Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Energy Management in RFID-Sensor Networks: Taxonomy and Challenges

Ubiquitous Computing is foreseen to play an important role for data production and network connectivity in the coming decades. The Internet of Things (IoT) research which has the capability to encapsulate identification potential and sensing capabilities, strives towards the objective of developing seamless, interoperable and securely integrated systems which can be achieved by connecting the Internet with computing devices. This gives way for the evolution of wireless energy harvesting and power transmission using computing devices. Radio Frequency (RF) based Energy Management (EM) has become the backbone for providing energy to wireless integrated systems. The two main techniques for EM in RFID Sensor Networks (RSN) are Energy Harvesting (EH) and Energy Transfer (ET). These techniques enable the dynamic energy level maintenance and optimisation as well as ensuring reliable communication which adheres to the goal of increased network performance and lifetime. In this paper, we present an overview of RSN, its types of integration and relative applications. We then provide the state-of-the-art EM techniques and strategies for RSN from August 2009 till date, thereby reviewing the existing EH and ET mechanisms designed for RSN. The taxonomy on various challenges for EM in RSN has also been articulated for open research directives

Wireless sensors networks

After studying in depth look at wireless sensor networks are quite clear improvement compared to traditional wireless networks due to several factors as are the durability of the lifetime of the batteries, allowing greater portability of sensor nodes and that can record more events to power stay longer in some places, the routing protocols networks sensors allow gain than in durability also gain in efficiency the avoidance of collisions between packets, which also ensures a lower number of unnecessary network traffic. Because of the great features of such networks are currently using sensor networks in many projects related to different fields such as: environment, health, military, construction and structures, automotive, home automation, agriculture, etc. This type of network currently is leading a technological revolution similar to that had appearance of internet, because the applications appear to be infinite, also speaks global surveillance network on the planet capable of recording and tracking people specific goods and research projects have generated great interest for application in practice

Towards self-powered wireless sensor networks

Ubiquitous computing aims at creating smart environments in which computational and communication capabilities permeate the word at all scales, improving the human experience and quality of life in a totally unobtrusive yet completely reliable manner. According to this vision, an huge variety of smart devices and products (e.g., wireless sensor nodes, mobile phones, cameras, sensors, home appliances and industrial machines) are interconnected to realize a network of distributed agents that continuously collect, process, share and transport information. The impact of such technologies in our everyday life is expected to be massive, as it will enable innovative applications that will profoundly change the world around us. Remotely monitoring the conditions of patients and elderly people inside hospitals and at home, preventing catastrophic failures of buildings and critical structures, realizing smart cities with sustainable management of traffic and automatic monitoring of pollution levels, early detecting earthquake and forest fires, monitoring water quality and detecting water leakages, preventing landslides and avalanches are just some examples of life-enhancing applications made possible by smart ubiquitous computing systems. To turn this vision into a reality, however, new raising challenges have to be addressed, overcoming the limits that currently prevent the pervasive deployment of smart devices that are long lasting, trusted, and fully autonomous. In particular, the most critical factor currently limiting the realization of ubiquitous computing is energy provisioning. In fact, embedded devices are typically powered by short-lived batteries that severely affect their lifespan and reliability, often requiring expensive and invasive maintenance. In this PhD thesis, we investigate the use of energy-harvesting techniques to overcome the energy bottleneck problem suffered by embedded devices, particularly focusing on Wireless Sensor Networks (WSNs), which are one of the key enablers of pervasive computing systems. Energy harvesting allows to use energy readily available from the environment (e.g., from solar light, wind, body movements, etc.) to significantly extend the typical lifetime of low-power devices, enabling ubiquitous computing systems that can last virtually forever. However, the design challenges posed both at the hardware and at the software levels by the design of energy-autonomous devices are many. This thesis addresses some of the most challenging problems of this emerging research area, such as devising mechanisms for energy prediction and management, improving the efficiency of the energy scavenging process, developing protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support. %, including the design of mechanisms for energy prediction and management, improving the efficiency of the energy harvesting process, the develop of protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support

Markov decision processes with applications in wireless sensor networks: A survey

Ministry of Education, Singapore under its Academic Research Funding Tier

Acoustic power distribution techniques for wireless sensor networks

Recent advancements in wireless power transfer technologies can solve several residual problems concerning the maintenance of wireless sensor networks. Among these, air-based acoustic systems are still less exploited, with considerable potential for powering sensor nodes. This thesis aims to understand the significant parameters for acoustic power transfer in air, comprehend the losses, and quantify the limitations in terms of distance, alignment, frequency, and power transfer efficiency. This research outlines the basic concepts and equations overlooking sound wave propagation, system losses, and safety regulations to understand the prospects and limitations of acoustic power transfer. First, a theoretical model was established to define the diffraction and attenuation losses in the system. Different off-the-shelf transducers were experimentally investigated, showing that the FUS-40E transducer is most appropriate for this work. Subsequently, different load-matching techniques are analysed to identify the optimum method to deliver power. The analytical results were experimentally validated, and complex impedance matching increased the bandwidth from 1.5 to 4 and the power transfer efficiency from 0.02% to 0.43%. Subsequently, a detailed 3D profiling of the acoustic system in the far-field region was provided, analysing the receiver sensitivity to disturbances in separation distance, receiver orientation and alignment. The measured effects of misalignment between the transducers are provided as a design graph, correlating the output power as a function of separation distance, offset, loading methods and operating frequency. Finally, a two-stage wireless power network is designed, where energy packets are inductively delivered to a cluster of nodes by a recharge vehicle and later acoustically distributed to devices within the cluster. A novel dynamic recharge scheduling algorithm that combines weighted genetic clustering with nearest neighbour search is developed to jointly minimise vehicle travel distance and power transfer losses. The efficacy and performance of the algorithm are evaluated in simulation using experimentally derived traces that presented 90% throughput for large, dense networks.Open Acces

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

Recent advances in wireless technologies have led to an increased deployment of Wireless Sensor Networks (WSNs) for a plethora of diverse surveillance applications such as health, military, and environmental. However, sensor nodes in WSNs usually suffer from short device lifetime due to severe energy constraints and therefore, cannot guarantee to meet the Quality of Service (QoS) needs of various applications. This is proving to be a major hindrance to the widespread adoption of WSNs for such applications. Therefore, to extend the lifetime of WSNs, it is critical to optimize the energy usage in sensor nodes that are often deployed in remote and hostile terrains. To this effect, several energy management schemes have been proposed recently. Node scheduling is one such strategy that can prolong the lifetime of WSNs and also helps to balance the workload among the sensor nodes. In this article, we discuss on the energy management techniques of WSN with a particular emphasis on node scheduling and propose an energy management life-cycle model and an energy conservation pyramid to extend the network lifetime of WSNs. We have provided a detailed classification and evaluation of various node scheduling schemes in terms of their ability to fulfill essential QoS requirements, namely coverage, connectivity, fault tolerance, and security. We considered essential design issues such as network type, deployment pattern, sensing model in the classification process. Furthermore, we have discussed the operational characteristics of schemes with their related merits and demerits. We have compared the efficacy of a few well known graph-based scheduling schemes with suitable performance analysis graph. Finally, we study challenges in designing and implementing node scheduling schemes from a QoS perspective and outline open research problems

A POWER DISTRIBUTION SYSTEM BUILT FOR A VARIETY OF UNATTENDED ELECTRONICS

A power distribution system (PDS) delivers electrical power to a load safely and effectively in a pre-determined format. Here format refers to necessary voltages, current levels and time variation of either as required by the empowered system. This formatting is usually referred as "conditioning". The research reported in this dissertation presents a complete system focusing on low power energy harvesting, conditioning, storage and regulation. Energy harvesting is a process by which ambient energy present in the environment is captured and converted to electrical energy. In recent years, it has become a prominent research area in multiple disciplines. Several energy harvesting schemes have been exploited in the literature, including solar energy, mechanic energy, radio frequency (RF) energy, thermal energy, electromagnetic energy, biochemical energy, radioactive energy and so on. Different from the large scale energy generation, energy harvesting typically operates in milli-watts or even micro-watts power levels. Almost all energy harvesting schemes require stages of power conditioning and intermediate storage - batteries or capacitors that reservoir energy harvested from the environment. Most of the ambient energy fluctuates and is usually weak. The purpose of power conditioning is to adjust the format of the energy to be further used, and intermediate storage smoothes out the impact of the fluctuations on the power delivered to the load. This dissertation reports an end to end power distribution system that integrates different functional blocks including energy harvesting, power conditioning, energy storage, output regulation and system control. We studied and investigated different energy harvesting schemes and the dissertation places emphasis on radio frequency energy harvesting. This approach has proven to be a viable power source for low-power electronics. However, it is still challenging to obtain significant amounts of energy rapidly and efficiently from the ambient. Available RF power is usually very weak, leading to low voltage applied to the electronics. The power delivered to the PDS is hard to utilize or store. This dissertation presents a configuration including a wideband rectenna, a switched capacitor voltage boost converter and a thin film flexible battery cell that can be re-charged at an exceptionally low voltage. We demonstrate that the system is able to harvest energy from a commercially available hand-held communication device at an overall efficiency as high as 7.7 %. Besides the RF energy harvesting block, the whole PDS includes a solar energy harvesting block, a USB recharging block, a customer selection block, two battery arrays, a control block and an output block. The functions of each of the blocks have been tested and verified. The dissertation also studies and investigates several potential applications of this PDS. The applications we exploited include an ultra-low power tunable neural oscillator, wireless sensor networks (WSNs), medical prosthetics and small unmanned aerial vehicles (UAVs). We prove that it is viable to power these potential loads through energy harvesting from multiple sources