Congestion Prediction and Adaptive Rate Adjustment Technique for Wireless Sensor Networks

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.

Self-powered wireless sensor networks based on the radioisotope thermoelectric generator for aquatic temperature monitoring

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.

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.

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.

Most of the proposed algorithms focus on static networks of sensors with either static or mobile anchors, in which the Monte Carlo localization algorithm is a typical one for localizing nodes in a mobile wireless sensor network. But the radio range being all different or inconstant in this algorithm leads to reduce the accuracy of localization and the efficiency of the algorithm itself. In this article, we propose the novel rang-based stochastic Monte Carlo localization algorithm for wireless sensor networks specifically designed with mobility to improve the accuracy of localization by dealing with the different radio ranges of sensors, and being bound to the narrow sampling area. Our simulation experimental results show that the rang-based stochastic Monte Carlo localization algorithm has improved the accuracy and stability of the estimated locations.

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.

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.

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 (WSNs) the existence of malicious nodes is a serious challenge. Malicious nodes increase the traffic by sending distrusted messages. In this paper, we present CCTF: a congestion control protocol based on trustworthiness of nodes in WSNs using fuzzy logic. Through the use of CCTF, each node reduces malfunctioning neighbour(s) effects via dropping valueless packets and increases the buffer capacity; consequently, the chance of network congestion reduces. Simulation results show that when 16% of all nodes are malicious, CCTF increases the packet delivery ratio up to 40% meanwhile it reduces the packet drop ratio nearly 27.5%.

Indoor air quality (IAQ) is a term that describes the air quality of the building occupants. IAQ can be influenced by physical factors like dust particles, chemical or gaseous pollutants (including carbon dioxide, carbon monoxide and volatile organic compounds) and biological factors like molds and bacteria growth which largely depend on temperature and humidity condition of a room. These kinds of pollutants can induce a bad health effects to building occupants. In this study, a web-based system for monitoring indoor air quality is presented. The system has been developed to monitor the environmental parameters of air quality like gaseous pollutants, particulate matter, temperature and humidity that can affect the occupant's comfort, health and indoor work environment if not properly maintained. Generally, the system consists of a desktop computer acting as base station, an array of metal oxide gas sensor, temperature and humidity sensor, particle dust sensor and wireless sensor network (WSN) node as a wireless transmitter. The measured data from sensors are sent to base station via WSN node. The computer will log the collected data using a self-developed server program which can be accessed through the web.

A Markov based unified model for energy harvesting nodes in wireless sensor networks is proposed. Using the presented model, the probability of event loss due to energy run out as well as an analytical vulnerability metric, namely average time to energy run-out, are derived. The results provide insight into the performance of energy harvesting nodes in wireless sensor networks as well as design requirements for such nodes. The proposed vulnerability metric can be used in the various harvesting aware techniques at different protocol layers.

Contemporary Wireless Sensor Network (WSN) nodes can use one of the three options for their power supply: the electricity grid, the batteries or the wide range of energy harvesting systems. The possibility of identification for power source type used on the WSN node provides valuable information on the node potential resources, that could be used e.g. for the improvement of the whole WSN lifetime. Additionally, the possibility of WSN node Power Source Type Identification (PSTID) allows unifying the node's software through implementing the methods for WSN node operation adaptation based on the information about identified power source type and allows the support of efficient WSN node power source changeability without node reprogramming. To the best of our knowledge, this article is the first one which focuses on the problem of WSN node PSTID and suggests the appropriate solution. The suggested method allows implementing efficient PSTID with minimum resource consumption while not requiring any hardware components added to the WSN node. The suggested method has been implemented and tested in practice using different WSN node platforms and power source options. The evaluation results are presented in the paper as well.

Designing a low power Wireless Sensor Network (WSN) Node is highly important for getting longer lifetime. Radio transmission and data processing are energy consuming processes. No compensation can be done in the field of energy consumption in radio transmission. This paper concentrates on energy reduction in the field of On the node processing. Due to limited energy supply from batteries the node must trade communication for on the node computation. Many of the WSN data processing algorithms use parallel prefix operations as common denominator. Parallel Prefix Operations have the form of Binary Tree Architecture. Binary tree architecture uses more number of Processing Element (PE) s. Parallel Prefix operations using Binary Tree Architecture (BTA) uses more area and more power. Binary Tree Architecture can be modified in to area and power efficient Folded Tree Architecture (FTA). A Digital comparator is a hardware electronic device that has two binary inputs and determines whether one number is greater than, less than or equal to the other number. The comparators are widely used in Micro Controller Units (MCUs) of WSN nodes. This is a crucial data path element of image and signal processing architectures. By using Folded Tree Architecture Area and Power Efficient Comparator Architecture can be formed. Thus Power and Area efficient WSN node can be formed.