The IEEE 802.11ad standard extends WiFi operation to the millimeter wave frequencies, and introduces novel features concerning both the physical (PHY) and Medium Access Control (MAC) layers. The hybrid MAC layer provides for two different kinds of resource allocations: Contention Based Access Periods (CBAPs) and contention-free Service Periods (SPs). In this paper, we propose a Markov Chain model to represent CBAPs, taking into account operation interruptions due to scheduled SPs and the deafness and hidden node problems that directional communication exacerbates. We also propose a mathematical analysis to evaluate the interference among stations and derive analytical expressions to assess the impact of various system parameters on some key performance metrics such as throughput, delay, and packet drop rate. This information may be used to efficiently design a transmission scheduler that allocates contention-based and contention-free periods based on the application requirements.

Bike-sharing systems have become an established reality in cities all across the world and are a key component of the Smart City paradigm. However, the unbalanced traffic patterns during rush hours can completely empty some stations, while filling others, and the service becomes unavailable for further users. The traditional approach to solve this problem is to use rebalancing trucks, which take bikes from full stations and deposit them at empty ones, reducing the likelihood of system outages. Another paradigm that is gaining steam is gamification, i.e., incentivizing users to fix the system by influencing their behavior with rewards and prizes. In this work, we combine the two efforts and show that a joint optimization considering both rebalancing and incentives results in a higher service quality for a lower cost than using simple rebalancing. We use simulations based on the New York CitiBike usage data to validate our model and analyze several schemes to optimize the bike-sharing system.

Jamming may become a serious threat in Internet of Things networks of battery-powered nodes, as attackers can disrupt packet delivery and significantly reduce the lifetime of the nodes. In this work, we model an active defense scenario in which an energy-limited node uses power control to defend itself from a malicious attacker, whose energy constraints may not be known to the defender. The interaction between the two nodes is modeled as an asymmetric Bayesian game where the victim has incomplete information about the attacker. We show how to derive the optimal Bayesian strategies for both the defender and the attacker, which may then serve as guidelines to develop and gauge efficient heuristics that are less computationally expensive than the optimal strategies. For example, we propose a neural-network-based learning method that allows the node to effectively defend itself from the jamming with a significantly reduced computational load. The outcomes of the ideal strategies highlight the tradeoff between node lifetime and communication reliability and the importance of an intelligent defense from jamming attacks.

The collision resolution mechanism in the Random Access Channel (RACH) procedure of the Long-Term Evolution (LTE) standard is known to represent a serious bottleneck in case of Machine-Type Communication (MTC). Its main drawbacks are seen in the facts that Base Stations (eNBs) typically cannot infer the number of collided User Equipments (UEs) and that collided UEs learn about the collision only implicitly, through the lack of the feedback in the later stage of the RACH procedure. The collided UEs then restart the procedure, increasing the RACH load and making the system more prone to collisions. In this paper, we leverage machine learning techniques to design a system that, besides outperforming the state-of-the-art schemes in preamble detection for the LTE RACH procedure, is able to estimate the collision multiplicity and thus gather information about how many devices chose the same preamble. This data can be used by the eNB to resolve collisions, increase the supported system load and reduce transmission latency. Besides LTE, the presented approach is applicable to novel 3GPP standards that target massive Internet of Things (IoT), e.g., LTE-M and NB-IoT, as well as 5G, since their RACH procedures are based on the same principles.

The availability of cheap and easy-to-install sensors is bolstering the development of monitoring applications in IoT scenarios. Although there is a need for periodical measurements of the tracked signals to guarantee an accurate representation at the receiver, choosing an appropriate duration for the reporting window is not trivial. In fact, the energy restrictions of many devices and the interference caused by other users call for longer reporting windows. However, this causes a higher reconstruction error due to the lower sampling resolution, which may be unacceptable in some applications. We propose a probabilistic random channel access scheme for battery-powered devices which monitor time-correlated phenomena and report their measurements to a fusion center. Our goal is to minimize the energy consumption of the sensors, while guaranteeing that the error in the signal estimate at the receiver does not exceed a chosen threshold. We exploit Markov chains and stochastic geometry to characterize the interference caused by the other devices. The numerical evaluation proves that our scheme is scalable and may be used in highly dense scenarios, and that it outperforms other state-of-the-art approaches, which do not consider the impact of interference on both the energy consumption and the accuracy of data representation.

Monitoring applications are gaining a lot of momentum in the Internet of Things (IoT), bolstered by the availability of cheap and easy-to-install sensors. Often, random access schemes are preferred to coordinated ones because they are more flexible and have no synchronization overhead. The possibility of collisions with other packets and the limited energy availability of the battery-powered nodes demand strategies to reduce the number of transmissions. This, however, is counterbalanced by the need to accurately monitor the process of interest, which requires a sufficient amount of sensed data. In this study, we propose a novel compression and transmission strategy with the objective of prolonging sensors' lifetime while guaranteeing a desired reconstruction accuracy of the tracked data. We consider the effect of both sensing and transmissions on the energy consumption, and adapt the sampling and transmission rates based on the target estimation accuracy and on the probability of packet losses caused by the interference from other sensors.

We consider a monitoring application where sensors periodically report data to a common receiver using time division multiplexing. The sensors are constrained by the limited and unpredictable energy availability provided by energy harvesting (EH), and by the channel impairments. To maximize the quality of the reported data, the packets transmitted contain newly generated data blocks together with up to r -1 previously unsuccessfully delivered ones, where r is a design parameter. These data blocks are compressed, concatenated, and encoded with a channel code. The scheme applies lossy compression, such that the fidelity of the individual blocks is traded off with the reliability provided by the channel code. We show that the proposed strategy outperforms the one in which retransmissions are not allowed. We also investigate the tradeoff between the value of r, the compression and the coding rates, under the constraints of the energy availability, and, once r has been decided, use a Markov decision process (MDP) to optimize the compression/coding rates. Finally, we implement a reinforcement learning algorithm, through which devices can learn the optimal transmission policy without knowing a priori the statistics of the EH process, and show that it indeed reaches the performance obtained via MDP.

The presence of many battery-powered sensors in the Internet of Things paradigm calls for the design of energy-aware protocols. Source coding techniques make it possible to save some energy by compressing the packets sent over the network, but at the cost of poorer accuracy in the representation of the data. This paper addresses the problem of designing efficient policies to jointly perform processing and transmission tasks. In particular, we aim at defining a scheduling strategy with the twofold goal of extending the network lifetime and guaranteeing a low overall distortion of the transmitted data. We propose a time division multiple access-based access scheme that efficiently allocates resources to heterogeneous nodes. We use realistic rate-distortion curves to quantify the impact of compression on the data quality and propose a complete energy model that includes the energy spent for processing and transmitting the data, as well as the circuitry energy costs. We consider both full and statistical knowledge of the wireless channels and derive communication policies for the two cases. The overall problem is structured in modules and solved through convex and alternate programming techniques. Finally, we thoroughly evaluate the proposed algorithms and the influence of the design variables on the system performance adopting parameters taken from real sensors.

Energy harvesting is a promising technology for the Internet of Things (IoT) towards the goal of self-sustainability of the involved devices. However, the intermittent and unreliable nature of the harvested energy demands an intelligent management of devices' operation in order to ensure a sustained performance of the IoT application. In this work, we address the problem of maximizing the quality of the reported data under the constraints of energy harvesting, energy consumption and communication channel impairments. Specifically, we propose an energy-aware joint source-channel coding scheme that minimizes the expected data distortion, for realistic models of energy generation and of the energy spent by the device to process the data, when the communication is performed over a Rayleigh fading channel. The performance of the scheme is optimized by means of a Markov Decision Process framework.

The spreading of video streaming services in the last few years is presenting new challenges in wireless networking; Video Rate Adaptation (VRA) is a technique that optimizes the bandwidth usage by adapting video quality as network conditions change. We propose two Quality of Experience (QoE) aware algorithms that perform VRA while guaranteeing user satisfaction.