the levels specified by future wireless communications, a paradigm shift from conventional small-scale MIMO to large-scale (LS)-MIMO is highly desirable. LS-MIMO technology as a "cleanslate" approach is shown to be capable of dramatically increasing the area spectral efficiency (SE, as measured in bits per second per Hertz per square kilometer) while simultaneously improving the energy efficiency as measured in bits per Joule. Furthermore, the concept of LS-MIMO has established itself as a beneficial transmission/detection paradigm, thus substantially reducing the impact of interference relying on some advanced transmit precoding/ beamforming/detection techniques. This article is intended to offer a state-of-the-art survey on LS-MIMO research, to promote the discussion of its beneficial application areas and the research challenges associated with BF aided wireless backhaul, LS-MIMO channel modeling, signal detection, and so on. Additionally, a joint group power allocation and pre-beamforming scheme called JGPAPBF is proposed to substantially improve the performance of LS-MIMO-based wireless backhaul in heterogeneous networks. Our hope is that this article will stimulate future research efforts.

The proliferation of new online Internet services has substantially increased the energy consumption in wired networks, which has become a critical issue for Internet service providers. In this paper, we target the network-wide energy-saving problem by leveraging speed scaling as the energy-saving strategy. We propose a distributed routing scheme-HDEER-to improve network energy efficiency in a distributed manner without significantly compromising traffic delay. HDEER is a two-stage routing scheme where a simple distributed multipath finding algorithm is firstly performed to guarantee loop-free routing, and then a distributed routing algorithm is executed for energy-efficient routing in each node among the multiple loop-free paths. We conduct extensive experiments on the NS3 simulator and simulations with real network topologies in different scales under different traffic scenarios. Experiment results show that HDEER can reduce network energy consumption with a fair tradeoff between network energy consumption and traffic delay.

Compromised or misconfigured routers have been a major concern in large-scale networks. Such routers sabotage packet delivery, and thus hurt network performance. Data-plane fault localization (FL) promises to solve this problem. Regrettably, the path-based FL fails to support dynamic routing, and the neighbor-based FL requires a centralized trusted administrative controller (AC) or global clock synchronization in each router and introduces storage overhead for caching packets. To address these problems, we introduce a dynamic distributed and low-cost model, D2FL. Using random 2-hop neighborhood authentication, D2FL supports volatile path without the AC or global clock synchronization. Besides, D2FL requires only constant tens of KB for caching which is independent of the packet transmission rate. This is much less than the cache size of DynaFL or DFL which consumes several MB. The simulations show that D2FL achieves low false positive and false negative rate with no more than 3% bandwidth overhead. We also implement an open source prototype and evaluate its effect. The result shows that the performance burden in user space is less than 10% with the dynamic sampling algorithm. 

Machine learning (ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the forefront in recent years partly owing to the advert of big data. ML algorithms have never been better promised while challenged by big data. Big data enables ML algorithms to uncover more fine-grained patterns and make more timely and accurate predictions than ever before; on the other hand, it presents major challenges to ML such as model scalability and distributed computing. In this paper, we introduce a framework of ML on big data (MLBiD) to guide the discussion of its opportunities and challenges. The framework is centered on ML which follows the phases of preprocessing, learning, and evaluation. In addition, the framework is also comprised of four other components, namely big data, user, domain, and system. The phases of ML and the components of MLBiD provide directions for the identification of associated opportunities and challenges and open up future work in many unexplored or under explored research areas.

Due to reliance on batteries, energy consumption has always been of significant concern for sensor node networks. This work presents the design and implementation of a house-build experimental platform, named Energy Management System for Wireless Sensor Networks (EMrise) for the energy management and exploration on wireless sensor networks. Consisting of three parts, the SystemC-based simulation environment of EMrise enables the HW/SW co-simulation for energy evaluation on heterogeneous sensor networks. The hardware platform of EMrise is further designed to facilitate the realistic energy consumption measurement and calibration as well as accurate energy exploration. In the meantime, a generic genetic algorithm based optimization framework of EMrise is also implemented to automatically, quickly and intelligently fine tune hundreds of possible solutions for the given task to find the best suitable energy-aware tradeoffs

Due to reliance on batteries, energy consumption has always been of significant concern for sensor node networks. This work presents the design and implementation of a house-build experimental platform, named EMrise (Energy Management System for Wireless Sensor Networks) for the energy management and exploration on wireless sensor networks. Consisting of three parts, the SystemC-based simulation environment of EMrise enables the HW/SW co-simulation for energy evaluation on heterogeneous sensor networks. The hardware platform of EMrise is further designed to facilitate the realistic energy consumption measurement and calibration as well as accurate energy exploration. In the meantime, a generic GA (genetic algorithm) based optimization framework of EMrise is also implemented to automatically, quickly and intelligently fine tune hundreds of possible solutions for the given task to find the best suitable energy-aware tradeoffs.

In wireless sensor networks (WSNs), there is no predefined infrastructure. Nodes need to frequently flood messages to discover routes, which badly decreases the network performance. To overcome such drawbacks, WSNs are often grouped into several disjointed clusters, each with a representative cluster head (CH) in charge of the routing process. In order to further improve the efficiency of WSNs, it is crucial to find a cluster partition with minimum number of clusters and the distance between each node to its corresponding CH can be bounded by a constant number of hops. Finding such a partition is defined as minimum d-hop cluster head set (d-MCHS) problem, which is proved to be NP-hard. In this paper, we propose a distributed approximation algorithm, named d^2-Cluster, to address d-MCHS problem and prove that the approximation ratio of d^2-Cluster under unit disk graph (UDG) is a constant factor \lambda which is related to d. To the best of our knowledge, it is the first constant approximation ratio for d-MDS problem in UDG

The full-duplex (FD) based devices are capable of concurrently transmitting and receiving signals with a single frequency band. However, a severe self-interference (SI) due to the large difference between the power of the devices' own transmission and that of the signal of interest may be imposed on the FD based devices, thus significantly eroding the received signal-to-interference-plus-noise ratio (SINR). To implement the FD devices, the SI power must be sufficiently suppressed to provide a high-enough received SINR for satisfying the decoding requirement. In this paper, the design and implementation of the duplexer for facilitating SI cancellation in FD based devices are investigated, with a new type of duplexer (i.e. an improved directional coupler) designed and verified. It is shown that the SI suppression capability may be up to 36 dB by using the proposed design, which is much higher than that attainable in the commonly designed ferrite circulator