We present the design of a multiuser networked wireless system to remotely configure and control the lighting of multiple webcam users at different locations. This system makes use of a Raspberry Pi and a wireless DMX transmitter as the wireless interface that can be used to control the DMX webcam lights. A lighting control software called OLA is used on the Raspberry Pi. A web interface is designed to issue commands to OLA API running on the Raspberry Pi to control DMX lights associated with Raspberry Pi. Multiple wireless interfaces, each for a specific user at a different location, can be simultaneously configured and managed using the web interface. The interactive web interface can be used to control the intensity and color of the DMX lights. The web interface follows a model controller view design and makes HTTP calls to the OLA software running on Raspberry pi. The proposed system enables an operator to provide optimum and artistic lighting effects for a group of online presenters.

The Internet of Things (IoT) enables us to gain access to a wide range of data from the physical world that can be analyzed for deriving critical state information. In this regard, machine learning (ML) is a valuable tool that can be used to develop models based on observed physical data, leading to efficient analytical decisions, including anomaly detection. In this work, we address some key challenges for applying ML in IoT applications that include maintaining privacy considerations of user data that are needed for developing ML models and minimizing the communication cost for transmitting the data over the IoT network. We consider a representative application of the anomaly detection of ECG signals that are obtained from a set of low-cost wearable sensors and transmitted to a central server using LoRaWAN, which is a popular and emerging low-power wide-area network (LPWAN) technology. We present a novel framework utilizing federated learning (FL) to preserve data privacy and appropriate features for uplink and downlink communications between the end devices and the gateway to optimize the communication cost. Performance results obtained from computer simulations demonstrate that the proposed framework leads to a 98% reduction in the volume of data that is required to achieve the same level of performance as in traditional centralized ML.

This paper addresses the issue of meeting communication performance requirements for Internet of Things (IoT) applications using LoRaWAN, an emerging low–power wide– area networking (LPWAN) technology. The proposed approach involves the development of rules for the assignment of LoRa spreading factors to IoT devices to limit the number of packet collisions selectively for devices having high Quality of Service (QoS) requirements. Two different spreading factor assignment schemes are proposed that differ in the assignment rules applied. Performance results, obtained using mathematical models and simulations, indicate that the proposed schemes adequately distinguish between high priority and low priority devices and can meet QoS requirements within limitations of network size.

Real-time prediction of traffic congestion enables intelligent transportation systems to improve traffic mobility, reduce delays, and enhance road safety. In this paper, an intelligent traffic congestion prediction system is presented to classify the traffic status across a road network using machine learning. It applies a long short term memory (LSTM) model to estimate the traffic congestion for short term future for LoRa networks. The proposed system, once trained, can efficiently predict traffic congestion using low bandwidth real-time traffic data that can be collected from roadside sensors using a low-power wide area network (LPWAN) technology such as LoRa. We use an online dataset to train and evaluate the proposed model, which shows that the proposed traffic congestion prediction model achieves high performance in terms of accuracy, precision, recall, and success rate. It reduces the error rates and the computing time for fast and accurate future predictions.

The received signal strength indicator (RSSI) of RF signals is a cost-effective solution for distance estimation, which makes it a practical choice for localization schemes in wireless sensor networks (WSN). However, RF propagation channels in most WSN deployment environments, including dense cities and natural habitats, are commonly affected by shadowing due to obstructions caused by natural and man-made obstacles. RF signal attenuation from shadowing introduces uncharacteristically high errors in RSSI-based distance estimates, which result in large errors in RSSI-based localization schemes. This paper proposes the use of outlier detection methods for removing the effect of such disproportionately erroneous distance estimates in location estimation using RSSI. Three different localization schemes are proposed that apply outlier detection to effectively reduce localization errors in shadowed environments. Performance results of the proposed schemes are obtained using computer simulations and experimental tests.

Long Range (LoRa) technology is receiving increasing attention in recent years for addressing the challenges of providing wireless connections to a large number of end devices in the field of Internet of Things (IoT). This is due to its long transmission range, low power consumption, and large network capacity. Despite these benefits, LoRa networks may not be able to achieve their full potential unless additional improvements are achieved in the network scalability. Specifically, the probability of success under heavy network traffic loads or large number of end devices needs to be improved. This paper explores the possibility of reducing the number of collisions in LoRa networks by allocating multiple spreading factors within the same network zones. We demonstrate that an optimum allocation of spreading factors increases the average probability of successful packet transmission to the gateway. Simulation results show that the proposed approach can substantially improve the network performance even for dense LoRa networks.

A major constraint for long term sustainability of wireless sensor networks (WSN) and the Internet of Things (IoT) is the limitation of their energy resources. Battery replacement in sensor nodes can be a prohibitively expensive exercise because of their abundance. RF energy harvesting (R-EH) has emerged as a promising technique to improve the lifespan, reliability, and capacity of WSN and IoT networks. In this work, information based smart RF energy harvesting is proposed, which reduces the total number of RF energy transmitters required to cover a certain area. An energy harvesting development kit for wireless sensors by Powercast Corporation is being used for implementation and analysis of this scheme.

Energy efficiency and scalability continue to be key considerations for the development of low cost wireless networks for meeting the needs of the emerging world of Internet of Things (IoT). Recent developments in low power wide area networks (LPWAN) promise to meet these requirements by achieving long communication ranges at low data rates without increasing the energy cost. Consequently, LPWANs are rapidly gaining prominence in the development of IoT networks in comparison to legacy WLANs that use multihop mesh networking for increasing connectivity and coverage. Inspired by this trend, we review and evaluate various recently developed LPWAN technologies and present key performance measures of LoRa (Long Range), which is a leading LPWAN technology.

Received RF signal strength provides a cost-effective mechanism for distance estimation that is popularly used in wireless sensor networks (WSN) range-based localization. However, such range estimates are heavily affected by shadowing that can be caused by obstructions of the line-of-sight radio frequency (RF) signal. Multilateration using several range estimates obtained from RSSI can lead to large errors in sensor location if one or more of the range estimates are affected by shadowing. In this paper, we present a scheme that applies spatial correlation and a clustering mechanisms to remove the effect of shadowing in range based location estimation. We show the effectiveness of this scheme in minimizing the adverse effects of signal outliers using simulations.

Wireless sensor networks (WSN) that are powered by energy harvested from the environment, also known as rechargeable WSNs, typically experience wide variations of energy availability across the network. Such variations can cause frequent node outages at specific locations where energy availability is low, whereas the remaining nodes may receive sufficient energy for continuous operation. The energy availability also varies over time due to environmental factors, which exacerbates the problem. To minimize the impact of such spatial and temporal variations of energy resources, we propose a joint Power COntrol and Routing scheme (PCOR) that adapts the energy consumption in the sensor nodes according to its available energy resources in order to facilitate uninterrupted operations. The scheme is applied to data collecting WSNs with a MAC that utilizes asynchronous duty-cycling for energy conservation. PCOR achieves its objective by reducing the energy consumption at energy-critical sensor nodes, i.e. nodes that have comparatively lower energy resources, using network-wide cooperative power control and route adaptations. This is accomplished by reducing the overhearing at energy-critical nodes, which is a key cause of energy consumption in such networks. We demonstrate through simulations and experimental results that PCOR reduces overhearing at energy-constrained nodes by up to 75% without significantly affecting the end-to-end packet delivery rates.

Batteryless wireless sensor networks that rely on energy harvested from the environment often exhibit random power outages due to limitations of energy resources, which give rise to intermittent connectivity and long transmission delays. To improve the delay performance in such networks, we consider a design strategy that uses predictive retransmissions to maximize the probability of success for each transmission. This is applied to two different transmission diversity schemes: cooperative relaying over unicast routes and opportunistic routing. Performance evaluations from theoretical models and simulations are presented that show that significant gains can be achieved using the proposed approach in such networks.

In this paper, we investigate mechanisms for improving the quality of communications in wireless-optical broadband access networks (WOBAN), which present a promising solution to meet the growing needs for capacity of access networks. This is achieved by using multiple gateways and multi-channel operation along with a routing protocol that effectively reduces the effect of radio interference. We present a joint route and channel assignment scheme with the objective of maximizing the end-to-end probability of success and minimizing the end-to-end delay for all active upstream traffic in the WOBAN. Performance evaluations of the proposed scheme are presented using ns-2 simulations, which show that the proposed scheme improves the network throughput up to three times and reduces the traffic delay by six times in presence of 12 channels and four network interface cards (NICs), compared to a single channel scenario.

Extreme weather events have been known to cause massive disruptions in power leading to significant outages in the recent past. The main goal of this study is to improve situation intelligence in the distribution grid to determine the downed lines, impacted areas, location of healthy lines and nodes, and the amount of distributed generation available at customer locations to serve the critical loads (health care facilities, law enforcement, natural gas delivery network, gasoline pumping stations, water distribution facilities, etc.). A multi-tiered wireless sensor network is designed that has the ability to report to the substation with information on the attributes of an outage, including location, possible extent of the outage, and potential use of DER for backup power. An interactive visualization software is developed to display the sensor data.

A Distributed Routing and Channel Selection Scheme for Multi-Channel Wireless Sensor Networks Amitangshu Pal 1, Sarbani Roy 2 and Asis Nasipuri 3 1 Temple University, Philadelphia, PA; amitangshu.pal@temple.edu 2 Jadavpur University, Kolkata, West Bengal; sarbani.roy@cse.jdvu.ac.in 3 The University of North Carolina at Charlotte, Charlotte, NC; anasipur@uncc.edu * Correspondence: amitangshu.pal@temple.edu; Tel.: +1-980-229-3383 ‡ These authors contributed equally to this work. Academic Editor: name Version January 19, 2017 submitted to Entropy; Typeset by LATEX using class file mdpi.cls Abstract: We propose a joint channel selection and quality aware routing scheme for multi-channel 1 wireless sensor networks that apply asynchronous duty cycling to conserve energy, which is 2 common in many environmental monitoring applications. A data collection traffic pattern is 3 assumed, where all sensor nodes perform periodic sensing and forwarding of data to a centralized 4 base station (sink). Under these assumptions, the effect of overhearing dominates the energy 5 consumption of the nodes. The proposed scheme achieves lifetime improvement by reducing 6 the energy consumed by overhearing and also by dynamically balancing the lifetimes of nodes. 7 Performance evaluations are presented from experimental studies as well as from extensive 8 simulation studies to show the effectiveness of the proposed scheme. 9

In this paper, we investigate mechanisms for improving the quality of communications in wirelessoptical broadband access networks (WOBAN), which present a promising solution to meet the growing needs for capacity of access networks. This is achieved by using multiple gateways and multi-channel operation along with a routing protocol that effectively reduces the effect of radio interference. We present a joint route and channel assignment scheme with the objective of maximizing the end-to-end probability of success and minimizing the end-to-end delay for all active upstream traffic in the WOBAN. Performance evaluations of the proposed scheme are presented using ns-2 simulations, which show that the proposed scheme improves the network throughput up to 3 times and reduces the traffic delay by 6 times in presence of 12 channels and 4 network interface cards (NICs), compared to a single channel scenario.

This paper presents an approach to monitor actuation events of pneumatic valves using piezo film sensors. These sensors are very low-cost, flexible, wide-band, and are ideally suited to acquire acoustic signals non-intrusively from the valve. Event-specific signal signatures based on the envelope of the sensor signals are used to determine the behavior of the valves under operation. A sequential state logic is developed to detect the signatures for normal valve events. All design considerations are derived from experimental data obtained from a laboratory testbed. The performance results, including analysis of signals at various stages of the algorithm are presented using MATLAB.

We consider energy harvesting sensor networks that are characterized by intermittent connectivity between sensor nodes due to random sleep and wake cycles caused by sporadic availability of energy sources. A retransmission strategy is developed that probabilistically predicts the best transmission interval between successive retransmissions in order to maximize the likelihood of successful packet delivery. We utilize this strategy for a cooperative routing approach to further improve the delay performance in such intermittently connected networks. An analytical model is formulated to study the delay characteristics of the proposed retransmission scheme. The performance of the proposed protocol in a multi-hop environment is evaluated through simulations. Results show that the retransmission scheme can provide significant performance improvements when compared to existing approaches.

We consider rechargeable wireless sensor networks that experience intermittent connectivity due to power outages caused by insufficient energy harvesting and limited storage. We present a predictive retransmission strategy that is integrated with opportunistic routing and unicast routing with cooperative relaying to reduce the transmission delay in such networks. Simulation results are presented to demonstrate the effectiveness of the proposed predictive retransmission scheme and the relative performance of opportunistic and cooperative unicast routing under realistic network models.

A game theoretic approach is proposed for joint power control and route adaptations for wireless sensor networks to improve the network lifetime. A data collecting sensor network is assumed that employs asynchronous duty-cycling for energy conservation. In such networks, overhearing dominates the energy consumption, which can be controlled by adapting the transmit power levels as well as the route selections. The goal of this work is to determine assignments of transmit power levels and parent selections for all sensor nodes to maximize the lifetime of the network by controlling the overhearing in the nodes, while maintaining acceptable quality of routes. Performance results of the proposed schemes from computer simulations are presented.

We consider wireless sensor networks (WSN) powered by energy harvested from the environment, which is assumed to be sporadic and random in nature. Wireless nodes in such networks experience random active and sleep periods depending on their energy availability, leading to intermittent connectivity in the network. This paper addresses the problem of minimizing the multi-hop transmission delay in such networks by applying cooperative relaying. We develop a theoretical model to analyze the transmission delay over such intermittently connected links with selective cooperative relaying. A novel two-hop cooperative relaying scheme is proposed that balances the trade-off between the benefit from cooperative relaying and the additional energy consumption caused by it. Performance of the proposed two-hop cooperative relay scheme is presented using simulations.