Wireless Sensor Network Paradigm Research Articles

Secure identity key and blockchain‐based authentication approach for secure data communication in multi‐WSN

SummaryBlockchain technology is an effective way to protect data that is handled by wireless sensor network (WSN). The traditional authentication protocol relies on trusted third parties, and most of the WSN paradigm does not allow these conditions, which results in single‐point failure. Sometimes the authentication process is slower, eventually hampers user experience, and thus users are inclined towards other alternatives that can offer multi‐factor authentication. To solve such issues, a secure identity key and blockchain‐based authentication framework is designed using a hybrid blockchain system in multi‐WSN to enhance the system's security. The nodes cooperate in the network to perform specific tasks such that interaction between nodes ensures the legitimacy of node's identity. With hybrid blockchain technology, nodes' identity information is authenticated and stored in the network after verification to establish communication among ordinary nodes and between the ordinary nodes and end user. The proposed method obtains higher security by achieving less computation time, higher detection rate, and limited memory usage of 0.072 s, 92.48%, and 3.925 MB, respectively. The developed authentication method is proved capable of proving resistance in case of different attacks in the network.

AIMD Rule-Based Duty Cycle Scheduling in Wireless Sensor Networks Using Quartile-Directed Adaptive Genetic Algorithm

Wireless sensor networks (WSNs) are attractive, in large part because they do not require wired infrastructure. However, this very characteristic renders them energy-constrained. Duty cycle scheduling (DCS) is thought to contribute to the energy efficiency of sensing. We develop a novel paradigm for modeling WSNs and a DCS strategy based on the additive increase/multiplicative decrease (AIMD) rule. The scheduling problem is framed as an optimization problem with the optimization objectives of lowering energy consumption and enhancing detection ability. The classic GA converges slowly in late evolution phases for using fixed crossover and mutation probability. An adaptable genetic algorithm (AGA) eliminates the above concern by allowing modifying crossover and mutation probability as per fitness values, but, unfortunately, it often fails to select the best individuals. We suggest a quartile-directed adaptive genetic algorithm (Q-AGA) in which individuals with fitness above the first quantile of the last generation were selected. The interquartile range (IQR) is utilized to weight crossover and mutation probability. If IQR is larger, the fitness distribution is more concentrated, and then, we can diminish the crossover and mutation probability, allowing favorable traits to be retained to the greatest extent feasible. Simulation results show that Q-AGA has a faster convergence speed and a stronger global search ability. Furthermore, we change the hypothesis of the system model by considering that the two objectives can be weighted, thereby broadening the application scenarios.

An Optimized Data Fusion Paradigm for WSN Based on Neural Networks

Wireless sensor networks (WSNs) have gotten a lot of attention as useful tools for gathering data. The energy problem has been a fundamental constraint and challenge faced by many WSN applications due to the size and cost constraints of the sensor nodes. This paper proposed a data fusion model based on the back propagation neural network (BPNN) model to address the problem of a large number of invalid or redundant data. Using three layered-based BPNNs and a TEEN threshold, the proposed model describes the cluster structure and filters out unnecessary details. During the information transmission process, the neural network’s output function is used to deal with a large amount of sensing data, where the feature value of sensing data is extracted and transmitted to the sink node. In terms of life cycle, data traffic, and network use, simulation results show that the proposed data fusion model outperforms the traditional TEEN protocol. As a result, the proposed scheme increases the life cycle of the network thereby lowering energy usage and traffic.

Performance evaluation of LoRa LPWAN technology for IoT-based blast-induced ground vibration system

The recent proliferation of wireless sensor networks (WSNs) evolution into the Internet of Things (IoT) vision enables a variety of low-cost monitoring applications which allows a seamless transfer of information via embedded computing and network devices. Ambiguous ground vibration can be induced by blasting demolition is a severe concern which grievously damages the nearby dwellings and plants. It is an indispensable prerequisite for measuring the blast-induced ground vibration (BIGV), accomplishing a topical and most active research area. Thus, proposed and developed an architecture which emphasizes the IoT realm and implements a low-power wide-area networks (LPWANs) based system. Especially, using the available Long-Range (LoRa) Correct as Radio Frequency (RF) module, construct a WSN configuration for acquisition and streaming of required data from and to an IoT gateway. The system can wirelessly deliver the information to mine management and surrounding rural peoples to aware of the intensity of BIGV level. In this article, an endeavor has been made to introduce a LoRa WAN connectivity and proved the potentiality of the integrated WSN paradigm by testing of data transmission-reception in a non-line of sight (NLOS) condition. The path loss metrics and other required parameters have been measured using the LoRa WAN technology at 2.4 GHz frequency.

A solid phase bacteria-powered biobattery for low-power, low-cost, internet of Disposable Things

The Internet of Disposable Things (IoDT) has recently emerged as a simple, low-cost, but powerful paradigm for wireless sensor networks. Stand-alone, self-sustaining IoDT devices are essential to providing effective and reliable functioning even in resource-limited environments. A stable power supply is the most critical factor in developing practical IoDT applications because their performance and deployment depend significantly on power availability. In this work, we created a micro-sized (∼62 μL) bacteria-powered biobattery for potentially powering unattended IoDT applications. The biobattery stores solid-phase microbial anodic nutrients and ionic pathways in microliter-scale chambers without an energy-intensive fluidic system, providing a relatively long-term operational capability (>8 days). We revolutionarily converted the liquid anolyte, salt bridge, and cathodic compartment into solid counterparts, increasing their densities and enabling their slow and continuous reactions. Furthermore, the solid-phase components will make the device favorable in miniaturization, integration, and operation with the solid-state IoDT applications. Our micro-biobattery produced a maximum power density of 4 μW/cm2 (0.33 mW/cm3) and current density 45 μA/cm2 (0.37 mA/cm3) after 96 h of operation while a liquid-based control device stopped generating power within 4 h.

Secure Sensor Cloud

Abstract The sensor cloud is a new model of computing paradigm for Wireless Sensor Networks (WSNs), which facilitates resource sharing and provides a platform to integrate different sensor networks...

Distributed Systematic Network Coding for Reliable Content Uploading in Wireless Multimedia Sensor Networks.

Recently, the wireless sensor network paradigm is shifting toward research aimed at enabling the robust delivery of multimedia content. A challenge is to deliver multimedia content with predefined levels of Quality of Service (QoS) under resource constraints such as bandwidth, energy, and delay. In this paper, we propose a distributed systematic network coding (DSNC) scheme for reliable multimedia content uploading over wireless multimedia sensor networks, in which a large number of multimedia sensor nodes upload their own content to a sink through a cluster head node. The design objective is to increase the reliability and bandwidth-efficient utilization in uploading with low decoding complexity. The proposed scheme consists of two phases: in the first phase, each sensor node distributedly encodes the content into systematic network coding packets and transmits them to the cluster head; then in the second phase, the cluster head encodes all successfully decoded incoming packets from multiple sensor nodes into innovative systematic network coding packets and transmits them to the sink. A bandwidth-efficient and channel-aware error control algorithm is proposed to enhance the bandwidth-efficient utilization by dynamically determining the optimal number of innovative coded packets. For performance analysis and evaluation, we firstly derive the closed-form equations of decoding probability to validate the effectiveness of the proposed uploading scheme. Furthermore, we perform various simulations along with a discussion in terms of three performance metrics: decoding probability, redundancy, and image quality measurement. The analytical and experimental results demonstrate that the performance of our proposed DSNC outperforms the existing uploading schemes.

A cluster prediction model-based data collection for energy efficient wireless sensor network

Wireless sensor networks (WSN) are expected to cover the major portion of the earth’s surface in the coming years. In the era of IoT, the WSN is the major data collection framework. To manage with the energy efficient data collection paradigm in WSN, numerous techniques have been suggested by the research community. In this paper, a data-aware energy conservation technique is proposed. Here, the inherent correlation between the consecutive observations of the sensor node and the data trend similarity between the neighboring sensor nodes are utilized to reduce the data transmission. A prediction-based data collection framework reduces the temporal data redundancy. ARIMA modeling is used to predict the data. The model is constructed by the (Clusterhead) CH node and is communicated to the cluster nodes. On every data collection round, the nodes compare the model predicted data and the observed data of the instant. If there is a deviation beyond the specified threshold, the nodes communicate the data difference to the CH. The data differences collected by the CH are compressed by using PCA technique. The compressed data are then sent to the sink node. Using this method, a huge portion of redundant data transmission is cut off. The method also maintains the collected data’s accuracy within the predefined error threshold. Being a data reduction-based energy conservation technique, this results in reduced data collision. This method conserves 82% of energy with the error threshold of minimum level.

A Joint Routing and MAC Protocol for Transmission Delay Reduction in Many-to-One Communication Paradigm for Wireless Sensor Networks

We propose a joint routing and medium access control (MAC) protocol, named as JRAM, for reducing transmission delays in a many-to-one communication paradigm for wireless sensor networks (WSNs). Due to the wide variety of WSN applications, there is a need for protocol solutions optimized for specific application classes. JRAM is proposed for WSNs deployed for monitoring multiple events in the same geographic region which require prompt detection and response. In existing contention-based synchronous MAC protocols designed for this, a node gets only one chance to succeed in data transmission scheduling per cycle, and a sink can also only receive data packets from at most one node in a cycle. Therefore, endto-end transmission delay (E2ETD) and packet delivery ratio (PDR) of these protocols drastically degrade with the increase in event occurrence rate (EOR). In contrast, JRAM proposes a novel approach to provide k (k > 1) chances to a node to succeed in data transmission scheduling in a cycle, and also allows a sink to receive data packets from k nodes in the same cycle. This is done, in JRAM, by partitioning the network nodes into k disjoint sets and then using a novel cycle structure. We evaluate JRAM through extensive NS-2.35 simulations and compare its performance with existing pipelined data collection (PDC), adaptive data collection (ADC), and CROPMAC protocols, for different types of traffic loads and traffic patterns. Results suggest that in case of high EOR, JRAM outperforms PDC, ADC and CROPMAC both in terms of the E2ETD and the PDR.

Information-centric sensor networks for cognitive IoT: an overview

Information-centric sensor networks (ICSNs) are a paradigm of wireless sensor networks that focus on delivering information from the network based on user requirements, rather than serving as a point-to-point data communication network. Introducing learning in such networks can help to dynamically identify good data delivery paths by correlating past actions and results, make intelligent adaptations to improve the network lifetime, and also improve the quality of information delivered by the network to the user. However, there are several factors and limitations that must be considered while choosing a learning strategy. In this paper, we identify some of these factors and explore various learning techniques that have been applied to sensor networks and other applications with similar requirements in the past. We provide our recommendation on the learning strategy based on how well it complements the needs of ICSNs, while keeping in mind the cost, computation, and operational overhead limitations.

On the Use of Magnetically Coupled Resonators for Chirp-Based Timestamping

The knowledge of the time value at which an event of interest occurred is the typical feature needed to perform many kinds of postprocessing in distributed sensor networks. Consequently, network-based distributed systems must support time dissemination in order to ensure that relative clock errors among nodes remain below a certain threshold. Despite many protocols being well established for wired connections, the wireless sensor network paradigm required a new approach to address adverse characteristics of the wireless medium. In this paper, the adoption of low-frequency signaling is proposed for overcoming some issues of radio frequency transmissions when used for timestamping events. The proposed approach is characterized using numerical simulations and an experimental prototype, based on inductive coupling of tuned resonators. Experimental tests confirm the effectiveness and feasibility of this approach that achieves microsecond-order time recovery performance in practical system configurations.

A data delivery framework for cognitive information-centric sensor networks in smart outdoor monitoring

Cognitive information-centric sensor networks represent a paradigm of wireless sensor networks in which sensory information is identified from the network using named-data, and elements of cognition are used to deliver information to the sink with quality that satisfies the end-user requirements. Specialized nodes called Local Cognitive Nodes (LCNs) implement knowledge representation, reasoning and learning as elements of cognition in the network. These LCNs identify user-requested sensory information, and establish data delivery paths to the sink by prioritizing Quality of Information (QoI) attributes (e.g., latency, reliability, and throughput) at each hop based on the network traffic type. Analytic Hierarchy Processing (AHP) is the reasoning tool used to identify these paths based on QoI-attribute priorities set by the user. From extensive simulations, parameters that can be controlled to improve the values of QoI attributes along each hop were identified, and performance of the AHP-based data-delivery technique was compared with two traditional data-centric techniques in terms of lifetime and QoI attribute performance. It was found that the use of cognition improves the number of successful transmissions to the sink by almost 30%, while closely adapting the data delivery paths to the QoI requirements of the user.

On intrusion detection in opportunistic networks

As the interest of infrastructureless wireless sensor networks (WSN) grows, intrusion has been recognised to cause chronic problems on security and has become the major factor for the increasing volume of hacking incidents. For that reason, research that relates to intrusion detection becomes of paramount importance. In this paper, we concentrate on the intrusion detection for the opportunistic networks (oppnets), a new paradigm of WSN. More specifically, we deal with a multilayer anomaly intrusion detection technique applied to oppnets. Even though the method is well established by other research, it is proved to conjecture attacks which are yet unknown without prior knowledge of the network topology and the hardware idiosyncrasies. Furthermore, it offers increased situational awareness and understanding of the occurred security events due to the usage of corroborating evidence/information from different layers. Extended simulations on a variety of node and multilayer parameters show a good performance of the technique under the constraints of energy efficiency, low resource availability and protocol independency. Furthermore, comparative simulations verify that the multilayer approach can be a valid security mechanism for the future of oppnets.

Improving sensor network performance with wireless energy transfer

Through recent technology advances in the field of wireless energy transmission wireless rechargeable sensor networks have emerged. In this new paradigm for wireless sensor networks a mobile entity called mobile charger (MC) traverses the network and replenishes the dissipated energy of sensors. In this work we first provide a formal definition of the charging dispatch decision problem and prove its computational hardness. We then investigate how to optimise the trade-offs of several critical aspects of the charging process. In the light of these optimisations, we study the impact of the charging process to the network lifetime for three characteristic underlying routing protocols. Finally, we propose a mobile charging protocol that locally adapts the circular trajectory of the MC to the energy dissipation rate of each sub-region of the network. We compare this protocol against several MC trajectories by a detailed experimental evaluation. The derived findings demonstrate significant performance gains.

An Empirical Evaluation of Lightweight Random Walk Based Routing Protocol in Duty Cycle Aware Wireless Sensor Networks

Energy efficiency is an important design paradigm in Wireless Sensor Networks (WSNs) and its consumption in dynamic environment is even more critical. Duty cycling of sensor nodes is used to address the energy consumption problem. However, along with advantages, duty cycle aware networks introduce some complexities like synchronization and latency. Due to their inherent characteristics, many traditional routing protocols show low performance in densely deployed WSNs with duty cycle awareness, when sensor nodes are supposed to have high mobility. In this paper we first present a three messages exchange Lightweight Random Walk Routing (LRWR) protocol and then evaluate its performance in WSNs for routing low data rate packets. Through NS-2 based simulations, we examine the LRWR protocol by comparing it with DYMO, a widely used WSN protocol, in both static and dynamic environments with varying duty cycles, assuming the standard IEEE 802.15.4 in lower layers. Results for the three metrics, that is, reliability, end-to-end delay, and energy consumption, show that LRWR protocol outperforms DYMO in scalability, mobility, and robustness, showing this protocol as a suitable choice in low duty cycle and dense WSNs.

Fusion function placement for Active Networks paradigm in wireless sensor networks

Active Networks paradigm integrated with distributed data fusion has the potential to significantly reduce energy dissipation in wireless sensor networks, where energy conservation is the most challenging issue. This work aims to minimize energy cost when distributed data fusion is deployed for the Active Networks computing paradigm. First we propose an optimal solution for mapping task graph of distributed data fusion application into network. Optimal solution uses an exhaustive search algorithm for finding the placements with minimized power consumption. However, optimal solution has high computational complexity—O(mn k ), where n denotes the number of network nodes, m is the number of fusion functions, and k is the maximum number of children a fusion function has in task graph and its children are also fusion functions. Then, an approximate solution with low complexity (O(mlog n + log2 m)) is proposed called P2lace, which includes two phases, task graph partition and task graph placement. Finally, an extensive evaluation compares approximate solution with optimal solution. The results show that approximate solution is scalable with different task graph characteristics and network size and only causes slightly more transmission cost than optimal solution. And the algorithm without optimizing is shown to be applicable to the network, where the sink node does not have global information of entire network.

Design and Implementation of a Smart Home Based on WSN and AMR

The purpose of the smart home application is to devise a set of intelligent home appliances that can provide an awareness of the users’ needs, providing them with a better home life experience without overpowering them with complex technologies and intuitive user interfaces. There are a large number of reseaches dealing with Wireless Sensor Networks(WSN) applications, but it is still possible to explored in WSN development and maintenance. The WSN paradigm has been stimulated by considering the integration of the WSN with sophisticated Autonomous Mobile Robots(AMR), having advanced mobility,processing and communication capabilities. The complementarities of the WSN and AMR technologies may result in a symbiotic coexistence with mutual advantages for both systems and may enable novel services in advanced scenarios,such as ubiquitous robotics,networked robotics,ambient intelligence. AMR can be considered to be mobile nodes that provide additional sensorial information, improve/repair the connectivity and collect information from wireless sensor nodes. On the other hand, the WSN can be considered as an extension of the sensorial capabilities of the robots and it can provide a smart environment for AMR. This paper examines the possibility of integration WSN and AMR into a smart home application.

Reduced‐frame TDMA protocols for wireless sensor networks

SUMMARYTime‐division multiple‐access (TDMA) is a common medium access control paradigm in wireless sensor networks. However, in its traditional form, the TDMA‐based protocols suffer from low channel utilization and high message delay because of a long frame length needed to provide collision‐free transmissions, which is particularly damaging in dense wireless sensor networks. In this paper, we investigate the performance and the energy efficiency of a class of TDMA‐based protocols, called reduced‐frame TDMA, where every TDMA slot is augmented with a short time period dedicated for carrier sense multiple access‐based contention resolution mechanism. Because of their ability to dynamically resolve collisions caused by conflicting slot assignments, the reduced‐frame TDMA protocols can be configured with any frame length, independently of node density. In addition, we present a distributed heuristic slot assignment algorithm that minimizes interslot interference in the presence of limited number of slots per frame. The simulation results indicate that the reduced‐frame TDMA protocols significantly reduce the message delay and increase the maximum throughput without incurring significant penalty in energy efficiency compared with the traditional TDMA scheme. Copyright © 2012 John Wiley & Sons, Ltd.

Resource Re-use in Wireless Sensor Networks: Realizing a Synergetic Internet of Things

The race for realizing a feasible framework for the Internet of Things (IoT) is indeed of increasing pace. Yet, none of the paradigms on the table consider building a system from scratch. Simply put, much has been invested (research and industry) in developing two key enabling technologies; namely Wireless Sensor Networks (WSNs) and RFID systems. The abundance and self-sustained operation of these technologies potentiate a truly diverse bed for the plethora of applications the IoT is envisioned to encompass. We note the application-specific approach, currently dominant in WSN research, a true hindrance to its adaptability in a realizable IoT framework. In remedy, we present a novel paradigm in WSNs to efficiently utilize network resources, and extend it to a platform for multiple applications to cross-utilize resources over multiple WSNs. Our system is composed of three successive phases, namely: identifying the resources available in a given deployment of WSNs, and calibrating their usability based on a set of attributes. Then, a set of functional requirements is drawn from the applications to run on these WSNs. Finally we present a formulation for an optimization problem that maps these functional requirements to the available resources. The resulting paradigm potentiates the utilization of WSNs, not only for accommodating multiple applications, but for dynamically allocating resources when needed in a larger IoT framework. We present the formulation aided by a use case. Finally, this work concludes with a set of open research topics stemming from IoT realization efforts, and the integration efforts for its enabling technologies.

Mobile multimedia in wireless sensor networks

One of the most referred and promising Wireless Sensor Network (WSN) applications is health monitoring. The small size and portability of nodes have made WSNs the perfect tool to easily monitor a person's health condition. In this type application, as well as in several other critical applications, reliability and mobility are paramount. In this paper we propose a method, based on WSNs and mobile intra-body sensors, to accurately detect the fertile period of women on time and other applications based on intra-vaginal temperature monitoring. In addition, our proposal also introduces the proposal of intra-body micro-cameras to monitor the women's cervix, capable to detect related pathologies. To efficiently support this mobile multimedia application, guaranteeing reliability in a continuous monitoring mode, we make use of a new WSN paradigm based on mobility proxies.