We consider the problem of utility-optimal link scheduling in a time-slotted multiple access point (AP) wireless network. In each slot, given the received signal strengths (RSS) from all potential transmitters to all receivers, and the link-wise scheduling weights, the objective is to select a subset of communication links such that the sum of the weighted long-term average link throughputs is maximized. Exhaustive search for the optimal configuration, in each slot, is computationally infeasible even with a few APs and stations, due to exponential complexity.We introduce a two-stage approach that combines graph-based approximation algorithms with deep learning models. In the data generation stage, in each slot, the scheduling problem is approximately reduced to finding a maximal independent set on an interference graph, which, in practice, yields an order of magnitude reduction in schedule search time. We parametrically unify three heuristics for generating the interference graph in each slot and obtain the best parameter set. The generated dataset is used to train a deep neural network (DNN) to predict link-sets when given as input the RSS values (we assume equal link weights in this paper) during the actual operation of the wireless network.The graph-based data generation method achieves over 98% of the optimal network utility with a per slot computation that is at least an order of magnitude smaller than optimal data generation but still requires milliseconds per slot, while the DNN trained on this data, in online usage, achieves over 94% of the optimal utility with about 100−200 µsec computation time per slot on commodity hardware. These results confirm that the hybrid approach offers a practical trade-off between computational efficiency and scheduling performance, making it well-suited for scaling to practical wireless network environments.

This paper addresses the problem of quickest change detection (QCD) at two spatially separated locations monitored by a single unmanned aerial vehicle (UAV) equipped with a sensor. At any location, the UAV observes i.i.d. data sequentially in discrete time instants. The distribution of the observation data changes at some unknown, arbitrary time and the UAV has to detect this change in the shortest possible time. Change can occur at most at one location over the entire infinite time horizon. The UAV switches between these two locations in order to quickly detect the change. To this end, we propose Location Switching and Change Detection (LS-CD) algorithm which uses a repeated one-sided sequential probability ratio test (SPRT) based mechanism for observation-driven location switching and change detection. The primary goal is to minimize the worst-case average detection delay (WADD) while meeting constraints on the average run length to false alarm (ARL2FA) and the UAV's time-averaged energy consumption. We provide a rigorous theoretical analysis of the algorithm's performance by using theory of random walk. Specifically, we derive tight upper and lower bounds to its ARL2FA and a tight upper bound to its WADD. In the special case of a symmetrical setting, our analysis leads to a new asymptotic upper bound to the ARL2FA of the standard CUSUM algorithm, a novel contribution not available in the literature, to our knowledge. Numerical simulations demonstrate the efficacy of LS-CD.

We report on the design and implementation of multiAP (Access Point) coordination in a Wi-Fi network, using fine-grained overlay time-slicing, centralized queueing and link activity scheduling. Our approach achieves network wide utility optimal downlink and uplink TCP throughputs, while permitting rate guarantees to designated TCP flows. We also obtain significant improvement in the performance of downlink streaming video, in the presence of cochannel interference from other APs. This work is a substantial extension of the ADWISER (ADvanced WI-fi Service EnhanceR) system reported in Hegde et al. (2013), Sunny et al. (2017), and Sevani et al. (2022). ADWISER is a central scheduler through which all downlink and uplink traffic passes; it overlays periodic time-slices, during which sets of AP-STA links are scheduled. During the time-slices the scheduled AP-STA links use the default IEEE 802.11 PHY/MAC mechanisms; thus ADWISER works without any modifications to the APs and the STAs. Our earlier work required the knowledge of the sets of the AP-STA links that can be scheduled without mutual interference between them, and used time-slices of 100s of milliseconds, which were statically partitioned into scheduling slots to which the known sets of AP-STA links were mapped. The techniques that we now develop and implement perform fine-grained time-slicing, down to 20 ms, thus permitting the handling of delay sensitive Internet applications. ADWISER, now, only needs to know the AP-STA associations. By a process of scheduling and measuring throughputs, using stochastic approximation algorithms, ADWISER dynamically determines the set of AP-STA links to schedule in successive time-slices, thereby achieving global network utility optimal throughputs. We have implemented ADWISER on a Linux OS based server machine, and all performance results we report are from test-beds that use commercial off-the-shelf APs and laptops as STAs.

We consider a system of several collocated nodes sharing a time slotted wireless channel, and seek a MAC (medium access control) that (i) provides low mean delay, (ii) has distributed control (i.e., there is no central scheduler), and (iii) does not require explicit exchange of state information or control signals. The design of such MAC protocols must keep in mind the need for contention access at light traffic, and scheduled access in heavy traffic, leading to the long-standing interest in hybrid, adaptive MACs. Working in the discrete time setting, for the distributed MAC design, we consider a practical information structure where each node has local information and some common information obtained from overhearing. In this setting, "ZMAC" is an existing protocol that is hybrid and adaptive. We approach the problem via two steps (1) We show that it is sufficient for the policy to be "greedy" and "exhaustive". Limiting the policy to this class reduces the problem to obtaining a queue switching policy at queue emptiness instants. (2) Formulating the delay optimal scheduling as a POMDP (partially observed Markov decision process), we show that the optimal switching rule is Stochastic Largest Queue (SLQ). Using this theory as the basis, we then develop a practical distributed scheduler, QZMAC, which is also tunable. We implement QZMAC on standard off-the-shelf TelosB motes and also use simulations to compare QZMAC with the full-knowledge centralized scheduler, and with ZMAC. We use our implementation to study the impact of false detection while overhearing the common information, and the efficiency of QZMAC. Our simulation results show that the mean delay with QZMAC is close that of the full-knowledge centralized scheduler.

Since the radio channel between the base-station and a User Equipment (UE) is stochastic, semi-persistent scheduling (SPS), commonly used for Voice over New Radio (VoNR), may be suboptimal in the utilisation of time-frequency resources, thus providing lower residual bit-rates for enhanced mobile broadband (eMBB) flows. We consider optimal scheduling of voice packets so that their resource utilisation is minimised, without violating their transmission deadlines. Assuming continuous time-frequency resources, we compare the performance of the following algorithms: SoA: Schedule on Arrival, which is one version of semi-persistent scheduling; RDR: Rate to (Residual) Deadline ratio based policy, a natural heuristic; OST: Optimal Stopping Time scheduling, which uses the finite horizon backward dynamic programming approach. We compare the above policies with GAS: Genie-Aided Scheduling, a bounding approach, in which the scheduler, non-causally, gets the resource requirements in every slot up to the deadline of the packet. We emperically evaluate the impact of these scheduling policies on VoNR resource utilisation and packet loss, and on the eMBB throughput region. We also study the effect of advancing the deadline for packet delivery, to reduce the downlink scheduling overhead and UE power consumption. For a 100 MHz system, i.i.d. channel over slots, independent across users, 100 VoNR calls, and call-by-call scheduling, we find that, for a packet deadline of 20 ms (40 slots), state dependent policies can provide up to 80 % reduction in resource utilisation by packet-voice, whereas a deadline of 1.5 ms (3 slots) already yields a reduction of up to 60 %. When there are slot-to-slot correlations in the channel, there is reduction in the above gains for coherence times 5 ms (10 slots) and 10 ms (20 slots).. but there is still room for optimisation beyond SPS.

One of the requirements of network slicing in 5G networks is RAN (radio access network) scheduling with rate guarantees. We study a three-time-scale algorithm for maximum sum utility scheduling, with minimum rate constraints. As usual, the scheduler computes an index for each UE in each slot, and schedules the UE with the maximum index. This is at the fastest, natural time-scale of channel fading. The next time-scale is of the exponentially weighted moving average (EWMA) rate update. The slowest time scale in our algorithm is an "index-bias" update by a stochastic approximation algorithm, with a step-size smaller than the EWMA. The index-biases are related to Lagrange multipliers, and bias the slot indices of the UEs with rate guarantees, promoting their more frequent scheduling. We obtain a pair of coupled ordinary differential equations (o.d.e.) such that the unique stable points of the two o.d.e.s are the primal and dual solutions of the constrained utility optimization problem. The UE rate and index-bias iterations track the asymptotic behaviour of the o.d.e. system for small step-sizes of the two slower time-scale iterations. Simulations show that, by running the index-bias iteration at a slower time-scale than the EWMA iteration and using the EWMA throughput itself in the index-bias update, the UE rates stabilize close to the optimum operating point on the rate region boundary, and the index-biases have small fluctuations around the optimum Lagrange multipliers. We compare our results with a prior two-time-scale algorithm and show improved performance.

Our work explores the efficacy of time-slicing and a centralized scheduler for performance enhancement in IEEE 802.11 networks. Our framework takes over control from the WiFi access points (APs) by queuing packets at the central scheduler, before the APs, and releasing packets efficiently for selected sets of STAs in appropriate time slices. We propose ‘index’ based proportional fair scheduling for interference mitigation in dense WiFi deployments. The framework results in near optimal utility of the wireless network, without explicit knowledge of complete network graph and without on-line RSSI measurements, achieving over-all coordination among the APs. We carried out extensive experiments to evaluate the performance of our framework with both TCP & interactive real-time traffic. Our work holds significant potential for addressing the challenges posed by dense deployment of WiFi networks.

The major difference between underwater sensor network and terrestrial sensor network is use of acoustic signals as communication medium rather than radio signals.The main reason behind this is the poor performance of radio signal in water.UWSNs have some distinct characteristics which makes them more research oriented like large propagation delay, high error rate, low bandwidth and limited energy.UWSNs have their application in the field of oceanographic, data collection, pollution monitoring, off shore exploration, disaster prevention, assisted navigation, tactical surveillance etc. In UWSNs the main advantages of protocol, design is to a reliable and effective data transmission from source to destination. Among those energy efficiency plays an important role in underwater communication. The main energy sources of UWSNs are batteries, which are very difficult to replace frequently. Two popular underwater protocols are DBR and EEDBR. DBR is one of the popular routing techniques, which don't use the full dimensional location information. In this research work, we use an efficient area localization scheme for UWSNs to minimize the energy hole created. Rather than finding the exact sensor position, this technique will estimate the position of every sensor node within certain area. In addition to that we introduced a RF based location finding and multilevel power transmission scheme.Simulation results shows that our proposed scheme produces better result than its counter parts.

Motivated by Industry 4.0 applications, we consider quickest change point detection (QCD) when process measurements are transmitted by a sensor over a lossy wireless link to a decision maker (DM). The sensor node samples measurements using a Bernoulli sampling process, and places the measurement samples in a transmit queue of the transmitter. The transmitter uses a retransmit-until-success transmission strategy to deliver packets to the DM over the lossy link, which is modeled as an independent Bernoulli process and has different loss probabilities before and after the change. We pose the QCD problem in the non-Bayesian setting under Lorden's framework [1], and derive a CUSUM algorithm. By defining a suitable Markov process, involving the DM measurements and the queue length process, we show that the problem reduces to QCD of a Markov process. Characterizing the information measure $I$ per measurement sample at the DM, our analysis proves the asymptotic optimality of our algorithm when the false alarm rate tends to zero. We discuss extensions of the analysis to periodic sampling and no-retransmission cases. Through numerical analysis, we demonstrate trade-offs that can be used to optimize system design parameters such as the sampling rate of the measurement process in the non-asymptotic regime.

We study Bandwidth Reservation (BR) policies for the Bandwidth on Demand (BoD) problem in a class of multihop networks. We motivate an Erlang fixed-point BR heuristic for the general BoD problem by first establishing the optimality of BR on a class of multihop networks. The motivating problem is a wireline network comprising $k$ links in tandem, each link of which is shared by two types of bandwidth demands, one type requiring one unit of bandwidth from every link, and the other type (being dedicated to the link) requiring one unit of bandwidth as well. First, for this $k$ -hop tandem network, when each link has unit bandwidth, we demonstrate that a policy of BR form is optimal. We then study the BoD problem for a more general $k$ -hop tandem network, in the “Kelly” limiting regime, where the arrival rates as well as the link bandwidths become large. For certain parameter regimes of the $k$ -hop tandem network, we show that an admission control policy of BR form is asymptotically optimal. Motivated by these results, we propose an Erlang fixed-point based, link-by-link, heuristic algorithm for computing a BR policy for the BoD problem in a general network. We, finally, evaluate this proposal numerically.

It is needless to mention the plenitude of research literature available today on Mobile Ad Hoc Networks (MANETs). Diverse issues about MANETs like medium access scheduling, routing protocols, transmission power control and performance analysis have been the focus of research in the past few years. In this paper, we study the optimal next hop distance that maximizes the end-to-end flow throughput in a mobile multi-hop wireless network environment subject to a network average power constraint. In our investigation we assume a spatially dense spreadout of nodes and we incorporate channel gain due to path-loss caused by the mobility of nodes. We consider a periphery limited mobility scenario in which nodes are restricted to move in their own local, approximately circular periphery and follow the random waypoint mobility model within this circle. For the calculation of the average throughput with path-loss, this kind of a mobility model leads us to compute the probability density function (PDF) of random distance between two nodes moving inside their local circular periphery. Computation of this PDF constitutes a problem in Geometric Probability Theory and to the best of our knowledge the derivation of PDF of random distance between two circles has never been investigated before. This is thus the first main contribution of this paper. The second main contribution of this paper is that, with reference to the vast literature available on MANETs, ours is the first attempt to derive a throughput maximizing optimal hop distance in a dense ad hoc network environment with mobility.

We study fine grained (10s of ms) overlay time-slicing , and centralized queuing and scheduling, for the performance management of "high throughput" (HT) IEEE 802.11 standards, where cochannel interference reduces PHY rates and aggregation, causing poor performance. In overlay time-slicing, interference between AP-STA (Access Point and associated Station) links is eliminated by queuing downlink packets in a scheduler, between the wireline network and the APs, and releasing packets to a set of AP-STA links only in their time-slice. This can manage downlink and uplink FTP and HTTP transfers, and downlink packet voice traffic. We utilize a stochastic approximation based closed-loop mechanism that releases only as much data in a time-slice as can be "served," so that the AP-STA links mapped to that time-slice are inactive at the end of their time-slice, thus, eliminating cochannel interference. Fine-grained overlay time-slicing is demonstrated on an experimental network with two cochannel AP-STA pairs, a setting that we see in our campus WiFi network. In our approach, even for small time-slices (20ms to 50ms), for downlink and uplink TCP bulk transfers, in spite of the scheduler working with partial information, the time-slice boundaries are respected, and performance is close to network utility optimal. Fine-grained time-slicing reduces HTTP access delay, prevents TCP connections over the wide area network from reacting to the path interruptions, and also permits slicing of applications such as interactive packet voice.

We consider a system of several collocated nodes sharing a time slotted wireless channel, and seek a MAC that (i) provides low mean delay, (ii) has distributed control (i.e., there is no central scheduler), and (iii) does not require explicit exchange of state information or control signals. The design of such MAC protocols must keep in mind the need for contention access at light traffic, and scheduled access in heavy traffic, leading to the long-standing interest in hybrid, adaptive MACs. We first propose EZMAC, a simple extension of an existing decentralized, hybrid MAC called ZMAC. Next, motivated by our results on delay and throughput optimality in partially observed, constrained queuing networks, we develop another decentralized MAC protocol that we term QZMAC. A method to improve the short-term fairness of QZMAC is proposed and analysed, and the resulting modified algorithm is shown to possess better fairness properties than QZMAC. The theory developed to reduce delay is also shown to work %with different traffic types (batch arrivals, for example) and even in the presence of transmission errors and fast fading. Extensions to handle time critical traffic (alarms, for example) and hidden nodes are also discussed. Practical implementation issues, such as handling Clear Channel Assessment (CCA) errors, are outlined. We implement and demonstrate the performance of QZMAC on a test bed consisting of CC2420 based Crossbow telosB motes, running the 6TiSCH communication stack on the Contiki operating system over the 2.4GHz ISM band. Finally, using simulations, we show that both protocols achieve mean delays much lower than those achieved by ZMAC, and QZMAC provides mean delays very close to the minimum achievable in this setting, i.e., that of the centralized complete knowledge scheduler.

Motivated by the emerging delay-sensitive applications of the Internet of Things (IoT), there has been a resurgence of interest in developing medium access control (MAC) protocols in a time-slotted framework. The resource-constrained, ad-hoc nature of wireless networks typical of the IoT also forces the amount of control information exchanged across the network -- required to make scheduling decisions -- to a minimum. In a previous article we proposed a protocol called QZMAC that (i) provides provably low mean delay, (ii) has distributed control (i.e., there is no central scheduler), and (iii) does not require explicit exchange of state information or control signals. In the present article, we implement and demonstrate the performance of QZMAC on a test bed consisting of CC2420 based Crossbow telosB motes, running the 6TiSCH communication stack on the Contiki operating system over the 2.4GHz ISM band. QZMAC achieves its near-optimal delay performance using a clever combination of polling and contention modes. We demonstrate the polling and the contention modes of QZMAC separately. We use an Adaptive Synchronization Technique in our implementation which we also demonstrate. Our network shows good delay performance even in the presence of heavy interference from ambient WiFi networks.

Motivated by medium access control for resource-challenged wireless Internet of Things (IoT) networks, we consider the problem of queue scheduling with reduced queue state information. In particular, we consider a time-slotted scheduling model with N wireless links, such that links i and i + 1, 1 <= i <= N - 1 cannot transmit together. Our aim in this paper is to study throughput-optimal, and even delay optimal, scheduling policies that require only the empty-nonempty state of the packet queues associated with these links (Queue Nonemptiness Based, or QNB, policies). We focus on Maximum Size Matching (MSM) policies, and provide an analysis of all the QNB-MSM policies for N = 3, thereby comparing their performance, and revisiting a delay optimal scheduling result. Our study shows that, while scheduling a maximum size matching would seem intuitive, there are important performance differences between different QNB-MSM policies. Further, it is not necessary for a QNB policy to be MSM for it to be throughput optimal. We develop a new Policy Splicing technique to combine scheduling policies for small networks to construct throughput-optimal policies for larger networks, some of which also aim for low delay. For N = 3 there exists a QNB-MSM policy that is sum-queue optimal over the entire stability region. We show, however, that for N >= 4, there is no QNB scheduling policy that is sum-queue length optimal over all arrival rate vectors in the capacity region. Our throughput-optimality results rely on two new arguments: a Lyapunov drift lemma specially adapted to policies that are queue length-agnostic, and a priority queueing analysis for showing strong stability. We then extend our results to a more general class of interference constraints that we call cluster-of-cliques (CoC) conflict graphs. We consider two types of CoC networks, namely, Linear Arrays of Cliques (LAoC) and Star-of-Cliques (SoC) networks. We develop QNB policies for these classes of networks, study their stability and delay properties, and propose and analyze techniques to reduce the amount of state information to be disseminated across the network for scheduling.

Motivated by medium access control for resource-challenged wireless Internet of Things (IoT), we consider the problem of queue scheduling with reduced queue state information. In particular, we consider a time-slotted scheduling model with $N$ sensor nodes, with pair-wise dependence, such that Nodes $i$ and $i + 1,~0 < i < N$ cannot transmit together. We develop new throughput-optimal scheduling policies requiring only the empty-nonempty state of each queue that we term Queue Nonemptiness-Based (QNB) policies. We propose a Policy Splicing technique to combine scheduling policies for small networks in order to construct throughput-optimal policies for larger networks, some of which also aim for low delay. For $N = 3,$ there exists a sum-queue length optimal QNB scheduling policy. We show, however, that for $N > 4,$ there is no QNB policy that is sum-queue length optimal over all arrival rate vectors in the capacity region. We then extend our results to a more general class of interference constraints that we call cluster-of-cliques (CoC) conflict graphs. We consider two types of CoC networks, namely, Linear Arrays of Cliques (LAoC) and Star-of-Cliques (SoC) networks. We develop QNB policies for these classes of networks, study their stability and delay properties, and propose and analyze techniques to reduce the amount of state information to be disseminated across the network for scheduling. In the SoC setting, we propose a throughput-optimal policy that only uses information that nodes in the network can glean by sensing activity (or lack thereof) on the channel. Our throughput-optimality results rely on two new arguments: a Lyapunov drift lemma specially adapted to policies that are queue length-agnostic, and a priority queueing analysis for showing strong stability.

A cheaper, non-vacuum-based routes are required for the large-scale implementation of TCOs in solar cells and LEDs. Generally, solution-based processing routes result in lower transparency and greater resistivity. To achieve electrical conductivity and transparency via solution processing route, greater insight into the effect of processing and microstructure/defects on the electrical and optical properties is needed. In this work, Al-doped ZnO films were deposited on glass substrates by sol–gel spin coating route. The formation of wurtzite structure was confirmed, and the crystallite size of the films was estimated by X-ray diffraction (XRD) pattern analysis. Greater than 85% transparency in the films was obtained in visible and near IR regime as examined by UV–Vis spectroscopy. An increase in the band gap was observed with increasing Al concentration from 0 to 3 at.%. The electrical properties were evaluated by the Hall measurement, and obtained resistivity was in the order of 10–2 Ωcm. The presence of defect states and their co-relation with the electrical properties were investigated by photoluminescence spectroscopy and X-ray photoelectron spectroscopy.

Microstrip patch antenna (MPA) are used for communication because of its various benefits such as low profile, light weight and small size. In the given work, antenna design parameters such as length and width are optimized for Ku-Band (12 GHz ≤ f ≤ 18 GHz) applications using artificial neural network. Back propagation algorithm is used to train the neural network as it gives accurate predictions, fast convergence and takes less training time. After optimizations, antenna is designed to improve gain characteristics in HFSS.

We consider the problem of deployment of indoor multihop wireless networks for connecting sensors to a data collection station, in the context of Internet of Things (IoT) applications. The locations of the source nodes and the sink are fixed, and additional router nodes might be needed to create a connected network that provides the required quality of service (QoS). Practical constraints often dictate that these cannot be placed just anywhere, and so we assume that several potential relay locations are provided. The problem is then to design a multihop network connecting the sensors to the sink, using a minimal set of the potential relay locations, that meets the network QoS. A priori, the qualities of links terminating on potential locations are not known. We are interested in a predict-place-iterate approach for relay deployment. Thus, the quality of the deployed network depends on the quality of the link prediction model. In this work, we study the improvement in network deployment that is provided by including the number of intervening walls on the link, in addition to using link length, in the link prediction model. Our comprehensive study involving analysis, simulations and experimental validation demonstrates that including the number of walls in the link prediction model can lead to a larger probability of successful design, fewer router nodes, and fewer iterations until successful design.

Radiation safety has become a major concern in recent years in healthcare, mining, security and nuclear applications. The problem is aggravated because of the absence of a real-time, accumulative radiation dosimeter of small form-factor that can provide digital output for continuous monitoring. In this paper, the first monolithically integrated wearable CMOS radiation dosimeter is presented, which consists a floating-gate resistive sensor with a sensitivity of 14Ω/rad, and a time-based resistance-to-digital-converter (RDC) functioning as an 18-bit ADC at 861nJ (at a measurement frequency of 100S/s) and 3.29pJ/ conversion-step energy efficiency. Unlike traditional ADCs, the time-based RDC exhibits in-field resolution-energy scalability by controlling the total integrated time for measurement, and achieves 6-bit better resolution than state-of-the-art VCO-based low-frequency (≈ 1kS/s) ADC architectures. The implemented integrated dosimeter achieves 8-bit better resolution and 7.5X lower power than the state-of-the-art CMOS Floating-Gate dosimeters. The dosimeter sensitivity is 40X better than the state-of-the-art with minimum resolvable dose reaching up to 10mrad.