无线网络中谈判解的协作带宽分配策略

为了促使无线网络中的"自私"节点参与合作,提出了谈判解协作带宽分配(CBA)策略,解决了节点间采用交换带宽资源协作传输,彼此以多大带宽中继对方数据的问题.首先,将两个节点的协作带宽分配问题建模为合作博弈中的谈判过程;之后,采用拉格朗日乘数法得到两个用户的纳什谈判解(NBS)协作带宽分配;其次,提出了一种新的Kalai-Smorodinsky谈判解(KSBS)协作带宽分配策略;最后,对两种谈判解协作带宽分配策略的公平性进行了研究.仿真表明,KSBS协作带宽分配策略和NBS协作带宽分配策略对提升用户效用的作用基本相同,但KSBS策略比NBS策略更为公平.     

Mobile anchor assisted particle swarm optimization (PSO) based localization algorithms for wireless sensor networks

Node localization is essential to wireless sensor networks (WSN) and its applications. In this paper, we propose a particle swarm optimization (PSO) based localization algorithm (PLA) for WSNs with one or more mobile anchors. In PLA, each mobile anchor broadcasts beacons periodically, and sensor nodes locate themselves upon the receipt of multiple such messages. PLA does not require anchors to move along an optimized or a pre‐determined path. This property makes it suitable for WSN applications in which data‐collection and network management are undertaken by mobile data sinks with known locations. To the best of our knowledge, this is the first time that PSO is used in range‐free localization in a WSN with mobile anchors. We further derive the upper bound on the localization error using Centroid method and PLA. Simulation results show that PLA can achieve high performance in various scenarios. Copyright © 2011 John Wiley & Sons, Ltd.     

基于议价博弈论的无线协作中继网络性能改进算法

针对自私性无线协作中继(CR)网络中的放大再中继(AF)和解码再中继(DF)协议,分别给出 公平且有效率的协作中继功率分配方案。两种方案虽然具体研究内容有差别,但有着共同 的研究策略。首先,将节点间的协作功率选择问题建模为纳什议价博弈问题(NBP);然后, 通过求取其纳什议价解(NBS)获得协作节点的共赢策略;最后,针对AF和DF协议分别给出计 算机仿真。仿真结果表明,所提出协作中继性能改进算法的效率性体现在所有协作节点在AF 和DF协议中均获得信号接收质量的提高,其公平性则体现在任意节点为其协作伙伴所贡献的 转发功率大小仅取决于对方为其带来的性能增益大小。     

Multi-objective-derived energy efficient routing in wireless sensor networks using hybrid African vultures-cuckoo search optimization

Wireless Sensor Network (WSN) plays an essential role in consumer electronics, remote monitoring, an electromagnetic signal, and so forth. The functional capacity of WSN gets enhanced everyday with different technologies. The rapid development of wireless communication, as well as digital electronics, provides automatic sensor networks with low cost and power in various functions, but the challenge faced in WSN is to forward a huge amount of data between the nodes, which is a highly complex task to provide superior delay and energy loss. To overcome these issues, the development of a routing protocol is used for the optimal selection of multipath to perform efficient routing in WSN. This paper developed an energy-efficient routing in WSNs utilizing the hybrid meta-heuristic algorithm with the help of Hybrid African Vultures-Cuckoo Search Optimization (HAV-CSO). Here, the designed method is utilized for choosing the optimal cluster heads for progressing the routing. The developed HAV-CSO method is used to enhance the network lifetime in WSN. Hence, the hybrid algorithm also helps select the cluster heads by solving the multi-objective function in terms of distance, intra-cluster distance, delay, inter-cluster distance, throughput, path loss, energy, transmission load, temperature, and fault tolerance. The developed model achieved 7.8% higher than C-SSA, 25.45% better than BSO-MTLBO, 23.21% enhanced than AVOA, and 1.29% improved than CSO. The performance of the suggested model is validated, and the efficacy of the developed work is proved over other existing works.     

无线传感器网络中带复杂联盟的自适应任务分配算法

针对无线传感器网络任务调度的实时性及节点计算及能量受限的特点,根据任务截止期赋予任务优先级,优先考虑高优先级任务,设计了一个无线传感器网络中带复杂联盟的自适应任务分配算法。为尽最大努力确保任务在截止期前完成,对截止期较为紧迫的任务采用历史信息生成历史联盟,并执行快速子任务分配算法;而对截止期较为宽裕的任务,在满足任务截止期约束条件下,以节点能耗和网络能量分布平衡为优化目标,采用矩阵的二进制编码形式,设计了一种离散粒子群优化算法以并行生成联盟,并执行基于负载和能量平衡的子任务分配算法。仿真实验结果表明所构造的自适应算法是有效的,在局部求解与全局探索之间能够取得较好的平衡,并能够在较短的时间内取得满意解。     

Nash bargaining solution based multi-cell and multi-user interference alignment scheme for cellular networks

In wireless cellular networks, the interference alignment （IA） is a promising technique for interference management. A new IA scheme for downlink cellular network with multi-cell and multi-user was proposed, in the proposed scheme, the interference in the networks is divided into inter-cell interference （ICI） among cells and inter-user interference （IUI） in each cell. The ICI is aligned onto a multi-dimensional subspace by multiplying the ICI alignment precoding matrix which is designed by the singular value decomposition （SVD） scheme at the base station （BS） side. The aligned ICI is eliminated by timing the interference suppression matrix which is designed by zero-forcing （ZF） scheme at the user equipment （UE） side. Meanwhile, the IUI is aligned by multiplying the IUI alignment precoding matrix which is designed based on Nash bargaining solution （NBS） in game theory. The NBS is solved by the particle swarm optimization （PSO） method. Simulations show that, compared with the traditional ZF IA scheme, the proposed scheme can obtain higher data rate and guarantee the data rate fairness of UEs with little additional complexity.     

无线传感器网络任务分配动态联盟模型与算法研究

为了延长网络生命周期,减少网络能量消耗和均衡网络负载,引入了动态联盟思想,构造了无线传感器网络任务分配的动态联盟模型,继而提出了一种基于离散粒子群优化的任务分配算法.该算法根据任务总完成时间、能量损耗以及网络负载状况,建立代价函数,结合粒子群优化算法,实现优化任务分配策略.引入了变异算子,在很好地保持了种群的多样性的同时提高了算法的全局搜索能力.仿真实验结果表明了该分配算法在局部求解与全局探索之间取得了较好的平衡,能有效减少无线传感器网络的计算时间和网络能耗,并有效地均衡网络负载.     

基于分布式与联合优化的无线传感器网络数据汇聚机制

针对大规模周期汇报型传感器网络提出了一种基于分布式与联合优化的数据汇聚机制,简称DDSM。该机制采用节点周期睡眠/工作的节能调度和分布式的工作机制,并提出一种将节点的路由建立与工作时隙分配相结合的联合优化方法,综合优化网络层和MAC层功能。最后,通过模拟实验对所提机制及其实现算法的性能进行比较和评价。实验结果表明,所提机制有效降低了节点的能耗,延长了网络的生命周期。     

Cooperative bargaining solution for efficient and fair spectrum management in cognitive wireless networks

This paper studies the fairness among the primary users (PUs) and the secondary users (SUs) for resource allocation in cognitive radio systems. We propose a novel co‐opetition strategy based on the Kalai–Smorodinsky bargaining solution to balance the system efficiency and the fairness among users. The strategy formulates the spectrum sharing problem as a nonlinear and integral sum utility maximization subject to a set of constraints describing the co‐opetition among the PUs and the SUs. Then, we solve the maximization problem by proposing a heuristical method that consists of four steps: multi‐PU competition, PU's subcarrier contribution, multi‐SU competition, and SU's subcarrier contribution. Extensive simulation results are presented by comparing the co‐opetition strategy with several conventional ones, including the Kalai–Smorodinsky bargaining solution, sum rate maximization as well as the Max–Min. Results indicate that the co‐opetition strategy can jointly balance the system efficiency and fairness in multiuser resource allocation, as it is able to support more satisfied users and in the meanwhile improve the utility of those unsatisfied. Moreover, the co‐opetition can help enable the coexistence of the PUs and the SUs in cognitive radio systems. Copyright © 2013 John Wiley & Sons, Ltd.     

SKWN: Smart and dynamic key management scheme for wireless sensor networks

In the era of the Internet of Things (IoT), we are witnessing to an unprecedented data production because of the massive deployment of wireless sensor networks (WSNs). Typically, a network of several hundred sensors is created to ensure the interactions between the cyber world and the physical world. Unfortunately, the intensive use of this kind of networks has raised several security issues. Indeed, many WSN‐based applications require secure communication in order to protect collected data. This security is generally ensured by encryption of communication between sensors, which requires the establishment of many cryptographic keys. Managing these keys, within a protocol, is an important task that guarantees the effectiveness of the security mechanism. The protocol should be intelligently adaptable not only to intrusion events but also to the security level needed by some applications. An efficient protocol optimizes also sensors energy and consequently increases the network life cycle. In this paper, we propose, a smart and dynamic key management scheme for hierarchical wireless sensor networks (SKWN). Our protocol offers three subschemes to deal with key establishment, key renewal, and new node integration. Regarding existing schemes, SKWN does not only provide reliable security mechanisms, but it also optimizes energy consumption and overheads related to the communication and memory usage. Furthermore, our approach relies on a machine learning approach to monitor the state of the network and decide the appropriate security level. We provide a formal approach and its implementation, together with simulations allowing to compare resources usage with respect to existing approaches.     

An efficient and fair cooperative approach for resource management in wireless networks

In cellular networks, the implementation of various resource management processes, such as bandwidth reservation and location updates, has been based on the one‐to‐one resource management information exchange paradigm, between the mobile nodes and the base stations. In this paper, we design and demonstrate the use of a distributed cooperative scheme that can be applied in the future wireless networks to improve the energy consumption for the routine management processes of mobile terminals, by adopting the peer‐to‐peer communication concept of wireless ad hoc networks. In our approach, the network is subdivided into one‐hop ad hoc clusters where the members of each cluster cooperate to perform the required management functions, and conventional individual direct report transmissions of the mobile terminals to the base stations are replaced by two‐hop transmissions. The performance evaluation and the corresponding numerical results presented in this paper confirm that our proposed scheme reduces significantly the overall system energy consumption when compared with the conventional one‐to‐one direct information management exchange approach. Furthermore the issue of fairness in dynamically selecting the various cluster heads in successive operational cycles of the proposed scheme is analyzed, and an enhanced algorithm is proposed and evaluated, which improves significantly the cluster head selection fairness, in order to balance the energy consumption among the various mobile terminals. Copyright © 2005 John Wiley & Sons, Ltd.     

Cross-layer optimization for end-to-end rate allocation in multi-radio wireless mesh networks

In this paper, we study rate allocation for a set of end-to-end communication sessions in multi-radio wireless mesh networks. We propose cross-layer schemes to solve the joint rate allocation, routing, scheduling, power control and channel assignment problems with the goals of maximizing network throughput and achieving certain fairness. Fairness is addressed using both a simplified max-min fairness model and the well-known proportional fairness model. Our schemes can also offer performance upper bounds such as an upper bound on the maximum throughput. Numerical results show that our proportional fair rate allocation scheme achieves a good tradeoff between throughput and fairness. Jian Tang is an assistant professor in the Department of Computer Science at Montana State University. He received the Ph.D. degree in Computer Science from Arizona State University in 2006. His research interest is in the area of wireless networking and mobile computing. He has served on the technical program committees of multiple international conferences, including ICC, Globecom, IPCCC and QShine. He will also serve as a publicity co-chair of International Conference on Autonomic Computing and Communication Systems (Autonomics’2007). Guoliang Xue is a Full Professor in the Department of Computer Science and Engineering at Arizona State University. He received the Ph.D. degree in Computer Science from the University of Minnesota in 1991 and has held previous positions at the Army High Performance Computing Research Center and the University of Vermont. His research interests include efficient algorithms for optimization problems in networking, with applications to fault tolerance, robustness, and privacy issues in networks ranging from WDM optical networks to wireless ad hoc and sensor networks. He has published over 150 papers in these areas. His research has been continuously supported by federal agencies including NSF and ARO. He is the recipient of an NSF Research Initiation Award in 1994 and an NSF-ITR Award in 2003. He is an Associate Editor of Computer Networks (COMNET), the IEEE Network Magazine, and Journal of Global Optimization. He has served on the executive/program committees of many IEEE conferences, including INFOCOM, SECON, IWQOS, ICC, GLOBECOM and QShine. He is the General Chair of IEEE IPCCC’2005, a TPC co-Chair of IPCCC’2003, HPSR’2004, IEEE Globecom’2006 Symposium on Wireless Ad Hoc and Sensor Networks, IEEE ICC’2007 Symposium on Wireless Ad Hoc and Sensor Networks, and QShine’2007. He is a senior member of IEEE. Weiyi Zhang received the M.E. degree in 1999 from Southeast University, China. Currently he is a Ph.D. student in the Department of Computer Science and Engineering at Arizona State University. His research interests include reliable communication in networking, protection and restoration in WDM networks, and QoS provisioning in communication networks.     

A game theoretic approach to balancing energy consumption in heterogeneous wireless sensor networks

Energy balancing is an effective technique in enhancing the lifetime of a wireless sensor network (WSN). Specifically, balancing the energy consumption among sensors can prevent losing some critical sensors prematurely due to energy exhaustion so that the WSN's coverage can be maintained. However, the heterogeneous hostile operating conditions—different transmission distances, varying fading environments, and distinct residual energy levels—have made energy balancing a highly challenging task. A key issue in energy balancing is to maintain a certain level of energy fairness in the whole WSN. To achieve energy fairness, the transmission load should be allocated among sensors such that, regardless of a sensor's working conditions, no sensor node should be unfairly overburdened. In this paper, we model the transmission load assignment in WSN as a game. With our novel utility function that can capture realistic sensors’ behaviors, we have derived the Nash equilibrium (NE) of the energy balancing game. Most importantly, under the NE, while each sensor can maximize its own payoff, the global objective of energy balancing can also be achieved. Moreover, by incorporating a penalty mechanism, the delivery rate and delay constraints imposed by the WSN application can be satisfied. Through extensive simulations, our game theoretic approach is shown to be effective in that adequate energy balancing is achieved and, consequently, network lifetime is significantly enhanced. Copyright © 2012 John Wiley & Sons, Ltd.     

Energy efficient path selection for mobile sink and data gathering in wireless sensor networks

Mobile sink (MS) has drawn significant attention for solving hot spot problem (also known as energy hole problem) that results from multihop data collection using static sink in wireless sensor networks (WSNs). MS is regarded as a potential solution towards this problem as it significantly reduces energy consumption of the sensor nodes and thus enhances network lifetime. In this paper, we first propose an algorithm for designing efficient trajectory for MS, based on rendezvous points (RPs). We next propose another algorithm for the same problem which considers delay bound path formation of the MS. Both the algorithms use k-means clustering and a weight function by considering several network parameters for efficient selection of the RPs by ensuring the coverage of the entire network. We also propose an MS scheduling technique for effective data gathering. The effectiveness of the proposed algorithms is demonstrated through rigorous simulations and comparisons with some of the existing algorithms over several performance metrics.     

Improved bat optimization algorithm and enhanced artificial bee colony-based cluster routing scheme for extending network lifetime in wireless sensor networks

In this paper, improved bat and enhanced artificial bee colony optimization algorithm-based cluster routing (IBEABCCR) scheme is proposed for optimal cluster head (CH) selection with the merits of global diversity and improved convergence rate. It is proposed for achieving optimal CH selection by balancing the tradeoff between the phases of exploration and exploitation. It specifically targeted on the formulation of an ideal CH selection scheme using improved bat optimization algorithm (IBOA) for minimizing the energy depletion rate. It also focuses on the design of an enhanced artificial bee colony (EABC)-based sink node mobility scheme for determining the optimal points of deployment over which sink nodes can be moved to achieve better delivery of packets from CH to sink node. This CH selection and sink node mobility schemes are contributed for extending the network lifespan using the fitness function, which adopted the factors of node centrality, node degree, distance amid CH and base station (BS), distance among sensor nodes, and residual energy during CH selection process. The simulation experiments were performed using MATLAB version 2018, which confirmed that the number of alive nodes realized in the network is enhanced by 39.21% with the location of BS positioned at (100, 100). The number of rounds (network lifetime) is enhanced by 23.84% with different BS locations in the network. Furthermore, the packets received at the BS are also realized to be enhanced by 26.32% on an average in contrast to the baseline CH schemes used for investigation.     

Network lifetime maximization for time‐sensitive data gathering in wireless sensor networks with a mobile sink

With the advances of more and more mobile sink deployments (e.g., robots and unmanned aerial vehicles), mobile sinks have been demonstrated to play an important role in the prolongation of network lifetime. In this paper, we consider the network lifetime maximization problem for time‐sensitive data gathering, which requires sensing data to be sent to the sink as soon as possible, subject to several constraints on the mobile sink. Because the mobile sink is powered by petrol or electricity, its maximum travel distance per tour is bounded. The mobile sink's maximum moving distance from its current location to the next must also be bounded to minimize data loss. As building a new routing tree rooted at each new location will incur an overhead on energy consumption, the mobile sink must sojourn at each chosen location at least for a certain amount of time. The problem, thus, is to find an optimal sojourn tour for the mobile sink such that the network lifetime is maximized, which is subject to a set of constraints on the mobile sink: its maximum travel distance, the maximum distance of each movement, and the minimum sojourn time at each sojourn location. In this paper, we first formulate this novel multiple‐constrained optimization problem as the distance‐constrained mobile sink problem for time‐sensitive data gathering. We then devise a novel heuristic for it. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the performance of the proposed algorithm is very promising, and the solution obtained is fractional of the optimal one. Copyright © 2011 John Wiley & Sons, Ltd.