Automated Driving (AD) vehicles are slowly being deployed on public roads. These AD vehicles will encounter hazardous (dangerous) scenarios due to unforeseen edge cases at design time and changing environments on the road after deployment. To allow developers of AD systems to mitigate such unforeseen risks, the safety of AD vehicles needs to be continuously monitored after deployment. To this end, the UL4600 standard and AVSC guidelines recommend the use of safety performance indicators (SPIs) by AD vehicle developers. Our paper presents a framework that uses SPIs to identify potentially hazardous scenarios specific to the evaluated AD system, covering both AD vehicles and cloud operations. The framework uses the perception systems and motion planners of heterogeneous redundant multi-channel architectures to detect hazards invisible in single-channel-based systems, provided one of the channels observes the environment correctly. We propose three cross-channel SPIs and use them to identify hazardous scenarios in the AD vehicle and validate this approach with a proof-of-concept implementation. In a test of 6 challenging routes in the CARLA simulator, our framework automatically identifies 86% of hazardous situations. Next, it identifies contributing issues in the AD vehicle, such as missed object detections or dangerous planned trajectories. With this proof of concept, we show that this framework provides evidence on the safety of deployed systems, identifies AD vehicle functions in need of improvement and provides lessons for the development of future AD systems.

LoRaWANs, a widely accepted IoT connectivity solution, adopt a simple (ALOHA-like) MAC layer, enabling low-power communication at the cost of scalability due to packet collisions. Hence, current studies on LoRaWAN conclude that the network does not support dense deployments. Several alternative MACs are proposed but they stumble upon well-known limitations: time division eliminates the asynchrony of LoRa nodes but requires feedback from the gateways; carrier-sensing-based protocols are heavily constrained by the reduced sensing ranges of the devices, thus creating a large number of hidden terminals, leading to collisions.To enhance LoRaWAN to cater to both low- and high-density deployments, in this paper, we propose Spreading Factor MAC (SFMAC), a novel, practical, distributed, and energy-efficient MAC protocol. SFMAC, a channel-sensing-based MAC, takes an unconventional approach to eliminate hidden terminals – by operating with pairs of SFs, wherein the higher SF is used for channel sensing and the lower for data transmission. Bleeps are transmitted in the higher SF as they can be sensed at longer ranges. SFMAC does not require any change in hardware or the LoRaWAN protocol. We demonstrate that the fundamental trade-off made by SFMAC – utilizing two SFs per data transmission instead of using all for data – works extremely well due to the elimination of hidden terminals. Through real-world experiments on 30 SX1261 devices and data-driven ns-3 simulations, we showcase that SFMAC increases goodput and channel utilization by manifolds over state-of-the-art protocols such as p-CARMA, np-CECADA, and LMAC.

The efficient execution of inferences at the edge is becoming increasingly critical for communication systems that are expected to provide users with fast and accurate mobile data analytics. These inference tasks are inherently latency-sensitive and computationally demanding, whereas edge nodes are limited by energy budgets and heterogeneous resources. This paper studies how a set of edge nodes can collaborate in executing demanding streaming inference tasks to optimize their aggregate performance. Such collaborative task exchange schemes enable the sharing of scarce computing resources and machine learning models (which perform the inferences), and constitute a scalable approach to this intricate problem. We formulate this exchange process as an online convex optimization problem, and design a dynamic task assignment algorithm, which is proven to have optimality guarantees even when the network and service parameters (resources and task properties) are unknown and vary arbitrarily over time. The algorithm aims to maximize inference accuracy while minimizing overall task latency and energy (including for data transfers), and simultaneously ensures that collaborating nodes do not suffer imbalanced energy costs. Through a series of data-driven experiments, we quantify the cooperation benefits under different weight combinations and validate the convergence and adaptability of the proposed learning algorithm across diverse conditions, including variations of system parameters as well as heterogeneity across nodes and tasks.

Haptic teleoperation is a promising technology with applications in telemaintenance and disaster management. However, it faces significant challenges when the application is subjected to a high network latency and environments with moving objects. This work aims to extend Model Mediated Teleoperation (MMT) to overcome challenges in supporting dynamic environments. Instead of striving for perfect model alignment, we acknowledge the inevitable mismatch between the remote environment and its model at the operator. We propose a set of design principles and an accompanying framework for designing MMT solutions that prioritize operator intent. Our approach is exemplified through an application where an operator, located 8000 km away (The Netherlands – India) and subjected to an average of 179 ms end-to-end latency, guides a robot arm to draw on a whiteboard whose position is actively altered. We evaluate the effectiveness of our approach through a user study. We show a 3-point improvement on a 7-point Likert scale when users utilize our approach to teleoperate over significant network latency of up to 1 second.

As a key technology of intelligent satellite-enabled services in B5G or 6G networks, deploying Deep Neural Networks (DNN) models on satellites has been a notable trend, catering to the daily demand for extensive computing-intensive and latency-sensitive tasks. The computing resources are strategically deployed on satellites where sensor data is generated or collected, facilitating the fine-grained computational inference of DNN-based tasks. However, no prior study has comprehensively explored the crucial inference challenges – e.g., the trade-off between the number of tasks completed and accuracy and partitioning models in multi-exit models – in the resource-constrained space environment. Effective scheduling frameworks cater to various streams of inference tasks are scarce because inference performance may deviate from the ideal situation due to changes in task system status, such as task profiles and network state. To this end, we first formulate a gain-aware in-orbit computing inference problem to strike a proper trade-off between inference latency and the number of tasks completed by dynamically selecting optimal early exit points and model partitioning points. We propose an offline dynamic programming-based algorithm that provides an effective solution when comprehensive system details are to be predicted. We have developed an online learning-based method to schedule inference tasks with uncertain and dynamic system statuses in real-world situations. Our evaluation shows that, compared to baseline methods, the online learning-based algorithm can improve task gain by an average of 87.3% across various tasks.

The rapid expansion of Low Earth Orbit (LEO) satellite constellations presents immense potential for in-orbit services. However, the large-scale and dynamic nature of LEO constellations creates unstable communication environments, where traditional methods struggle to ensure the efficiency and stability of onboard inference services. This highlights the need for an advanced knowledge service framework capable of both inference and root cause analysis of service disruptions. To address this, we propose a novel knowledge service framework that integrates data-driven and knowledge-driven models through satellite-ground collaboration. The framework leverages lightweight onboard models for real-time data processing and ground-based knowledge graphs for advanced inference and cause analysis. To further enhance stability within complex interconnected onboard systems, we propose a prediction-based algorithm for LEO satellite networks that uses joint spatio-temporal modeling to achieve accurate link prediction. Additionally, we formulate an optimization problem aimed at minimizing path distance variance and maximizing path stability across LEO topologies, and we propose a heuristic path selection strategy to ensure efficient inter-satellite routing. Extensive in-orbit deployments and simulation experiments demonstrate the feasibility and effectiveness of the proposed framework. Satellite-ground verification on the BUPT-1 satellite shows its ability to provide real-time services, while inter-satellite simulations using real constellation data indicate significant improvements in response latency and path stability. Compared with baseline methods, our proposed method significantly reduces path jitter by up to 62.6% and improves path availability by up to 17.3% across various LEO constellations.

Collaborative execution of inference tasks by extreme-edge nodes can effectively address the challenge of scarce resources in the Next Generation IoT and 6G networks. In this paper, we study how such nodes can coordinate the execution of streaming inferences to jointly optimize their task performance (accuracy and latency) and energy consumption. We formulate this process as an online learning problem, and design an online task assignment algorithm, which is proven to provide optimality guarantees even when the network parameters (node resources and task properties) are unkown and subject to arbitrary variations over time. Further, we validate the performance of the proposed algorithm using data-driven simulations of representative scenarios and compare it with non-cooperative benchmarks.

We present HUM-High-frequency UAV Messaging: an acoustic side channel communication system we design for localized drone-to-drone communications. We generate Pulse Width Modulated (PWM) signals from drone motors to carry information and improve communication reliability by mitigating propeller noise interference through modifications to the propeller's physical design. These modifications reduce propeller noise in the designated acoustic spectrum by up to 7 dB. We deploy a custom ultrasonic microphone shield specifically designed for decoding in the receiver. HUM's improved signal-to-noise ratio enables up to 80x higher data rates compared to the existing design from the literature while providing better scalability. HUM supports simultaneous decoding across 16 drones within 8 m, range as seen in real flight tests. The cost of this performance is minimal; we experimentally demonstrate that HUM has a marginal impact on flight dynamics and battery life.

With the advent of the multimodal immersive communication system, people can interact with each other using multiple devices for sensing, communication and/or application level control either onsite or remotely. As a breakthrough concept, a distributed sensing, computing, communications, and control (DS3C) fabric is introduced in this paper for provisioning 6G services in multi-tenant environments in a unified manner. The DS3C fabric can be further enhanced by natively incorporating intelligent algorithms for network automation and managing networking, computing, and sensing resources efficiently to serve vertical use cases with extreme and/or conflicting requirements. As such, the paper proposes a novel end-to-end 6G system architecture with enhanced intelligence spanning across different network, computing, and business domains, identifies vertical use cases and presents an overview of the relevant standardisation and pre-standardisation landscape.

The next frontier in communications isteleoperation– manipulation and control of remote environments with haptic feedback. Compared to conventional networked applications, teleoperation poses widely different requirements, ultra-low latency (ULL) is primary. Realizing ULL communication demands significant redesign of conventional networking techniques, and the network infrastructure envisioned for achieving this is termed asTactile Internet(TI). The design of meaningful performance metrics is crucial for seamless TI communication. However, existing performance metrics fall severely short of comprehensively characterizing TI performance due to their inability to capture how well sensed signals are reproduced. We take Dynamic Time Warping(DTW) as the basis of our work and identify necessary changes for characterizing TI performance. Through substantial refinements to DTW, we designEffective Time- and Value-Offset (ETVO)– a new method for measuring the fine-grained performance of TI systems. Through an in-depth objective analysis, we demonstrate the improvements of ETVO over DTW. Through subjective experiments, we demonstrate that existing QoS and QoE methods fall short of estimating the TI session performance accurately. Using subjective experiments, we demonstrate the behavior of the proposed metrics, their ability to match theoretically derived performance, and finally, their ability to reflect user satisfaction in a practical setting.

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist. To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects, such as scalability, exploration, adaptation to dynamic environments, and multiagent learning. Current research challenges are also discussed, including sample efficiency, exploration versus exploitation, dealing with sparse rewards, and learning to plan. Then, the benefits of hybrid algorithms that combine DRL and ESs are highlighted.

Surveillance and monitoring are highly critical in many application scenarios like wildlife conservation, restricted areas such as nuclear spillover, and border security. Moreover, in these scenarios, intrusions do not happen frequently thus, conventional surveillance is overkill and expensive that also requires extensive human involvement which can be arduous, expensive, and inefficient. To address these issues we propose an end-to-end smart acoustic surveillance solution for intrusion detection using a simple low-cost system called Balls for Walls (B4 W). The objective is to create a network of sensors that could also be remotely launched. The nodes responsible for surveillance employ audio sensors which are packaged within hard balls thus allowing the launch of these sensors from a distance of over 500 m. We use microphones for detecting human activity inferred through sensing the sound of footsteps against background noise. We evaluate the systems across five different terrain types. We propose a novel, low complexity detection algorithm called SEED which leverages signal energy and shape to distinguish humans from ambient noise. B4 W offers a maximum detection rate of 98.3% on dry leaves and a low false alarm rate of 0.9%. The system is energy efficient to last a maximum of 170 days and it is orientation agnostic. The proposed system has been extensively tested across varying terrains and ambient signal scenarios to demonstrate its efficacy.

UAVs are becoming versatile and valuable platforms for various applications. However, the main limitation is their flying time. We present BEAVIS, a novel aerial robotic platform striking an unparalleled trade-off between the maneuverability of drones and the long-lasting capacity of blimps. BEAVIS scores highly in applications where drones enjoy unconstrained mobility yet suffer from limited lifetime. A nonlinear flight controller exploiting novel, unexplored, aerodynamic phenomena to regulate the ambient pressure and enable all translational and yaw degrees of freedom is proposed without direct actuation in the vertical direction. BEAVIS has built-in rotor fault detection and tolerance. We explain the design and the necessary background in detail. We verify the dynamics of BEAVIS and demonstrate its distinct advantages, such as agility, over existing platforms including the degrees of freedom akin to a drone with 11.36× increased lifetime. We exemplify the potential of BEAVIS to become an invaluable platform for many applications.

The pioneering field of tactile Internet (TI) will enable the transfer of human skills over long distances through haptic feedback. Realizing this demands a roundtrip latency of sub-5 ms. In this work, we investigate the capability of Wi-Fi 6 and existing TI scheduling/multiplexing schemes in meeting this stringent latency constraint. Taking the concrete example of the state-of-the-art video-haptic multiplexer (VH-multiplexer), we highlight the pitfalls of relying on the existing Wi-Fi 6 systems for TI communication. To circumvent this, we propose video-tactile latency scheduler (ViTaLS)—a novel link layer framework for tuning the video-tactile frame transmissions to suit their heterogeneous Quality of Service requirements. We present a mathematical model to characterize the packet transmission duration of ViTaLS. Using a custom simulator, we validate our model and measure the objective performance improvement of ViTaLS over VH-multiplexer. We also present ViTaLS-optimal—a variant of ViTaLS, for further 4 reducing the tactile latency. Objectively, we show that ViTaLS-optimal yields a latency improvement of up to 82 %. Based on experiments conducted on a real TI testbed, we subjectively demonstrate that ViTaLS-optimal outperforms the VH-multiplexer.

We present radio-frequency (RF) information harvesting, a chan-nel sensing technique that takes advantage of the energy in the wireless medium to detect channel activity at essentially no en-ergy cost. RF information harvesting is essential for event-driven wireless sensing applications using battery-less devices that har-vest tiny amounts of energy from impromptu events, such as op-erating a switch, and then transmit the event notification to a one-hop gateway. As multiple such devices may concurrently de-tect events, coordinating access to the channel is key. RF infor-mation harvesting allows devices to break the symmetry between concurrently-transmitting devices based on the harvested energy from the ongoing transmissions. To demonstrate the benefits of RF information harvesting, we integrate it in a tailor-made ultra low-power hardware MAC protocol we call Radio Frequency-Distance Packet Queuing (RF-DiPaQ). We build a hardware/software proto-type of RF-DiPaQ and use an established Markov framework to study its performance at scale. Comparing RF-DiPaQ against sta-ple contention-based MAC protocols, we show that it outperforms pure Aloha and 1-CSMA by factors of 3.55 and 1.21 respectively in throughput, while it saturates at more than double the offered load compared to 1-CSMA. As traffic increases, the energy saving of RF-DiPaQ against CSMA protocols increases, consuming 36% less energy than np-CSMA at typical offered loads.

Due to power limitations and coexistence in ISM bands, up to 50% of the Long Range (LoRa)-frames are corrupted at low signal strengths (≈ -115dBm) and the built-in redundancy schemes in LoRa-Wide Area Network (LoRaWAN) cannot correct the corrupted bytes. To address this, higher Spreading Factors (SF) are used resulting in wasted energy, increased traffic load, and highly compromised effective data rate. Our on-field experiments showed a high correlation in the corruption of close-by frames. We propose a novel Divide & Code (DC) scheme for LoRaWANs as an alternative to using higher SF. DC pre-encodes LoRa payloads using lightweight and memoryless encoding. After receiving a corrupted frame, DC uses a combination of most probable patterns of errors, Time Thresholds (TT), and splitting of payloads into subgroups for batch processing to recover frames effectively and maintain low complexity and timely operation. By implementing DC on our LoRa-testbed, we show it outperforms vanilla-LoRaWAN and Reed-Solomon codes in decoding and energy consumption. Our schemes decode up to 80.5% of corrupted payloads on SF10 by trying only 0.03% of all patterns of error combinations. TT keeps processing times below 2 ms with only minor reductions in the decoding ratio of corrupted payloads. Finally, we showcase that introducing 30% redundancy with DC results in minimum energy consumption and high decoding ratio at low SNRs.

Long-range wide-area network (LoRaWAN) is an energy-efficient and inexpensive networking technology that is rapidly being adopted for many Internet-of-Things applications. In this study, we perform extensive measurements on a new LoRaWAN deployment to characterise the spatio-temporal properties of the LoRaWAN channel. Our experiments reveal that LoRaWAN frames are mostly lost due to the channel effects, which are adverse when the end-devices are mobile. The frame losses are up to 70 percent, which can be bursty for both mobile and stationary scenarios. Frame losses result in data losses since the frames are transmitted only once in the basic configuration. To reduce data losses in LoRaWAN, we design a novel coding scheme for data recovery called DaRe that works on the application layer. DaRe combines techniques from convolutional and fountain codes. By implementing DaRe, we show that 99 percent of the data can be recovered with a code rate of 1/2 when the frame loss is up to 40 percent. Compared to the repetition coding scheme, DaRe provides 21 percent higher data recovery and can save up to 42 percent of the energy consumed on a transmission for 10-byte data units. We also show that DaRe provides better resilience to bursty frame losses.

The ability to localize a sound source is essential for safe integration of robots in our society. However, its high computational demand makes it challenging to equip small swarm robots with such capability. Therefore, this paper investigates potential means for developing a lightweight sound source localization method. It introduces the Cutting The Plane (CTP) algorithm which leverages recent advancements in embedded microphone technology to reduce the computation complexity of audio source localization. We analyzed its performance and compared it to a software-based approach using simulation. Finally, we performed tests on real hardware to show the feasibility of using our system for relative localization between swarm robots.

High frame-corruption is widely observed in Long Range Wide Area Networks (LoRaWAN) due to the coexistence with other networks in ISM bands and an Aloha-like MAC layer. LoRa's Forward Error Correction (FEC) mechanism is often insufficient to retrieve corrupted data. In fact, real-life measurements show that at least one-fourth of received transmissions are corrupted. When more frames are dropped, LoRa nodes usually switch over to higher spreading factors (SF), thus increasing transmission times and increasing the required energy. This paper introduces ReDCoS, a novel coding technique at the application layer that improves recovery of corrupted LoRa frames, thus reducing the overall transmission time and energy invested by LoRa nodes by several-fold. ReDCoS utilizes lightweight coding techniques to pre-encode the transmitted data. Therefore, the inbuilt Cyclic Redundancy Check (CRC) that follows is computed based on an already encoded data. At the receiver, we use both the CRC and the coded data to recover data from a corrupted frame beyond the built-in Error Correcting Code (ECC). We compare the performance of ReDCoS to (i) the standard FEC of vanilla-LoRaWAN, and to (ii) Reed Solomon (RS) coding applied as ECC to the data of LoRaWAN. The results indicated a 54x and 13.5x improvement of decoding ratio, respectively, when 20 data symbols were sent. Furthermore, we evaluated ReDCoS on-field using LoRa SX1261 transceivers showing that it outperformed RS-coding by factor of at least 2x (and up to 6x) in terms of the decoding ratio while consuming 38.5% less energy per correctly received transmission.

Autonomous vehicles have created a sensation in both indoor and outdoor applications. The famous indoor use-case is process automation inside a warehouse using Autonomous Indoor Vehicles (AIV). These vehicles need to locate themselves not only with an accuracy of a few centimeters but also within a few milliseconds in an energy-efficient manner. Due to these challenges, localization is a holy grail. In this paper, we propose FEEL – an indoor localization system that uses a fusion of three low-energy sensors: IMU, UWB, and radar. We provide detailed software and hardware architecture of FEEL. Further, we propose Adaptive Sensing Algorithm (ASA) for optimizing for localization accuracy and energy consumption of FEEL by adjusting the sensing rate to the dynamics of the physical environment in real-time. Our extensive performance evaluation over diverse test settings reveals that FEEL provides a localization accuracy of sub-7 cm with an ultra-low latency of around 3 ms. Additionally, ASA yields up to 20% energy savings with only a marginal trade off in accuracy. Furthermore, we show that FEEL outperforms state of the art in indoor localization.

Long Range Wide Area Networks (LoRaWAN) offer ubiquitous communications for The Internet of Things (IoT). However, there are many challenges in rolling out LoRaWAN - mainly scalability, energy efficiency, Packet Reception Ratio (PRR), and keeping the channel access as simple as unslotted ALOHA. To this end, we design non-persistent Capture Effect Channel Activity Detection Algorithm (np-CECADA), which is a novel, distributed protocol for the MAC layer of LoRaWAN. It utilizes Channel Activity Detection (CAD), which is a built-in imperfect mechanism for channel sensing and minimal feedback from the gateways. In np-CECADA each device independently adapts backoff times based on the traffic in its vicinity and the transmission power based on the heuristically inferred probability of capturing the channel. To achieve this, first, we carried out an extensive on-field evaluation to measure the effectiveness of CAD and capture effect in LoRa. Using them we designed np CECADA and developed $ns-3$ modules. Packet Reception Ratio of np-CECADA is $ 15.74\times$ and $ 5.13\times$ higher than vanilla LoRaWAN and p-CARMA, respectively. Channel utilization is $ 11.24\times$ higher compared to LMAC. Further, on a testbed of 30 LoRa devices np-CECADA outperforms LoRaWAN up to 5 times.

With the advent of Space-IoTs, the rate of launch of satellites has grown significantly. Alongside, the failure rate of satellites has also surged increased tremendously. Satellites are non-repairable systems in orbit, and the financial loss incurred when the satellites fail before their expected mission time is substantial. If the source of a failure is known while the satellite is in orbit, then there is a possibility to revive it by sending appropriate commands from ground stations. In this work, we present a simple, independent satellite health monitoring system called Chirper. The Chirper is equipped with multiple modules such as IMU, isolated voltage and current measurement probes, and an onboard communication channel. We present a new approach to measure low DC voltages in an isolated way, providing a resolution and accuracy of around 1 V. We evaluated the design and performance of the Chirper through simulation, testing it in space systems test facility, and by mounting it on a helium balloon. With extensive experiments we show that 90% of the time the dc voltage measurement error is within 0.8 V, and the maximum error is 0.9 V. We expect to launch the Chirper soon on a space system.

With the recent advances in wireless communications, the automotive industry is leading to evolution. To succeed in this emerging era of technology, the Internet of Connected Vehicles (IoCV) has emerged as one of the potential applications of the Internet of Things (IoT). It refers to the dynamic mobile communication systems that communicate between vehicles and public networks to enhance the connectivity between cars via technology. By offering a wide variety of infotainment services, fleet operations, and in-vehicle applications, IoCV has gained the tremendous capacity to provide a safer and sustainable transportation system to the society. According to Gartner Inc., “the connected car is already a reality, and in-vehicle wireless connectivity is expanding rapidly.” As a result, the evolution of cars into the IoT will keep on accelerating the global market which is expected to grow by 270% by 2022. Furthermore, the increasing deployment of sensors and ever-evolving cognitive technology opens up new opportunities for IoCV. Due to these significant developments, connected vehicles are receiving widespread attention from the major automotive giants such as Tesla, BMW, Waymo (Google), Uber, Volvo, and so on. Despite all the opportunities offered by the IoCV, their highly dynamic topology and the increasing number of vehicles pose challenges regarding delivering low-latency vehicle-to-everything (V2X) communications.

Information and Communication Technology (ICT) is now touching various aspects of our lives. The electricity grid with the help of ICT is transformed into Smart Grid (SG) which is highly efficient and responsive. It promotes two-way energy and information flow between energy distributors and consumers. Many consumers are becoming prosumers by also producing energy. The trend is to form small communities of consumers and prosumers leading to Micro-grids (MG) to manage energy locally. MGs are parts of SG that decentralize the energy flow by allocating the produced energy within the community. Energy allocation amongst them needs to solve issues viz., (i) how to balance supply/demand within micro-grids; (ii) how allocating energy to a user affects his/her community. To address these issues we propose six Energy Allocation Strategies (EASs) for MGs - ranging from simple to optimal. We maximize the usage of the energy generated by prosumers within MG. We form household-groups sharing similar characteristics to apply EASs by analyzing thoroughly energy and socioeconomic data of households. We propose four metrics to evaluate EASs. We test our EASs on the data from 443 households over a year. By prioritizing specific households, we increase the number of fully served households up to 81% compared to random sharing.

The next frontier in communications is teleoperation – manipulation and control of remote environments. Compared to conventional networked applications, teleoperation poses widely different requirements, ultra-low latency (ULL) being the primary one. Teleoperation, along with a host of other applications requiring ULL communication, is termed as Tactile Internet (TI). A significant redesign of conventional networking techniques is necessary to realize TI applications. Further, these advancements can be evaluated only when meaningful performance metrics are available. However, existing TI performance metrics fall severely short of comprehensively characterizing TI performance. In this paper, we take the first step towards bridging this gap. To this end, we propose a method that captures the fine-grained performance of TI in terms of delay and precision. We take Dynamic Time Warping (DTW) as the basis of our work and identify whether it is sufficient in characterizing TI systems. We refine DTW by developing a framework called Effective Time- and Value-Offset (ETVO) that extracts fine-grained time and value offsets between input and output signals of TI. Using ETVO, we present two quantitative metrics for TI – Effective Delay-Derivative (EDD) and Effective Root Mean Square Error. Through rigorous experiments conducted on a realistic TI setup, we demonstrate the potential of the proposed metrics to precisely characterize TI interactions.

High accuracy and device-free indoor localization is still a holy grail to enable smart environments. With the growing privacy concerns and regulations, it is necessary to develop methods and systems that can be low-power, device-free as well as privacy-aware. While IR-based solutions fit the bill, they require many modules to be installed in the area of interest for higher accuracy, or proper planning during installation, or they may not work if the background has multiple heat-emitting objects, etc. In this paper, we propose a custom-built miniature device called LOCI that uses IR sensing. One unit of LOCI can provide three-dimensional localization at best. LOCI uses only a thermopile and a PIR sensor built within a 5x5x2 cm3module. Since IR-based sensing is used, LOCI consumes around 80 mW. LOCI uses analog waveform from the PIR sensor with the gain of the PIR sensor dynamically controlled through software in real-time to simulate spatial diversity. LOCI proposes low-complexity techniques with sensor fusion to eliminate the noise in the background, which has not been handled in previous works even with sophisticated signal processing techniques. Since LOCI uses raw data from the thermopile, the computations are power-efficient. We present the complete design of LOCI and the proposed methodology to estimate height and location. LOCI achieves accuracies of sub-22 cm with a confidence of 0.5 and sub-35 cm with a confidence of 0.8. The best-case location accuracy is 12.5 cm. The accuracy of height estimation is within 8 cm in majority cases. LOCI can easily be extended to recognize activities.

Global Positioning System is a widely adopted localization technique. With the increasing demand for small satellites, the need for a low-power GPS for satellites is also increasing. To enable many state-of-the-art applications, the exact position of the satellites is necessary. However, building low-power GPS receivers which operate in low earth orbit pose significant challenges. This is mainly due to the high speed (~7.8 km/s) of small satellites. While duty-cycling the receiver is a possible solution, the high relative Doppler shift between the GPS satellites and the small satellite contributes to the increase in Time To First Fix (TTFF), thus increasing the energy consumption. Further, if the GPS receiver is tumbling along with the small satellite on which it is mounted, longer TTFF may lead to no GPS fix due to disorientation of the receiver antenna. In this paper, we elucidate the design of a low-cost, low-power GPS receiver for small satellite applications. We also propose an energy optimization algorithm called F3to improve the TTFF which is the main contributor to the energy consumption during cold start. With simulations and in-orbit evaluation from a launched nanosatellite with our μGPS and high-end GPS simulators, we show that up to 96.16% of energy savings (consuming only ~ 1/25th energy compared to the state of the art) can be achieved using our algorithm without compromising much (~10 m) on the navigation accuracy. The TTFF achieved is at most 33 s.

Ultra-low latency and high reliability communications are the two defining characteristics of tactile Internet (TI). Nevertheless, some TI applications would also require high data-rate transfer of audiovisual information to complement the haptic data. Using millimeter Wave (mmWave) communications is an attractive choice for high data-rate TI applications due to the availability of large bandwidth in the mmWave bands. Moreover, mmWave radio access is also advantageous to attain the air-interface-diversity required for high reliability in TI systems as mmWave signal propagation significantly differs to sub-6 GHz propagation. However, the use of narrow beamwidth in mmWave systems makes them susceptible to link misalignment-induced unreliability and high access latency. In this article, we analyze the tradeoffs between high gain of narrow beamwidth antennas and corresponding susceptibility to misalignment in mmWave links. To alleviate the effects of random antenna misalignment, we propose a beamwidth-adaptation scheme that significantly stabilize the link throughput performance.

Many IoT applications require the knowledge of crowd distribution, particularly in indoor scenarios. To this end, we leverage the lighting grid infrastructure in buildings by using smart light bulbs, which can include variety of sensors, in these grids. The exponentially increasing adoption of smartphones and the Wi-Fi infrastructure has motivated us to tackle this problem using Wi-Fi. As we seek a solution that works in many buildings, relying on active user participation or installing apps is not an option. Therefore, we need a Wi-Fi based crowd distribution estimation technique that is non-participatory and non-intrusive, and works with very few Wi-Fi packets generated by users' smartphones sporadically. We approach the problem by analyzing the Wi-Fi packets for counting people (smartphones) and estimating their position within a pre-defined accuracy. To this end, extensive experiments are conducted in a real-world testbed with controlled settings as well as in test setups in office spaces with no control. We propose improvised counting techniques that results in people counts close to 75% of the ground truth. We further propose improvements to range-free localization techniques to refine the position estimation accuracy and reduce the execution time. Our algorithm estimates the location with an accuracy of 2m 74% of the time, when Wi-Fi sniffers are placed in bulbs every 4m in the grid.

Many Internet of Things smart-* applications are being powered solely through ambient energy-harvested energy. These applications require periodic data collection with low latency and high reliability. Since the energy is harvested in small amounts from ambient sources and is stochastic in nature, it is extremely challenging to achieve low latency and high reliability for such applications. To this end, we propose a distributed, energy-management module called ReNEW, for Constructive Interference (CI) based protocols that utilizes the available energy effectively in order to achieve our target of increased reliability in EH-WSN, especially in the low harvesting regimes. We choose CI-based protocols to leverage the low latency guarantees. Specifically, we propose a Markov decision model to maximize the energy utility in the infinite horizon by allocating energy optimally. To this end, we also propose a threshold optimal policy. As we find that just a energy scheduler cannot achieve the goal, we also propose distributed techniques to conserve energy on the redundant nodes in the network, and dynamically activate them based on feedback. We also improve the performance of CI by adapting the transmit powers on nodes. We implement and evaluate ReNEW on Indriya testbed for real-world scenarios. We show that in a network of 20 source nodes out of the 30 nodes in the network can perform periodic data collection with an improvement of 2.5 times higher packet reception ratio as compared to LWB. This is one of the worst case scenarios as the harvested energy is as low as 50uJ/s and packets of size 100 B is sent every 30 s. Furthermore, in this scenario, ReNEW saves around 25% higher residual energy on the average as compared to the standard LWB. In a nutshell, by integrating ReNEW with CI based protocols, we enable guaranteed latency and increased reliability for the batteryless EH-WSNs.

Two nanosatellites recently launched into space had issues with respect to its stabilization, power and orientation. The signals were intermittent, and amateur radio enthusiasts around the globe were requested to observe the satellites so as to get their health information. As decoding the received signals required proprietary hardware (that could not be sent to everyone), amateur radio receivers recorded the signal using Software Defined Radios (SDRs) and sub-sampled the carrier signals to make it easy to share. The captured signals, modulated using binary Frequency Shift Keying (FSK), included noise and more importantly the frequency shifts due to Doppler, caused by the speed of the satellites (of about 7.8 km/s), thus making decoding a major challenge even for the designated proprietary receivers (failed in some cases). As the existing FSK methods did not work effectively, we were motivated by this challenge to design an effective FSK decoder that works in the presence of Doppler and noise. In this paper, we propose Teager Energy Decoder (TED) based on Teager Energy Operator to decode such Doppler and noise influenced sub-sampled data. TED does not need any Doppler correction mechanisms and can dynamically adapt to the changing frequency shifts. We evaluate TED using simulation as well as from the signals from those two satellites. We show that TED performs better than COTS transceivers and available GNU-radio-based solutions using SDRs. TED is low-complexity algorithm, O(N2), and has been prototyped on a low-power micro-controller. TED can be easily adopted on satellites to decode signals for space Internet of Things applications.

The IEEE “Tactile Internet” (TI) Standards working group (WG), designated the numbering IEEE 1918.1, undertakes pioneering work on the development of standards for the TI. This paper describes the WG, its intentions, and its developing baseline standard and the associated reasoning behind that and touches on a further standard already initiated under its scope: IEEE 1918.1.1 on “Haptic Codecs for the TI.” IEEE 1918.1 and its baseline standard aim to set the framework and act as the foundations for the TI, thereby also serving as a basis for further standards developed on TI within the WG. This paper discusses the aspects of the framework such as its created TI architecture, including the elements, functions, interfaces, and other considerations therein, as well as the novel aspects and differentiating factors compared with, e.g., 5G Ultra-Reliable Low-Latency Communication, where it is noted that the TI will likely operate as an overlay on other networks or combinations of networks. Key foundations of the WG and its baseline standard are also highlighted, including the intended use cases and associated requirements that the standard must serve, and the TI's fundamental definition and assumptions as understood by the WG, among other aspects.

The constructive interference (CI) phenomenon has been exploited by a number of protocols for providing energy-efficient, low-latency, and reliable data collection and dissemination services in wireless sensor networks. These protocols consider CI to provide highly reliable packet delivery. This has attracted attention to understand the working of CI; however, the existing works present inconsistent views. Furthermore, these works do not study in the real-world settings where the physical conditions of deployment and unreliable wireless channels also impact the performance of CI. Therefore, we study the phenomenon of CI, considering a receiver's viewpoint and analyze the parameters that affect CI. We validate our arguments with results from extensive and rigorous experimentation in real-world settings. This paper presents comprehensive insights into the CI phenomenon. With the understanding, we develop the destructive interference-based power adaptation (DIPA), an energy-efficient and distributed algorithm, that adapts transmission power to improve the performance of CI. Since CI-based protocols cannot have an explicit acknowledgment packet, we make use of destructive interference on a designated byte to provide a feedback. We leverage this feedback to adapt transmission powers. We compared CI with and without DIPA in two real-life testbeds. On one testbed, we achieve around 25% lower packet losses while using only half of its transmission power for 64-B packets. On the other testbed, we achieve 25% lower packet losses while consuming only 47% of its transmission power for 128-B packets. Existing CI-based protocols can easily incorporate DIPA into them to achieve lower packet losses and higher energy efficiencies.

Depletion of fossil fuel and the ever-increasing need for energy in residential and commercial buildings have triggered in-depth research on many energy saving and energy monitoring mechanisms. Currently, users are only aware of their overall energy consumption and its cost in a shared space. Due to the lack of information on individual energy consumption, users are not being able to fine-tune their energy usage. Further, even-splitting of energy cost in shared spaces does not help in creating awareness. With the advent of the Internet of Things (IoT) and wearable devices, apportioning of the total energy consumption of a household to individual occupants can be achieved to create awareness and consequently promoting sustainable energy usage. However, providing personalized energy consumption information in real-time is a challenging task due to the need for collection of fine-grained information at various levels. Particularly, identifying the user(s) utilizing an appliance in a shared space is a hard problem. The reason being, there are no comprehensive means of collecting accurate personalized energy consumption information. In this paper we present the Personalized Energy Apportioning Toolkit (PEAT) to accurately apportion total energy consumption to individual occupants in shared spaces. Apart from performing energy disaggregation, PEAT combines data from IoT devices such as smartphones and smartwatches of occupants to obtain fine-grained information, such as their location and activities. PEAT estimates energy footprint of individuals by modeling the association between the appliances and occupants in the household. We propose several accuracy metrics to study the performance of our toolkit. PEAT was exhaustively evaluated and validated in two multi-occupant households. PEAT achieves 90% energy apportioning accuracy using only the location information of the occupants. Furthermore, the energy apportioning accuracy is around 95% when both location and activity information is available.

Progress in low power miniaturized electronics and wireless technologies have enabled many innovative applications. Of particular interest is the Internet of Things (IoT) that has dominated the world of ICT in the last half a decade enabling many smart systems and applications. At the same time, we also see much enthusiasm with respect to space missions and applications including terrestrial applications, apart from exploring the universe. We believe that innovations in the domain of IoT will significantly influence the space related activities -- both research and development. In this article, we chart out the innovations in space and the vision with respect to embedded and wireless systems for space applications. In particular, we bring in the notion of Sensor Wireless Actuator Networks in Space (SWANS) and Space Pixels to explain IoT in space. We explain with examples what we envision for the next decade and also the challenges therein. We briefly put forth our four major targets in the next five years.

Ambient energy-harvesting technology is a promising approach to keep wireless sensor networks (WSNs) operating perpetually. Depending on the harvesting source nodes can either be active (alive) or inactive (dead) at any instant in such Energy-Harvesting WSNs (EH-WSNs). Thus, even in a static deployment of EH-WSNs, the network topology is no longer static. A popular method to increase energy-efficiency in WSNs is by employing topology control algorithms. Most of the topology control algorithms in the literature cannot handle the situation when nodes have different energy-levels, and when number of active nodes varies with time in EH-WSN. To address this issue, we present two localized energy based topology control algorithms, viz., EBTC-1 and EBTC-2. EBTC-1 is for convergecast applications of WSNs and EBTC-2 is for a generic scenario where all nodes are required to be connected. While typical topology control algorithms select a particular number of neighbors, the distinguishing feature of both these algorithms is that they select neighbors based on energy-levels, and render the global topology strongly-connected. Simulation results confirm that EBTC-1 and EBTC-2 reduce the transmission power and they let nodes have neighbors with high remaining energy. Results show that our proposed algorithms increase at least 33% in the remaining energy per neighbor. In addition, in terms of energy consumption and fault-tolerance, our proposed algorithms typically achieve 1-connected topology using 74% less energy compared to K-Neigh.

Smart grids offer better energy management at consumer premises as well as energy companies side using bi- directional communication and control. Energy companies can balance energy supply and demand to a large extent, with the advent of smart homes. They can also nudge consumers to shift their demands to off-peak hours for load balancing and monetary benefits. We propose a decentralized demand scheduling algorithm that minimizes consumer discomfort and electricity cost of a household. Our algorithm utilizes only aggregated energy consumption of a household to derive optimal appliance level demand schedules. Furthermore, a low-complexity energy disaggregation algorithm is proposed to derive fine- grained appliance information and consumer preferences. We propose three important coefficients related to energy usage of consumers. We utilize them to derive optimal day- ahead demand schedules. The decentralized algorithm is empirically evaluated using real-world energy usage data from open datasets, which include our own deployment. Our proposed scheduling algorithm saves up to 30% energy cost. This work is one of the first to derive day-ahead schedules using real-world data from multiple households.

Constructive Interference (CI) phenomenon has been exploited by Glossy, a mechanism for low-latency and reliable network flooding and time synchronization for wireless sensor networks. Recently, CI has also been used for other applications such as data collection and multicasting in static and mobile WSNs. These applications base their working on the high reliability promised by Glossy regardless of the physical conditions of deployment, number of nodes in the network, and unreliable wireless channels that may be detrimental for CI. There are several works that study the working of CI, but they present inconsistent views. We study CI from a receiver's viewpoint, list factors that affect CI and also specify how and why they affect. We validate our arguments with results from extensive and rigorous experimentation in real-world settings. This paper presents comprehensive insights into CI phenomenon. With this understanding, we improve the performance of CI through an energy-efficient and distributed algorithm. We cause destructive interference on a designated byte to provide negative feedback. We leverage this to adapt transmission powers. Compared to Glossy, we achieve 25% lesser packet losses while using only half of its transmission power.

Typical Wireless Sensor Networks (WSN) deployments use more nodes than needed to accurately sense the phenomena of interest. This redundancy can be leveraged by switching-on only a subset of nodes at any time instant (node-scheduling) and putting the remaining nodes sleep. This effectively extends the network lifetime. In addition to sensing coverage, node-scheduling schemes must also ensure that (i) the network stays connected, and (ii) the time needed to wake-up the complete protocol stack after sleeping is minimized. We present Sleeping Beauty, a highly-efficient data collection protocol that aids node-scheduling schemes in both aspects. Sleeping Beauty uses a slotted and tightly synchronized communication primitive, where a node keeps its radio off for most of the time, except in the slots when it needs to participate for successful communication. Further, an efficient neighbor-discovery mechanism is included that provides partial, but sufficient topology information (potential parents) to avoid network partitions. Furthermore, Sleeping Beauty employs a novel, yet simple clock-offset estimation technique that maintains highly-accurate time synchronization over long radio-off periods (i.e., less than 500 us deviation even after 45, min of sleeping). This minimizes time wasted in resynchronizing the network in between data collection rounds. Through experiments on two different testbeds, we verified that Sleeping Beauty decreases the duty cycle up to a factor of 3 compared to state-of-the-art techniques, while achieving similar delivery ratios.

In this paper we study a general energy minimization problem for mixed-criticality systems on multi-cores, considering different system operation modes, and static & dynamic energy consumption. While making global scheduling decisions, trade-offs in energy consumption between different modes and also between static and dynamic energy consumption are required. Thus, such a problem is challenging. To this end, we first develop an optimal solution analytically for unicore and a corresponding low-complexity heuristic. Leveraging this, we further propose energy-aware mapping techniques and explore energy savings for multi-cores. To the best of our knowledge, we are the first to investigate mixed-criticality energy minimization in such a general setting. The effectiveness of our approaches in energy reduction is demonstrated through both extensive simulations and a realistic industrial application.

The increase in the deployment of smart meters has enabled collection of fine-grained energy consumption data at consumer premises. Analysis of this real-time energy consumption data bestows new opportunities for better demand-response (DR) programs. This paper offers a new perspective to study energy demand and helps in designing novel mechanisms for decentralized demand-side management. Specifically, a new concept of finding the demand states using energy consumption of consumers over time and feasible transitions therein is introduced. It is shown that the orchestration of temporal transitions between the demand states can meet broad range of smart grid objectives. An online demand regulation model is developed that captures the temporal dynamics of energy demand to identify target consumers for different DR programs. This methodology is empirically evaluated and validated using data from more than 4000 households, which were part of a real-world smart grid project. This paper is the first one to comprehensively analyze the temporal dynamics of demands.

Communication at mmWave frequencies has been the focus in the recent years. In this paper, we discuss standardization efforts in 60 GHz short range communication and the progress therein. We compare the available standards in terms of network architecture, medium access control mechanisms, physical layer techniques and several other features. Comparative analysis indicates that IEEE 802.11ad is likely to lead the short-range indoor communication at 60 GHz. We bring to the fore resolved and unresolved issues pertaining to robust WLAN connectivity at 60 GHz. Further, we discuss the role of mmWave bands in 5G communication scenarios and highlight the further efforts required in terms of research and standardization.

The 60GHz frequency band promises very high data rates - in the order of Gb/s - due to the availability of high bandwidth. However, high free-space path loss makes it necessary to employ beamforming capable directional antennas. When beamforming is used, the links are sensitive to misalignment in antenna directionality because of movement of devices. To identify and circumvent the misalignments, we propose to use the motion sensors (i.e., accelerometer and gyroscope) which are already present in most of the modern mobile devices. By finding the extent of misaligned beams, corrective actions are carried out to reconfigure the antennas. Motion sensors on mobile devices provide means to estimate the extent of misalignments. We collected real data from motion sensors and steer the beams appropriately. The results from our study show that the sensors are capable of detecting the cause of errors as translational or rotational movements. Furthermore it is also shown that the sensor data can be used to predict the next location of the user. This can be used to reconfigure the directional antenna to switch the antenna beam directions and hence avoid frequent link disruptions. This decreases the number of beam searches thus lowering the MAC overhead.

Pyroelectric Infra-Red (PIR) sensors are used in many applications including security. PIRs detect the presence of humans and animals from the radiation of their body heat. This could be used to trigger events, e.g., opening doors, recording video, etc. PIRs are used widely because of their low power consumption.

In this paper, we emphasize on indoor networks in 5G era. We explore the possible technologies and architectural solutions for 5G indoor communications. Owing to the fact that requirements for indoor and outdoor communications will be quite different in the next generation networks, we try to define an architectural framework for 5G indoor communications. Our proposed architecture focuses on three aspects of indoor network architecture, namely; air interface, backhaul connectivity and indoor signal distribution.

Ambient energy harvesting for Wireless Sensor Networks (WSNs) is being pitched as a promising solution for long-lasting deployments in various WSN applications. However, the sensor nodes most often do not have enough energy to handle application, network and house-keeping tasks because amount of energy harvested highly varies spatially and temporally. Moreover the ambient source cannot be assumed to be continuously available. When harvested energy is in excess, it is desirable that the nodes take up higher loads. The nodes should switch to highly energy efficient schemes when the energy is not sufficient. Hence harvesting-aware scheduling of tasks is required. The two most important challenges for harvesting-aware scheduling are (a) to determine the amount of energy to be expended in a time slot, and (b) to utilize this energy for execution of tasks maximally. To increase energy utilization for task execution, we decompose application level tasks into subtasks, some of which can be executed concurrently. In this article, we propose a dynamic optimization model, based on Markov Decision Process (MDP) that takes into account priorities and deadlines of the tasks, and stored and harvested energy to derive an optimal scheduling policy. Since the complexity of the MDP is intractable in realtime, we propose a greedy scheduling policy. We compare its performance with the optimal policy.

The advent of Internet of Things (IoT) has boosted the growth in number of devices around us and kindled the possibility of umpteen number of applications. One of the major challenges in the realization of IoT applications is interoperability among various IoT devices and deployments. Thus, the need for a new architecture-comprising smart control and actuation-has been identified by many researchers. In this paper, we propose a Distributed Internet-like Architecture for Things (DIAT), which will overcome most of the obstacles in the process of large-scale expansion of IoT. It specifically addresses heterogeneity of IoT devices, and enables seamless addition of new devices across applications. In addition, we propose an usage control policy model to support security and privacy in a distributed environment. We propose a layered architecture that provides various levels of abstraction to tackle the issues such as scalability, heterogeneity, security, and interoperability. The proposed architecture is coupled with cognitive capabilities that helps in intelligent decision-making and enables automated service creation. Using a comprehensive use-case, comprising elements from multiple-application domains, we illustrate the usability of the proposed architecture.

60 GHz wireless local area networks (WLANs) standards (e.g., IEEE 802.1 lad and IEEE 802.15.3c) employ hybrid MAC protocols consisting of contention based access using CSMA/CA as well as dedicated service periods using time division multiple access (TDMA). To provide the channel access in the contention part of the protocol, quasi omni (QO) antenna patterns are defined which span over the particular spatial directions and cover a limited area around access points. In this paper, we propose an algorithm to determine the beamwidth of each QO level. The proposed algorithm takes into account the spatial distribution of nodes to allocate the beamwidth of each QO level in an adaptive fashion in order to maximizes the channel utilization and satisfy the required link budget criterion. Since the proposed algorithm minimizes the collisions, it also minimizes the average time required to transmit total packets in a QO level. Proposed algorithm improves the average channel utilization up to 20–30% and reduces the time required to transmit total packets up to 40–50% for the given network parameters.

IEEE 802.22 standard is the first worldwide standard using cognitive radio (CR) technology for wireless regional area networks (WRANs). Orthogonal frequency-division multiple access (OFDMA) is employed for point-to-multi-point (P2M) communication between base stations (BS) and customer premises equipments (CPEs) in WRANs. The large coverage of a single cell (up to 100 km of radius) is the unique feature of WRANs. However, channel reuse is not considered, i.e., in one slot the channel can be allocated to only one CPE, and every intra-cell packet needs to be routed through the BS. Therefore, device-to-device WRAN (D2DWRAN) has been proposed to support direct intra-cell CPE-CPE communication. Power control strategies are used to achieve higher reuse of OFDMA slots. In this paper, we take one step further to enhance the network capacity by adopting multichannel management (MCM) with infrastructure support. This enables multiple operating channels to be used simultaneously in D2DWRANs. New channel switching strategies are also studied. We implement a MCM scheme in an OFDMA setting for D2DWRANs, and examine it in the upstream (US). For downstream (DS), there are already many proposals in the literature, which can be adopted directly by D2DWRAN. The simulation results show that MCM scheme significantly improves the network performance compared to single-channel D2DWRANs and WRANs.

IEEE 802.22 wireless regional area network (WRAN) standard employing cognitive radios is gaining much attention recently. The WRAN standard targets reuse of unused TV channels. We propose a device-to-device wireless regional area network (D2DWRAN) to extend the capacity of IEEE 802.22. Network capacity can be increased by supporting direct intra-cell device to device (D2D) communication through channel reuse and also with aggregation of nonadjacent multiple operating channels. Self-coexistence of neighboring IEEE 802.22 cells is a major challenge in WRANs, since the availability of channels varies frequently and channels are reused by every cell as much as possible. Currently, IEEE 802.22 does not consider D2D communication. We propose a two-tier spectrum sharing mechanism for channel allocation in D2DWRANs - both at intra-cell and inter-cell levels. Algorithms and a thorough analysis are presented. We examine our proposal through simulations. Results show significant performance improvement compared to IEEE 802.22. However, there are many hurdles to cross, such as, coexistence with other cognitive radio networks (CRNs), the intra-cell routing, allocation of other network resources, etc. We believe that further work in this domain can lead to increase in capacity not only in WRANs but also in other cellular networks.

In data gathering wireless sensor network applications, data correlation among the sensor nodes have been utilized to extend network lifetimes. It has been shown that the data correlation also exists between nodes that are far away, contrary to the assumption that correlation decreases as a function of distance. Therefore, it is possible to group the nodes based on the correlation among their data regardless of their location. Given that data from one active node per group is sufficient to reconstruct the sensed data for the remaining sensor nodes, most of the nodes can be kept in low-power sleep mode. However, only few active nodes will usually create a disconnected network, and hence failing the purpose of the deployment. In this paper we formalize this problem, referred to as Sleep-route, of selecting the minimum number of connected active nodes that are sufficient to predict the sensed data for remaining sleeping nodes with high accuracy. We prove that the problem is NP-hard. Thus, we develop a greedy algorithm, Sleep-route heuristic that provides near-optimal solutions. Using Contiki-based simulations, we show that our scheme can extend network lifetime up to 42% as compared to the state-of-the-art solutions.

We propose two novel energy-aware routing algorithms for wireless ad hoc networks, called reliable minimum energy cost routing (RMECR) and reliable minimum energy routing (RMER). RMECR addresses three important requirements of ad hoc networks: energy-efficiency, reliability, and prolonging network lifetime. It considers the energy consumption and the remaining battery energy of nodes as well as quality of links to find energy-efficient and reliable routes that increase the operational lifetime of the network. RMER, on the other hand, is an energy-efficient routing algorithm which finds routes minimizing the total energy required for end-to-end packet traversal. RMER and RMECR are proposed for networks in which either hop-by-hop or end-to-end retransmissions ensure reliability. Simulation studies show that RMECR is able to find energy-efficient and reliable routes similar to RMER, while also extending the operational lifetime of the network. This makes RMECR an elegant solution to increase energy-efficiency, reliability, and lifetime of wireless ad hoc networks. In the design of RMECR, we consider minute details such as energy consumed by processing elements of transceivers, limited number of retransmissions allowed per packet, packet sizes, and the impact of acknowledgment packets. This adds to the novelty of this work compared to the existing studies.

Homes, offices and vehicles are getting networked. This will enable context aware, autonomous operation of many support systems that could be controlled remotely. To achieve this there would be a large number of tiny devices - sensors and actuators - which are networked and they are termed generally as Internet of Things (IoT) devices. In future, they will be powered through harvested energy from the ambience to enable perennial lifetime and minimal manual maintenance. Some examples of energy sources are photovoltaic panels and piezoelectric crystals. Several challenges arise due to the nature of sources of energy. One of these challenges is that the devices (nodes) leave and re-enter networks due to fluctuating availability of harvested energy. As a result, the neighbor table maintained at each node changes quite often leading to complications in forming and maintaining routes. In fact initial neighbor discovery (ND) itself is a difficult task. Further, usage of directional antennas would increase the time taken to complete ND. We study such a network through exhaustive simulation study considering various parameters. We demonstrate the benefits and challenges of using directional antennas for ND. We present a scheme that nodes could use to discover their neighbor during initial deployment and another scheme that could be used for subsequent discovery on re-entry into the network. We show that a dedicated ND protocol is necessary for energy harvesting networks and that directional ND is beneficial in these networks under some circumstances.

We propose energy harvesting technologies and cooperative relaying techniques to power the devices and improve reliability. We propose schemes to (a) maximize the packet reception ratio (PRR) by cooperation and (b) minimize the average packet delay (APD) by cooperation amongst nodes. Our key result and insight from the testbed implementation is about total data transmitted by each relay. A greedy policy that relays more data under a good harvesting condition turns out to be a sub optimal policy. This is because, energy replenishment is a slow process. The optimal scheme offers a low APD and also improves PRR.

Even though 60 GHz frequency band has limited coverage, due to the availability of higher bandwidth worldwide (unlicensed, approximately 5 GHz between 57–62 GHz), it is one of the promising candidates for future broadband employing both Fiber and Wireless (Fi-Wi) technology. In this article we briefly explain an in-home radio over fiber (RoF) architecture employing 60 GHz spectrum band. The main challenge for designers of such networks is the additional delay introduced by the optical distribution network. Later in the light of this increased delay, we provide an investigation on the feasibility of applying the IEEE 802.15.3c MAC protocol in such a RoF system at 60 GHz. Mainly, we analyse various ACK mechanisms in the IEEE 802.15.3c when it is used in conjunction with RoF network. The analytical results show that the fiber optic network causes a slight drop in the throughput but it could still be easily used in home networks. This gives us an opportunity to sustain high bandwidth at low cost for home networking, also supporting mobility. Modelling of a RoF system with IEEE 802.15.3c and numerical results thereof are the contributions of this article.

IEEE 802.22 is the first standard to uitilize cognitive radio technology for wireless regional area networks (WRANs). It has adopted a cellular topology containing one base station (BS) and multiple customer premises equipments (CPEs) in a cell. It covers a very large area with radius ranging up to 100 km. Orthogonal frequency-division multiple access (OFDMA) system is employed and it is further slotted in time-domain. In one slot only one CPE can be allocated, and every intra-cell packet needs to be routed through the BS. This point-to-multi-point structure significantly limits the network capacity in the case of CPE-CPE communication, which is predicted to happen, and even the use of OFDMA does not help much. Therefore, peer-to-peer WRAN (P2PWRAN) has been proposed to support direct intra-cell communication in order to extend the network capacity. In this paper, the IEEE 802.22 OFDMA system is adapted to P2PWRAN to support direct CPE-CPE communications. The downstream burst allocation in P2PWRAN is similar to the OFDMA system of IEEE 802.16, which has been widely studied in the literature. Therefore, in this paper we look at the unexplored upstream burst allocation where slots can be reused among CPEs and the BS with power control mechanisms. We propose a burst allocation algorithm that maximizes the network capacity greedily. The algorithm is examined under various conditions such as, number of CPEs, size of requests, length of US subframes and type of requests (CPE-BS or CPE-BS). We also show that our algorithm performs better than the existing solutions.

IEEE 802.22 is proposed for reusing the TV channels with cognitive radio technology to build Wireless Regional Area Networks (WRANs). The cellular topology of a WRAN makes the spectrum management easy. However, this topology reduces the network capacity significantly, because in one slot only one user can be allocated with a channel. Therefore, Peer-toPeer WRAN (P2PWRAN) is proposed, in which direct CPE(Consumer Premises Equipment)-to-CPE intra-cell communication is supported. While increasing the network capacity significantly, reducing the total energy consumption is a challenge.The energy efficiency of nodes in the standard for WRAN is difficult to improve, since the transmission distance of nodes to the BS is relatively static. P2PWRAN enables to achieve higher energy efficiency through better possibilities of channel allocation. Channel allocation influences energy consumption and network capacity significantly. Channel allocation also affects the upper layers, for example the routing protocol. Therefore, guaranteeing the network performance and fairness amongst CPEs, while achieving green networks, needs cross-layer design. In this paper, we define the channel allocation problem in P2PWRAN and we recognize it as a Multi-objective Quadratic Programming (MOQP) problem. An Energy-aware Channel Allocation (ECA) algorithm is proposed for exploiting the P2PWRAN paradigm to achieve the lower communication energy consumption and higher network energy efficiency and throughput than simple greedy policies. ECA jointly considers the network layer and Media Access Control (MAC) layer. By controlling the parameters in ECA, the fairness of channel allocation can also be guaranteed.

We implement two energy models that accurately and comprehensively estimates the system energy cost and communication energy cost for using Bluetooth and Wi-Fi interfaces. The energy models running on a system is used to smartly pick the most energy optimal network interface so that data transfer between two end points is maximized.

Peer to peer wireless regional area network (P2PWRAN) is proposed as an extension to support peer to peer communication based on IEEE 802.22 [1]. Multiple channel allocation and reuse of channels in the same time slot in P2PWRAN significantly increase the network capacity compared to standard IEEE 802.22 networks. One of the key issues in bringing P2PWRAN into reality is building the interference map. The concept of interference map/mapping has been mentioned in literature however, how to build an interference map is still an open issue. Therefore, we propose a simple and self-adapting interference map building protocol (SIMBP) for P2PWRAN, which can also be used in other multi-channel wireless networks with minor modifications. The simulation results show that SIMBP converges under the P2PWRAN setting, and with number of available channels and growing number of nodes, the capacity of the network is reached eventually.

Wireless Regional Area Networks(WRAN), IEEE 802.22 standard, adopts a centralized cellular topology including base station (BS) and customer-premise equipments (CPEs). However this may lead to limited network capacity, since every CPE needs to communicate to the BS and only one channel can be used to communicate in the whole cell per time slot with one BS antenna. Peer to Peer WRAN (P2PWRAN) was proposed [3] to circumvent this, where CPEs can communicate with each other directly. P2PWRAN increases network capacity compared to the standard WRAN because of spatial reuse. Multiple communication channels can be simultaneously allocated and reused in one time slot by the BS and multiple CPEs. In IEEE 802.22b [4], a standard in progress, peer to peer communication plays an important role for enabling smart grids. Channel allocation influences the network performance significantly, therefore, we formulate the spectrum allocation problem in P2PWRAN as a quadratically constrained programming (QCP) problem. It is also proved as a computationally hard problem. The Greedy Coloring Algorithm (GCA) is examined in P2PWRAN channel allocation, and the results show that it may cause severe unfairness in allocation of channels. Thus a Fair Greedy Coloring Algorithm (FGCA) is proposed to guarantee a fair allocation by queuing flows considering previous allocations. However, FGCA guarantees fairness but leads to decrease in performance of P2PWRAN. Therefore, a Trade-off FGCA (TFGCA) is proposed considering fairness and network performance at the same time during allocation. Simulation results show that with the adjustment of two factors in TFGCA, network performance and fairness can be balanced.

The rapid developments in Internet-of-Things (IoT) and Machine-to-Machine (M2M) communication make it necessary to design communication systems operating in different wireless spectrum as an alternative to highly congested wireless access systems. In addition, the deployment of wireless smart meter devices is ramping up and it is expected that such devices will flood the market in the near future competing for the same wireless spectrum. The IEEE 802.11ah standardization task group is developing a global Wireless LAN (WLAN) standard that will allow wireless access using carrier frequencies below 1 GHz in the ISM (Industrial, Scientific, and Medical) band and will help Wi-Fi-enabled devices to get guaranteed access for short-burst data transmissions, such as meter data. In addition to exploiting the underutilized sub 1 GHz spectrum the improved coverage range allows new applications to emerge such as wide area based sensor networks, sensor backhaul systems and potential Wi-Fi off-loading functions. This paper summarizes the IEEE 802.11ah standardization activities in progress and discusses advantages and challenges in the design of physical layer (PHY) and media access control (MAC) schemes in the sub 1 GHz band.

Wireless communication in the sub 1GHz band is going to be one of the key enabler for the wireless smart grid. As part of the smart grid, home area networks (HAN) have to be reliable, robust, and secure. Frequency bands at 2.4 GHz are widely used, e.g., by Wi-Fi and ZigBee systems, but are highly congested. Frequency bands below 1 GHz, such as the ISM (industry, scientific and medical) 920/950MHz bands, offer less interfered wireless channels. There are few wireless transceivers available in the consumer market, which operate in the 950MHz ISM band. However, a thorough performance evaluation of the transmission characteristics is missing. We conducted a performance analysis of 950MHz transceivers which we deployed in a multi-node network setup to emulate a wireless HAN system. We contribute on details regarding the data rate of sub 1 GHz wireless sensors and the related error statistics. We discuss the transmission boundaries and investigate the optimal modulation schemes of sub 1GHz wireless sensors.

Abstract The newer context‐aware applications require many inputs and amongst them the location information is one of the most important. In the near future, we see the potential in using wireless sensors deployed inside buildings to support in generating the location information besides their other routine tasks. This paper records the efforts involved in designing and prototyping a centralized indoor positioning system for tracking a target's position. We present an in‐depth discussion on the RSSI based positioning algorithms both, range‐based and range‐free. Considering that the signal strength can be distorted heavily due to multi‐path and shadowing, we have proposed the weighing schemes to leverage the credibility of the measured RSSI values. For online tracking, we have also proposed boundary selection and local grid scanning to lower the searching time, and the RSSI data dissemination and collection schemes to reduce traffic overhead. Evaluations have been done in both the field measurements and with an RSSI generator. The generator has been developed to simulate and replace the real measurements for the ease of algorithm design and testing, thus avoiding repetitive field measurements. The results show that positioning systems with adequate accuracy can be built with our proposed schemes. We expect that these proposed schemes to be integrated in multitude of systems with context‐aware applications, which use location information. Copyright © 2010 John Wiley & Sons, Ltd.

In this work, we study the adaptability of well known cryptography algorithms to energy harvesting wireless sensor networks. We are particularly interested in algorithms that have the ability to adapt to varying power in such networks. Our investigations and implementation on hardware platforms indicate that it is optimal to precompute a few key stream bytes, store in memory and later used during the time when the system is low on harvested power level. Our demonstrable setup shows using a precomputed key stream can decrease the energy consumption by 14%. We have implemented the Trivium stream cipher for two different microcontrollers, the MSP430 and the AVR ATmega1281 and show the performance results for these implementations. We have implemented an algorithm based on universal hash functions to provide message authentication with the assistance of stream ciphers. We show that this authentication algorithm has exciting properties for energy harvesting system and more generally for resource constrained devices.

Energy-aware routing is an effective way to extend the operational lifetime of wireless ad hoc networks. In this paper, we propose a new energy-aware routing scheme called Reliable Minimum Energy Cost Routing (RMECR). RMECR finds routes which require least amount of energy for reliable packet transfer in ad hoc networks. It defines the energy cost of packet forwarding by a node as the fraction of remaining battery energy which is consumed by a node to forward a packet. This includes the energy consumed for retransmission of the packet as well, when the packet or its acknowledgment is lost. We show that RMECR can effectively reduce the energy consumption of nodes and balance the traffic load among them. Furthermore, RMECR is able to find reliable routes, in which constituent links require less number of packet retransmissions due to packet loss. This in turn decreases the latency of packet delivery and saves energy as well.

Cognitive Radios (CRs) address spectrum scarcity and under-utilization of the spectrum. Allocating spectrum to CR nodes in a network is neither easy nor straight-forward since spectrum availability is space and time-varying. The Link layer always sets up communication between nodes in a CR ad hoc network by allocating the available spectrum suitably. In a time-slotted model, allocation of channels amongst CR nodes is a NP complete problem. Moreover, since the availability of spectrum is time varying, all the nodes in the network may not be allocated sufficient resources. Thus there is always a possibility that a node may starve compared to its counterparts. In this article, we address the issue of fairness in a CR ad hoc network and propose novel distributed heuristics to allocate spectrum fairly. In literature, max-min, max-sum and max-proportional fairness based schemes are proposed to provide fairness in these CR ad hoc networks. We compare our heuristic results with these schemes, and discuss why our method is significantly better.

60 GHz radio is a very attractive technology for short-range wireless communication due to its capability to provide Gbps data rate. To address the neighbor discovery (ND) problem in 60 GHz networks, we propose a novel analytical framework to investigate the ND performance. The main difficulty in modeling the ND process in a 60 GHz network is the involvement of the directional antennas with gain differences between the antenna's main lobe and side lobes. Different antenna modes - directional or omni-directional - coupled with different ND mechanisms make the analytical study demanding. In this article, we propose a comprehensive theoretical model to demonstrate the performance of ND processes using one-way ND and handshake-based ND mechanisms. Moreover, we combine them with different antenna modes, i.e., directional transmitting with omni-directional listening (DO) and directional transmitting with directional listening (DD) modes. The impact of antenna modes on the ND process is analyzed. Since 60 GHz radio is prone to co-channel interference, we examine a realistic interference-aware link model and antenna pattern via simulation studies. Our work is specifically beneficial to provide guidelines for applying directional neighbor discovery process within 60 GHz wireless networks.

Wireless personal area networks (WPANs) usually have very limited coverage. In this paper, we focus on methods to extend this range through multi-hop communication crossing several inter-connected WPANs called piconets. Multi-hop communication between piconets is possible through the bridge devices. While extending the range of WPANs, bridge devices become the capacity bottlenecks for these connected piconets. Therefore, an unoptimized resource allocation mechanism involving the bridges could degrade system performance severely. In this work we propose a novel resource allocation mechanism that outperforms other schemes proposed so far. Moreover, our scheme is QoS aware due to its cross-layer design. In our proposed scheme, resource allocation cooperates with the route discovery process to mitigate collisions due to inter-piconet resource reservations. Simulation results show that our approach significantly increases network capacity. This work is expected to pave the way for QoS support in WPANs.

Body area sensor networks (BASNs) are powered by batteries and hence energy is a scarce resource. Thus communication protocols in BASN need to be energy efficient. This paper addresses this problem by proposing a Minimum Energy Packet Forwarding Protocol (MEPF). First, we analyze the tradeoff of energy consumption between lower transmission power and packet retransmission. Then we propose the transmission power control part of MEPF. It transmits a packet using the minimum transmission power that can guarantee a high packet reception rate. Unlike the previous work, we not only need to adjust the power of the forward transmission but also the ACKs, which further reduces the consumption. MEPF outstands from the earlier works by also retransmitting lost packets when a link is good enough. The quality of the link is dynamically judged by a machine learning algorithm which also considers the limited buffer space in a sensor node. By MEPF, the energy consumption to forward a packet is minimized without sacrificing the packet reception ratio. Experimental results show quantitatively that MEPF save significant amounts of energy.

Cognitive radios (CRs) address the problems of spectrum scarcity and under-utilization of the spectrum. However, realizing a CR network is neither easy nor straight-forward. The link layer in CR ad hoc networks is responsible for choosing suitable channels, out of the available channels, for setting up communication between nodes. However, the selection of channels amongst the CR nodes in the network is proven to be NP-complete. The need of maximizing the spectrum efficiency in a fair way can be modeled as a graph coloring problem. An edge coloring heuristic and a clique determination heuristic algorithms are proposed in this article. We compare the results of these heuristics with the algorithm proposed in. We also compare the spectrum utilization in all these algorithms.

Cognitive Radios (CRs) address spectrum scarcity and under-utilization of the spectrum. However, realizing a CR network is neither easy nor straight-forward. In particular the link layer should facilitate setting up of communication between nodes enabling sharing of channels, while maximizing the spectrum utilization. We address the problem of allocation of channels to CR nodes. For time-slotted model, allocating the channels among CR nodes is an NP complete problem. We propose novel distributed heuristics, one based on clique sets called Clique Based Heuristic and the second one is a low complexity, locally adapting Localized Heuristic; both use local control channel. We compare the results of these heuristics with the optimal and a distributed graph coloring algorithm proposed in the literature. We also compare the spectrum utilization and overheads for all these algorithms.

Realtime voice communication over the Internet has rapidly gained popularity. It is indeed essential to reduce the total bandwidth consumption to efficiently use the available bandwidth for the subscribers having low speed connectivity and even otherwise. In this paper we introduce a novel technique to identify the voice and silent regions of a speech stream that is very much suitable for VoIP calls. We use an entropy measure, which is based on the spacings of order statistics of speech frames to differentiate the silence zones from the speech zones. We developed an algorithm that uses an adaptive thresholding to minimize the misdetection. The performance of our approach is compared with the built-in VAD of AMR codec. Our approach yields comparatively better saving in bandwidth yet maintaining a good quality of the speech streams. Further, the proposed approach has improved voice detection compared to the AMR schemes under noisy conditions. The ideas presented in this paper has been identified novel during the WIPO international patent search.

We study the application of multipath routing to enhance reliability of high quality video in 60 GHz radio indoor networks. We propose a cross-layer approach taking into account the possibility of layered-coding for video stream transportation and the characteristics of 60 GHz radio links based on our earlier studies on the stability of 60 GHz radio links. Further three traffic allocation schemes have been investigated and compared for the best use of multipath capacity.

Wireless indoor networks employing Radio over Fiber technique combining 60 GHz wireless networks with wired networks is a newer solution to provide high definition in-home multimedia applications. About 5 Gbps is available in this band. In 60 GHz band, each antenna can cover a small space such as a single room in a building. Thus more antennas are needed to cover the whole building. This leads to frequent handoffs. In addition, the overlapping between two adjacent cells is normally small; therefore a handoff should be completely done in a relatively shorter time compared to cellular networks. Since there are many research results and commercial systems of wireless indoor positioning, predicting the direction of the movement of a user is very easy. This paper proposes the direction assisted handoff algorithm. In this paper we consider such a scenario and we study direction assisted handoff. The simulation results show that the proposed handoff algorithm can reduce the total number of handoffs while increasing the percentage of successful handoffs.

The emerging high definition in-home multimedia applications require higher capacity from future wireless networks in the indoor environments. In this paper, we consider a novel mesh-like architecture for indoor wireless networks using 60 GHz radio for supporting multimedia applications with sufficient data rate and stringent quality-of-service (QoS) guarantees. We concentrate on studying the application of multipath routing to enhance reliability of transporting high quality video since 60 GHz transmission is highly dependent on line-of-sight (LOS). Based on our previous study on the stability of 60 GHz radios, we propose a cross-layer approach taking into account layered coding technique for video stream transportation. We present our model along with an analysis to assess multipath routing in this scenario. Further, we give an in-depth discussion on two traffic allocation schemes to harness multipath capability.

60 GHz radio is a promising technology which can handle the data rate of the order of gigabits-per-second. Hence, it is an attractive candidate for wireless personal area networks (WPANs). Due to the high path loss, directional antennas are recommended for use in 60 GHz systems. To set-up directional communications, it is essential to obtain the knowledge of direction of the surrounding devices. However, a neighbor discovery process using directional antennas is nontrivial. In this paper, we provide a comprehensive investigation of using directional antenna for neighbor discovery process. We demonstrate the performance of neighbor discovery using different antenna modes combining different neighbor discovery mechanisms. Our results may aid in designing directional antennas for 60 GHz WPANs.

User centered personal network (PN) interconnects various devices belonging to a user in different geographic locations, such as home, office, car, etc., to form one network to improve the quality of life of the user. In PNs, the user's devices move in different groups, where these groups merge and split. A PN mobility model (PNMM) has been proposed to model the devices' movements in PNs. In this paper, PNMM has been evaluated: stability evaluation proves that PNMM maintains a long-term steady state. The impact of PNMM on the performance of protocols designed for PNs has been discussed in this paper. Simulation results give insights of the performance of the PN clustering protocol, and provide guidelines for future design of clustering in PNs.

With the evolution of wireless sensor network, body area sensor networks are expected to realize many realtime monitoring applications. Localization is one of the most important amongst all contexts. In indoor environments signal strength-based localization algorithms usually fail to achieve good accuracy due to deficient antenna coverage and multi- path interference. We propose spatial diversification method, which solves this problem by combining multiple receivers in a body area sensor network to estimate the location with a higher accuracy. This method mitigates the errors caused by antenna orientations and beam forming properties. With a range free localization algorithm that we developed, we show with experimental results that with spatial diversity the localization accuracy is improved compared to using single receiver alone.

Clustering has been used as a fundamental tool to improve scalability in large wireless ad hoc networks. Techniques based on clustering, such as routing, self-organization, context management, service discovery, etc., ensure QoS in wireless ad hoc networks when the number of devices increases. The crucial issue of clustering is the overhead generated during the cluster formation and maintenance phases. An in-depth study of the influence of node mobility on cluster formation and maintenance overhead is still missing. There is little insight into the performance of clustering in the presence of different node mobility patterns. This paper presents a detailed analytical study of the clustering overhead considering node mobility in wireless ad hoc networks, and offers a way to understand how different the node mobility parameters influence the clustering overhead. The analytical results are compared with the simulation results with sufficient accuracy. In summary, this paper provides a fundamental understanding of mobility behaviors of devices and the clustering overhead.

Group mobility has been modeled for simulators to evaluate protocols in wireless ad hoc networks. These mobility models have to be verified in terms of their instant and long-run behaviors. This paper discusses importance and challenges of the stability evaluation for ad hoc group mobility models; proposes analytical technique for the stability of group mobility model and evaluation of the same via simulation. As a case study, we evaluate the personal network group mobility model (PNMM). The evaluation method presented in this paper can be applied to all generic ad hoc group mobility models in terms of stability analysis and simulations.

Personal networks (PN) is a concept in the area of modern communication networks in which devices belonging to a person cooperate to offer services to the person regardless of their geographical locations. A PN may consist of several ad-hoc sub-networks (or clusters) which are interconnected through infrastructure networks such as the Internet. Failure of personal devices due to battery shortage is one of the main threads for service availability in PNs. Power-aware routing (PAR) has been proposed as an effective scheme to prolong the lifetime of battery-operated nodes in wireless ad-hoc networks. This scheme, however, could be utilized in PNs as well, since a PN cluster can be modeled as a wireless ad-hoc network. Aiming at prolonging the lifetime of nodes in PNs, in this paper we propose a PAR algorithm for PNs and we evaluate and compare its performance with the performance of other PAR algorithms proposed so-far for wireless ad-hoc networks. We analyze the effect of node density, routing overhead, heterogeneity of PN nodes in terms of their power supplies, and communication pattern within PN clusters on the performance of the PAR algorithms. Taking into account various parameters, we show that in a PN environment, our proposed scheme can profoundly extend the lifetime of PN nodes compared to the other algorithms. We also show that there can be an optimal value for route refresh interval, as a parameter which affects the generated routing overhead, and the node density of PN clusters, in such a way that the network lifetime is maximized.

To achieve ultra high data rate communications in 60 GHz Wireless Personal Area Networks (WPANs), frame aggregation is devised in IEEE 802.15.3c to increase the MAC transmission efficiency. In this work, we propose a novel statistical model to analyze the behavior of two types of frame aggregation mechanisms: standard frame aggregation and low latency frame aggregation. Through our model, we could fully understand the performance of the frame aggregation mechanisms in terms of transmission delay and system capacity. The accuracy of our model is validated by extensive simulations. Our work provides valuable insights for ultra high performance transmission using 60 GHz radio.

In this paper, we propose a solution to resolve link blockage problem in 60 GHz WPANs. Line-of-Sight (LOS) link is easily blocked by a moving person, which is concerned as one of the severe problems in 60 GHz systems. Beamforming is a feasible technique to resolve link blockage by switching the beam path from LOS link to a Non-LOS (NLOS) link. We propose and evaluate two kinds of Beam Switching (BS) mechanisms: instant decision based BS and environment learning based BS. We examine these mechanisms in a typical indoor WPAN scenario. Extensive simulations have been carried out, and our results reveal that combining angle-of-arrival with the received signal to noise ratio could make better decision for beam switching. Our work provides valuable observations for beam switching during point-to-point communication using 60 GHz radio.

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.

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.

In this paper we introduce and examine via simulations, a Markov decision process (MDP) based approach for vertical handover decision-making. We consider here the case of a heterogeneous network environment where both the legacy WLAN and the novel line-of-sight (LOS)- dependent 60 GHz radio systems are deployed to support indoor high quality multi-media applications. The MDP decision approach takes into account multiple factors such as user preference, network situation, device capability and effects of environment. We show its ability to effectively make handover decision in uncertain situations. The method and results herein are applicable, in general, to any other situation where such a decision is to be made.

With the advent of opening up 60 GHz Radio with 5 GHz of available spectrum, many bandwidth-hungry applications can be supported. The immediate concern, however, is the constraint on the line-of-sight transmission as well as the short transmission range of signals. As a result, in an indoor network, a mobile user might experience frequent breaks or losses of connection when one moves from one cell to another. To mitigate this problem, the Extended Cell (EC) architecture is proposed. In this paper, a dynamic Extended Cell formation algorithm is proposed based on the actual floor plan and the traffic situation under the network. Moreover, by applying this dynamic EC formation algorithm, we show that the call blocking probability is reduced. The dynamic EC formation also eases the deployment and maintenance cost due to its adaptability to the changing environment.

With the advent of miniaturization and higher computing capabilities of wireless devices, there is an exponential rise in the number of devices such as personal digital assistants, etc. Application development for these devices is the main focus for many device manufacturers and software developers. The applications developed need to be tested and tried before releasing it to the market. There are cross-compilers to produce the binary files of the software applications that can run on these devices. However, it is rather difficult for developers to test it on the actual devices every time a change is made in the program. Further, a wireless network infrastructure should also be in place for testing. To expedite application development FEW-PNets - a Framework for Emulation of Wireless Personal Networks is proposed. It is a networked application to emulate a Personal Network (PN) owned by a person. It supports emulation of the most functions of PNs. We explain our model by emulating simple PN devices. In this article we elucidate the concepts, advantages, application domains and limitations of FEW-PNets.

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.

Personal network (PN) as a user centric enabler for future 4G wireless communications starts from the user and extends the user's personal area network (PAN) to a global coverage of his personal devices and services in his home, car and office etc. as well as other foreign networks and services regardless of their geographical locations. Personal networks are dynamic in nature and consist of heterogeneous mobile devices and technologies. As a result, one of the main challenges in the development of personal networks is self-organization in order to be adaptive to the dynamics and heterogeneity of personal networks. A framework that consists of different self-organizing functions for personal networks is proposed in this paper. A cross layer function, context awareness, is discussed in details on how personal networks could make use of context information at various layers to facilitate self-organization.

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.

This paper addresses the issues of designing an infrastructure to support seamless in-building communication at the 60 GHz band. Recently, the 60 GHz band has received much attention due to its 5 GHz of available spectrum. However, the propagation of signals in this band is strongly hindered by attenuation and line-of-sight requirements. The situation gets worse in the in-building environment where signal propagation is obstructed by physical objects such as walls, furniture etc. In this paper, we present a novel radio over fiber (RoF) architecture that is cost-effective and is able to deliver high data-rate of the order of gigabits. To ensure a seamless communication environment at the 60 GHz band, we propose the concept of extended cells (EC) in order to create sufficient overlap areas. Finally, we illustrate the effectiveness of the proposed architecture by simulating an in-building network at the 60 GHz band employing the RoF and EC concepts

A personal network is an overlay network which builds on different wireless networking technologies. This overlay network is responsible of glueing the different wireless networking technologies. In this paper, we consider the cooperation of two key technologies to realize a personal network, namely, Wireless Personal Area Network and Universal Mobile Telecommunications System (UMTS). The co-operation poses a new set of problems as these technologies were not designed to intework with each other. In this paper, we describe each of these problems and their solutions.

Personal networks (PN) is a new concept related to pervasive computing with a strong user-focus view. The key to a successful PN realization is a general network architecture that is capable of bridging different current and future technologies and offers a homogeneous and clear view to the end-user. In this paper, we focus on forming a PN by connecting remote personal devices using infrastructure-based IP networks, including 3G networks and WLAN hotspots. One way is to upgrade the current access networks with new functionality to support PNs. Since many devices in PNs are mobile and battery powered, this may help them to achieve a faster service and to save energy. However, to deploy such functionality is not easy and may hamper the adoption of PNs altogether. Therefore, in this paper we study three possible inter-cluster communication architectures that can use current IP networks. To discern the above proposal we also give a detailed picture of PN network architecture supported by infrastructure. We believe that this detailed discussion will help the success of PNs

To allocate audio conference servers (CS) [R. V. Prasad et al., June 2003] for virtual conferencing over IP, we leverage session initiation protocol (SIP) for signaling. We address here the problem of facilitating seamless conference amongst participants using CSs. This demands a proper allocation of CSs to clients to maximize the number of participants served and at a reduced cost. Seeking a more realistic approach, we avoid over-simplifying assumptions; hence the problem becomes relatively harder. We present three heuristic algorithms for these NP-hard problems and bring about the effectiveness of their performance.

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.

Session initiation protocol seems to be proffered standard for voice-over-IP. In this paper, we consider the limitations of existing SIP conferencing methods and propose a distributed architecture using controllers (SIP proxy servers) and conference servers which facilitate the control and media handling of a VoIP conference. The conference servers are designed on the basis of H.323's multipoint processors and handle the audio part of the conference. We provide conferencing messages on SIP that bring these controllers and conference servers together along with the clients for scalable conference on the Internet. The schematic of the proxy server and conference server is presented to aid in implementation. We also explain the mechanism we use to improve the quality of mixed output speech by selecting the streams at the clients.

Real-time services have been supported by and large on circuit-switched networks. Recent trends favour services ported on packet-switched networks. For audio conferencing, we need to consider many issues -- scalability, quality of the conference application, floor control and load on the clients/servers -- to name a few. In this paper, we describe an audio service framework designed to provide a Virtual Conferencing Environment (VCE). The system is designed to accommodate a large number of end users speaking at the same time and spread across the Internet. The framework is based on Conference Servers [14], which facilitate the audio handling, while we exploit the SIP capabilities for signaling purposes. Client selection is based on a recent quantifier called "Loudness Number" that helps mimic a physical face-to-face conference. We deal with deployment issues of the proposed solution both in terms of scalability and interactivity, while explaining the techniques we use to reduce the traffic. We have implemented a Conference Server (CS) application on a campus-wide network at our Institute.

We discuss techniques for voice activity detection (VAD) for voice over Internet Protocol (VoIP). VAD aids in saving the bandwidth requirement of a voice session, thereby increasing the bandwidth efficiently. We compare the quality of speech, level of compression and computational complexity for three time-domain and three frequency-domain VAD algorithms. Implementation of time-domain algorithms is computationally simple. However, better speech quality is obtained with the frequency-domain algorithms. A comparison of the merits and demerits along with the subjective quality of speech after removal of silence periods is presented for all the algorithms. A quantitative measurement of speech quality for different algorithms is also presented.

In a conference, considering the packets only from a set of selected clients can reduce the degradation of the quality of speech because mixing packets from all clients can lead to lack of clarity in the speech of any participants. The automatic selection should be smooth and should not cause frequent interruptions. A method of selecting the clients for mixing is suggested here based on a new quantifier of the voice activity called loudness number (LN). The dependence of the loudness number on the amplitude of the packet at present and the past activity is clearly brought out. The structure of the packet used has been explained. A method to avoid echo and enhance the quality of the conference is presented. The contributions of the paper are expected to aid in the implementation of H.323 recommendations for the multipoint processors (MP). A working prototype based on the proposed loudness number is already functional.