Ekhiotz Jon Vergara

Bio: Ekhiotz Jon Vergara is an academic researcher from Linköping University. The author has contributed to research in topics: Energy consumption & Efficient energy use. The author has an hindex of 9, co-authored 15 publications receiving 179 citations.

EnergyBox: Disclosing the wireless transmission energy cost for mobile devices

Abstract: While evolving mobile technologies bring millions of users closer to the vision of information anywhere-anytime, device battery depletions still hamper the quality of experience to a great extent. The energy consumption of data transmission is highly dependent on the traffic pattern, and we argue that designing energy efficient data transmissions starts by energy awareness. Our work proposes EnergyBox, a parametrised tool that facilitates accurate and repeatable energy consumption studies for 3G and WiFi transmissions at the user end using real traffic data. The tool takes as input the parameters of a network operator and the power draw for a given mobile device in the 3G and WiFi transmission states. It outputs an estimate of the consumed energy for a given packet trace, either synthetic or captured in a device using real applications. Using nine different applications with different data patterns the versatility and accuracy of the tool was evaluated. The evaluation was carried out for a modern and popular smartphone in the WiFi setting, a specific mobile broadband module for the 3G setting, and within the operating environment of a major mobile operator in Sweden. A comparison with real power traces indicates that EnergyBox is a valuable tool for repeatable and convenient studies. It exhibits an accuracy of 94–99% for 3G, and 95–99% for WiFi given the studied applications’ traces. Next the tool was deployed in a use case where a location sharing application was ran on top of two alternative application layer protocols (HTTP and MQTT) and with two different data exchange formats (JSON and Base64). The illustrative use case helped to identify the appropriateness of the pull and push strategies in sharing location data, and the benefit of EnergyBox in characterising where the breaking point lies for preferring one or the other protocol, under which network load, or exchange data format.

EnergyBox: A Trace-Driven Tool for Data Transmission Energy Consumption Studies

Abstract: Although evolving mobile technologies bring millions of users closer to the vision of information anywhere-anytime, device battery depletions hamper the quality of experience to a great extent. We argue that the design of energy-efficient solutions starts by energy-awareness and propose EnergyBox, a tool that provides accurate and repeatable energy consumption studies for 3G and WiFi transmissions at the user end. We recognize that the energy consumption of data transmission is highly dependable on the traffic pattern, and provide the means for trace-based iterative packet-driven simulation to derive the operation states of wireless interfaces. The strength of EnergyBox is that it allows to modularly set the 3G network parameters specified at operator level, the adaptive power save mode mechanism for a WiFi device, and the different power levels of the operation states for different handheld devices. EnergyBox enables efficient energy consumption studies using real data, which complements the device-dependent laborious physical power measurements. Using real application transmission traces, we have validated EnergyBox showing an accuracy range of 94-99% for 3G and 93-99% for WiFi compared to the real measured energy consumption by a 3G modem and a smartphone with WiFi.

When mice consume like elephants: instant messaging applications

Abstract: A recent surge in the usage of instant messaging (IM) applications on mobile devices has brought the energy efficiency of these applications into focus of attention. Although IM applications are changing the message communication landscape, this work illustrates that the current versions of IM applications differ vastly in energy consumption when using the third generation (3G) cellular communication. This paper shows the interdependency between energy consumption and IM data patterns in this context. We analyse the user interaction pattern using a IM dataset, consisting of 1043370 messages collected from 51 mobile users. Based on the usage characteristics, we propose a message bundling technique that aggregates consecutive messages over time, reducing the energy consumption with a trade-off against latency. The results show that message bundling can save up to 43% in energy consumption while still maintaining the conversation function. Finally, the energy cost of a common functionality used in IM applications that informs that the user is currently typing a response, so called typing notification, is evaluated showing an energy increase ranging from 40-104%.

Energy-aware cross-layer burst buffering for wireless communication

Abstract: The massive explosion of mobile applications with the ensuing data exchange over the cellular infrastructure is not only a blessing to the mobile user but also has a price in terms of regular discharging of the device battery. A big contributor to this energy consumption is the power hungry wireless network interface. We leverage a measurement kit to perform accurate physical energy consumption measurements in a third generation (3G) telecommunication modem thus isolating the energy footprint of data transfers as opposed to other mobile phone-based measurement studies. Using the measurement kit we show how the statically configured network parameters, i.e., channel switch timers, and buffer thresholds, in addition to the transfer data pattern and the radio coverage, impact the communication energy footprint. We then demonstrate that being aware of static network parameters creates room for energy savings. This is done by devising a set of algorithms that (a) infer the network parameters efficiently, and (b) use the parameters in a new packet scheduler in the device. The combined regime is shown to transfer background uplink data, from real world traces of Facebook and Skype, with significant energy saving compared to the state-of-the-art.

Kernel level energy-efficient 3G background traffic shaper for android smartphones

Abstract: Reducing the energy consumption of wireless devices is paramount to a wide spread adoption of mobile applications. Cellular communication imposes high energy consumption on the mobile devices due to the radio resource allocation, which differs from other networks such as WiFi. Most applications are unaware of the energy consumption characteristics of third generation cellular communication (3G). This makes the background small data transfers of undisciplined applications an energy burden due to inefficient utilisation of resources. While several approaches exist to reduce the energy consumption of this best-effort background traffic by means of traffic shaping, we find that they are mostly evaluated with simulations and the actual energy overhead for the traffic shaper itself has not been studied. In order to cover this gap, our work realises an existing energy saving algorithm as a Kernel Level Shaper (KLS) within the Android platform, and measures its energy footprint. The total energy savings of our implementation range from 8% to 58% for emulated real background traffic, that is categorised as best-effort traffic. We further show the implications of running the KLS during live operation of applications as an exploratory study.

Comparison of two lightweight protocols for smartphone-based sensing

Abstract: Smartphones are equipped with numerous sensors and have become sophisticated sensing platforms. However, several sensing applications running on a smartphone can degrade the device performance. This can be overcome by using lightweight application protocols which improve the smartphone performance in terms of bandwidth consumption, battery lifetime and communication latency. This work focuses on two emerging application protocols: the Message Queuing Telemetry Transport (MQTT) and the Constrained Application Protocol (CoAP). Although both protocols have been designed for highly constrained environments such as sensors, they are also appropriate to be adopted in smartphone applications. We provide a qualitative and quantitative comparison between MQTT and CoAP when used as smartphone application protocols and we give preliminary indications on the application scenarios in which either protocol should be adopted. While MQTT has already been adopted in smartphone applications, CoAP is relatively new and has up to now mainly been considered for sensors and actuators. Our comparison shows that CoAP can be a valid alternative to MQTT for certain application scenarios.

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Abstract: Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices at the edge of the current network. To achieve higher performance in this new paradigm, one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis are used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource and less extensive towards the estimation, discovery, and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of nonfunctional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

IoT real time data acquisition using MQTT protocol

Abstract: The Internet of Things (IoT) provides ease to monitor and to gain sensor data through the Internet [1]. The need of high quality data is increasing to the extent that data monitoring and acquisition system in real time is required, such as smart city or telediagnostic in medical areas [2]. Therefore, an appropriate communication protocol is required to resolve these problems. Lately, researchers have developed a lot of communication protocols for IoT, of which each has advantages and disadvantages. This study proposes the utilization of MQTT as a communication protocol, which is one of data communication protocols for IoT. This study used temperature and humidity sensors because the physical parameters are often needed as parameters of environment condition [3]. Data acquisition was done in real-time and stored in MySQL database. This study is also completed by interface web-based and mobile for online monitoring. This result of this study is the enhancement of data quality and reliability using MQTT protocol.

On the compound impact of opportunistic scheduling and D2D communications in cellular networks

Abstract: Opportunistic scheduling was initially proposed to exploit user channel diversity for network capacity enhancement. However, the achievable gain of opportunistic schedulers is generally restrained due to fairness considerations which impose a tradeoff between fairness and throughput. In this paper, we show via analysis and numerical simulations that opportunistic scheduling not only increases network throughput dramatically, but also increases energy efficiency and can be fair to the users when they cooperate, in particular by using D2D communications. We propose to leverage smartphone's dual-radio interface capabilities to form clusters among mobile users. We design simple, scalable and energy-efficient D2D-assisted opportunistic strategies, which would incentivize mobile users to form clusters. We use a coalitional game theory approach to analyze the cluster formation mechanism, and show that proportional fair-based intra-cluster payoff distribution brings significant incentive to all mobile users regardless of their channel quality.

The general nucleolus and the reduced game property

Abstract: The nucleolus of a TU game is a solution concept whose main attraction is that it always resides in any nonempty ɛ-core. In this paper we generalize the nucleolus to an arbitrary pair (Π,F), where Π is a topological space andF is a finite set of real continuous functions whose domain is Π. For such pairs we also introduce the “least core” concept. We then characterize the nucleolus forclasses of such pairs by means of a set of axioms, one of which requires that it resides in the least core. It turns out that different classes require different axiomatic characterizations. One of the classes consists of TU-games in which several coalitions may be nonpermissible and, moreover, the space of imputations is required to be a certain “generalized” core. We call these gamestruncated games. For the class of truncated games, one of the axioms is a new kind ofreduced game property, in which consistency is achieved even if some coalitions leave the game, being promised the nucleolus payoffs. Finally, we extend Kohlberg's characterization of the nucleolus to the class of truncated games.