Mobile Charging Strategy for Wireless Rechargeable Sensor Networks

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.

Monitoring Neuronal Network Disturbances of Brain Diseases: A Preclinical MRI Approach in the Rodent Brain

Functional and structural neuronal networks, as recorded by resting-state functional MRI and diffusion MRI-based tractography, gain increasing attention as data driven whole brain imaging methods not limited to the foci of the primary pathology or the known key affected regions but permitting to characterize the entire network response of the brain after disease or injury. Their connectome contents thus provide information on distal brain areas, directly or indirectly affected by and interacting with the primary pathological event or affected regions. From such information, a better understanding of the dynamics of disease progression is expected. Furthermore, observation of the brain's spontaneous or treatment-induced improvement will contribute to unravel the underlying mechanisms of plasticity and recovery across the whole-brain networks. In the present review, we discuss the values of functional and structural network information derived from systematic and controlled experimentation using clinically relevant animal models. We focus on rodent models of the cerebral diseases with high impact on social burdens, namely, neurodegeneration, and stroke.

Data transfer in a decentralized network of robots using a local voting protocol

The construction of an effective intelligent information transmission system in a group of cyber-physical systems is one of the important problems in both practical and theoretical contexts. Such a system for transmitting information for a group is being built for a network consisting of separate robotic complexes. Increasingly, decentralized solutions are used to build effective interaction between group members. As a rule, in networks, decentralization is present in computing software modules, and the data transmission system between nodes is centralized. One of the aspects of such centralization is the need to send data to a specific destination directly or by relaying through other nodes - routing data in the network. In this work, a method of data transmission in a decentralized network between robotic complexes without reference to routing is proposed. The method consists of the exchange of data on the state of the entire network as a whole between the nodes.

Design and Implementation of Distributed Controller Clustering for Solving the Issue of Single Failure in SDN Networks

Software-Defined Networks (SDN) It is a centralized control structure in the network that opens up new possibilities that did not exist before. The significant characteristic of this innovative approach is the focus on the capability of proposing networks of high dynamicity and programmability to transform the intelligence of underlying systems to the networks via controllers. The main issue of the SDN approach is found in its security, mainly due to its central-controlling architecture since the entire network is controlled from a central point. This makes it very vulnerable to single-point failure. In this paper, a fully Distributed SDN controller is proposed for solving the one point failure which exists within the single SDN controller. In general, the concept involves forming cluster of distributed controllers whereby each controller controls its domain and can thereby share the load within the network. The experimental results of the proposed system show an increase and enhancement in the performance of the network. The single-point failure issues have been overcome. The throughput of the proposed system increased with 20% while the packet loss rate was minimize with 33%.

Node Importance Based Protection in Power-Grid Optical Backbone Communication Networks

Power-grid optical backbone communication network is a special communication network serving for power system. With the development of new internet technology, there are more and more services carried by power-grid optical backbone communication networks. It plays an important role in the protection of nodes, especially important nodes which often carry important information of the network, when the network is under heavy traffic load. Hench, to solve this problem, we propose the concept of node importance and design a node importance-based protection algorithm under heavy traffic load scenario in power-grid optical backbone communication networks. Simulation results show that the proposed node importance based protection algorithm can obviously reduce blocking probability of the important nodes and improve the performance of the entire network in terms of blocking probability.

Deep brain stimulation for movement disorder treatment: exploring frequency-dependent efficacy in a computational network model

A large-scale computational model of the basal ganglia network and thalamus is proposed to describe movement disorders and treatment effects of deep brain stimulation (DBS). The model of this complex network considers three areas of the basal ganglia region: the subthalamic nucleus (STN) as target area of DBS, the globus pallidus, both pars externa and pars interna (GPe-GPi), and the thalamus. Parkinsonian conditions are simulated by assuming reduced dopaminergic input and corresponding pronounced inhibitory or disinhibited projections to GPe and GPi. Macroscopic quantities are derived which correlate closely to thalamic responses and hence motor programme fidelity. It can be demonstrated that depending on different levels of striatal projections to the GPe and GPi, the dynamics of these macroscopic quantities (synchronisation index, mean synaptic activity and response efficacy) switch from normal to Parkinsonian conditions. Simulating DBS of the STN affects the dynamics of the entire network, increasing the thalamic activity to levels close to normal, while differing from both normal and Parkinsonian dynamics. Using the mentioned macroscopic quantities, the model proposes optimal DBS frequency ranges above 130 Hz.

Enhanced GAF protocol based on graph theory to optimize energy efficiency and lifetime in WSN technology

PurposeThe purpose of this paper is to design an enhanced routing protocol to minimize energy consumed and extend network lifetime in sensor network (WSN).Design/methodology/approachWith the use of appropriate routing protocols, data collected by sensor nodes reache the BS. The entire network lifetime can be extended well beyond that of its single nodes by putting the nodes in sleep state when they are not in use, and make active just a single node at a time within a given area of interest. So that, the lowest-cost routing arises by minimizing the communication cost. This paper proposes an enhanced adaptive geographic fidelity (E-GAF) routing protocol based on theory of graphs approach to improve the discovery phase, select the optimal path, reduce the energy used by nodes and therefore extend the network lifetime. Following the simulations established by varying the number of grids and tests, a comparison is made between the E-GAF and basic GAF (B-GAF) based on the number of dead nodes and energy consumption.FindingsThe results obtained show that E-GAF is better than the existing basic GAF protocol in terms of energy efficiency and network lifetime.Originality/valueThis paper adopts the latest optimization algorithm know as E-GAF, which is used to solve the problem of energy and improve the network lifetime in a WSN. This is the first work that utilizes network lifetime in WSN.

SCR-CC: A Novel Sensing Clustering Routing Algorithm Based on Collaborative Computing in Heterogeneous Sensor Networks

In order to better improve the reliability of data transmission and extend the network lifetime, the paper proposes the Sensing Clustering Routing Algorithm Based on Collaborative Computing (SCR-CC). The proposed algorithm uses the characteristics of the perceptual radius, which obey the normal distribution, and gives the process of completing the expected value of the data transmission of any two nodes in the cluster. Secondly, the paper analysed the necessary conditions of the existence for the expected value of the number of neighbour nodes when the redundant nodes are closed and the working nodes meet arbitrary differences. Thirdly, the cluster angle formed by the base station and the cluster is used to optimize the clustering structure and complete the dynamic clustering process to achieve the energy balance of the entire network. Finally, the simulation experiments show that the proposed SCR-CC algorithm compared with the other three algorithms reduces the number of failed nodes by 11.37% on average and increases the network lifetime by 27.09% on average; therefore, the efficiency and effectiveness of the SCR-CC algorithm are verified.

Anti-Quantum Lattice-Based Ring Signature Scheme and Applications in VANETs

Message authentication is crucial because it encourages participants to accept countermeasures and further transmit messages to legitimate users in a network while maintaining the legitimacy of the identity of network members. An unauthorized user cannot transmit false messages to a given network. Although traditional public key cryptography is suitable for message authentication, it is also easy to manage and generate keys, and, with the expansion of an entire network, the system needs a lot of computing power, which creates additional risks to network security. A more effective method, such as ring signature, can realize this function and guarantee more security. In this paper, we propose an anti-quantum ring signature scheme based on lattice, functionality analysis, and performance evaluation to demonstrate that this scheme supports unconditional anonymity and unforgeability. After efficiency analysis, our scheme proved more effective than the existing ring signature schemes in processing signature generation and verification. The proposed scheme was applied to VANETs that support strong security and unconditional anonymity to vehicles.

An Image Change Detection Algorithm Based on Multi-Feature Self-Attention Fusion Mechanism UNet Network

Various UNet architecture-based image change detection algorithms promote the development of image change detection, but there are still some defects. First, under the encoder–decoder framework, the low-level features are extracted many times in multiple dimensions, which generates redundant information; second, the relationship between each feature layer is not modeled so sufficiently that it cannot produce the optimal feature differentiation representation. This paper proposes a remote image change detection algorithm based on the multi-feature self-attention fusion mechanism UNet network, abbreviated as MFSAF UNet (multi-feature self-attention fusion UNet). We attempt to add multi-feature self-attention mechanism between the encoder and decoder of UNet to obtain richer context dependence and overcome the two above-mentioned restrictions. Since the capacity of convolution-based UNet network is directly proportional to network depth, and a deeper convolutional network means more training parameters, so the convolution of each layer of UNet is replaced as a separated convolution, which makes the entire network to be lighter and the model’s execution efficiency is slightly better than the traditional convolution operation. In addition to these, another innovation point of this paper is using preference to control loss function and meet the demands for different accuracies and recall rates. The simulation test results verify the validity and robustness of this approach.