Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Networks, Communication, and Computing Vol. 2

Networks, communications, and computing have become ubiquitous and inseparable parts of everyday life. This book is based on a Special Issue of the Algorithms journal, and it is devoted to the exploration of the many-faceted relationship of networks, communications, and computing. The included papers explore the current state-of-the-art research in these areas, with a particular interest in the interactions among the fields

Resource Optimization in Wireless Sensor Networks for an Improved Field Coverage and Cooperative Target Tracking

There are various challenges that face a wireless sensor network (WSN) that mainly originate from the limited resources a sensor node usually has. A sensor node often relies on a battery as a power supply which, due to its limited capacity, tends to shorten the life-time of the node and the network as a whole. Other challenges arise from the limited capabilities of the sensors/actuators a node is equipped with, leading to complication like a poor coverage of the event, or limited mobility in the environment. This dissertation deals with the coverage problem as well as the limited power and capabilities of a sensor node. In some environments, a controlled deployment of the WSN may not be attainable. In such case, the only viable option would be a random deployment over the region of interest (ROI), leading to a great deal of uncovered areas as well as many cutoff nodes. Three different scenarios are presented, each addressing the coverage problem for a distinct purpose. First, a multi-objective optimization is considered with the purpose of relocating the sensor nodes after the initial random deployment, through maximizing the field coverage while minimizing the cost of mobility. Simulations reveal the improvements in coverage, while maintaining the mobility cost to a minimum. In the second scenario, tracking a mobile target with a high level of accuracy is of interest. The relocation process was based on learning the spatial mobility trends of the targets. Results show the improvement in tracking accuracy in terms of mean square position error. The last scenario involves the use of inverse reinforcement learning (IRL) to predict the destination of a given target. This lay the ground for future exploration of the relocation problem to achieve improved prediction accuracy. Experiments investigated the interaction between prediction accuracy and terrain severity. The other WSN limitation is dealt with by introducing the concept of sparse sensing to schedule the measurements of sensor nodes. A hybrid WSN setup of low and high precision nodes is examined. Simulations showed that the greedy algorithm used for scheduling the nodes, realized a network that is more resilient to individual node failure. Moreover, the use of more affordable nodes stroke a better trade-off between deployment feasibility and precision

Energy-efficient wireless sensor networks via scheduling algorithm and radio Wake-up technology

One of the most important requirements for wireless sensor networks (WSNs) is the energy efficiency, since sensors are usually fed by a battery that cannot be replaced or recharged. Radio wake-up - the technology that lets a sensor completely turn off and be reactivated by converting the electromagnetic field of radio waves into energy - is now one of the most emergent strategies in the design of wireless sensor networks. This work presents Scheduled on Demand Radio WakeUp (SORW), a flexible scheduler designed for a wireless sensor network where duty cycling strategy and radio wake-up technology are combined in order to optimize the network lifetime. In particular, it tries to keep sensors sleeping as much as possible, still guaranteeing a minimum number of detections per unit of time. Performances of SORW are provided through the use of OMNet++ simulator and compared to results obtained by other basic approaches. Results show that with SORW it is possible to reach a theoretical lifetime of several years, compared to simpler schedulers that only reach days of activity of the network

ZigBee-assisted ad-hoc networking of multi-interface mobile devices

Wireless ad hoc network is decentralized wireless network, which does not rely on a preexisting infrastructure, such as routers in wired networks or access points in managed (infrastructure) wireless networks. Instead, each node participates in routing by forwarding data for other nodes. The determination of which nodes forward data is made dynamically based on the network connectivity. Node density has a great impact on the performance and efficiency of wireless ad hoc networks by influencing some factors such as capacity, network contention, routing efficiency, delay, and connectivity. On one hand, maintaining stable connectivity is a big challenge for sparsely deployed and highly dynamic ad hoc wireless network. Vehicle ad hoc network (VANET) which consists of highly mobile vehicles with wireless interfaces is one type of such network, especially in rural areas where vehicles traffic are very sparse. One of the most important applications built on top of VANET is the safety application. In VANET safety applications, source vehicles that observe accidents or some other unsafe conditions of the roads generate warning messages about the conditions, and propagate the warning messages to the following vehicles. In this way, the following drivers have the opportunity to do some necessary action before they reach the potential danger zone to avoid accident. The safety application requires timely and accurate warning message detection and delivery. However, recent researches have shown that sparse and highly dynamic vehicle traffic leads network fragmentation, which poses a crucial research challenge for VANET safety application. On the other hand, reducing contention and thus maximizing the network throughput is also a big challenge for densely deployed ad hoc wireless network, especially when many devices are located in a small area and each device has heavy duty message to transmit. The WiFi interface perhaps is the most common interface found in mobile devices for data transfer as it provides good combination of throughout, range and power efficiency. However, the WiFi interface may have to consume a large amount of bandwidth and energy for contention and combating collision, especially when mobile devices located in a small area all have heavy traffic to transmit. Meanwhile, ZigBee is an emerging wireless communication technology which supports low-cost, low-power and short-range wireless communication. Nowadays, it has been common for a mobile device, such as smart phone, PDA and laptop, to have both WiFi and Bluetooth interfaces. As the ZigBee technology becomes more and more mature, it will not be surprising to see the ZigBee interface commonly embedded in mobile devices together with WiFi and Bluetooth interfaces in the near future. The co-existence of the ZigBee and the WiFi interfaces in the same mobile device inspires us to develop new techniques to address the above two issues. Specifically, this thesis presents two systems built based on ZigBee-assisted ad-hoc networking of multi-interface mobile devices. In order to achieve stable connectivity in a sparse and dynamic VANET, the first system integrates a network of static roadside sensors and highly mobile vehicles to improve driving safety. In order to reduce contention in a densely deployed ad hoc wireless network, the second system assists WiFi transmission with ZigBee interface for multi-interface mobile devices. Extensive implementations and experiments have been conducted to demonstrate the effectiveness of our proposed systems

Utilizing ZigBee Technology for More Resource-efficient Wireless Networking

Wireless networks have been an essential part of communication in our daily life. Targeted at different applications, a variety of wireless networks have emerged. Due to constrained resources for wireless communications, challenges arise but are not fully addressed. Featured by low cost and low power, ZigBee technology has been developed for years. As the ZigBee technology becomes more and more mature, low-cost embedded ZigBee interfaces have been available off the shelf and their sizes are becoming smaller and smaller. It will not be surprising to see the ZigBee interface commonly embedded in mobile devices in the near future. Motivated by this trend, we propose to leverage the ZigBee technology to improve existing wireless networks. In this dissertation, we classify wireless networks into three categories (i.e., infrastructure-based, infrastructure-less and hybrid networks), and investigate each with a representative network. Practical schemes are designed with the major objective of improving resource efficiency for wireless networking through utilizing ZigBee technology. Extensive simulation and experiment results have demonstrated that network performance can be improved significantly in terms of energy efficiency, throughput, packet delivery delay, etc., by adopting our proposed schemes

A Multi-Hop 6LoWPAN Wireless Sensor Network for Waste Management Optimization

In the first part of this Thesis several Wireless Sensor Network technologies, including the ones based on the IEEE 802.15.4 Protocol Standard like ZigBee, 6LoWPAN and Ultra Wide Band, as well as other technologies based on other protocol standards like Z-Wave, Bluetooth and Dash7, are analyzed with respect to relevance and suitability with the Waste Management Outsmart European FP7 Project. A particular attention is given to the parameters which characterize a Large Scale WSN for Smart Cities, due to the amount of sensors involved and to the practical application requested by the project. Secondly, a prototype of sensor network is proposed: an Operative System named Contiki is chosen for its portability on different hardware platforms, its Open Source license, for the use of the 6LoW-PAN protocol and for the implementation of the new RPL routing protocol. The Operative System is described in detail, with a special focus on the uIPv6 TCP/IP stack and RPL implementation. With regard to this innovative routing proto col designed specifically for Low Power Lossy Networks, chapter 4 describes in detail how the network topology is organized as a Directed Acyclic Graph, what is an RPL Instance and how downward and upward routes are constructed and maintained. With the use of several AVR Atmel modules mounting the Contiki OS a real WSN is created and, with an Ultrasonic Sensor, the filling level of a waste basket prototype is periodically detected and transmitted through a multi-hop wireless network to a sink nodeope

Distributed Joint Source-Channel Coding With Copula-Function-Based Correlation Modeling for Wireless Sensors Measuring Temperature

Wireless sensor networks (WSNs) deployed for temperature monitoring in indoor environments call for systems that perform efficient compression and reliable transmission of the measurements. This is known to be a challenging problem in such deployments, as highly efficient compression mechanisms impose a high computational cost at the encoder. In this paper, we propose a new distributed joint source-channel coding (DJSCC) solution for this problem. Our design allows for efficient compression and error-resilient transmission, with low computational complexity at the sensor. A new Slepian-Wolf code construction, based on non-systematic Raptor codes, is devised that achieves good performance at short code lengths, which are appropriate for temperature monitoring applications. A key contribution of this paper is a novel Copula-function-based modeling approach that accurately expresses the correlation amongst the temperature readings from colocated sensors. Experimental results using a WSN deployment reveal that, for lossless compression, the proposed Copula-function-based model leads to a notable encoding rate reduction (of up to 17.56%) compared with the state-of-the-art model in the literature. Using the proposed model, our DJSCC system achieves significant rate savings (up to 41.81%) against a baseline system that performs arithmetic entropy encoding of the measurements. Moreover, under channel losses, the transmission rate reduction against the state-of-the-art model reaches 19.64%, which leads to energy savings between 18.68% to 24.36% with respect to the baseline system