Battery-free computer gaming offers a vision of sustainable interaction in which games run on hardware that does not require a battery, yet this approach introduces uncertainty due to frequent power failures. Rather than viewing these failures as limitations, this work examines how integrating energy harvesting with application design can encourage users to reimagine and work with such failures, thus shaping behaviour and supporting device use. We present TURNER, a state-of-the-art modular battery-free games console powered by a hand crank and solar cells, created as a research probe to study how energy harvesting mediates the relationship between power and interaction. In a mixed-methods study (N = 60), we explored the influence of energy harvesting on gameplay. Findings show significant variations in harvesting strategies, with interviews surfacing strategies for creating applications that respond to and build on the patterns of system power failure, the ergonomics of energy harvesting, and the value of embedding energy generation into play. Our work offers insights for interactive, sustainable battery-free computers.

Despite decades of research on battery-free systems, their adoption in everyday electronics remains limited. Interactive Internet of Things devices such as wearables, personal trackers, and health monitors are increasingly widespread, yet almost all depend on batteries that are environmentally harmful, slow to charge, and have limited lifespans. Existing battery-free devices have seen use only in niche applications with minimal user interaction, primarily due to slow energy harvesting, frequent power interruptions, and restricted sensing capabilities under tight energy constraints. To address these limitations, we present Cheetah, a battery-free architecture that charges rapidly and reliably from ubiquitous wireless chargers, reduces power consumption, and enhances usability. We implement and evaluate Cheetah architecture as a smartwatch and a wearable patch, capable of operating for a full day after only six seconds of charging. Our results demonstrate that battery-free design can move beyond niche deployments to become a practical and sustainable alternative for mainstream interactive electronics.

Intermittently-operating embedded computing platforms powered by energy harvesting must frequently checkpoint their computation state. Using non-volatile memory reduces checkpoint size by eliminating the need to checkpoint volatile memory but increases checkpoint frequency to cover Write After Read (WAR) dependencies. Additionally, non-volatile memory is significantly slower to access - while consuming more energy than its volatile counterpart - suggesting the use of a data cache. Unfortunately, existing data cache solutions do not fit the challenges of intermittent computing and often require additional hardware or software to detect WARs. In this paper, we extend the data cache by integrating it with WAR detection - dropping the need for an additional memory tracker. This idea forms the basis of NACHO: a data cache tailored to intermittent computing. NACHO, on average, reduces intermittent computing runtime overhead by 54% compared to state of the art cache-based systems. It also reduces the number of non-volatile memory writes by 82% compared to a data cache-less system, and 18% on average compared to multiple state of the art cache-based systems.

Battery-free sensing devices have gained growing popularity as they can operate relying solely on harvested energy and environmentally friendly capacitors. However, despite the increasing number of battery-free solutions, their design remains a difficult task. In fact, the limited energy storage capacity and the resulting coupling between energy supply and demand introduce new design trade-offs that cannot be explored using conventional tools that consider a constant power supply. To enable fast design space exploration and facilitate the development of battery-free systems, we introduce Simba, an open-source simulation framework that allows to investigate in detail the complex interplay between various device components. We demonstrate the benefits of Simba in two case studies, evaluated experimentally, targeting real-world, state-of-the-art battery-free devices. First, we illustrate how Simba can explore the dependencies between different component configurations and assess their impact on the overall system performance. Among others, we show that changing the storage capacity or slightly modifying the load behavior can improve data throughput by a factor of up to 5.1x and 9.7x, respectively. Second, we present how Simba allows to automatically select key parameters that optimize the operations of a battery-free system (e.g., its checkpointing mechanism), and showcase how Simba enables performance evaluations based on real-world energy harvesting traces.CCS CONCEPTS• Computer systems organization → Embedded systems.

We present an architecture for intermittently-powered wireless communication systems that does not require any changes to the official protocol specification. Our core idea is to save the intermediate state of the wireless protocol to non-volatile memory within each connection interval. The protocol state is then deterministically restored at a predefined (harvested energy-dependent) time, which follows the connection interval. As a case study for our architecture, we introduce FreeBie: a battery-free intermittently-powered Bluetooth Low Energy (BLE) mote. To the best of our knowledge FreeBie is the first battery-free active wireless system that sustains bi-directional communication on intermittent harvested energy. The strength of our architecture is articulated by FreeBie consuming at least 9.5 times less power during device inactivity periods than a state-of-the-art BLE device.

Intermittently operating embedded computing platforms powered by energy harvesting require software frameworks to protect from errors caused by Write After Read (WAR) dependencies. A powerful method of code protection for systems with non-volatile main memory utilizes compiler analysis to insert a checkpoint inside each WAR violation in the code. However, such software frameworks are oblivious to the code structure---and therefore, inefficient---when many consecutive WAR violations exist. Our insight is that by transforming the input code, i.e., moving individual write operations from unique WARs close to each other, we can significantly reduce the number of checkpoints. This idea is the foundation for WARio: a set of compiler transformations for efficient code generation for intermittent computing. WARio, on average, reduces checkpoint overhead by 58%, and up to 88%, compared to the state of the art across various benchmarks.

Battery-free and intermittently powered devices offer long lifetimes and enable deployment in new applications and environments. Unfortunately, developing sophisticated inference-capable applications is still challenging due to the lack of platform support for more advanced (32-bit) microprocessors and specialized accelerators---which can execute data-intensive machine learning tasks, but add complexity across the stack when dealing with intermittent power. We present Protean to bridge the platform gap for inference-capable battery-free sensors. Designed for runtime scalability, meeting the dynamic range of energy harvesters with matching heterogeneous processing elements like neural network accelerators. We develop a modular "plug-and-play" hardware platform, SuperSensor, with a reconfigurable energy storage circuit that powers a 32-bit ARM-based microcontroller with a convolutional neural network accelerator. An adaptive task-based runtime system, Chameleon, provides intermittency-proof execution of machine learning tasks across heterogeneous processing elements. The runtime automatically scales and dispatches these tasks based on incoming energy, current state, and programmer annotations. A code generator, Metamorph, automates conversion of ML models to intermittent safe execution across heterogeneous compute elements. We evaluate Protean with audio and image workloads and demonstrate up to 666x improvement in inference energy efficiency by enabling usage of modern computational elements within intermittent computing. Further, Protean provides up to 166% higher throughput compared to non-adaptive baselines.

Debugging and testing battery-free intermittently-powered systems is notoriously difficult. This is not only due to the additional complexity of maintaining state through power failures but also due to the lack of proper tools to test and debug these systems. As a solution, we present DIPS: a fully-featured hardware debugger for battery-free intermittently-powered systems capable of automatically verifying memory and peripheral state between power failures. Our solution seamlessly integrates an emulator allowing for emulation of any power scenario to the device under test. This allows our debugger to pause emulation and program execution when debugging or when state restoration issues are detected. Our new system is built around GNU Debugger (GDB): a widely-used debugging tool. Therefore, DIPS allows for a debugging process identical to state-of-the-art debuggers for continuously-powered devices. User studies found that our debugger is easy and intuitive to use. It allows embedded system developers to find bugs quicker in code written for battery-free devices. With our debugger we found unseen errors in state-of-the-art software frameworks for intermittently-powered systems.

Age of Information (AoI) is a key metric to understand data freshness in Internet of Things (IoT) devices. In this paper we analyse an intermittently—powered IoT sensor-with mixed-memory (volatile and non-volatile) architecture—that uses a Time-Dependent Checkpointing (TDC) scheme. We derive the average Peak Age of Information (PAoI) and average AoI of the system, and use these metrics to understand which device parameters most significantly influence performance. We go on to consider how the average PAoI of a mixed-memory system compares with entirely volatile or entirely non-volatile architecture, and also introduce an alternative TDC strategy to improve system resilience in unpredictable environmental conditions.

Tiny energy harvesting sensors that operate intermittently, without batteries, have become an increasingly appealing way to gather data in hard to reach places at low cost. Frequent power failures make forward progress, data preservation and consistency, and timely operation challenging. Unfortunately, state-of-the-art systems ask the programmer to solve these challenges, and have high memory overhead, lack critical programming features like pointers and recursion, and are only dimly aware of the passing of time and its effect on application quality. We present Time-sensitive Intermittent Computing System (TICS), a new platform for intermittent computing, which provides simple programming abstractions for handling the passing of time through intermittent failures, and uses this to make decisions about when data can be used or thrown away. Moreover, TICS provides predictable checkpoint sizes by keeping checkpoint and restore times small and reduces the cognitive burden of rewriting embedded code for intermittency without limiting expressibility or language functionality, enabling numerous existing embedded applications to run intermittently.

Energy-harvesting devices have enabled Internet of Things applications that were impossible before. One core challenge of batteryless sensors that operate intermittently is reliable timekeeping. State-of-the-art low-power real-time clocks suffer from long start-up times (order of seconds) and have low timekeeping granularity (tens of milliseconds at best), often not matching timing requirements of devices that experience numerous power outages per second. Our key insight is that time can be inferred by measuring alternative physical phenomena, like the discharge of a simple RC circuit, and that timekeeping energy cost and accuracy can be modulated depending on the run-time requirements. We achieve these goals with a multi-tier timekeeping architecture, named Cascaded Hierarchical Remanence Timekeeper (CHRT), featuring an array of different RC circuits to be used for dynamic timekeeping requirements. The CHRT and its accompanying software interface are embedded into a fresh batteryless wireless sensing platform, called Botoks, capable of tracking time across power failures. Low start-up time (max 5 ms), high resolution (up to 1 ms) and run-time reconfigurability are the key features of our timekeeping platform. We developed two time-sensitive batteryless applications to demonstrate the approach: a bicycle analytics tool, where the CHRT is used to track time between revolutions of a bicycle wheel, and wireless communication, where the CHRT enables radio synchronization between two intermittently-powered sensors.

Energy-neutral Internet of Things requires freeing embedded devices from batteries and powering them from ambient energy. Ambient energy is, however, unpredictable and can only power a device intermittently. Therefore, the paradigm of intermittent execution is to save the program state into non-volatile memory frequently to preserve the execution progress. In task-based intermittent programming, the state is saved at task transition. Tasks are fixed at compile time and agnostic to energy conditions. Thus, the state may be saved either more often than necessary or not often enough for the program to progress and terminate. To address these challenges, we propose Coala, an adaptive and efficient task-based execution model. Coala progresses on a multi-task scale when energy permits and preserves the computation progress on a sub-task scale if necessary. Coala’s specialized memory virtualization mechanism ensures that power failures do not leave the program state in non-volatile memory inconsistent. Our evaluation on a real energy-harvesting platform not only shows that Coala reduces runtime by up to 54% as compared to a state-of-the-art system, but also it is able to progress where static systems fail.

Distributed and collaborative computation has never been considered before in networks of batteryless sensors. This can bring many advantages for applications (e.g. longer transmission ranges, lower network costs), however introducing new research challenges. In this paper, we focus on the well-known distributed sensor fusion but in an intermittently-powered batteryless sensor network. The goal is to estimate a parameter collaboratively by considering individual sensor measurements. We show that, even though the nodes stop operation with high probability due to random power failures and they neither communicate with their neighbors nor perform computation most of the time, the simplest implementation of the fully-distributed sensor fusion based on average consensus improves the overall estimation quality of the network considerably. In the light of this, we anticipate that if harvested energy is used efficiently so that nodes have more opportunity to receive and send packets, existing fully-distributed protocols can be implemented with tiny modifications in networks of batteryless sensors.

We characterize the performance of a backscatter tag-to-tag (T2T) multi-hop network. For this, we developed a discrete component-based backscatter T2T transceiver and a communication protocol suite. The protocol composed of a novel (i) flooding-based link control tailored towards backscatter transmission, and (ii) low-power listening MAC. The MAC design is based on the new insight that backscatter reception is more energy costly than transmission. Our experiments show that multi-hopping extends the coverage of backscatter networks by enabling longer backward T2T links (tag far from the exciter sending to the tag close to the exciter). Four hops, for example, extend the communication range by a factor of two. Furthermore, we show that dead spots in multi-hop T2T networks are far less significant than those in the single-hop T2T networks.

Quantum communication brings radically new capabilities that are provably impossible to attain in any classical network. Here, we take the first step from a physics experiment to a quantum internet system. We propose a functional allocation of a quantum network stack, and construct the first physical and link layer protocols that turn ad-hoc physics experiments producing heralded entanglement between quantum processors into a well-defined and robust service. This lays the groundwork for designing and implementing scalable control and application protocols in platform-independent software. To design our protocol, we identify use cases, as well as fundamental and technological design considerations of quantum network hardware, illustrated by considering the state-of-the-art quantum processor platform available to us (Nitrogen-Vacancy (NV) centers in diamond). Using a purpose built discrete-event simulator for quantum networks, we examine the robustness and performance of our protocol using extensive simulations on a supercomputing cluster. We perform a full implementation of our protocol in our simulator, where we successfully validate the physical simulation model against data gathered from the NV hardware. We first observe that our protocol is robust even in a regime of exaggerated losses of classical control messages with only little impact on the performance of the system. We proceed to study the performance of our protocols for 169 distinct simulation scenarios, including trade-offs between traditional performance metrics such as throughput, and the quality of entanglement. Finally, we initiate the study of quantum network scheduling strategies to optimize protocol performance for different use cases.

Battery-free computational RFID platforms, such as WISP (Wireless Identification and Sensing Platform), are intermittently-powered devices designed for replacing existing sensor networks. Accordingly, synchronization appears as one of the crucial building blocks for collaborative and coordinated actions in these platforms. However, intermittent power leads to frequent loss of computational state and short-term clock frequency instability that makes synchronization challenging. In this article, we introduce the WISP-Sync protocol that provides synchronization among WISP tags in the communication range of an RFID reader. WISP-Sync overcomes the aforementioned challenges by employing a Proportional-Integral (PI) controller-inspired algorithm which (i) is adaptive-reactive to short-term clock instabilities; (ii) requires only a few computation steps-suitable for limited harvested energy; and (iii) keeps a few variables to hold the synchronization state-minimum overhead to recover from power interrupts. Evaluations in our testbed showed that WISP-Sync ensured an average synchronization error of approximately 1 ms among the tags with an average energy overhead of 1.85 μJ per synchronization round.

Tiny energy harvesting battery-free devices promise maintenance free operation for decades, providing swarm scale intelligence in applications from healthcare to building monitoring. These devices operate intermittently because of unpredictable, dynamic energy harvesting environments, failing when energy is scarce. Despite this dynamic operation, current programming models are static; they ignore the event-driven and time-sensitive nature of sensing applications, focusing only on preserving forward progress while maintaining performance. This paper proposes InK; the first reactive kernel that provides a novel way to program these tiny energy harvesting devices that focuses on their main application of event-driven sensing. InK brings an event-driven paradigm shift for batteryless applications, introducing building blocks and abstractions that enable reacting to changes in available energy and variations in sensing data, alongside task scheduling, while maintaining a consistent memory and sense of time. We implemented several event-driven applications for InK, conducted a user study, and benchmarked InK against the state-of-the-art; InK provides up to 14 times more responsiveness and was easier to use. We show that InK enables never before seen batteryless applications, and facilitates more sophisticated batteryless programs.

We propose a novel incomplete cooperative algorithm for distributed constraint optimization problems (DCOPs) denoted as Cooperative Constraint Approximation (CoCoA). The key strategy of the algorithm is to use a semi-greedy approach in which knowledge is distributed amongst neighboring agents, and assigning a value only once instead of an iterative approach. Furthermore, CoCoA uses a unique-first approach to improve the solution quality. It is designed such that it can solve DCOPs as well as Asymmetric DCOPS, with only few messages being communicated between neighboring agents. Experimentally, through evaluating graph coloring problems, randomized (A)DCOPs, and a sensor network communication problem, we show that CoCoA is able to very quickly find solutions of high quality with a smaller communication overhead than state-of-the-art DCOP solvers such as DSA, MGM-2, ACLS, MCS-MGM and Max-Sum. In our asymmetric use case problem of a sensor network, we show that CoCoA not only finds the best solution, but also finds this solution faster than any other algorithm.

We present Stork - an extension of the EPC C1G2 protocol allowing streaming of data to multiple Computational Radio Frequency IDentification tags (CRFIDs) simultaneously at up to 20 times faster than the prior state of the art. Stork introduces downstream attributes never before seen in (C)RFIDs: (i) fast feedback for CRFID downstream verification based on the internal EPC C1G2 memory check command - which we analytically and experimentally show to be the best possible downstream verification process based on EPC C1G2; (ii) ability to perform multi-CRFID transfer - which in our experiments speeds up downstream by more than two times compared to sequential transmission; and (iii) the use of compressed data streams - which improves firmware reprogramming times by up to 10% at large reader-to-CRFID distances.

Computational RFID (CRFID) devices are emerging platforms that can enable perennial computation and sensing by eliminating the need for batteries. Although much research has been devoted to improving upstream (CRFID to RFID reader) communication rates, the opposite direction has so far been neglected, presumably due to the difficulty of guaranteeing fast and error-free transfer amidst frequent power interruptions of CRFID. With growing interest in the market where CRFIDs are forever-embedded in many structures, it is necessary for this void to be filled. Therefore, we propose Wisent — a robust downstream communication protocol for CRFIDs that operates on top of the legacy UHF RFID communication protocol: EPC C1G2. The novelty of Wisent is its ability to adaptively change the frame length sent by the reader, based on the length throttling mechanism, to minimize the transfer times at varying channel conditions. We present an implementation of Wisent for the WISP 5 and an off-the-shelf RFID reader. Our experiments show that Wisent allows transfer up to 16 times faster than a baseline, non-adaptive shortest frame case, i.e. single word length, at sub-meter distance. As a case study, we show how Wisent enables wireless CRFID reprogramming, demonstrating the world's first wirelessly reprogrammable (software defined) CRFID.

A wireless power transfer network (WPTN) aims to support devices with cable-less energy on-demand. Unfortunately, wireless power transfer itself-especially through radio frequency radiation rectification-is fairly inefficient due to decaying power with distance, antenna polarization, etc. Consequently, idle charging needs to be minimized to reduce the already large costs of providing energy to the receivers. In turn, energy saving in a WPTN can be boosted by simply switching off the energy transmitter when the received energy is too weak for rectification. Therefore in this paper we propose, and experimentally evaluate, two “green” protocols for the control plane of static charger/mobile receiver WPTN aimed at optimizing the charger workflow to make the WPTN reduce idle time of transmitters. Those protocols are: “beaconing,” where receivers advertise their presence to the WPTN, and “probing” exploiting the receiver feedback from the WPTN on the level of received energy. We demonstrate that both protocols reduce the unnecessary WPTN uptime, however trading it for the reduced energy provision, compared to the base case of “WPTN charger always on.” For example, our system (in our experiments) saves at most ≈80 % of energy at the charger with only ≈17% less energy possibly harvested.

This paper focuses on analytical studies of the primary user (PU) traffic classification problem. colorblack{Observing} that the gamma distribution can represent positively skewed data and exponential distribution (popular in communication networks performance analysis literature) it is considered here as the PU traffic descriptor. We investigate two PU traffic classifiers utilizing perfectly measured PU activity (busy) and inactivity (idle) periods: (i) maximum likelihood classifier (MLC) and (ii) multi-hypothesis sequential probability ratio test classifier (MSPRTC). Then, relaxing the assumption on perfect period measurement, we consider a PU traffic observation through channel sampling. For a special case of negligible probability of PU state change in between two samplings, we propose a minimum variance PU busy/idle period length estimator. Later, relaxing the assumption of the complete knowledge of the parameters of the PU period length distribution, we propose two PU traffic classification schemes: (i) estimate-then-classify (ETC), and (ii) average likelihood function (ALF) classifiers considering time domain fluctuation of the PU traffic parameters. Numerical results show that both MLC and MSPRTC are sensitive to the periods measurement errors when the distance among distribution hypotheses is small, and to the distribution parameter estimation errors when the distance among hypotheses is large. For PU traffic parameters with a partial prior knowledge of the distribution, the ETC outperforms ALF when the distance among hypotheses is small, while the opposite holds when the distance is large.

We propose a primary user (PU) traffic distribution classifier for dynamic spectrum access networks based on multi-hypothesis sequential probability ratio test (MSPRT). In specific, we propose two classifiers: (i) an estimate-then-classify classifier, and (ii) a modified MSPRT classifier based on the average likelihood function considering partial knowledge of the PU traffic parameters. Using the sequential algorithm, we show that our proposed classifiers can achieve higher classification performance compared to the traditional maximum likelihood classifier using constant number of samples.

In this work we investigate the bounds on the estimation accuracy of Primary User (PU) traffic parameters with exponentially distributed busy and idle times. We derive closed-form expressions for the Cramér-Rao bounds on the mean squared estimation error for the blind joint estimation of the PU traffic parameters, specifically, the duty cycle, and the mean arrival and departure rates. Moreover, we present the corresponding maximum-likelihood estimators for the traffic parameters and discuss the effect of sensing errors in the joint estimation of PU traffic.

We present results on the system level performance of the IEEE 802.22 standard with sensing functionality, using a highly detailed implementation of the IEEE 802.22 protocol stack in the NS-2 simulator. Our attention is focused on the effect of spatio-temporal wireless microphone (WM) activity on the performance of the IEEE 802.22 network with spectrum sensing considered. In general we find that the frequency of WM appearance and activity duration should be quite high in all channels not used by TV broadcasters to reduce IEEE 802.22 throughput, for example about 50% WM occupancy in each of total of four channels. Impact on WM performance is found to be low in general using the two-stage spectrum sensing strategy with frequent sensing stages.

Wireless microphones operating in the TV white spaces often appear at specific venues such as schools or churches, and at specific times. Hence, their location and appearance pattern can be predicted from spectrum sensing statistics. In this paper we propose and evaluate three spectrum selection functions that utilize sensing results to provide long-term spectrum usage statistics as basis for channel selection to enhance performance by reducing interference and increasing throughput. To evaluate performance of the spectrum selection functions, these are implemented in a detailed system level simulator for the IEEE 802.22 standard. We find that the spectrum selection function that uses statistics about channel idle and busy periods performs best when primary user activity is high, and that the spectrum selection function that uses predictions about location and distance to primary users performs best when IEEE 802.22 radio users are mobile and the primary user activity is low.

This paper presents a mathematical analysis of the accuracy of estimating Primary User's (PU's) mean duty cycle u, as well as the mean off- and on-times, where the estimation accuracy is expressed in terms of the Cramer-Rao bound on the mean squared estimation error. For estimating u, we derive the mean squared estimation error for uniform, non-uniform, and weighted sample stream averaging, as well as maximum likelihood (ML) estimation. The estimation accuracy of the mean PU off- and on-times is studied when ML estimation is employed. Besides, the impact of spectrum sensing errors on the estimation accuracy is studied analytically for the averaging estimators, while simulation results are used for the ML estimators. Furthermore, we develop algorithms for the blind estimation of the traffic parameters based on the derived theoretical estimation accuracy expressions.

In this paper we present new results on the problem of finding the best channel sensing order for multi-channel Dynamic Spectrum Access (DSA) networks. We start with the general assumption that all Secondary Users (SUs) cooperatively sense each Primary User (PU) channel at one time. Then, the SU sensing results are reported to a DSA base station that schedules SU transmissions in order to maximize DSA network throughput. We then assume that PU traffic parameters are not perfectly known to DSA network and change over time, and propose a novel PU channel sensing order scheme based on the quality of PU traffic estimation. We adopt a maximum likelihood estimator to estimate the traffic statistics of PU channels and derive the Cramer-Rao (CR) bounds for the PU traffic estimation performance. Based on the CR bound and its Gaussian approximation, we analyze the impact of the estimation error on the DSA network throughput by computing a new metric called sensing order confidence, i.e., the probability that the best selected sensing order is not affected by PU traffic estimation errors. Finally, we formulate a convex optimization problem to determine the minimum number of PU channel state samples required for estimating PU traffic parameters after determining a certain constraint on the sensing order confidence metric to achieve the best sensing order.

We present an analytical framework for modeling a priority-based load balancing scheme in cellular networks based on a new algorithm called direct retry with truncated offloading channel resource pool (DRK). The model, developed for a baseline case of two cell network, differs in many respects from previous works on load balancing. Foremost, it incorporates the call admission process, through random access. In specific, the proposed model implements the Physical Random Access Channel used in 3GPP network standards. Furthermore, the proposed model allows the differentiation of users based on their priorities. The quantitative results illustrate that, for example, cellular network operators can control the manner in which traffic is offloaded between neighboring cells by simply adjusting the length of the random access phase. Our analysis also allows for the quantitative determination of the blocking probability individual users will experience given a specific length of random access phase. Furthermore, we observe that the improvement in blocking probability per shared channel for load balanced users using (DRK) is maximized at an intermediate number of shared channels, as opposed to the maximum number of these shared resources. This occurs because a balance is achieved between the number of users requesting connections and those that are already admitted to the network. We also present an extension of our analytical model to a multicell network (by means of an approximation) and an application of the proposed load balancing scheme in the context of opportunistic spectrum access.

In this paper we propose an analytical framework which allows the investigation of the average channel throughput at the medium access control (MAC) layer for opportunistic spectrum access (OSA) networks with channel bonding enabled. We show that channel bonding is beneficial in certain cases only (e.g. low primary user traffic), though the extent of the benefit depend on the features of the OSA network including secondary network size and the total number of channels available for bonding.

Transmission over multiple frequency bands combined into one logical channel speeds up data transfer for wireless networks. On the other hand, the allocation of multiple channels to a single user decreases the probability of finding a free logical channel for new connections, which may result in a network-wide throughput loss. While this relationship has been studied experimentally, especially in the WLAN configuration, little is known on how to analytically model such phenomena. With the advent of Opportunistic Spectrum Access (OSA) networks, it is even more important to understand the circumstances in which it is beneficial to bond channels occupied by primary users with dynamic duty cycle patterns. In this paper we propose an analytical framework which allows the investigation of the average channel throughput at the medium access control layer for OSA networks with channel bonding enabled. We show that channel bonding is generally beneficial, though the extent of the benefits depend on the features of the OSA network, including OSA network size and the total number of channels available for bonding. In addition, we show that performance benefits can be realized by adaptively changing the number of bonded channels depending on network conditions. Finally, we evaluate channel bonding considering physical layer constraints, i.e. throughput reduction compared to the theoretical throughput of a single virtual channel due to a transmission power limit for any bonding size.

We present an analytical framework to assess the link layer throughput of multichannel Opportunistic Spectrum Access (OSA) ad hoc networks. Specifically, we focus on analyzing various combinations of collaborative spectrum sensing and Medium Access Control (MAC) protocol abstractions. We decompose collaborative spectrum sensing into layers, parametrize each layer, classify existing solutions, and propose a new protocol called Truncated Time Division Multiple Access (TTDMA) that supports efficient distribution of sensing results in “\kappa out of N” fusion rule. In case of multichannel MAC protocols, we evaluate two main approaches of control channel design with 1) dedicated and 2) hopping channel. We propose to augment these protocols with options of handling secondary user (SU) connections preempted by primary user (PU) by 1) connection buffering until PU departure and 2) connection switching to a vacant PU channel. By comparing and optimizing different design combinations, we show that 1) it is generally better to buffer preempted SU connections than to switch them to PU vacant channels and 2) TTDMA is a promising design option for collaborative spectrum sensing process when \kappa does not change over time.

In this paper a novel signaling protocol for co-existence and spectrum sharing among cognitive radio nodes is proposed. This protocol allows the radios to rendezvous with each other in a statically allocated spectrum band through on-off keying signaling and reliable spectrum sensing. It enables the radios to have non-cooperative communication in the sense that they do not need to exchange their modulation schemes and frequency channel. The proposed protocol requires no synchronization and is augmented with power control mechanisms. Analysis of the proposed protocol in terms of timing, probability of a successful connection and energy efficiency is also given.

We present an analytical framework for modeling a priority-based load balancing scheme in cellular networks. The model differs in many respects from previous work on load balancing, in particular by incorporating the call admission process, through random access; allowing differentiation of users based on their priorities; and by incorporating the received signal properties. The analysis illustrates that, for example, ignoring channel quality has a non- negligible impact on the performance of load balancing. Moreover, the system model allows for the scenario-based determination of whether blocking probability or collision probability (during call admission) is the more dominant factor in the degradation of the performance of UEs affected by load balancing.

We propose an analytical model to assess the throughput and call blocking rate of Opportunistic Spectrum Orthogonal Frequency Division Multiple Access network constituted of multiple classes of Secondary Users (SUs) and temporarily active Primary Users (PUs) of different priorities. In the case of PUs we consider low activity wideband and high activity narrowband users. While in the case of SUs we consider subscribers transmitting real-time constant bit rate (CBR) and elastic variable bit rate (VBR) traffic. We conclude that with the increased activity of PUs, CBR traffic experiences larger throughput decrease than VBR. Moreover, blocking rate of CBR connections decreases exponentially with increased inter-arrival rate of PU, while we observe no direct correlation between obtained SU network throughput and CBR blocking rate.

Many protocols in wireless sensor networks use packet delivery ratio (PDR) as a metric to select the best route, transmission rate or power. PDR is normally estimated either by counting the number of received hello/data messages in a small period of time, i.e., less than 1 second, or by taking the history of PDR into account. The first method is accurate but requires many packets to be sent, which costs too much energy. The second one is energy efficient, but fails to achieve good accuracy. Therefore in this paper we propose a novel estimation method which takes advantage of receiving signal strength. We show with extensive experimental results that the proposed method is 25% more accurate than the second estimation method, while being simple and energy efficient at the same time.

With the multitude of mobile communication devices supporting users, it is natural to expect that these devices would work ubiquitously anywhere and any time with high data rate support. 60 GHz is an obvious choice for the high data rate indoor communication. In this paper we present an holistic analytical model of 60 GHz link stability for indoor wireless networks. The model is built considering the indoor channel characteristics of 60 GHz radio, the antenna configuration and the mobility of persons. Also, we take an example to show the relevance of our model and its applicability in numerical evaluation.

QoS-sensitive applications transmitted over wireless links require precise knowledge of the wireless environment. However, the dynamic nature of wireless channel, together with its different configurations and types, makes so called link quality estimation (LQE) a difficult task. This paper looks into the design of an accurate and fast LQE method for a multi data rate environment. We investigate the impact of various conditions on the LQE accuracy. In result, two different link quality estimation sources, i.e., based on hello packet delivery ratio and signal strength, are measured and their performances are compared. We find that these two methods are not always accurate. As an improvement we propose an adaptive LQE method that chooses different LQE indicators depending on the wireless environment. The performance of the proposed method is verified via an extensive measurement campaign.

We adopt a machine learning approach towards the problem of identifying wireless systems present in a dynamic radio environment with heterogeneous usage. To classify the wireless systems, we utilize two features that typify spectrum use-center frequency and the frequency spread-and cluster the measurement data in this space. Since the systems are unknown prior to clustering, we use an unsupervised clustering method that uses the Chinese restaurant process implemented using Gibbs sampling. The system identification is divided into two parts: training and online classification. In the training phase, we assign wireless systems present in the surrounding to the clusters while the online classification uses this trained data to perform classification. By means of an extensive measurement campaign, we show that the proposed machine learning process achieves up to 90% correctness in classifying the wireless systems considered here.

VoIP packets, when transported over the Internet, experience loss and variable delay. The effect of the network not only depends on the background flows but also on the parameters of VoIP packets itself, such as VoIP packet size and the packet generation intervals. While higher sized packets experience more losses, they experience less delay jitter and handling them is thus easy at the playout buffer. To investigate the effect of various network conditions on VoIP session holistically, we present a complete end to end study considering various states of the underlying network. We present as a case study of G.711 coded packets generated at 20 and 40 ms intervals for comparison. While packets carrying 20 ms data are better when the network is loaded, 40 ms packetization is favored when the network is not saturated. This affects the jitter and loss thus affecting the quality. We explain this trade-off using mean opinion scores.

This work comprehensively compares four possible multichannel medium access control (MAC) approaches for opportunistic spectrum access (OSA). One of important conclusions to be drawn from the analysis is that multichannel OSA MACs that spread both control and data between all available channels, e.g., hopping MACs, perform best among possible OSA MAC implementations when the PU traffic has long ON/OFF periods compared to the time-scale of the SU channel access.

Sharing of the frequency spectrum between licensed primary users and unlicensed secondary users (SUs) requires reliable detection of spectrum occupancy by the SUs. Due to fading, single terminal detection is unreliable and results in a high probability of missed detection. This problem is solved by applying cooperative detection. In this paper two novel energy-based cooperative detection methods using weighted combining for Dynamic Spectrum Access are presented and analyzed. Weighting is based on the local mean SNR and the optimum log-likelihood ratio. Simulation results show a substantial improvement for the proposed weighting methods compared to equal gain combining and hard decision combining.

We propose an architecture using the 60 GHz Radio over Fiber along with a novel concept of forming Extended Cells (EC). Since in the indoor environment, the propagation of millimeter wave signals is strongly hindered by walls, people, furniture, etc., a mobile user might experience frequent loss of connection as soon as one moves from one cell to another. With ECs one can create sufficient overlap areas between cells, thus ensuring a seamless communication environment to achieve lesser handovers and call drop. We illustrate the effectiveness of the proposed architecture through simulation. We continue further to model the mobility of users in the indoor environment to arrive at an expression to find the call blocking probability.

Opportunistic Spectrum Access (OSA) is being seriously considered for the future spectrum needs. In this paper we propose a simple and effective multichannel multiple access technique for OSA networks. In our design users of an OSA network must contact the OSA Base Station to gain access to the radio resources. In an OSA environment each of the channels can be arbitrarily occupied by the Primary Users (PUs) of the specific band. Thus the OSA nodes should cause least interference to the PUs while exploiting the voids in the PU usage. We analyze the OSA network where many OSA nodes would be competing amongst themselves and with the PU, using fast retrials. We also propose mechanisms to minimize the probability of collisions and interference caused to the PUs, while maximizing the throughput of the OSA network.

A wireless sensor network (WSN) should operate unattended for a long period of time before battery replacement, therefore characteristics such as self-organization and energy efficiency are of utmost importance to the WSN. One way to meet these requirements is to partition the WSN into clusters, where each cluster managed by a cluster head (CH) covers a number of cluster members. In this paper we provide an analysis of energy consumption in a clustered network and quantify the energy gain of clustering with different number of nodes and traffic levels. Following the analysis we propose a novel clustering protocol which minimizes the energy consumption by selecting less CHs. The proposed protocol also prolongs the lifetime of selected CHs and eases the maintenance of the WSN.

In this paper we give insight into the performance of Dynamic Spectrum Access Networks (DSAN), analyzing Quality of Detection of Primary User (PU) and DSAN blocking probability, when the channel is under the effect of log-normal shadowing. Specifically we propose a distributed power conserving PU detection architecture and investigate the impact of PU detection accuracy on DSAN performance. We measure DSAN blocking probability as a function of the number of PU channels and their utilization. Finally we propose two efficient DSAN channel access schemes called Least-used and Least-used with Channel Hopping which aim at minimizing packet dropping due to the arrival of the PU.

Voice enabled applications over the Internet are rapidly gaining popularity. Reducing the total bandwidth requirement can make a non-trivial difference for the subscribers having low speed connectivity. Voice activity detection algorithms for VoIP applications can save bandwidth by filtering the frames that do not contain speech. In this paper we introduce a novel technique to identify the voice and silent regions of a speech stream very much suitable for VoIP calls. We use an information theoretic measure, called spectral entropy, for differentiating the silence from the speech zones. Specifically we developed a heuristic approach that uses an adaptive threshold to minimize the miss detection in the presence of noise. The performance of our approach is compared with the relatively new 3GPP TS 26.194 (AMR-WB) standard, along with the listeners' intelligibility rating. Our algorithm yields comparatively better saving in bandwidth, yet maintaining good quality of the speech streams.

For efficient computer supported cooperative work (CSCW) audio conferencing is an essential component where video and text are add-ons. The specifications for enabling CSCW over Internet are incomplete if they are blind to actual conduct of participants. Indeed, a blind conference mimics quite closely a virtual voice-only conference. In this paper, we analyze the results of sessions of face-to-face blind conversations and gain penetrating insights. In particular, we focus on the impact of users' behavior on the design of a scalable architecture for virtual voice-only conferencing over VoIP and arrive at a meaningful number of floors for such conferences. We also present the features and the requirements for the proposed service.