A Wireless Sensor Network Routing Algorithm Based on Zigbee

WITHDRAWN: An efficient biometric based authenticated geographic opportunistic routing for IoT applications using secure wireless sensor network

Wide usage of wireless sensor network (WSN) is the reason for development of many routing protocols. Recent advances in WSN observe the increased interest in the potential use in applications like Military, Environmental, Health, Vehicular, Mechanical stress levels on attached objects, disaster management etc. Wireless sensor networks are callously restricted by storage capacity, energy and computing power. So it is essential to design an energy efficient aware protocol in order to augment the network lifetime. WSN also has the influencing features to perform remote communication, it is also shrouded with various loopholes in its precise operation, where majority of the communication issues are raised from deployment of existing standards of routing protocols and algorithm. Sensors are expected to be remotely deployed in unnoticed environments. Routing as one key technologies of wireless sensor network has now become very important research work because the applications of WSN is everywhere, it is impractical that there is a routing protocol suitable for all applications. In this paper, a review on routing protocol in WSNs is carried out which are classified as data-centric, hierarchical and location based depending on the network structure. Then some of the multipath routing protocols which are widely used in WSNs like Multipath Multispeed Protocol (MMSPEED), Braided Multipath Routing Protocol, Energy-Aware Routing to improve network performance are also discussed. Massive research work has already been carried out in past to introduce some of the effective routing protocols to enable robust communication system in WSN. Advantages and disadvantages of each routing algorithm are discussed thereafter. This paper also discusses the critical open issues that are yet to be solved. This paper compares and summarizes the performances of routing protocols.

Routing Algorithm determines the optimal route to transmit data packet from source to destination. The nodes in wireless sensor networks (WSN) are devoid of abundant energy and hence energy efficiency is the vital benchmarks for data transmission in WSN. The basic importance of wireless sensor networks (WSN) in present scenario is to reduce the node energy consumption such that it improves the data transmission efficiency and reliability to extend entire network life time. In these respect, we need to search the optimal route for data transmission in WSN. Here an Improved Ant Colony Optimization (IACO) based energy efficient routing algorithm is proposed in WSN to solve the issue. The function of improved Low Energy Adaptive Clustering Hierarchy (LEACH) routing algorithm is to select cluster heads (CH) by considering residual energy and the distance in between the nodes. To balance the energy consumption between the nodes, residual energy is used as an energy control factor, which affects the network life time and enhances the data transmission efficiency. An ACO algorithm is applied to search the optimal path from the various available routes in between CH and sink for data transmission. Simulation results show that the proposed method Improved Ant Colony Optimization based Low Energy Adaptive Clustering Hierarchy (IACOLEACH) is found to yield better results over other conventional algorithms.

A common time reference across nodes is required in most Wireless Sensor Networks (WSNs) applications. Also many routing protocols require that nodes communicate according to some predefined schedule for reasons of energy efficiency. However, independent distribution of the time information, without considering the routing algorithm schedule or network topology, may lead to a failure of the synchronisation protocol. This can be avoided by integrating the synchronisation service into the network layer with a so-called cross-layer approach. We present two novel cross-layer routing algorithms, CLEAR and RISS. We implemented and tested the performance of these solutions in simulations and also deployed these routing techniques on sensor nodes using TinyOS. All proposed schemes extend the network lifetime and due to their lightweight architecture they are very efficient on WSN nodes with constrained resources. Hence, it is recommended that a cross-layer approach should be a feature of any routing algorithm for WSNs.

Resource Management Approach to an Efficient Wireless Sensor Network

The wireless sensor network (WSN), is a type of selforganized distribution system without complex infrastructure requirement. However, WSN?s independence and un-attended usage limit its power supply and life expectancy. These become a critical issue for any WSN implementation. The in-network processing which, intends to minify data volume locally can greatly reduce the energy consumption of data delivery over a long distance to the sink. However, open problems are still remain with in-network processing, such as how to carry out in-network processing, and how to combine routing scheme to the sink (e.g. the long distance delivery). For any WSN application, the following preassumption is vital. There exists a physical signal field which bridges physical events to the sensors of WSN, otherwise WSN does not work. In fact, the physical signal field can be depicted by sensory data of all the nodes, and can even be used for data local convergence (e.g. innetwork processing). An immediate solution which we propose is that, sensory data of the nodes can be converged to local extremes of the physical signal field along its gradient direction. Furthermore, the physical signal field is integrated with the cost field routing scheme [2, 3]. The cost field routing is in charge of delivering data to the sink. During the in-network processing, the delivery direction is derived by combining linearly the gradient directions of the cost field and physical signal field. Also simulation results show that the proposed schemes are robust, adaptable, and reliable to the variation of physical events.

Mobile security is one of the most fundamental problems in Wireless Sensor Networks (WSNs). The data transmission path will be compromised for some disabled nodes. To construct a secure and reliable network, designing an adaptive route strategy which optimizes energy consumption and network lifetime of the aggregation cost is of great importance. In this paper, we address the reliable data aggregation route problem for WSNs. Firstly, to ensure nodes work properly, we propose a data aggregation route algorithm which improves the energy efficiency in the WSN. The construction process achieved through discrete particle swarm optimization (DPSO) saves node energy costs. Then, to balance the network load and establish a reliable network, an adaptive route algorithm with the minimal energy and the maximum lifetime is proposed. Since it is a non-linear constrained multi-objective optimization problem, in this paper we propose a DPSO with the multi-objective fitness function combined with the phenotype sharing function and penalty function to find available routes. Experimental results show that compared with other tree routing algorithms our algorithm can effectively reduce energy consumption and trade off energy consumption and network lifetime.

Energy conservation is crucial in Wireless Sensor Networks (WSNs) for prolonging sensor node's life. This research attempts to gain benefits of Bayesian classifier through development of a Bayesian Classifier-Based Energy Aware Routing algorithm (BBEAR) for mobile WSNs. Through implementing a Bayesian classifier for routing process, a superior routing algorithm is designed with an intelligent node message rebroadcast decision. This paper discusses the proposed algorithm design and presents a comparison of its performance with flooding technique. BBEAR workflow is described, along with its classifier mechanism that allows the message routing judgment by each sensor node to be based on several node's and message's classifying attributes. In contrast to other routing schemes developed for WSN, BBEAR algorithm incorporates several node attributes, including: remaining energy that the nodes have, distance to the sink, number of messages being queued, node's success rate, delay of the received message, as well as the number of message duplicates received by the node. The BBEAR performance is also discussed and evaluated in terms of various measures such as: throughput, number of duplicates received, average delay, and the lifetime of the nodes. The simulation study of the proposed algorithm shows its superiority over flooding: as it provides 15% gain in message throughput, four times reduction of duplicates, and gives an extension of 60% nodes' lifetime while still maintaining the delay and delay jitter at low levels.

Energy conservation and fault tolerance are the most two important challenging issues for the development of large scale wireless sensor networks (WSNs). Failure of cluster heads in a cluster based WSN is more catastrophic as they are responsible not only for data aggregation and transmission of the aggregated data to the base station but also relaying data for multi-hop communication. Therefore, a routing algorithm in WSNs should be energy aware as well as fault tolerant. In this paper, we present a distributed energy efficient and fault tolerant routing algorithm for WSNs. The algorithm selects next-hop cluster head in energy efficient manner in the data routing phase and carefully restores the connectivity of the neighbours of a cluster head in case of its failure. The algorithm is tested extensively by considering several scenarios of WSN. The experimental results are compared with the existing algorithms in terms of several metrics to show the effectiveness of the proposed algorithm.

In order to reduce or avoid the coal mine accidents, this paper proposes the application of wireless sensor networks (WSN) in the coal mine safety intelligent monitoring system, and discusses the principle and advantages of wireless sensor networks. According to the current situation of low level of intellectualization in coal mine safety monitoring system and the existing problems, this paper proposes the designing proposal and the monitoring mechanism of the coal mine safety intelligent monitoring system, discusses the feasibility, the method and the way of the application of the wireless sensor networks in the coal mine safety intelligent monitoring system.

Wireless sensor networks consist of a large number of low-cost, low-power, and multifunctional sensor nodes that communicate over short distances through wireless links. Such sensor networks are ideal candidates for a wide range of applications such as monitoring of critical infrastructures, data acquisition in hazardous environments, industry control systems, vehicular networks, and military operations. Wireless sensor networks introduce new security challenges due to their dynamic topology, severe resource constraints, and absence of a trusted infrastructure. The purpose of this special issue is to publish high-quality research papers as well as review articles addressing recent advances of wireless sensor network security. In this special issue, we will explore the topic of security challenges and solutions for the wide application of wireless sensor networks in different areas, such as RFID and smart grid. RFID has been widely used in logistics and internet of things. Smart grid leverages sensor networks to better balance the load of the power grid. Both of them have great impact on the daily life and areweak points of thewhole economy,which attract the attention of terrorists. The feasible attacks and defense solutions are still not clear.The papers in this volume also cover several important foundational security services for wireless sensor networks, such as key management, key distribution, secure cluster formation, secure architecture for multihop communication, intrusion prediction, and secure localization. All these foundational security services are critical bases for application of wireless sensor networks. We hope that the papers in this volume engender further thinking about new security challenges for wireless sensor networks and even further “out-of-the-box” ideas about how to solve new security problems for wireless sensor networks.

Timeliness in query response is the major quality metric for query processing in the real-time applications of Wireless Sensor Networks (WSNs). The structure of the query routing tree directly affects the whole query processing delay as it provides the path to forward a query to the relevant nodes and return the response to the sink. In the current literature, query routing structure is designed irrespective of the variation in query loads among the sensors. As a consequence, current schemes do not guarantee for the routing tree to provide a faster path to the sensors with higher query load. This motivates the current work to consider query load in constructing and self-reconfiguring the routing tree. In this paper, we present a query load-based spanning tree construction method that reduces the query response delay as well as energy consumption in query execution and provides query response with the best possible accuracy. Simulation results illustrate the efficacy of the proposed framework.

Developing Wireless Sensor Networks (WSN) applications is not an easy task, since it demands different expertise and knowledge. To address this problem, we propose a model-driven approach to build WSN applications. It promotes the separation of concerns between two levels of requirements involved: (i) domain experts, with no knowledge on WSN platforms, contribute to the development of applications, (ii) network experts, with no knowledge on the application domain, program nodes to meet application requirements. Our approach also promotes the reuse of software artifacts: an application model can be reused on different sensor platforms and a platform model can be reused for different WSN applications.

Now-a-days the power generation sector is focussing more attention towards using renewable energy resource e.g. wind power. However the reliable operation of off-shore located wind farms is one of the major requirements to ensure consistent power supply to the grid. This can be ensured by using suitable real-time automated and remote structural health monitoring of the wind farm. This is one of the major applications of wireless sensor networks (WSN). This paper is an extension to our work in which we proposed an application-specific clustering and routing algorithm (NETCRP) for increasing the networklifetime. This paper presents a novel loss-less packet-size reduction algorithm using flexible-thresholds for real-time fault detection in wind farms. The method utilizes the correlation between data samples collected at different times of the day for selecting the suitable thresholds. The method is compared with the primitive Mean-method which computes only one threshold for all data samples independent of the day and time of sample collection. This method then uses suitable fault detection methods using combination-summation and flow-direction of received data. The proposed method FTSFD (Flexible Threshold Selection and Fault Detection) provides two benefits — firstly, it is a simple method by which a remote observer can easily understand and predict real-time fault occurrences in the tower, and, secondly, it also helps in reducing the message size considerably and helps in increasing the overall network-lifetime of the system. The simulation results confirm that the proposed method has better fault-detection accuracy over the existing method and the overall network lifetime of the WSN in wind farm is increased by nearly ten times as compared to previous methods.

With development of Internet of Things, wireless sensor networks (WSN) become larger and larger and if the present clustering and routing methods are widely used in large scale WSN, “energy hole” problem will deteriorate. To alleviate “energy hole” problem of WSN, a dynamic broadcast radius-based clustering and routing algorithm (DBRCR) is proposed. The algorithm calculates node broadcast radius every round, and based on the broadcast radius the neighbor table is established. Then according to the degrees of nodes, construct clusters iteratively. Finally, finding intra-cluster communication path based on data transmission success rate and routing for the inter-cluster communication based on nodes' residual energy and transmission energy consumption to minimize the data packet-loss rate and prolong the network lifetime.