Releasing network isolation problem in group-based industrial wireless sensor networks

In this paper, we propose a cross-layer optimization scheme named Adjusting the Transmission Radius (ATR), which is based on the Energy Consumed uniformly Connected K-Neighborhood (EC-CKN) sleep scheduling algorithm in wireless sensor networks (WSNs). In particular, we discovered two important problems, namely, the death acceleration problem and the network isolation problem, in EC-CKN-based WSNs. Furthermore, we solve these two problems in ATR, which creates sleeping opportunities for the nodes that cannot get a chance to sleep in the EC-CKN algorithm. Simulation and experimental results show that the network lifetime of ATR-Connected-K-Neighborhood-based WSNs increases by 19%, on average, and the maximum increment is 41%. In addition, four important insights were discovered through this research work and presented in this paper

LEVERAGING PEER-TO-PEER ENERGY SHARING FOR RESOURCE OPTIMIZATION IN MOBILE SOCIAL NETWORKS

Mobile Opportunistic Networks (MSNs) enable the interaction of mobile users in the vicinity through various short-range wireless communication technologies (e.g., Bluetooth, WiFi) and let them discover and exchange information directly or in ad hoc manner. Despite their promise to enable many exciting applications, limited battery capacity of mobile devices has become the biggest impediment to these appli- cations. The recent breakthroughs in the areas of wireless power transfer (WPT) and rechargeable lithium batteries promise the use of peer-to-peer (P2P) energy sharing (i.e., the transfer of energy from the battery of one member of the mobile network to the battery of the another member) for the efficient utilization of scarce energy resources in the network. However, due to uncertain mobility and communication opportunities in the network, resource optimization in these opportunistic networks is very challenging. In this dissertation, we study energy utilization in three different applications in Mobile Social Networks and target to improve the energy efficiency in the network by benefiting from P2P energy sharing among the nodes. More specifi- xi cally, we look at the problems of (i) optimal energy usage and sharing between friendly nodes in order to reduce the burden of wall-based charging, (ii) optimal content and energy sharing when energy is considered as an incentive for carrying the content for other nodes, and (iii) energy balancing among nodes for prolonging the network lifetime. We have proposed various novel protocols for the corresponding applications and have shown that they outperform the state-of-the-art solutions and improve the energy efficiency in MSNs while the application requirements are satisfied

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 such as: a) the trajectory of the charger; b) the different charging policies; c) the impact of the ratio of the energy the Mobile Charger may deliver to the sensors over the total available energy in the network. In the light of these optimisations, we then study the impact of the charging process to the network lifetime for three characteristic underlying routing protocols; a Greedy protocol, a clustering protocol and an energy balancing protocol. 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 for all three routing families by a detailed experimental evaluation. The derived findings demonstrate significant performance gains, both with respect to the no charger case as well as the different charging alternatives; in particular, the performance improvements include the network lifetime, as well as connectivity, coverage and energy balance properties

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime

Assessing the impact of sensor-based task scheduling on battery lifetime in IoT devices

A well-known system-level strategy to reduce the energy consumption of microprocessors or microcontrollers is to organize the scheduling of the executed tasks so that it is aware of the main battery nonidealities. In the Internet-of-Things (IoT) domain, devices rely on simpler microcontrollers, workloads are less rich, and batteries are typically sized to guarantee lifetimes of more extensive orders of magnitude (e.g., days, as opposed to hours). Load current magnitudes in these IoT devices are, therefore, relatively small compared to other more powerful devices, and they hardly trigger the conditions that emphasize the battery nonidealities. In this work, we carry out a measurement-based assessment about whether task scheduling is really relevant to extend the lifetime of IoT devices. We run experiments both on a physical commercial IoT device hosting four sensors, an MCU, and a wireless radio, as well as on a “synthetic” device emulated with a programmable load generator. We used both secondary lithium-ion and primary alkaline batteries to explore the impact of battery chemistries further. Results show that the impact of different schedules is essentially irrelevant, with a maximum difference of only 3.98% in battery lifetime between the optimal and worst schedules

An Efficient Energy Harvesting Assited Clustering Scheme for Wireless Sensor Networks

One of the prominent challenges in Wireless sensor networks (WSNs) is the energy conservation of sensor nodes irrespective of the nature of the sensor applications due to the tiny, limited batteries of the nodes.One promising solution to preserve energy is the clustering phenomenon and this mechanism also requires adequate stress on overall overhead of the network. Various clustering solutions have been addressed to extricate the power constraints of the sensor networks and they fluctuate in their boundaries owing to the multifaceted nature of this problem. In a typical clustering process in a WSN, energy is consumed in three phases: data sensing, data forwarding and data aggregation. A potential green, untrammeled replacement towards the conventional clustered sensor networks is the harvesting and utilization of energy from an ambient power resource. Unlike many other solutions, this approach overcomes the customary trade-offs but hosts economic and application-specific constraints. Our proposed Efficient Energy Harvestingassisted Clustering (EEHC) scheme contributes the idea of forming effective clusters that are free of residual nodes and overlapping. In this environment each sensor node is equipped with the energy harvesting device.The cluster head effectively balances the load in a cluster based on energy budgeting and nearly reduces the need for reclustering. Our approach is compared against the conventional and modern clustering algorithms for WSNs and yields significant improvement in the scope of lifetime from the pecuniary perspective

Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

Clustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency

An Autonomous Wearable Sensor Node for Long-Term Healthcare Monitoring Powered by a Photovoltaic Energy Harvesting System

oai:ojs.ijet.ise.pw.edu.pl:article/2503In this paper, an autonomous wearable sensor node is developed for long-term continuous healthcare monitoring. This node is used to monitor the body temperature and heart rate of a human through a mobile application. Thus, it includes a temperature sensor, a heart pulse sensor, a low-power microcontroller, and a Bluetooth low energy (BLE) module. The power supply of the node is a lithium-ion rechargeable battery, but this battery has a limited lifetime. Therefore, a photovoltaic (PV) energy harvesting system is proposed to prolong the battery lifetime of the sensor node. The PV energy harvesting system consists of a flexible photovoltaic panel, and a charging controller. This PV energy harvesting system is practically tested outdoor under lighting intensity of 1000 W/m2. Experimentally, the overall power consumption of the node is 4.97 mW and its lifetime about 246 hours in active-sleep mode. Finally, the experimental results demonstrate long-term and sustainable operation for the wearable sensor node