Narrow Results

Opportunistic Network (OppNet) IoT is a subsection of Mobile Adhoc Network (MANET) in which the connection between nodes is not regulated. In MANET, the message is transmitted to the destination with the known routing path, whereas OppNet can transmit data without having a predefined path for data transmission. Estimating the path between sources and destinations is complicated due to the lack of infrastructure and the frequently changing environment. In this research, efficient routing with the detection or classification of nodes is accomplished with a multi-hop routing-based deep learning model. The EPRoPHET routing algorithm is based on a deep learning strategy in which the energy-efficient routing decision is made based on node classification. The deep learning model optimized deep convolutional neural network (DCNN) is utilized to classify reliable and unreliable nodes based on their ability to deliver the message. The hyperparameters used in the DCNN are updated with the Bird Swarm bionic Model (BSBM). Information, such as node movement, location, distance between nodes, and energy status, is considered when estimating the delivery probability. The decision about the forwarder node is taken with the memory of individual nodes and the previous routing information. The performance of a proposed approach is evaluated and compared with the existing state-of-the-art approaches. For 150 nodes, the proposed model achieved a better delivery probability of 0.96, an overhead ratio of 15.6, a latency of 4,100ms and an energy consumption of 37.5J, respectively. The higher performance obtained with the EPRoPHET routing algorithm represents the efficiency of a proposed approach.

In Wireless Sensor Network (WSN), node localization is a crucial need for precise data gathering and effective communication. However, high energy requirements, long inter-node distances and unpredictable limitations create problems for traditional localization techniques. This study proposes an innovative two-stage approach to improve localization accuracy and maximize route selection in WSNs. In the first stage, the Self-Adaptive Binary Waterwheel Plant Optimization (SA-BWP) algorithm is used to evaluate a node’s trustworthiness to achieve accurate localization. In the second stage, the Gazelle-Enhanced Binary Waterwheel Plant Optimization (G-BWP) method is employed to determine the most effective data transfer path between sensor nodes and the sink. To create effective routes, the G-BWP algorithm takes into account variables like energy consumption, shortest distance, delay and trust. The goal of the proposed approach is to optimize WSN performance through precise localization and effective routing. MATLAB is used for both implementation and evaluation of the model, which shows improved performance over current methods in terms of throughput, delivery ratio, network lifetime, energy efficiency, delay reduction and localization accuracy in terms of various number of nodes and rounds. The proposed model achieves highest delivery ratio of 0.97, less delay of 5.39, less energy of 23.3 across various nodes and rounds.

Routing algorithm is an important factor and a key technology for Flying Ad hoc Networks (FANETs), which can safeguard FANET network communication. In FANETs, the fast-changing nature of FANET network topology makes the traditional Mobile Ad-hoc Networks (MANET) network routing algorithm not directly usable, which leads to challenges for the design of routing algorithms. In this paper, when the FANET nodes need to communicate, the route search program performs route search based on the ant colony algorithm, so as to obtain a stable route with high efficiency. Making use of NS3 for simulation of ACA, Dynamic Source Routing (DSR), and Ad hoc On demand Distance Vector Routing (AODV), the results of simulation show that ACA can improve FANET performance.

To perceive the data utilizing sensor nodes, wireless sensor network (WSN) consists of several nodes connected to a wireless channel. However, the sink node, also known as a base station (BS), provides power to the WSN and acts as an access node for a number of the network’s sensor devices. Weather monitoring, field surveillance, and the collection of meteorological data are just a few of the various uses for WSN. The energy of each node directly affects how long a wireless network will last. So, to increase the lifespan of WSN, effective routing is required. Using the suggested Taylor sea lion optimization-based deep belief network (TSLnO-based DBN), the ultimate purpose of this research is to build a method for energy-aware communication in WSN. In the setup stage, cluster head (CH) is chosen using a hybrid optimization technique called ant lion whale optimization (ALWO), which is created by fusing the whale optimization algorithm (WOA) and ant lion optimizer (ALO). It is important to note that CH’s selection criteria are solely based on fitness factors such as energy and distance. The second phase, known as the steady state step, is when the updating of energy and trust takes place. In the prediction phase, the network classifier is trained using a newly created optimization method called TSLnO, and the age of neighbor nodes is predicted by estimating the energy of neighbors using DBN. By combining the Taylor Series and the sea lion optimization (SLnO) method, the proposed TSLnO is produced. The communication/route discovery phase, which occurs in the fourth phase, is where the path through nearby nodes is chosen. The maintenance phase of the route is the fifth phase.

Medical information system, like the Internet of Medical Things (IoMT), has gained more attention in recent decades. Disease diagnosis is an important facility of the medical healthcare system. Wearable devices become popular in a wide range of applications in the health monitoring system and this has stimulated the increasing growth of IoMT. Recently, a smart healthcare system has been more effective, and various methods have been developed to classify the disease at the beginning stage. To capture the patient’s information and detect the disease, a new framework is designed using the developed Conditional Auto regressive Mayfly Algorithm (CAMA)-based Deep Residual Network (DRN). Initially, pre-processing is done by the T2FCS filtering technique to increase the image quality by eliminating noises. The second step is segmentation. Here, the segmentation of brain tumor is done using U-Net. After that, data augmentation is performed to enhance image dimensions using the techniques, such as flipping, shearing, and translation to solve the issues of data samples. After processing the data augmentation mechanism, the next step is brain tumor detection, which is done using DRN. Here, DRN is trained by the proposed CAMA, which is the integration of conditional auto regressive value at risk (CAViaR) with the mayfly algorithm (MA). The developed model reduces computational complexity and increases effectiveness and robustness. The proposed CAMA-based DRN outperformed with an utmost testing accuracy of 0.921, sensitivity of 0.931, specificity of 0.928, distance of 52.842 and trust of 0.697.

In massively parallel systems, the performance gains are often significantly diminished by the inherent communication overhead. This overhead is caused by the required message passing resulting from the task allocation scheme. In this paper, techniques to reduce this communication overhead by both scheduling the communication and determining the routing that the messages should take within a tightly-coupled processor network are presented. Using the recently developed Collision Graph model, static scheduling algorithms are derived which work at compile-time to determine the ordering and routing of the individual message transmissions. Since a priori knowledge about the network traffic required by static scheduling may not be available or accurate, this work also considers dynamic scheduling. A novel hybrid technique is presented which operates in a dynamic environment yet uses known information obtained by analyzing the communication patterns. Experiments performed show significant improvement over baseline techniques.

With the increase of the complexity of circuits, fast estimation can provide some vital information for optimal layout decisions. Fast congestion prediction plays an important role in the physical layout of VLSI design. In this paper, we present a probabilistic estimation approach with via minimization constraints. Our model is more realistic than previous models. It has more flexibility for wires to have more usage area to bypass congested regions and blockages. The experiment on routing benchmarks demonstrates the effectiveness of our approach.

C-based cycle-accurate simulations are used to evaluate the performance of a Network-on-Chip (NoC) based on an improved version of the modified Fat Tree topology. The modification simplifies routing further and guarantee orderly reception of packets without any loss of performance. Several traffic models have been used in these simulations; Bursty and non-bursty traffic with uniformly-distributed destination addresses and non-uniformly-distributed destination addresses. A simple new traffic model has been developed for generating non-uniformly-distributed destination addresses. This model is general enough to be used in developing new NoC architectures and captures universally accepted place-and-route methodologies. Simulation results are used to illustrate how the hardware resources of a modified Fat Tree NoC can be minimized without affecting the network performance. The performance of a NoC with regular Mesh topology was also evaluated for comparison with the modified Fat Tree topology.

In this paper, a new algorithm is proposed for computing the node-disjoint optimal transmission energy consumption route for coded cooperative mobile IoT networks. Inspired by the potential benefits user cooperation can provide, we incorporate user cooperation to the mobile ad-hoc multi-hop IoT networks. Our results include a novel ant colony system-based node-disjoint energy-efficient routing algorithm. Ant colony system can approximate the optimal route by local information and is thus very suitable for mobile IoT network environment. In particular, ant algorithm makes history-sensitive choice and thus can significantly outperform the greedy algorithm. In addition, it can efficiently handle the case of multiple sources and multiple destinations. For a large IoT network, we investigate the multi-scale ant colony system and when compared to Dijkstra algorithm, such an algorithm usually shortens the runtime by a factor of several hundred.

The applications of wireless sensor network (WSN) are growing very rapidly, so utilizing the energy in an efficient manner is a challenging task as the battery life of nodes in WSN is very limited. For enhancing the lifetime of the network, various clustering protocols have been proposed earlier. In this paper, a clustering protocol named Energy Efficient Clusterhead Selection Scheme (ECSS) is proposed. The proposed ECSS protocol focusses on selecting an energy-efficient cluster head (CH), which helps in enhancing the overall lifetime and performance of the network. The proposed ECSS protocol uses the energy levels of nodes for the CH selection process. The proposed protocol is designed for the heterogeneous environment and it aims in minimizing the energy usage in the network and thereby improving the lifespan of the network. To measure the performance of the proposed ECSS protocol, the comparison is performed with the various existing protocols using MATLAB simulator. The results of simulation show that the proposed ECSS protocol has enhanced the network lifespan, throughput, and energy usage of the network as contrasted to the existing protocols.

Wireless Sensor Networks (WSNs) are used for data collection from the surrounding environment using the enormous amount of sensor nodes. Energy saving is the most fundamental challenge of WSNs which primarily depends on the Cluster Head (CH) selection and packet routing strategy. In this paper, we are proposing an Energy Aware Distance-based Cluster Head selection and Routing (EADCR) protocol to extend the lifetime of WSN using the FCM technique, residual energy of the nodes, their relative Euclidean distances from the Base Station (BS) and cluster centroid. Since the nodes consume a considerable amount of energy during the clustering phase, thus to avoid this, here, we are introducing a new clustering approach where the CH selection is now based on the newly proposed fitness function. We are also providing a new strategy for packet routing using the shortest path technique for routing between node and its destination which reduces the energy consumption of the CHs by employing the multi-hop communication. We also save the energy of the nodes using their Euclidean distances among them, from their CH and from BS. The simulation results exhibit that the EADCR enhances the network lifetime as compared to the other similar algorithms, e.g., FCM, REHR, UCRA–GSO and CCA–GWO under different scenarios. It also saves the residual energy of the network and enhances the network coverage.

Wireless Sensor Networks (WSNs) have come across several things which include collecting data, handling data and distribution for super visioning specific applications such as the services needed, managing anything that occurred naturally, etc. They totally rely on applications. Therefore, the WSNs are classified under major networks. This is very essential. It can be defined as a network of networks that helps in proper flow of data. The main characteristics of WSN include its continuous changes in topologies, connected nodes with several chips and tunic routing protocol. There should be the better utilization of the available resources so that its life span may exceed. There should be an effective usage of available assets to avoid the waste. In our research, we propose a hybrid approach, namely, the Power Control Tree-Based Cluster (PCTBC) to identify the Sybil attacks in WSNs. It employs several stages structured clustering of nodes based on position and identity verification. This approach is utilized for the usage of energy consumption, effectiveness of detecting Sybil attacks inside clusters. The main aspects put into consideration are the efficient routing protocol of the distance between the hops and the energy power remains, where the distance between the hops is being computed using the Received Signal Strength Indication (RSSI) and also the packet transmission can be properly tuned on the basis of the distance. Also, the proposed approach considers the energy consumption for the transmission of the defined packet.

Wireless Sensor Networks (WSN) became a novel technology for ubiquitous livelihood and still remains a hot research topic because of its applicability in diverse domains. Energy efficiency treated as a crucial factor lies in the designing of WSN. Clustering is commonly applied to increase the energy efficiency and reduce the energy utilization. The proper choice of cluster heads (CHs) and cluster sizes is important in a cluster-based WSN. The CHs which are placed closer to base station (BS) are affected by the hot spot issue and it exhausts its energy faster than the usual way. For addressing this issue, a new unequal clustering and routing technique using ant colony optimization (ACO) algorithm is presented. Initially, CHs are chosen and clusters are constructed based on several variables. Next, the ACO algorithm with levy distribution is applied for the selection of optimal paths between two nodes in the network. A comprehensive validation set takes place under diverse situations under the position of BS. The experimental outcome verified the superiority of the presented model under several validation parameters.

In parallel and distributed systems, multistage interconnection network (MIN) plays an important role for its efficient communication between processor and memory at a very low cost. A major class of MIN called Gamma network is known for its redundant network topology and is being used in broadband communication systems. The increased redundancy incorporation makes these networks more complex and hence reliability evaluation becomes complex. The performance evaluation of these network topologies requires reliability evaluation utilizing routing mechanism or techniques. In this paper, we have proposed four topologies of Gamma-Minus network using MUX and DEMUX. Terminal Reliability (TR), fault tolerance and routing schemes of Gamma-Minus network topologies proposed have been computed by utilizing different connection patterns of MUX/DEMUX. Also, performance indices such as TR, Reliability Cost Ratio (RCR), Fault Tolerance, etc. computed for different Gamma-Minus architectures have been compared with the existing Gamma networks, other than Gamma-Minus. All the performance indices for different Gamma-Minus topologies show improvement over the performance indices of different Gamma networks. The proposed Gamma-Minus architectures have been compared among themselves and also Gamma-Minus2 shows the best performance for all performance indices. To the best of our knowledge, most of the researchers have not compared fault tolerance and RCR performance measure.

Gamma Interconnection Network (GIN) is characterized as Redundant Multistage Interconnection Network (MIN) which is considered as a potential candidate for use in broadband communications. Several advancements have been made to improve Reliability indices such as Terminal Reliability (TR), Broadcast Reliability (BR) and Network Reliability (NR) of these networks. But inspite of these advancements, there are certain issues which are yet to be explored such as Complexity, Cost, Number of disjoint paths on presumption that source/destination are failure free. Most of the work done in the literature addresses TR only and less work has been done on analysis of BR and NR. In literature networks, $8\times 8$ size has been explored and no attention has been paid to bigger network sizes although reliability of bigger size network is important for parallel processing systems. In this paper, existing class of Gamma Networks has been studied extensively and modifications have been proposed in existing Gamma Network which minimizes or resolves most of the limitations mentioned above. The proposed Gamma-Minus Network has more redundant paths than other networks. Proposed Network has been compared with some recently introduced members of this class. The results show that newly proposed Gamma-Minus Network has better reliability with lowest cost and path length and provides disjoint minimal-path set for $8\times 8$ to $1024\times 1024$ network size. The routing used in this paper eliminates the backtracking overhead which minimizes transmission delay.

The increase in demand for mobile telecommunication systems, and the limited bandwidth allocated to these systems, have led to systems with smaller cell dimensions, which in turn led to the increase of control messages. In order to prevent controller bottle necks, it is desirable to distribute the network control functions throughout the network. To satisfy this requirement, a mobile network structure characterized by its hierarchical and decentralized network control is presented in this paper. The area served by the mobile system is divided into regions, and the regions are further divided into cells. Each cell is served by a base station, each base station is connected to a regional network through a base station interface unit (BIU). Each region has its own regional network. Connected to each regional network are the cellular controller, the home database, the visitor database, the trunk interface unit (TIU) and the gateway interface unit (GIU). The TIU connects the regional network to the public switched telephone network (PSTN). The GIU connects the regional network to other regional networks through the gateway network. This architecture distributes the network control functions among a large number of processing elements, thus preventing controller bottle necks — a problem faced by centralized controlled systems. The information and network control messages are transferred in the form of packets across this network. Processes inherent to the operation of this network structure are illustrated and discussed. These processes include the location update process, the setting up of a call, the handoff process (both the intra-region handoff process and the inter-region handoff process are considered), and the process of terminating a call.

We take advantage of the hierarchical structure of the star graph network to obtain an efficient method for constructing node-disjoint paths between arbitrary pairs of nodes in the network. A distributed fault-tolerant routing algorithm for the star network based on this construction method is then presented and evaluated. The proposed algorithm adapts the routing decisions in response to node failures. Node failure and repair conditions may arise dynamically (at any time) provided that the total number of faulty nodes at any given time is less than the node-connectivity n - 1 of the n-star. When a message is blocked due to faulty components, the source of the message is warned and requested to switch to a different node-disjoint path. The methods used to identify the paths, to propagate failure information back to source nodes, and to switch from a routing path to another incur little communication and computation overhead. We show that if the node failures occur 'reasonably' apart in time, then all messages will be routed on paths of length δ + ε where δ is the minimum distance between the source and the destination and ε is 0, 2, or 4. In the unlikely case where more failures occur in a 'short period', the algorithm still delivers all messages but via possibly longer paths.

We define the higher dimensional honeycomb graphs as a generalization of hexagonal plane tessellation, and consider it as a multiprocessor interconnection network. A 3-D honeycomb mesh network with n nodes has degree 4 and diameter approximately 3.63n. The network cost, defined as the product of degree and diameter, is about 20 percents better for the 3-D honeycomb than for the 3-D mesh. We describe the addressing scheme, the routing and broadcasting algorithms for three-dimensional and higher dimensional honeycombs. Furthermore, a formula for the diameter of a higher dimensional honeycomb network of given size is determined.

Efficient routing among a set of mobile hosts is one of the most important functions in ad hoc wireless networks. Routing based on a connected dominating set is a promising approach, where the search space for a route is reduced to the hosts in the set. A set is dominating if all the hosts in the system are either in the system are either in the set or neighbors of hosts in the set. In this paper, we first review a distributed formation of a connected dominating set called marking process and dominating-set-based routing. Then we propose several ways to reduce the size of the dominating set and study the locality of dominating set and study the locality of dominating set in ad hoc wireless networks with switch-on/off operations. Results show that the dominating set derived from the marking process exhibits good locality properties; i.e., the change of a host status, gateway (dominating) or non-gateway (dominated), affects only the status of hosts in a restricted vicinity. In addition, locality of host status updated is also verified through simulation.

The Mobile Ad Hoc Networks (MANETs) are useful in many applications environments and do not need any infrastructure support. Much work has been done on routing in MANETs. However, the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention in the wireline network domain. However, these QoS routing algorithms can not be applied directly to MANETs, because of the bandwidth constraints and dynamic network topology of MANETs. Searching for the shortest path with minimal cost and finding delay constrained least-cost paths are NP-complete problems. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, to cope with changing of MANET topology, routing methods should be adaptive, flexible, and intelligent. In this paper, we propose a Genetic Algorithm (GA) based routing method for Mobile Ad-hoc Networks (GAMAN). Robustness rather than optimality is the primary concern of GAMAN. The GAMAN uses two QoS parameters for routing. The performance evaluation via simulations shows that GAMAN is a promising QoS routing algorithm for MANETs.