Energy sink-holes avoidance method based on fuzzy system in wireless sensor networks

The existence of a mobile sink for gathering data significantly extends wireless sensor networks (WSNs) lifetime. In recent years, a variety of efficient rendezvous points-based sink mobility approaches has been proposed for avoiding the energy sink-holes problem nearby the sink, diminishing buffer overflow of sensors, and reducing the data latency. Nevertheless, lots of research has been carried out to sort out the energy holes problem using controllable-based sink mobility methods. However, further developments can be demonstrated and achieved on such type of mobility management system. In this paper, a well-rounded strategy involving an energy-efficient routing protocol along with a controllable-based sink mobility method is proposed to extirpate the energy sink-holes problem. This paper fused the fuzzy A-star as a routing protocol for mitigating the energy consumption during data forwarding along with a novel sink mobility method which adopted a grid partitioning system and fuzzy system that takes account of the average residual energy, sensors density, average traffic load, and sources angles to detect the optimal next location of the mobile sink. By utilizing diverse performance metrics, the empirical analysis of our proposed work showed an outstanding result as compared with fuzzy A-star protocol in the case of a static sink

A Survey on Routing Protocols for Large-Scale Wireless Sensor Networks

With the advances in micro-electronics, wireless sensor devices have been made much smaller and more integrated, and large-scale wireless sensor networks (WSNs) based the cooperation among the significant amount of nodes have become a hot topic. “Large-scale” means mainly large area or high density of a network. Accordingly the routing protocols must scale well to the network scope extension and node density increases. A sensor node is normally energy-limited and cannot be recharged, and thus its energy consumption has a quite significant effect on the scalability of the protocol. To the best of our knowledge, currently the mainstream methods to solve the energy problem in large-scale WSNs are the hierarchical routing protocols. In a hierarchical routing protocol, all the nodes are divided into several groups with different assignment levels. The nodes within the high level are responsible for data aggregation and management work, and the low level nodes for sensing their surroundings and collecting information. The hierarchical routing protocols are proved to be more energy-efficient than flat ones in which all the nodes play the same role, especially in terms of the data aggregation and the flooding of the control packets. With focus on the hierarchical structure, in this paper we provide an insight into routing protocols designed specifically for large-scale WSNs. According to the different objectives, the protocols are generally classified based on different criteria such as control overhead reduction, energy consumption mitigation and energy balance. In order to gain a comprehensive understanding of each protocol, we highlight their innovative ideas, describe the underlying principles in detail and analyze their advantages and disadvantages. Moreover a comparison of each routing protocol is conducted to demonstrate the differences between the protocols in terms of message complexity, memory requirements, localization, data aggregation, clustering manner and other metrics. Finally some open issues in routing protocol design in large-scale wireless sensor networks and conclusions are proposed

Routing Protocols for Large-Scale Wireless Sensor Networks: A Review

With the advances in micro-electronics, wireless sensor gadgets have been made substantially littler and more coordinated, and large-scale wireless sensor networks (WSNs) based the participation among the noteworthy measure of nodes have turned into a hotly debated issue. "Large-scale" implies for the most part large region or high thickness of a system. As needs be the routing protocols must scale well to the system scope augmentation and node thickness increments. A sensor node is regularly energy-constrained and can't be energized, and in this manner its energy utilization has a very critical impact on the adaptability of the protocol. To the best of our insight, at present the standard strategies to tackle the energy issue in large-scale WSNs are the various leveled routing protocols. In a progressive routing protocol, every one of the nodes are separated into a few gatherings with various task levels. The nodes inside the abnormal state are in charge of data aggregation and administration work, and the low level nodes for detecting their environment and gathering data. The progressive routing protocols are ended up being more energy-proficient than level ones in which every one of the nodes assume a similar part, particularly as far as the data aggregation and the flooding of the control bundles. With concentrate on the various leveled structure, in this paper we give an understanding into routing protocols planned particularly for large-scale WSNs. As per the distinctive goals, the protocols are by and large ordered in light of various criteria, for example, control overhead decrease, energy utilization mitigation and energy adjust. Keeping in mind the end goal to pick up a thorough comprehension of every protocol, we feature their imaginative thoughts, portray the basic standards in detail and break down their points of interest and hindrances. Also a correlation of each routing protocol is led to exhibit the contrasts between the protocols as far as message unpredictability, memory necessities, localization, data aggregation, bunching way and different measurements. At last some open issues in routing protocol plan in large-scale wireless sensor networks and conclusions are proposed

Routing Protocols for Large-Scale Wireless Sensor Networks: A Review

With the advances in micro-electronics, wireless sensor gadgets have been made substantially littler and more coordinated, and large-scale wireless sensor networks (WSNs) based the participation among the noteworthy measure of nodes have turned into a hotly debated issue. "Large-scale" implies for the most part large region or high thickness of a system. As needs be the routing protocols must scale well to the system scope augmentation and node thickness increments. A sensor node is regularly energy-constrained and can't be energized, and in this manner its energy utilization has a very critical impact on the adaptability of the protocol. To the best of our insight, at present the standard strategies to tackle the energy issue in large-scale WSNs are the various leveled routing protocols. In a progressive routing protocol, every one of the nodes are separated into a few gatherings with various task levels. The nodes inside the abnormal state are in charge of data aggregation and administration work, and the low level nodes for detecting their environment and gathering data. The progressive routing protocols are ended up being more energy-proficient than level ones in which every one of the nodes assume a similar part, particularly as far as the data aggregation and the flooding of the control bundles. With concentrate on the various leveled structure, in this paper we give an understanding into routing protocols planned particularly for large-scale WSNs. As per the distinctive goals, the protocols are by and large ordered in light of various criteria, for example, control overhead decrease, energy utilization mitigation and energy adjust. Keeping in mind the end goal to pick up a thorough comprehension of every protocol, we feature their imaginative thoughts, portray the basic standards in detail and break down their points of interest and hindrances. Also a correlation of each routing protocol is led to exhibit the contrasts between the protocols as far as message unpredictability, memory necessities, localization, data aggregation, bunching way and different measurements. At last some open issues in routing protocol plan in large-scale wireless sensor networks and conclusions are proposed

Energy Efficient Scheme for Wireless Sensor Networks

Recent advances in wireless sensor networks have commanded many new protocols specifically designed for sensor networks where energy awareness is an important concern. This routing protocols might differ from depending on the application and the network architecture. To extend the lifetime of Wireless sensor network (WSN), an energy efficient scheme can be designed and developed via an algorithm to provide reasonable energy consumption and network for WSN. To maintain high scalability and better data aggregation, sensor nodes are often grouped into disjoint, non-overlapping subsets called clusters. Clusters create hierarchical WSNs which incorporate efficient utilization of limited resources of sensor nodes to reduce energy consumption, thus extend the lifetime of WSN. The objective of this paper is to present a state of the art survey and classification of energy efficient schemes for WSNs. Keywords: Wireless Sensor Network, clustering, energy efficient clustering, network lifetime, energy efficient algorithms, energy efficient routing, and sensor networks. DOI: 10.17762/ijritcc2321-8169.15024

Supplementing an AD-HOC Wireless Network Routing Protocol with Radio Frequency Identification (RFID) Tags

Wireless sensor networks (WSNs) have a broad and varied range of applications, yet all of these are limited by the resources available to the sensor nodes that make up the WSN. The most significant resource is energy. A WSN may be deployed to an inhospitable or unreachable area, leaving it with a non-replenishable power source. This research examines a way of reducing energy consumption by augmenting the nodes with radio frequency identification (RFID) tags that contain routing information. It was expected that RFID tags would reduce the network throughput, the ad hoc on-demand distance vector (AODV) routing traffic sent, and the amount of energy consumed. However, the results show that RFID tags have little effect on the network throughput or the AODV routing traffic sent. They also increase ETE delays in sparse networks as well as the amount of energy consumed in both sparse and dense networks. Furthermore, there was no statistical difference in the amount of user data throughput received. The density of the network is shown to have an effect on the variation of the data but the trends are the same for both sparse and dense networks. This counter-intuitive result is explained, and conditions for such a scheme to be effective are discussed

Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies

[[abstract]]Over the last few years, we have witnessed a growing interest in Cyber Physical Systems (CPSs) that rely on a strong synergy between computational and physical components. CPSs are expected to have a tremendous impact on many critical sectors (such as energy, manufacturing, healthcare, transportation, aerospace, etc) of the economy. CPSs have the ability to transform the way human-to-human, human-toobject, and object-to-object interactions take place in the physical and virtual worlds. The increasing pervasiveness of Wireless Sensor Networking (WSN) technologies in many applications make them an important component of emerging CPS designs. We present some of the most important design requirements of CPS architectures. We discuss key sensor network characteristics that can be leveraged in CPS designs. In addition, we also review a few well-known CPS application domains that depend on WSNs in their design architectures and implementations. Finally, we present some of the challenges that still need to be addressed to enable seamless integration of WSN with CPS designs.[[incitationindex]]SCI[[booktype]]紙

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Energy-aware routing protocols in wireless sensor networks

Saving energy and increasing network lifetime are significant challenges in the field of Wireless Sensor Networks (WSNs). Energy-aware routing protocols have been introduced for WSNs to overcome limitations of WSN including limited power resources and difficulties renewing or recharging sensor nodes batteries. Furthermore, the potentially inhospitable environments of sensor locations, in some applications, such as the bottom of the ocean, or inside tornados also have to be considered. ZigBee is one of the latest communication standards designed for WSNs based on the IEEE 802.15.4 standard. The ZigBee standard supports two routing protocols, the Ad hoc On-demand Distance Vector (AODV), and the cluster-tree routing protocols. These protocols are implemented to establish the network, form clusters, and transfer data between the nodes. The AODV and the cluster-tree routing protocols are two of the most efficient routing protocols in terms of reducing the control message overhead, reducing the bandwidth usage in the network, and reducing the power consumption of wireless sensor nodes compared to other routing protocols. However, neither of these protocols considers the energy level or the energy consumption rate of the wireless sensor nodes during the establishment or routing processes. (Continues...)