Multi-point path planning based on the algorithm of colony-particle swarm optimization

The problem of multi-point path planning is a NP-hard problem,which is equivalent to finding the shortest path of a starting point and some specific node.Aiming at the problem of multi-point path planning,a retrospective ant colony-particle swarm optimization algorithm was proposed.This algorithm used Floyd-Warshall to transform the graph and combined ant colony algorithm and particle swarm algorithm to find the shortest path.The experimental results show that this algorithm can find the precise solution under small data,at the same time,under a large amount of data,can be better than the maximum minimum ant colony algorithm and genetic algorithm.     

移动无线传感网的生存时间优化算法研究

当sink节点位置固定不变时,分布在sink 节点周围的传感节点很容易成为枢纽节点,因转发较多的数据而过早失效。为解决上述问题,提出移动无线传感网的生存时间优化算法（LOAMWSN）。LOAMWSN算法考虑sink节点的移动,采用减聚类算法确定sink节点移动的锚点,采用最近邻插值法寻找能遍历所有锚点的最短路径近似解,采用分布式非同步Bellman-Ford算法构建sink节点k跳通信范围内的最短路径树。最终,传感节点沿着最短路径树将数据发送给sink节点。仿真结果表明：在节点均匀分布和非均匀分布的无线传感网中,LOAMWSN算法都可以延长网络生存时间、平衡节点能耗,将平均节点能耗保持在较低水平。在一定的条件下,比Ratio_w、TPGF算法更优。     

Method for optimal allocation of network resources based on discrete probability model

The method for optimal allocation of network resources based on discrete probability model is proposed. In order to take into account multiple coverage of the monitored points, the method constructs the discrete probability perception model of the network nodes. The model is introduced into the solution of the node coverage area, and the optimized parameters of the sensor optimization arrangement are used to optimize the layout of the multimedia sensor nodes. After setting the node scheduling standard, the interaction force between the sensor nodes and the points on the curve path is analyzed by the virtual force analysis method based on the discrete probability model At the same time On this basis, the path coverage algorithm based on the moving target is used to optimize the coverage of the wireless sensor network node in order to achieve optimal configuration of network resources. The experimental results show that the proposed method has good convergence and can complete the node coverage process in a short time. The introduction of the node selection criteria and the adoption of the dormant scheduling mechanism greatly improve the energy saving effect and enhance the network resource optimization effect.

     

无线传感网络安全防护路径激光追踪方法

为了提高无线传感网络安全防护能力,需要进行网络安全防护路径设计,提出基于联合节点行为覆盖的无线传感网络安全防护路径激光追踪方法.构建无线传感网络安全防护路径的覆盖关系模型,根据传感器节点与目标节点从属关系进行无线传感网络安全防护的路径空间规划设计,采用最短路径寻优方法进行无线传感网络安全防护路径的激光控制,采用激光扫描...     

无线传感器网络中移动sink节点的路径规划

在无线传感器网络中,大量感知数据汇集到sink节点的采集方法会导致sink节点附近的节点能量耗尽,造成能量空洞。针对该问题,利用移动的sink节点进行数据收集是一种解决方法,其中移动sink的路径规划成为一个重要的问题。提出了一个移动sink路径规划算法,将无线传感器中随机分布的节点划分为不同的子区域,寻找sink节点移动的最佳转向点,最终得到最优的移动路径,以实现无线传感器网络生命周期最大化。仿真实验表明,与现有方案相比,该算法能显著延长网络的生命周期。     

边缘计算中云边任务下发策略研究

目前边缘计算的相关研究大部分着眼于如何将设备端数据卸载至边缘端进行处理,而未考虑云中心如何高效率、低延时地将不同任务下发至边缘节点的问题.针对该问题,文中提出了一种边缘计算架构模型,通过对任务进行统一建模,使用改进的Dijkstra算法得到任务下发最优路径,减少所需计算节点数量和提升计算性能,使其能在最短的时间内下发到...     

Wireless Sensor Network Path Optimization Using Sensor Node Coverage Area Calculation Approach

The proposed work is based on the path optimization approach for wireless sensor network (WSN). Path optimization is achieved by using the NSG 2.1 Tool, TCL Script file and NS2 simulator to improve the quality of service (QoS). Path optimization approach finds best suitable path between sensor nodes of WSN. The routing approach is not only the solution to improve the quality but also improves the WSN performance. The node cardinally is taken under consideration using the ad-hoc on demand distance vector routing protocol mechanism. Ad hoc approach emphasize on sensor nodes coverage area performance along with simulation time. NSG 2.1 Tool calculates the sensor node packet data delivery speed which can facilitate inter-node communication successfully. An experimental result verified that the proposed design is the best possible method which can escape from slow network response while covering maximum sensor nodes. It achieves coverage support in sensor node deployment. The result outcomes show best path for transferring packet from one sensor node to another node. The coverage area of sensor node gives the percentage of average coverage ratio of each node with respect to the simulation time.

     

Computing shortest paths for any number of hops

In this paper, we introduce and investigate a "new" path optimization problem that we denote the all hops optimal path (AHOP) problem. The problem involves identifying, for all hop counts, the optimal, i.e., minimum weight, path(s) between a given source and destination(s). The AHOP problem arises naturally in the context of quality-of-service (QoS) routing in networks, where routes (paths) need to be computed that provide services guarantees, e.g., delay or bandwidth, at the minimum possible "cost" (amount of resources required) to the network. Because service guarantees are typically provided through some form of resource allocation on the path (links) computed for a new request, the hop count, which captures the number of links over which resources are allocated, is a commonly used cost measure. As a result, a standard approach for determining the cheapest path available that meets a desired level of service guarantees is to compute a minimum hop shortest (optimal) path. Furthermore, for efficiency purposes, it is desirable to precompute such optimal minimum hop paths for all possible service requests. Providing this information gives rise to solving the AHOP problem. The paper's contributions are to investigate the computational complexity of solving the AHOP problem for two of the most prevalent cost functions (path weights) in networks, namely, additive and bottleneck weights. In particular, we establish that a solution based on the Bellman-Ford algorithm is optimal for additive weights, but show that this does not hold for bottleneck weights for which a lower complexity solution exists.     

Network correlated data gathering with explicit communication: NP-completeness and algorithms

We consider the problem of correlated data gathering by a network with a sink node and a tree-based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy-based coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NP-hard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal.     

物联网数据收集中无人机路径智能规划

为解决无人机在数据收集过程中的路径规划问题,将其分为全局路径规划和局部路径规划。针对全局路径规划,将其建模为一个定向问题,定向问题是背包问题和旅行商问题2种经典优化问题的组合。采用指针网络深度学习对该模型进行求解,并在无人机能量约束下得到其服务节点集合及服务顺序。针对局部路径规划,基于无人机接收到节点的参考信号强度,通过深度Q网络学习对无人机局部飞行路径进行规划,使无人机逼近节点位置并服务各节点。仿真结果表明,所提方案能够在无人机能量约束下有效提升其数据收集的收益。     

A Fuzzy Logic-Based Method for Replica Placement in the Peer to Peer Cloud Using an Optimization Algorithm

The Peer to Peer-Cloud (P2P-Cloud) is a suitable alternative to distributed cloud-based or peer-to-peer (P2P)-based content on a large scale. The P2P-Cloud is used in many applications such as IPTV, Video-On-Demand, and so on. In the P2P-Cloud network, overload is a common problem during overcrowds. If a node receives many requests simultaneously, the node may not be able to respond quickly to user requests, and this access latency in P2P-Cloud networks is a major problem for their users. The replication method in P2P-Cloud environments reduces the time to access and uses network bandwidth by making multiple data copies in diverse locations. The replication improves access to the information and increases the reliability of the system. The data replication's main problem is identifying the best possible placement of replica data nodes based on user requests for data access time and an NP-hard optimization problem. This paper proposes a new replica replacement to improve average access time and replica cost using fuzzy logic and Ant Colony Optimization algorithm. Ants can find the shortest path to discover the optimal node to place the duplicate file with the least access time latency. The fuzzy module evaluates the historical information of each node to analyze the pheromone value per iteration. The fuzzy membership function is also used to determine each node's degree based on the four characteristics. The simulation results showed that the access time and replica cost are improved compared to other replica replacement algorithms.

     

最短路程的运输问题及解法

针对最短路程的运输问题,建立了模型,研究了其解的最优性充分条件,提出了一种简捷、快速的求解方法,并给出了具体的求解步骤,最后用实例表明了解法的可操作性。     

移动协助传感器网络中Sink的路径优化策略

在无线传感器网络中引入移动Sink来解决。静态无线传感器网络(所有节点均为静止)存在的能量空洞、冗余覆盖和热点等问题。传感器节点将数据发送给汇聚节点(CP,collection point),移动Sink访问CP节点收集数据。提出了一种最短移动距离最小能耗的路径优化模型(MEMD)。证明了该模型是一个NP-hard问题,给出了一种基于效用的贪心启发式方法用于确定最佳的CP节点队列。为了在规定的最大传输延时的范围内访问尽可能多的CP节点,提出了一种基于CP节点访问概率的路径选择算法。通过模拟实验以及实验床的真实数据,提出的算法能很好地在满足延时要求的同时节约网络的能量。     

物联网感知层多路径传输策略研究

多路径数据传输是无线传感器网络亟需解决的一个关键问题.本文针对节点故障、链路失效和外界干扰影响网络稳定性和可靠性,提出一种基于混合蛙跳算法的无线传感器网络多路径传输策略.首先我们详细介绍了蛙跳算法及其原理,之后我们将其应用到无线传感器网络多路径传输策略之中,接着运用混合蛙跳算法对传感网络节点其进行更新、划分、重组以便选择出最优节点建立传输最优路径,提高网络的稳定性和可靠性.通过算法仿真与结果对比提出的算法与AODV、粒子群PSO算法相比,在网络能耗、传输时延、丢包率、连通率和可靠度等方面都具有较好的性能.其中网络能耗比AODV、PSO算法降低了62.5％和35.8％.     

Optimal Speed Control of Mobile Node for Data Collection in Sensor Networks

A data mule represents a mobile device that collects data in a sensor field by physically visiting the nodes in a sensor network. The data mule collects data when it is in the proximity of a sensor node. This can be an alternative to multihop forwarding of data when we can utilize node mobility in a sensor network. To be useful, a data mule approach needs to minimize data delivery latency. In this paper, we first formulate the problem of minimizing the latency in the data mule approach. The data mule scheduling (DMS) problem is a scheduling problem that has both location and time constraints. Then, for the 1D case of the DMS problem, we design an efficient heuristic algorithm that incorporates constraints on the data mule motion dynamics. We provide lower bounds of solutions to evaluate the quality of heuristic solutions. Through numerical experiments, we show that the heuristic algorithm runs fast and yields good solutions that are within 10 percent of the optimal solutions.     

Network Lifetime Maximization for Estimation in Multihop Wireless Sensor Networks

We consider the distributed estimation by a network consisting of a fusion center and a set of sensor nodes, where the goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional. In energy-limited wireless sensor networks, both local quantization and multihop transmission are essential to save transmission energy and thus prolong the network lifetime. The network lifetime optimization problem includes three components: i) optimizing source coding at each sensor node, ii) optimizing source throughput of each sensor node, and iii) optimizing multihop routing path. Fortunately, source coding optimization can be decoupled from source throughput and multihop routing path optimization, and is solved by introducing a concept of equivalent 1-bit MSE function. Based on the optimal source coding, the source throughput and multihop routing path optimization is formulated as a linear programming (LP) problem, which suggests a new notion of character-based routing. The proposed algorithm is optimal and the simulation results show that a significant gain is achieved by the proposed algorithm compared with heuristic methods.     

A piecewise geometry method for optimizing the motion planning of data mule in tele-health wireless sensor networks

In tele-health wireless sensor networks (WSNs), data mules (DMs) can be despatched to collect and deliver distributed healthcare data via wireless communication. However, to improve the delivery efficiency, it is very important to reduce the data latency. In this paper, we aim at minimizing the data latency through DM motion planning, which includes path selection and speed control. We first model and formulate the problem as a geometric optimization problem and then propose a convex hull based two-phase method. In the first phase of the method, the convex hull structure is utilized to depict a path skeleton, and in the second phase, speed control is considered to regulate the path skeleton. Simulation experiments show that the proposed method exhibits an average of 10–20 % decrease in the latency of data collection. The results suggest that the data collection efficiency can be improved by the proposed method, and the DM approach is feasible and effective for healthcare data delivery in WSN-based tele-health applications.     

An efficient rate-distortion optimal shape coding approach utilizing a skeleton-based decomposition

In this paper, we present a new shape-coding approach, which decouples the shape information into two independent signal data sets; the skeleton and the boundary distance from the skeleton. The major benefit of this approach is that it allows for a more flexible tradeoff between approximation error and bit budget. Curves of arbitrary order can be utilized for approximating both the skeleton and distance signals. For a given bit budget for a video frame, we solve the problem of choosing the number and location of the control points for all skeleton and distance signals of all boundaries within a frame, so that the overall distortion is minimized. An operational rate-distortion (ORD) optimal approach using Lagrangian relaxation and a four-dimensional direct acyclic graph (DAG) shortest path algorithm is developed for solving the problem. To reduce the computational complexity from O(N/sup 5/) to O(N/sup 3/), where N is the number of admissible control points for a skeleton, a suboptimal greedy-trellis search algorithm is proposed and compared with the optimal algorithm. In addition, an even more efficient algorithm with computational complexity O(N/sup 2/) that finds an ORD optimal solution using a relaxed distortion criterion is also proposed and compared with the optimal solution. Experimental results demonstrate that our proposed approaches outperform existing ORD optimal approaches, which do not follow the same decomposition of the source data.     

M‐RPSS: A modified RPSS for path scheduling of mobile sink in wireless sensor network

In wireless sensor networks (WSNs), data gathering is the main concern, since it directly affects the network lifetime and data latency. Rendezvous Point Selection Scheme (RPSS) is a mobile sink node approach; it offers superior performance than its preceding mobile sink schemes like Rendezvous Design for Variable Track (RD‐VT), RD‐VT with Steiner Minimum Tree (RD‐VT‐SMT), and Weight Rendezvous Planning with Steiner Minimum Tree (WRP‐SMT). However, a more uniform distribution of the rendezvous node leads to less energy consumption in WSNs. The more optimum path offers less data latency. In the proposed approach, we use particle swarm optimization (PSO) to find the optimum rendezvous point and adaptive PSO (APSO) to find an optimum path by solving the travelling salesman problem. By rigorous simulation, we prove that modified RPSS (M‐RPSS) increases the network lifetime by more than 10% and decreases the data latency.     

基于多准则遗传算法的航线规划

飞行航线规划是中小型飞机执行长途飞行任务时的重要工作。通过建立问题的多准则最短路径问题模型,提出一种多准则最短路径遗传算法求解优化飞行航线。通过理论分析证明了算法的收敛性。仿真实验验证了该算法的有效性。