Ahmed Bader

Spatiotemporal Stochastic Modeling of IoT Enabled Cellular Networks: Scalability and Stability Analysis

The Internet of Things (IoT) is large scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ubiquitous, reliable, and efficient wireless access, will play fundamental rule in delivering the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the traffic requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT devices. To this end, the developed model is utilized to characterize the extent to which cellular networks can accommodate IoT traffic as well as to assess and compare three different transmission strategies that incorporate a combination of transmission persistency, backoff, and power-ramping. The analysis and the results clearly illustrate the scalability problem imposed by IoT on cellular network and offer insights into effective scenarios for each transmission strategy.

Front-End Intelligence for Large-Scale Application-Oriented Internet-of-Things

The Internet-of-things (IoT) refer to the massive integration of electronic devices, vehicles, buildings, and other objects to collect and exchange data. It is the enabling technology for a plethora of applications touching various aspects of our lives, such as healthcare, wearables, surveillance, home automation, smart manufacturing, and intelligent automotive systems. Existing IoT architectures are highly centralized and heavily rely on a back-end core network for all decision-making processes. This may lead to inefficiencies in terms of latency, network traffic management, computational processing, and power consumption. In this paper, we advocate the empowerment of front-end IoT devices to support the back-end network in fulfilling end-user applications requirements mainly by means of improved connectivity and efficient network management. A novel conceptual framework is presented for a new generation of IoT devices that will enable multiple new features for both the IoT administrators as well as end users. Exploiting the recent emergence of software-defined architecture, these smart IoT devices will allow fast, reliable, and intelligent management of diverse IoT-based applications. After highlighting relevant shortcomings of the existing IoT architectures, we outline some key design perspectives to enable front-end intelligence while shedding light on promising future research directions.

Localized Power Control for Multihop Large-Scale Internet of Things

In this paper, we promote the use of multihop networking in the context of large-scale Internet of Things (IoT). Recognizing concerns related to the scalability of classical multihop routing and medium access techniques, we advocate the use of blind cooperation in conjunction with multihop communications. However, we show that blind cooperation is actually inefficient unless power control is applied. Inefficiency in this paper is projected in terms of the transport rate normalized to energy consumption. To that end, we propose an uncoordinated power control mechanism whereby each device in a blind cooperative cluster randomly adjusts its transmit power level. We derive an upper bound on the mean transmit power that must be observed at each device. We also devise a practical mechanism for each device to infer about the size of its neighborhood, a requirement necessary for the operation of the power control scheme. Finally, we assess the performance of the developed power control mechanism and demonstrate how it consistently outperforms the point-to-point case.

A Fully Distributed Geo-Routing Scheme for Wireless Sensor Networks

When marrying randomized distributed space-time coding (RDSTC) to beaconless geo-routing, new performance horizons can be created. In order to reach those horizons, however, beaconless geo-routing protocols must evolve to operate in a fully distributed fashion. In this letter, we expose a technique to construct a fully distributed geo-routing scheme in conjunction with RDSTC. We then demonstrate the performance gains of this novel scheme by comparing it to one of the prominent classical schemes.

Mobile Ad Hoc Networks in Bandwidth-Demanding Mission-Critical Applications: Practical Implementation Insights

There has been recently a growing trend of using live video feeds in mission-critical applications. Real-time video streaming from the front-end personnel or mobile agents is believed to substantially improve the situational awareness in mission-critical operations, such as disaster relief, law enforcement, and emergency response. Mobile ad hoc networks (MANETs) are a natural contender in such contexts. However, classical MANET routing schemes fall short in terms of scalability, bandwidth, and latency; all the three metrics being quite essential for mission-critical applications. As such, autonomous cooperative routing (ACR) has gained traction as the most viable MANET proposition. Nonetheless, ACR is also associated with a few implementation challenges. If they go unaddressed, will deem ACR practically useless. In this paper, efficient and low-complexity remedies to those issues are presented, analyzed, and validated. The validation is based on field experiments carried out using software-defined radio platforms. Compared with the classical MANET routing schemes, ACR was shown to offer up to two times better throughput, more than four times reduction in end-to-end latency, while observing a given target of transport rate normalized to energy consumption.

Blind Cooperative Routing for Scalable and Energy-Efficient Internet of Things

Multihop networking is promoted in this paper for energy-efficient and highly-scalable Internet of Things (IoT). Recognizing concerns related to the scalability of classical multihop routing and medium access techniques, the use of blind cooperation in conjunction with multihop communications is advocated herewith. Blind cooperation however is actually shown to be inefficient unless power control is applied. Inefficiency in this paper is projected in terms of the transport rate normalized to energy consumption. To that end, an uncoordinated power control mechanism is proposed whereby each device in a blind cooperative cluster randomly adjusts its transmit power level. An upper bound is derived for the mean transmit power that must be observed at each device. Finally, the uncoordinated power control mechanism is demonstrated to consistently outperform the simple point-to-point routing case.

Reduction of Buffering Requirements: Another Advantage of Cooperative Transmission

Yet another advent of cooperative transmission is exposed in this letter. It is shown that cooperation lends itself to the reduction of buffer sizes of wireless sensor nodes. It is less likely to find the channel busy when cooperative transmission is employed in the network. Otherwise, in the lack of cooperation, the probability of build up of packet queues in transmission buffers increases.

A spatiotemporal model for the LTE uplink: Spatially interacting tandem queues approach

With the proliferation of the Internet-of-things (IoT), there is an undeniable consensus that cellular LTE networks will have to support a dramatically larger number of uplink connections. This is true since most of the devices to be added incur machine-type communications which is dominantly upstream. Can current LTE network withstand this challenge? To answer this question, the joint performance of random access process and the uplink data transmission should be investigated. These two problems have been classically treated in the literature in a disjoint fashion. In this paper, they are jointly analyzed as an inseparable couple. To do that, a tandem queuing model is adopted whereby devices are represented as spatially interacting queues. The interaction between queues is governed by the mutual inter-cell and intra-cell interference. To that end, a joint stochastic geometry and queueing theory model is exploited to study this problem and a spatiotemporal analytical model is developed accordingly. Network stability and scalability are two prime performance criteria for performance assessment. In light of these two criteria, the developed model is poised to offer valuable insights into efficient access and resource allocation strategies.

CFO and channel estimation for MISO-OFDM systems

This study deals with the joint channel and carrier frequency offset (CFO) estimation in a Multiple Input Single Output (MISO) communications system. This problem arises in OFDM (Orthogonal Frequency Division Multiplexing) based multi-relay transmission protocols such that the geo-routing one proposed by A. Bader et al in 2012. Indeed, the outstanding performance of this multi-hop relaying scheme relies heavily on the channel and CFO estimation quality at the PHY layer. In this work, two approaches are considered: The first is based on estimating the overall channel (including the CFO) as a time-varying one using an adaptive scheme under the assumption of small or moderate CFOs while the second one performs separately, the channel and CFO parameters estimation based on the considered data model. The two solutions are analyzed and compared in terms of performance, cost and convergence rate.

An Ultra-Low-Latency Geo-Routing Scheme for Team-Based Unmanned Vehicular Applications

Results and lessons learned from the implementation of a novel ultra low-latency geo-routing scheme are presented in this paper. The geo-routing scheme is intended for team-based mobile systems whereby a cluster of unmanned autonomous vehicles are deployed to accomplish a critical mission under human supervision. The contention-free nature of the developed scheme lends itself to jointly achieve lower latency and higher throughput. Implementation challenges are presented and corresponding resolutions are discussed herewith.