In the past two decades, several applications have been proposed for Wireless Sensor Networks (WSNs). Some of these applications, such as habitat monitoring, active volcano monitoring, and toxic gas detection, exclude the involvement of human presence because the environments are either dangerous, inaccessible, or too extensive. The scope of these applications can be extended if the ground networks are assisted by a network of Unmanned Aerial Vehicles (UAVs). Paramount to this is the stability of the wireless links the UAVs establish with the ground nodes. Lossy and unstable links are costly to maintain the UAVs may exhaust their batteries prematurely. In this paper, we experimentally investigate the stability of aerial-to-ground links and propose a stochastic model to predict link quality as a function of time. The model can be used to estimate goodput, determine mission duration, and estimate short-term link connection and disconnection durations.

The future, in which mobile robots and human beings intermingle in industrial complexes, shopping malls, airports, and similar areas, is not far. The condition, however, requires the realization of several features, including self-localization, self-navigation, identification and avoidance of obstacles, and dynamic route discovery. Indoor localization has been the subject of interest for over two decades, most recent advances attempting to take advantage of freely available signals from a plethora of indoor sources. As far as the estimation task is concerned, most of the models employ extended kalman filters and particle filters. Each technique has its own merits and demerits, as well as a set of assumptions. The purpose of this paper to identify the most significant components of these techniques and to closely examine the prevailing assumptions underlying the selection process of indoor localization techniques. Moreover, the paper experimentally demonstrates the error modeling process in kalman and particle filters.

Wireless sensor networks accommodating the mobility of nodes will play important roles in the future. In residential, rehabilitation, and clinical settings, sensor nodes can be attached to the body of a patient for long-term and uninterrupted monitoring of vital biomedical signals. Likewise, in industrial settings, workers as well as mobile robots can carry sensor nodes to augment their perception and to seamlessly interact with their environments. Nevertheless, such applications require reliable communications as well as high throughput. Considering the primary design goals of the sensing platforms (low-power, affordable cost, large-scale deployment, longevity, operating in the ISM band), maintaining reliable links is a formidable challenge. This challenge can partially be alleviated if the nature of link quality fluctuation can be known or estimated on time. Indeed, higher-level protocols such as handover and routing protocols rely on knowledge of link quality fluctuation to seamlessly transfer communication to alternative routes when the quality of existing routes deteriorates. In this article, we present the result of extensive experimental study to characterise link quality fluctuation in mobile environments. The study focuses on slow movements (<5 km h -1 ) signifying the movement of people and robots and transceivers complying to the IEEE 802.15.4 specification. Hence, we deployed mobile robots that interact with strategically placed stationary relay nodes. Our study considered different types of link quality characterisation metrics that provide complementary and useful insights. To demonstrate the usefulness of our experiments and observations, we implemented a link quality estimation technique using a Kalman Filter. To set up the model, we employed two link quality metrics along with the statistics we established during our experiments. The article will compare the performance of four proposed approaches with ours.

Wireless sensor networks are useful for a large number of healthcare applications which require mobile nodes. Most existing applications rely on body area networks (BAN) which require the presence of additional devices, such as mobile phones, to transfer data from the BAN to a remote base station or a server. In this paper we propose to extend BAN with personal area networks (PAN) so that enhanced mobility and seamless collection of data can be possible. In order to improve the reliability of this merge and support a high goodput, we also propose a seamless handover mechanism which enables mobile transmitters to discover and transfer communication to reliable relay nodes when the quality of an existing link deteriorates. In this paper we shall report how we implemented our scheme for TinyOS and TelosB platforms and compared it with four other competitive schemes.

Wireless sensor networks supporting the free mobility of nodes can be useful for several applications. For example, in residential areas and rehab centres, sensors can be attached to subjects to monitor their movements, body exertions, and cardiac activities. These benefits, however, are also challenged by the difficulty of establishing reliable and stable links. In cellular networks, mobile stations are always associated with the nearest base station through intra-and inter-cellular handover. The underlying process is that the quality of an established link is continually evaluated and handover decisions are dully made by resource rich base stations. In wireless sensor networks, should a seamless handover be carried out, the task has to be accomplished by energy constraint, resource-limited, and low-power wireless sensor nodes in a distributed manner. In this paper we propose a sender-initiated mobility management protocol to enable seamless handover. We have fully implemented the protocol in TinyOS environment for the TelosB and Imote2 platforms, experiment results showing that our protocol achieves high reliability and triggers less handover requests (less than 50% to 80%) compared to three state-of-the-arts. Furthermore, our protocol reduces the signalling overhead by up to 95%.

Wireless sensor networks supporting aggressive sampling in harsh environments (to deal with high packet loss or to provide reliable data from sensors such as 3D accelerometers and 3D gyroscopes) require transmission schemes which can achieve a relatively high throughput. Existing contention-based, low-power listening MAC protocols are not apt for these types of networks because their channel utilisation is considerably low. In this paper we propose a hybrid burst transmission scheme to achieve high throughput between static relay nodes, such as nodes deployed on a civil infrastructure (bridge or building). Our transmission scheme deals with link quality fluctuations and adaptively adjust the number of packets that can be transmitted in burst. Its essential features are relying on statistics that are obtained (1) offline and reflect the long-term characteristic of a link and (2) online and reflect the short-term link quality fluctuation. We experimentally compared our transmission scheme with two proposed state-of-the-art schemes in terms of throughput, transmission delay, packet loss, and energy consumption. We implemented all transmission schemes and integrated them into the TinyOS environment and the TelosB platform.

Several reproducible experiments are required before actual deployment of wireless sensor networks takes place if stable and predictable outcomes of protocols and data processing algorithms are desired. Considering the typical size of wireless sensor networks and the number of parameters that can be configured or tuned, conducting repeated and reproducible experiments can be both time consuming and costly. The conventional way of evaluating the performance of different protocols and algorithms under different network configurations is by changing the source code and reprogramming the testbed, which requires some effort. In this paper, we propose a traffic flow control management system that facilitates the execution of repeated experiments in an efficient and flexible way. We implemented our system on top of TinyOS for the TelosB platform and demonstrated the scope and usefulness of the system by conducting several experiments in two real testbeds.

Understanding fluctuations of link quality in a wireless sensor network is useful for different reasons. For example, nodes can determine when and for how long they should transmit packets, so that they can reduce packet loss rate and the cost of retransmission (delay as well as power consumption). However, because the quality of a link depends on many factors, it cannot be known except in a probabilistic sense. In this paper we estimate the expected duration in which the quality of a specific link remains stable using the conditional distribution function of the signal-to-noise ratio (SNR) of received acknowledgment packets. We employ the expected duration to determine how long nodes should transmit packet in burst and how long they should refrain from contention. To develop our model, we deployed Imote2 sensor platforms in indoor and outdoor places and transmitted more than 70, 000 packets. We transmitted additional 16,900 packets to test our model. 90% of the time, our approach resulted in high packet delivery compared with the case in which packets were transmitted without knowledge of link quality fluctuations.

The energy consumption of a wireless sensor network affects its lifetime which in turn affects the scope and usefulness of the network. Most existing or proposed MAC protocols enable nodes to specify a duty cycle, so that they can sleep much of the time to save energy. However, only very few models exist to determine the appropriate time and duration of a sleep phase. Existing approaches rely on pre-calculated sleep durations or are difficult to implement on real platforms. We propose a runtime and adaptive model to estimate the sleep time and duration of wireless sensor nodes. Our model takes the statistics of incoming and outgoing packets at a relay node which is then supplied to a general queueing model. The model is lightweight and can be fitted into any existing MAC protocol. We have implemented our model for TelosB platform and TinyOS environment. We integrated our model with two existing protocols (TinyOS LPL MAC and XMAC) and compared the performance of these protocols with and without our model. The performance evaluation results show that the energy consumption of a relay node reduced by 11.4 - 64.8%. The overall throughput of the network increased by up to 24%. Moreover, our model readily responded to changes in packet traffic rate while at the same time increasing the packet transmission reliability by 64.5 - 67.4% for different traffic scenarios.

This paper addresses link quality fluctuation and its impact on the packet delivery capacity of wireless sensor networks. Independent studies have previously confirmed that link quality fluctuates even in a static deployment and understanding stable durations, good and bad alike, can contribute to the efficient transmission of packets. We propose a two stage Markov model to characterise link quality fluctuation and to determine when and for how long nodes should transmit packets in burst. Both to develop and test our model, we deployed a wireless sensor network consisting of 14 nodes in a garden and transmitted more than 120,000 packets with different links. The experiment results confirm that our approach improved the packet delivery capacity of the links by up to 40% when compared with a baseline and by up to 25% when compared with a scheme that employs conditional distribution functions.

We propose a MAC protocol that supports the mobility of nodes in wireless sensor networks. The protocol enables burst transmission and seamless handover to achieve high throughput and to reduce packet delivery latency and packet loss. An adaptive filter continuously evaluates the RSSI values of received acknowledgment packets and decides whether a mobile node should transfer a communication to a nearby relay node with a better link quality. The handover process itself takes place without breaking an existing link. This paper presents the design, implementation and evaluation of the MAC protocol.