Constantinos Marios Angelopoulos

Wireless energy transfer in sensor networks with adaptive, limited knowledge protocols

We investigate the problem of efficient wireless energy transfer in Wireless Rechargeable Sensor Networks (WRSNs). In such networks a special mobile entity (called the Mobile Charger) traverses the network and wirelessly replenishes the energy of sensor nodes. In contrast to most current approaches, we envision methods that are distributed, adaptive and use limited network information. We propose three new, alternative protocols for efficient charging, addressing key issues which we identify, most notably (i) to what extent each sensor should be charged, (ii) what is the best split of the total energy between the charger and the sensors and (iii) what are good trajectories the Mobile Charger should follow. One of our protocols (LRP) performs some distributed, limited sampling of the network status, while another one (RTP) reactively adapts to energy shortage alerts judiciously spread in the network. We conduct detailed simulations in uniform and non-uniform network deployments, using three different underlying routing protocol families. In most cases, both our charging protocols significantly outperform known state of the art methods, while their performance gets quite close to the performance of the global knowledge method (GKP) we also provide.

Efficient energy management in wireless rechargeable sensor networks

Through recent technology advances in the field of wireless energy transmission, Wireless Rechargeable Sensor Networks (WRSN) have emerged. In this new paradigm for WSNs a mobile entity called Mobile Charger (MC) traverses the network and replenishes the dissipated energy of sensors. In this work we first provide a formal definition of the charging dispatch decision problem and prove its computational hardness. We then investigate how to optimize the trade-offs of several critical aspects of the charging process such as a) the trajectory of the charger, b) the different charging policies and c) the impact of the ratio of the energy the MC may deliver to the sensors over the total available energy in the network. In the light of these optimizations, we then study the impact of the charging process to the network lifetime for three characteristic underlying routing protocols; a greedy protocol, a clustering protocol and an energy balancing protocol. Finally, we propose a Mobile Charging Protocol that locally adapts the circular trajectory of the MC to the energy dissipation rate of each sub-region of the network. We compare this protocol against several MC trajectories for all three routing families by a detailed experimental evaluation. The derived findings demonstrate significant performance gains, both with respect to the no charger case as well as the different charging alternatives; in particular, the performance improvements include the network lifetime, as well as connectivity, coverage and energy balance properties.

A smart system for garden watering using wireless sensor networks

As water supplies become scarce and polluted, there is an urgent need to irrigate more efficiently in order to optimize water use. In this paper, we present a WSN based, smart home-irrigation system that consists of heterogeneous motes, special sensors and actuators. The system is fully adaptive not only to environmental conditions but also to the specific water needs that different plants may have. This way, it manages to perform efficient home irrigation, while it provides an IPv6-capable managing system.

A new random walk for efficient data collection in sensor networks

Motivated by the problem of efficiently collecting data from wireless sensor networks via a mobile sink, we present an accelerated random walk on Random Geometric Graphs. Random walks in wireless sensor networks can serve as fully local, very simple strategies for sink motion that significantly reduce energy dissipation but introduce higher latency in the data collection process. While in most cases random walks are studied on graphs like Gn,p and Grid, we define and experimentally evaluate our newly proposed random walk on the Random Geometric Graphs model, that more accurately abstracts spatial proximity in a wireless sensor network. We call this new random walk the γ-stretched random walk, and compare it to two known random walks; its basic idea is to favour visiting distant neighbours of the current node towards reducing node overlap. We also define a new performance metric called Proximity Cover Time which, along with other metrics such as visit overlap statistics and proximity variation, we use to evaluate the performance properties and features of the various walks.

Traversal Strategies for Wireless Power Transfer in Mobile Ad-Hoc Networks

We investigate the problem of wireless power transfer in mobile ad-hoc networks. In particular we investigate which traversal strategy should a Mobile Charger follow in order to efficiently recharge agents that are randomly and dynamically moving inside an area of interest. We first formally define this problem as the Charger Traversal Decision Problem and prove its computational hardness. We then define a weighting function which evaluates several network parameters in order to prioritize the nodes during the charging process. Based on this function we define three traversal strategies for the MC; a global-knowledge strategy that uses an Integer Linear Program to optimize its trajectory; a global-knowledge strategy which tessellates the network area and prioritizes the charging process over each tile; a local-knowledge strategy that uses local network information collected and ferried distributively by the moving agents. We also evaluate two naive zero-knowledge strategies; a space-filling deterministic one in which the MC systematically sweeps the network area and a randomized one in which the MC performs a blind random walk. We evaluate these strategies both in homogeneous and heterogeneous agent distributions and for various network sizes with respect to number of alive nodes over time, energy distribution among the nodes over time and charging efficiency over distance traveled. Our findings indicate that in small networks network agnostic strategies are sufficient. However, as the network scales the use of local distributed network information achieves good performance-overhead trade-offs.

A user-enabled testbed architecture with mobile crowdsensing support for smart, green buildings

We present an IoT testbed architecture for Smart Buildings that enables the seamless and scalable integration of crowd-sourced resources such as smartphones and tablets. The purpose of this integration is dual. First, the embedded sensory capabilities of the resources provided by the crowd are combined with the sensing capabilities of the building for efficient smart actuations. Second, the system is able to interact with its users in a direct, personal way both for incentivising them to provide sensory data from their devices and to receive feed-back on their preferences and experienced comfort. The above are exposed to the experimenter as a set of services thus providing great agility on developing and evaluating a broad range of use case scenarios. We demonstrate this flexibility by deploying a testbed in the premises of a building and by evaluating several crowd incentive policies in the context of a smart luminance scenario. The scenario is based on Participatory Sensing principles to create live luminance maps, aggregate user preferences and accordingly adjust the luminance units.

Improving sensor network performance with wireless energy transfer

Through recent technology advances in the field of wireless energy transmission wireless rechargeable sensor networks have emerged. In this new paradigm for wireless sensor networks a mobile entity called mobile charger MC traverses the network and replenishes the dissipated energy of sensors. In this work we first provide a formal definition of the charging dispatch decision problem and prove its computational hardness. We then investigate how to optimise the trade-offs of several critical aspects of the charging process. In the light of these optimisations, we study the impact of the charging process to the network lifetime for three characteristic underlying routing protocols. Finally, we propose a mobile charging protocol that locally adapts the circular trajectory of the MC to the energy dissipation rate of each sub-region of the network. We compare this protocol against several MC trajectories by a detailed experimental evaluation. The derived findings demonstrate significant performance gains.

IoT Lab: Towards co-design and IoT solution testing using the crowd

IoT Lab is a European funded project researching the potential of crowdsourcing as an extension to the traditional IoT testbed infrastructures. The project proposes an innovative co-design, implementation and testing of solutions with the close involvement of the crowd in the process. Through the use of a smart phone application the crowd can participate in experiments by contributing with sensory data and knowledge. The IoT Lab platform leveraged the IoT ARM (architecture reference model) design framework to create an initial architecture that includes virtualization of crowdsourcing and testbed components as well as ability to federate with other testbeds.

Characteristic utilities, join policies and efficient incentives in Mobile Crowdsensing Systems

In this paper we identify basic design issues of Mobile Crowdsensing Systems (MCS) and investigate some characteristic challenges. We define the basic components of an MCS - the Task, the Server and the Crowd - and investigate the functions describing/governing their interactions. We identify three qualitatively different types of Tasks; a) those whose added utility is proportional to the size of the Task, b) those whose added utility is proportional to the progress of the Task and c) those whose added utility is reversely proportional to the progress of the Task. For a given type of Task, and a finite Budget, the Server makes offers to the agents of the Crowd based on some Incentive Policy. On the other hand, each agent that receives an offer decides whether it will undertake the Task or not, based on the inferred cost (computed via a Cost function) and some Join Policy. In their policies, the Crowd and the Server take into account several aspects, such as the number and quality of participating agents, the progress of execution of the Task and possible network effects, present in real-life systems. We evaluate the impact and the performance of selected characteristic policies, for both the Crowd and the Server, in terms of Task execution and Budget efficiency of the Crowd. Experimental findings demonstrate key performance features of the various policies and indicate that some policies are more effective in enabling the Server to efficiently manage its Budget while providing satisfactory incentives to the Crowd and effectively executing the system Tasks. Interestingly, incentive policies that take into account the current crowd participation achieve a better trade-off between Task completion and budget expense.

Design and evaluation of characteristic incentive mechanisms in Mobile Crowdsensing Systems

Abstract In this paper we identify basic design issues of Mobile Crowdsensing Systems and investigate some characteristic challenges. We define the basic components of an MCS – the Task, the Server and the Crowd – and investigate the functions describing/governing their interactions. We identify three qualitatively different types of Tasks; (a) those whose added utility is proportional to the size of the Task, (b) those whose added utility is proportional to the progress of the Task and (c) those whose added utility is reversely proportional to the progress of the Task. For a given type of Task, and a finite Budget, the Server makes offers to the agents of the Crowd based on some Incentive Policy. On the other hand, each agent that receives an offer decides whether it will undertake the Task or not, based on the inferred cost (computed via a Cost function) and some Join Policy. In their policies, the Crowd and the Server take into account several aspects, such as the number and quality of participating agents, the progress of execution of the Task and possible network effects, present in real-life systems. We evaluate the impact and the performance of selected characteristic policies, for both the Crowd and the Server, in terms of Task execution, Budget efficiency and Workload balance of the Crowd. Experimental findings demonstrate key performance features of the various policies and indicate that some policies are more effective in enabling the Server to efficiently manage its Budget while providing satisfactory incentives to the Crowd and effectively executing the system Tasks. Interestingly, incentive policies that take into account the current crowd participation achieve a better trade-off between Task completion and budget expense.