Search

This paper studies the impact of demand-prioritization on Space-Division Multiplexing Elastic Optical Networks (SDM-EON). For this purpose, we solve the static Routing, Modulation Level, Spatial Mode, and Spectrum Assignment (RMLSSA) problem using 34 different explainable demand-prioritization strategies. Although previous works have applied heuristics or meta-heuristics to perform demand-prioritization, they have not focused on identifying the best prioritization strategies, their inner operation, and the implications behind their good performance by thorough profiling and impact analysis. We focus on a comprehensive analysis identifying the best explainable strategies to sort network demands in SDM-EON, considering the physical-layer impairments found in optical communications. Also, we show that simply using the common shortest path routing might lead to higher resource requirements. Extensive simulation results show that up to 8.33% capacity savings can be achieved on average by balanced routing, up to a 16.69% capacity savings can be achieved using the best performing demand-prioritization strategy compared to the worst-performing ones, the most used demand-prioritization strategy in the literature (serving demands with higher bandwidth requirements first) is not the best-performing one but the one sorting based on the path lengths, and using double-criteria strategies to break ties is key for a good performance. These results are relevant showing that a good combination of routing and demand-prioritization heuristics impact significantly on network performance. Additionally, they increase the understanding about the inner workings of good heuristics, a valuable knowledge when network settings forbid using more computationally complex approaches.

In order to mitigate congestion of urban transportation network, this paper propose a reservation scheme. Given users’ preferred departure time, arrival time and flexible time windows at both origins and destinations, the centralized reserving and scheduling system determines agents’ departure times, routes and trajectories with appropriate resolution, which aims to minimize the total traveling disutility involving travel times, waiting times queues and delays. After dividing traffic condition into various modes such as free flow, congestion, hyper-congestion and so on, the reservation model is built on a high-dimensional discretized space-time-mode network. This reservation and sketching timetabling problem can be formulated as a multi-commodity flow optimization problem with additional side constraints, such as capacity constraints under different operating mode. The multi-commodity flow problem (i.e. the master problem) is divided into two sub-problems. One is knapsack problem which determine the sequence of activated space-time-mode arcs. The other is agents’ shortest path problem. We further use Alternative Direction Method of Multiplier (ADMM) to solve the shortest path problem above based on dual decomposition methodology. Finally numerical cases are used to show the benefits of reservation timetabling.

In order to solve the NP-hard problem of mobile sink path planning in wireless sensor networks (WSN) where the communication range is modeled as a circular area and overlaps with each other, this paper proposes a sink node path planning method guided by the Hamiltonian of quantum annealing algorithm (EMGH) to balance the energy consumption of wireless sensor networks, improve the network life and solve the energy hole problem. First of all, this paper analyzes the problem in theory, and transforms the characteristics of the problem into a mathematical model. The mathematical model considers that the sensor network itself is a travelling salesman problem (TSP), but also requires the shortest path of the mobile sink node. Then, the path of each node in the sensor network is iterated by quantum annealing algorithm, and an optimal TSP path is obtained by using quantum tunneling effect and quantum circuit to achieve parallelism. Finally, in the case of compressing the solution space, the moving path of the mobile sink node is quantum coded, and the Hamiltonian in the quantum annealing algorithm is taken as the guiding factor, and then the individual dimensions are moved, which improves the accuracy of the algorithm and speeds up the convergence speed of the algorithm. Finally, the feasibility of EMGH is verified by simulation experiments and comparison with other algorithms, which provides a reference for the optimization and improvement of path planning method.

The generalized recursive circulant networking can be widely used in the design and implementation of interconnection networks. It consists of a series of processors, each is connected through bidirectional, point-to-point communication channels to different neighbors. In this work, we apply the shortest path routing concept to build independent spanning trees on the generalized recursive circulant graphs. The proposed strategy loosen the restricted conditions in previous research and extended the result to a more general vertex setting by design the specific algorithm to deal with the constraint issue.

Considerable effort has been devoted to the study of network structures and connectivity patterns and their influence on network dynamics. A widely used assumption in network analysis models is that traffic follows the shortest paths connecting pairs of nonneighboring vertices. For example, graph centrality measures, community extraction algorithms, and core-periphery detection algorithms use this assumption. However, this is a very restricted perspective and can be misleading as a consequence of its focus on shortest path communications. In this work, we study the utilization of shortest paths in complex networks in different data dissemination scenarios. We also explore whether there are general properties that can make networks utilize shortest paths more effectively. By conducting simulations on a set of real-world and artificial networks, we show that the utilization of shortest paths in complex networks may not be as common as assumed. This implies that longer paths can be as important (in some cases) as the shortest paths. Our results show that at least two factors clearly influence shortest path utilization in a network: the structure of the network and the data dissemination algorithm. We also find that the type of a network is not a good indicator of its shortest path utilization.

Edge storage, as a supplement to cloud storage, reduces latency by providing services in a timely and efficient manner near the source. In a collaborative edge storage datacenter network (CESN), not only does the edge storage datacenter (ESDC) that is closest to the user provide services, but multiple ESDCs work together to provide better services. In this collaborative work mechanism, different application session requests create large persistent multicast flows with diverse performance requirements. Existing multicast scheduling methods such as unicast shortest path (USP) and static single tree (SST) do not consider flow characteristics or performance requirements. In this paper, we first modeled the multicast flow scheduling problem in a CESN. The model is based on different types of flows with diverse network requirements. Then, we tailored a multicast flow scheduling method based on multiple-attribute decision-making and a genetic algorithm (MDGA). MDGA selects appropriate multicast routing paths for flows in a CESN by considering the requested flow types and network status. The experimental results show that the proposed MDGA method can balance network loads and reduce the average transmission delay for high-priority flows better than USP and SST.

The traveling salesman problem (TSP), can be used as a typical combinatorial optimization problem, to describe a wide variety of practical engineering optimization problems in various fields. In this study, the problem of personnel and equipment utilization in the transportation industry was abstracted, a more general class of TSPs with replenishment arcs was proposed, an optimization model to minimize the total travel time was established, the ant colony optimization algorithm to solve the standard TSP was improved, and an improved ant colony algorithm based on dynamic heuristic information was designed. Simulation experiments showed that the algorithm can account for the cumulative mileage constraint and search for the shortest path, effectively solving the TSP with replenishment arcs.

The intelligent patrol car with environmental sensing and autonomous navigation is a special robot, which is mostly used for equipment defect detection in industrial areas such as the power distribution room or data center room. A path planning algorithm for the navigation system of intelligent patrol car is proposed to ensure efficient and secure navigation in the complex indoor environment, and its effect is verified by simulation and experiment. First, a patrol car platform integrated with several intelligent devices is built to achieve global localization, mapping and path planning. Then a new co-optimization on multi-objective Cauchy mutation cat swarm optimization (MOCMCSO) and artificial potential field method (APFM) is proposed to solve the multi-objective optimization problem on shortest global path length and minimum total turning-angle variation. The optimal path is written into the navigation module to drive the patrol car to move and navigate. The simulations are carried out to confirm that the method can achieve a balance between the shortest path and good path smoothness, which has less optimization time and lower fitness value compared with multi-objective cat swarm optimization (MOCSO) and multi-objective particle swarm optimization (MOPSO), and is more suitable for global path planning in indoor environment. Finally, the experiments have been carried out in the data center equipment room to verify the effectiveness and superiority over the path planning algorithm on MOCMCSO.

When an emergency occurs in the city, a large area of road congestion usually occurs. Therefore, it is particularly important to provide an effective emergency path planning strategy for vehicles. However, existing emergency path planning methods do not take into account the connectivity characteristics of the road network and commuting capacity. For this purpose, a Grid Map Emergency Path Planning (GMEPP) framework based on a novel model Grid Road Network (GRN) is designed in this paper. First, the road network data is divided into grids under equal spacing bands, and the roads data divided into different grids and use the commuting capacity of each road as the weight of each edge in the grid. Then a Grid PageRank (GPR) algorithm will be introduced, the output value of this methodology is calculated based on the capacity and number of connected edges of all vertices pointed by the external grid in each grid. The higher value of the grid will be recommended to users first when the path is planning. According to the GRN model, an improved Bidirectional Dijkstra will be applied to query the shortest path between two points, which is called Gird Bidirectional Dijkstra (GBD). At last, GMEPP uses Reverse Contraction Hierarchies (RCH) and Multiple Reverse Contraction Hierarchies (MRCH) originality methodologies based on intersection type to speed up the query algorithm GBD. To compare the efficiency of the proposed method, this paper conducted extensive experiments to verify. The results of the test showed that the Gird Bidirectional Dijkstra Multiple Reverse Contraction Hierarchies (GBD-MRCH) is better than other methods in different grid distributions.

Graphs in real-world applications are dynamic both in terms of structures and inputs. Information discovery in such networks, which present dense and deeply connected patterns locally and sparsity globally can be time consuming and computationally costly. In this paper we address the shortest path query in spatio-temporal graphs which is a fundamental graph problem with numerous applications. In spatio-temporal graphs, shortest path query classical algorithms are insufficient or even flawed because information consistency can not be guaranteed between two timestamps and path recalculation is computationally costly. In this work, we address the complexity and dynamicity of the shortest path query in spatio-temporal graphs with a simple, yet effective model based on Reinforcement Learning with Proximal Policy Optimization. Our solution simplifies the problem by decomposing the spatio-temporal graph in two components: a static and a dynamic sub-graph. The static graph, known and immutable, is efficiently solved with $A^{*}$ algorithm. The sub-graphs interconnecting the static graph have unknown dynamics and we address such issue by estimating the unknown dynamic portion of the graph as a Markov Chain which correlates the observations of the agents in the environment and the path to be followed. We then derive an action policy through Proximal Policy Optimization to select the local optimal actions in the Markov Process that will lead to the shortest path, given the estimated system dynamics. We evaluate the system in a simulation environment constructed in Unity3D. In partially structured and unknown environments, with variable environment parameters we’ve obtained an efficiency 75% greater than the comparable deterministic solution.

Vehicle path selection follows the shortest path principle, while taking the shortest travel time as the optimization goal. In the real road network, affected by signal control, the travel time of the vehicle path has significant uncertainty. The shortest path algorithm with static road indicators as the weights has obvious defects in path selection. In order to solve this problem, according to the theory of distributed wave, the operating state of the vehicle under the influence of the downstream signal control is classified. The travel time of the vehicle on the road segment is classified and predicted, and the travel time prediction value set is further transformed into the travel time reliability. For the vehicle route selection of a dynamic road network, the path travel time reliability determined by the product of the road segment travel time reliability is logarithmically converted, and the Dijkstra algorithm is used to find the most reliable path as the target solution. Then, a simulation model is constructed to verify the validity of the algorithm. The experimental results prove that using the reliability of travel time as the weight of path selection and solving by Dijkstra algorithm can reflect the actual vehicle path selection more accurately. This method is a beneficial improvement to the problem of static path selection.

Quantitative identification of key nodes in complex networks is of great significance for studying the robustness and vulnerability of complex networks. Although various centralities have been proposed to solve this issue, each approach has its limitations for its own perspective of determining an actor to be “key”. In this paper, we propose a novel method to identify key nodes in complex networks based on global structure. Three aspects including the shortest path length, the number of shortest paths and the number of non-shortest paths are considered, and we establish three corresponding influence matrices. Node efficiency, which can reflect the contribution of one node to the information transmission of the entire network, is selected as the initial value of node's influence on other nodes, and then the comprehensive influence matrix is constructed to reflect the influence among nodes. The proposed method provides a new measure to identify key nodes in complex networks from the perspective of global network structure, and can obtain more accurate identification results. Four experiments are conducted to evaluate the performance of our proposed method based on Susceptible-Infected (SI) model, and the results demonstrate the superiority of our method.

Resilient circuits represent promising approaches for improving both circuit performance and tolerance for dynamic variations. However, the short path padding problem becomes severe during the implementation, resulting in significant area overhead and even frequency degradation, which might nullify the benefits of resilience. The present work addresses this issue by proposing a progressive resilient design methodology, including a clock period prediction method that can accurately access the minimum clock period possible in an early stage and a short path padding method, based on the greedy heuristic algorithm to reduce both the total padding delay and the runtime. The runtime for the padding stage is further minimized by introducing an accelerated padding method that decreases the number of point visits required during the greedy padding process. The proposed methodology is applied to several benchmark circuits, decreasing averagely 26.1% in the total number of padding buffers and 38.1% in the runtime, compared to a present state of the art methodology. Thus, this proposal not only avoids iterations of padding assignments by early period prediction, but also is a feasible and effective short path padding methodology for resilient circuits.

The classical Isomap is the most common unsupervised nonlinear manifold method and widely being used in visualizations and dimension reductions. However, when it applied to real-world datasets, it shows shortcomings for the shortest path between all pairs of data points, which are based on the nearest neighborhood $G$ graph via the Dijkstra algorithm, which makes it a very time-consuming step. The other critical problem is the classical Isomap has a lack of topological stability on the nearest neighborhood $G$ graph. In this paper, we propose a novel technique called the FastIsomapVis for the above problems of the classical Isomap. The FastIsomapVis uses hierarchal divide, conquer, and combine approach through two algorithms, which are randomized division tree (KD-tree) and Dijkstra Buckets Double (DKD). The primary aim of the FastIsomapVis is to increase the efficiency and accuracy of the graph. This research paper focuses on transforming the high dimensional datasets into a low dimensional Isomap visualization. The FastIsomapVis makes it easy to construct an accurate $K$ nearest neighborhood $G$ graph and scale high dimensional data points into low dimensional space. Our proposed method is compared to the classical Isomap to verify its effectiveness and provide highly authentic results of the high dimensional datasets. The finding of the current study shows that our proposed method is much fastened than classical Isomap.

Recently, the Internet of Things (IoT) is widely considered in vehicular ad-hoc networks (VANETs) for use in intelligent transportation systems. In particular, the pervasive deployment of different sensors in modern vehicles has unlocked interesting possibilities for improving routing performance in VANETs. Nevertheless, the discovery of short single loop-free routes for effective and efficient information dissemination in VANETs remains a challenge. This challenge proves more difficult to solve since it reduces to the case of finding the shortest Hamiltonian path for effective routing in VANETs. Consequently, in this paper, we propose two discretized variants of the cuckoo search optimization (CSO) algorithm, namely, the Lévy flight-based discrete CSO (LF-DCSO) and the random walk-based discrete CSO (RW-DCSO) for effective route discovery in VANETs. In addition, we investigated the inverse mutation operator gleaned from genetic algorithm (GA) in order to improve the exploration properties of our DCSO variants. We describe a new objective function that effectively models the reliability of individual links between nodes that comprise a single route. A detailed report of the routing protocol that controls the routing process is presented. Our proposed methods were compared against the roulette wheel-based GA and the improved k-means-based GA termed IGAROT. Specifically, our findings reveal that there was no significant difference in the performance of the different methods in the low vehicle density scenario, however, in the medium vehicle density scenario, the RW-DCSO algorithm achieved 2.56%, 100%, and 128.57% percentage increment in its route reliability score over the LF-DCSO, RW-GA, and IGAROT algorithms, respectively. Whereas in the high vehicle density scenario, the LF-DCSO algorithm achieved a percentage increment of 42.85%, 525%, and 733.33% in the route reliability score obtained over the RW-DCSO, IGAROT, and RW-GA algorithms, respectively. Such results suggest that our methods are able to guarantee effective routing in VANETs.

Sensor-cloud infrastructure provides a storage platform for the massive sensed data, that is flexible and re-configurable, for various application areas which are monitored through the resource-limited networks such as wireless sensor networks (WSNs), ad hoc networks, and Internet of things (IoT). Due to their overwhelming characteristics, these networks are used in different application areas to assist human beings in their daily-life activities. However, these networks have different challenging issues such as reliability in communication and processing, storage of the massive data, efficient utilization of on-board battery, maximum lifetime achievement, minimum possible average packet loss ratio, and reliable routing mechanisms. Although various communication and load balancing mechanisms have been proposed in the literature to resolve this issue, however, these schemes are either application specific or overlay complex. In this paper, a reliable communication and load balancing scheme for the resource-limited networks is presented to resolve these issues, particularly with available resources. To achieve these goals, the proposed scheme bounds every sensing device $C_{i}$ to compute the transmission capabilities of its neighboring devices that is residual energy $E_{r}$ , hop count $H_{c}$ , round trip time ( $RTT_{i}$ ), and processing cost. Initially, to guarantee reliable wireless communication, a source device prefers a neighboring device $C_{i}$ with minimum $H_{c}$ value over those having maximum $H_{c}$ values. Moreover, this scheme bounds every device $C_{i}$ to find four shortest & reliable paths and forward maximum packets on two of these paths preferably on the most reliable and optimal route. Therefore, unlike the traditional shortest path scheme, devices $C_{i}$ reside on these paths do not deplete their on-board battery more rapidly than others. To further improve the reliability of the proposed scheme, the assigned weight-age factors are fine-tuned if one or two of the neighboring devices $C_{i}$ consume 80% of their on-board battery, that is now maximum weight-age is assigned to the residual energy $E_{r}$ and minimum to $H_{c}$ value respectively. Simulation results show the exceptional performance of the proposed reliable communication and load balancing scheme against the field-proven schemes in terms of average packet delivery ratio, average throughput, end-to-end delay, and overall network lifetime.

This paper studies the trajectory optimization problem of a single cellular-enabled unmanned aerial vehicle (UAV), taking into account the outage performance of the entire trajectory. To provide real-time control, it is critical for UAV to maintain reliable connectivity with the ground base station (GBS). We first consider the connectivity outage performance, which is defined as the sum of the time duration of the outage performance not meeting a predefined threshold during the entire UAV mission. Then we formulate a trajectory optimization problem to minimize the mission completion time, while ensuring a sum constraint of the connectivity outage performance. We show that the connectivity outage constraint can be transformed into a flying area constraint. Since the formulated problem is NP-hard, a low-complexity method is proposed to solve the problem by finding the shortest path in an undirected weighted graph with enlarged GBS coverage. Simulation results demonstrate the superiority of the proposed scheme over other state-of-the-art schemes, in terms of trajectory length and computational complexity.

Advances in mobile technologies and map-based applications enables users to utilize sophisticated spatial queries, including k-nearest neighbor and shortest path queries. Often, location-based servers are used to handle multiple simultaneous queries because of the popularity of map-based applications. This study focuses on the efficient processing of multiple concurrent k-farthest neighbor (kFN) queries in road networks. For a positive integer k, query point q, and set of data points P, a kFN query returns k data points farthest from the query point q. For addressing multiple concurrent spatial queries, traditional location-based servers based on one-query-at-a-time processing are unsuitable owing to high redundant computation costs. Therefore, we propose a group processing of multiple kFN (GMP) algorithm to process multiple kFN queries in road networks. The proposed GMP algorithm uses group computation to avoid the redundant computation of network distances between the query and data points. The experiments using real-world roadmaps demonstrate the proposed solution's effectiveness and efficiency.

Rapidly-exploring Random Trees (RRTs) have been widely used for motion planning problems due to their ability to efficiently find solutions. Informed RRT* is an optimized version of RRT, which not only implements the rewiring process to optimize the tree but also limits the search area to a subset of the state space to return near-optimal solutions faster. However, limiting the state space is a function of the obtained shortest path so that before a solution is found, the planner cannot limit the state space to a subset. Moreover, unidirectional RRTs such as Informed RRT* take more time to find initial solutions in comparison to the bidirectional RRTs. In this paper, we propose Hybrid RRT, which divides the planning process into three parts: finding initial solutions by a dual-tree search, combining two trees into one, and optimizing the solution. In order to obtain an initial solution, Hybrid RRT implements a dual-tree search, which helps it find solutions faster than unidirectional searches. Then, it combines the start tree and the goal tree of the dual-tree search into one so as to implement informed sampling for a single tree to optimize the current solution. The simulation carried out in Open Motion Planning Library (OMPL), which shows that Hybrid RRT achieved outstanding improvement over RRT* and Informed RRT*.

This paper proposes a local flooding-based on-demand routing protocol for mobile ad hoc networks to reduce the flooding overhead and offers effective alternative paths between the source and destination nodes. The proposed protocol first uses overhearing to identify the one-hop neighbors along the shortest path between the source and destination. Then, it performs periodic local flooding initiated by the destination node, which offers the latest route information along the routing path. This technique only involves one-hop neighbors along the shortest path, with the result that the flooding overhead is significantly reduced and seamless rerouting is possible when the current routing path is either disconnected or outdated. Intensive simulations show that the proposed routing protocol improves the routing performance in terms of data delivery ratio, end-to-end delay, and jitter, and it significantly reduces the control overhead in a wide range of node mobility and density, compared to conventional routing protocols.

Given the growth, complexity, and size of the Internet, new methodologies are needed to support cost-effective resource provisioning. This paper provides a cost-based polynomial-time heuristic algorithm for resource provisioning optimization called multi-layered market algorithm (MMA). The MMA is solvable for multi-layered, multi-technology, and practical-sized networks, where the traffic is modeled as a combination of constant bit-rate and variable bit-rate (VBR) traffic streams. A VBR stream is modeled either by a Gaussian process or by a Poisson Pareto burst process (PPBP) which under certain parameter values is long-range dependent – a known characteristic of the Internet traffic streams. The consideration of VBR traffic models in a multi-layered network optimization is a key novel aspect of MMA. The MMA considers a range of transport technologies operating in layers and traffic sharing schemes. The MMA implements flow-size-based routing where flows according to their sizes are routed independently. As routing affects resource requirement, such considerations are important for resource provisioning by the given cost models. The complexity resulting from these considerations, including layering and PPBP traffic, requires a simplified design philosophy which in this paper, is based on adopting the shortest path routing in each layer. This is achieved by MMA which is based on an iterative algorithm, and resource provisioning that is performed link-by-link in all layers. As a benchmark for MMA, we provide an integer linear programming (ILP) formulation for a multi-layered network optimization problem with fixed end-to-end demands. The MMA is validated by comparing its solutions to those ILP results in different variants of a six-node network, and its software is verified using double-entry bookkeeping – a method commonly used in accounting systems. The MMA runs on a platform called network mark-up language, which enables visualization and further validation of the results.

Self-aware and self-expressive physical systems are inspiring new methodologies for engineering solutions of complex computing problems. Among many other examples, the slime mold Physarum Polycephalum exhibits self-awareness and self-expressiveness while adapting to changes in its dynamical environment and solving resource-consuming problems like shortest path, proximity graphs or optimization of transport networks. As such, the modeling of the slime mold’s behavior is essential when designing bio-inspired algorithms and hardware prototypes. The goal of this paper is to combine one of the powerful parallel computational tools, cellular automata (CA) with the adaptive potential of Physarum slime mold. Namely, we propose a CA model and multi-agent approach to imitate the behavior of the plasmodium. We then test the efficacy of the proposed model on graph problems such as the maze problem or the traveling salesman problem (TSP). Finally, the virtual Physarum model is evaluated on a data set for pattern recognition purposes and achieves to form very effectively the letters of the alphabet, especially when compared with real experiments performed to prove the efficacy of the proposed model. Furthermore, to exploit the CA’s inherent parallelism and make the model’s responses faster, both GPU and hardware implementations are proposed and compared. As a result, an accelerated virtual lab is developed which uses a multi-agent CA model to describe the behavior of plasmodium and can be used as an intelligent, autonomous, self-adaptive system in various heterogeneous and unknown environments spanning from different types of graph problems up to real life-time applications.

Compressed sensing technology is one of the effective techniques to effectively reduce the amount of data transmission in wireless sensor networks. Compressed sensing technology can reduce the amount of data that a node undertakes from n to m, where m ≪ n, but we still hope to further reduce the amount of data that the node bears to improve network lifetime. In this paper, a Compressive Sensing based Clustering Joint Annular Routing Data Gathering (CS-CARDG) scheme is proposed to improve the network life. The key technology adopted by CS-CARDG scheme is: data is collected by cluster. The network first forms a cluster, and each node in the cluster sends the data packet to the cluster head. Each cluster forms m-dimensional data according to the requirements of the compressed sensing technology to ensure that the data can be recovered. When the cluster head node routes the m-dimensional data to the sink, the CS-CARDG scheme adopts a two-stage routing scheme with the same ring routing and shortest path that is completely different from the previous scheme. The same ring routing means that the cluster heads with the same number of sink hops are routed around the ring for one week to route the compressed sensing data of the same ring to a node in the ring. In this way, each sub-dimension data in the same ring is routed to the corresponding node of each ring through the same-loop route, and then the shortest-circuit strategy of the second phase is started. That is, from the outermost ring, the same fractal data is sequentially compressed from the outside to the inside, and is routed to the sink by the shortest path. In this round of data collection, the number of data packets that the nodes in the near-sink one-hop range bears is only m, and the nodes in the near-sink region directly send data to the sink node, thereby reducing the amount of data that the node bears to m/k + 1, where k is the number of nodes within the node's broadcast radius.. In this paper, the compressed sensing strategies proposed in the past are compared by detailed theoretical analysis. The theoretical analysis results show that the CS-CARDG strategy proposed in this paper can effectively reduce the amount of data carried by nodes. This scheme reduces the amount of data in the network from (1+r/l2) λ + (1+r/l3) λ + ··· + m/k+d to m/k+1. Reduced the amount of data by at least 20%. In the network with R=480 m, the energy utilization rate can reach more than 90%.

A novel clustering algorithm by fast search and find of density peaks (DP) was proposed in Science, 2014. It has attracted much attention from researchers. It can easily select clusters centers with decision graph. However, it cannot be used to cluster manifold data sets as the existing distance measurement is not suitable to evaluate the dissimilarity between objects on manifold structure. Some researchers use graph-based distance to measure the dissimilarity between objects on manifold clusters, but computing the graph-based distance on the original data set is time consuming. An improved density peaks clustering algorithm based on shared-neighbors between local cores, SLORE-DP, is proposed in this paper. First, it finds local cores to represent the data set and redefines the graph-based distance between local cores with shared-neighbors-based distance. Then natural neighbor-based density and the new defined graph-based distance are used to construct decision graph on local cores and DP algorithm is employed to cluster local cores. Finally, the remaining points are assigned to the same cluster as their local cores belong to. Since we use the new defined graph-based distance to estimate the dissimilarity between local cores, SLORE-DP can be used to cluster manifold data sets and at the same time it only calculates the shortest path between local cores, which greatly reduces the running time of the algorithm. We do experiments on several synthetic data sets containing manifold clusters and several real data sets from UCI. The results show that SLORE-DP is more effective and efficient than other algorithms when clustering manifold data sets.

Regional failures, such as natural disaster or malicious attack, have become a major threat to the construction of future reliable communication network. The regional failures usually cause a large number of disconnected nodes simultaneously and influence the network for a long time. However, a routing scheme that is resilient to such geographically correlated failures is still unexplored. In this paper, we provide a comprehensive study of the disaster resilient routing dealing with the regional failure in operational IP backbone networks. It is notable that the path with minimal risk (i.e., minimal failure probability) is not necessarily the shortest path. The main challenge of finding such paths is that regional failure is unpredictable in terms of time, location, and the affected area. To this end, in combination with the computational geometry tool, we develop effective algorithms to find the minimal risk path between end node pairs to tolerate random regional failures. We show that in contrast to the conventional shortest path, a little longer path can be more effective to the disasters. After selecting such a path as the primary path, we turn to find a secondary backup path. In contrast to the conventional single link/node failure, a regional failure disrupts a large number of network components, simultaneously. As a result, how to find backup paths for re-establishment of the corrupted paths will raise a novel fairness issue. Specifically, during the backup path allocation, we focus on routing fairness to bound the worst-case user experience. A metric is proposed based on which an ILP is formulated. The extensive simulations validate that such an issue is non-negligible in face of regional failure scenarios.

Person re-identification has become increasing popular because of its widely application in computer vision. In this paper, we propose a novel, simple and efficient person re-id network called MPLFN. The network combines two tasks: the classification task and the metric learning task. In the classification task, we uniformly partition N feature parts from an image, and compute the person classification loss in each part separately. Computing the part loss separately guides the network to focus on every body part and learn discriminative representations for each of them. And then in the metric learning task, we recalculate the distance of two images by the shortest path between two sets of feature parts. Then the distances are put into a triplet loss to perform a dynamic part alignment during the training. With the joint learning of these two tasks, the performance of the network is significantly enhanced. Compared with existing person re-id works, MPLFN achieves a better performance on three mainstream person re-identification datasets. Extensive experiments have been conducted to validate our proposed method.

Facility layout problem is one of the most important problems in a huge range of industries and services organizations. Simultaneous study of some qualitative and quantitative parameters like closeness relationship between facilities, physical constraints such as input/output points and how to arrange facilities can play a key role to determine the facility layout. Considering these parameters can lead to reduce production costs, increase production capacity, and remove additional displacements. A two-stage approach is proposed to achieve these goals. In the first stage, a goal programming model is proposed to determine weights of the attributes based on the experts' opinions with different preference representation structures. Afterwards, the closeness ratings of the facilities are calculated using weights of attributes. In the second stage, an efficient layout is designed by determining facilities placement sequence, location of the next facility adjacent to the previous one, location of input/output points, and rectilinear feasible shortest path between facilities. Two meta-heuristic algorithms including particle swarm optimization and genetic algorithm are designed due to computational complexity. The objective function is to minimize the sum of products distance between facilities and closeness rating and also to minimize the dead space. A case study of an Auto Body Parts company is demonstrated to verify the efficiency of the proposed two-stage approach. Furthermore, in order to assess the performance of the proposed approach, a comparison is drawn between the proposed approach and five existing approaches in the literature to solve different problems by using the two meta-heuristic algorithms.

Wireless body area networks (WBANs) are composed of several tiny sensor devices which are embedded on or implanted in the human body. They are designed to continuously transmit sensed physiological data and act as key infrastructures for remote healthcare monitoring and treatment. Considering the limited power supply, varying link connectivity, and complicated channel environment, the design of routing protocol in WBANs is challenging. The existing algorithms cannot fully satisfy the application requirements. Focusing on lower energy consumption, a directional diffusion routing protocol for WBANs is proposed in this paper. The concept of the gradient is introduced to indicate the direction and the rate of data transmission and the minimum hop count in directed diffusion is used as the criterion for establishing the gradient. Each node is required to maintain the gradient information of its neighbor nodes with the shortest path. Simultaneously, the residual energy is also considered to further improve the performance. The simulation results show that the proposed protocol has significantly lower packet loss rate and power consumption in both static and mobile scenarios. The reliability of the data transmission is improved and the networks life is extended effectively.

A cooperative path searching approach is proposed to decouple cooperative path planning for multiple robots into the path planning phase and the trajectory tracking phase. Unexpected local environment changes or failures for some robots will not affect the regular running of other robots. The collision between robots and the motion constraints of robots are not considered in the first phase. A new connection point method suitable for the trap-like map is used to find the shortest path for every robot. Connection point method does not require much computation cost even if the grid map is zoomed in. In the second phase, a cooperative search tracking approach based on modified coevolution pigeon-inspired optimization algorithm is proposed to enable every robot to track the grid path obtained by the connection point method. A competition mechanism with serial number priority is used to cope with collisions between robots. The numerical simulations are performed to verify the effectiveness of the proposed approach.

This paper presents a path planning approach for mobile robots in 2D spaces. The algorithm uses a quadtree decomposition where the discretization precision is improved until a path to the goal is found if one exists. The algorithm uses interval analysis-based methods to categorize the quadtree decomposition to occupied, free and partly occupied cells. The proposed algorithm is compared against other concurrent path planning algorithms, A on an ordinary quadtree, A for shortest path on a binary occupancy grid, and a Dijkstra's algorithm for lowest collision probability in a continuous-valued occupancy grid, in five different scenarios.Compared to the other methods, the main advantage of our method is achieving a compromise between driving distance, safety, and computation time. The proposed algorithm was found to require significantly fewer collision checks in all scenarios while providing sub-optimum results, based on the obstacle distance and path length criteria. The algorithm is suitable for further extension to include non-euclidean measures and for higher dimensions of configuration spaces. The proposed algorithm will be publicly available on GitHub repository. acceptedVersion

In recent years, service-oriented-based Internet of Things (IoT) has received massive attention from research and industry. Integrating and composing smart objects functionalities or their services is required to create and promote more complex IoT applications with advanced features. When many smart objects are deployed, selecting the most appropriate set of smart objects to compose a service by considering both energy and quality of service (QoS) is an essential and challenging task. In this paper, we reduced the problem of finding an optimal balance between QoS level and the consumed energy of the IoT service composition to a bi-objective shortest path optimization (BSPO) problem and used an exact algorithm named pulse to solve the problem. The BSPO has two objectives, minimizing the QoS including execution time, network latency, and service price, and minimize the energy consumption of the composite service. Experimental evaluations show that the proposed approach has short execution time in various complex service profiles. Meanwhile, it can obtain good performance in energy consumption and thus network lifetime while maintaining a reasonable QoS level.