Digital biomarker technologies coupled with predictive models are increasingly applied for early detection of age-related potentially reversible conditions including mild cognitive impairment (MCI) and pre-frailty (PF). We aimed to determine the predictive accuracy of digital biomarker technologies to detect MCI and PF with systematic review and meta-analysis. A computer-assisted search on major academic research databases including IEEE-Xplore was conducted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were adopted for reporting in this study. Summary receiver operating characteristic curve based on random-effect bivariate model was used to evaluate overall sensitivity and specificity for detection of the respective age-related conditions. A total of 43 studies were selected for final systematic review and meta-analysis. 26 studies reported on detection of MCI with sensitivity and specificity of 0.48-1.00 and 0.55-1.00, respectively. On the other hand, there were 17 studies that reported on the detection of PF with reported sensitivity of 0.53-1.00 and specificity of 0.61-1.00. Meta-analysis further revealed pooled sensitivities of 0.84 (95% CI: 0.79-0.88) and 0.82 (95% CI: 0.74-0.88) for in-home detection of MCI and PF, respectively, while pooled specificities were 0.85 (95% CI: 0.80-0.89) and 0.82 (95% CI: 0.75-0.88), respectively. Besides MCI, and PF, in this work during systematic review, we also found one study which reported a sensitivity of 0.93 and a specificity of 0.57 for detection of cognitive frailty (CF). The meta-analytic result, for the first time, quantifies the predictive efficacy of digital biomarker technologies for detection of MCI and PF. Additionally, we found the number of studies for detection of CF to be notably lower, indicating possible research gaps to explore predictive models on digital biomarker technology for detection of CF.

Nocturia, or the need to void (or urinate) one or more times in the middle of night time sleeping, represents a significant economic burden for individuals and healthcare systems. Although it can be diagnosed in the hospital, most people tend to regard nocturia as a usual event, resulting in underreported diagnosis and treatment. Data from self-reporting via a voiding diary may be irregular and subjective especially among the elderly due to memory problems. This study aims to detect the presence of nocturia through passive in-home monitoring to inform intervention (e.g., seeking diagnosis and treatment) to improve the physical and mental health of community-dwelling elderly living alone. With continuous and objective data from motion sensors installed in each zone of the apartment (bedroom, living room, kitchen, and bathroom) and a contact sensor on the main door from 39 elderly, we derive a sensor-based nocturia classification model, where nocturia labeling is done based on psychosocial survey data. Our evaluation of the model reveals that (i) the use of sensor-derived features (e.g., bedroom and living room occupancy and activity level as well as going out patterns) beyond nocturia events and (ii) the extraction and use of usual sleep location as a feature improves the classification performance, where perfect accuracy can be achieved with support vector machine. Further analysis on the survey findings also reveals that elderly with nocturia are more likely to have poor sleep quality, and suffer from conditions related to physical frailty. Our findings lend support to the efficacy of passive in-home monitoring as a digital biomarker for detection of nocturia and related conditions in live-alone elderly.

Singapore has the fastest ageing population in the Asia Pacific region, with an estimated 82,000 seniors living with dementia. These figures are projected to increase to more than 130,000 by 2030. The challenge is to identify more community dwelling seniors with Mild Cognitive Impairment (MCI), a prodromal state, as it provides an opportunity for evidence-based early intervention to delay the onset of dementia. In this paper, we explore the use of Internet of Things (IoT) systems in detecting MCI symptoms in seniors who are living alone, and accurately grouping them into MCI positive and negative subjects. We present feature extraction methods and findings from real data captured via selected sensors installed in the homes of 49 seniors for up to two months. Performance evaluation shows that the sleep state variability, as measured through bed sensors, yields a recall of over 70% in predicting MCI in these community dwelling seniors.

At the Bloomberg Live `Sooner Than You Think' forum [1] held in Singapore in 2018, nearly 75% of delegates picked inclusiveness to be the key measure of success for a smart city. An inclusive smart city is a citizen-centered approach that extends the experiences provided by smart city solutions to all citizens, including seniors and persons with disabilities (PwDs).Despite existing regulations on barrier-free accessibility for buildings and public infrastructure, pedestrian infrastructure is generally still inaccessible to PwDs in many parts of the world. In this paper, we present SmartBFA (Smart Mobility and Accessibility for Barrier Free Access) - a publicly-funded initiative in Singapore that aims to design a scalable and sustainable system that can collect, classify and determine accessible point-to-point routes to address interconnection gaps in first and last mile BFA paths for persons requiring barrier-free access (such as wheelchair users and seniors with mobility aids).In SmartBFA, point-to-point accessibility information is passively crowdsourced from IoT devices that are retrofitted on the wheelchairs of participants, as they go about their daily commute. We share preliminary findings from data acquired from 68 wheelchair participants between May 2018 to Mar 2019, spanning across 23,000 hrs and 40,000 km of traveled paths. We compare travel patterns of participants with varying wheelchair types, as well as demonstrate the feasibility and scalability of such a crowdsourced approach for acquiring accessibility data.

It is projected that Singapore will become superaged (where 20% of its population will comprise seniors) by 2025. Although various community programs are available to promote active ageing among seniors who are well, provide befriending services for seniors at risk of isolation and care and support for frail and vulnerable seniors, it is not easy to differentiate between `well' seniors and `at risk' seniors. While privacy-preserving z-wave based sensor-enabled homes have been piloted in 100 homes of seniors living alone and have been successful in the timely detection of at-risk seniors, they have limited scalability due to high costs, reliability issues and high maintenance needs. In this paper, we experimentally evaluate Sigfox-based sensor-enabled homes for detecting of at-risk seniors, benchmark our results against the incumbent system, and study the tradeoffs between battery lifespan and detection accuracy. From the evaluation, we observe that while maintaining a similar battery lifespan, the Sigfox-based system is able to match the accuracy in detecting at-risk seniors at a fraction of the cost of the incumbent sensor system.

We are increasingly reliant on cellular data services for many types of day-to-day activities, from hailing a cab, to searching for nearby restaurants. Geo-location has become a ubiquitous feature that underpins the functionality of such applications. Network operators can also benefit from accurate mobile terminal localization in order to quickly detect and identify location-related network performance issues, such as coverage holes and congestion, based on mobile measurements. Current implementations of mobile localization on the wildly-popular Android platform depend on either the Global Positioning System (GPS), Android's Network Location Provider (NLP), or a combination of both. In this paper, we extensively study the performance of such systems, in terms of its localization accuracy. We show through real-world measurements that the performance of GPS+NLP is heavily dependent on the mobility of the user, and its gains on localization performance is minimal, and often even detrimental, especially for network round-trip delays up to 1s. Building upon these findings, we evaluate the efficacy of using Tattle, a cooperative local measurement-exchange system, and propose Delay-Adjusted U-CURE, a clustering algorithm that greatly improves the localization performance of both GPS-only, and GPS+NLP techniques, without keeping expensive system states, nor requiring any location anchors nor additional instrumentation, nor any external knowledge that is not available programmatically to application designers. Our results are promising, demonstrating that median location accuracy improvements of over 30% is achievable with just 3 co-located devices, and close to 60% with just 6 co-located devices. These findings can be used by operators to better manage their networks, or by application designers to improve their location-based services.

Cellular service subscribers are increasingly reliant on cellular data services for all kinds of mobile applications. Oftentimes, when subscribers experience frustratingly high network delays and timeouts, they like to know whether their experiences are shared by other users nearby. The question that is often asked is essentially this: “is it just me, or do others around me face the same problem?” In this paper, we describe how we use Tattle, a distributed real-time participatory sensing and monitoring framework, to glean network performance information from users nearby. Tattle relies on recent advances in peer-to-peer device networking, such as Wi-Fi Direct, Bluetooth Low Energy, and Apple's iBeacon, to exchange key snippets of diagnostic information using very low-power, very short-range local-area wireless interfaces, between participating devices. We propose and develop a robust statistical algorithm, based on quantile regression, which identifies key points in time where a device experiences high delays and outages that are not observed by its neighbors, and decides if the device is performing “normally”, or “abnormally”. This directly answers the “me, or others?” question. We demonstrate and validate the efficacy of our system through real-world measurements of network delay, consisting of over 7,300 time-series that comprises over 443,500 data samples, using commodity smart devices attached to two different providers' networks.

Crowdsourcing can be modeled as a principal-agent problem in which the principal (crowdsourcer) desires to solicit a maximal contribution from a group of agents (participants) while agents are only motivated to act according to their own respective advantages. To reconcile this tension, we propose an all-pay auction approach to incentivize agents to act in the principal’s interest, i.e., maximizing profit, while allowing agents to reap strictly positive utility. Our rationale for advocating all-pay auctions is based on two merits that we identify, namely all-pay auctions (i) compress the common, two-stage “bid-contribute” crowdsourcing process into a single “bid-cum-contribute” stage, and (ii) eliminate the risk of task nonfulfillment. In our proposed approach, we enhance all-pay auctions with two additional features: an adaptive prize and a general crowdsourcing environment. The prize or reward adapts itself as per a function of the unknown winning agent’s contribution, and the environment or setting generally accommodates incomplete and asymmetric information, risk-averse (and risk-neutral) agents, and a stochastic (and deterministic) population. We analytically derive this all-pay auction-based mechanism and extensively evaluate it in comparison to classic and optimized mechanisms. The results demonstrate that our proposed approach remarkably outperforms its counterparts in terms of the principal’s profit, agent’s utility, and social welfare.

Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers' types (e.g., abilities or costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this paper, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions each for a potential winner. We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, called strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In game-theoretical terms, it says that an asymmetric auction admits a symmetric equilibrium. Not only theoretically interesting, but SA also has important practical implications on mechanism complexity, energy efficiency, crowdsourcing revenue, and system scalability. By scrutinizing seven mechanisms, our extensive performance evaluation demonstrates the superior performance of our mechanism as well as offers insights into the SA property.

Incentive mechanisms for crowdsourcing have been extensively studied under the framework of all-pay auctions. Along a distinct line, this paper proposes to use Tullock contests as an alternative tool to design incentive mechanisms for crowdsourcing. We are inspired by the conduciveness of Tullock contests to attracting user entry (yet not necessarily a higher revenue) in other domains. In this paper, we explore a new dimension in optimal Tullock contest design, by superseding the contest prize - which is fixed in conventional Tullock contests - with a prize function that is dependent on the (unknown) winner's contribution, in order to maximize the crowdsourcer's utility. We show that this approach leads to attractive practical advantages: (a) it is well-suited for rapid prototyping in fully distributed web agents and smartphone apps; (b) it overcomes the disincentive to participate caused by players' antagonism to an increasing number of rivals. Furthermore, we optimize conventional, fixed-prize Tullock contests to construct the most superior benchmark to compare against our mechanism. Through extensive evaluations, we show that our mechanism significantly outperforms the optimal benchmark, by over three folds on the crowdsourcer's utility cum profit and up to nine folds on the players' social welfare.

Compressive sensing has been successfully used for optimized operations in wireless sensor networks. However, raw data collected by sensors may be neither originally sparse nor easily transformed into a sparse data representation. This paper addresses the problem of transforming source data collected by sensor nodes into a sparse representation with a few nonzero elements. Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized learning algorithm of the sparsifying dictionary. We introduce an unsupervised neural network to extract an intrinsic sparse coding of the data. The sparse codes are generated at the activation of the hidden layer using a sparsity nomination constraint and a shrinking mechanism. Our analysis using real data samples shows that the proposed method outperforms conventional sparsity-inducing methods.

The operating environment of cellular networks can be in a constant state of change. One Singaporean operator expressed difficulty with the coverage assertion (CA) problem of whether regulated minimum coverage is met, especially in urban areas. Currently, the operator manually appraises coverage through laborious and expensive walk/drive-tests. In this paper, we propose Tattle, a distributed, low-cost and comprehensive cellular network measurement collection and processing framework. We exemplify Tattle by leveraging on participating UEs to report on network coverage in real-time. Tattle exploits wireless local-area interfaces to exchange RSCP measurements amongst devices to preserve the co-locality of readings and conserve power. We propose U-CURE, a clustering algorithm which considers sample location uncertainty and the knowledge of device co-location to remove erroneously localized readings. We develop a prototype app on the Android™ platform as a proof-of-concept of the Tattle framework. We then use the Tattle framework to perform extensive RSCP measurement collection and processing in various areas in Singapore, collecting over 3.78 million readings. We present visualizations of mean signal coverage and RSCP CDFs for various areas of interest. The latter is a key output of Tattle, which helps operators to appraise coverage and solve the CA problem by relying on subscriber measurements, instead of expensive, laborious and limited-scale walk-/drive-tests.

We design an incentive mechanism based on all-pay auctions for participatory sensing. The organizer (principal) aims to attract a high amount of contribution from participating users (agents) while at the same time lowering his payout, which we formulate as a profit-maximization problem. We use a contribution-dependent prize function in an environment that is specifically tailored to participatory sensing, namely incomplete information (with information asymmetry), risk-averse agents, and stochastic population. We derive the optimal prize function that induces the maximum profit for the principal, while satisfying strict individual rationality (i.e., strictly have incentive to participate at equilibrium) for both risk-neutral and weakly risk-averse agents. The thus induced profit is demonstrated to be higher than the maximum profit induced by constant (yet optimized) prize. We also show that our results are readily extensible to cases of risk-neutral agents and deterministic populations.

Incentive is key to the success of crowd sourcing which heavily depends on the level of user participation. This paper designs an incentive mechanism to motivate a heterogeneous crowd of users to actively participate in crowd sourcing campaigns. We cast the problem in a new, asymmetric all-pay contest model with incomplete information, where an arbitrary n of users exert irrevocable effort to compete for a prize tuple. The prize tuple is an array of prize functions as opposed to a single constant prize typically used by conventional contests. We design an optimal contest that (a) induces the maximum profit -- total user effort minus the prize payout -- for the crowdsourcer, and (b) ensures users to strictly have incentive to participate. In stark contrast to intuition and prior related work, our mechanism induces an equilibrium in which heterogeneous users behave independently of one another as if they were in a homogeneous setting. This newly discovered property, which we coin as strategy autonomy (SA), is of practical significance: it (a) reduces computational and storage complexity by n-fold for each user, (b) increases the crowdsourcer's revenue by counteracting an effort reservation effect existing in asymmetric contests, and (c) neutralizes the (almost universal) law of diminishing marginal returns (DMR). Through an extensive numerical case study, we demonstrate and scrutinize the superior profitability of our mechanism, as well as draw insights into the SA property.

We present a data compression and dimensionality reduction scheme for data fusion and aggregation applications to prevent data congestion and reduce energy consumption at network connecting points such as cluster heads and gateways. Our in-network approach can be easily tuned to analyze the data temporal or spatial correlation using an unsupervised neural network scheme, namely the autoencoders. In particular, our algorithm extracts intrinsic data features from previously collected historical samples to transform the raw data into a low dimensional representation. Moreover, the proposed framework provides an error bound guarantee mechanism. We evaluate the proposed solution using real-world data sets and compare it with traditional methods for temporal and spatial data compression. The experimental validation reveals that our approach outperforms several existing wireless sensor network's data compression methods in terms of compression efficiency and signal reconstruction.

In wireless sensor networks powered by ambient energy harvesting (WSNs-HEAP), sensor nodes' energy harvesting rates are spatially heterogeneous and temporally variant, which impose difficulties for medium access control (MAC). In this paper, we first derive the necessary conditions under which channel utilization and fairness are optimal in a WSN-HEAP, respectively. Based on the analysis, we propose an earliest deadline first (EDF) polling MAC protocol, which regulates transmission sequence of the sensor nodes based on the spatially heterogeneous energy harvesting rates. It also mitigates temporal variations in energy harvesting rates by a prediction and update mechanism. Simulation results verify the performance tradeoff predicted by our analysis for the proposed HEAP-EDF protocol. In the presence of spatial heterogeneity and temporal variations in energy harvesting rates, our proposed protocol exhibits significant performance advantages compared to the existing MAC protocols for WSNs-HEAP in the literature.

This paper presents a solution to the problem of monitoring a region of interest (RoI) using a set of nodes that is not sufficient to achieve the required degree of monitoring coverage. In particular, sensing coverage of wireless sensor networks (WSNs) is a crucial issue in projects due to failure of sensors. This scenario of limited funding hinders the traditional method of using mobile robots to move around the RoI to collect readings. Instead, our solution employs supervised neural networks to produce the values of the uncovered locations by extracting the non-linear relation among randomly deployed sensor nodes throughout the area. Moreover, we apply a hybrid backpropagation method to accelerate the learning convergence speed to a local minimum solution. We use a real-world data set from meteorological deployment for experimental validation and analysis.

Two crucial issues to the success of participatory sensing are (a) how to incentivize the large crowd of mobile users to participate and (b) how to ensure the sensing data to be trustworthy. While they are traditionally being studied separately in the literature, this paper proposes a Simple Endorsement Web (SEW) to address both issues in a synergistic manner. The key idea is (a) introducing a social concept called nepotism into participatory sensing, by linking mobile users into a social “web of participants” with endorsement relations, and (b) overlaying this network with investment-like economic implications. The social and economic layers are interleaved to provision and enhance incentives and trustworthiness. We elaborate the social implications of SEW, and analyze the economic implications under a Stackelberg game framework. We derive the optimal design parameter that maximizes the utility of the sensing campaign organizer, while ensuring participants to strictly have incentive to participate. We also design algorithms for participants to optimally “sew” SEW, namely to manipulate the endorsement links of SEW such that their economic benefits are maximized and social constrains are satisfied. Finally, we provide two numerical examples for an intuitive understanding.

In dense deployments of Wireless Body Sensor Networks (WBSNs), inter-user interference significantly degrades network performance when multiple WBSN users stay in a small area such as hospitals. Due to the high dynamics of the environments, it is usually hard to schedule the transmissions of multiple WBSNs without a central controller. In this paper, we propose a robust and lightweight interference mitigation scheme for realistic WBSN systems through adaptive channel hopping. A WBSN testbed is set up to investigate the impact of inter-user interference on various performance metrics when different severity levels of interference are present. Based on the measurement results, we propose a distributed interference detection and mitigation scheme without any central controller. A WBSN prototype system has been set up and shown that our proposed scheme can effectively detect and mitigate the multi-user interference, with tripling the network throughput in severely interfered environments.

With wireless energy transfer, a mobile node can operate perpetually without having a wired connection to charge its battery. In this paper, we present a quality of service (QoS) aware data transmission and wireless energy transfer for the mobile node. The node can request for wireless energy transfer or transmit a packet when the node is in a coverage area of an access point. The node supports service differentiation for different type of traffic (i.e., low and high priority data). To meet the QoS requirement of each traffic type, we present the performance modeling and optimization framework. The objective is to maximize the throughput, while the packet loss probabilities are maintained below the target levels. The optimal policy for the node is obtained from solving the constrained Markov decision process (CMDP). In addition, we present an application of the framework to a data mule for collecting, carrying, and transmitting data from sensors to the access point.

The low sound speed in water makes propagation delay (PD)-based range estimation attractive for underwater acoustic localization (UWAL). However, due to the long channel impulse response and the existence of reflectors, PD-based UWAL suffers from significant degradation when PD measurements of nonline-of-sight (NLOS) communication links are falsely identified as line-of-sight (LOS). In this paper, we utilize expected variation of PD measurements due to mobility of nodes and present an algorithm to classify the former into LOS and NLOS links. First, by comparing signal strength-based and PD-based range measurements, we identify object-related NLOS (ONLOS) links, where signals are reflected from objects with high reflection loss, for example, ships hull, docks, rocks and so on. In the second step, excluding PD measurements related to ONLOS links, we use a constrained expectation-maximization algorithm to classify PD measurements into two classes: LOS and sea-related NLOS (SNLOS), and to estimate the statistical parameters of each class. Since our classifier relies on models for the underwater acoustic channel, which are often simplified, alongside simulation results, we validate the performance of our classifier based on measurements from three sea trials. Both our simulation and sea trial results demonstrate a high detection rate of ONLOS links, and accurate classification of PD measurements into LOS and SNLOS.

Inter-user interference degrades the reliability of data delivery in Wireless Body Sensor Networks (WBSNs) in dense deployments when multiple users wearing WBSNs are in close proximity to one another. The impact of such interference in realistic WBSN systems is significant but has not been well explored. To this end, we investigate and analyze the impact of inter-user interference in practical WBSN systems based on TelosB platform. We capture packet delivery ratio (PDR) and throughput considering unslotted carrier sense multiple access with collision avoidance (unslotted CSMA/CA) and slotted CSMA/CA modes in IEEE 802.15.4 MAC. Our experimental results show that the unslotted CSMA/CA is only effective in light inter-user interference scenarios. Comparably, the slotted CSMA/CA can provide dramatic performance improvement (2.7 times higher in PDR and 1.7 times higher in throughput on average), when severe inter-user interference occurs in WBSN deployment.

We expect femto-cells to be massively and densely deployed in the future. Numerous existing works on femto-cell interference management assume that the local topology of interfering femto-cells can be sufficiently approximated through sensing, if not already known in advance. We show that this assumption results in poor throughput performance in dense femto-cell networks. For some cell-edge users, using conventional sensing in dense deployments can result in almost 50 times less instantaneous throughput, as compared to having oracular knowledge of interference topology. This sub-optimality is caused by “far-away” interferers. These are femto-cells that are deployed just far enough such that their presence will not be detected by conventional sensing. We then introduce a mobile sensing scheme to detect these “far-away” interferers by exploiting the inherent mobility of femto-cell users. We show through packet-level simulation that this sensing scheme is able to better approximate the interference topology. This results in significantly improved performance over conventional sensing, in dense deployment scenarios.

In this paper, we propose a medium access control protocol (EH-MAC) for multi-hop energy harvesting wireless sensor networks (EH-WSNs). In EH-WSNs, the main goal is to match energy consumption with the energy harvesting rate which is different from minimizing energy consumption in battery-operated WSNs. Unlike most existing MAC protocols that are designed to extend network lifetime, EH-MAC is designed to achieve high throughput given the varying amount of ambient energy that can be harvested from the environment at different locations and times. EH-MAC is based on asynchronous, receiver-initiated polling; however, unlike traditional random backoff mechanisms, it uses probabilistic polling to reduce data packet collisions. EH-MAC also dynamically adjusts the number of polling packets to minimize interference. Performance results show that EH-MAC increases network capacity and data throughput over other MAC protocols for EH-WSNs.

Using energy harvesting WSNs (EH-WSNs) are attractive as they can be solely powered by ambient energy sources. Multi-hop routing is important to achieve wide coverage as the transmission range of each node is limited. In this paper, we propose an adaptive opportunistic routing (AOR) protocol for multi-hop EH-WSNs that achieves high throughput using a regioning scheme that adapts to network conditions and energy availability. We evaluate AOR using extensive simulations incorporating experimental results from the characterization of different types of energy harvesters. The results show that AOR increases throughput in both monitoring and event-driven WSNs with different node densities and energy harvesting rates compared to traditional opportunistic routing protocols and other non-opportunistic routing protocols. We have also implemented AOR on a testbed of 20 energy harvesting sensor nodes and results show that AOR works well in EH-WSNs.

Wireless ad hoc routing protocols must employ accurate and rapid link failure detection mechanisms in order to maintain valid multihop routes and provide high data delivery rates. Hello-based failure detection is the predominant failure detection mechanism due to its ease of implementation. Despite its prevalence, no analytical work has been carried out to better understand its fundamental behavior. In this paper, we study the performance of hello-based link failure detection via analysis and experimentation. Our analytical results, which are validated by experimental results, show the existence of optimal hello beaconing parameters which depend on network conditions such as traffic load, link failure rate and hello delivery rate. These results can be applied in real-world network deployments for obtaining the optimal hello beaconing parameters that can provide the highest data delivery rate.

There are many technical challenges to realize the myriad of applications envisaged for underwater acoustic sensor networks (UASNs), in particular, determining the location of each node or localization. While various schemes have been proposed recently, the impact of MAC protocols for localization has not been studied. A MAC protocol that can enable many sensor nodes in large-scale networks to share the limited channel resource is an indispensable component to maximize localization coverage and speed, while minimizing communication costs. This can be achieved with MAC schemes that require little or no node coordination. In this paper, we evaluate the performance of a multi-stage localization scheme for a large-scale two-dimensional UASN under CSMA (requiring no node coordination) and T-Lohi (requiring light coordination). Our simulation results show that CSMA achieves better localization performance than T-Lohi at the expense of higher communication costs.

In wireless networks, it is important to determine the outcome of packet transmissions for networking protocols. In this paper, we design a transmission outcome classifier for IEEE 802.15.4 wireless networks based on received signal strength indicator and link quality indicator values. Our classifier performs loss differentiation by analyzing statistical differences between weak signal and collision losses. We implement our proposed classifier using the CC2500 RF transceiver and evaluate it experimentally. The results show that our classifier can accurately detect packet transmissions as well as distinguish wireless losses due to weak signals and multiple access collisions, with a maximum error rate of 15%. We apply the classifier to probabilistic polling, which is a MAC protocol designed for energy harvesting wireless sensor networks, and show experimentally that it is able to achieve close to or even exceed the theoretical throughput due to packet capture effect.

We use a Bayesian game-theoretic approach to model transmission control in energy-harvesting wireless sensor networks. In general, the energy state of an energy-harvesting sensor varies more dramatically with time as compared to traditional battery-powered sensors. Therefore, each energy-harvesting sensor is aware of its instantaneous energy state, which is modeled as its private information. Each sensor decides its transmission strategy according to its belief of its opponents' energy states. There exists a Bayesian Nash equilibrium (BNE) where a sensor with energy higher than its energy threshold will decide to transmit at fixed power, and wait otherwise. We show how each sensor determines its threshold to maximize its utility function. Moreover, we show via simulations that the performance of the Bayesian game model is close to that of a perfect-information game where energy states are common information to all sensors. In addition, since the proposed Bayesian game has the advantage of requiring less information exchange overhead, it seems to be more feasible to implement than the perfect-information game.

In this demonstration, we implement and deploy a solar-powered wireless sensor network in an outdoor carpark to provide parking guidance to motorists. Combining energy harvesting, multi-hop opportunistic routing and adaptive duty-cycling technologies, the system provides low-cost, real-time, sustainable and eco-friendly operation. We make use of solar energy harvesting sources to power our wireless vehicle detection sensor nodes. Upon detecting the presence of a car, this information is transmitted via a multi-hop wireless network to a base station, before being forwarded to the central server for information dissemination to motorists. We will display, in real-time, the variance in the duty cycle and energy level according to the time of day throughout the conference period. For similar demos we typically provide both a live video stream and real-time sensor data from the carpark, displayed on desktop web browsers and mobile devices, so that visitors can see parking lot occupancy data in real-time and compare it with a live video feed, but due to the time difference between Singapore and Seattle, we will be showing a video recording instead.

Energy harvesting wireless sensor networks (EH-WSNs) are gaining importance in smart homes, environmental monitoring, health care and transportation systems, since they enable much longer operation time as energy can be replenished through energy harvesting. This is unlike sensor nodes that use non-rechargeable batteries which need to be replaced once energy is depleted. However, the sporadic availability of ambient energy makes the design of networking protocols and predicting network performance very challenging. In this paper, we perform an empirical energy characterization of a time-slotted solar energy harvesting node with different system and environmental parameters. We use six different statistical models (uniform distribution, geometric distribution, transformed geometric distribution, Poisson distribution, transformed Poisson distribution and a Markovian model) to fit the empirical datasets. Our results show that there is no single statistical model that can fit all the datasets, thus justifying the need to use empirical data to validate the theoretical analysis of time-slotted MAC protocols for EH-WSNs.

In the offshore engineering community, reliable deep-water anchor performance is critical for mooring floating platforms such as Mobile Offshore Drilling Units. In a typical installation, an anchor is fully embedded into the seabed (up to 100 m). This has to be done with high fidelity as anchor failure can cause floating units to go adrift, damaging oil and gas pipelines. The likelihood of this happening may be reduced with a data acquisition system comprising various sensors/measurement instruments housed in the anchor, where their data is then transmitted in some ways towards the seabed, and then onwards to the installation vessel. In this paper, we explore the feasibility of electromagnetic wave underground wireless communications as a low cost means to transmit key anchor parameters from the anchor to the seabed. We employ two semi-empirical models to obtain the path loss characteristics at different operating frequencies and volumetric water contents.

Wireless Sensor Networks Powered by Ambient Energy Harvesting (WSN-HEAP) can perform the task of continuous and remote monitoring of the environment without the need for replacement of batteries. We identify three important design considerations for wireless networking protocols in WSN-HEAP: the unpredictable energy supply, the propagation losses in different environments and the suitable power level to use. In this paper, we perform an empirical characterization of commercially available solar and thermal energy harvesting sensor nodes. We deploy a transmitter-receiver pair at different distances and in various environments to conduct link measurements to determine the packet delivery ratio and RSSI values. We also quantified the energy harvesting characteristics of the sensor node. Then, we analyze the collected data to provide insights and guidelines for designing networking protocols for WSN-HEAP. Our analysis shows that the transmission range of the node is highly dependent on the environment in which it is deployed in and the RSSI values can only be used to estimate the transmitter-receiver distance in some environments. Furthermore, the charging time exhibits large variances even in the absence of mobility.

Underwater localization is challenging as its efficacy is affected by propagation delays, motion-induced doppler shift, phase and amplitude fluctuations, multipath interference etc that are inherent in underwater acoustic channels. In this paper, we consider a recently proposed Underwater Positioning Scheme, which offers unique localization only in a finite region. We quantify the conditions for unique localization and propose a variant that offers unique localization with high probability regardless of the reference and unknown node deployment. We demonstrate the trade-offs between both schemes in terms of localizability space, localization latency and energy consumption.

We are witnessing pervasive use of wireless sensor networks (WSN)s in a wide variety of applications such as monitoring of road infrastructure. As they are expected to be deployed in harsh environments for long durations, the research community have turned their attention to tapping on ambient energy to power such networks. However, since energy harvesting rates are still significantly lower than the power consumption in each wireless sensor node, the energy availability is sporadic, making the design of runtime policies in WSN powered by ambient energy harvesting (WSN-HEAP) to maximize performance an important but challenging task. In this paper, using extensive simulations, we evaluate the efficacy of transmission power control for 2-D WSN-HEAP deployed in a grid topology in terms of throughput, data delivery ratio and fairness. When a fixed power is assigned to all nodes, we observe a trade-off between throughput and fairness: throughput is maximized at lower powers at the expense of fairness and vice versa. When nodes are assigned powers according to their proximity from the sinks, we observe that assigning the minimum transmission power required for each node to communicate with its nearest sink maximizes all performance metrics. This indicates that minimizing interference dominates over multi-sink redundancy in a 2-D WSN-HEAP.

Wireless multihop ad hoc networks face a multitude of challenging problems including highly dynamic multihop topologies, lossy and noisy communication channels, and sporadic connectivity which contribute to frequent link failures. Rapid and accurate link failure detection is therefore important to maintain correct and optimum operation of network routing protocols. In this paper, we propose a unified link failure detection and recovery architecture (ulfra) which uses link layer feedback for rapid failure detection and packet salvaging for packet recovery. While link layer feedback and packet salvaging have been studied in simulations and simple experiments, no thorough experimental study have been undertaken to evaluate their real-world performance. This paper essentially fills this void as we implement and evaluate ulfra in an IEEE 802.11 multihop ad hoc network. Our experimental results show that link layer feedback, as modeled in current network simulators, actually performs worse than hello beaconing as it generates excessive false failure detections. To improve its performance, we implement a veto mechanism to reduce spurious detections. Experimental results show that the veto mechanism dramatically improves the performance of link layer feedback in terms of packet delivery, delay, and routing overhead as it considerably reduces the number of false detections. Compared with hello, it delivers 15–20% more packets at high node failure and 12–20% more at high network traffic.

Motion and intrusion detection are often cited among various Wireless Sensor Network (WSN) applications. A typical configuration comprises clusters of wireless nodes equipped with motion sensors to detect human motion. Currently, the performance of WSN is subject to several constraints, mainly the phenomenon of radio irregularity and finite onboard computation/energy resources. In Radio Frequency (RF) propagation, radio irregularity rises to a higher level in the presence of human activity due to the absorption effect of the human body. In this paper, the feasibility of monitoring RF transmission for the purpose of intrusion detection is investigated. With empirical data obtained from the Crossbow TelosB platform in several different environments, the impact of human activity on the signal strength of RF signals in a WSN is evaluated. This paper offers a novel approach to intrusion detection by turning a constraint in WSN, namely radio irregularity, into an advantage for the purpose of intrusion detection. Unlike most related work, the ¿intruders¿ neither transmit nor receive any RF signals. By enabling existing wireless infrastructures to serve as intrusion detectors instead of deploying numerous costly sensors, this approach shows great promise for providing novel solutions.

Energy consumption is an important issue in the design of wireless sensor networks which typically rely on nonrenewable energy sources like batteries for power. Recent advances in ambient energy harvesting technologies have made it a viable alternative source of energy for powering wireless sensor networks perpetually. In this paper, we optimize network performance by finding the optimal routing algorithm and relay node placement scheme for wireless sensor networks powered by ambient energy harvesting. We evaluate the performance of three different variants of geographic routing algorithms and consider two relay node placement schemes, viz. uniform string topology and a cluster string topology. The performance metrics are network throughput (T), goodput (G), source sending rate (SR), efficiency (eta), data delivery ratio (DR) and hop count (H). Simulation results obtained using the Qualnet simulator show that there is an optimal combination of routing algorithm and relay node placement scheme that maximizes the required performance metric. These results aim to provide insights into the impact of routing algorithms and relay node placement schemes on wireless sensor networks that rely solely on ambient energy harvesting for power.

Wireless sensor networks are expected to be deployed in harsh environments characterised by extremely poor and fluctuating channel conditions. With the commonly adopted single-sink architecture, such conditions are exemplified by contention near the sink as a result of multipath delivery. This may be reduced by deploying multiple sinks spatially- apart e.g., along the edges of the network such that multiple spatially diverse paths that diverge like a starburst from each node towards these sinks can be set-up. Such an architecture opens up new challenges to the data delivery scheme, which determines the performance of the network. Since the compactness of sensors with limited energy resources restrict the use of sophisticated mechanisms, we consider simple data delivery schemes suited for such a multi-sink architecture. We optimise a single-path data delivery scheme with simple ARQ for a spatially-invariant environment, and demonstrate that its optimality over a spatially-diverse multipath scheme extends to spatially-variant environments. We also verify our analysis with simulations obtained using the Qualnet simulator.

One of the major challenges for the deployment of underwater acoustic sensor networks (UASN) is the design of a suitable medium access control (MAC) protocol, and CDMA has been earmarked as the most promising candidate. While several works have considered the problems of allocation of code and transmission power separately, in this paper, we propose a receiver-centric interference management approach for joint code/power assignment for MAC in CDMA UASNs. Through extensive numerical simulations, we illustrate its efficacy and demonstrate its superiority to conventional transmitter-centric approaches, for both fixed-power code assignments and joint code/power assignments.

With the advent of cognitive radio technology, new paradigms for spectrum access can achieve near-optimal spectrum utilisation by letting each user sense and utilise available spectrum opportunistically while regulating the interference it imposes on other users through interference constraints. However, the simplest and most common forms of such constraints are binary and transmitter-centric, which are often inefficient since they only consider pair-wise sets of transmitters. Hence, we propose a non-binary receiver-centric constraint model for spectrum access in cognitive radio networks. Such a model is in line with the recently proposed interference temperature metric that constraints whole subsets of transmitters, thereby permitting interfering signals to be introduced and enabling additional communication, leading to improved spectrum utilisation. These constraints are easy to generate and check, and are currently being used to devise a co-operative negotiated etiquette for cognitive radios offering heterogeneous services in a wireless office networking scenario.

Abstract — With the advent of cognitive radio technology, new paradigms for spectrum access can achieve near-optimal spectrum utilization by letting users sense and utilize available spectrum opportunistically. Recently, graph-coloring models have been used to produce fair, efficient and optimal spectrum allocations for a given network topology. However, topology changes (e.g., due to user mobility) can incur significant signaling traffic to reallocate spectrum, consuming valuable resources. We propose a frequency-spread multiple access (FSMA) scheme that is topology-transparent, and numerical results suggest that it is suited for opportunistic spectrum access in a sparsely-connected, low-density mobile ad-hoc network. I.

Third generation wireless systems can simultaneously accommodate flow transmissions of users with widely heterogeneous applications. As resources are limited (particularly in the air interface), admission control is necessary to ensure that all active users are accommodated with sufficient capacity to meet their specific Quality of Service requirements. Our admission control rule protects users with stringent capacity requirements ("streaming traffic") while offering sufficient capacity over longer time intervals to delay-tolerant users ("elastic traffic"). Performance evaluation of wireline differentiated-services platforms is already difficult due to the inherently large dimensionality of models to capture the diversity of user applications. In wireless systems, this is further exemplified as the location of users adds to the dimensionality problem. Using time-scale decomposition, we develop approximations to evaluate the performance of a differentiated admission control strategy to support integrated services with capacity requirements in a realistic downlink transmission scenario for a single radio cell.

In this paper we investigate the limiting properties, in terms of capacity and delay, of an ad hoc network employing a topology-transparent scheduling scheme. In particular, we focus on Time-Spread Multiple Access (TSMA) protocols, which are able to offer, in a distributed fashion, a deterministic upper bound on the access delay. The analysis is based on some asymptotic (focusing) properties of geometric random graphs. The analytical framework is applied to both static and mobile networks. The obtained results are compared with the results present in the literature for the case of an optimum (centralized) scheduling scheme.

Although many wireless channel-state dependent (CSD) schedulers have been proposed recently, their contributions lie in the design of the scheduling mechanism to meet some performance objectives. However, these objectives are often first-order statistics such as average or worst-case delay, which are insufficient to characterize the scheduler's performance. In this paper, we propose a matrix formulation to derive the delay probability density function for CSD schedulers over a Markovian wireless channel. Our analysis is then used to determine the admissibility of a wireless scheduler in terms of a minimum throughput requirement and a real-time QoS requirement. In addition, we evaluate the buffer size requirement of the wireless receiver and highlight the trade-off between buffer size requirements and channel efficiency.

Abstract In the last few years, wireless scheduling algorithms have been proposed by supplementing wireline scheduling algorithms with a wireless adaptation scheme. However, Quality of Service (QoS) bounds have either been derived for flows that perceive error‐free conditions or a static worst‐case channel condition. Such an assumption of the channel condition is unrealistic, since channel errors are known to be bursty in nature. Hence, these bounds are inadequate to characterize the scheduler's QoS performance. Our research focuses on performing an extensive analysis of wireless scheduling in order to derive statistical QoS performance bounds under realistic channel conditions. In this paper, we develop stochastic models for various wireless schedulers. Based on these models, we define and evaluate statistical QoS performance metrics in terms of throughput, delay and fairness under various channel conditions and over different time scales. Numerical results indicate that no single scheduler outperforms the others in terms of all the QoS metrics under all channel conditions. The choice of an optimal scheduling mechanism depends on the priority of QoS requirements as well as the channel conditions. Copyright © 2004 John Wiley & Sons, Ltd.

We develop Markov models for various schedulers in order to evaluate their performance tradeoffs at wireless links. While FIFO scheduling aggregates flows into a single flow just prior to the wireless link, channel-state dependent (or CSD) schedulers maintain a queue for each flow, and use predicted channel information to make scheduling decisions. We present results of tradeoffs in terms of overall throughput and per-flow QoS performance obtained with each scheduler under different channel conditions.