Hwee-Pink Tan

Wireless sensor networks powered by ambient energy harvesting (WSN-HEAP) - Survey and challenges

Wireless sensor networks (WSNs) research has pre-dominantly assumed the use of a portable and limited energy source, viz. batteries, to power sensors. Without energy, a sensor is essentially useless and cannot contribute to the utility of the network as a whole. Consequently, substantial research efforts have been spent on designing energy-efficient networking protocols to maximize the lifetime of WSNs. However, there are emerging WSN applications where sensors are required to operate for much longer durations (like years or even decades) after they are deployed. Examples include in-situ environmental/habitat monitoring and structural health monitoring of critical infrastructures and buildings, where batteries are hard (or impossible) to replace/recharge. Lately, an alternative to powering WSNs is being actively studied, which is to convert the ambient energy from the environment into electricity to power the sensor nodes. While renewable energy technology is not new (e.g., solar and wind) the systems in use are far too large for WSNs. Those small enough for use in wireless sensors are most likely able to provide only enough energy to power sensors sporadically and not continuously. Sensor nodes need to exploit the sporadic availability of energy to quickly sense and transmit the data. This paper surveys related research and discusses the challenges of designing networking protocols for such WSNs powered by ambient energy harvesting.

Sensor OpenFlow: Enabling Software-Defined Wireless Sensor Networks

While it has been a belief for over a decade that wireless sensor networks (WSN) are application-specific, we argue that it can lead to resource underutilization and counter-productivity. We also identify two other main problems with WSN: rigidity to policy changes and difficulty to manage. In this paper, we take a radical, yet backward and peer compatible, approach to tackle these problems inherent to WSN. We propose a Software-Defined WSN architecture and address key technical challenges for its core component, Sensor OpenFlow. This work represents the first effort that synergizes software-defined networking and WSN.

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

Design and performance analysis of MAC schemes for Wireless Sensor Networks Powered by Ambient Energy Harvesting

Energy consumption is a perennial issue in the design of wireless sensor networks (WSNs) which typically rely on portable sources like batteries for power. Recent advances in ambient energy harvesting technology have made it a potential and promising alternative source of energy for powering WSNs. By using energy harvesters with supercapacitors, WSNs are able to operate perpetually until hardware failure and in places where batteries are hard or impossible to replace. In this paper, we study the performance of different medium access control (MAC) schemes based on CSMA and polling techniques for WSNs which are solely powered by ambient energy harvesting using energy harvesters. We base the study on (i) network throughput (S), which is the rate of sensor data received by the sink, (ii) fairness index (F), which determines whether the bandwidth is allocated to each sensor node equally and (iii) inter-arrival time (@c) which measures the average time difference between two packets from a source node. For CSMA, we compare both the slotted and unslotted variants. For polling, we first consider identity polling. Then we design a probabilistic polling protocol that takes into account the unpredictability of the energy harvesting process to achieve good performance. Finally, we present an optimal polling MAC protocol to determine the theoretical maximum performance. We validate the analytical models using extensive simulations incorporating experimental results from the characterization of different types of energy harvesters. The performance results show that probabilistic polling achieves high throughput and fairness as well as low inter-arrival times.

Profit-Maximizing Incentive for Participatory Sensing

We design an incentive mechanism based on all-pay auctions for participatory sensing. The organizer (principal) aims to attract a high amount of contribution from participating users (agents) while at the same time lowering his payout, which we formulate as a profit-maximization problem. We use a contribution-dependent prize function in an environment that is specifically tailored to participatory sensing, namely incomplete information (with information asymmetry), risk-averse agents, and stochastic population. We derive the optimal prize function that induces the maximum profit for the principal, while satisfying strict individual rationality (i.e., strictly have incentive to participate at equilibrium) for both risk-neutral and weakly risk-averse agents. The thus induced profit is demonstrated to be higher than the maximum profit induced by constant (yet optimized) prize. We also show that our results are readily extensible to cases of risk-neutral agents and deterministic populations.

Opportunistic routing in wireless sensor networks powered by ambient energy harvesting

Energy consumption is an important issue in the design of wireless sensor networks (WSNs) which typically rely on portable energy sources like batteries for power. Recent advances in ambient energy harvesting technologies have made it possible for sensor nodes to be powered by ambient energy entirely without the use of batteries. However, since the energy harvesting process is stochastic, exact sleep-and-wakeup schedules cannot be determined in WSNs Powered solely using Ambient Energy Harvesters (WSN-HEAP). Therefore, many existing WSN routing protocols cannot be used in WSN-HEAP. In this paper, we design an opportunistic routing protocol (EHOR) for multi-hop WSN-HEAP. Unlike traditional opportunistic routing protocols like ExOR or MORE, EHOR takes into account energy constraints because nodes have to shut down to recharge once their energy are depleted. Furthermore, since the rate of charging is dependent on environmental factors, the exact identities of nodes that are awake cannot be determined in advance. Therefore, choosing an optimal forwarder is another challenge in EHOR. We use a regioning approach to achieve this goal. Using extensive simulations incorporating experimental results from the characterization of different types of energy harvesters, we evaluate EHOR and the results show that EHOR increases goodput and efficiency compared to traditional opportunistic routing protocols and other non-opportunistic routing protocols suited for WSN-HEAP.

Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors

Motivated by recent developments in Wireless Sensor Networks (WSNs), we present several efficient clustering algorithms for maximizing the lifetime of WSNs, i.e., the duration till a certain percentage of the nodes die. Specifically, an optimization algorithm was proposed for maximizing the lifetime of a single-cluster network, followed by an extension to handle multi-cluster networks. Then we study the joint problem of prolonging network lifetime by introducing energy-harvesting (EH) nodes. An algorithm is proposed for maximizing the network lifetime where EH nodes serve as dedicated relay nodes for cluster heads (CHs). Theoretical analysis and extensive simulation results show that the proposed algorithms can achieve optimal or suboptimal solutions efficiently, and therefore help provide useful benchmarks for various centralized and distributed clustering scheme designs.

Routing and Relay Node Placement in Wireless Sensor Networks Powered by Ambient Energy Harvesting

Energy consumption is an important issue in the design of wireless sensor networks which typically rely on non-renewable energy sources like batteries for power. Recent advances in ambient energy harvesting technologies have made it a viable alternative source of energy for powering wireless sensor networks perpetually. In this paper, we optimize network performance by finding the optimal routing algorithm and relay node placement scheme for wireless sensor networks powered by ambient energy harvesting. We evaluate the performance of three different variants of geographic routing algorithms and consider two relay node placement schemes, viz. uniform string topology and a cluster string topology. The performance metrics are network throughput (T), goodput (G), source sending rate (SR), efficiency (η), data delivery ratio (DR) and hop count (H). Simulation results obtained using the Qualnet simulator show that there is an optimal combination of routing algorithm and relay node placement scheme that maximizes the required performance metric. These results aim to provide insights into the impact of routing algorithms and relay node placement schemes on wireless sensor networks that rely solely on ambient energy harvesting for power.

A Multi-hop ARQ Protocol for Underwater Acoustic Networks

Underwater acoustic networks are envisaged to be the enabling technology for oceanographic data collection, pollution monitoring, offshore exploration and tactical surveillance applications. Unique characteristics of underwater acoustic channels such as large propagation delays and high bit error rates pose a challenge to designing reliable and efficient communication protocols. In this paper, we propose an opportunistic acknowledgement scheme suited for Stop and Wait ARQ protocols and demonstrate using simulations that it achieves better latency and energy efficiency than traditional non-opportunistic schemes for both one and two-dimensional multi-hop acoustic channels.

Modelling Interference Temperature Constraints for Spectrum Access in Cognitive Radio Networks

With the advent of cognitive radio technology, new paradigms for spectrum access can achieve near-optimal spectrum utilisation by letting each user sense and utilise available spectrum opportunistically while regulating the interference it imposes on other users through interference constraints. However, the simplest and most common forms of such constraints are binary and transmitter-centric, which are often inefficient since they only consider pair-wise sets of transmitters. Hence, we propose a non-binary receiver-centric constraint model for spectrum access in cognitive radio networks. Such a model is in line with the recently proposed interference temperature metric that constraints whole subsets of transmitters, thereby permitting interfering signals to be introduced and enabling additional communication, leading to improved spectrum utilisation. These constraints are easy to generate and check, and are currently being used to devise a co-operative negotiated etiquette for cognitive radios offering heterogeneous services in a wireless office networking scenario.