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Radio frequency identification (RFID) is an important internet of things (IoT) enabling technology. In RFIDs collision occur among tags because tags share communication channel. This is called tag collision problem. The problem becomes catastrophic when dense population of tags are deployed like in IoT. Hence, the need to enhance existing dynamic frame slotted ALOHA (DFSA) based electronic product code (EPC) C1G2 media access control (MAC) protocol. Firstly, this paper validates through simulation the DFSA theory that efficiency of the RFID system is maximum when the number tags is approximately equal to the frame size. Furthermore, literature review shows tag grouping is becoming popular to improving the efficiency of the RFID system. This paper analyzes selected grouping-based algorithms. Their underlining principles are discussed including their tag estimation methods. The algorithms were implemented in MATLAB while extensive Monte Carlo simulation was performed to evaluate their strengths and weaknesses. Results show that with higher tag density, fuzzy C-means based algorithm (FCMBG) outperformed traditional DFSA by over 40% in terms of throughput rate. The results also demonstrate FCMBG bettered other grouping-based algorithms (GB-DFSA and GBSA) whose tag estimation method are based on collision slots in terms slot efficiency by over 10% and also in terms of identification time.

Over the recent years, multimedia applications have begun to be used over wireless sensor networks (WSN), which hitherto has been primarily used to carry small amount of data. To meet the demand for higher throughput and real-time characteristics of multimedia contents, the routing protocol need to be simplified and optimized. In this paper, we present a routing algorithm that provides a low end-to-end packet delay and low packet loss, referred to as smart, greedy forwarding algorithm based on throughput and energy-awareness (SGFTEM). This algorithm routes multimedia packets across a WSN by choosing high- throughput paths rather than always choosing the shorter path to the sink. When it encounters network holes, a network void-bypass is applied, which enhances the reliability of the network. Energy management is also implemented to extend sensor node lifetime by way of reducing the radio transmission coverage to a suitable distance reachable by the sensor nodes The simulation results shows that, SGFTEM reduces the end-to-end delay by 40% and packet loss ratio by 35% and maintain energy consumption against other algorithms. With energy management, the residual energy of sensor nodes are made to be equally distributed across the entire network which results in load balancing. The performance of SGFTEM are also compared via simulation against AGEM, TPGF, GPSR, and AODV showing superior performance under similar scenarios i.e. low packet end-to-end delay, low packet loss and load balancing of routing paths.

Underwater wireless sensor network (UWSN) is the enabling technology for a new era of underwater monitoring and actuation applications. In this network, data aggregation and forwarding are intensely constrained due to channel impairment, and therefore require due consideration. One way to address the data collection of UWSN is by enhancing the routing protocol using the Opportunistic Routing (OR) technique. This article proposes a normalized advancement based opportunistic routing protocol called NA-TORA. NA-TORA is a geographically opportunistic routing protocol in which the next-hop forwarder is selected based on Normalized Advancement (NA). NA is calculated from Expected Transmission Count (ETX) and node' s energy consumption to find an optimal forwarding node. However, the forwarded data may not be received on the designated sink node due to the existence of a void node in the data forwarding route. To overcome the issue of void nodes, we have incorporated a void node detection and avoiding mechanism on NA-TORA, called NA-TORA with VA. The proposed scheme recursively detect void nodes and avoid these nodes to participate in data routing by utilizing the angle of transmission adjustment and transmission range extension method. The novelty of this work lies within its data transmission phase, where normalized advancement is used to select a potential candidate forwarder. Apart from that, the proposed routing protocol operates in two different modes, i.e., standard operating mode (NA-TORA), and void avoidance mode (NA-TORA with VA). Comprehensive simulations were performed to compare the performance of NA-TORA and NA-TORA with VA with some well-known existing routing protocols.

The importance of wireless sensor networks in structural health monitoring is unceasingly growing, because of the increasing demand for both safety and security in the cities. The speedy growth of wireless technologies has considerably developed the progress of structural monitoring systems with the combination of wireless sensor network technology. Wireless sensor network–based structural health monitoring system introduces a novel technology with compelling advantages in comparison to traditional wired system, which has the benefits of reducing installation and maintenance costs of structural health monitoring systems. However, structural health monitoring has brought an additional complex challenges in network design to wireless sensor networks. This article presents a contemporary review of collective experience the researchers have gained from the application of wireless sensor networks for structural health monitoring. Technologies of wired and wireless sensor systems are investigated along with wireless sensor node architecture, functionality, communication technologies, and its popular operating systems. Then, comprehensive summaries for the state-of-the-art academic and commercial wireless platform technologies used in laboratory testbeds and field test deployments for structural health monitoring applications are reviewed and tabulated. Following that, classification taxonomy of the key challenges associated with wireless sensor networks for structural health monitoring to assist the researchers in understanding the obstacles and the suitability of implementing wireless technology for structural health monitoring applications are deeply discussed with available research efforts in order to overcome these challenges. Finally, open research issues in wireless sensor networks for structural health monitoring are explored.

Indoor localization systems, especially its accuracy, have become a major topic in recent research. Moreover, location-based services have been used in many mobile applications and wireless sensor networks. High accuracy and fast convergence are crucial requirements for a good localization system. However, the type of obtained received signal strength (RSS) data is vital to obtain high accuracy. In this paper, we perform a simulation and an experimental analysis for an indoor localization system. Moreover, 2D and 3D localization models are considered to investigate the localization error. The simulation is developed to provide an accurate RSS compared with an experimental testbed by considering the multiwall path loss model. Results demonstrate that the simulated RSS data are consistent with the experimental RSS data; that is, the simulated data almost have same distribution form as the experimental data. Therefore, the simulation can be used to develop high-accuracy localization systems.

Indoor positioning remains a serious issue due to the difficulty in attaining sufficient accuracy within an indoor environment using tracking technology of low complexity. Currently, most positioning systems do not embed the off-the-shelf (OTS) system which allows mobile devices to estimate location without using any additional hardware. In this paper, we propose a robust 3D indoor positioning system that is suitable for an indoor IoT application. This system based on Bayesian network that depends on Wi-Fi signals strength. It was experimentally tested in a building with pre-deployed access points (APs). The experimental results indicate that localization accuracy of the proposed system is high with the use of a small-sized radio map.

Underwater Wireless Sensor Network (UWSN) has emerged as promising networking techniques to monitor and explore oceans. Research on acoustic communication has been conducted for decades, but had focused mostly on issues related to physical layer such as high latency, low bandwidth, and high bit error. However, data gathering process is still severely limited in UWSN due to channel impairment. One way to improve data collection in UWSN is the design of routing protocol. Opportunistic Routing (OR) is an emerging technique that has the ability to improve the performance of wireless network, notably acoustic network. In this paper, we propose an anycast, geographical and totally opportunistic routing algorithm for UWSN, called TORA. Our proposed scheme is designed to avoid horizontal transmission, reduce end to end delay, overcome the problem of void nodes and maximize throughput and energy efficiency. We use TOA (Time of Arrival) and range based equation to localize nodes recursively within a network. Once nodes are localized, their location coordinates and residual energy are used as a matrix to select the best available forwarder. All data packets may or may not be acknowledged based on the status of sender and receiver. Thus, the number of acknowledgments for a particular data packet may vary from zero to 2-hop. Extensive simulations were performed to evaluate the performance of the proposed scheme for high network traffic load under very sparse and very dense network scenarios. Simulation results show that TORA significantly improves the network performance when compared to some relevant existing routing protocols, such as VBF, HHVBF, VAPR, and H2DAB, for energy consumption, packet delivery ratio, average end-to-end delay, average hop-count and propagation deviation factor. TORA reduces energy consumption by an average of 35% of VBF, 40% of HH-VBF, 15% of VAPR, and 29% of H2DAB, whereas the packet delivery ratio has been improved by an average of 43% of VBF, 26% of

Indoor localization techniques that use wireless local area network beacon signals have recently gained considerable attention among research communities. System accuracy is one of the most important issues in indoor localization technology. We propose a Bayesian graphical model based on fingerprinting location algorithm in this study. The proposed Bayesian model was simulated using OpenBUGS, a graphical user interface. We conducted an experiment to collect a sample of reference points in a testbed with a dimension of 51 × 22 m 2 . Results show that the proposed model has improved the accuracy by 25.65% using 15 reference points compared with Madigan model.

In Cognitive Radio (CR), the conventional narrow band spectrum sensing requires either random channel sensing order or predefined channel sensing sequence to sense all channels in a specified spectrum band in order to detect vacant channels. This may be inefficient in energy constraint devices networks such as Cognitive Radio Wireless Sensor Network (CR-WSN). In this paper, we propose a Reinforcement Learning based clustered Cooperative Channel Sensing (RL-CCS) that learns channels’ dynamic behaviors in terms of channel availability, sensing energy cost, and channel impairment to achieve optimal sensing sequence and optimal set of channels. The problem of selecting optimal policy is formulated as a Markov Decision Problem (MDP) to determine optimal solutions that minimize sensing energy while improving Primary User (PU) detection and channel utilization in CR-WSN. Simulation results show convergence and adaptability of the algorithm to dynamic environment in achieving optimal solutions. The results also indicate that with optimal channel sensing sequence and optimal sets of channels, sensing energy cost savings of 15.14% per channel sensing cycle can be achieved while improving PU detection accuracy and channel utilization compared to the sensing sequence based on Greedy search approach. Performance comparison of the proposed algorithm with other benchmark schemes indicates viability of our proposed approach over the other schemes in minimizing sensing energy and improving PU detection performance.

Indoor localization system becomes a substantial issue in recent research, especially in terms of the accuracy. Location based services have been used in many mobile applications as well as wireless sensor networks. High accuracy and fast convergence are very important issues for a good localization system. However, the type of obtained received signal strength (RSS) data is very important in order to get high accuracy. In this paper, we introduce three types of RSS data, which are: measured RSS, simulated RSS and average combined RSS. Bayesian network based on fingerprinting technique is used to investigate the effect of the three different types of RSS. The results show the effect of the three different RSS data on the accuracy of estimated location. The measured RSS has achieved an average accuracy of 4.3 meters using 10 training points while the average combined RSS has achieved a good accuracy of 2.1 meters.

This paper explores the concept of a Forward Scattering Radar (FSR) system with a rotational transmitter for target detection and localization. Most of the research and development in FSR used a fixed dedicated transmitter; therefore, the detection of stationary and slow moving target is very difficult. By rotating the transmitter, the received signals at the receiver contain extra information carried by the Doppler due to the relative movement of the transmitter-target-receiver. Hence, rotating the transmitter enhances the detection capability especially for a stationary and slow-moving target. In addition, it increases the flexibility of the transmitter to control the signal direction, which broadens the coverage of FSR networks. In this paper, a novel signal processing for the new mode of FSR system based on the signal’s joint time-frequency is proposed and discussed. Additionally, the concept of the FSR system with the rotational transmitter is analyzed experimentally for the detection and localization of a stationary target, at very low speed and a low profile target crossing the FSR baseline. The system acts as a virtual fencing of a remote sensor for area monitoring. The experimental results show that the proposed mode with the new signal processing scheme can detect a human intruder. The potential applications for this system could be used for security and border surveillance, debris detection on an airport runway, ground aerial monitoring, intruder detection, etc.

In this paper we focus on routing's issues in UWSN and propose an adaptive and lifetime aware routing protocol, REEP (Reliable and Energy Efficient Protocol). Research on acoustic channel has conducted from decades but that focused mostly on issues related to physical layer such as high latency, low bandwidth and high bit error. However, research on routing layer in UWSN is still in its infant stage and in need of a standard operational protocol. Due to unique nature of acoustic communication we cannot directly implement protocols defined for terrestrial sensor network or MANET. However, different researchers put their efforts to define a standard operational protocol for UWSN but most of these protocols follow OSI layer structure, which were proposed for wired Network and does not suit well acoustic environment. Our proposed routing protocol uses generic MAC protocol and aims to improve network lifetime by evenly distributing residual energy of nodes and by finding most suitable and economical routing path for data transmission. REEP uses ToA (Time of Arrival) to find node distance to its respective sink and utilizes this information to find best available routing path. We use NS2 based simulator, Aquasim to simulate and compare performance of REEP with a well known pre-existing UWSN routing protocol, DBR (Depth Based Routing). Simulation results show that REEP out-perform DBR by energy efficiency, extending network life time and by reducing end to end delay.

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

A long term healthcare monitoring system requires battery operated devices with low-power technologies. Researchers tried to adapt various short-range technologies for Wireless Body Area Networks (WBANs) in ubiquitous health monitoring. The classical Bluetooth is known for its greedy power consumption, IrDA and NFC require line-of-sight conditions, and ANT has weak coexistence features and interference issues. A typical choice remains ZigBee/6LoWPAN over IEEE 802.15.4 based solutions in WBANs because of their low-power consumption. However, the recently proposed Bluetooth Low Energy (BLE) announced more compelling features in various aspects. Only few studies have been published supporting these claims on BLE. In this paper, we present a BLE based remote healthcare monitoring platform and we study its compatibility for ECG monitoring. ECG data requires continuous and real-time transmissions, making it particularly challenging for resource constrained devices. In our system, a BLE112 module from Blue giga and a BLE USB dongle are used for WBAN. The performance of the system is evaluated experimentally and the results showed good potential of this proposed BLE platform in meeting the main QoS requirements of medical applications in terms of throughput, end-to-end delay, and packet error rate, while staying energy efficient.

Multi-user spatial sensing diversity exploration through cooperation spectrum sensing greatly improves sensing performance. However, high communication overhead and energy costs for exchanging sensing results may limit its viability in a realistic large scale resource constraint network such as cognitive radio wireless sensor networks. This paper presents a Weighted Hard Combination (WHC) scheme that combines features of both quantized and hard combining schemes to minimize energy cost for reporting sensing result and improve primary user detection performance in cooperative sensing. We evaluate the effectiveness of the scheme through simulation. Performance comparison of the WHC scheme in terms of detection performance, reporting energy cost and reporting time ratio with conventional hard combination, soft combination and quantized schemes indicates viability of the scheme. The results indicate that the WHC scheme minimizes reporting energy cost by 70% and improves detection performance by 5.6% compared to the quantized 3-bits scheme.