https://hal.archives-ouvertes.fr/hal-01009386/document

Energy efficiency in wireless sensor networks: A top-down survey

Among the panoply of applications enabled by the Internet of Things (IoT), smart and connected health care is a particularly important one. Networked sensors, either worn on the body or embedded in our living environments, make possible the gathering of rich information indicative of our physical and mental health. Captured on a continual basis, aggregated, and effectively mined, such information can bring about a positive transformative change in the health care landscape. In particular, the availability of data at hitherto unimagined scales and temporal longitudes coupled with a new generation of intelligent processing algorithms can: (a) facilitate an evolution in the practice of medicine, from the current post facto diagnose-and-treat reactive paradigm, to a proactive framework for prognosis of diseases at an incipient stage, coupled with prevention, cure, and overall management of health instead of disease, (b) enable personalization of treatment and management options targeted particularly to the specific circumstances and needs of the individual, and (c) help reduce the cost of health care while simultaneously improving outcomes. In this paper, we highlight the opportunities and challenges for IoT in realizing this vision of the future of health care.

/pdf/health-monitoring-and-management-using-internet-of-things-2lx1tqm8h2.pdf

Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges

A new design for an energy harvesting device is proposed in this paper, which enables scavenging energy from radiofrequency (RF) electromagnetic waves. Compared to common alternative energy sources like solar and wind, RF harvesting has the least energy density. The existing state-of-the-art solutions are effective only over narrow frequency ranges, are limited in efficiency response, and require higher levels of input power. This paper has a twofold contribution. First, we propose a dual-stage energy harvesting circuit composed of a seven-stage and ten-stage design, the former being more receptive in the low input power regions, while the latter is more suitable for higher power range. Each stage here is a modified voltage multiplier, arranged in series and our design provides guidelines on component choice and precise selection of the crossover operational point for these two stages between the high (20 dBm) and low power (-20 dBm) extremities. Second, we fabricate our design on a printed circuit board to demonstrate how such a circuit can run a commercial Mica2 sensor mote, with accompanying simulations on both ideal and non-ideal conditions for identifying the upper bound on achievable efficiency. With a simple yet optimal dual-stage design, experiments and characterization plots reveal approximately 100% improvement over other existing designs in the power range of -20 to 7 dBm.

https://repository.library.northeastern.edu/files/neu:1340/fulltext.pdf

Design Optimization and Implementation for RF Energy Harvesting Circuits

Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this article, we review the state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy-harvesting-based WSNs, along with some future work directions to address these challenges.

https://dl.acm.org/doi/pdf/10.1145/3183338

Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review

This paper investigates collaborative task execution between a mobile device and a cloud clone for mobile applications under a stochastic wireless channel. A mobile application is modeled as a sequence of tasks that can be executed on the mobile device or on the cloud clone. We aim to minimize the energy consumption on the mobile device while meeting a time deadline, by strategically offloading tasks to the cloud. We formulate the collaborative task execution as a constrained shortest path problem. We derive a one-climb policy by characterizing the optimal solution and then propose an enumeration algorithm for the collaborative task execution in polynomial time. Further, we apply the LARAC algorithm to solving the optimization problem approximately, which has lower complexity than the enumeration algorithm. Simulation results show that the approximate solution of the LARAC algorithm is close to the optimal solution of the enumeration algorithm. In addition, we consider a probabilistic time deadline, which is transformed to hard deadline by Markov inequality. Moreover, compared to the local execution and the remote execution, the collaborative task execution can significantly save the energy consumption on the mobile device, prolonging its battery life.

/pdf/collaborative-task-execution-in-mobile-cloud-computing-under-xgq6gy6j2g.pdf

Collaborative Task Execution in Mobile Cloud Computing Under a Stochastic Wireless Channel

Cloud computing provides an approach to accessing shared computing resources. However, a traditional cloud is composed of powerful but energy-hungry workstations. The growth of the population of mobile devices such as smart phones and tablets provides huge amount of idling computing power. In this paper, we describe the design and implementation of a mobile computing system prototype named GEMCloud that utilizes energy efficient mobile devices (e.g., smartphones and tablets) as computing resources. We evaluate the computing power and energy efficiency of the mobile devices through comprehensive experiments. The results show that a cloud computing system with enough mobile devices working cooperatively is able to save 55% to 98% of the energy consumption of conventional server-based clouds while providing comparable computing speed.

Mobile computing - A green computing resource

Use of a passive wake-up radio can drastically increase the network lifetime in a sensor network by reducing or even completely eliminating unnecessary idle listening. A sensor node with a wake-up radio receiver (WuRx) can operate in an extremely low power sleep mode until it receives a trigger signal sent by a wake-up radio transmitter (WuTx). After receiving the trigger signal, the attached WuRx wakes up the sensor node to start the data communication. In this paper, we implement and compare the performance of three passive wake-up radio-based sensor nodes: 1) WISP-Mote, which is a sensor mote that employs an Intel WISP passive RFID tag as the WuRx; 2) EH-WISP-Mote, which combines a novel energy harvester with the WISP-Mote; and 3) REACH-Mote, which uses the energy harvester circuit combined with an ultra-low-power pulse generator to trigger the wake-up of the mote. Experimental results show that the wake-up range and wake-up delay for the EH-WISP-Mote are improved compared with the WISP-Mote, while providing the ability to perform both broadcast-based and ID-based wake-ups. On the other hand, the REACH-Mote, which can only provide broadcast-based wake-up, can achieve a much longer wake-up range than any known passive wake-up radio to date, achieving feasible wake-up at a range of up to 37 ft.

/pdf/range-extension-of-passive-wake-up-radio-systems-through-115vobli5y.pdf

Range extension of passive wake-up radio systems through energy harvesting

Energy efficiency is one of the crucial design criteria for wireless sensor networks. Idle listening constitutes a major part of energy waste, and thus solutions such as duty cycling and the use of wake-up radios have been proposed to reduce idle listening and save energy. Compared to duty cycling, wake-up radios save more energy by reducing unnecessary wake-ups and collisions. In this paper, we investigate the feasibility and potential benefits of using passive RFID as a wake-up radio. We first introduce a physical implementation of sensor nodes with passive RFID wake-up radios and measure their energy cost and wake-up probability. Then, we compare the performance of our RFID wake-up sensor nodes with duty cycling in a Data MULE scenario through simulations with realistic application parameters. The results show that using a passive RFID wake-up radio offers significant energy efficiency benefits at the expense of delay and the additional low-cost RFID hardware, making RFID wake-up radios beneficial for many delay-tolerant sensor network applications.

/pdf/feasibility-and-benefits-of-passive-rfid-wake-up-radios-for-3zff4ul23t.pdf

Feasibility and Benefits of Passive RFID Wake-Up Radios for Wireless Sensor Networks

As servers are placed in diverse locations in networked services today, it becomes vital to direct a client's request to the best server(s) to achieve both high performance and reliability. In this distributed setting, non-negligible latency and server availability become two major concerns, especially for highly-interactive applications. Profiling latencies and sending redundant data have been investigated as solutions to these issues. The notion of a cloudlet in mobile-cloud computing is also relevant in this context, as the cloudlet can supply these solution approaches on behalf of the mobile. In this paper, we investigate the effects of profiling and redundancy on latency when a client has a choice of multiple servers to connect to, using measurements from real experiments and simulations. We devise and test different server selection and data partitioning strategies in terms of profiling and redundancy. Our key findings are summarized as follows. First, intelligent server selection algorithms help find the optimal group of servers that minimize latency with profiling. Second, we can achieve good performance with relatively simple approaches using redundancy. Our analysis of profiling and redundancy provides insight to help designers determine how many servers and which servers to select to reduce latency.

Use of Network Latency Profiling and Redundancy for Cloud Server Selection

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today’s mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources. Accelerating MobileCloud Computing: A Survey

He Ba

Papers

Mobile computing - A green computing resource

Range extension of passive wake-up radio systems through energy harvesting

Feasibility and Benefits of Passive RFID Wake-Up Radios for Wireless Sensor Networks

Use of Network Latency Profiling and Redundancy for Cloud Server Selection

Accelerating Mobile-Cloud Computing: A Survey