Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

The fault diagnosis in wireless sensor networks is one of the most important topics in the recent years of research work. The problem of fault diagnosis in wireless sensor network can be resembled with artificial immune system in many different ways. In this paper, a detection algorithm has been proposed to identify faulty sensor nodes using clonal selection principle of artificial immune system, and then the faults are classified into permanent, intermittent, and transient fault using the probabilistic neural network approach. After the actual fault status is detected, the faulty nodes are isolated in the isolation phase. The performance metrics such as detection accuracy, false alarm rate, false‐positive rate, fault classification accuracy, false classification rate, diagnosis latency, and energy consumption are used to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm gives superior results as compared with existing algorithms in terms of the performance metrics. The fault classification performance is measured by fault classification accuracy and false classification rate. It has also seen that the proposed algorithm provides less diagnosis latency and consumes less energy than that of the existing algorithms proposed by Mohapatra et al, Panda et al, and Elhadef et al for wireless sensor network.     

Energy Attentive and Pre-fault Recognize Mechanism for Distributed Wireless Sensor Network Using Fuzzy Logic Approach

Wireless sensor networks (WSNs) have been transforming over recent years with development in the design of smart real-time applications. However, it presents numerous challenges in terms of fault-tolerant communication, low latency, scalability, and transmission efficiency. It is extremely difficult for WSNs to detect runtime faults since they're unaware of the internal processes at work within the sensor node. As a result, valuable sensed information cannot reach its destination and performance starts degrading. Towards this objective, the proposed mechanism applies a novel pre-fault detection mechanism based on a fuzzy rule-based method for multilevel transmission in distributed sensor networks. The proposed mechanism uses a fuzzy rule set to make routing decisions. A fuzzy decision rule set is proposed to perform routing based on the fuzzy fault count status of a node. The proposed mechanism assists in identifying the fault in advance and determining the optimal routing path to save energy and improve network performance. In accordance with the node fault status, the data transmission rate is finalized to prevent further energy consumption. The results demonstrated that the proposed mechanism performed well on judgment evaluation metrics like the energy dissipation ratio, throughput, packet loss rate and communication delay.

     

Clustering Based Two Dimensional Motion of Sink Node in Wireless Sensor Networks

The wireless sensor network (WSN) is always known for its limited-energy issues and finding a good solution for energy minimization in WSNs is still a concern for researchers. Implementing mobility to the sink node is used widely for energy conservation or minimization in WSNs which reduces the distance between sink and communicating nodes. In this paper, with the intention to conserve energy from the sensor nodes, we designed a clustering based routing protocol implementing a mobile sink called ‘two dimensional motion of sink node (TDMS)’. In TDMS, each normal sensor node collects data and send it to their respective leader node called cluster head (CH). The sink moves in the two dimensional direction to collect final data from all CH nodes, particularly it moves in the direction to that CH which has the minimum remaining energy. The proposed protocol is validated through rigorous simulation using MATLAB and comparisons have been made with WSN’s existing static sink and mobile sink routing protocols over two different geographical square dimensions of the network. Here, we found that TDMS model gives the optimal result on energy dissipation per round and increased network lifetime.

     

A Game Theory Based Multi Layered Intrusion Detection Framework for Wireless Sensor Networks

Most of the existing intrusion detection frameworks proposed for wireless sensor networks (WSNs) are computation and energy intensive, which adversely affect the overall lifetime of the WSNs. In addition, some of these frameworks generate a significant volume of IDS traffic, which can cause congestion in bandwidth constrained WSNs. In this paper, we aim to address these issues by proposing a game theory based multi layered intrusion detection framework for WSNs. The proposed framework uses a combination of specification rules and a lightweight neural network based anomaly detection module to identify the malicious sensor nodes. Additionally, the framework models the interaction between the IDS and the sensor node being monitored as a two player non-cooperative Bayesian game. This allows the IDS to adopt probabilistic monitoring strategies based on the Bayesian Nash Equilibrium of the game and thereby, reduce the volume of IDS traffic introduced into the sensor network. The framework also proposes two different reputation update and expulsion mechanisms to enforce cooperation and discourage malicious behavior among monitoring nodes. These mechanisms are based on two different methodologies namely, Shapley Value and Vickery–Clark–Grooves (VCG) mechanism. The complexity analysis of the proposed reputation update and expulsion mechanisms have been carried out and are shown to be linear in terms of the input sizes of the mechanisms. Simulation results show that the proposed framework achieves higher accuracy and detection rate across wide range of attacks, while at the same time minimizes the overall energy consumption and volume of IDS traffic in the WSN.     

A Cognitive Energy Efficient and Trusted Routing Model for the Security of Wireless Sensor Networks: CEMT

There are many smart applications evolved in the area of the wireless sensor networks. The applications of WSNs are exponentially increasing every year which creates a lot of security challenges that need to be addressed to safeguard the devices in WSN. Due to the dynamic characteristics of these resource constrained devices in WSN, there must be high level security requirements to be considered to create a high secure environments. This paper presents an efficient multi attribute based routing algorithm to provide secure routing of information for WSNs. The work proposed in this paper can decrease the energy and enhances the performance of the network than the currently available routing algorithm such as multi-attribute pheromone ant secure routing algorithm based on reputation value and ant-colony optimization algorithm. The proposed work secures the network environment with the improved detection techniques based on nodes’ higher coincidence rates to find the malicious behavior using trust calculation algorithm. This algorithm uses some QoS parameters such as reliability rate, elapsed time to detect impersonation attacks, and stability rate for trust related attacks, to perform an efficient trust calculation of the nodes in communication. The outcome of the simulation show that the proposed method enhances the performance of the network with the improved detection rate and secure routing service.

     

Intrusion Detection in Mobile Sensor Networks: A Case Study for Different Intrusion Paths

Optimization of energy consumption in the batteries of a sensor node plays an essential role in wireless Sensor networks (WSNs). The longevity of sensor nodes depends on efficiency of energy utilization in batteries. Energy is consumed by sensor nodes in WSNs to perform three significant functions namely data sensing, transmitting and relaying. The battery energy in WSNs depletes mainly due to sampling rate and transmission rate. In the present work, the most important parameters affecting the longevity of network are indentified by modeling the energy consumption. The parameters are expressed as a fuzzy membership function of variables affecting the life time of network. Fuzzy logic is used at multiple levels to optimize the parameters. Network simulator-2 is used for experimentation purpose. The proposed work is also compared with the existing routing protocols like Enhanced Low Duty Cycle, Threshold Sensitive Energy Efficient Sensor Network and Distributed Energy Efficient Adaptive Clustering Protocol with Data Gathering. The proposed solution is found to be more energy efficient and hence ensures longer network lifetime.

     

A Novel QoS Based Secure Unequal Clustering Protocol with Intrusion Detection System in Wireless Sensor Networks

Wireless sensor network (WSN) becomes a hot research topic owing to its application in different fields. Minimizing the energy dissipation, maximizing the network lifetime, and security are considered as the major quality of service (QoS) factors in the design of WSN. Clustering is a commonly employed energy-efficient technique; however, it results in a hot spot issue. This paper develops a novel secure unequal clustering protocol with intrusion detection technique to achieve QoS parameters like energy, lifetime, and security. Initially, the proposed model uses adaptive neuro fuzzy based clustering technique to select the tentative cluster heads (TCHs) using three input parameters such as residual energy, distance to base station (BS), and distance to neighbors. Then, the TCHs compete for final CHs and the optimal CHs are selected using the deer hunting optimization (DHO) algorithm. The DHO based clustering technique derives a fitness function using residual energy, distance to BS, node degree, node centrality, and link quality. To further improve the performance of the proposed method, the cluster maintenance phase is utilized for load balancing. Finally, to achieve security in cluster based WSN, an effective intrusion detection system using a deep belief network is executed on the CHs to identify the presence of intruders in the network. An extensive set of experiments were performed to ensure the superior performance of the proposed method interms of energy efficiency, network lifetime, packet delivery ratio, average delay, and intrusion detection rate.

     

Adaptive Load-Aware Congestion Control Protocol for Wireless Sensor Networks

Congestion control in wireless sensor networks (WSNs) is crucial. In this article, we discuss congestion control and the adaptive load-aware problem for sensor nodes in WSNs. When the traffic load of a specific node exceeds its the available capacity of the node, a congestion problem occurs because of buffer memory overflow. Congestion may cause serious problems such as packet loss, the consumption of power, and low network throughput for sensor nodes. To address these problems, we propose a distributed congestion control protocol called adaptive load-aware congestion control protocol (ALACCP). The protocol can adaptively allocate the appropriate forwarding rate for jammed sensor nodes to mitigate the congestion load. Through the buffer management mechanism, the congestion index of neighboring sensor nodes, and an adjustment of the adaptive forwarding rate, the degree of congestion is alleviated markedly. The performance in allocating the forwarding rate effectively to neighboring sensor nodes also improves. The ALACCP can avoid packet loss because of traffic congestion, reduce the power consumption of nodes, and improve the network throughput. Simulation results revealed that the proposed ALACCP can effectively improve network performance and maintain the fairness of networks.     

Detection of Replica Node Attack Based on Exponential Moving Average Model in Wireless Sensor Networks

Due to the broadcast nature of wireless communication, wireless sensor networks (WSNs) are susceptible to several attacks. Amongst them, replica attack is one of the predominates as it facilitates the attackers to perform some other attacks. So, it is of immense significance to design a competent security method for WSNs. Introducing a trust method is the primary concern for assisting well-organized use of the available energy in each node in the energy restricted environment. In order to tradeoff between energy usage and attack detection, energy-based prediction approach is deemed to be a suitable one. A statistical method, exponential moving average (EMA) model based replica detection is proposed to detect replica node attack based on energy consumption threshold in WSNs. The difference between actual and predicted energy consumption exceeding the threshold level is considered as malicious. In this paper, future energy drop of a sensor node is forecasted using statistical measure instead of probabilistic method. In EMA model, the transition from higher power consuming state (active state) to lower power consuming states (sleep and sense states) is controlled by a fixed schedule. The accumulated average time of the node was in any state in the past is used to estimate the time duration of a node that spends in that state. Unlike Markov Model, the estimations of energy are made periodically. By this, computational overhead on the microcontroller of the sensor is greatly reduced in EMA approach. The simulation results taken using TRM simulator shows that choosing the threshold value which is neither too large nor too small results in optimum level of detection accuracy and lifetime of the network.

     

3HA: Hybrid Hole Healing Algorithm in a Wireless Sensor Networks

In wireless sensor networks (WSNs), the appearance of coverage holes over a large target field is mostly possible. Those holes reduce network performance and may affect the network efficiency. Several approaches were proposed to heal coverage holes in WSNs, but they still suffer from some weaknesses. In this paper we suggest a distributed algorithm, named hybrid hole healing algorithm (3HA), to find the minimum effective patching positions to deploy additional nodes to cover the holes. A hole manager node of each hole is responsible for operating the 3HA algorithm which requires two phases. The first phase finds all candidate patching positions using a Voronoi diagram. It takes all Voronoi vertices within the hole as the initial patching positions list. The second phase reduces as much as possible this list based on integer linear programming and on a probabilistic sensor model. The 3HA algorithm repeats the above phases in rounds, until all Voronoi vertices are covered. Simulation results show that our solution offers a high coverage ratio for various forms and sizes of holes and reduces the number of additional sensors when compared to some algorithms like the Perimeter-based, the Delaunay triangulation-based, the Voronoi-based, and the Trees-based coverage hole healing methods.

     

面向物联网的无线传感器网络综述

在分析无线传感器网络国内外研究现状及技术成熟度的基础上,从技术层面阐述了无线传感器网络与物联网之间的相互关系,总结了无线传感器网络系统执行所需要的信息采集系统设计、网络服务支持和网络通信协议设计等关键技术,说明了无线传感器网络未来发展所面临的挑战,并提出了面向物联网的无线传感器网络发展新思路。     

EARP: An Enhanced ACO-Based Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks

A plenty of Ant Colony Optimization (ACO)-based routing algorithms have been proposed to find optimal path of mobile sinks in Wireless Sensor Networks (WSNs). However, they concentrate on energy efficiency and ignore fault tolerance for critical data collection points like Cluster Heads (CHs). They supposed an ideal scenario where there are no failures which is not the case in reality due to failures resulting from unattended and hostile deployment environments and so on. Moreover, the existing routing protocols are not application-specific enabled (i.e., the parameters cannot be adapted to the application’s requirements). In this paper, we propose an energetically-optimized multi-sink-based clustered WSN model along with a fault-tolerant and energy-efficient Enhanced ACO based Routing Protocol (EARP) to provide reliable data transmission in case of encountering a faulty path. Unlike existing studies, EARP addresses jointly the different constraints of forest fires detection application like fault tolerance, network lifetime and response time. The proposed EARP is simulated along with its counterparts in a general scenario based on various main metrics and also in an application-specific scenario (forest fires detection) based on network lifetime and response time. The simulations results prove its superiority, compared to its peers, in both scenarios.

     

A Detailed Review of Multi-Channel Medium Access Control Protocols for Wireless Sensor Networks

Most applications of Wireless Sensor Networks (WSNs) assume the presence of single-channel Medium Access Control (MAC) protocols. In the usual dense deployment of the sensor networks, single-channel MAC protocols may be deficient because of radio collisions and limited bandwidth. Hence, using multiple channels can significantly improve the performance of WSN. Recent developments in sensor technology, as seen in Crossbow’s MICAz Mote, Rockwell’s WINS nodes and IEEE 802.15.4 Zigbee, have enabled support for multi-channel communications. Several multi-channel MAC protocols with different objectives have been proposed for WSNs in literature. This paper surveys and classifies the state-of-the-art multi-channel MAC protocols that are proposed for WSNs. It first outlines the sensor network properties that are crucial for designing a MAC protocol. It subsequently reviews the existent challenges to design a good multi-channel MAC protocol for the sensor networks. Then, several multi-channel MAC protocols specifically proposed for the WSNs are inspected in detail and compared with each other. Finally, some open issues in this area are outlined for future research.     

PRIN: A Priority-Based Energy Efficient MAC Protocol for Wireless Sensor Networks Varying the Sample Inter-Arrival Time

Wireless sensor networks (WSNs) are used in a variety of applications to sense and transfer information to the centralized node with energy efficiency increasing the network’s lifespan. Other factors, such as quality of service (QoS) is also important to improve the performance of the WSNs, by increasing throughput and reducing end-to-end delay. In this paper, we evaluate the importance of QoS in the Medium Access Control (MAC) protocol for WSNs using different metrics and parameters such as energy efficiency, throughput, delay, and the network lifespan. We propose a new QoS MAC protocol, “PRIority in Node” (PRIN), using static priority in the source and the intermediate node and priority among the node which is one hop from the sink node to achieve QoS in WSNs. Simulation results are compared with those of the synchronous MAC protocol in terms of QoS parameters to show the improved performance of the proposed MAC protocol.     

A Robust Mutual Authentication Protocol for Wireless Sensor Networks

Authentication is an important service in wireless sensor networks (WSNs) for an unattended environment. Recently, Das proposed a hash‐based authentication protocol for WSNs, which provides more security against the masquerade, stolen‐verifier, replay, and guessing attacks and avoids the threat which comes with having many logged‐in users with the same login‐id. In this paper, we point out one security weakness of Das' protocol in mutual authentication for WSN's preservation between users, gateway‐node, and sensor nodes. To remedy the problem, this paper provides a secrecy improvement over Das' protocol to ensure that a legal user can exercise a WSN in an insecure environment. Furthermore, by presenting the comparisons of security, computation and communication costs, and performances with the related protocols, the proposed protocol is shown to be suitable for higher security WSNs.     

A Supervised Learning Based Decision Support System for Multi-Sensor Healthcare Data from Wireless Body Sensor Networks

Wireless body sensor network (WBSN) is also known as wearable sensors with transmission capabilities, computation, storage and sensing. In this paper, a supervised learning based decision support system for multi sensor (MS) healthcare data from wireless body sensor networks (WBSN) is proposed. Here, data fusion ensemble scheme is developed along with medical data which is obtained from body sensor networks. Ensemble classifier is taken the fusion data as an input for heart disease prediction. Feature selection is done by the squirrel search algorithm which is used to remove the irrelevant features. From the sensor activity data, we utilized the modified deep belief network (M-DBN) for the prediction of heart diseases. This work is implemented by Python platform and the performance is carried out of both proposed and existing methods. Our proposed M-DBN technique is compared with various existing techniques such as Deep Belief Network, Artificial Neural Network and Conventional Neural Network. The performance of accuracy, recall, precision, F1 score, false positive rate, false negative and true negative are taken for both proposed and existing methods. Our proposed performance values for accuracy (95%), precision (98%), and recall (90%), F1 score (93%), false positive (72%), false negative (98%) and true negative (98%).

     

Optimal Node Clustering and Scheduling in Wireless Sensor Networks

Selection and rotation of cluster head (CH) is a well known optimization problem in hierarchical Wireless sensor networks (WSNs), which affects its overall network performance. Population-based metaheuristic particularly Artificial bee colony (ABC) has shown to be competitive over other metaheuristics for solving optimization problems in WSNs. However, its search equation contributes to its insufficiency due to poor exploitation phase and low convergence rate. This paper, presents an improved artificial bee colony (iABC) metaheuristic with an improved search equation, which will be able to search an optimal solution to improve its exploitation capabilities moreover, in order to increase the global convergence of the proposed metaheuristic, an improved approach for population sampling is introduced through Student’s-t distribution. The proposed metaheuristic maintain a balance between exploration and exploitation search abilities with least memory requirements, with the use of first of its kind compact Student’s-t distribution, which is particularly suitable for WSNs limited hardware environment. Further utilising the capabilities of the proposed metaheuristic, an improved artificial bee colony based clustering and scheduling (iABC-CS) scheme is introduced, to obtain optimal cluster heads (CHs) along with optimal CH scheduling in WSNs. Simulation results manifest that iABC-CS outperform over other well known clustering algorithms on the basis of packet delivery ratio, energy consumption, network lifetime and end to end delay.     

Broadcasting with Controlled Redundancy and Improved Localization in Wireless Sensor Networks

Wireless sensor networks （WSNs） consist of sensor nodes that broadcast a message within a network. Efficient broadcasting is a key requirement in sensor networks and has been a focal point of research over the last few years. There are many challenging tasks in the network, including redundancy control and sensor node localization that mainly depend on broadcasting. In this paper, we propose a broadcasting algorithm to control redundancy and improve localization （BACRIL） in WSNs. The proposed algorithm incorporates the benefits of the gossip protocol for optimizing message broadcasting within the network. Simulation results show a controlled level of redundancy, which is up to 57.6% if the number of sensor nodes deployed in a 500 m×500 m area are increased from 50 to 500.     

A novel real-time scheme for (m,k)-firm streams in wireless sensor networks

The multimedia transmission based real-time applications have posed a big challenge to wireless sensor networks (WSNs) where both reliability and timeliness need to be guaranteed at the same time, to support an acceptable Quality of Service (QoS). The existing real-time routing protocols, however, are not able to meet the QoS requirements of realtime applications because of the inherent resource constraint of sensor nodes and instability of wireless communication. Therefore, we propose a real-time scheme in this paper, including a QoS-aware routing protocol and a set of fault recovery mechanisms, for (m,k)-firm based real-time applications over WSNs. A local status indicator which is specially devised for (m,k)-firm stream, is used for intermediate nodes to monitor and evaluate their local conditions. The proposed routing protocol takes into account of packet deadline, node condition and remaining energy of next hop, to make optimal forwarding decision. Additionally, according to the stream QoS and node condition, the proposed fault recovery mechanisms are utilized for nodes to handle the congestion, link failure and void problems occurred during transmission and remain the desired reliability and timeliness requirements. The proposed scheme has been well studied and verified through simulations. The results have proved the efficiency of the proposed scheme in terms of high successful transmission ratio, small end-to-end delay and long lifetime of network.     

加权选举的无线传感器网络协议

为了提高无线传感器网络的稳定期,提出了一种高效节能的加权选举协议。该协议使用集群策略结合链状路由算法,在异构的无线传感器网络环境下改善节能并且延长稳定期。仿真结果表明该协议在网络寿命和稳定期方面的性能都优于LEACH,SEP和HEARP。另外,实验表明在异构环境下稳定期依赖于节点的额外能量。