TPR: Dead end aware table less position based routing scheme for low power data-centric wireless sensor networks

Reputation and Trust‐based Monitoring Systems (RTMSs) have provided a ubiquitous framework for secure Wireless Sensor Network (WSN) computing. However, neighborhood monitoring, though useful, consumes substantial amounts of sensor resources. Therefore, employing sensors for neighborhood monitoring, which is secondary to their intended duties, depletes valuable and scarce resources. This eventually leads to shortened network lifetime, which is counter‐productive in WSNs. In this paper, we propose a novel, Connected Dominating Set (CDS)‐based reputation monitoring system. Our model is the first attempt to employ a CDS‐based monitoring backbone to securely aggregate the reputation of sensors without subjecting them to energy depletion or reputation pollution attacks encountered in existing reputation monitoring systems. Secure and certificateless node mobility and robustness to node replication and ID spoofing attacks are two vital by‐products of our model. We confirm the performance of our model via simulation studies. Copyright © 2010 John Wiley & Sons, Ltd.

Wireless sensor networks (WSN) have been recently be proposed for great deal of applications in industrial automation, health and environment monitoring. Increasingly there is a need to access wireless sensor networks from other IP-based networks. However, WSN has its own application-specific non-standardized protocols such as data-centric router protocol, which is a significant challenge to establish interconnection with IP networks which is based on address-centric router protocols. Service-Oriented Architecture (SOA) has been chosen to implement the communication among distributed applications. In this paper, we propose a Service-Oriented framework for inter-connection between data-centric WSN and IP network. This framework enables service-based query for data-centric WSN based on Device Profile for Web service (DPWS). It provides transparent access from one network to the other without modifying protocols running in either network.

The wireless sensor network (WSN) with fluctuating environs might be susceptible to diverse types of malicious cyber‐attacks, and they are mostly dependent on the authentication and encryption algorithm to astound this challenge. Most predominant routing schemes in literature are fall backs in characterizing the malicious nodes on networks due to the real time variation of routing information. Therefore, a reliable and trustworthy inter‐correlated routing scheme based on Block chain, Meta‐heuristic, and Deep Learning Algorithms are presented in this paper. The disseminated routing info in the WSN is handled by Block chain strategy, in which the optimal routing is accomplished with the help of Salp Swarm Optimization algorithm. The routing info variations between the nodes are envisaged and the optimal routing decisions are done by using the Deep Convolutional Neural network algorithm. The proposed routing scheme is implemented in NS2 and its performance is evaluated based on latency, energy consumption, and throughput metrics are analyzed. The efficiency of the method is improved as 97% and the evaluation is done for the malicious attacks, latency, and the delay. The comparison is made for the existing methods as particle swarm optimization, Markov decision process, security disjoint routing‐based verified message, trusted‐cluster–based routing, and reinforcement learning‐based neural network (RLNN) with the proposed method for the delay ratio.

Wireless sensor networks (WSNs) offer much promise for target tracking and environmental monitoring. While many WSN routing protocols have been proposed to date, most of these focus on the mobility of observers and assume that targets are fixed. However, in reality, many applications require for sensing data to be propagated from multiple mobile targets to multiple mobile observers. In addition, WSNs often operate under strict energy constraints, and therefore reducing energy dissipation is also an important issue. In this paper, we present a grid-based routing scheme known as TRENS. First, we address the issue of the WSN comprising multiple mobile targets and observers--with TRENS being the first scheme of its kind to use tracking technology to increase the efficiency of routing procedures in the context of dynamic topology. Next, we introduce a shortcutting approach to resolve energy issues by optimizing routing paths and thus decreasing communication costs and latency. Finally, we conduct extensive simulations to show how TRENS conserves energy and performs better than other grid-based schemes.

Generally, routing techniques are essential for Wireless Sensor Networks (WSNs) to deliver data packets to their destinations (also known as sinks). For a practical application, sensors are deployed to monitor environmental changes and events. Most WSNs detect specific events in their specific environments. But, different WSNs may monitor different events with different sensors in the same area. For example, a smart-home WSN deploys thermal sensors/meters to measure indoor and outdoor temperatures, body-temperature sensors to detect thief intrusion, etc. Also, in the underlying house, there is a health WSN which utilizes physiological sensors to monitor patients’ health condition. In other words, several WSNs of different purposes co-exist in the same geographical area. Currently, each WSN’s data delivery is independent from others’. Basically, if all sensors in such a multi-WSNs environment can share their routing paths/nodes and relay event packets for other WSNs, the delivery efficiency can be enhanced since many more sensors can be found there for packet relay. Consequently, the transmission energy can be reduced since energy consumed for wireless transmission is proportional to d2 where d is the transmission distance between sender and receiver. Therefore, in this study, we propose an energy saving routing mechanism, named Energy-Efficient Cooperative Routing Scheme for Heterogeneous Wireless Sensor Networks (EERH for short), in which several WSNs deployed in the same geographical environment form a heterogeneous sensor network and sensors relay packets for its own WSN and also for other WSNs. Routing paths are dynamically established according to the transmission directions of event packets and the residual energy of the underlying sensors and their neighbors. In addition, the packets routed to the same direction by the same sensors are aggregated to save delivering energy. Moreover, the network parameters of the EERH, like propagation delay of an event packet and the transmission distance of a sensor, are adjustable so as to satisfy the practical environment needs. Simulation results show that the EERH efficiently extends the lifetime of a heterogeneous WSN.

Wireless Sensor Networks (WSNs) are gaining immense popularity as a result of their wide potential applications in industry, military, and academia such as military surveillance, agricultural monitoring, industrial automation, and smart homes. Currently, WSN has garnered tremendous significance as it is has become the core component of the Internet of Things (IOT) area. Modern-day applications need a high level of security and quick response mechanism to deal with the emerging data trends where the response is measured in terms of latency, throughput, and scalability. Further, critical security issues need to be considered due to various types of threats and attacks WSNs are exposed to as they are deployed in harsh and hostile environments unattended in most of the mission critical applications. The fact that a complex sensor network consisting of simple computing units has similarities with specific animal communities, whose members are often very simple but produce together more sophisticated and capable entities. Thus, from an algorithmic viewpoint, bio-inspired framework such as swarm intelligence technology may provide valuable alternative to solve the large scale optimization problems that occur in wireless sensor networks. Self-organization, on the other hand, can be useful for distributed control and management tasks. In this chapter, swarm intelligence and social insects-based approaches developed to deal with a bio-inspired networking framework are presented. The proposed approaches are designed to tackle the challenges and issues in the WSN field such as large scale networking, dynamic nature, resource constraints, and the need for infrastructure-less and autonomous operation having the capabilities of self-organization and survivability. This chapter covers three phases of the research work carried out toward building a framework. First phase involves development of SIBER-XLP model, Swarm Intelligence Based Efficient Routing protocol for WSN with Improved Pheromone Update Model, and Optimal Forwarder Selection Function which chooses an optimal path from source to the sink to forward the packets with the sole objective to improve the network lifetime by balancing the energy among the nodes in the network and at the same time selecting good quality links along the path to guarantee that node energy is not wasted due to frequent retransmissions. The second phase of the work develops a SIBER-DELTA model, which represents Swarm Intelligence Based Efficient Routing protocol for WSN taking into account Distance, Energy, Link Quality, and Trust Awareness. WSNs are prone to behavior related attacks due to the misbehavior of nodes in forwarding the packets. Hence, trust aware routing is important not only to protect the information but also to protect network performance from degradation and protect network resources from undue consumption. Finally, third phase of the work involves the development of SIBER-DELTAKE hybrid model, an improved ACO-KM-ECC trust aware routing protocol based on ant colony optimization technique using K-Medoids (KM) algorithm for the formation of clusters with Elliptical Curve Cryptography (ECC). KM yields efficiency in setting up a cluster head and ECC mechanism enables secure routing with key generation and management. This model takes into account various critical parameters like distance, energy, link quality, and trust awareness to discover efficient routing.

Data aggregation is an important process for wireless sensor networks (WSNs). The important aspects with WSNs are to increase the lifetime and to reserve the battery power of sensor nodes. The energy of nodes is mainly utilized in the process of sensing and transmitting the data. Clustering is one of the processes in which the total WSNs are divided into the form of groups and each group has a cluster head (CH) and advantage of doing this is decrease of energy consumption and reduce the data collision. The blockchain technology is utilized in WSN by so many ways for example security, data storing, node recovery etc. because the role of blocks in blockchain is same as the role of node in WSN. In this paper, we propose a cluster-based data aggregation framework for wireless sensor networks based on the blockchain technique. The proposed scheme is based on cluster head (CH) selection based on energy, and nodes communicate with each other via the shortest path. Further, the proposed scheme is also using the concept of blockchain technology for the purpose of maximum data storage. KeywordsWireless sensor networksLow energy adaptive clustering hierarchy (LEACH)Data aggregationCluster headBlockchainAnd Energy consumption

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.

Mobile sink based routing strategies for wireless sensor networks (WSNs) have drawn a lot of research attention in recent years. If sink mobility pattern is well designed, better WSNs performance can be achieved. In this paper, we provide a survey of state-of-the-art mobile sink based location-based routing strategies for WSNs routing strategies. Location-based routing strategies can be further classified into backbone-based and rendezvous-based routing protocols. We first describe the main principles of the two location-based routing protocols with sink mobility support respectively. Then we analyze their advantages and disadvantages.

Reliable event delivery at MAC, routing and transport level has been explored to great extent to provide improved throughput and route resilience in wireless sensor networks. The evolution of wireless sensor and actor networks (WSAN) have posed an extra constraint in terms of reliable event response. The reliable response is defined as the the in time event delivery from sensor nodes to actors and timely action of actors against the sensors input. This might be required in an application that includes a diverse mixture of sensors for monitoring multiple events. In this paper, we propose a reliable event response (RER) framework for WSANs that addresses the issue of reliable action. RER implements two functions, reliable event delivery (RED) and prioritized event action scheduling. RED is based on a a novel cluster-based multipath routing protocol. While, action reliability is ensured through a prioritized action scheduling based on earliest deadline shortest positioning (EDSP) algorithm. The results reveal that RER achieved its goal of action reliability which is not possible with simple scheduling first-in first-out (FIFO) scheduling algorithm.

Wireless sensor nodes are battery-powered communication devices. Their limited capabilities have imposed various constraints to the system design of wireless sensor networks (WSNs). These constraints are interrelated, and are usually in conflict with each other. Clustering is often used to reduce energy consumption in WSNs. However, an arbitrary selection of clustering parameters may lead to severe degradation in other aspects, such as extra delays in data collection processes (DCPs). In this paper, a multi-objective optimization (MOO) framework for cluster-based WSNs is proposed. The proposed framework considers both the energy consumption and the duration of a DCP as its objective functions. Simulation results show that networks optimized using the proposed framework can obtain reasonable trade-offs between the two objectives. Nevertheless, the networks optimized using the proposed framework can obtain non-dominated solutions that cannot be achieved by using energy-aware clustering algorithms.

Major points of concern in implementing a wireless sensor network (WSN) are the network lifetime and energy utility within any delay tolerant network. Both these parameters define the success of the sensor network. The higher the expectancy of network Lifetime, the higher is the probability of acceptance of the network. Similarly, better the energy utilization in the network, better are the chances of success and implementation of the sensor network. Clustering is one such scheme adopted in WSN towards harnessing the best of above specified parameters for the network implemented. Most popular clustering techniques are the variants of LEACH protocol that facilitate cluster formation based on the proximity of an individual node to other nodes in the sensor network. These protocols are based on a single hop structure from the selected cluster heads in the network. This paper embarks on a multi-hop clustering algorithm that takes into consideration the spatial correlation between the nodes to form clusters and implements a highly energy efficient routing scheme which selects the multi-hop path in the network in a dynamic fashion.

In wireless sensor networks constructed from battery driven nodes, it is difficult to supply electric power to the nodes. Because of this, the power consumption must be reduced. To cope with this problem, clustering techniques have been proposed. EACLE is a method that uses a clustering technique. In EACLE, the route selection is executed independently after the CH (Cluster Head) selection. These two phase controls increase overheads and reduce the battery power, which leads to shorten the lifetime of wireless sensor networks. This paper proposes a novel routing and clustering method called PARC for wireless sensor networks that reduces these overheads by integrating the cluster selection phase and the route construction phase into a single phase. However, PARC has a following weak point that the batteries of CHs around the sink node are depleted earlier than the other nodes and the sink node cannot collect sensing data. This phenomenon is called a hot spot problem. In order to cope with this problem of PARC, we propose PARC+, which extends the CH selection method of PARC such as more nodes around the sink can be selected as a CH node. We evaluate our proposed methods by simulation experiments and show its effectiveness.

Aiming at the problem that the current wireless sensor network performance is low and cannot meet the requirements of high-precision synchronous acquisition of mechanical vibration signals, this paper proposes a synchronous acquisition method (SACD) based on cross-layer design. Based on this monitoring mode, an experimental platform is constructed to conduct a vibration signal measurement experiment, and compared with the wired vibration signal test and analysis method. The results show that the vibration monitoring mode based on the wireless sensor network can meet the basic requirements of mechanical vibration monitoring applications.

Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.