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 homogeneous 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.

Object tracking applications are gaining popularity and will soon utilize energy harvesting low-power wireless nodes where power is mostly consumed for neighbor discovery. Such applications require the design and experimentation with low-power neighbor discovery protocols. We demonstrate the Panda protocol [4, 5] implementation using commercial off-the-shelf energy harvesting devices, based on the TI eZ430-RF2500-SEH prototype. The prototypes harvest indoor light energy to perform power-aware neighbor discovery, while maintaining a power budget. A custom-designed online monitoring system interactively demonstrates the network dynamics, including the energy storage levels of the devices, the neighbor discovery events, and aggregate discovery statistics.

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 tailoredfor 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 ND 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 homogeneous 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 than 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3× compared with related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting.

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.

Security-sensitive applications, such as patient health monitoring and credit card transactions, are increasingly utilizing wireless communication systems, RFIDs, wireless sensor networks, and other wireless communication systems. The use of interference-emitting jammers to protect these sensitive communications has been recently explored in the literature, and has shown high potential. In this paper we consider optimization problems relating to the temporal distributions of jammers' activity, and the suitable coding regimes used for communication. Solving the joint problem optimally enables comprehensive security in space, at a low power consumption and low communication overhead. The joint optimization of jamming in space and time is driven by a new framework that uses the bit-error probability as a measure of communication quality. Under this framework, we show how to guarantee information-theoretic security within a geographic region, and with increased flexibility to tailor the coding regime to the problem's geometry. We present efficient algorithms for different settings, and provide simulations for various scenarios using the bit-error probability functions. These simulations demonstrate the efficiency of the scheme. We believe that our scheme can lead to practical, economical and scalable solutions for providing another layer of protection of sensitive data, in cases where encryption schemes are limited or impractical.

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.

In modern broadband cellular networks, the omnidirectional antenna at each cell is replaced by three or six 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 worst-case performance guarantee for solving it. We then show that the proposed algorithms indeed substantially increase the network throughput.

Advanced wireless technologies such as multiple-input-multiple-output (MIMO) require each mobile station (MS) to send a lot of feedback to the base station. This periodic feedback consumes much of the uplink bandwidth. This expensive bandwidth is very often viewed as a major obstacle to the deployment of MIMO and other advanced closed-loop wireless technologies. This paper is the first to propose a framework for efficient allocation of periodic feedback channels to the nodes of a wireless network. Several relevant optimization problems are defined and efficient algorithms for solving them are presented. A scheme for deciding when the base station (BS) should invoke each algorithm is also proposed and shown through simulations to perform very well.

LTE Advanced and other 4G cellular standards allow relay nodes (RNs) to be deployed as a substitute for base stations (BSs). Unlike a BS, an RN is not directly connected to the backbone. Rather, each RN is associated with a donor BS, to which it is connected through the OFDMA wireless link. A very important task in the operation of a wireless network is packet scheduling. In a network with RNs, such scheduling decisions must be made in each cell not only for the BS, but also for the RNs. Because the scheduler in a network with RNs must take into account the transmission resources of the BS and the RNs, it needs to find a feasible schedule that does not exceed the resources of a multidimensional resource pool. This makes the scheduling problem computationally harder than in a network without RNs. In this paper, we define and study the packet-level scheduling problem for a network with RNs. This problem is not only NP-hard, but also admits no efficient polynomial-time approximation scheme. To solve it, we propose an efficient algorithm with a performance guarantee and a simple water-filling heuristic. To the best of our knowledge, our algorithm is the first packet-level scheduling algorithm that provides a performance guarantee for a network with RNs. Using simulations, we evaluate our new algorithms and show that they perform very well.

LTE-advanced and other 4G cellular standards allow relay nodes (RNs) to be deployed as a substitute for base stations (BSs). Unlike a BS, an RN is not directly connected to the backbone. Rather, each RN is associated with a donor BS, to which it is connected through the OFDMA wireless link. A very important task in the operation of a wireless network is packet scheduling. In a network with RNs, such scheduling decisions must be made in each cell not only for the BS, but also for the RNs. Because the scheduler in a network with RNs must take into account the transmission resources of the BS and the RNs, it needs to find a feasible schedule that does not exceed the resources of a multi-dimensional resource pool. This makes the scheduling problem computationally harder than in a network without RNs. In this paper we define and study for the first time the packet-level scheduling problem for a network with RNs. This problem is shown to be not only NP-hard, but also very hard to approximate. To solve it, we propose an approximation with a performance guarantee, and a simple water-filling heuristic. Using simulations, we evaluate our new algorithms and show that they perform very well.

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.

In an OFDMA network, the modulation and coding scheme (MCS) of the messages sent to the mobile stations (MSs) varies according to channel condition. To determine the appropriate MCS level, the base station (BS) allocates to every active MS a CQI (Channel Quality Information) channel. The CQI bandwidth is a scarce resource whose allocation must be adjusted to the actual needs of the MSs. However, allocations and deallocations of CQI channels require expensive signaling messages between the BS and the MSs, and therefore should be minimized. In this paper we propose a framework for the management of the CQI bandwidth by the BS. We identify three related optimization problems and propose efficient algorithms for solving them.

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 forward error correction (FEC) codes. Every data block to be multicast is translated into a sequence of packets, from which every receiver must receive at least in order to correctly decode the block. The new problem is to determine which PHY-layer modulation and coding scheme (MCS) the base station should use for each packet. We present several variants of this problem, which differ in the number of automatic repeat request (ARQ) 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.

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.