FiFo: Fishbone Forwarding in Massive IoT Networks

Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages. Although various benchmark algorithms have been developed to date for message delivery in such applications, they pose several practical challenges such as insufficient network coverage and/or highly redundant transmissions to expand the coverage area, resulting in considerable energy consumption for each IoT device. To overcome this problem, we first characterize a new performance metric, forwarding efficiency, which is defined as the ratio of the coverage probability to the average number of transmissions per device, to evaluate the data dissemination performance more appropriately. Then, we propose a novel and effective forwarding method, fishbone forwarding (FiFo), which aims to improve the forwarding efficiency with acceptable computational complexity. Our FiFo method completes two tasks: 1) it clusters devices based on the unweighted pair group method with the arithmetic average; and 2) it creates the main axis and sub axes of each cluster using both the expectation-maximization algorithm for the Gaussian mixture model and principal component analysis. We demonstrate the superiority of FiFo by using a real-world dataset. Through intensive and comprehensive simulations, we show that the proposed FiFo method outperforms benchmark algorithms in terms of the forwarding efficiency.Comment: 13 pages, 16 figures, 5 tables; to appear in the IEEE Internet of Things Journal (Please cite our journal version that will appear in an upcoming issue.

Framework for Content Distribution over Wireless LANs

Wireless LAN (also called as Wi-Fi) is dominantly considered as the most pervasive technology for Intent access. Due to the low-cost of chipsets and support for high data rates, Wi-Fi has become a universal solution for ever-increasing application space which includes, video streaming, content delivery, emergency communication, vehicular communication and Internet-of-Things (IoT). Wireless LAN technology is defined by the IEEE 802.11 standard. The 802.11 standard has been amended several times over the last two decades, to incorporate the requirement of future applications. The 802.11 based Wi-Fi networks are infrastructure networks in which devices communicate through an access point. However, in 2010, Wi-Fi Alliance has released a specification to standardize direct communication in Wi-Fi networks. The technology is called Wi-Fi Direct. Wi-Fi Direct after 9 years of its release is still used for very basic services (connectivity, file transfer etc.), despite the potential to support a wide range of applications. The reason behind the limited inception of Wi-Fi Direct is some inherent shortcomings that limit its performance in dense networks. These include the issues related to topology design, such as non-optimal group formation, Group Owner selection problem, clustering in dense networks and coping with device mobility in dynamic networks. Furthermore, Wi-Fi networks also face challenges to meet the growing number of Wi Fi users. The next generation of Wi-Fi networks is characterized as ultra-dense networks where the topology changes frequently which directly affects the network performance. The dynamic nature of such networks challenges the operators to design and make optimum planifications. In this dissertation, we propose solutions to the aforementioned problems. We contributed to the existing Wi-Fi Direct technology by enhancing the group formation process. The proposed group formation scheme is backwards-compatible and incorporates role selection based on the device's capabilities to improve network performance. Optimum clustering scheme using mixed integer programming is proposed to design efficient topologies in fixed dense networks, which improves network throughput and reduces packet loss ratio. A novel architecture using Unmanned Aeriel Vehicles (UAVs) in Wi-Fi Direct networks is proposed for dynamic networks. In ultra-dense, highly dynamic topologies, we propose cognitive networks using machine-learning algorithms to predict the network changes ahead of time and self-configuring the network

A Lightweight Distributed Solution to Content Replication in Mobile Networks

Performance and reliability of content access in mobile networks is conditioned by the number and location of content replicas deployed at the network nodes. Facility location theory has been the traditional, centralized approach to study content replication: computing the number and placement of replicas in a network can be cast as an uncapacitated facility location problem. The endeavour of this work is to design a distributed, lightweight solution to the above joint optimization problem, while taking into account the network dynamics. In particular, we devise a mechanism that lets nodes share the burden of storing and providing content, so as to achieve load balancing, and decide whether to replicate or drop the information so as to adapt to a dynamic content demand and time-varying topology. We evaluate our mechanism through simulation, by exploring a wide range of settings and studying realistic content access mechanisms that go beyond the traditional assumptionmatching demand points to their closest content replica. Results show that our mechanism, which uses local measurements only, is: (i) extremely precise in approximating an optimal solution to content placement and replication; (ii) robust against network mobility; (iii) flexible in accommodating various content access patterns, including variation in time and space of the content demand.Comment: 12 page

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

WiFi-Direct InterNetworking

We are on the verge of having ubiquitous connectivity. However, there are still scenarios where public communication networks are not reachable, are saturated or simply cannot be trusted. In such cases, our mobile phones can leverage device-to-device communication to reach the public network or to enable local connectivity. A device-to-device communication technology, with at least WiFi speed and range, will offer sufficient connectivity conditions for interconnection in areas/situations where it is not currently possible. Such advance will foster a new breed of systems and applications. Their widespread adoption is, nonetheless, bound to their usage in off-the-shelf devices. This raises a problem because the device-to-device communication technologies currently available in off-the-shelf mobile devices have several limitations: Bluetooth is limited in speed and range,Wi-Fi Direct is limited in speed and connectivity for medium and large scenarios, and WiFi-Aware is a new and untested technology, whose specification does not cover large scenarios. In this thesis, we address this problem by presenting two communication topologies and a network formation algorithm that enable the use of Wi-Fi Direct communication between off-the-shelf mobile devices in medium and large scale scenarios. The communication topologies, named Group-Owner Client-Relay Group-Owner and Group-Owner Group-Owner, allow for Wi-Fi Direct intergroup communication, whilst the network formation algorithm, named RedMesh, systematically creates networks of Wi-Fi Direct groups. The algorithm proved to be very effective, achieving full connectivity in 97.28% of the 1 250 tested scenarios. The RedMesh algorithm distinguishes itself for being the first one to useWi-Fi Direct communication topologies that can form tree and mesh structures, and for being the first algorithm able to build networks that can rely only on unicast communication. We may hence conclude that the work developed in this thesis makes significant progress in the formation of large scale networks of off-the-shelf mobile devices

Fast recovery from node compromise in wireless sensor networks

Wireless Sensor Networks (WSNs) are susceptible to a wide range of security attacks in hostile environments due to the limited processing and energy capabilities of sensor nodes. Consequently, the use of WSNs in mission critical applications requires reliable detection and fast recovery from these attacks. While much research has been devoted to detecting security attacks, very little attention has been paid yet to the recovery task. In this paper, we present a novel mechanism that is based on dynamic network reclustering and node reprogramming for recovering from node compromise. In response to node compromise, the proposed recovery approach reclusters the network excluding compromised nodes; thus allowing normal network operation while initiating node recovery procedures. We propose a novel reclustering algorithm that uses 2-hop neighbourhood information for this purpose. For node reprogramming we propose the modified Deluge protocol. The proposed node recovery mechanism is both decentralized and scalable. Moreover, we demonstrate through its implementation on a TelosB-based sensor network testbed that the proposed recovery method performs well in a low-resource WSN.<br /