A Novel IEEE 802.11 Power Save Mechanism for Energy Harvesting Motivated Networks

The spread of wirelessly connected computing sensors and devices and hybrid networks are leading to the emergence of an Internet of Things (IoT), where a myriad of multi-scale sensors and devices are seamlessly blended for ubiquitous computing and communication. However, the communication operations of wireless devices are often limited by the size and lifetime of the batteries because of the portability and mobility. To reduce energy consumption during wireless communication, the IEEE 802.11 standard specifies a power management scheme, called Power Saving Mechanism (PSM), for IEEE 802.11 devices. However, the PSM of IEEE 802.11 was originally designed for battery-supported devices in single-hop Wireless Local Area Networks (WLANs), and it does not consider devices that are equipped with rechargeable batteries and energy harvesting capability. In this thesis, the original PSM is extended by incorporating with intermittent energy harvesting in the IEEE 802.11 Medium Access Control (MAC) layer specification, and a novel energy harvesting aware power saving mechanism, called EH-PSM, is proposed. The basic idea of EH-PSM is to assign a longer contention window to a device in energy harvesting mode than that of a device in normal mode to make the latter access the wireless medium earlier and quicker. In addition, the device in energy harvesting mode stays active as far as it harvests energy and updates the access point of its harvesting mode to enable itself to be ready for receiving and sending packets or overhearing any on-going communication. The proposed scheme is evaluated through extensive simulation experiments using OMNeT++ and its performance is compared with the original PSM. The simulation results indicate that the proposed scheme can not only improve the packet delivery ratio and throughput but also reduce the packet delivery latenc

تحسين أداء خوارزمية توفير الطّاقة الديناميكية في الهواتفِ الذّكية

تُعتبر خوارزميات توفير الطّاقة المُستهلكة من قبل تقنية Wi-Fi (الستاتيكية والديناميكية) مُساهم قوي في تحسينِ زمنِ تفريغِ بطاريةِ الهواتفِ الذّكية، إلاّ أنَّ أحدَ أهمِ العوامل التّي قد تُؤثّر على أدائِها، هو زيادة عدد الهواتف الذّكية المُتنافِسة على النفاذِ إلى الوسطِ اللاسلكي. اقترحَ هذا البحث تحسيناً للخوارزميةِ الديناميكية، بحيث تمَّ استغلال معيار خاص بالشبكة اللاسلكية (قوة الإشارة المُستقبلة)، لإنقاصِ مُستوى الازدحامِ الشبكي، وهذا ما يترك أثراً إيجابياً على كل من الطّاقة المُستهلكة، ومعاييرِ جودةِ الخدمة المطلوبة للتطبيقاتِ الشبكية. تمَّ التحقّق من الخوارزميةِ المُقترحة (RS-DPSM)، عن طريقِ مقارنةِ أدائِها معَ أداءِ خوارزمياتِ توفيرِ الطّاقة القياسية، وذلكَ باستخدامِ المُحاكي NS-2، وقد أظهرت النتائج تفوّقاً ملحوظاً للخوارزميةِ المُقترحة في أغلبِ السيناريوهات. Power Saving algorithms consumed by Wi-Fi technology (static and dynamic) are considered an essential contributor in improving the battery discharge time in smart phones, Meanwhile, one of most major factors which may affect their performance is that many more numbers of smart phones competing to access into the wireless medium. This research has suggested an improvement for the dynamic algorithm, where one special metric for wireless network is going to be used (the received signal strength), in order to decrease the network congestion, and this results in positive effects on both of consumed power and quality of service metrics required for network applications. Then verifying of the proposed algorithm (RS-DPSM), by comparing its performance with the performance of standard power saving algorithms, through using the simulator NS-2.The results showed a marked superiority of the proposed algorithm in most scenarios

تحسين آلية التحكم في إدارة نمط توفير الطّاقة

إنَّ خوارزميةَ توفيرِ الطّاقة الافتراضيّة، التّي تتبّناها الهواتف الذّكية من أجلِ الحدِّ من استهلاكِ الطّاقة الناتج عن استعمالِ النظامِ الفرعي المضمّن Wi-Fi، هي نمط توفير الطّاقة القياسي، الذّي طرحهُ البروتوكول 802.11. يتصف تنفيذ النمط القياسي بعدّةَ سلبياتٍ لعلَّ أبرزها عدم تمكّنهِ منَ الاستفادةِ من نموذجِ حركةِ المعطيات، لإنقاصِ مُستوى استهلاكِ الطّاقة، بالإضافةِ إلى عدمِ مرونتهِ من حيث السّماح للمستخدمين بالتحكمِ في زمنِ تأخيرِ الرّزم. اقترحَ هذا البحث تحسيناً للخوارزميةِ القياسية، بحيث تمَّ العمل على تلافي ما سبقَ ذكرهُ، بالإضافةِ إلى مراعاةِ قيدِ الطّاقة على اعتبار أنَّ Wi-Fi ما هي إلاّ جزء من نظامٍ مضمّنٍ أكبر، يجب أن يؤدي وظائفَ أُخرى. تمَّ التحقّق منَ الخوارزميةِ المُقترحة ((EDPSM عن طريقِ مقارنةِ أدائِها معَ النمطِ القياسي، باستخدامِ المُحاكي NS2، وذلكَ وفقاً لمجموعةٍ من البارامتراتِ المؤثرةِ على الأداء. The default power saving algorithm adopted by smartphones in order to reduce power consumption resulting from the use of embedded subsystem Wi-Fi, is the standard power save mode which put forward by the 802.11 protocol. Implementation of the standard mode characterizes several disadvantages, the most notably is not being able to take advantage of the data traffic model, to reduce the level of power consumption, in addition to the lack of flexibility in terms of allowing users to control with the delay time of packets. This research suggested an improvement to the standard algorithm, so that it has been working to avoid the foregoing, in addition to taking into account the limitation of power because that Wi-Fi is only part of a larger embedded system should achieve other functions. The verification of the proposed algorithm (EDPSM) is done by comparing its performance with the standard mode, using the simulator NS2, and according to a set of parameters affecting the performance

Towards Optimizing WLANs Power Saving: Context-Aware Listen Interval

Despite the rapid growth of Wireless Local Area Networks (WLANs), the energy consumption caused by wireless communication remains a significant factor in reducing the battery life of power-constrained wireless devices. To reduce the energy consumption, static and adaptive power saving mechanisms have been deployed in WLANs. However, some inherent drawbacks and limitations remain. We have developed the concept of Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. In this paper we develop the power saving modes of CALI. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to Pyles’ et al. SAPSM power saving approach, which also employs an ML classifier

Video Streaming Energy Consumption Analysis for Content Adaption Decision-Taking

Over recent years, rapid growth of smartphone technology and capabilities makes it an important tool in our daily activities. Despite increasing processing power and capabilities as well as decreasing price, these consumer smartphones are still limited in term of batteries capacity. The heterogeneity properties of these devices, subscribed network as well as its users also lead to mismatch problem. Usage in power-hungry multimedia applications such as streaming video players and 3D games, the limited battery capacity motivates smartphone energy aware content adaptation research to address these problems. This paper present experiments of energy consumption of video streaming in various video encoding properties as well as different network scenarios. The result of the experiments shows that energy savings up to 40% can be achieved by using different encoding property

The impact of exporting on SME capital structure and debt maturity choices. National Bank of Belgium Working Paper No. 311

Using a longitudinal dataset comprising of detailed financial and exporting data from Belgian small and medium-sized enterprises (SME) between 1998 and 2013, this article examines the manner in which firms manage to finance their export activities and the resulting impact on corporate capital structure. We find that exporters have to finance relatively more working capital as compared to their non-exporting peers and that they resolve this financing need by carrying more short-term debt. In addition, we evidence that the relationship between pledgeable short-term assets, such as working capital, and short-term debt financing is more pronounced for exporters. In particular, we show that the ties between pledgeable short-term assets and short-term debt financing are stronger for export- intensive firms and firms that serve distant and risky export destinations. Overall, what our empirical findings seem to suggest is that developing tools that facilitate the pledging of assets is likely to boost SME export activities by widening access to bank financing and reducing financial constraints

Is Fragmentation a Threat to the Success of the Internet of Things?

The current revolution in collaborating distributed things is seen as the first phase of IoT to develop various services. Such collaboration is threatened by the fragmentation found in the industry nowadays as it brings challenges stemming from the difficulty to integrate diverse technologies in system. Diverse networking technologies induce interoperability issues, hence, limiting the possibility of reusing the data to develop new services. Different aspects of handling data collection must be available to provide interoperability to the diverse objects interacting; however, such approaches are challenged as they bring substantial performance impairments in settings with the increasing number of collaborating devices/technologies.Comment: 16 pages, 2 figures, Internet of Things Journal (http://ieee-iotj.org

Optimising WLANs Power Saving: Context-Aware Listen Interval

Energy is a vital resource in wireless computing systems. Despite the increasing popularity of Wireless Local Area Networks (WLANs), one of the most important outstanding issues remains the power consumption caused by Wireless Network Interface Controller (WNIC). To save this energy and reduce the overall power consumption of wireless devices, a number of power saving approaches have been devised including Static Power Save Mode (SPSM), Adaptive PSM (APSM), and Smart Adaptive PSM (SAPSM). However, the existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. This thesis proposes a novel Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. We focused on the network activity of a single smartphone application while ignoring the network activity of applications running simultaneously. We introduced a context-aware network traffic classification approach based on ML classifiers to classify the network traffic of wireless devices in WLANs. Smartphone applications’ network traffic reflecting a diverse array of network behaviour and interactions were used as contextual inputs for training ML classifiers of output traffic, constructing an ML classification model. A real-world dataset is constructed, based on nine smartphone applications’ network traffic, this is used firstly to evaluate the performance of five ML classifiers using cross-validation, followed by conducting extensive experimentation to assess the generalisation capacity of the selected classifiers on unseen testing data. The experimental results further validated the practical application of the selected ML classifiers and indicated that ML classifiers can be usefully employed for classifying the network traffic of smartphone applications based on different levels of behaviour and interaction. Furthermore, to optimise the sleep and awake cycles of the WNIC in accordance with the smartphone applications’ network activity. Four CALI power saving modes were developed based on the classified output traffic. Hence, the ML classification model classifies the new unseen samples into one of the classes, and the WNIC will be adjusted to operate into one of CALI power saving modes. In addition, the performance of CALI’s power saving modes were evaluated by comparing the levels of energy consumption with existing benchmark power saving approaches using three varied sets of energy parameters. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to SAPSM power saving approach, which also employs an ML classifier