With the tremendous growth in wireless network deployment and increasing use of mobile devices, e.g., smartphones and tablets, improving energy efficiency in such devices, especially with communication driven workloads, is critical to providing a satisfactory user experience. Studies show that signal strength plays an important role on energy consumption of cellular data communications. While energy consumption can be minimized by accurately predicting signal strengths and reacting to it in real-time, the dynamic nature of wireless environments makes signal strengths highly unpredictable. In this paper, after analyzing in detail the signal strength variation and its impact on energy consumption, we propose to use crowdsourcing approach to optimize mobile devices' energy efficiency by utilizing signal strength traces reported/shared by other users/devices in cellular networks. Via a comprehensive measurement study, we observe that signal strength traces collected from different devices are pseudo-identical, and they even exhibit similar threshold-based behaviors in the relationship between signal strength and device power consumption. Based on our observations, we propose a predictive scheduling algorithm that: (i) selects the right set of signal strength traces based on its location, (ii) applies a filter to smooth out signal strengths and hide abrupt changes, (iii) digitizes the signal strength to “good” and “bad” areas, and (iv) schedules transmissions based on power-throughput characteristics to optimize the transmission energy efficiency. To demonstrate the efficacy of the proposed algorithms, we prototype the crowdsourcing-based predicative scheduling algorithm on Android-based smartphones. Our experiment results from real-life driving tests demonstrate that, by leveraging others' signal traces, mobile devices can save energy up to 35 percent compared to the conventional opportunistic scheduling, i.e., schedule transmissions only based on instantaneous channel conditions.

To provide the ultimate mobile user experience, extended battery life is critical to small form-factor mobile platforms such as smartphones and tablets. Dynamic voltage and frequency scaling (DVFS) and low-power CPU/platform sleep states are commonly used power management features, as they allow dynamic control of power and performance to the time-varying needs of workloads. Despite the potential power saving benefit from synergistic integration of DVFS and sleep-state selection, it is challenging to optimize them jointly for mobile workloads (e.g., video streaming), and most existing work considers them only individually. To address this problem, we study joint optimization of CPU frequency (a.k.a. CPU P-states) and CPU/platform sleep-state selections to reduce energy consumption in mobile platforms. This joint optimization becomes feasible with advanced power management techniques and power aware software development methodologies that regulate (e.g., coalesce/align) system activities, making workload characteristics and system idle duration more deterministic and predictable. We then analyze the optimal operating state that minimizes the expected platform energy consumption based on workload characteristics, and present an algorithm to adapt to it at run time. Our evaluation results on mobile workloads show that the proposed scheme can reduce system power consumption by up to 24%, compared to the conventional CPU-utilization-based approach, which seeks mainly to minimize processor energy.

Our principal motivation is to reduce the energy consumption of display subsystems in mobile devices by introducing a hybrid frame buffer architecture into the platform. We observed that display contents on a screen are quite static for certain mobile workloads, such as web browsing. As a result, data reading from the display frame is much more frequent than the writing of new data onto the frame buffer, a state we refer to as read dominance. Based on this observation, we propose a hybrid frame buffer architecture that exploits the display contents' read-dominant property to improve the energy efficiency of display subsystems. Specifically, we employ two memory types: DRAM and Phase-Change Memory (PCM), in the display frame buffer to exploit their different read/write energy characteristics. We also present an analysis of the energy efficiency of the hybrid frame buffer based on our display content and energy consumption models. Our evaluation results show that the proposed hybrid frame buffer reduces frame buffer energy consumption by up to 43%, compared to the conventional DRAM-only frame buffer.

Spectrum sensing is key to the realization of dynamic spectrum access. To protect primary users' communications from the interference caused by secondary users, spectrum sensing must meet the strict detectability requirements set by regulatory bodies, such as the FCC. Such strict detection requirements, however, can hardly be achieved using PHY-layer sensing techniques alone with one-time sensing by only a single sensor. In this paper, we jointly exploit two MAC-layer sensing methods-cooperative sensing and sensing scheduling- to improve spectrum sensing performance, while incurring minimum sensing overhead. While these sensing methods have been studied individually, little has been done on their combinations and the resulting benefits. Specifically, we propose to construct a profile of the primary signal's RSSs and design a simple, yet near-optimal, incumbent detection rule. Based on this constructed RSS profile, we develop an algorithm to find 1) an optimal set of sensors; 2) an optimal point at which to stop scheduling additional sensing; and 3) an optimal sensing duration for one-time sensing, so as to make a tradeoff between detection performance and sensing overhead. Our evaluation results show that the proposed sensing algorithms reduce the sensing overhead by up to 65 percent, while meeting the requirements of both false-alarm and misdetection probabilities of less than 0.01.

In cognitive radio networks (CRNs), secondary users must be able to accurately and reliably track the location of small-scale mobile primary users/devices (e.g., wireless microphones) in order to efficiently utilize spatial spectrum opportunities, while protecting primary communications. However, accurate tracking of the location of mobile primary users is difficult due mainly to the CR-unique constraint, i.e., localization must rely solely on reported sensing results (i.e., measured primary signal strengths), which can easily be compromised by malicious sensors (or attackers). To cope with this challenge, we propose a new framework, called Sequential mOnte carLo combIned with shadow-faDing estimation (SOLID), for accurate, attack/fault-tolerant tracking of small-scale mobile primary users. The key idea underlying SOLID is to exploit the temporal shadow fading correlation in sensing results induced by the primary user's mobility. Specifically, SOLID augments conventional Sequential Monte Carlo (SMC)-based target tracking with shadow-fading estimation. By examining the shadow-fading gain between the primary transmitter and CRs/sensors, SOLID 1) significantly improves the accuracy of primary tracking regardless of the presence/absence of attack, and 2) successfully masks the abnormal sensing reports due to sensor faults or attacks, preserving localization accuracy and improving spatial spectrum efficiency. Our extensive evaluation in realistic wireless fading environments shows that SOLID lowers localization error by up to 88 percent in the absence of attacks, and 89 percent in the presence of the challenging "slow-poisoning” attack, compared to the conventional SMC-based tracking.

Collaborative spectrum sensing is vulnerable to data falsification attacks, where malicious secondary users (attackers) submit manipulated sensing reports to mislead the fusion center's decision on spectrum occupancy. This paper considers a challenging attack scenario, where multiple attackers cooperatively maximize their aggregate spectrum utilization. Without attack-prevention mechanisms, we show that honest secondary users (SUs) are unable to opportunistically transmit over the licensed spectrum and may even get penalized for collisions caused by attackers. To prevent such attacks, we propose two attack-prevention mechanisms with direct and indirect punishments. Our key idea is to identify collisions with the primary user (PU) that should not happen if all SUs follow the fusion center's decision. Unlike prior work, the proposed simple mechanisms do not require the fusion center to identify and exclude attackers. The direct punishment can effectively prevent all attackers from behaving maliciously. The indirect punishment is easier to implement and can prevent attacks when the attackers care enough about their long-term reward.

The dynamic spectrum market (DSM) is a key economic vehicle for realizing the opportunistic spectrum access that will mitigate the anticipated spectrum-scarcity problem. DSM allows legacy spectrum owners to lease their channels to unlicensed spectrum consumers (or secondary users) in order to increase their revenue and improve spectrum utilization. In DSM, determining the optimal spectrum leasing price is an important yet challenging problem that requires a comprehensive understanding of market participants' interests and interactions. In this paper, we study spectrum pricing competition in a duopoly DSM, where two wireless service providers (WSPs) lease spectrum access rights, and secondary users (SUs) purchase the spectrum use to maximize their utility. We identify two essential, but previously overlooked, properties of DSM: 1) heterogeneous spectrum resources at WSPs and 2) spectrum sharing among SUs. We demonstrate the impact of spectrum heterogeneity via an in-depth measurement study using a software-defined radio (SDR) testbed. We then study the impacts of spectrum heterogeneity on WSPs' optimal pricing and SUs' WSP selection strategies using a systematic three-step approach. First, we study how spectrum sharing among SUs subscribed to the same WSP affects the SUs' achievable utility. Then, we derive the SUs' optimal WSP selection strategy that maximizes their payoff, given the heterogeneous spectrum propagation characteristics and prices. We analyze how individual SU preferences affect market evolution and prove the market convergence to a mean-field limit, even though SUs make local decisions. Finally, given the market evolution, we formulate the WSPs' pricing strategies in a duopoly DSM as a noncooperative game and identify its Nash equilibrium points. We find that the equilibrium price and its uniqueness depend on the SUs' geographical density and spectrum propagation characteristics. Our analytical framework reveals the impact of spectrum heterogeneity in a real-world DSM, and can be used as a guideline for the WSPs' pricing strategies.

Mobile data usage over cellular networks has been dramatically increasing over the past years. Wi-Fi based wireless networks offer a high-bandwidth alternative for offloading such data traffic. However, intermittent connectivity, and battery power drain in mobile devices, inhibits always-on connectivity even in areas with good Wi-Fi coverage. This paper presents WiFisense, a system that employs user mobility information retrieved from low-power sensors (e.g., accelerometer) in smartphones, and further includes adaptive Wi-Fi sensing algorithms, to conserve battery power while improving Wi-Fi usage. We implement the proposed system in Android-based smartphones and evaluate the implementation in both indoor and outdoor Wi-Fi networks. Our evaluation results show that WiFisense saves energy consumption for scans by up to 79% and achieves considerable increase in Wi-Fi usage for various scenarios.

Cognitive radios (CRs) can mitigate the impending spectrum scarcity problem by utilizing their capability of accessing licensed spectrum bands opportunistically. While most existing work focuses on enabling such opportunistic spectrum access for stationary CRs, mobility is an important concern to secondary users (SUs) because future mobile devices are expected to incorporate CR functionality. In this paper, we identify and address three fundamental challenges encountered specifically by mobile SUs. First, we model channel availability experienced by a mobile SU as a two-state continuous-time Markov chain (CTMC) and verify its accuracy via in-depth simulation. Then, to protect primary/incumbent communications from SU interference, we introduce guard distance in the space domain and derive the optimal guard distance that maximizes the spatio-temporal spectrum opportunities available to mobile CRs. To facilitate efficient spectrum sharing, we formulate the problem of maximizing secondary network throughput within a convex optimization framework, and derive an optimal, distributed channel selection strategy. Our simulation results show that the proposed spectrum sensing and distributed channel access schemes improve network throughput and fairness significantly, and reduce SU energy consumption for spectrum sensing by up to 74%.

In cognitive radio networks (CRNs), detecting small-scale primary devices, such as wireless microphones, is a challenging, but very important, problem that has not yet been addressed well. Recently, cooperative sensing and sensing scheduling have been advocated as an effective MAC (medium access control) layer approach to detecting large-scale primary signals. However, it is unclear whether and how they can improve the detection of a small-scale primary signal because of (i) its small signal footprint due to the use of weak transmit-power, and (ii) the unpredictability of its spatial and temporal spectrum-usage patterns. Based on extensive analysis and simulation, we identify the data-fusion range as a key factor that enables effective cooperative sensing for detection of small-scale primary signals. In particular, we derive a closed-form expression for the optimal data-fusion range that minimizes the average detection delay. We also observe that the sensing performance is sensitive to the accuracy in estimating the primary's location and transmit-power. Based on these observations, we propose an efficient sensing framework that jointly performs \underline{De}tection, \underline{LOC}ation estimation, and transmit-power estimation ({t DeLOC}) for small-scale primary users. Our extensive evaluation results in a realistic CRN environment show that {t DeLOC} achieves near-optimal detection performance, while meeting the detection requirements specified in the IEEE 802.22 standard draft. These findings provide useful insights and guidelines in designing a sensing scheme for detection of small-scale primaries in CRNs.

Detecting misbehaving users in wireless networks is an important problem that has been drawing considerable attention. Even though there is a plethora of work on 802.11 wireless local area networks (WLANs), most existing schemes employ behavior-based anomaly detection, assuming that the backoff-time information of each transmitting node is available to the monitoring node. Unfortunately, it is practically infeasible to obtain the accurate backoff value chosen by other transmitting nodes because this MAC-layer information is not readily available. In this paper, we propose a practical way of pinpointing the misbehaving nodes without requiring access of hardware-level (e.g., backoff time) information in 802.11 WLANs. In contrast to most prior work, our scheme exploits the sequence of successfully received packets, which are readily observable at the access point. The distinct features of our scheme are that it 1) promptly detects a misbehaving node using a sequential hypothesis test, 2) performs well in realistic erroneous channel conditions due to its ability to accurately capture link heterogeneity, and 3) incurs negligible memory and computation overheads as it makes detection decisions based on runtime observations. The effectiveness of the proposed scheme is evaluated via extensive simulation as well as implementation, demonstrating its capability of accurately detecting nodess' selfish behavior in realistic 802.11 WLAN environments.

Cooperative (or distributed) sensing has been recognized as a viable means to enhance the incumbent signal detection by exploiting the diversity of sensors. However, it is challenging to secure such distributed sensing due mainly to the unique features of dynamic spectrum access networks-openness of low-layer protocol stacks in software-defined radio devices and the absence of interactions/coordination between primary and secondary devices. To meet this challenge, we propose an attack-tolerant distributed sensing protocol (ADSP) for DTV signal detection in IEEE 802.22 WRANs, under which sensors in close proximity are grouped as a cluster, and sensors within a cluster cooperate to safeguard the integrity of sensing. The heart of ADSP is a novel filter based on shadow-fading correlation, by which the fusion center cross-validates reports from the sensors to identify and penalize abnormal sensing reports. By realizing this correlation filter, ADSP significantly reduces the impact of an attack on the performance of distributed sensing, while incurring minimal processing and communication overheads. ADSP also guarantees the detectability requirements of 802.22 to be met even with the presence of sensing report manipulation attacks by scheduling sensing within the framework of sequential hypothesis testing. The efficacy of ADSP is validated on a realistic 2D shadow-fading field. Our extensive simulation-based study shows that ADSP reduces the false-alarm rate by 99.2 percent while achieving 97.4 percent of maximum achievable detection rate, and meets the detection requirements of IEEE 802.22 in various attack scenarios.

In cognitive radio networks (CRNs), detecting small-scale primary devices---such as wireless microphones (WMs)---is a challenging, but very important, problem that has not yet been addressed well. We identify the data-fusion range as a key factor that enables effective cooperative sensing for detection of small-scale primary devices. In particular, we derive a closed-form expression for the optimal data-fusion range that minimizes the average detection delay. We also observe that the sensing performance is sensitive to the accuracy in estimating the primary's location and transmit-power. Based on these observations, we propose an efficient sensing framework, called DeLOC, that iteratively performs location/transmit-power estimation and dynamic sensor selection for cooperative sensing. Our extensive simulation results in a realistic CRN environment show that DeLOC achieves near-optimal detection performance, while meeting the detection requirements specified in the IEEE 802.22 standard draft.

Information on site-specific spectrum characteristics is essential to evaluate and improve the performance of wireless networks. However, it is usually very costly to obtain accurate spectrum-condition information in heterogeneous wireless environments. This paper presents a novel spectrum-survey system, called Sybot (Spectrum survey robot), that guides network engineers to efficiently monitor the spectrum condition (e.g., RSS) of WiFi networks. Sybot effectively controls mobility and employs three disparate monitoring techniques - complete, selective, and diagnostic - that help produce and maintain an accurate spectrum-condition map for challenging indoor WiFi networks. By adaptively triggering the most suitable of the three techniques, Sybot captures spatio-temporal changes in spectrum condition. Moreover, based on the monitoring results, Sybot automatically determines several key survey parameters, such as site-specific measurement time and space granularities. Sybot has been prototyped with a commodity IEEE 802.11 router and Linux OS, and experimentally evaluated, demonstrating its ability to generate accurate spectrum-condition maps while reducing the measurement effort (space, time) by more than 56%.

Accurate sensing of the spectrum condition is of crucial importance to the mitigation of the spectrum scarcity problem in dynamic spectrum access (DSA) networks. Specifically, distributed sensing has been recognized as a viable means to enhance the incumbent signal detection by exploiting the diversity of sensors. However, it is challenging to make such distributed sensing secure due mainly to the unique features of DSA networks - openness of a low-layer protocol stack in SDR devices and non-existence of communications between primary and secondary devices. To address this challenge, we propose attack-tolerant distributed sensing protocol (ADSP), under which sensors in close proximity are grouped into a cluster, and sensors in a cluster cooperatively safeguard distributed sensing. The heart of ADSP is a novel shadow fading correlation-based filter tailored to anomaly detection, by which the fusion center prefilters abnormal sensor reports via cross-validation. By realizing this correlation filter, ADSP minimizes the impact of an attack on the performance of distributed sensing, while incurring minimal processing and communications overheads. The efficacy of our scheme is validated on a realistic two-dimensional shadow-fading field, which accurately approximates real-world shadowing environments. Our extensive simulation-based evaluation shows that ADSP significantly reduces the impact of attacks on incumbent detection performance.

In cognitive radio networks (CRNs), regulatory bodies, such as the FCC, enforce an extremely demanding detectability requirement to protect primary users' communications, which can hardly be achieved with one-time sensing using only a single sensor. Most of the previous work focused on either cooperative sensing or sensing scheduling as a viable means to improve the detection performance without studying their interactions. In this paper, we propose an optimal spectrum sensing framework in CRNs that jointly exploits sensors' cooperation and sensing scheduling to meet the desired detection performance with minimum sensing overhead. Specifically, we propose an optimal sensing framework for the IEEE 802.22 wireless regional area networks (WRANs) that directs the base station (BS) to manage spectrum sensing by (i) constructing each primary signal's spatial profile of received signal strengths (RSSs) as a detection criterion, (ii) selecting an optimal set of sensors for cooperative sensing, and (iii) finding an optimal time to stop sensing. This framework will ensure the desired sensing performance of 802.22 with minimum sensing overhead. Our evaluation results show that the proposed sensing algorithms reduce the sensing overhead significantly and lower the feasible operation region of energy detector by 13 dB for practical scenarios.

Recently, spectrum-agile (SA) networks have been recognized as a viable solution to the spectrum-scarcity problem in spectrum-scarcity problem. In SA networks, secondary (unlicensed) users are allowed to opportunistically utilize idle licensed spectrum bands, thus improving spectrum utilization efficiency and accommodating more users and applications. We take a two- step approach to the problem of maximizing the throughput of an SA network. The first step is to determine a subset of "candidate" channels that a secondary device will consider for its channel-switching. The candidate channels are selected based on their estimated utilization. We then propose channel-aware switching to determine when and where to switch to, among the candidate channels. Wireless channels are assumed to experience independent Rayleigh fading, and modeled with a finite-state Markov channel (FSMC) model. Our evaluation results show that the proposed channel-aware switching scheme significantly outperforms the traditional forced-switching scheme in terms of average throughput.

The family of IEEE 802.11 Wireless Local Area Network (WLAN) standards supports multiple transmission rates in the physical layer (PHY). This multi-rate capability offers a viable means of coping with dynamically-fluctuating wireless channel conditions. We propose a cross-layer approach for the optimal PHY mode control to maximize the system goodput. Our key idea is to exploit the underlying channel fading characteristics and the history of PHY mode control and observations to infer the current channel condition, so that the optimal PHY mode may be selected. Assuming Rayleigh fading, we describe the receiver-side signal-to-noise ratio (SNR) fluctuations as a finite-state Markov channel (FSMC) model. Since the channel condition (i.e., fading level) is not directly observable by the transmitter, we formulate the PHY rate adaptation problem as a partially-observable Markov decision process (POMDP) to find the optimal transmission policy. We use the belief state vector to represent the channel state probabilistically. The belief state is updated solely based on the channel state-transition matrix and acknowledgement (ACK) information. Our evaluation results show that the POMDP-based rate adaptation outperforms two most well-known rate adaptation schemes, Auto Rate Fallback (ARF) and Adaptive ARF (AARF), in terms of average goodput under various fading conditions, and achieves up to 92% of the ideal performance.