Papers by Usman Raza
One of the key technologies for future IoT/M2M systems are low power wide area networks, which ar... more One of the key technologies for future IoT/M2M systems are low power wide area networks, which are designed to support a massive number of low-end devices often in the unlicensed shared spectrum using random access protocols. However these usually operate without centralised control and since Automatic Repeat request and acknowledgement mechanisms are not very effective due to the strict duty cycles limits and high interference in the shared bands, many packets are lost from collisions. In this paper we analyse a recently proposed application layer coding scheme, which aims to recover lost packets by introducing redundancy in the form of a fountain code. We show how latency and decoding complexity is affected by the packet loss rate but also prove that there is a limit to what can be achieved by introducing more redundancy. The analysis is backed up by simulation results
Low Power Wide Area (LPWA) networks are making spectacular progress from design, standardisation,... more Low Power Wide Area (LPWA) networks are making spectacular progress from design, standardisation, to commercialisation. At this time of fast-paced adoption, it is of utmost importance to analyse how well these technologies will scale as the number of devices connected to the Internet of Things (IoT) inevitably grows. In this letter, we provide a stochastic geometry framework for modelling the performance of a single gateway LoRa network, a leading LPWA technology. Our analysis formulates unique peculiarities of LoRa, including its chirp spread-spectrum modulation technique, regulatory limitations on radio duty cycle, and use of ALOHA protocol on top, all of which are not as common in today's commercial cellular networks. We show that the coverage probability drops exponentially as the number of end-devices grows due to interfering signals using the same spreading sequence. We conclude that this fundamental limiting factor is perhaps more significant towards LoRa scalability than for instance spectrum restrictions. Our derivations for co-spreading factor interference found in LoRa networks enables rigorous scalability analysis of such networks.
IEEE GLOBECOM 2017, 2017
The need for low power, long range and low cost connectivity to meet the requirements of IoT appl... more The need for low power, long range and low cost connectivity to meet the requirements of IoT applications has led to the emergence of Low Power Wide Area (LPWA) networking technologies. The promise of these technologies to wirelessly
connect massive numbers of geographically dispersed devices at a low cost continues to attract a great deal of attention in the academic and commercial communities. Several rollouts are already underway even though the performance of these technologies is yet to be fully understood. In light of these developments, tools to carry out ‘what-if analyses’ and predeployment studies are needed to understand the implications of choices that are made at design time. While there are several
promising technologies in the LPWA space, this paper specifically focuses on the LoRa/LoRaWAN technology. In particular, we present LoRaWANSim, a simulator which extends the LoRaSim tool to add support for the LoRaWAN MAC protocol, which
employs bidirectional communication. This is a salient feature not available in any other LoRa simulator. Subsequently, we provide vital insights into the performance of LoRaWAN based networks through extensive simulations. In particular, we show
that the achievable network capacity reported in earlier studies is quite optimistic. The introduction of downlink traffic can have a significant impact on the uplink throughput. The number of transmit attempts recommended in the LoRaWAN specification
may not always be the best choice. We also highlight the energy consumption versus reliability trade-offs associated with the choice of number of retransmission attempts.
Low Power Wide Area (LPWA) networks are attracting a lot of attention primarily because of their ... more Low Power Wide Area (LPWA) networks are attracting a lot of attention primarily because of their ability to offer affordable connectivity to the low-power devices distributed over very large geographical areas. In realizing the vision of the Internet of Things (IoT), LPWA technologies complement and sometimes supersede the conventional cellular and short range wireless technologies in performance for various emerging smart city and machine-to-machine (M2M) applications. This review paper presents the design goals and the techniques, which different LPWA technologies exploit to offer wide-area coverage to low-power devices at the expense of low data rates. We survey several emerging LPWA technologies and the standardization activities carried out by different standards development organizations (e.g., IEEE, IETF, 3GPP, ETSI) as well as the industrial consortia built around individual LPWA technologies (e.g., LORa Alliance,WEIGHTLESS-SIG, and DASH7 Alliance). We further note that LPWA technologies adopt similar approaches, thus sharing similar limitations and challenges. This paper expands on these research challenges and identifies potential directions to address them. While the proprietary LPWA technologies are already hitting the market with large nationwide roll-outs, this paper encourages an active engagement of the research community in solving problems that will shape the connectivity of tens of billions of devices in the next decade.
A Software-Defined Device-to-Device Communication Architecture for Public Safety Applications in 5G Networks
IEEE Access, 2015
Energy neutral operation of WSNs can be achieved by exploiting the idleness of the workload to br... more Energy neutral operation of WSNs can be achieved by exploiting the idleness of the workload to bring the average power consumption of each node below the harvesting power available. This paper proposes a combination of state-of-the-art low-power design techniques to minimize the local and global impact of the two main activities of each node: sampling and communication. Dynamic power management is adopted to exploit low-power modes during idle periods, while asynchronous wake-up and prediction-based data collection are used to opportunistically activate hardware components and network nodes only when they are strictly required. Furthermore, the concept of " model-based sensing " is introduced to push prediction-based data collection techniques as close as possible to the sensing elements. The results achieved on representative real-world WSN case studies show that the combined benefits of the design techniques adopted is more than linear, providing an overall power reduction of more than 3 orders of magnitude.
Data prediction is proposed in wireless sensor networks (WSNs) to extend the system lifetime by ... more Data prediction is proposed in wireless sensor networks (WSNs) to extend the system lifetime by enabling the sink to determine the data sampled, within some accuracy bounds, with only minimal communication from source nodes. Several theoretical studies clearly demonstrate the tremendous potential of this approach, able to suppress the vast majority of data reports at the source nodes. Nevertheless, the techniques employed are relatively complex,and their feasibility on resource-scarce WSN devices is often not ascertained. More generally, the literature lacks reports from real-world deployments, quantifying the overall system-wide lifetime improvements determined by the interplay of data prediction with the underlying network. These two aspects, feasibility and system-wide gains, are key in determining the practical usefulness of data prediction in real-world WSN applications.
In this paper, we describe Derivative-Based Prediction (DBP), a novel data prediction technique much simpler than those found in the literature. Evaluation with real data sets from diverse WSN deployments shows that DBP often performs better than the competition, with data suppression rates up to 99% and good prediction accuracy. However, experiments with a real WSN in a road tunnel show that, when the network stack is taken into consideration, DBP only triples lifetime---a remarkable result per se, but a far cry from the data suppression rates above. To fully achieve the energy savings enabled by data prediction, the data and network layers must be jointly optimized. In our testbed experiments, a simple tuning of the MAC and routing stack, taking into account the operation of DBP, yields a remarkable seven-fold lifetime improvement w.r.t. the mainstream periodic reporting.
Energy autonomy and system lifetime are critical concerns in wireless sensor networks (WSNs), for... more Energy autonomy and system lifetime are critical concerns in wireless sensor networks (WSNs), for which energy harvesting (EH) is emerging as a promising solution. Nevertheless, the tools supporting the design of EH-WSNs are limited to a few simulators that require developers to re-implement the application with programming languages different from WSN ones. Further, simulators notoriously provide only a rough approximation of the reality of low-power wireless communication.
In this paper we present SENSEH, a software framework that allows developers to move back and forth between the power and speed of a simulated approach and the reality and accuracy of in-field experiments. SENSEH relies on COOJA for emulating the actual, deployment-ready code, and provides two modes of operation that allow the reuse of exactly the same code in realworld WSN deployments. We describe the toolchain and software architecture of SENSEH, and demonstrate its practical use and benefits in the context of a case study where we investigate how the lifetime of a WSN used for adaptive lighting in road tunnels can be extended using harvesters based on photovoltaic panels.
One of the most challenging goals of many wireless sensor network (WSN) deployments is the reduct... more One of the most challenging goals of many wireless sensor network (WSN) deployments is the reduction of energy consumption to extend system lifetime. This paper considers a novel combination of techniques that address energy savings at the hardware and application levels: wake-up receivers and node level power management, plus prediction-based data collection. Individually, each technique can achieve significant energy savings, but in combination, the results are impressive. This paper presents a case study of these techniques as applied in a road tunnel for light monitoring. Preliminary results show the potential for two orders of magnitude reduction in power consumption. This savings of 380 times allows the creation of an energetically sustainable system by considering integration with a simple, photovoltaic energy harvester.
Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefo... more Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed.
Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefo... more Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed. In contrast, this paper investigates in practice whether i) model-driven data acquisition works in a real application; ii) the energy savings it enables in theory are still worthwhile once the network stack is taken into account. We do so in the concrete setting of a WSN-based system for adaptive lighting in road tunnels. Our novel modeling technique, Derivative-Based Prediction (DBP), suppresses up to 99% of the data reports, while meeting the error tolerance of our application. DBP is considerably simpler than competing techniques, yet performs better in our real setting. Experiments in both an indoor testbed and an operational road tunnel show also that, once the network stack is taken into consideration, DBP triples the WSN lifetime-a remarkable result per se, but a far cry from the aforementioned 99% data suppression. This suggests that, to fully exploit the energy savings enabled by data modeling techniques, a coordinated operation of the data and network layers is necessary.
SenSys 2013, Nov 2013
Time series forecasting aims at improving energy efficiency in wireless sensor networks (WSNs) by... more Time series forecasting aims at improving energy efficiency in wireless sensor networks (WSNs) by reducing the amount of data traffic. One such technique has each node generate a model that predicts the sampled data. When the actual, sensed data deviates from the model, a new model is generated and transmitted to the sink. Reductions in application data traffic as high as two orders of magnitude can be achieved. However, our experience in applying such forecasting in a real world deployment shows that the actual lifetime improvement is significantly less due to networking overheads. The study reported here reveals that careful, coordinated network parameter tuning can leverage the reduced traffic of forecasting techniques to increase lifetime without compromising application performance.
In recent years, indoor localization has become a hot research topic with some sophisticated solu... more In recent years, indoor localization has become a hot research topic with some sophisticated solutions reaching accuracy on the order of ten centimeters. While certain classes of applications can justify the corresponding costs that come with these solutions, a wealth of applications have requirements that can be met at much lower cost by accepting lower accuracy. This paper explores one specific application for monitoring patients in a nursing home, showing that sufficient accuracy can be achieved with a carefully designed deployment of low-cost wireless sensor network nodes in combination with a simple RSSI-based localization technique. Notably our solution uses a single radio sample per period, a number that is much lower than similar approaches. This greatly eases the power burden of the nodes, resulting in a significant lifetime increase. This paper evaluates a concrete deployment from summer 2012 composed of fixed anchor motes throughout one floor of a nursing home and mobile units carried by patients. We show how two localization algorithms perform and demonstrate a clear improvement by following a set of simple guidelines to tune the anchor node placement. We show both quantitatively and qualitatively that the results meet the functional and non-functional system requirements.
Wireless Sensor Networks (WSNs) are distributed systems composed of battery-powered nodes that se... more Wireless Sensor Networks (WSNs) are distributed systems composed of battery-powered nodes that sense and collect information about the physical world. They enable applications in a wide variety of domains including but not limited to environmental monitoring, health care and disaster management. In such applications, nodes communicate the
sensed information over multiple radio links until it reaches its destination referred to as the sink. As wireless communication is the most energy hungry operation, the data collection causes the biggest drain from battery. This motivates research on energy efficient mechanisms for data collection.
Though there is a plethora of protocols proposed in research literature, they are not designed to collaborate with the appli-cations. One opportunity from such collaboration is exploiting complete knowledge about application characteristics to make data collection more energy efficient. This enables underlying layers not to provision the resources more than the needs of the application and therefore save valuable battery power. The aim of this thesis is to explore a complex interplay between application characteristics and adaptive mechanisms across network stack using concrete real world deployments. It will propose a generic framework that integrates the adaptations to achieve near-optimal energy efficiency for heterogeneous applications.
Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefo... more Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed.
In contrast, this paper investigates in practice whether i) model-driven data acquisition works in a real application; ii) the energy savings it enables in theory are still worthwhile once the network stack is taken into account. We do so in the concrete setting of a WSN-based system for adaptive lighting in road tunnels. Our novel modeling technique, Derivative-Based Prediction (DBP), suppresses up to 99% of the data reports, while meeting the error tolerance of our application. DBP is considerably simpler than competing techniques, yet performs better in our real setting. Experiments in both an indoor testbed and an operational road tunnel show also that, once the network stack is taken into consideration, DBP triples the WSN lifetime—a remarkable result per se, but a far cry from the aforementioned 99% data suppression. This suggests that, to fully exploit the energy savings enabled by data modeling techniques, a coordinated operation of the data and network layers is necessary.
Conference Presentations by Usman Raza
One of the most challenging goals of many wireless sensor network (WSN) deployments is the reduct... more One of the most challenging goals of many wireless sensor network (WSN) deployments is the reduction of energy consumption to extend system lifetime. This paper considers a novel combination of techniques that address energy savings at the hardware and application levels: wake-up receivers and node level power management, plus prediction-based data collection. Individually, each technique can achieve significant energy savings, but in combination, the results are impressive. This paper presents a case study of these techniques as applied in a road tunnel for light monitoring. Preliminary results show the potential for two orders of magnitude reduction in power consumption. This savings of 380 times allows the creation of an energetically sustainable system by considering integration with a simple, photovoltaic energy harvester.
In recent years, indoor localization has become a hot research topic with some sophisticated solu... more In recent years, indoor localization has become a hot research topic with some sophisticated solutions reaching accuracy on the order of ten centimeters. While certain classes of applications can justify the corresponding costs that come with these solutions, a wealth of applications have requirements that can be met at much lower cost by accepting lower accuracy. This paper explores one specific application for monitoring patients in a nursing home, showing that sufficient accuracy can be achieved with a carefully designed deployment of low-cost wireless sensor network nodes in combination with a simple RSSI-based localization technique. Notably our solution uses a single radio sample per period, a number that is much lower than similar approaches. This greatly eases the power burden of the nodes, resulting in a significant lifetime increase. This paper evaluates a concrete deployment from summer 2012 composed of fixed anchor motes throughout one floor of a nursing home and mobile units carried by patients. We show how two localization algorithms perform and demonstrate a clear improvement by following a set of simple guidelines to tune the anchor node placement. We show both quantitatively and qualitatively that the results meet the functional and non-functional system requirements.
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Papers by Usman Raza
connect massive numbers of geographically dispersed devices at a low cost continues to attract a great deal of attention in the academic and commercial communities. Several rollouts are already underway even though the performance of these technologies is yet to be fully understood. In light of these developments, tools to carry out ‘what-if analyses’ and predeployment studies are needed to understand the implications of choices that are made at design time. While there are several
promising technologies in the LPWA space, this paper specifically focuses on the LoRa/LoRaWAN technology. In particular, we present LoRaWANSim, a simulator which extends the LoRaSim tool to add support for the LoRaWAN MAC protocol, which
employs bidirectional communication. This is a salient feature not available in any other LoRa simulator. Subsequently, we provide vital insights into the performance of LoRaWAN based networks through extensive simulations. In particular, we show
that the achievable network capacity reported in earlier studies is quite optimistic. The introduction of downlink traffic can have a significant impact on the uplink throughput. The number of transmit attempts recommended in the LoRaWAN specification
may not always be the best choice. We also highlight the energy consumption versus reliability trade-offs associated with the choice of number of retransmission attempts.
In this paper, we describe Derivative-Based Prediction (DBP), a novel data prediction technique much simpler than those found in the literature. Evaluation with real data sets from diverse WSN deployments shows that DBP often performs better than the competition, with data suppression rates up to 99% and good prediction accuracy. However, experiments with a real WSN in a road tunnel show that, when the network stack is taken into consideration, DBP only triples lifetime---a remarkable result per se, but a far cry from the data suppression rates above. To fully achieve the energy savings enabled by data prediction, the data and network layers must be jointly optimized. In our testbed experiments, a simple tuning of the MAC and routing stack, taking into account the operation of DBP, yields a remarkable seven-fold lifetime improvement w.r.t. the mainstream periodic reporting.
In this paper we present SENSEH, a software framework that allows developers to move back and forth between the power and speed of a simulated approach and the reality and accuracy of in-field experiments. SENSEH relies on COOJA for emulating the actual, deployment-ready code, and provides two modes of operation that allow the reuse of exactly the same code in realworld WSN deployments. We describe the toolchain and software architecture of SENSEH, and demonstrate its practical use and benefits in the context of a case study where we investigate how the lifetime of a WSN used for adaptive lighting in road tunnels can be extended using harvesters based on photovoltaic panels.
sensed information over multiple radio links until it reaches its destination referred to as the sink. As wireless communication is the most energy hungry operation, the data collection causes the biggest drain from battery. This motivates research on energy efficient mechanisms for data collection.
Though there is a plethora of protocols proposed in research literature, they are not designed to collaborate with the appli-cations. One opportunity from such collaboration is exploiting complete knowledge about application characteristics to make data collection more energy efficient. This enables underlying layers not to provision the resources more than the needs of the application and therefore save valuable battery power. The aim of this thesis is to explore a complex interplay between application characteristics and adaptive mechanisms across network stack using concrete real world deployments. It will propose a generic framework that integrates the adaptations to achieve near-optimal energy efficiency for heterogeneous applications.
In contrast, this paper investigates in practice whether i) model-driven data acquisition works in a real application; ii) the energy savings it enables in theory are still worthwhile once the network stack is taken into account. We do so in the concrete setting of a WSN-based system for adaptive lighting in road tunnels. Our novel modeling technique, Derivative-Based Prediction (DBP), suppresses up to 99% of the data reports, while meeting the error tolerance of our application. DBP is considerably simpler than competing techniques, yet performs better in our real setting. Experiments in both an indoor testbed and an operational road tunnel show also that, once the network stack is taken into consideration, DBP triples the WSN lifetime—a remarkable result per se, but a far cry from the aforementioned 99% data suppression. This suggests that, to fully exploit the energy savings enabled by data modeling techniques, a coordinated operation of the data and network layers is necessary.
Conference Presentations by Usman Raza