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Nowadays Wi-Fi is the most mature technology for wireless-Internet access. Despite the large (and ever increasing) diffusion of Wi-Fi hotspots, energy limitations of mobile devices are still an issue. To deal with this, the standard 802.11 includes a Power-Saving Mode (PSM), but not much attention has been devoted by the research community to under...
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... of Wi-Fi hotspots are nowadays very frequent, for example in company and education buildings, coffee shops, airports, and so on. Figure 1 shows a simple Wi-Fi installation, where users carrying mobile hosts (e.g., laptops, PDAs, . . . ) exploit an Access Point to connect to legacy Internet services. This is the scenario used in the paper. ...
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... our analysis, we consider the typical Wi-Fi hotspot scenario, depicted in Figure 1 and replicated in Figure 2(a) for the reader convenience, in which a mobile user accesses the Internet through an Access Point. We focus on best-effort Internet applications, such as Web browsing, e-mail, file transfer (hereafter referred to as reference ap- plications). ...
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... show that this "shielding property" can be exploited to eliminate the energy wastage related to the transport protocol (i.e., cause i) above). To this end, we run simulations by replacing the standard TCP architecture with the architecture shown in Figure 10. This is similar to the original Indirect TCP, except for the transport protocol used over the WLAN. ...
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... we use the Simplified Transport Protocol (STP), which is essentially a Stop-and-Wait transport protocol, optimized for the one-hop wireless environment [6,3]. Figures 11(c,d,e) show that the Indirect-TCP architecture actually shields the TCP sender at the fixed host from frame losses in the WLAN (note that the TCP throughput is measured at the fixed host). Specifically, even though the WLAN frame loss probability increases just as in the legacy TCP architecture (compare Figures 11(c) and 9(b)), the number of timeouts registered at the TCP sender (Figure 11(d)) and the throughput experienced by the TCP connection over the wired network (Figure 11(e)) are independent of that. ...
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... 11(c,d,e) show that the Indirect-TCP architecture actually shields the TCP sender at the fixed host from frame losses in the WLAN (note that the TCP throughput is measured at the fixed host). Specifically, even though the WLAN frame loss probability increases just as in the legacy TCP architecture (compare Figures 11(c) and 9(b)), the number of timeouts registered at the TCP sender (Figure 11(d)) and the throughput experienced by the TCP connection over the wired network (Figure 11(e)) are independent of that. Hence, the effect of the reduced TCP throughput on the energy consumption, registered in the previous set of experiments, disappears. ...
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... 11(c,d,e) show that the Indirect-TCP architecture actually shields the TCP sender at the fixed host from frame losses in the WLAN (note that the TCP throughput is measured at the fixed host). Specifically, even though the WLAN frame loss probability increases just as in the legacy TCP architecture (compare Figures 11(c) and 9(b)), the number of timeouts registered at the TCP sender (Figure 11(d)) and the throughput experienced by the TCP connection over the wired network (Figure 11(e)) are independent of that. Hence, the effect of the reduced TCP throughput on the energy consumption, registered in the previous set of experiments, disappears. ...
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... the MAC- delay increase (cause ii) above) is thus responsible for the additional energy consumption. It should be noted that PSM is not able to face this problem, as it appears from Figures 11(a,b). Figure 11(b) shows the Idleness index as a function of M . ...
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... should be noted that PSM is not able to face this problem, as it appears from Figures 11(a,b). Figure 11(b) shows the Idleness index as a function of M . The Idleness index is defined as the fraction of time (within bursts) during which the tagged mobile host is idle because there are no frames buffered for it at the Access Point. ...
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... the tagged mobile host is never idle, and the PSM can never switch the wireless interface to the sleep mode. In conclusion, for high contention levels, either enabling the PSM or not leads to similar results (Figure 11(a)). Based on these observations, we can conclude that the effect of the MAC delay on the energy consumption can be contrasted only by reducing the MAC delay itself through MAC-level modifications (e.g., as proposed in In [14]). ...
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... it switches it off, and reactivates it t oa seconds before the idle-time endpoint. If E O (t i ) denotes the energy spent in this case, the following equation holds: Figure 12(a) plots Equations 14 ("ideal sleep" curve) and 15 ("ideal off" curve) as functions of t i . It confirms that for "short" idle times the best policy consists in putting the wireless interface in the sleep mode, while for "long" idle times the off-based policy exhibits the best performance. ...
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... 16 is plotted in Figure 12(a), with label "PSM". This plot confirms that PSM is effective with respect to interarrival times, i.e., for idle times below 1 s. to the "ideal-sleep" policy is always below 20%. ...
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... in this region, PSM is a close approximation of the best, ideal, policy. Figure 12(a) also shows that the PSM discrepancy with off-based policies increases as idle times become longer and longer. The "ideal-off" policy cannot be implemented in practice. ...
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... let us consider a very simple timeout-based policy that lets the mobile host active for the first t oa seconds of an idle time, and then switches it off 11 . The energy spent by this policy is plotted in Figure 12(a) for comparison ("timeout-based off" label). This policy is known to be 2-competitive, i.e., it never consumes more than twice the energy spent by the ideal off-based policy [26]. ...
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... us define E BR as the average energy spent by PSM to download a single burst, and E UT T as the average energy spent by PSM during a User Think Time 12 . In Figure 12(b) the index R(E UT T , E BR ) is plotted for increasing User Think Times. Three different plots are drawn for three different average burst sizes, i.e., a = 1, a = 10, and a = 100. ...
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... different plots are drawn for three different average burst sizes, i.e., a = 1, a = 10, and a = 100. Figure 12(b) shows that the energy spent during User Think Times is not negligible with respect to the energy spent during bursts, for any average burst size. Specifically, for small bursts (i.e., a = 1), R(E UT T , E BR ) is around 20 for UTTs equal to 30 s, and raises up to about 40 for UTTs equal to 60 s (not shown in the plot). ...
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... this implementation of the Cross-Layer Energy Manager depends on the specific application it is designed for, it is hereafter referred to as the Application-dependent Cross-Layer Energy Manager (A-XEM). The pseudo-code specification of this Energy Manager is shown in Figure 14(left) (Web browsing is used as the reference application). ...
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... us focus on line 2, and assume that a burst just started. According to the general XEM scheme depicted in Figure 13, A-XEM relies upon the standard PSM during bursts (lines 2-6), and switches the wireless interface off during User Think Times (lines 7-8). The completion of a Web-page download triggers the start of a User Think Time (line 6), while a new request from the user indicates that a new burst is starting (line 8). ...
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... pseudo-code specification of T-XEM is detailed in Figure 14(right). As in the case of A-XEM, T-XEM is implemented at the mobile host (apart from the PSM functionalities already implemented at the Access Point). ...
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... first set of experiments is aimed at evaluating the sensitiveness of the Cross-Layer Energy Managers with respect to the User Think Time duration. Figure 15(a) shows the energy consumption of T-XEM and A-XEM for increasing UTTs (for T-XEM, a different curve is plotted for each RTT value). The energy consumption of PSM, derived from derived from Equation 16, is also shown for comparison. ...
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... highlighted in Section 3, the probability of having such small UTTs is very low. Figure 15(a) shows that, for typical UTT values (tens of seconds), the Cross-Layer Energy Managers greatly outperform PSM. It is also interesting to compare the performance of the two XEM implementations. ...
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... P ) are evaluated and plotted in Figure 15(b) for increasing UTTs. In Figure 15(b) we only consider the lower and upper value for T-XEM, i.e., 0.1 s and 1 s. ...
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... P ) are evaluated and plotted in Figure 15(b) for increasing UTTs. In Figure 15(b) we only consider the lower and upper value for T-XEM, i.e., 0.1 s and 1 s. Furthermore, to investigate the performance of the Cross-Layer Energy Managers for a wide range of burst sizes, we considered both short (i.e., a = 1) and long (i.e., a = 100) burst sizes. ...
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... expected, the performance gains are reduced if we focus on a particular User Think Time, and increase the burst sizes (e.g., set a to 100). This is because, for a given User Think Time, the (energetic) cost of a burst (with respect to the cost of the User Think Time) increases with the burst size (see Figure 12(b)). Since A-XEM and T-XEM differ from PSM in the way they handle User Think Times, the energy saved with respect to PSM is reduced when the burst size increases. ...
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... the difference between XEM and the other techniques relies in the different values of β they are able to achieve. Figure 16 shows the range of XEM performance presented in Figure 15(b), and the maximum expected performance of PSM, STPM, BSD and SPSM. Specifically, i) STPM behaves exactly like PSM during a UTT; ii) BSD in the best case listens for a Beacon just every 900 ms, and sleeps for the rest of the time; iii) SPSM may be able to sleep for the whole UTT. ...
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... can be set to 0 also in the case of XEM, since it introduces just 100 ms to the PSM additional delay. Therefore, the difference between XEM and the other techniques relies in the different values of β they are able to achieve. Figure 16 shows the range of XEM performance presented in Figure 15(b), and the maximum expected performance of PSM, STPM, BSD and SPSM. Specifically, i) STPM behaves exactly like PSM during a UTT; ii) BSD in the best case listens for a Beacon just every 900 ms, and sleeps for the rest of the time; iii) SPSM may be able to sleep for the whole UTT. ...
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... However, stations with traffic to send/receive will compete just after the reception of a beacon, resulting in traffic peaks and collisions. In addition, a station stays awake until all its packets are received and/or transmitted, causing relatively long active times for PSM devices even if they have only a little data to send or receive [22]. ...
... If a station has multiple packets to send, it cannot sleep until its buffer is empty. These limits affect the energy consumption [22]. ...
Wi-Fi is a popular wireless technology and is continuously extended to keep pace with requirements such as high throughput, real-time communication, dense networks, or resource and energy efficiency. The IEEE 802.11ax standard, also known as Wi-Fi 6, promises to provide data rates of up to almost 10 Gb/s, lower energy consumption, and higher reliability. Its capabilities go far beyond Wi-Fi 5 (802.11ac) and novel technical concepts have been introduced for this purpose. As such, the Wi-Fi 6 standard includes Multi-User Orthogonal Frequency Division Multiple Access (MU OFDMA), Multi-User Multiple-Input Multiple-Output (MU MIMO), new mechanisms for Spatial Reuse (SR), new mechanisms for power saving, higher-order modulation, and additional minor improvements. In this paper, we provide a survey of Wi-Fi 6. Initially, we provide a compact technological summary of Wi-Fi 5 and its predecessors. Then, we discuss the potential application domains of Wi-Fi 6, which are enabled through its novel features. Subsequently, we explain these features and review the related works in these areas. Finally, performance evaluation tools for Wi-Fi 6 and future roadmaps are discussed.
... The energy-saving approach is interesting; however, it is only applied to the Radio Access Network. issues, and concerns [6]. The PSM is a sleep scheduling technique that allows the radio interface, which consumes a significantly high amount of energy, to enter sleep mode whenever it is idle or not in use. ...
Wireless visual sensor networks based on IEEE 802.11 mesh networks are effective and suitable solutions for video surveillance systems in monitoring intrusions in selected areas. The IEEE 802.11-based visual sensor networks offer high bit rate video transmissions but suffer from energy inefficiency issues. Moreover, the video transmission in the visual sensor networks requires strict quality of service (QoS) in terms of bandwidth and delay. Also, it is challenging to decrease the overall energy consumption of the network while assuring guaranteed QoS in terms of bandwidth and delay in energy-constrained wireless visual sensor networks. The main contribution of this dissertation is to provide an energy-efficient video surveillance network without compromising the QoS requirement of video transmission. First, we propose a new hybrid IoT network architecture for a video surveillance system that detects and tracks an intruder in the monitoring area. The hybrid IoT network integrates the IEEE 802.11-based multi-hop visual Sensor Networks and LoRa network to provide an autonomous, energy-efficient, high-bitrate video surveillance system. Leveraging the LoRa network characteristics, the LoRa network is utilized as an always-active network for preliminary motion detection and tracking. Moreover, the LoRa network also decides which visual sensor nodes to wake up depending on the tracking information. The Kalman filter is investigated to track the intruder's trajectory from noise measurements of low-power motion sensors to activate only the visual sensor nodes along the intruder's trajectory to provide effective video vigilance. We showed through simulation that Kalman filter estimates and predicts intruder trajectory with reasonable accuracy. Moreover, the proposed hybrid IoT network approach reduces energy consumption significantly compared with a traditional, always active continuous monitoring single-tier visual sensor network. Next, the contribution of this dissertation focuses on an energy-aware and QoS routing mechanism for the IEEE 802.11-based multi-hop visual sensor network of the hybrid IoT network. We propose a routing algorithm that routes a set of video streams to the gateway with guaranteed QoS in terms of bandwidth and delay while minimizing the number of visual sensor nodes that are involved in routing. This maximizes the number of nodes that can be turned off completely to optimize the overall energy consumption of the network without compromising QoS performance. The proposed routing problem is formulated as an Integer Linear Program (ILP) and solved using the branch-and-bound algorithm. Through computer simulation, the performance of the proposed approach is compared with the existing state-of-the-art routing algorithms from the literature. The results clearly show that the proposed mechanism saves a significant amount of the overall energy consumption while guaranteeing QoS in terms of bandwidth and delay.
... Finally, V2N is used to utilize the various applications inter-vehicle. To this end, vehicles use public networks using wireless communications as Wi-Fi hotspots [8]. Figure 1 shows the communication methods of V2X communication in VANET. ...
... [17], and cellular networks [18]), social networking [19] and cloud computing [20] support internet of things to compose a extensive network infrastructure. on the reader and their attendance will be taken immediately. ...
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... standard, has several issues which leads to power consumption and performance degradation issues. For example, even in the power saving mode, the wireless device lasts for 3124 VOLUME 7, 2019 long in the transmitting or awake state while the PS-Poll frames are sent to AP in order to retrieve the buffered frames once at a time from the AP [9], [10]. Nevertheless, the SPSM adds delay of 100 to 300ms when the WNIC of a wireless device is off between the beacon intervals, this delay impacts and causes issues regarding performance on both interactive applications such as web browsers and real-time applications such as VoIP [11]- [13]. ...
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, most approaches proposed to-date are focused on static and adaptive power saving modes. Existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. In this paper, we propose a novel context-aware network traffic classification approach based on Machine Learning (ML) classifiers for optimizing WLAN power saving. The levels of traffic interaction in the background are contextually exploited for application of ML classifiers. Finally, the classified output traffic is used to optimize our proposed, Context-aware Listen Interval (CALI) power saving modes. A real-world dataset is recorded, based on nine smartphone applications’ network traffic, reflecting different types of network behaviour and interaction. This is used to evaluate the performance of eight ML classifiers in this initial study. Comparative results show that more than 99% of accuracy can be achieved. Our study indicates that ML classifiers are suited for classifying smartphone applications’ network traffic based on levels of interaction in the background.
... Related work on power-saving mechanism design. Agrawal et al. [194] propose an opportunity-based power-saving mechanism (OPSM) for web browsing [196] propose a cross-layer energy management mechanism based on traffic differences in the application layer (e.g., web browsing, e-mail, file transfer, etc.) that makes STAs switch between an active state, sleep state, and off mode to enhance power-saving efficiency. Si et al. [197] are the first to model and analyze the DCF protocol based on PSM using a Markov model and find that after each AP Beacon transmission, there are more STA contention channels, resulting in excessive collision and throughput degradation. ...
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... This study can help system developers set reasonable system parameters for WiFi downclocking. So far, downclocking has received great attention [2][3][4][5][6][7][8][9][10][11][12][13]. Among the most relevant works, [2] is the first paper that brought downclocking to low-power WiFi networks and proposed the SRID algorithm, which was considered as one of the most classical amendments on power saving of 802.11 protocols [12,13]. ...
... Among the most relevant works, [2] is the first paper that brought downclocking to low-power WiFi networks and proposed the SRID algorithm, which was considered as one of the most classical amendments on power saving of 802.11 protocols [12,13]. In WiFi, the dominant source of energy consumption is the idle listening operation [7,8], where a node needs to frequently detect unpredictably arriving packets or assess a clear channel with high power. Therefore, SRID reduced the power consumption by allowing a WiFi node to downclock its sample rate in idle listening mode. ...
... , ) and (0, 1, . . . , ) (8) 1 ← the total number of sampling points that satisfy Condition 1. where 1 is the probability of Condition 1 being satisfied under H 1 and ER1 is the probability of Condition 2 being satisfied under H 1 . ...
Mobile edge computing (MEC) enables battery-powered mobile nodes to acquire information technology services at the network edge. These nodes desire to enjoy their service under power saving. The sampling rate invariant detection (SRID) is the first downclocking WiFi technique that can achieve this objective. With SRID, a node detects one packet arrival at a downclocked rate. Upon a successful detection, the node reverts to a full-clocked rate to receive the packet immediately. To ensure that a node acquires its service immediately, the detection performance (namely, the miss-detection probability and the false-alarm probability) of SRID is of importance. This paper is the first one to theoretically study the crucial impact of SRID attributes (e.g., tolerance threshold, correlation threshold, and energy ratio threshold) on the packet detection performance. Extensive Monte Carlo experiments show that our theoretical model is very accurate. This study can help system developers set reasonable system parameters for WiFi downclocking.
... The applications of WSNs include disaster control, environment and habitat monitoring, battlefield surveillance, traffic control, and health care applications [15]. Additionally, many other technologies and devices such as Near Field Communication (NFC) [16], short-range wireless communication (i.e., ZigBee [17] and Bluetooth [18]), universal mobile accessibility (i.e., Wi-Fi hotspots [19], cellular networks [20]), social networking [21] and cloud computing [22] support Internet of Things paradigm for defining network infrastructure. ...
Internet of Things (IoT) is rapidly emerging as the next generation of communication infrastructure, where myriad of multi-scale sensors and devices are seamlessly blended for ubiquitous computing and communication. The rapid growth of IoT applications has increased the demand for experienced professionals in the area. Since few, if any, dedicated IoT courses are currently offered, most Science, Technology, Engineering, and Mathematics (STEM) students will have limited or no exposure to IoT development until after graduation, and inadequately prepared in IoT development to enter into the workforce. Therefore, we have implemented an IoT-based learning framework to facilitate STEM undergraduate education. The design challenges of the novel learning framework are discussed in the paper. Subsequently, we established effective learning approaches to address the challenges. Specifically, we have introduced a lab development kit composed of Raspberry™ Pi/Arduino™ boards and sets of sensors with Zigbee™ supporting to provide wireless communication.
... Thus the power saving mechanism is most useful when all of a MP's neighbouring MPs support power saving. A similar power saving mechanism was also a part of the initial IEEE 802.11 standard but its actual implementation in commercial products was found by Anastasi et al. (2008) to be limited due to the additional complexity involved in the synchronisation process, decrease in QoS and increases in energy consumption due to the resultant additional transfer times. ...
Topology Control (TC) has been previously reported to improve the energy-efficiency and network performance of wireless ad-hoc networks, including Infrastructure Wireless Mesh Networks (I-WMNs). However, simulation-based studies have been relied upon to reach these conclusions and the study of TC prototypes applicable to I-WMNs has largely been limited to design issues. Thus, the study of the efficacy of TC prototypes as a mechanism for improving energy-efficiency and network performance remains an open issue.
The thesis addresses this knowledge gap by studying the dynamic, run-time behaviours and the network topologies created by two standards-compatible TC prototypes. This study provides unique insight into how the prototypes consume computational resources,
maintain network connectivity, produce cumulative transceiver power savings and affect the workings of the routing protocol being employed.
This study also documents the topology instability caused by transceiver power oscillations produced by the PlainTC prototype. A context-based solution to reduce transceiver power oscillations and the subsequent topology instability is proposed. This solution applies the Principal Component Analysis statistical method to historical network data in order to derive the weights associated with each of the identified context variables. A threshold value is defined that only permits a node to adjust its transceiver power output if the observed change in a node’s context exceeds the threshold. The threshold mechanism is incorporated into the PlainTC+ prototype and is shown to reduce topology instability whilst improving network performance when compared to PlainTC. The results obtained in this study suggest that I-WMN topologies formed by TC are
able to closely match the performance of networks that do not employ TC. However,
this study shows that TC negatively affects the energy efficiency of the network despite
achieving cumulative transceiver power savings.
... Le mode la plus économe en énergie du Wi-Fi nommée Power Saving Mode (PSM) permet de former un réseau entre un point d'accès et des stations à portée radio consommant peu d'énergie [9,182]. Cependant ce mode de fonctionnement impose d'utiliser le Wi-Fi en mode infrastructure avec une topologie en étoile ce qui limite la portée du réseau. ...
Les réseaux de capteurs sans fil (aussi appelés LLNs en anglais) sont des réseaux contraints composés de nœuds ayant de faibles ressources (mémoire, CPU, batterie). Ils sont de nature très hétérogène et utilisés dans des contextes variés comme la domotique ou les villes intelligentes. Pour se connecter nativement à l’Internet, un LLN utilise une passerelle, qui a une vue précise du trafic transitant entre Internet et le LLN du fait de sa position. Le but de cette thèse est d’exposer comment des fonctionnalités peuvent être ajoutées à une passerelle d’un LLN dans le but d’optimiser l’utilisation des ressources limitées des nœuds contraints et d’améliorer la connaissance de leur état de fonctionnement. La première contribution est un estimateur non intrusif utilisant le trafic passant par la passerelle pour inférer l’utilisation de la radio des nœuds contraints. La seconde contribution adapte la durée de vie d’informations mises en cache (afin d’utiliser les ressources en cache au lieu de solliciter le réseau) en fonction du compromis entre le coût et l’efficacité. Enfin, la troisième contribution est Makesense, un framework permettant de documenter, d’exécuter et d’analyser une expérience pour réseaux de capteurs sans fil de façon reproductible à partir d’une description unique.
