The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic

Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively

Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach

Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes

An Energy Aware Unequal Clustering Algorithm using Fuzzy Logic for Wireless Sensor Networks

In wireless sensor networks, clustering provides an effective way of organising the sensor nodes to achieve load balancing and increasing the lifetime of the network. Unequal clustering is an extension of common clustering that exhibits even better load balancing. Most existing approaches do not consider node density when clustering, which can pose significant problems. In this paper, a fuzzy-logic based cluster head selection approach is proposed, which considers the residual energy, centrality and density of the nodes. In addition, a fuzzy-logic based clustering range assignment approach is used, which considers the suitability and the position of the nodes in assigning the clustering range. Furthermore, a weight function is used to optimize the selection of the relay nodes. The proposed approach was compared with a number of well known approaches by simulation. The results showed that the proposed approach performs better than the other algorithms in terms of lifetime and other metrics

City Sustainable Development Evaluation Based on Hesitant Multiplicative Fuzzy Information

Sustainable development evaluation is the basis of city sustainable development research, and effective evaluation is the foundation for guiding the formulation and implementation of sustainable development strategy. In this paper, we provided a new city sustainable development evaluation method called hesitant multiplicative fuzzy TODIM (HMF-TODIM). The main advantage of this method is that it can deal with the subjective preference information of the decision-makers. The comparison study of existing methods and HMF-TODIM is also carried out. Additionally, real case analysis is presented to show the validity and superiority of the proposed method. Research results in this paper can provide useful information for the construction of sustainable cities

A novel topology control approach to maintain the node degree in dynamic wireless sensor networks

Topology control is an important technique to improve the connectivity and the reliability of Wireless Sensor Networks (WSNs) by means of adjusting the communication range of wireless sensor nodes. In this paper, a novel Fuzzy-logic Topology Control (FTC) is proposed to achieve any desired average node degree by adaptively changing communication range, thus improving the network connectivity, which is the main target of FTC. FTC is a fully localized control algorithm, and does not rely on location information of neighbors. Instead of designing membership functions and if-then rules for fuzzy-logic controller, FTC is constructed from the training data set to facilitate the design process. FTC is proved to be accurate, stable and has short settling time. In order to compare it with other representative localized algorithms (NONE, FLSS, k-Neighbor and LTRT), FTC is evaluated through extensive simulations. The simulation results show that: firstly, similar to k-Neighbor algorithm, FTC is the best to achieve the desired average node degree as node density varies; secondly, FTC is comparable to FLSS and k-Neighbor in terms of energy-efficiency, but is better than LTRT and NONE; thirdly, FTC has the lowest average maximum communication range than other algorithms, which indicates that the most energy-consuming node in the network consumes the lowest power

Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

In multi-hop routing, cluster heads close to the base station functionaries as intermediate nodes for father cluster heads to relay the data packet from regular nodes to base station. The cluster heads that act as relays will experience energy depletion quicker that causes hot spot problem. This paper proposes a dynamic multihop routing algorithm named Data Similarity Aware for Dynamic Multi-hop Routing Protocol (DSA-DMRP) to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. The DSA-DMRP uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters. In this mechanism, each node can recognize and note its similar neighbor nodes. Next, K-hop Clustering Algorithm (KHOPCA) that is modified by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra-cluster and inter-cluster transmission. The DSA-DMRP was compared against the KHOPCA to justify the performance. Simulation results show that, the DSA DMRP can improve the network lifetime longer than the KHOPCA and can satisfy the requirement of the dynamic multi-hop routing protocol

Energy-efficient mobile sink routing scheme for clustered corona-based wireless sensor networks

Wireless Sensor Networks (WSNs) are generally composed of several tiny, inexpensive and self-configured sensor nodes, which are able to communicate with each other via wireless communication devices. The main duty of the nodes is to sense data and transmit to a sink via multi- or single-hop data transmission manners. Since the sensor nodes generally are limited in power resources, they deplete their energy rapidly. In addition, sensor nodes are usually distributed in places, where may be too harsh to be accessible for human. Consequently, exchanging or recharging the power supplies of the sensor nodes is difficult. Therefore, energy efficiency is the most critical issue in design of WSN, which affects the lifetime and performance of the network. Several cluster-based schemes are proposed to enhance the energy efficiency; however, most of them generate sub-optimal clusters without considering both coverage and energy issues simultaneously. Furthermore, several mobility-based schemes are proposed in order to achieve balanced energy consumption through optimizing the sojourn time and sojourn location of Mobile Sinks (MS). Nevertheless, most of them adjust the sojourn time of MS under predictable mobility pattern. Moreover, in most of existing mobility based schemes, time limitation is not considered for optimizing the sojourn location of MS. The aim behind this research is to develop an Energy-efficient Mobile Sink Routing (EMSR) Scheme, which improves the energy efficiency. The EMSR is the incorporation of three schemes: Energyefficient based Unequal-sized Clustering (EUC) mechanism aims to construct the optimal sized clusters, which ensures the energy conservation and coverage preservation. Collaborative Mobile Sink-based Inter-Cluster Routing (CMSICR) mechanism aims to optimize the sojourn time of MS to balance the energy consumption among Cluster Heads (CH). An Energy-efficient Intra-cluster Movement of Mobile Sink (EIM2S) mechanism, which identifies the optimal sojourn locations of the MS within clusters in order to balance the energy consumption among Member Nodes (MN). The EMSR partitions the network field into optimal clusters and employs MSs in order to balance the energy consumption among CHs and MNs. Simulation results show that EMSR achieved improved performance in terms of network lifetime by 51%, total energy consumption by 28% wasted energy by 36% compared to existing schemes. In conclusion, the proposed routing scheme proves to be a viable solution for multi hop cluster based WSN