Energy harvesting is a promising solution to prolong the operation time of energy-constrained wireless networks. In particular, scavenging energy from ambient radio signals, namely wireless energy harvesting (WEH), has recently drawn significant attention. In this paper, we consider a point-to-point wireless link over the flat-fading channel, where the receiver has no fixed power supplies and thus needs to replenish energy via WEH from the signals sent by the transmitter. We first consider a SISO (single-input single-output) system where the singleantenna receiver cannot decode information and harvest energy independently from the same signal received. Under this practical constraint, we propose a dynamic power splitting (DPS) scheme, where the received signal is split into two streams with adjustable power levels for information decoding and energy harvesting separately based on the instantaneous channel condition that is assumed to be known at the receiver. We derive the optimal power splitting rule at the receiver to achieve various trade-offs between the maximum ergodic capacity for information transfer and the maximum average harvested energy for power transfer, which are characterized by the boundary of a so-called "rateenergy" region. Moreover, for the case when the channel state information is also known at the transmitter, we investigate the joint optimization of transmitter power control and receiver power splitting. The achievable rate-energy (R-E) region by the proposed DPS scheme is compared against that by the existing time switching scheme as well as a performance upper bound by ignoring the practical receiver constraint. Finally, we extend the result for DPS to the SIMO (single-input multiple-output) system where the receiver is equipped with multiple antennas. In particular, we investigate a low-complexity power splitting scheme, namely antenna switching, which can be practically implemented to achieve the near-optimal rate-energy trade-offs as compared to the optimal DPS.

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Electric vehicles (EVs) are expected to become widespread in future years. Thus, it is foreseen that EVs will become the new high-electricity-consuming appliances in the households. The characteristics of the extra power load that they impose on the distribution grid follow the patterns of people's random usage behaviors. In this paper, we seek to provide answers to the following question: assigning real-world randomness to the EVs' availability in the households and their charging requirements, how can EVs' demand response (DR) help to minimize the peak power demand and, in general, shape the aggregated demand profile of the system? We present a general demandshaping problem applicable for limit order bids to a day-ahead (DA) energy market. We propose an algorithm for distributed DR of the EVs to shape the daily demand profile or to minimize the peak demand. Additionally, we put these problems in a game framework. Extensive simulations show that, for certain practical distributions of EVs' usage, it is possible to accommodate EVs for all the users in the system and yet achieve the same peak demand as when there is no EV in the system without any changes in the users' commuting behaviors.Index Terms-Day-ahead (DA) market, demand response (DR), electric vehicle (EV), flexible load, limit order bids, random usage patterns, residential load, smart grids, vehicle-to-grid (V2G).

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Electric vehicles (EVs) add significant load on the power grid as they become widespread. The characteristics of this extra load follow the patterns of people's driving behaviours. In particular, random parameters such as arrival time and charging time of the vehicles determine their expected charging demand profile from the power grid. In this paper, we first develop a model for uncoordinated charging power demand of an EV based on a stochastic process in order to characterize its expected daily power demand profile. Next, we illustrate it for different charging time distributions through simulations. This gives us useful insights into the long-term planning for upgrading the power systems' infrastructure to accommodate EVs. Then, we incorporate departure time as another random variable into this modelling and introduce an autonomous demand response (DR) technique to manage the EVs' charging demand. Our results show that, it is possible to accommodate a large number of EVs and achieve the same peak-to-average ratio (PAR) in daily aggregated power consumption of the grid as when there is no EV in the system without any change in the users' commuting behaviours. We also show that this peak value can be further decreased significantly when we add vehicle-to-grid (V2G) capability in the system.Index Terms-Demand response, electric vehicle, load model, residential load.

The key to a long healthy life is the adoption of a balanced diet and regular exercise. The paper describes the use of a Body Sensor Network (BSN) for real-time corrective feedback during the performance of an exercise routine. The feedback utility will log important parameters during an exercise session to allow one to keep track of fitness progress. Inclusion of vital sign sensors such as pulse sensors avoid over exertion and exercise mishaps. A means of creating new exercise routines easily is an additional important functionality.We detail the design of a working prototype exercise system, showing how it differs from related work. We follow up with a study of its effectiveness. The system based on a combination of accelerometers and ultrasound hardware is shown to recognize correctly a subset of exercises.