Rest Of Nodes Research Articles

On correctly evaluating orders of truncation error for node-centered edge-based schemes

In this short note, we present a simple analysis to show that an observed order of truncation error on a regular grid can change, if not correctly evaluated, depending on the choice of the norm and also on the discretization scheme. For a second-order scheme, it can be first order, second order, or anywhere in between. Using the node-centered edge-based discretization as an example, we provide a guide on how to evaluate the order of truncation error correctly: one should evaluate it separately for a set of nodes, where the residual stencil is symmetric and the same at every node, and the rest of nodes.

An Improved Gateway-Based Energy-Aware Multi-Hop Routing Protocol for Enhancing Lifetime and Throughput in Heterogeneous WSNs

In this work, an improved version of the gateway based multi-hop routing protocol was studied. The MGEAR protocol is mainly used for prolonging network lifetime in homogeneous wireless sensor network. Herein, the proposed approach aims to prolong network lifetime and enhance the throughput of this protocol in the case of heterogeneous wireless sensor networks (HWSNs). In the MGEAR, the network is divided into several fields; sensor nodes in the first field communicate directly with the base station. Sensors in the center of the network send their data to the gateway which perform data fusion and spread to the base station. The rest of nodes are divided into two equal regions, in each region sensor nodes are grouped into clusters with a leading node as cluster-head. The central point of our approach is the selection of cluster-heads which is based on a ratio between the residual energy of each sensor node and the average energy of the region which it belongs. In order to equalize the load and prolong the lifetime of sensors, the cluster-head election probability is computed in each round according to the residual energy of each sensor node. Finally, the simulation results showed that this model had higher throughput and increased the lifetime by 130%, 151%, 167%, 171%, and 215% compared to HCR, ERP, ModLEACH, D-MSEP and DDEEC protocols respectively in the case of 2-level heterogeneity. In case of 3-level heterogeneity, the network lifetime is increased by 123%, 150%, 163% and 218% compared to HCP, ModLEACH, hetSEP and hetDEEC.

SALP Swarm Optimization Approach for Maximization The Lifetime of Wireless Sensor Network

In recent years, the maximization of a lifetime for wireless sensor networks is considered an important area for researchers. The wireless sensor networks (WSNs) contain two types of sensors that called sensor nodes and sink nodes which sensor node send information to the central node (sink node) that collected its data. Choosing the best location of sink node considered the critical problem that faces the lifetime of wireless sensor networks. In this paper, we propose a method that choosing best location of a sink node by applying Salp Swarm Algorithm (SSA) after determining sink node location we create transmission paths between the sink node and rest of nodes using Prim’s minimum spanning tree to choose shortest paths. Accordingly, for fitness function that used to decrease energy consumption for a network. Simulation results clarify that our proposed algorithm that solves localization of sink node presents the best results for prolonging the network’s lifetime compared to Cat Swarm Optimization algorithm (CSA) and Particle Swarm Optimization (PSO).

SALP Swarm Optimization Approach for Maximization The Lifetime of Wireless Sensor Network

In recent years, the maximization of a lifetime for wireless sensor networks is considered an important area for researchers. The wireless sensor networks (WSNs) contain two types of sensors that called sensor nodes and sink nodes which sensor node send information to the central node (sink node) that collected its data. Choosing the best location of sink node considered the critical problem that faces the lifetime of wireless sensor networks. In this paper, we propose a method that choosing best location of a sink node by applying Salp Swarm Algorithm (SSA) after determining sink node location we create transmission paths between the sink node and rest of nodes using Prim's minimum spanning tree to choose shortest paths. Accordingly, for fitness function that used to decrease energy consumption for a network. Simulation results clarify that our proposed algorithm that solves localization of sink node presents the best results for prolonging the network's lifetime compared to Cat Swarm Optimization algorithm (CSA) and Particle Swarm Optimization (PSO).

Top-Down Construction of Independent Spanning Trees in Alternating Group Networks

A set of spanning trees in a graph G is called independent spanning trees (ISTs) if they are rooted at the same vertex r, and for each vertex v(= r) in G, the two paths from v to r in any two trees share no common vertex expect for v and r. ISTs can be applied in many research fields, such as fault-tolerant broadcasting and secure message distribution in reliable communication networks. Since Cayley graphs have been widely used to design interconnection networks, constructing ISTs on cayley graphs is worth studying. The alternating group network is a subclass of Cayley graphs, and the approach of constructing ISTs in alternating group networks is still unknown. In this paper, we propose a novel and simple top-down approach for constructing ISTs in alternating group networks. The main ideas of the algorithm are to use induction to develop small trees to big trees, to use a triangle breadth-first search (TBFS) process to create a backbone of an IST, and to use breadth-first search (BFS) process to connect the rest of nodes. Compared to other methods of different interconnection networks in the literature, the uniqueness of our method is that it does not need to determine the parent of one node by any rule, on the contrary others determine that by rules. The time complexity in n-dimensional alternating group network AN n is O(n 2 × n!), where n! is twice the number of nodes of AN n ; hence it is polynomial time. We implement the algorithm in PHP and run cases from AN 3 to AN 10. The results reveal that all spanning trees of AN 3 to AN 10 are independent and that our algorithm is accurate and efficient. INDEX TERMS Independent spanning trees, alternating group networks, triangle breadth-first search, interconnection networks, Cayley graph.

Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing.

Conventional partial correlation coefficients (PCC) were extended to the non-Gaussian case, in particular to independent component analysis (ICA) models of the observed multivariate samples. Thus, the usual methods that define the pairwise connections of a graph from the precision matrix were correspondingly extended. The basic concept involved replacing the implicit linear estimation of conventional PCC with a nonlinear estimation (conditional mean) assuming ICA. Thus, it is better eliminated the correlation between a given pair of nodes induced by the rest of nodes, and hence the specific connectivity weights can be better estimated. Some synthetic and real data examples illustrate the approach in a graph signal processing context.

Combining Solar Energy Harvesting with Wireless Charging for Hybrid Wireless Sensor Networks

The application of wireless charging technology in traditional battery-powered wireless sensor networks (WSNs) grows rapidly recently. Although previous studies indicate that the technology can deliver energy reliably, it still faces regulatory mandate to provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and the relatively low energy supplies from wireless chargers. Fortunately, solar energy harvesting can provide high power density without health risks. However, its reliability is subject to weather dynamics. In this paper, we propose a hybrid framework that combines the two technologies - cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. We divide the network into three hierarchical levels. On the first level, we study a discrete placement problem of how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed 1:61(1+ϵ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -approximation algorithm for the placement. Then, we extend the discrete problem into continuous space and develop an iterative algorithm based on the Weiszfeld algorithm. On the second level, we establish an energy balance in the network and explore how to maintain such balance for wireless-powered nodes when sunlight is unavailable. We also propose a distributed cluster head re-selection algorithm. On the third level, we first consider the tour planning problem by combining wireless charging with mobile data gathering in a joint tour. We then propose a polynomial-time scheduling algorithm to find appropriate hitting points on sensors' transmission boundaries for data gathering. For wireless charging, we give the mobile chargers more flexibility by allowing partial recharge when energy demands are high. The problem turns out to be a Linear Program. By exploiting its particular structure, we propose an efficient algorithm that can achieve near-optimal solutions. Our extensive simulation results demonstrate that the hybrid framework can reduce battery depletion by 20 percent and save vehicles' moving cost by 25 percent compared to previous works. By allowing partial recharge, battery depletion can be further reduced at a slightly increased cost. The results also suggest that we can reduce the number of high-cost mobile chargers by deploying more low-cost solar-powered sensors.

An integrated restoration framework for coverage and communication within wireless sensor networks

The paper presents a novel integrated framework to restore the coverage and communication failures within a wireless sensor network (WSN) without adding new resources. We view WSN as a collection of tree topologies, where each tree comprises of base station (BS) as a root, and the sensors as rest of nodes. The sensor/BS failures produce a disjoint set of sensors, thus generating coverage and communication issues simultaneously. We have formulated the restoration within WSN as an optimisation problem, where the objective function is to maximise the lifespan of WSN, subject to energy constraints. The restoration framework employs Simulated Annealing to select the best alternate neighbouring sensor/BS to restore the coverage and communication. The simulation results of a typical WSN with 26% sensor failures and 40% BS failures demonstrate the capability of our framework to maintain 100% communications for up to 200 days of operation than without utilising restoration.

Localization Technology Based on Quantum-Behaved Particle Swarm Optimization Algorithm for Wireless Sensor Network

This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references for the rest of nodes to localize. The ranging-based localization problem is formulated as a multidimensional optimization issue, and the quantum-behaved particle swarm optimization algorithm (QPSO) is used to exploit their quick convergence to quality solutions. Finally, the simulation results compared with the particle swarm optimization algorithm (PSO) algorithm show that QPSO outperforms the PSO and improve the node position accuracy, which prove the validity of the presented method.

An Energy-Efficient MAC Protocol with Probabilistic Scheduled Listen-Sleep Cycles for Wireless Sensor Networks

In this paper, we propose an energy efficient MAC protocol for wireless sensor networks. In sensor networks, reducing energy consumption is one of the critical issues for extending network lifetime. One good solution to resolve this issue is introducing listen-sleep cycles, allowing sensor nodes to turn their transceiver off during sleep periods, which was adopted by S-MAC [1]. However, in S-MAC, due to the synchronized scheduling, transmission collisions will increase in heavy traffic situations, resulting in energy waste and low throughput. Hence, in this paper, we propose probabilistic scheduled MAC (PS-MAC), in which each node determines ‘listen’ or ‘sleep’ pseudo-randomly based on its own pre-wakeup probability and pre-wakeup probabilities of its neighbor nodes in each time slot. This allows the listen-sleep schedule of nodes in each transmitter and receiver pair to be synchronized, while maintaining those of the rest of nodes to be asynchronous. Therefore, collisions can be reduced even under heavy traffic conditions, resulting in reduced energy waste and high throughput. In addition, by dynamically adjusting the pre-wakeup probabilities of sensor nodes based on the change of the network environment, system throughput and latency can be further improved. Simulation results show that PS-MAC provides significant energy savings, low delay, and high network throughput.