A Hierarchical and Role Based Secure Routing Protocol for Mobile Wireless Sensor Networks

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

Profiling environmental parameter using a large number of spatially distributed wireless sensor network (WSN) NODEs is an extensive illustration of advanced modern technologies, but high power requirement for WSN NODEs limits the widespread deployment of these technologies. Currently, WSN NODEs are extensively powered up using batteries, but the battery has limitation of lifetime, power density, and environmental concerns. To overcome this issue, energy harvester (EH) is developed and presented in this paper. Solar-based EH has been identified as the most viable source of energy to be harvested for autonomous WSN NODEs. Besides, a novel chemical-based EH is reported as the potential secondary source for harvesting energy because of its uninterrupted availability. By integrating both solar-based EH and chemical-based EH, a hybrid energy harvester (HEH) is developed to power up WSN NODEs. Experimental results from the real-time deployment shows that, besides supporting the daily operation of WSN NODE and Router, the developed HEH is capable of producing a surplus of 971 mA·hr equivalent energy to be stored inside the storage for NODE and 528.24 mA·hr equivalent energy for Router, which is significantly enough for perpetual operation of autonomous WSN NODEs used in environmental parameter profiling.

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

Wireless Sensor Networks (WSN), consists tiny but powerful devices to monitor or sense specific phenomena, e.g. temperature, visual, humidity etc. In WSNs nodes are deployed in the harsh and unattended environment. In such scenario, nodes may fail due to various reasons, e.g. link failure, battery depletion, hardware/software failure etc. In order to provide a reliable network, fault-tolerant network is required. Furthermore in WSN, energy is one of the major constraints, due to irreplaceable batteries. There have been various techniques proposed for fault tolerance and energy saving by detecting and correcting node failure. In this paper, the proposed algorithm called Cluster Head Fault Detection and Correction algorithm (FDFC), detects Cluster Head (CH) failure in a distributed manner and replace a faulty node. In this work, all the CHs present in network share status data with all the other CHs. Besides fault detection, this algorithm also suggests faulty node replacement technique. The effectiveness of the proposed algorithms is validated through simulation experiments.

This paper is a performance survey on the residual energy of nodes in a wireless sensor network (WSN). Wireless sensor nodes are limited in application due to constraints in energy of nodes’ battery and addressing the problem of energy management is essential to lengthen the life of a deployed network of wireless sensors. In the method used, Sensor nodes were equally distributed on the complete border of the network field. An exclusive cluster, six hierarchical, and eighteen hierarchical clusters were formed based on even partition of the border of the network field. The choice of Cluster Head (CH) within each cluster formed was based on selection of a sensor node with minimal transmission distance to the Base Station. A comparison of the various hierarchical scenarios was simulated in MATLAB. The mean residue energies (MREs) after the network lifetime are 3%, 52%, and 35% of the initial node energy for single, six and eighteen hierarchical clusters respectively. The residual energies of sensor nodes above the recorded MREs were taken on a class interval of 50’s of the node identity (nid) and respective averages of residual energies of sensor nodes (AEr) of each selected class interval were evaluated. Based on the result, certain categories of sensor nodes at a section of the network field indicated that the battery energy of the affected nodes was not maximally utilized thereby leading to enormous residual energy. It was concluded that the residual energy of nodes in a WSN could be optimized to extend the lifetime.

For the past few years, centralized decision-making is being used for malicious node identification in wireless sensor networks (WSNs). Generally, WSN is the primary technology used to support operations, and security issues are becoming progressively worse. In order to detect malicious nodes in WSN, a blockchain-routing- and trust-model-based jellyfish search optimizer (BCR-TM-JSO) is created. Additionally, it provides the complete trust-model architecture before creating the blockchain data structure that is used to identify malicious nodes. For further analysis, sensor nodes in a WSN collect environmental data and communicate them to the cluster heads (CHs). JSO is created to address this issue by replacing CHs with regular nodes based on the maximum remaining energy, degree, and closeness to base station. Moreover, the Rivest–Shamir–Adleman (RSA) mechanism provides an asymmetric key, which is exploited for securing data transmission. The simulation outcomes show that the proposed BCR-TM-JSO model is capable of identifying malicious nodes in WSNs. Furthermore, the proposed BCR-TM-JSO method outperformed the conventional blockchain-based secure routing and trust management (BSRTM) and distance degree residual-energy-based low-energy adaptive clustering hierarchy (DDR-LEACH), in terms of throughput (5.89 Mbps), residual energy (0.079 J), and packet-delivery ratio (89.29%).

In wireless sensor networks, power is the most essential resource because each sensor node has limited batteries. Many kinds of existing clustering protocols have been developed to balance and maximize lifetime of the sensor nodes in wireless sensor networks. These protocols select cluster heads periodically, and they considered only `How can we select cluster heads energy-efficiently?' or `What is the best selection of cluster heads?' without considering energy-efficient period of the cluster heads replacement. Unnecessary head selection may dissipate limited battery power of the entire sensor networks. In this paper, we present T-LEACH, which is a threshold-based cluster head replacement scheme for clustering protocols of wireless sensor networks. T-LEACH minimizes the number of cluster head selection by using threshold of residual energy. Reducing the amount of head selection and replacement cost, the lifetime of the entire networks can be extended compared with the existing clustering protocols. Our simulation results show that T-LEACH outperformed LEACH in terms of balancing energy consumption and network lifetime.

Wireless Sensor Network (WSN) suffers from different malware attacks. Several traditional approaches are proposed for detecting fault nodes in WSN. It is necessary to redistribute the nodes and detect the fault nodes while modifying the WSN parameters. Faults can occur in the nodes interrupting the continuous communication process of the nodes in the WSN. The main reason for a fault is also the induction of the energy drain in a node to the maximum possible level, failure of links in communicating nodes due to bandwidth constraints, and attacks induced by the malicious nodes. To solve this problems, Extended Finite State Machine based Fault Tolerance (EFSM_FT) in the WSN is used to detect the fault sensor nodes in the WSN. In this approach, called the finite state machine (FSM), state prompted is modified while a set of situations are true. Every node can be absolutely to be faulty or not establishing on the sensor node states. Using sensor energy, drop rate, forward sensor rate, sensor obtained rate, bandwidth, and transmission delay, this method finds the node with the fault. As a result, this approach accurately detects the faulty nodes in the WSN. The simulation outcomes illustrate the proposed method has a better fault detection ratio and minimizes the false negative ratio in the WSN.

An adaptive cellular automata scheme for diagnosis of fault tolerance and connectivity preserving in wireless sensor networks

An efficient cluster head selection for wireless sensor network-based smart agriculture systems

Recently, the technology of wireless sensor networks (WSNs) experiences a growing use in home automation or advanced industry infrastructure applications. Despite a strong interest of industries in this technology, key issues like miniaturization and security of WSN nodes has not been solved yet. State-of-the-art WSN nodes do not provide credible security nor satisfying configurability and miniaturized implementations. This publication deals with these limitations and presents a WSN node that provides security, configurability, and a miniaturized design. To show the sensor node feasibility, the WSN node is implemented within a smart home demonstrator. Additionally, a miniaturized pre-study WSN node design is presented using the novel embedded wafer level ball grid array (eWLB) packaging technology. Furthermore, an eWLB based WSN node design is proposed that further miniaturize the presented WSN node.

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

Clustering is an effective approach to hierarchically organizing network topology and to prolong the lifetime of a wireless sensor network. Current clustering protocols usually utilize two techniques: selecting cluster heads with more residual energy and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster. Most of the researches in this field has focused on energy-efficient solutions, but caring less about the communication between Cluster Head (CH) nodes and Base Station (BS). When the sensor field is far away from the base station, the cluster heads are burdened with heavier relay traffic and tend to die much faster. To mitigate the problem, we propose an Energy-Efficient Hierarchical Routing Protocol (EEHRP) for long range transmission in the wireless sensor networks. It uses a number of gateway nodes, which do not engage in clustering, to connect the CHs and base station. They are responsible for transmitting packets received from the CHs to the base station, thus the CHs can preserve some energy in data forwarding and the gateway nodes can ease their burden by not participating in clustering. Simulation results show that EEHRP obviously increases the network lifetime and well balances the energy consumption among the sensor nodes.

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.

Over the past decades, hardware and software technologies for wireless sensor networks (WSNs) have significantly progressed, and WSNs are widely used in various areas including Internet of Things (IoT). In general, existing WSNs are mainly used for applications that require delay-tolerance and low-computation due to the poor resources of traditional sensor nodes in WSNs. However, compared to the traditional sensor nodes, today’s devices for WSNs have more powerful resource. Thus, sensor nodes these days not only conduct sensing and transmitting data to servers but also are able to process many operations, so more diverse applications can be applied to WSNs. Especially, many applications using audio data have been proposed because audio is one of the most widely used data types, and many mobile devices already have a built-in microphone. However, many of the applications have a requirement that heavy-operations should be done by a tight deadline, so it is difficult for a single node in WSNs to run relatively heavy applications by itself. In this paper, to overcome this limitation of WSNs, we propose a new emerging system, HeaLow, a cooperative computing system for heavy-computation and low-latency processing in WSNs. We designed HeaLow and carried out the practical implementation on real devices. We confirmed the effectiveness of HeaLow through various experiments using the real devices and simulations. Using HeaLow, nodes in WSNs are able to perform heavy-computation processes while satisfying a completion time requirement.