基于能量变化的无线传感器网络覆盖算法

设计了一种基于能量变化的无线传感器网络覆盖算法.通常网络节点连接的通信范围应该大于两倍的感应范围,但这将导致网络能耗的增加.提出的算法采用最少数量的节点来解决传感器网络的覆盖率和网络的连通性问题.测量结果表明,提出的覆盖算法可以满足传感器节点的通信范围小于两倍的感应范围.这将降低传感器网络的能量损耗.算法通过使用不同能量水平的活跃节点来降低能量损耗,并且可以延长系统的使用寿命.     

无线传感器网络孤立点数目的近似分布

利用微积分计算和Brun's筛法,得到了部署在在单位正方形区域上服从均匀分布n个传感器节点构成的无线安全传感器网络孤立点数目的近似分布.我们证明了对于某个常数c,如果任意两个传感器节点之间最大通信半径rn满足nπr_n~2p'=ln n+c,这里p'是两个传感器节点至少有一个公共密钥的概率,则无线传感器网络孤立点数目近似服从参数为e~(-c)的Poisson分布.     

无线传感器网络传输可靠性计算

无线传感器网络是由传感器节点和汇聚节点组成的以数据为中心的无线网络.汇聚节点根据一个或多个源节点传送的采集数据对事件进行监测和判断,而数据传输的可靠性直接影响到监测和判断的准确性.在无线传感器网络中,一方面,网络拓扑结构是动态变化的,数据传输的可靠性与网络拓扑结构有关;另一方面,网络中的传感器节点是能最受限的,因此传输的可靠性还与节点的能昔密切相关-针对无线传感器网络的特点,给出了无线传感器网络的传输可靠性概念,提出一种传输可靠性度量,分别在有数据融合和无数据融合两种情况下,对网络节点的能耗情况进行了分析,获得r网络节点正常工作的概率随时间的变化关系,并导出数学表达式,用于计算节点所产生的数据包成功传输给汇聚节点的概率,从而获得了求整个网络传输可靠性的计算方法.     

基于二叉树的簇内分层密钥管理方案

通过对基于分簇的数据融合隐私保护算法CPDA的分析,提出了一种基于簇内分层随机密钥管理方案.它使用二叉树的逻辑结构,对簇内节点进行重新组织后,传输各自的切片数据,再通过建立密钥树的逻辑层次结构.对整个组密钥进行管理最终实现了在数据融合的同时,保护数据安全通过实验分析,相比于分簇隐私数据融合协议CPDA在具有更好的隐私保护性的同时,更低的数据通信量以及良好的可扩展性.     

基于罚函数方法的Leach协议

为延长无线传感器网络生存时长、减少网络能量消耗,首先将自适应粒子群优化算法应用于Leach协议,获得每一轮的最优簇头集;再基于罚函数方法,对集合中处于边缘位置的感知节点以及基站附近能量较低的感知节点进行惩罚,降低其当选为簇头的概率.通过大量仿真实验表明,协议对网络中簇头节点的选取更加合理,死亡节点分布由外而内,使节点能量负载更加均衡.     

基于离散群居蜘蛛算法的WSNs分簇路由优化

对无线传感器网络(WSNs)路由优化问题进行研究,提出一种基于离散群居蜘蛛算法的WSNs分簇路由优化方案.首先定量分析节点覆盖冗余度期望值与网络覆盖率的关系,筛选出能够保证网络覆盖率要求的最少网络工作节点,其次研究分簇大小与网络节点密度的关系,动态地确定最佳的分簇个数.基于此,以簇间距离和簇首能量为评价指标构建簇间通信模型,重新定义蜘蛛个体编码方式和更新策略,采用离散群居蜘蛛算法对模型进行求解,最终实现WSNs分簇路由优化.仿真结果表明,方案能够满足网络覆盖要求,而且与其它路由优化算法相比,延长了网络生命周期,降低了网络能耗.     

弧k/n(G)与节点k/n(G)网络模型可靠性估计的对偶变量蒙特罗洛方法

针对弧k/n(G)网络无法刻画网络节点对上游节点输入需求的问题,提出了节点k/n(G)网络模型.在节点k/n(G)网络中,通过设定网络节点工作条件为接收工作输入点集中n个节点里的至少k个输入,节点k/n(G)性质被进一步延伸至对上游任意节点.为采用蒙特卡洛方法对弧与节点k/n(G)网络的可靠性进行估计,分别对两类k/n(G)网络设计了基于随机邻接矩阵的网络连通性算法.并结合k/n(G)网络结构函数的单调性,将对偶变量方差缩减技术应用于两类k/n(G)网络的蒙特卡洛方法,仿真实验表明:所设计的仿真方案能够有效地对两类k/n(G)网络的可靠性进行估计,对偶变量方法提高了蒙特卡洛方法的计算精度并减少了计算时间.     

光通信链路建模与网络规划

光纤是通信网络中优质的传输媒介,具有传输频带宽、容量大、抗干扰性能好等优点.首先考虑光通信链路中放大器自发辐射噪声和光纤非线性效应等效噪声对其信道进行建模,并分析了三种不同调制格式下的误码率(BER)性能.进一步,基于联盟形成算法,考虑存在中间节点和不存在中间节点两种情况,对光通信网络进行最优规划以获得最大化网络价值.此外,基于概率整形和几何整形原理对星座图进行设计,实现了误码率性能的改善.     

量子计算与量子通信浅说(上)

基于大学本科非数学专业的高等数学知识对量子计算与量子通信做一个简介,期望普通理工科大学本科生可以理解这些概念及原理.本文将介绍量子力学的基本原理和量子Bit;以BB84方案为例介绍量子密码原理以及如何利用纠缠态实现密钥分配.     

一类强部分平衡设计的构造

利用强部分平衡设计可以构造最佳认证码和密钥预分配方案,本文提出了一种强 部分平衡设计的构造方法,并给出了利用这种方法得到的一些结果.     

Unconditionally-secure key pre-distribution for triangular grid based wireless sensor network

We present an unconditionally-secure key pre-distribution scheme for a wireless sensor network using t-degree bivariate polynomials. The proposed scheme is proven to be perfectly resilient against both node disconnection and link failure. The memory requirements, computation and communication overheads of our scheme are also favorable. Our scheme demonstrates superior performance compared to the existing similar schemes.     

A key design to prolong lifetime of wireless sensor network

In order to solve the contradiction between the connectivity of the wireless sensor network and the key storage consumption, under the premise of reducing network storage consumption, the key pre-distribution management scheme with higher connectivity rate is proposed using the hexagonal network deployment information, which adopts the idea of the matrix space for the square deployment information strategy to reduce the burden of the network storage. Ability against the capture attack is improved obviously. The results show that contradiction between the network connectivity rate and the energy consumption has a better solution, and the proposed algorithm is suitable for the wireless sensor networks of energy limited.     

Key pre-distribution in a non-uniform rectangular grid for wireless sensor networks

A deterministic key pre-distribution scheme is proposed in the paper. The distribution of keys to the nodes precedes a virtual arrangement of the nodes into a non-uniform rectangular grid structure. Distribution of keys is based on projective planes and pairwise connectivity. With small memory requirements, the nodes induce a network which offers a trade-off between connectivity and resilience. The impact of resilience and connectivity can be controlled by choosing the number of rows and columns suitably. Another significant aspect of the proposed scheme is that the path between any two nodes is not unique, which leads to a well-connected network.     

Adaptive energy efficient sensor scheduling for wireless sensor networks

In this paper, we consider an adaptive energy efficient sensor scheduling mechanism. We consider a wireless sensor network where the sink sends queries form time to time, and sensors are equipped with one or more sensing components. Our goal is to design an adaptive sensor scheduling mechanism to choose sets of active sensors to work alternatively such that different types of queries are served, the global connectivity requirements can be met, and network lifetime is maximized. A connected dominating set (CDS) based localized mechanism is proposed. Initially, a basic backbone is constructed, then when a query is issued, new sensors are activated locally such that to meet the requirements of the query and global connectivity. When a query expires, some sensors return to sleep and the CDS is restored. Our simulation results show that the solution is effective and it improved network lifetime.     

An Iterative Exact Solution for the Dual Power Management Problem in Wireless Sensor Network

We study the dual power management problem in wireless sensor networks. Given a wireless sensor network with two possible power levels (heigh and low) for each sensor, the problem consists in minimizing the number of sensors assigned heigh power while ensuring the connectivity of the network. We formulate the problem by a binary integer programming model to minimize the total power consumption. Since the problem is NP-complete, we provide an iterative approximation based on iterative methods in combinatorial optimization. We solve the separation subproblem as a minimum spanning tree.     

Clustering intelligent transportation sensors using public transportation

Advanced transportation sensors use a wireless medium to communicate and use data fusion techniques to provide complete information. Large-scale use of intelligent transportation sensors can lead to data bottlenecks in an ad-hoc wireless sensor network, which needs to be reliable and should provide a framework to sensors that constantly join and leave the network. A possible solution is to use public transportation vehicles as data fusion nodes or cluster heads. This paper presents a mathematical programming approach to use public transportation vehicles as cluster heads. The mathematical programming solution seeks to maximize benefit achieved by covering both mobile and stationary sensors, while considering cost/penalty associated with changing cluster head locations. A simulation is developed to capture realistic considerations of a transportation network. This simulation is used to validate the solution provided by the mathematical model.     

Cooperative differential game model based on trade-off between energy and delay for wireless sensor networks

A wireless sensor network (WSN) consists of a large number of unattended sensors with limited storage, battery power, computation, and communication capabilities, where battery power (or energy) is the most crucial resource for sensor nodes. The information sensed by sensors needs to be transmitted to sink quickly especially for the applications with delay restriction. However, it is difficult to achieve optimal energy efficiency and source-to-sink delay simultaneously. So it is very necessary to find a power control solution based tradeoff between energy and delay. In this paper, a cooperative differential game model is proposed, and a power solution is obtained which determines a fair distribution of the total cooperative cost among sources.     

Efficient public key encryption with smallest ciphertext expansion from factoring

For public key encryption schemes, adaptive chosen ciphertext security is a widely accepted security notion since it captures a wide range of attacks. SAEP and SAEP+ are asymmetric encryption schemes which were proven to achieve semantic security against adaptive chosen ciphertext attacks. However, the bandwidth for message is essentially worse, that is the ciphertext expansion (the length difference between the ciphertext and the plaintext) is too large. In most of the mobile networks and bandwidth constrained communication systems, it is necessary to securely send as many messages as possible. In this article, we propose two chosen-ciphertext secure asymmetric encryption schemes. The first scheme is a generic asymmetric encryption padding scheme based on trapdoor permutations. The second one is its application to the Rabin-Williams function which has a very fast encryption algorithm. These asymmetric encryption schemes both achieve the optimal bandwidth w.r.t. the ciphertext expansion, namely with the smallest ciphertext expansion. Further, tight security reductions are shown to prove the security of these encryption schemes.     

Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing

Wireless sensor networks typically contain hundreds of sensors. The sensors collect data and relay it to sinks through single hop or multiple hop paths. Sink deployment significantly influences the performance of a network. Since the energy capacity of each sensor is limited, optimizing sink deployment and sensor-to-sink routing is crucial. In this paper, this problem is modeled as a mixed integer optimization problem. Then, a novel layer-based diffusion particle swarm optimization method is proposed to solve this large-scaled optimization problem. In particular, two sensor-to-sink binding algorithms are combined as inner layer optimization to evaluate the fitness values of the solutions. Compared to existing methods that the sinks are selected from candidate positions, our method can achieve better performance since they can be placed freely within a geometrical plane. Several numerical examples are used to validate and demonstrate the performance of our method. The reported numerical results show that our method is superior to those existing. Furthermore, our method has good scalability which can be used to deploy a large-scaled sensor network.     

A Study of the Connectivity Over Time Behavior of On-Demand Ad Hoc Path Selection Schemes

Selecting the most appropriate path between each pair of nodes in an ad hoc network is an issue with major impact on network performance. Many schemes were proposed and compared in the literature, using various criteria. However, the connectivity over time behavior of these schemes, which is important to some practical applications, was not well studied, especially with regards to the terrain type. In this work we use simulation to study this aspect of network performance. We demonstrate that a different connectivity requirement and a different time horizon may dictate a different scheme to use. We also demonstrate that path selection schemes are not equally sensitive to the terrain.