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In recent years, the axial radiation model has emerged as a pivotal framework for Wireless Sensor Networks (WSN), particularly in enhancing intelligent sensing platforms. This study delves into the WSN structured around the axial radiation model, encompassing critical aspects like model reconstruction, node deployment, and routing optimization. Our focus is on evaluating the performance metrics that influence the responsiveness of these intelligent platforms. We introduce the Multi-block Directed Radiation (MBDR) routing algorithm, designed to extend system operational time and boost data transmission efficiency. Comprehensive experimental analyses demonstrate the significant advancements of MBDR in survivability, data transmission rates, and regional balance within central axis radiation model environments.

The routing strategy plays a very important role in complex networks such as Internet system and Peer-to-Peer networks. However, most of the previous work concentrates only on the path selection, e.g. Flooding and Random Walk, or finding the shortest path (SP) and rarely considering the local load information such as SP and Distance Vector Routing. Flow-based Routing mainly considers load balance and still cannot achieve best optimization. Thus, in this paper, we propose a novel dynamic routing strategy on complex network by incorporating the local load information into SP algorithm to enhance the traffic flow routing optimization. It was found that the flow in a network is greatly affected by the waiting time of the network, so we should not consider only choosing optimized path for package transformation but also consider node congestion. As a result, the packages should be transmitted with a global optimized path with smaller congestion and relatively short distance. Analysis work and simulation experiments show that the proposed algorithm can largely enhance the network flow with the maximum throughput within an acceptable calculating time. The detailed analysis of the algorithm will also be provided for explaining the efficiency.

We propose a symmetrical scheme, by drawing results from group theory, and use it to build a new class of data center network models. The results are superior to current network models with respect to a number of performance criteria. Greater symmetry in networks is important, as it leads to simpler structure and more efficient communication algorithms. It also tends to produce better scalability and greater fault tolerance. Our models are general and are expected to find many applications, but they are particularly suitable for large-scale data-center networks.

With the development of mobile edge computing and neural network Deep Learning (DL), more and more scholars are studying the combination of the two. This paper mainly studies the application of mobile edge computing and neural network DL in IoT computing collaboration and data-aware routing algorithms. Therefore, this paper proposes the deployment options of MEC technology and ETSIMEC in mobile edge computing, combining mobile edge computing with DL, designs an optimization algorithm based on Markov decision process and feature expression learning, and then analyzes and optimizes IoT computing and VANET routing algorithm. In order to have a clearer direction for the optimization algorithm, this paper also designs the edge computing model training and experiment comparison, the DL algorithm comparison experiment, and the routing algorithm simulation experiment and performance analysis. Combined with the experimental results, it is optimized and compared with traditional IoT computing and routing algorithms. Finally, it is concluded that the computing efficiency of IoT computing based on edge computing and DL designed in this paper is 21.33% higher than that of traditional IoT computing. The efficiency of the routing algorithm based on edge computing and DL designed in this paper is 9.29% higher than that of the traditional routing algorithm.

This paper presents a hierarchical fault-tolerant routing algorithm called FXY, which is a hybrid method based on flooding and XY, and can balance performance and fault tolerance based on a predefined parameter m. First, FXY partitions the whole network into different equal size square submeshes with the size of m × m. At the first level of the hierarchy, packet routing within these submeshes is performed based on flooding routing algorithm. When the packets are received at effective boundary of each submesh, XY routing is performed to route the packet inter submeshes i.e., from one submesh to the neighbor submesh which is certainly one of its neighbor nodes. Here, the size of the submesh is defined as fault-tolerant granularity. As fault-tolerant granularity is increased, the size of the submeshes will be increased, therefore the method mainly floods packets in large-size submeshes and finally packets are received at their destinations correctly. On the other hand, when fault-tolerant granularity is decreased, the method mainly routes packets as XY method, which is not fault-tolerant, but has the best performance. The method is evaluated for various packet injection rates and fault rates. The experimental results reveal that the method presents a fault-tolerant routing algorithm, and can be adjusted so that it shows better fault-tolerance and performance trade-offs compared to XY and flooding which are two end-to-end cases of having the best performance and no fault-tolerance, having the least performance and the best fault tolerance, respectively. The experimental results for an 8 × 8 NoC size, have shown that 2-FXY, which is the proposed method with fault-tolerant granularity of two, offers the best trade-off between performance and fault tolerance compared to other methods, XY, flooding and probabilistic flooding.

A routing aggregation (RA) is proposed for load balancing network-on-chip (NoC). The computing nodes with dense traffic and long distance in network are gathered into the same routing node to form a super router. A load balancing routing algorithm for super router is presented to improve the overall performance of NoC. A simulation platform using System C is presented to confirm the feasibility of the proposed design in 2D mesh. The simulation results show that the proposed RA design can reduce the average packet latency and the standard deviation of host link utilization 8% and 33%, respectively compared with the reported routing methods. The area cost and power consumption compared with the reported schemes are 22% and 12% less, respectively.

Reliability is one of the main concerns in the design of networks-on-chip (NoCs) due to the use of deep submicron technologies in fabrication of such products. This paper presents a new fault-tolerant routing algorithm called double stairs for NoCs. Double stairs routing algorithm is a low overhead routing that has the ability to deal with fault. The proposed routing algorithm makes a redundant copy of each packet at the source node and routes the original and redundant packets in a new partially adaptive routing algorithm. The method is evaluated for various packet injection rates and fault rates. Experimental results show that the proposed routing algorithm offers the best trade-off between performance and fault tolerance compared to other routing algorithms, namely flooding, XYX and probabilistic flooding.

Wireless on-chip networks suffer from serious congestion problems due to the expansion of their network size and the joining of wireless nodes, and token passing is very inefficient in the case of low network injection. Aiming at the congestion problem and the inefficiency of token passing in wireless on-chip networks, this paper designs a three-hop congestion-aware routing mechanism in WiNoC. In the scheme of this paper, first, the congestion information is written into Head Flit for propagation through the information piggybacking method, in which the information piggybacking method does not introduce congestion information propagation overhead, second, the port with the smallest congestion value is selected for transmission through the three-hop congestion-aware routing algorithm to reduce the congestion within the subnetwork, and then the wireless node congestion is mitigated through the new wired and wireless packet division method, and finally, the power of wireless nodes is reduced through the dynamic MAC mechanism to reduce the power consumption of wireless nodes. The experimental results show that this paper’s scheme can reduce the intra-subnet congestion, balance the inter-subnet load, reduce the power consumption of wireless nodes, and reduce the latency by 44.6% compared with the basic wireless on-chip network using adaptive routing algorithm, and reduce the latency by 21.8% compared with the wireless on-chip network using global congestion-aware routing algorithm; in different traffic patterns, this paper achieves the highest saturation throughput in different traffic patterns, and the additional hardware overhead of the router increases by only 0.04%.

A family of regular graphs of degree 3, called chordal rings is presented as a possible candidate for the implementation of a distributed system and for fault-tolerant architectures. The symmetry of graphs makes it possible to determine message routing by using a simple distributed algorithm. Arbitrary data permutations are generally accomplished by sorting. For certain classes of permutations, however, there exist algorithms that are more efficient than the best sorting algorithm. One such class is the Bit Permute Complement (BPC) class of permutations.

In this paper, we first develop algorithms requiring two token storage registers in each node to realize an arbitrary BPC permutation. We next evaluate its ability to realize BPC permutations in networks of arbitrary size by estimating the number of required routing steps when a single fault is present and when not.

In this paper we investigate the configuration of switches suitable for high performance communication, and propose communication schemes which exploit the structural strengths of that configuration. In switch-based networks, communication performance heavily depends on the configuration of switches and communication schemes for the networks. The deadlock problem caused by wormhole routing is another crucial factor affecting communication performance. Thus, we first evaluate several candidate configurations in terms of deadlock avoidance, scalability, flexibility, cost, and network properties (bandwidth, diameter, and average distance), and verify that the incomplete fat tree is the most promising configuration. Next, we show how to implement a routing algorithm and a unicast-based multicast algorithm on the incomplete fat tree configuration. The routing algorithm always finds a shortest path and fully utilizes network resources without using a routing tables. The multicast algorithm is optimal in that it is contention-free and requires a minimum number of communication steps.

We consider routing in hypercube networks with a very large number (i.e., up to a constant fraction) of faulty nodes. Simple and natural conditions are identified under which hypercube networks with a very large number of faulty nodes still remain connected. The conditions can be detected and maintained in a distributed manner based on localized management. Efficient routing algorithms on hypercube networks satisfying these conditions are developed. For a hypercube network that satisfies the conditions and may contain up of 37.5% faulty nodes, our algorithms run in linear time and for any two given non-faulty nodes find a routing path of length bounded by four times the Hamming distance between the two nodes. Moreover, our algorithms are distributed and local-information-based in the sense that each node in the network knows only its neighbors' status and no global information of the network is required by the algorithms.

Extensive experiments and experience have shown that the well-known hypercube networks are highly fault tolerant. What is frustrating is that it seems very difficult to properly formulate and formally prove this important fact, despite extensive research efforts in the past two decades. Most proposed fault tolerance models for hypercube networks are only able to characterize very rare extreme situations thus significantly underestimating the fault tolerance power of hypercube networks, while for more realistic fault tolerance models, the analysis becomes much more complicated. In this paper, we develop new techniques that enable us to analyze a more realistic fault tolerance model and derive lower bounds for the probability of hypercube network fault tolerance in terms of node failure probability. Our results are both theoretically significant and practically important. From the theoretical point of view, our method offers very general and powerful techniques for formally proving lower bounds on the probability of network connectivity, while from the practical point of view, our results provide formally proven and precisely given upper bounds on node failure probabilities for manufacturers to achieve a desired probability for network connectivity. Our techniques are also useful and powerful for analysis of the performance of routing algorithms, and applicable to the study of other hierarchical network structures and to other network communication problems.

It is known that many networks modeling real-life complex systems are small-word (large local clustering and small diameter) and scale-free (power law of the degree distribution), and very often they are also hierarchical. Although most of the models are based on stochastic methods, some deterministic constructions have been recently proposed, because this allows a better computation of their properties. Here a new deterministic family of hierarchical networks is presented, which generalizes most of the previous proposals, such as the so-called binomial tree. The obtained graphs can be seen as graphs on alphabets (where vertices are labeled with words of a given alphabet, and the edges are defined by a specific rule relating different words). This allows us the characterization of their main distance-related parameters, such as the radius and diameter. Moreover, as a by-product, an efficient shortest-path local algorithm is proposed.

This paper introduces a new generic routing algorithm called QColony for packet-switched communications networks that support real-time flows. In this context, we introduce both a novel path-selection scheme, namely the best-fit scheme, and a novel routing technique, namely the multi-pheromone technique, inspired by observations of biological ant colonies. Simulation was carried out for our algorithm and two other routing algorithms under various traffic scenarios and different irregular network topologies. Simulation results show that we can achieve good performance for the QColony algorithm. According to our experiments, QColony is able to provide smooth performance for all operating conditions, especially with large networks and under traffic scenarios with failure conditions and improperly functioning nodes. We also demonstrate through simulation that the best-fit scheme is able to deliver higher routing performance than what the shortest-path scheme can achieve.

There are many routing problems belong to NP type, traditional methods will lose efficiency by ordinary because of large computation. So, in order to reduce the communication complexity, a high-speed and efficient algorithm using quantum superposition states technology has been proposed which can be used in routing choice. In this paper, we reported on the latest routing technology and constructed a general mathematical model of quantum routing algorithm according to the principle of quantum superposition states. Simulating results show that the routing through the algorithm gains an advantage over conventional algorithm. Furthermore, it has stronger practicality and robustness.