Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

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

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks

Description and composition of bio-inspired design patterns: a complete overview

In the last decade, bio-inspired self-organising mechanisms have been applied to different domains, achieving results beyond traditional approaches. However, researchers usually use these mechanisms in an ad-hoc manner. In this way, their interpretation, definition, boundary (i.e. when one mechanism stops, and when another starts), and implementation typically vary in the existing literature, thus preventing these mechanisms from being applied clearly and systematically to solve recurrent problems. To ease engineering of artificial bio-inspired systems, this paper describes a catalogue of bio-inspired mechanisms in terms of modular and reusable design patterns organised into different layers. This catalogue uniformly frames and classifies a variety of different patterns. Additionally, this paper places the design patterns inside existing self-organising methodologies and hints for selecting and using a design patter

Supporting Mobile Distributed Services

With sensors becoming increasingly ubiquitous, there is a tremendous potential for services which can take advantage of the data collected by these sensors, from the important -- such as detecting medical emergencies and imminent natural disasters -- to the mundane -- such as waiting times experienced by diners at restaurants. This information can then be used to offer useful services. For example, a busy professional could find a restaurant to go to for a quick lunch based on information available from smartphones of people already there having lunch, waiting to be seated, or even heading there; a government could conduct a census in real-time, or “sense” public opinion. I refer to such services as mobile distributed services. The barriers to offering mobile distributed services continue to be prohibitive for most: not only must these services be implemented, but they would also inevitably compete for resources on people's devices. This is in part because such services are poorly understood, and consequently, there is limited language support for programming them. In this thesis, I address practical challenges related to three important problems in mobile distributed services. In addition, I present my efforts towards a formal model for representing mobile distributed services. First, I address the challenge of enhancing the programmability of mobile distributed services. This thesis presents a set of core mechanisms underlying mobile distributed services. I interpret and implement these mechanisms for the domain of crowd-sourced services. A distributed runtime middleware, CSSWare, has been developed to simplify the burden of initiating and managing crowd-sourced services. CSSWare provides a set of domain-specific programming constructs for launching a new service. Service designers may launch novel services over CSSWare by simply plugging in small pieces of service specific code. Particularly, new services can be prototyped in fewer than 100 lines of code. This ease of programming promises to democratize the building of such services. Second, I address the challenge of efficiently supporting the sensing needs of mobile distributed services, and more generally sensor-based applications. I developed ShareSens, an approach to opportunistically merge sensing requirements of independent applications. When multiple applications make sensing requests, instead of serving each request independently, ShareSens opportunistically merges the requests, achieving significant power and energy savings. Custom filters are then used to extract the data required by each application. Third, I address the problem of programming the sensing requirements of mobile distributed services. In particular, ModeSens is presented to allow multi-modal sensing requirements of a service to be programmed separately from its function. Programmers can specify the modes in which a service can be, the sensing needs of each mode, and the sensed events which trigger mode transition. ModeSens then monitors for mode transition events, and dynamically adjusts the sensing frequencies to match the current mode's requirements. Separating the mode change logic from an application's functional logic leads to more modular code. In addition, I present MobDisS (Mobile Distributed Services), an early model for representing mobile distributed services, allowing them to be carefully studied. Services can be built by composing simpler services. I present the syntax and operational semantics of MobDisS. Although this work can be evaluated along multiple dimensions, my primary goal is to enhance programmability of mobile distributed services. This is illustrated by providing the actual code required for creating two realistic services using CSSWare. Each service demonstrates different facets of the middleware, ranging from the use of different sensors to the use of different facilities provided by CSSWare. Furthermore, experimental results are presented to demonstrate scalability, performance and data-contributor side energy efficiency of CSSWare and ShareSens. Finally, a set of experimental evaluation is carried out to measure the performance and energy costs of using ModeSens

NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA)

Target tracking in wireless sensor networks

The problem being tackled here relates to the problem of target tracking in wireless sensor networks. It is a specific problem in localization. Localization primarily refers to the detection of spatial coordinates of a node or an object. Target tracking deals with finding spatial coordinates of a moving object and being able to track its movements. In the tracking scheme illustrated, sensors are deployed in a triangular fashion in a hexagonal mesh such that the hexagon is divided into a number of equilateral triangles. The technique used for detection is the trilateration technique in which intersection of three circles is used to determine the object location. While the object is being tracked by three sensors, distance to it from a fourth sensor is also being calculated simultaneously. The difference is that closest three sensors detect at a frequency of one second while the fourth sensor detects the object location at twice the frequency. Using the distance information from the fourth sensor and a simple mathematical technique, location of object is predicted for every half second as well. The key thing to note is that the forth sensor node is not used for detection but only for estimation of the object at half second intervals and hence does not utilize much power. Using this technique, tracking capability of the system is increased. The scheme proposed can theoretically detect objects moving at speeds of up to 33 m/s unlike the paper [16] on which it is based which can detect objects moving only up to speeds of 5 m/s. While the earlier system [16] has been demonstrated with four sensors as well, but for that the arrangement of sensor nodes is a square. The technique demonstrated does not involve a change in the arrangement and utilizes the hexagonal mesh arrangement. Some other scenarios have been tackled such as when displacement of the object is zero at the end of one second. Its movement is predicted during that time interval. Also, incase an object moves in a circle, such motions are also tracked