Energy Consumption in Wireless Sensor Networks Using GSP

The energy consumption rate for sensors in a wireless sensor network varies greatly based on the protocols the sensors use for communications. The Gossip-Based Sleep Protocol (GSP) implements routing and some MAC functions in an energy conserving manner. The effectiveness of GSP has already been demonstrated via simulation. However, no prototype system has been previously developed. GSP was implemented on the Mica2 platform and measurements were conducted to determine the improvement in network lifetime. Results for energy consumption, transmitted and received power, minimum voltage supply required for operation, effect of transmission power on energy consumption, and different methods for measuring lifetime of a sensor node are presented. The behaviour of sensor nodes when they are close to their end of lifetime is described and analyzed. A comparison with other models for energy consumption is made and suggestions for future work are presented

THE EFFECT OF INTERACTIONS BETWEEN PROTOCOLS AND PHYSICAL TOPOLOGIES ON THE LIFETIME OF WIRELESS SENSOR NETWORKS

Wireless sensor networks enable monitoring and control applications such weather sensing, target tracking, medical monitoring, road monitoring, and airport lighting. Additionally, these applications require long term and robust sensing, and therefore require sensor networks to have long system lifetime. However, sensor devices are typically battery operated. The design of long lifetime networks requires efficient sensor node circuits, architectures, algorithms, and protocols. In this research, we observed that most protocols turn on sensor radios to listen or receive data then make a decision whether or not to relay it. To conserve energy, sensor nodes should consider not listening or receiving the data when not necessary by turning off the radio. We employ a cross layer scheme to target at the network layer issues. We propose a simple, scalable, and energy efficient forwarding scheme, which is called Gossip-based Sleep Protocol (GSP). Our proposed GSP protocol is designed for large low-cost wireless sensor networks with low complexity to reduce the energy cost for every node as much as possible. The analysis shows that allowing some nodes to remain in sleep mode improves energy efficiency and extends network lifetime without data loss in the topologies such as square grid, rectangular grid, random grid, lattice topology, and star topology. Additionally, GSP distributes energy consumption over the entire network because the nodes go to sleep in a fully random fashion and the traffic forwarding continuously via the same path can be avoided

Energy-aware Gossip Protocol for Wireless Sensor Networks

Dissertação de mestrado em Engenharia InformáticaIn Wireless Sensor Networks (WSNs), typically composed of nodes with resource constraints, leveraging efficient processes is crucial to enhance the network longevity and consequently the sustainability in ultra-dense and heterogeneous environments, such as smart cities. Epidemic algorithms are usually efficient in delivering packets to a sink or to all it’s peers but have poor energy efficiency due to the amount of packet redundancy. Directional algorithms, such as Minimum Cost Forward Algorithm (MCFA) or Directed Diffusion, yield high energy efficiency but fail to handle mobile environments, and have poor network coverage. This work proposes a new epidemic algorithm that uses the current energy state of the network to create a topology that is cyclically updated, fault tolerant, whilst being able to handle the challenges of a static or mobile heterogeneous network. Depending on the application, tuning in the protocol settings can be made to prioritise desired characteristics. The proposed protocol has a small computational footprint and the required memory is proportional not to the size of the network, but to the number of neighbours of a node, enabling high scalability. The proposed protocol was tested, using a ESP8266 as an energy model reference, in a simulated environment with ad-hoc wireless nodes. It was implemented at the application level with UDP sockets, and resulted in a highly energy efficient protocol, capable of leveraging extended network longevity with different static or mobile topologies, with results comparable to a static directional algorithm in delivery efficiency.Em Redes de Sensores sem Fios (RSF), tipicamente compostas por nós com recursos lim-itados, alavancar processos eficientes é crucial para aumentar o tempo de vida da rede e consequentemente a sustentabilidade em ambientes heterogéneos e ultra densos, como cidades inteligentes por exemplo. Algoritmos epidêmicos são geralmente eficientes em en-tregar pacotes para um sink ou para todos os nós da rede, no entanto têm baixa eficiência energética devido a alta taxa de duplicação de pacotes. Algoritmos direcionais, como o MCFA ou de Difusão Direta, rendem alta eficiência energética mas não conseguem lidar com ambientes móveis, e alcançam baixa cobertura da rede. Este trabalho propõe um novo protocolo epidêmico que faz uso do estado energético atual da rede para criar uma topologia que por sua vez atualizada ciclicamente, tolerante a falhas, ao mesmo tempo que é capaz de lidar com os desafios de uma rede heterogênea estática ou móvel. A depender da aplicação, ajustes podem ser feitos às configurações do protocolo para que o mesmo priorize determinadas características. O protocolo proposto tem um pequeno impacto computacional e a memória requerida é proporcional somente à quantidade de vizinhos do nó, não ao tamanho da rede inteira, permitindo assim alta escalabilidade. O algoritmo proposto foi testado fazendo uso do modelo energético de uma ESP8266, em um ambiente simulado com uma rede sem fios ad-hoc. Foi implementado à nível aplicacional com sockets UDP, e resultou em um protocol energeticamente eficiente, capaz de disponibilizar alta longevidade da rede mesmo com diferentes topologias estáticas ou móveis com resultados comparáveis à um protocolo direcional em termos de eficiência na entrega de pacotes

Analysis of sensory data using graph signal processing

Air pollution monitoring is an important topic that has been researched in the past few years thanks to the massive deployment of IoT platforms, as it affects the lives of both children and adults, and it kills millions of people worldwide every year. A new framework of tools called Graph Signal Processing was presented recently and it allows, among other things, the ability to predict data on a node that belongs to a network of sensors using both the data itself and the topology of the graph, which is based on the Laplacian matrix. This thesis is a comparative study on different prediction techniques for pollutant signals, such as Linear Combination, Multiple Linear Regression and GSP and it presents the results of all three methods in different scenarios, using RMSE and R2 indicators, and focusing the efforts on the understanding of how different parameters (such as the distance between nodes) affect the performances of these new tools. The results of the study show that pollutants O3 and NO2 are lowpass signals, and as the number of edges between nodes increases, GSP obtains a close performances to MRL. For PM10, we conclude that is not a low-pass signal, and the performance of the indicators drop massively compared with the previous ones. Linear combination is the worst of all three and MLR has a stable performance during all the scenarios

Energy-Constrained Delivery of Goods with Drones Under Varying Wind Conditions

In this paper, we study the feasibility of sending drones to deliver goods from a depot to a customer by solving what we call the Mission-Feasibility Problem (MFP). Due to payload constraints, the drone can serve only one customer at a time. To this end, we propose a novel framework based on time-dependent cost graphs to properly model the MFP and tackle the delivery dynamics. When the drone moves in the delivery area, the global wind may change thereby affecting the drone's energy consumption, which in turn can increase or decrease. This issue is addressed by designing three algorithms, namely: (i) compute the route of minimum energy once, at the beginning of the mission, (ii) dynamically reconsider the most convenient trip towards the destination, and (iii) dynamically select only the best local choice. We evaluate the performance of our algorithms on both synthetic and real-world data. The changes in the drone's energy consumption are reflected by changes in the cost of the edges of the graphs. The algorithms receive the new costs every time the drone flies over a new vertex, and they have no full knowledge in advance of the weights. We compare them in terms of the percentage of missions that are completed with success (the drone delivers the goods and comes back to the depot), with delivered (the drone delivers the goods but cannot come back to the depot), and with failure (the drone neither delivers the goods nor comes back to the depot).Comment: typo author's nam

Wireless sensor with data and Energy Packets

This paper develops a mathematical model to determine the balance of energy input and data sensing and transmission in a wireless sensing node. Since the node acquires energy through harvesting from an intermittent source, and sensing is also carried out intermittently, the node is modelled with random arrivals of both energy and data. A buffer in the node stores data packets while energy is stored in a battery acting as an energy buffer. The approach uses the “Energy Packet Network” paradigm so that both energy and data packets can be modelled as discrete quantities. We assume that for each data packet, the sensor consumes K e energy packets for node electronics including sensing, processing, and storing and K t energy packets for transmission. We model the node's energy and data flow by a two-dimensional random walk which represents the backlog of data and energy packets. We then simplify the model using companion matrices and matrix algebra techniques that allow us to obtain a closed-form solution for the stationary probability distribution for the random walk which allows us to compute important performance measures, including the energy consumed by the node, and its throughput in data packets transmitted as a function of the amount of power that it receives. The model also allows us to evaluate the effect of ambient noise and the needs for data retransmissions, including for the case where M sensors operate in proximity and create interference for each other

COMPLEMENTING THE GSP ROUTING PROTOCOL IN WIRELESS SENSOR NETWORKS

Gossip-Based Sleep Protocol (GSP) is a routing protocol in the flooding family with overhead generated by duplicate packets. GSP does not have other sources of overhead or additional information requirements common in routing protocols, such as routing packets, geographical information, addressing or explicit route computation. Because of its simple functionality, GSP is a candidate routing protocol for Wireless Sensor Networks. However, previous research showed that GSP uses the majority of energy in the network by keeping the nodes with their radios on ready to receive, even when there are no transmissions, situation known as Idle Listening. Complementing GSP implies creating additional protocols that make use of GSP particular characteristics in order to improve performance without additional overhead. The research analyzes the performance of GSP with different topologies, number of hops from source to destination and node densities, and presents one alternative protocol to complement GSP decreasing idle listening, number of duplicate packets in the network and overall energy consumption. The study compared the results of this alternative protocol, MACGSP6, to a protocol stack proposed for Wireless Sensor Networks: Sensor MAC (S-MAC) with Dynamic Source Routing (DSR), showing the advantages and disadvantages of the different approaches

Internet of Things in Emergency Medical Care and Services

Emergency care is a critical area of medicine whose outcomes are influenced by the time, availability, and accuracy of contextual information. In addition, the success of emergency care depends on the quality and accuracy of the information received during the emergency call and data collected during the emergency transportation. The success of a follow medical treatment at an emergency care unit depends too on data collected during the two phases: emergency call and transport. However, most information received during an emergency-call is inaccurate and the process of information collection, storage, processing, and retrieval, during an emergency-transportation, is remaining manual and time-consuming. Emergency doctors mostly lack patient’s health records and base the medical treatment on a set of collected information including information provided by the patient or his relatives. Hence, the emergency care delivery is more patient-centered than patient-centric information. Wireless body area network and Internet of Technology (IoT) enable accurate collection of data and are increasingly used in medical applications. This chapter discusses the challenges facing the emergency medical care services delivery, especially in the developing countries. It presents and discusses an IoT platform for a patient-centric-information-based emergency care services delivery. The study is focused on a case of road traffic injury. Results of conducted experiments are discussed