Unlike terrestrial networks that mainly rely on radio waves for communications, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, the use of acoustic waves pose a new research challenge in the networking area. While existing network schemes for terrestrial sensor networks are mainly designed for negligible propagation delay and high data rate, underwater acoustic communications are characterized by high propagation delay and low data rate. These terrestrial schemes, when directly applied to the underwater channel, will under-utilize its already limited capacity. We investigate how the underwater channel's throughput may be enhanced via medium access control (MAC) techniques that consider its unique characteristics. Specifically, we study the performance of Aloha-based protocols in underwater networks, and propose two enhanced schemes, namely, Aloha with collision avoidance (Aloha-CA), and Aloha with advance notification (Aloha-AN), which are capable of using the long propagation delays to their advantage. Simulation results have shown that both schemes can boost the throughput by reducing the number of collisions, and, for the case of Aloha-AN, also by significantly reducing the number of unproductive transmissions.

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Abstract-Fingerprint-based methods are widely adopted for indoor localization purpose because of their cost-effectiveness compared to other infrastructure-based positioning systems. However, the popular location fingerprint, Received Signal Strength (RSS), is observed to differ significantly across different devices' hardware even under the same wireless conditions. We derive analytically a robust location fingerprint definition, the Signal Strength Difference (SSD), and verify its performance experimentally using a number of different mobile devices with heterogeneous hardware. Our experiments have also considered both Wi-Fi and Bluetooth devices, as well as both access-point-based localization and mobile-node-assisted localization. We present the results of two well-known localization algorithms (K Nearest Neighbor and Bayesian Inference) when our proposed fingerprint is used, and demonstrate its robustness when the testing device differs from the training device. We also compare these SSD based localization algorithms' performance against that of two other approaches in the literature that are designed to mitigate the effects of mobile node hardware variations, and show that SSD based algorithms have better accuracy.

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).

Abstract-Indoor localization techniques using location fingerprints are gaining popularity because of their cost-effectiveness compared to other infrastructure-based location systems. However, their reported accuracy fall short of their counterparts. In this paper, we investigate many aspects of fingerprint-based location systems in order to enhance their accuracy. First, we derive analytically a robust location fingerprint definition, and then verify it experimentally as well. We also devise a way to facilitate under-trained location systems through simple linear regression technique. This technique reduces the training time and effort, and can be particularly useful when the surrounding or setup of the localization area changes. We further show experimentally that because of the positions of some access points or the environmental factors around them, their signal strength correlates nicely with distance. We argue that it would be more beneficial to give special consideration to these access points for location computation, owing to their ability to distinguish locations distinctly in signal space. The probability of encountering such access points will be even higher when we denote a location's signature using the signals of multiple wireless technologies collectively. We present the results of two well-known localization algorithms (K-Nearest Neighbor and Bayesian Probabilistic Model) when the above factors are exploited, using Bluetooth and Wi-Fi signals. We have observed significant improvement in their accuracy when our ideas are implemented.

Abstract-Although there are many MAC protocols that have been proposed for terrestrial wireless networks with a wide variety of aspects, these protocols cannot be applied directly in underwater acoustic networks due to the channel's uniqueness of having low data rate and long propagation delay. In order to achieve a high throughput, both characteristics must be taken into account in the MAC design. We propose a random access MAC protocol for multi-hop underwater acoustic networks based on receiver reservation, which we shall call the "Receiverinitiated Packet Train" (RIPT) protocol. It is a handshakingbased protocol that addresses the channel's long propagation delay characteristic by utilizing receiver-initiated reservations, as well as by coordinating packets from multiple neighboring nodes to arrive in a packet train manner at the receiver. Our simulation results have confirmed that the RIPT protocol can achieve our goal of having high and stable throughput performance while maintaining low collision rate.

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Abstract-Unlike terrestrial wireless communication which uses radio waves, underwater communication relies on acoustic waves. The long latency and limited bandwidth pose great challenges in underwater Media Access Control (MAC) protocol design. As a result, terrestrial MAC protocols perform inefficiently when deployed directly in an underwater environment. In this paper, we examine how an existing asynchronous handshaking based protocol called Multiple Access Collision Avoidance (MACA) can be adapted for use in multi-hop underwater networks. Three areas of improvement are investigated, namely, the state transition rules, the packet forwarding strategy, and the backoff algorithm. Throughput performance is also evaluated through extensive simulation in multi-hop underwater networks. Due to its simplicity and throughput stability, our proposed MAC protocol can be adopted as a reference MAC protocol for underwater networks, with which a more sophisticated underwater MAC may benchmark its performance.