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Minimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node.

In this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured.

Energy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods.

This paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.

Wireless sensor networks (WSNs) are periodically collecting data through randomly dispersed sensors (motes), which typically consume high energy in radio communication that mainly leans on data transmission within the network. Furthermore, dissemination mode in WSN usually produces noisy values, incorrect measurements or missing information that affect the behaviour of WSN. In this article, a Distributed Data Predictive Model (DDPM) was proposed to extend the network lifetime by decreasing the consumption in the energy of sensor nodes. It was built upon a distributive clustering model for predicting dissemination-faults in WSN. The proposed model was developed using Recursive least squares (RLS) adaptive filter integrated with a Finite Impulse Response (FIR) filter, for removing unwanted reflections and noise accompanying of the transferred signals among the sensors, aiming to minimize the size of transferred data for providing energy efficient. The experimental results demonstrated that DDPM reduced the rate of data transmission to ∼20%. Also, it decreased the energy consumption to 95% throughout the dataset sample and upgraded the performance of the sensory network by about 19.5%. Thus, it prolonged the lifetime of the network.

With the increasing acclaim of Wireless Sensor Networks and its diverse applications, research has been directed into optimising and prolonging the network lifetime. Energy efficiency has been a critical factor due to the energy resource impediment of batteries in sensor nodes. The proposed routing algorithm therefore aims at extending lifetime of sensors by enhancing load distribution in the network. The scheme is based on the chain-based routing technique of the PEGASIS (Power Energy GAthering in Sensor Information Systems) protocol and uses Ant Colony Optimisation to obtain the optimal chain. The contribution of the proposed work is the integration of the clustering method to PEGASIS with Ant Colony Optimisation to reduce redundancy of data, neighbour nodes distance and transmission delay associated with long links, and the employment an appropriate cluster head selection method. Simulation results indicates proposed method’s superiority in terms of residual energy along with considerable improvement regarding network lifetime, and significant reduction in delay when compared with existing PEGASIS protocol and optimised PEG-ACO chain respectively.

The purpose of this paper is to minimize energy usage by maximizing network life in the creation of applications and protocols

This paper presents a novel methodology for optimum energy consumption in wireless sensor networks. The proposed methodology introduces some protocols and logarithms that effectively contributed to reducing energy consumption in these types of networks.

The results of that comparison showed the ability of those logarithms and protocols to reduce that energy but in varying proportions. It can be concluded that a significant reduction in energy consumption approximately 50% could be obtained by the proposed methodology.

Here, a novel methodology for optimum energy consumption in wireless sensor networks has been introduced.

This paper aims to describe a method for Internet-of-Things-devices to achieve industrial grade reliability for information transfer from wireless sensor systems to production systems using blockchain technologies.

An increased security and reliability of submitted data within the sensor network could be achieved on an application level. Therefore, a lightweight, high-level communication protocol based on blockchain principles was designed.

Blockchain mechanisms can secure the wireless communication of Internet-of-Things-devices in a lightweight and scalable manner.

The innovation of this research is the successful application of general blockchain mechanisms to increase security of a wireless sensor system without binding to a dedicated blockchain technology.

The performance of Wireless Sensor Networks (WSNs) applications is bounded by the limited resources of battery-enabled Sensor Nodes (SNs), which include energy and computational power; the combination of which existing research seldom focuses on. Although bio-inspired algorithms provide a way to control energy usage by finding optimal routing paths, those which converge slower require even more computational power, which altogether degrades the overall lifetime of SNs.

Hence, two novel routing protocols are proposed using the Red-Deer Algorithm (RDA) in a WSN scenario, namely Horizontal PEG-RDA Equal Clustering and Horizontal PEG-RDA Unequal Clustering, to address the limited computational power of SNs. Clustering, data aggregation and multi-hop transmission are also integrated to improve energy usage. Unequal clustering is applied in the second protocol to mitigate the hotspot problem in Horizontal PEG-RDA Equal Clustering.

Comparisons with the well-founded Ant Colony Optimisation (ACO) algorithm reveal that RDA converges faster by 85 and 80% on average when the network size and node density are varied, respectively. Furthermore, 33% fewer packets are lost using the unequal clustering approach which also makes the network resilient to node failures. Improvements in terms of residual energy and overall network lifetime are also observed.

Proposal of a bio-inspired algorithm, namely the RDA to find optimal routing paths in WSN and to enhance convergence rate and execution time against the well-established ACO algorithm. Creation of a novel chain cluster-based routing protocol using RDA, named Horizontal PEG-RDA Equal Clustering. Design of an unequal clustering equivalent of the proposed Horizontal PEG-RDA Equal Clustering protocol to tackle the hotspot problem, which enhances residual energy and overall network lifetime, as well as minimises packet loss.

Due to its boundless potential applications, Wireless Sensor Networks have been subject to much research in the last two decades. WSNs are often deployed in remote environments making replacement of batteries not feasible. Low energy consumption being of prime requisite led to the development of energy-efficient routing protocols. The proposed routing algorithms seek to prolong the lifetime of sensor nodes in the relatively unexplored area of 3D WSNs. The schemes use chain-based routing technique PEGASIS as basis and employ genetic algorithm to build the chain instead of the greedy algorithm. Proposed schemes will incorporate an energy and distance aware CH selection technique to improve load balancing. Clustering of the network is also implemented to reduce number of nodes in a chain and hence reduce delay. Simulation of our proposed protocols is carried out for homogeneous networks considering separately cases for a static base-station inside and outside the network. Results indicate considerable improvement in lifetime over PEGASIS of 817% and 420% for base station inside and outside the network respectively. Residual energy and delay performance are also considered.

Designing an efficient routing protocol that opportunistically forwards data to the destination node through nearby sensor nodes or devices is significantly important for an effective incidence response and disaster recovery framework. Existing sensor routing protocols are mostly not effective in such disaster recovery applications as the networks are affected (destroyed or overused) in disasters such as earthquake, flood, Tsunami and wildfire. These protocols require a large number of message transmissions to reestablish the clusters and communications that is not energy efficient and result in packet loss. This paper introduces ODCR - an energy efficient and reliable opportunistic density clustered-based routing protocol for such emergency sensor applications. We perform simulation to measure the performance of ODCR protocol in terms of network energy consumptions, throughput and packet loss ratio. Simulation results demonstrate that the ODCR protocol is much better than the existing TEEN, LEACH and LORA protocols in term of these performance metrics.

Multipath routing holds a great potential to provide sufficient bandwidth to a plethora of applications in wireless sensor networks. In this paper, we consider the problem of interference that can significantly affect the expected performances. We focus on the performance evaluation of the iterative paths discovery approach as opposed to the traditional concurrent multipath routing. Five different variants of multipath protocols are simulated and evaluated using different performance metrics. We mainly show that the iterative approach allows better performances when used jointly with an interference-aware metric or when an interference-zone marking strategy is employed. This latter appears to exhibit the best performances in terms of success ratio, achieved throughput, control messages overhead as well as energy consumption.

The future construction site will be pervasive, context aware and embedded with intelligence. The purpose of this paper is to explore and define the concept of the digital skin of the future smart construction site.

The paper provides a systematic and hierarchical classification of 114 articles from both industry and academia on the digital skin concept and evaluates them. The hierarchical classification is based on application areas relevant to construction, such as augmented reality, building information model-based visualisation, labour tracking, supply chain tracking, safety management, mobile equipment tracking and schedule and progress monitoring. Evaluations of the research papers were conducted based on three pillars: validation of technological feasibility, onsite application and user acceptance testing.

Technologies learned about in the literature review enabled the envisaging of the pervasive construction site of the future. The paper presents scenarios for the future context-aware construction site, including the construction worker, construction procurement management and future real-time safety management systems.

Based on the gaps identified by the review in the body of knowledge and on a broader analysis of technology diffusion, the paper highlights the research challenges to be overcome in the advent of digital skin. The paper recommends that researchers follow a coherent process for smart technology design, development and implementation in order to achieve this vision for the construction industry.