Numerous energy harvesting wireless devices that will serve as building blocks for the Internet of Things (IoT) are currently under development. However, there is still only limited understanding of the properties of various energy sources and their impact on energy harvesting adaptive algorithms. Hence, we focus on characterizing the kinetic (motion) energy that can be harvested by a wireless node with an IoT form factor and on developing energy allocation algorithms for such nodes. In this paper, we describe methods for estimating harvested energy from acceleration traces. To characterize the energy availability associated with specific human activities (e.g., relaxing, walking, cycling), we analyze a motion dataset with over 40 participants. Based on acceleration measurements that we collected for over 200 hours, we study energy generation processes associated with day-long human routines. We also briefly summarize our experiments with moving objects. We develop energy allocation algorithms that take into account practical IoT node design considerations, and evaluate the algorithms using the collected measurements. Our observations provide insights into the design of motion energy harvesters, IoT nodes, and energy harvesting adaptive algorithms.

Object tracking applications are gaining popularity and will soon utilize Energy Harvesting (EH) low-power nodes that will consume power mostly for Neighbor Discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges posed by emerging EH low-power transceivers were not addressed. Therefore, we design an ND protocol tailored for the characteristics of a representative EH prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND accounting for unique prototype characteristics (i.e., energy costs for transmission/reception, and transceiver state switching times/costs). Then, we present the Power Aware Neighbor Discovery Asynchronously (Panda) protocol in which nodes transition between the sleep, receive, and transmit states. We analyze Panda and select its parameters to maximize the ND rate subject to a homogenous power budget. We also present Panda-D, designed for non-homogeneous EH nodes. We perform extensive testbed evaluations using the prototypes and study various design tradeoffs.We demonstrate a small difference (less then 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3x compared to related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting. Index TermsNeighbor discovery, energy harvesting, wireless A partial and preliminary version of this paper will appear in IEEE INFOCOM'16 [1], April 2016.

Wireless communication systems, such as RFIDs and wireless sensor networks, are increasingly being used in security-sensitive applications, e.g. credit card transactions or monitoring patient health in hospitals. Wireless jamming by transmitting artificial noise, which is traditionally used as an offensive technique for disrupting communication, has recently been explored as a means of protecting sensitive communication from eavesdroppers.In this paper, we consider location optimization problems related to the placement and power consumption of such friendly jammers in order to protect the privacy of wireless communications constrained within a geographic region. Under our model, we show that the problem of placing a minimum number of fixed-power jammers is NP-Hard, and we provide a PTAS ((1 + ")-approximation scheme) for the same, where " is a tunable parameter between 0 and 1.

Abstract-In this paper we define and address a new problem that arises when a base station in a broadband wireless network wishes to multicast information to a large group of nodes and to guarantee some level of reliability using Application layer FEC codes. Every data block to be multicast is translated into a sequence of K + n packets, from which every receiver must receive at least K in order to correctly decode the block. The new problem is to determine which PHY layer MCS (Modulation and Coding Scheme) the base station should use for each packet. We present several variants of this problem, which differ in the number of ARQ (Automatic Repeat reQuest) rounds during which the delivery of a data block must be completed. Most of these variants are shown to be NP-hard. However, we present optimal solutions for practical instances, where the number of MCSs is small, and efficient approximations and heuristics for the general case of each variant.

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Abstract-In modern broadband cellular networks, the omni-directional antenna at each cell is replaced by 3 or 6 directional antennas, one in every sector. While every sector can run its own scheduling algorithm, bandwidth utilization can be significantly increased if a joint scheduler makes these decisions for all the sectors. This gives rise to a new problem, referred to as "joint scheduling," addressed in this paper for the first time. The problem is proven to be NP-hard, but we propose efficient algorithms with a worstcase performance guarantee for solving it. We then show that the proposed algorithms indeed substantially increase the network throughput.

Abstract-In this paper we define and address a new problem that arises when a base station in a broadband wireless network wishes to multicast information to a large group of nodes and to guarantee some level of reliability using Application layer FEC codes. Every data block to be multicast is translated into a sequence of K + n packets, from which every receiver must receive at least K in order to correctly decode the block. The new problem is to determine which PHY layer MCS (Modulation and Coding Scheme) the base station should use for each packet. We present several variants of this problem, which differ in the number of ARQ (Automatic Repeat reQuest) rounds during which the delivery of a data block must be completed. Most of these variants are shown to be NP-hard. However, we present optimal solutions for practical instances, where the number of MCSs is small, and efficient approximations and heuristics for the general case of each variant.

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