A Comparison Analysis of Fault Detection Algorithms in Wireless Sensor Networks

Sensor nodes are small and low-cost portable devices that are connected wirelessly to form a wireless sensor network. Sensor nodes are typically powered by a chemical battery source that has a load-dependent finite lifetime. Most applications require wireless sensor networks to operate reliably for an extended period of time beginning with their deployment. To ensure both the longevity of a wireless sensor network via power management techniques and development of a self-powered sensor node, it is important to understand a sensor node’s operational energy consumption. It is well understood that wireless sensor network lifetime has a strong functional dependence on sensor node lifetime. This paper presents the development of a stochastic model to capture the expected energy consumption of a schedule-driven sensor node per cycle of operation by utilizing renewal theory, random stopping criteria, and Wald’s inequality. The model captures the expected energy consumed due to generic operation of a schedule-driven sensor node when an external event occurs (i.e., node level activities) and energy consumed due to carrier sense multiple access/collision avoidance medium access control protocol (i.e., retransmission attempts, channel listening, and other network level activities). The stochastic energy model is used to compute the expected lifetime and cycle lifetime of the sensor node. This paper also computes the deterministic energy bounds corresponding to the minimum and maximum energy consumption of a sensor node per cycle of operation. Analytical and numerical model validations are performed and presented.

In the era of New Generation Networks (NWGN) many emerging and future enable technologies accomplish the requirements of ubiquitous communication networks. Mobile wireless sensor network (mWSN) is one of the rising and emerging technologies for various application of NWGN, the enormous concerns of these networks are data gathering with energy efficiency. Routing protocol act as middleware, which is responsible to enhance the network performance with less energy consumption. In this paper we propose, analyze and validate mobile data collector (MDC) based LEACH routing protocol for environmental application which is based on multi-hop routing strategy. MDC based LEACH routing protocol utilizes self-organized sensor nodes with distributed cluster formation technique, randomly selection of cluster heads to equally balance the energy consumption among the sensor nodes and finally forward the data to base station by the support of MDC. Moreover, our approach shows the significant improvement in terms of energy consumption of sensor nodes, enhance network lifetime and data gathering at base station are outperform when compared with LEACH protocol. Key words: Mobile wireless sensor networks, wireless sensor networks, network protocol, cluster based routing, data gathering technique.

Wireless sensor networks (WSNs) have become an important part of our lives as they can be used in vast application areas from disaster relief to health care. As a consequence, the life span and the energy consumption of a WSN has become a challenging research area. According to the existing studies, instead of using direct transmission or multi-hop routing, clustering can significantly reduce the energy consumption of sensor nodes and can prolong the lifetime of a WSN. In this paper, we propose a low energy fixed clustering algorithm (LEFCA) for WSNs. With LEFCA, the clusters are constructed during the set-up phase. A sensor node which becomes a member of a cluster stays in the same cluster throughout the life span of the network. LEFCA not only improves the lifetime of the network, but also decreases the energy dissipation and increases the throughput significantly.

In wireless sensor networks, MAC protocol is used to achieve efficient, fair and balanced allocation of wireless channel resources and access control of nodes in the network, and to control the communication process of nodes in the network. The energy consumption of wireless sensor nodes is mainly concentrated in the communication unit, including: data transceiver, idle listening, protocol control overhead, etc. A good MAC protocol is beneficial to reduce unnecessary energy consumption and enhance node life time. At the same time, MAC protocol, as the bottom protocol of wireless sensor network, provides a stable and reliable communication foundation for the realization of the upper protocol. Therefore, a large number of scholars at home and abroad have carried out extensive and in-depth research on MAC layer protocol of network. In this paper, the research process and progress of MAC layer protocol in wireless sensor networks are analyzed, and the research progress of MAC layer protocol in multi-radio frequency and multi-channel wireless sensor networks is analyzed.

The mismatch of task scheduling results in rapid network energy consumption during data transmission in wireless sensor networks. To address this issue, the paper proposed an Energy-consumption Optimization-oriented Task Scheduling Algorithm (EOTS algorithm) which formally described the overall power dissipation in the network system. On this basis, a network model was built up such that both the idle energy consumption in sensor nodes and energy consumption during the execution of tasks were taken into account, with which the whole task was effectively decomposed into sub-task sequences. They underwent simulated annealing and iterative refinement, with the intention of improving sensor nodes’ utilization rate, reducing local idle energy cost, as well as cutting down the overall energy consumption accordingly. The experiment result shows that under the environment of multi-task operation, from the perspective of energy cost optimization, the proposed scheduling strategy recorded an increase of 21.24% compared with the FIFO algorithm, and an increase of 16.77% in comparison to the EMRSA algorithm; while in light of network lifetimes, the EOTS algorithm surpassed the ECTA algorithm by a gain of 19.21%. Therefore, the effectiveness of the proposed EOTS algorithm is verified.

Wireless sensor networks (WSNs) have become an important part of our lives as they can be used in vast application areas from disaster relief to health care. As a consequence, the life span and the energy consumption of a WSN have become a challenging research area. According to the existing studies, instead of using direct transmission or multihop routing, clustering can significantly reduce the energy consumption of sensor nodes and can prolong the lifetime of a WSN. In this paper, we propose a low energy fixed clustering algorithm (LEFCA) for WSNs. With LEFCA, the clusters are constructed during the set-up phase. A sensor node which becomes a member of a cluster stays in the same cluster throughout the life span of the network. LEFCA not only improves the lifetime of the network but also decreases the energy dissipation significantly.

Internet Protocol version 6 (IPv6) over Low-power Personal Area Network (6LoWPAN) is proposed by the Internet Engineering Task Force (IETF) working group to accomplish the concept of IP-based Wireless Sensor Network (WSN). A new layer is incorporated between IPv6 network layer and 802.15.4 MAC layer, which is entitled adaptation layer. Header compression, packet fragmentation, and layer two forwarding are the main tasks of the adaptation layer. An IPv6 packet is too big regarding to the Maximum Transmission Unit (MTU) size of 802.15.4 standard thus it has to be fragmented. Breaking up a big packet to number of small fragments and attaching new headers to each fragment may affect the energy level. In this paper, we place an attention on the impact of the adaptation layer on the energy consumption of a 6LoWPAN sensor node. From our analysis, we found that the adaptation layer, in particular the fragmentation process may increase the energy consumption of a sensor node by 5 to 10 percent.

Wireless sensor networks (WSN) are commonly formed by distributed sensor nodes equipped with wireless transmitter with limited bandwidth, computation power and battery sources. WSN are generally developed for wild life, military or environmental monitoring etc. These facts have highlighted challenges to minimize energy consumption of sensor nodes while still maintaining the goal of WSN developments. In this paper, an integration of cluster-based and position based routing protocols for multi-hop scenarios is proposed. The self-organized clusters are formed periodically where decision to become cluster head (CH) is performed individually by each node. Direct long communication between selected CH to sink node is avoided to preserve energy and minimize impact of overhearing transmission. Accordingly, a multi-hop communication scheme which adopts position based routing is applied. The proposed approach has been developed and tested using wireless simulation framework. It shows that the proposed approach can increase remaining battery energy, increase packet delivery ratio and reduce the number of dead nodes in various simulation settings.

The ability of wireless sensor network (WSN) can be observed in different fields like patient wearable monitoring, health applications, intruder detection, combat monitoring and battlefield surveillance in the last few years. In such applications, WSN contains a wide range of sensor nodes, which will monitor continuously, and passes the data to end-user through base station (BS) or sink node (SN). In such applications where users are unattended to replace the sensor node frequently, that indicates users can expect long-lasting of WSN. So in this paper, we concentrated on long-lasting of WSN, by minimizing the energy utilization of sensor node in WSN. So we are applying the clustering technique. So, clustering will minimize the energy usage of the sensor node, such that it will improve the lifetime of WSN. In this, we introduced our proposed protocol “Low Energy Utilization with Dynamic Cluster Head (LEU-DCH)” which reduces the energy consumption of sensor nodes. Besides, we also have shown the best way of selecting how many clusters we need.

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

A wireless sensor network (WSN) is consisting of a set of sensor nodes with a limited energy stored in their batteries. Generally, replacing or charging the battery is hard and inefficient. Further, the main critical aspect of applications based on wireless sensor networks is their lifetime. Therefore, judicious power management with optimized routing protocols can effectively optimize the energy consumption of sensor nodes and thus extend the network lifetime. In this paper, an optimized routing protocol for wireless sensor nodes is proposed. We are interested in constructing an efficient routing spanning tree that minimizes the energy consumption among all nodes in the network and fit for WSN with reduced energy for achieving a longer lifetime. The main idea of this algorithm comes from the Minimum Spanning Tree (MST) graph theory. This approach focuses on the minimal hop count of each node to reach the destination (sink node) within an optimal path.

Wireless sensor networks (WSNs) consist of many sensor nodes that are densely deployed throughout a randomized geographical area to monitor, detect, and analyze various physical phenomena. The primary obstacle encountered in WSNs pertains to the significant reliance of sensor nodes on finite battery power for wireless data transfer. Sensors as a crucial element inside Cyber-Physical Systems (CPS) renders them vulnerable to failures arising from intricate surroundings, substandard manufacturing, and the passage of time. Various anomalies can appear within WSNs, mostly attributed to defects such as hardware and software malfunctions and anomalies and assaults initiated by unauthorized individuals. These anomalies significantly impact the overall integrity and completeness of the data gathered by the networks. Therefore, it is imperative to provide a critical mechanism for the early detection of faults, even in the presence of constraints imposed by the sensor nodes. Machine Learning (ML) techniques encompass a range of approaches that may be employed to identify and diagnose sensor node faults inside a network. This paper presents a novel Energy-aware and Context-aware fault detection framework (ECFDF) that utilizes the Extra-Trees algorithm (ETA) for fault detection in WSNs. To assess the effectiveness of the suggested methodology for identifying context-aware faults (CAF), a simulated WSN scenario is created. This scenario consists of data from humidity and temperature sensors and is designed to emulate severe low-intensity problems. This study examines six often-seen categories of sensor fault, including drift, hard-over/bias, spike, erratic/precision, stuck, and data loss. The ECFDF approach utilizes an Energy-Efficient Fuzzy Logic Adaptive Clustering Hierarchy (EE-FLACH) algorithm to select a Super Cluster Head (SCH) within WSNs. The SCH is responsible for achieving optimal energy consumption within the network, and this selection process facilitates the early detection of faults. The results of the simulation indicate that the ECFDF technique has superior performance in terms of Fault Detection Accuracy (FDA), False-Positive Rate (FPR), and Mean Residual Energy (MRE) when compared to other detection and classification methods.

The rapid development of the agricultural Internet of Things (IoT) is inseparable from the support of wireless sensor networks (WSNs) in recent years. To further facilitate the adaptation of WSNs to agricultural applications, reducing the energy consumption of sensor nodes in agricultural environments has become a crucial problem. Sleep scheduling in randomly deployed WSNs is an effective method to reduce the energy consumption of sensor nodes, which can extend the network lifetime while ensuring network coverage. However, most of the existing sleep scheduling algorithms require frequent information exchange (such as broadcasting to find neighboring nodes and collecting nodes’ residual energy), which will inevitably lead to massive energy consumption. To address this problem, in this article, an unmanned aerial vehicle (UAV)-assisted sleep scheduling algorithm (UAVSS) is proposed, which avoids excessive information exchange among nodes and ensures sufficient network coverage with the least number of sensor nodes. The UAV traverses all the sensor nodes along the shortest path to help information exchange and gather sensing data. Furthermore, the UAV path is planned by using the latest monarch butterfly optimization (MBO) algorithm. Simulation results indicate that the proposed UAVSS can prolong the network lifetime while ensuring the required area coverage.

Wireless Sensor Networks (WSNs) are designed by hundreds or thousands of tiny, low cost and multifunctional sensor nodes. Each sensor node has very low battery life. Sensor nodes have finite storage capabilities, transmission and processing range and energy resources are also limited. There are many design issues in WSNs such as mobility, energy consumption, network topology, data aggregation, localization, production cost, security, network size and density etc. Routing protocols provide efficient working of the network, increase network lifetime, responsible for maintaining the routes in the network and perform reliable multi-hop communication under various conditions. LEACH (Low Energy Adaptive Clustering Protocol) is one of the hierarchical protocol in WSNs. LEACH uses TDMA MAC Protocol. During random data distribution, a number of TDMA slots are wasted. Because sensor nodes don't know either they have data to send or not, they continuously listen to the medium and this result in idle listening problem. This paper proposed OE-LEACH (An Optimized Energy Efficient LEACH Algorithm for WSNs) to enhance the performance of the LEACH Protocol, reduce time delay and energy consumption. Network Lifetime and throughput of WSN also increases. The Proposed method is simulated in MATLAB 2010a.

The energy efficiency of the Wireless Sensor Network (WSN) deployed in a Smart Grid facility is a key criterion for the performance of a WSN integrated supporting system. Since small form factor sensors used in the Smart Grid have limited battery capacity, the energy saving for sensor nodes is a major design goal for WSN protocols. In the past, our strategy is to install a large number of core nodes with backhaul connections to ensure the reliable connectivity for low power sensor nodes. This strategy leads to a high overall cost for WSN deployment. In this paper, we propose a lightweight mesh networking solution to reduce the WSN core network deployment cost while keeping the same level of energy consumption for sensor nodes. We have developed a prototype system in our lab to demonstrate the feasibility of our proposed mesh solution using BLE chipsets. Comparing with the BLE-Mesh solution, our solution can save 40-70% energy for low power sensors and increase the channel capacity between 100-200% depending on the size of sensor data. (Abstract)