Towards Efficient Mobile M2M Communications: Survey and Open Challenges <s> Smartphones as Mobile M2M Gateways <s> Battery lifetime has become one of the top usability concerns of mobile systems. While many endeavors have been devoted to improving battery lifetime, they have fallen short in understanding how users interact with batteries. In response, we have conducted a systematic user study on battery use and recharge behavior, an important aspect of user-battery interaction, on both laptop computers and mobile phones. Based on this study, we present three important findings: 1) most recharges happen when the battery has substantial energy left, 2) a considerable portion of the recharges are driven by context (location and time), and those driven by battery levels usually occur when the battery level is high, and 3) there is great variation among users and systems. These findings indicate that there is substantial opportunity to enhance existing energy management policies, which solely focus on extending battery lifetime and often lead to excess battery energy upon recharge, by adapting the aggressiveness of the policy to match the usage and recharge patterns of the device. We have designed, deployed, and evaluated a user- and statistics-driven energy management system, Llama, to exploit the battery energy in a user-adaptive and user-friendly fashion to better serve the user. We also conducted a user study after the deployment that shows Llama effectively harvests excess battery energy for a better user experience (brighter display) or higher quality of service (more application data) without a noticeable change in battery lifetime. <s> BIB001 </s> Towards Efficient Mobile M2M Communications: Survey and Open Challenges <s> Smartphones as Mobile M2M Gateways <s> In this paper we consider the interconnection of an IEEE 802.15.4 body area network (BAN) in which nodes sense physiological variables such as electrocardiography (EKG), electroencephalography (EEG), pulse oximeter data, blood pressure and cardiac output, with an IEEE 802.11b room/ward WLAN. We model the operation of this two-tier network assuming that 802.15.4 BAN operates in CSMA-CA mode and that the BAN coordinator acts as the bridge which conveys BAN packets to the 802.11b access point. We analyze the two-hop network delay and discuss the mutual interaction of different data streams as well as impact of the number of bridges on packet delay. <s> BIB002 </s> Towards Efficient Mobile M2M Communications: Survey and Open Challenges <s> Smartphones as Mobile M2M Gateways <s> In this paper, we present a measurement study of the energy consumption characteristics of three widespread mobile networking technologies: 3G, GSM, and WiFi. We find that 3G and GSM incur a high tail energy overhead because of lingering in high power states after completing a transfer. Based on these measurements, we develop a model for the energy consumed by network activity for each technology. Using this model, we develop TailEnder, a protocol that reduces energy consumption of common mobile applications. For applications that can tolerate a small delay such as e-mail, TailEnder schedules transfers so as to minimize the cumulative energy consumed meeting user-specified deadlines. We show that the TailEnder scheduling algorithm is within a factor 2x of the optimal and show that any online algorithm can at best be within a factor 1.62x of the optimal. For applications like web search that can benefit from prefetching, TailEnder aggressively prefetches several times more data and improves user-specified response times while consuming less energy. We evaluate the benefits of TailEnder for three different case study applications - email, news feeds, and web search - based on real user logs and show significant reduction in energy consumption in each case. Experiments conducted on the mobile phone show that TailEnder can download 60% more news feed updates and download search results for more than 50% of web queries, compared to using the default policy. <s> BIB003 </s> Towards Efficient Mobile M2M Communications: Survey and Open Challenges <s> Smartphones as Mobile M2M Gateways <s> As more and more physical information becomes available, a critical problem is enabling the simple and efficient exchange of this data. We present our design for a simple RESTful web service called the Simple Measuring and Actuation Profile (sMAP) which allows instruments and other producers of physical information to directly publish their data. In our design study, we consider what information should be represented, and how it fits into the RESTful paradigm. To evaluate sMAP, we implement a large number of data sources using this profile, and consider how easy it is to use to build new applications. We also design and evaluate a set of adaptations made at each layer of the protocol stack which allow sMAP to run on constrained devices. <s> BIB004 </s> Towards Efficient Mobile M2M Communications: Survey and Open Challenges <s> Smartphones as Mobile M2M Gateways <s> Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users. <s> BIB005 </s> Towards Efficient Mobile M2M Communications: Survey and Open Challenges <s> Smartphones as Mobile M2M Gateways <s> Machine-to-machine communications for healthcare is emerging for the benefit of humans. In addition to novel medium access, we provide a systematic view to look for ways to develop technology to accomplish this goal, and a thorough vision toward effective system and network design. Category: Smart and intelligent computing <s> BIB006 </s> Towards Efficient Mobile M2M Communications: Survey and Open Challenges <s> Smartphones as Mobile M2M Gateways <s> Machine-to-Machine (M2M) communication is expected to play a major role within the coming years towards the development of e-healthcare applications. The design of cellular networks, such as Long Term Evolution (LTE), is optimized to serve the data traffic of human-based communication with broadband requirements. E-healthcare traffic has different characteristics such as small packet sizes, narrowband requirements and huge number of devices. The focus of this work is to investigate the impact of e-healthcare traffic on LTE cellular networks. We develop a possible future scenario of electrocardiography (ECG) devices performing remote monitoring of patients with mobility support in our LTE simulation model. Regular LTE traffic is also deployed in the network and the influence of the varying ECG traffic is examined. The simulation results indicate that the e-healthcare related data traffic has a drastic influence on regular LTE traffic. <s> BIB007 </s> Towards Efficient Mobile M2M Communications: Survey and Open Challenges <s> Smartphones as Mobile M2M Gateways <s> An overview of several standardization activities for machine-to-machine (M2M) communications is presented, analyzing some of the enabling technologies and applications of M2M in industry sectors such as Smart Grid and e-Health. This summary and overview of the ongoing work in M2M from the industrial and standardization perspective complements the prevalent academic perspective of such publications to date in this field. <s> BIB008 </s> Towards Efficient Mobile M2M Communications: Survey and Open Challenges <s> Smartphones as Mobile M2M Gateways <s> Part I. Understanding Energy Consumption: 1. Introduction 2. Energy and power primer 3. Smartphone batteries 4. Energy measurement 5. On human behavior and energy efficiency Part II. Energy Management and Conservation: 6. Overview 7. Smartphone subsystems 8. Mobile operating systems 9. Power modeling 10. Power profilers Part III. Advanced Energy Optimization: 11. Overview 12. Traffic scheduling 13. Exploiting multiple wireless network interfaces 14. Mobile cloud offloading 15. Example scenarios for energy optimization 16. Future trends Appendix A. An energy profile application. <s> BIB009
A potential use case for mobile M2M communications is its application in healthcare for remote monitoring of patient vital signs, e.g., activity level, blood pressure, heart rate, temperature, either for ambulatory monitoring of chronic conditions, or for prophylactic reasons, in order to minimize the number of visits to the doctor [11, BIB007 . In the latter scenario, there are wearable sensors that continuously collect physiologic information and send it to a remote service to be processed and acted upon. Table 3 shows some characteristics of representative traffic originated in current healthcare sensors for remote vital sign monitoring, as presented in BIB002 . Different physiologic variables have different sampling rates and number of channels. The number of channels reflects the number of different locations of the body from where we are sampling the variables. Although it is not expected that an individual carries an Electroencephalogram (EEG) sensing device the entire day, we can envision the application of such devices in a medical facility, where it might be necessary/useful for continuous remote monitoring. As shown in the table, the EEG is expected to produce a higher data rate than, for example, the body temperature variable. Sensors often forward the collected data to a nearby mobile gateway using short-range wireless technologies, like Bluetooth, for energy saving. This mobile gateway can collect data from only one or several sensors, and forward it to destination service. Instead of simply forwarding information as it arrives, the mobile gateway can collect, aggregate, and eventually process information to reduce the number of transmissions and eventually the amount of data, and energy consumed in undesired network and protocol overheads. Transmitting the highest possible amount of information in the least number of packets improves resource utilization efficiency. However, different sensor data has different granularity, timeliness requirements, or tolerance to packet loss. For example, EEG data is more important than a room temperature, and, therefore, it will have stricter delivery requirements. Just a single blood pressure sensor with a sampling frequency of 120 Hz, needing 16 bits to send the data, results in 1920 bit per second (Table 3 ), 6.9 Mbit per hour, and 165 Mbit per day, excluding any communication protocol overheads. The more sensors the mobile gateway has to support, the more data needs to be collected and forwarded. This situation introduces a considerable challenge to the mobile gateways in terms of energy consumption, since they have limited battery life, and can have different time intervals between battery replacement/recharge. Smartphones are an ideal choice to play the role of a mobile M2M gateway in healthcare applications, as they are largely available to the population, are less resource constrained than specialized sensors, and have various connectivity alternatives. Although M2M communications have been thoroughly addressed for healthcare applications BIB008 BIB006 BIB007 BIB004  , most of the literature for healthcare still focus on BANs of sensors, and do not explore scenarios potentiated by the use of smartphones. The use of smartphones for transmission of sensing and monitoring data can lead to undesirable battery depletions, since users use their smartphones mainly for other purposes, mostly personal communication and browsing. For the user, it might be desirable to recharge the smartphone only during the night when the smartphone is no longer needed, which means that the collection, processing and forwarding of data should not exceed a certain amount of energy consumption during the day, in order for the battery to last at least 12 h, e.g., from 08:00 to 20:00. Several studies have been conducted on recharge patterns  BIB001 BIB009 . Battery recharges are triggered either by the current battery level, or by the context, which includes location and time of day. Users that recharge battery based on battery levels, notice differences in the recharging cycle of the phones, and tend to be irritated by the increase of energy consumption BIB009 . Results from  show two major charging schedules: one between 18:00 and 20:00, and another charging schedule between 01:00 and 02:00, and the majority of the charging durations are 2 h or less or 14 h or more. From interviews in BIB001 , persons usually recharge once their mobile equipment at the office, at home, or at night. Since user behaviour in terms of battery recharge may vary a lot  , we consider that, for user convenience, phones are recharged only once per day, at night, and they need to operate from 08:00 to 20:00. We seek to understand the feasibility of using a smartphone as an M2M gateway in terms of the energy consumption for forwarding data using a cellular network. The ubiquity and high data rates make cellular networks the most common choice for mobile M2M applications. As sensor data can be obtained using different short-range low-powered technologies, for the sake of simplicity, we exclude the data collection from the analysis. Nevertheless, we stress that obtaining the sensing data in an optimal way in terms of energy consumption is of extreme importance. For this study, we use the models for the average energy consumed for uploading data from a smartphone using a 3G network, obtained from BIB003 . Transmissions in 3G can be defined in 3 periods: the ramp, the transmission, and the tail, as can be seen in Figure 5 . The ramp and the tail are transitions to and from high-power states where the actual transmission takes place, respectively. The time duration of the ramp is relatively small when compared to the actual transmission and the tail. However, the tail can last up to 12.5 s BIB003 . Still, if the data to be transmitted is large, or if the interval between successive transfers is small, the tail and the ramp do not impact the energy consumption significantly BIB003 when compared to the actual transmission. The energy consumption model takes as inputs: the necessary energy to transfer x KB, R(x) = 0.025 × x + 3.5 J, which already includes the energy consumed in scanning and association with the network; M = 0.02 J/Sec as a maintenance energy value to keep using the interface; and the energy consumed during the tail time, E = 0.62 J/s, where the maximum time in this period is 12.5 s. Therefore, for example, transmissions of 100 KB with intervals of 50 s between consecutive transmissions (the device becomes idle after the tail) will consume 0.025 × 100 + 3.5 + 0.62 × 12.5 + 0.02 × 50 = 12.25 J. We assume that regular user activity depletes the battery in 100 mAh, an intermediate value between the 10 and 250 mAh presented in BIB005 . The average battery for a middle range priced smartphone, like the Samsung Galaxy Nexus  , has a capacity of 1750 mAh at a voltage of 3.6 V. Therefore, assuming the battery voltage remains constant during normal operation, the smartphone should last at least 17 h when not additionally functioning as a mobile M2M gateway. Please now consider the scenario where a smartphone collects data from several sensors to be forwarded, as depicted in our storyboard and in Figure 2 . In this case, we assume that there is only one sensor collecting the heart rate from an individual and forwarding the data to a smartphone. From Table 3 , we can conclude that, in each second, the sensor generates approximately 0.64 KB of data. The smartphone has to transmit this data through 3G and needs to append protocol headers and likely execute security mechanisms. We assume the use of CoAP application protocol, no header compression, and that each transmission includes a fixed overhead of 62 B to the useful data: 10 bytes for a common CoAP confirmable request [54] , 13 bytes for DTLS per packet [54] , 8 bytes for the UDP header, 20 bytes for the IP header, 2 bytes for the PDCP header, 5 bytes for the RLC and MAC Headers, and 4 bytes for CRC [82] . The data can be forwarded as 0.702 KB every second, 1.342 KB every two seconds, 6.462 KB every 10 s, and so forth, depending on the aggregation chosen. Figure 6 depicts the battery depletion of the smartphone for different transmission rates using 3G. Due to the scale in hours of the x axis, it is not possible to visualize the changes in slope when transmissions occur. The results show that only for larger aggregations of transmitted data, 64 KB every 100 s, we can achieve a full day, e.g., 08:00 -20:00, of battery operation. We conclude that frequent transmissions of small size data will have an undesired effect in the expected depletion time of a smartphone's battery in mobile M2M communications scenarios. In order to use smartphones as M2M gateways, it is a good advice to maximize the collection of data necessary to be forwarded from nearby sensors, and maximize the intervals between transmissions. More research is required to devise energy efficient transmission methods that enable the use of smartphones as mobile gateways. Techniques such as transmission scheduling, header compression, data aggregation, or leverage heterogeneous networks can enhance smartphones' capabilities acting as gateways while easing their energy consumption problems. 