Which Wireless Sensor Networks Research Articles

Reliability Evaluation for Chain Routing Protocols in Wireless Sensor Networks Using Reliability Block Diagram

There are many different fields in which wireless sensor networks (WSNs) can be used such as environmental monitoring, healthcare, military, and security. Due to the vulnerability of WSNs, reliability is a critical concern. Evaluation of a WSN’s reliability is essential during the design process and when evaluating WSNs’ performance. Current research uses the reliability block diagram (RBD) technique, based on component functioning or failure state, to evaluate reliability. In this study, a new methodology-based RBD, to calculate the energy reliability of various proposed chain models in WSNs, is presented. A new method called D-Chain is proposed, to form the chain starting from the nearest node to the base station (BS) and to choose the chain head based on the minimum distance D, and Q-Chain is proposed, to form the chain starting from the farthest node from the BS and select the head based on the maximum weight, Q. Each chain has three different arrangements: single chain/single-hop, multi-chain/single-hop, and multi-chain/multi-hop. Moreover, we applied dynamic leader nodes to all of the models mentioned. The simulation results indicate that the multi Q-Chain/single-hop has the best performance, while the single D-Chain has the least reliability in all situations. In the grid scenario, multi Q-Chain/single-hop achieved better average reliability, 11.12 times greater than multi D-Chain/single-hop. On the other hand, multi Q-Chain/single-hop achieved 6.38 times better average reliability than multi D-Chain/single-hop, in a random scenario.

Machine Learning Techniques for Precision Agriculture Using Wireless Sensor Networks

In India, approximately 70% of the total population is dependent on agriculture for their livelihood. Hence, it is essential to pay attention to agriculture to increase crop quality and quantity, thus increasing the overall cultivation yield. The traditional methods used require a lot of farmer’s effort and hard work, which results in delayed crop cultivation. Moreover, it’s challenging to predict the environmental conditions and detect the particular area where there are weeds, insects, etc., which requires immediate treatments, thus affecting overall crop production. So, there is a need to make it automated, and this can be done by adopting advanced techniques of precision agriculture (PA) or intelligent agriculture. Precision agriculture is one of the fields in which wireless sensor networks (WSNs) are widely adopted, which consists of a large number of sensors placed in the field to monitor and measure the various environmental parameters, such as humidity, temperature, soil moisture, soil PH value, precipitation, water level, etc., for enhancing the productivity, profitability, quantity, and quality of crops. The machine learning techniques can be applied to precision agriculture to increase crop growth, manage the process of crop cultivation, and create a perfect environment for the crops to increase productivity with less human effort. This paper provides an insight into various machine learning techniques used for precision agriculture using wireless sensor networks.

Clustering Isolated Nodes to Enhance Network's Life Time of WSNs for IoT Applications

Because of the metaphysical shift in the technological era, the world is witnessing machine to machine (M2M) communication. Sensors are omnipresent, owing to which wireless sensor networks (WSNs) are developing as a key enabling technology for the Internet of Things (IoT). M2M interaction poses a severe threat to the lifetime of the network. Clustering plays an essential role in elevating the energy efficiency of the WSN. Particularly, uniform distribution of cluster heads (CHs) is crucial for achieving better network lifetime and uninterrupted sensing from the area under observation. For this, we propose an efficient two-fold clustering algorithm, namely second-fold clustering (SFC). In the first phase, the selection of CH is made based on zonal residual energy and zonal degree of connectivity. In the second phase, the leftover nodes known as isolated nodes are clumped. CH is selected based on the zonal degree of connectivity to ensure that energy is disbursed (almost) uniformly by all the nodes. This uniformity makes it suitable for integration with the IoT. To find out the efficacy of SFC, carried rigorous experimental study out where it is compared to two existing clustering algorithms. The simulation results show that SFC outperforms its counterparts. The results showed that SFC improved network lifetime by approximately 9.6% and 10.2% compared to regional energy-aware clustering scheme and Residual-Low Energy Adaptive Clustering Hierarchy (R-LEACH), respectively.

ZIZO: A Zoom-In Zoom-Out Mechanism for Minimizing Redundancy and Saving Energy in Wireless Sensor Networks

In modern life, there is invisible data being continuously generated, data that if collected and processed can detect risks and changes and enable us to mitigate their effect. Thus, there is an essential need to sense everything around us in order to make better use of it. This lead us to an era known as “sensing-era” in which wireless sensor networks (WSN) play a vital role in the monitoring of natural and artificial environments. Indeed, the collection and transmission of huge amounts of redundant data by sensor nodes will lead to a faster consumption of their limited battery power, which is sometimes difficult to replace or recharge, reducing the overall lifetime of the network. Therefore, an effective way to increase lifetime by saving energy is to reduce the amount of transmitted data by eliminating redundancy along the path to the sink. In this paper, we propose a Zoom-In Zoom-Out (ZIZO) mechanism aimed to minimize data transmission in WSN. ZIZO works on two WSN levels: on the sensor level where we propose a compression method called index-bit-encoding (IBE) in order to aggregate similar readings before sending them to the second network level, e.g. cluster-head (CH). The CH searches then for correlation among node data in order to optimize the sampling rate of the sensors in the cluster through a process called sampling rate adjustment (SRA). We evaluate the performance of our mechanism based on both simulations and experiments while the obtained results are compared to other existing techniques, and we show reduced energy consumption by up to 90% in some cases.

NEOFog

Nonvolatile processors have emerged as one of the promising solutions for energy harvesting scenarios, among which Wireless Sensor Networks (WSN) provide some of the most important applications. In a typical distributed sensing system, due to difference in location, energy harvester angles, power sources, etc. different nodes may have different amount of energy ready for use. While prior approaches have examined these challenges, they have not done so in the context of the features offered by nonvolatile computing approaches, which disrupt certain foundational assumptions. We propose a new set of nonvolatility-exploiting optimizations and embody them in the NEOFog system architecture. We discuss shifts in the tradeoffs in data and program distribution for nonvolatile processing-based WSNs, showing how non-volatile processing and non-volatile RF support alter the benefits of computation and communication-centric approaches. We also propose a new algorithm specific to nonvolatile sensing systems for load balancing both computation and communication demands. Collectively, the NV-aware optimizations in NEOFog increase the ability to perform in-fog processing by 4.2X and can increase this to 8X if virtualized nodes are 3X multiplexed.

Middleware and communication technologies for structural health monitoring of critical infrastructures: A survey

Critical Infrastructure Protection (CIP) has become a priority for every country around the world with the aim of reducing vulnerabilities and improving protection of Critical Infrastructures (CI) against terrorist attacks or natural disasters, among other threats. As part of CIP, Structural Health Monitoring (SHM) is defined as the process of gathering basic information that allows detecting, locating and quantifying vulnerabilities early on (fatigue cracking, degradation of boundary conditions, etc.) thereby improving, the resilience of the CI. Recent advances in electronics, wireless communication and software are expected to open the door to a new era of densely connected devices sharing information worldwide, known as the Internet of Things (IoT), in which Wireless Sensor Networks (WSNs) play an important role. The combined use of IoT/WSNs together with industrial sensors in SHM provide an ad-hoc, inexpensive and easy way of deploying a monitoring system, where data can be shared among different entities. SHM requirements are challenging and diverse and therefore several different technologies may be used in the same deployment. At the same time the use of a middleware can substantially simplify and speed up the development of applications for SHM. Taking into account the challenges of SHM systems, this paper provides a review of the most novel and relevant wireless technologies and a state-of-the-art middleware for WSNs focusing on SHM specific requirements.

A confidential and DoS-resistant multi-hop code dissemination protocol for wireless sensor networks

Due to the open environment in which Wireless Sensor Networks (WSNs) are typically deployed, it is important to be able to authenticate transmitted data. In some applications it is also required that the data be kept confidential in spite of message interception. Authentication and confidentiality are typically implemented through cryptographic operations which may be expensive in power consumption, making a protocol with these features vulnerable to attack by an adversary who transmits forged data, so forcing nodes to waste energy in identifying it as invalid. Additionally, in multi-hop code dissemination protocols, a sensor node is required to broadcast its program image when requested by its neighbors. An adversary could repeatedly send spurious program image requests to its neighbors, making them exhaust their energy reserves. In this paper, we present a new approach to achieve confidentiality in multi-hop code dissemination. We propose countermeasures against both these types of attack. Our approach is based on Deluge, an open source, state-of-the-art code dissemination protocol for WSNs. We provide theoretical analysis and simulation/experimental results which show that our approach outperforms earlier attempts when a malicious code injector is present, as well as a performance evaluation of latency, energy consumption, dissemination rate, and other factors (Liu and Ning, November 2006).

Research Challenges in using Mobile Agents for Data Aggregation in Wireless Sensor Networks with Dynamic Deadlines

research has provided means by which Wireless Sensor Networks (WSN) with dynamic deadlines can make use of mobile agents for data aggregation. Lot of research has already been done with respect to using mobile agents in case of static wireless sensor networks. This paper proposes, along with brief explanation of research issues associated with applying mobile agents, two new techniques to improve the performance of data aggregation using mobile agents. First technique proposed in this paper makes mobile agents intelligent enough to support dynamic wireless sensor networks also. Second technique proposed shows that how multiple mobile agents can also be used, in partitions of the original given wireless sensor which are generated based on some chosen criteria, to reduce overall number of buffer overflows. Paper will be concluded with mentioning of future directions of research.