Theofanis P. Raptis

Distributed wireless power transfer in sensor networks with multiple Mobile Chargers

We investigate the problem of efficient wireless power transfer in wireless sensor networks. In our approach, special mobile entities (called the Mobile Chargers) traverse the network and wirelessly replenish the energy of sensor nodes. In contrast to most current approaches, we envision methods that are distributed and use limited network information. We propose four new protocols for efficient charging, addressing key issues which we identify, most notably (i) what are good coordination procedures for the Mobile Chargers and (ii) what are good trajectories for the Mobile Chargers. Two of our protocols (DC, DCLK) perform distributed, limited network knowledge coordination and charging, while two others (CC, CCGK) perform centralized, global network knowledge coordination and charging. As detailed simulations demonstrate, one of our distributed protocols outperforms a known state of the art method, while its performance gets quite close to the performance of the powerful centralized global knowledge method.

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.

Hierarchical, collaborative wireless energy transfer in sensor networks with multiple Mobile Chargers

Wireless energy transfer is used to fundamentally address energy management problems in Wireless Rechargeable Sensor Networks (WRSNs). In such networks mobile entities traverse the network and wirelessly replenish the energy of sensor nodes. In recent research on collaborative wireless charging, the mobile entities are also allowed to charge each other.In this work, we enhance the collaborative feature by forming a hierarchical charging structure. We distinguish the Chargers in two groups, the hierarchically lower Mobile Chargers which charge sensor nodes and the hierarchically higher Special Chargers which charge Mobile Chargers. We define the Coordination Decision Problem and prove that it is NP-complete. Also, we propose a new protocol for 1-D networks which we compare with a state of the art protocol. Motivated by the improvement in 1-D networks, we propose and implement four new collaborative charging protocols for 2-D networks, in order to achieve efficient charging and improve important network properties. Our protocols are either centralized or distributed, and assume different levels of network knowledge.Extensive simulation findings demonstrate significant performance gains, with respect to non-collaborative state of the art charging methods. In particular, our protocols improve several network properties and metrics, such as the network lifetime, routing robustness, coverage and connectivity. A useful feature of our methods is that they can be suitably added on top of non-collaborative protocols to further enhance their performance.

Hierarchical, collaborative wireless charging in sensor networks

Wireless power transfer is used to fundamentally address energy management problems in Wireless Rechargeable Sensor Networks. In such networks mobile entities traverse the network and wirelessly replenish the energy of sensor nodes. In recent research on collaborative wireless charging, the mobile entities are also allowed to charge each other. In this work, we enhance the collaborative feature by forming a hierarchical charging structure. We distinguish the chargers into two groups, the hierarchically lower Mobile Chargers (MCs) which charge sensor nodes and the hierarchically higher Special Chargers (SCs) which charge MCs. We propose and implement four new collaborative charging protocols, in order to achieve efficient charging and improve important network properties. Our protocols are either centralized or distributed, and assume different levels of network knowledge. Extensive simulation findings demonstrate significant performance gains, with respect to non-collaborative state of the art charging methods. In particular, our protocols improve several network properties and metrics, such as the network lifetime, routing robustness, coverage and connectivity. A useful feature of our methods is that they can be suitably added on top of non-collaborative protocols to further enhance their performance.

Efficient, distributed coordination of multiple mobile chargers in sensor networks

We investigate the problem of efficient wireless energy recharging in Wireless Rechargeable Sensor Networks (WRSNs). In such networks special mobile entities (called the Mobile Chargers) traverse the network and wirelessly replenish the energy of sensor nodes. In contrast to most current approaches, we envision methods that are distributed and use limited network information. We propose four new protocols for efficient recharging, addressing key issues which we identify, most notably (i) what are good coordination procedures for the Mobile Chargers and (ii) what are good trajectories for the Mobile Chargers. Two of our protocols (DC, DCLK) perform distributed, limited network knowledge coordination and charging, while two others (CC, CCGK) perform centralized, global network knowledge coordination and charging. As detailed simulations demonstrate, one of our distributed protocols outperforms a known state of the art method, while its performance gets quite close to the performance of the powerful centralized global knowledge method.

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.