Sharief M. A. Oteafy

Resource Re-use in Wireless Sensor Networks: Realizing a Synergetic Internet of Things

The race for realizing a feasible framework for the Internet of Things (IoT) is indeed of increasing pace. Yet, none of the paradigms on the table consider building a system from scratch. Simply put, much has been invested (research and industry) in developing two key enabling technologies; namely Wireless Sensor Networks (WSNs) and RFID systems. The abundance and self-sustained operation of these technologies potentiate a truly diverse bed for the plethora of applications the IoT is envisioned to encompass. We note the application-specific approach, currently dominant in WSN research, a true hindrance to its adaptability in a realizable IoT framework. In remedy, we present a novel paradigm in WSNs to efficiently utilize network resources, and extend it to a platform for multiple applications to cross-utilize resources over multiple WSNs. Our system is composed of three successive phases, namely: identifying the resources available in a given deployment of WSNs, and calibrating their usability based on a set of attributes. Then, a set of functional requirements is drawn from the applications to run on these WSNs. Finally we present a formulation for an optimization problem that maps these functional requirements to the available resources. The resulting paradigm potentiates the utilization of WSNs, not only for accommodating multiple applications, but for dynamically allocating resources when needed in a larger IoT framework. We present the formulation aided by a use case. Finally, this work concludes with a set of open research topics stemming from IoT realization efforts, and the integration efforts for its enabling technologies.

Dynamic adaptive streaming over popularity-driven caching in Information-Centric Networks

The growing demand for video streaming is straining the current Internet, and mandating a novel approach to future Internet paradigms. The advent of Information-Centric Networks (ICN) promises a novel architecture for addressing this exponential growth in data-intensive services, of which video streaming is projected to dominate (in traffic size). In this paper, we present a novel strategy in ICNs for adaptive caching of variable video contents tailored to different sizes and bit rates. Our objective is to achieve optimal video caching to reduce access time for the maximal requested bit rate for every user. At its core, our approach capitalizes on a rigorous delay analysis and potentiates maximal serviceability for each user. We incorporate predictors for requested video objects based on a popularity index (Zipf distribution). In our proposed model, named DASCache, we present delay queuing analysis for cached objects, providing a cap on expected delay in accessing video content. In DASCache, we present a Binary Integer Programming (BIP) formulation for the cache assignment problem, which operates in rounds based on changes in content requests and popularity scores. DASCache reacts to changes in network dynamics that impact bit rate choices by heterogeneous users and enables users to stream videos, maximizing Quality of Experience (QoE). To evaluate the performance of DASCache, in contrast to current benchmarks in video caching, we present an elaborate performance evaluation carried out on ndnSIM, over NS-3.

Resilient IoT Architectures Over Dynamic Sensor Networks With Adaptive Components

As competing industries delve into the Internet of Things (IoT), a growing challenge of interoperability and redundant deployments is magnified. Specifically, as we augment more “things” in the IoT fabric, how will these components interact across their heterogeneity, let alone collaborate. In this paper, we address the core issue of component interaction and operation under the IoT umbrella. We present our contribution in the framework of wireless sensor networks (WSNs), as a founding block in the IoT. More importantly, we present a novel paradigm in the design of WSNs, to build a resilient architecture that decouples operational mandates from the nodes. We abstract IoT things as wirelessly interfaced components, which introduce functionality physically decoupled from their devices; boosting resilience, dynamicity, and resource utilization. This approach dissects the study of any IoT nodal capacity to its “connected” components, and empowers dynamic associativity between things to serve varying functional requirements and levels. It also enables reintroducing only the components required to suffice for network operation, or only those needed to meet a new requirement. More importantly, critical resources in the network will be shared within their neighborhoods. Thus network lifetime will relate to functional cliques of dynamic IoT nodes, rather than individual networks. We evaluate the cost effectiveness and resilience of our paradigm via simulations.

Towards a global IoT: Resource re-utilization in WSNs

The Internet of Things (IoT) is envisioned as a paradigm shift, with a plethora of applications, on the premise of well-established enabling technologies; prominently Wireless Sensor Networks (WSNs) and RFIDs. The former has evolved to improve energy efficiency and resilient operation, yet true scalability has only been recently probed and quite sparsely advanced. Moreover, the traditional approach, whereby most WSN platforms are tailored for a single-application, imposes significant rigidity in re-utilizing platforms for new applications, and limitations on re-using previously deployed ones. In remedy, we present a novel paradigm in WSNs to efficiently utilize network resources, and extend it to a platform for multiple applications to cross-utilize resources over multiple WSNs. We present the approach in three phases; the first calibers resources in the network and their usability. Then applications are represented as finite sets of functional requirements. Finally, we present an optimization approach to find an optimal mapping between applications and resources. This paradigm presents a leap in scalability, not only in a WSN but across multiple ones, dynamically accommodating varying resources being introduced and removed; in addition to utilizing transient resources in their vicinity. To this end, we present an architecture to efficiently adopt WSNs in IoT with changing demands and scale. Our approach is further explained and demonstrated via a detailed use case depicting the premise of IoT applications.

Re-Usable Resources in Wireless Sensor Networks: A Linear Optimization for a Novel Application Overlay Paradigm over Multiple Networks

Todays abundance of sensors and their wireless/wired networks, coupled with a growing plethora of applications, necessitate a dynamic approach to the assignment of tasks to a network. The current practice in WSN design is almost always application specific, due to functional and resource tradeoffs that have justified much of the tailored research done so far. Identifying this as a major bottleneck in WSN advancement, this paper presents a new paradigm which decouples applications from WSN architectures and protocols. This paradigm views the network as an abundance of connected resources (hence functionalities) to match requirements of applications (old and new) based on utilization and feasibility factors. We present an elaborate abstraction of network resources, with detailed description of its governing utility attributes. Then we describe the view of applications as an aggregation of functional requirements based on a given set of resources. The intermediate mapping between applications and resources is then solved by a reduced linear optimization formulation, to realize the system as a whole. The paradigm is further explained via a multiple-application scenario and its representation and operation under our paradigm.

Towards Augmenting Federated Wireless Sensor Networks

Environmental Monitoring (EM) has witnessed significant improvements in recent years due to the great utility of Wireless Sensor Networks (WSNs). Nevertheless, due to harsh operational conditions in such applications, WSNs often suffer large scale damage in which nodes fail concurrently and the network gets partitioned into disjoint sectors. Thus, reestablishing connectivity between the sectors, via their remaining functional nodes, is of utmost importance in EM; especially in forestry. In this regard, considerable work has been proposed in the literature tackling this problem by deploying Relay Nodes (RNs) aimed at reestablishing connectivity. Although finding the minimum relay count and positions is NP-Hard, efficient heuristic approaches have been anticipated. However, the majority of these approaches ignore the surrounding environment characteristics and the infinite 3-Dimensional (3-D) search space which significantly degrades network performance in practice. Therefore, we propose a 3-D grid-based deployment for relay nodes in which the relays are efficiently placed on grid vertices. We present a novel approach, named FADI, based on a minimum spanning tree construction to re-connect the disjointed WSN sectors. The performance of the proposed approach is validated and assessed through extensive simulations, and comparisons with two main stream approaches are presented. Our protocol outperforms the related work in terms of the average relay node count and distribution, the scalability of the federated WSNs in large scale applications, and the robustness of the topologies formed.

RobP2P: A Robust Architecture for Resource Sharing in Mobile Peer-to-Peer Networks

Peer-to-peer (P2P) systems are constructed to provide resource sharing among interested participants (peers) in a distributed and self-organized fashion. The way P2P networks are formed is critical to the overall system performance due to communications and network maintenance overhead. Mobile environments pose additional challenges on P2P networks due to heterogeneity of nodes, inherent limited resources, dynamic context and wireless network characteristics. This paper presents RobP2P, a robust architecture to construct mobile P2P networks and efficiently maintain the network state. RobP2P introduces a novel super-peer selection protocol based on an aggregate utility function that takes into account peers’ capability and context. It also presents an agile scheme through which super-peers can delegate their responsibilities to more powerful and stable joining or existing peers. Our simulation results show that the RobP2P is efficient, less prone to failure, and generates lower overhead traffic, while reliably maintaining the consistency of network state . c

Pruned Adaptive Routing in the heterogeneous Internet of Things

Recent research endeavours are capitalizing on state of the art technologies to build a scalable Internet of Things (IoT). Envisioned as a technology to integrate the best of Wireless Sensor Networks and RFID systems, there is much promise for a global network of objects that are identifiable, track-able, and harmoniously informing. However, the realization of an IoT framework is hindered by many factors, the most pressing of which is attributed to the integration of these heterogeneous nodes and devices. A considerable subset of these nodes undergoes movement and dynamically enters and leaves the network backbone/topology. Routing packets and inter-nodal communication has received little attention; mainly due to the sheer reliance on the Internet as a backbone. However, spatially correlated entities in the IoT, and those which most often interact, would pose a significant overhead of communication if all intermediate packets need to be routed over distant backhauls. In remedy, we present a Pruned Adaptive IoT Routing (PAIR) protocol that selectively establishes routes of communication between IoT nodes. Since nodes in the IoT belong to different owners, we also introduce a pricing model to cater for the exchange of monetary costs by intermediate nodes to utilize their relaying resources. We also establish a cap on inter-nodal routing to dynamically utilize the Internet backbone if the source to destination distance surpasses a preset (case optimized) threshold. The PAIR routing protocol is elaborated upon, building upon the detailed system model presented in this paper. We finally present a use case to demonstrate the utility and practicality of PAIR in the heterogeneous IoT as it scales.

Dynamic Election-Based Sensing and Routing in Wireless Sensor Networks

Decentralized protocols offer high adaptability to topology changes prominent in Wireless Sensor Networks (WSN). Protocols resilient to topology changes stemming from nodes dying, being added, relocating or duty cycling, improve network performance in terms of lifetime and percent of events sensed and reported. Topology dependant protocols, such as cluster-based, face many hindrances especially in terms of scalability, dynamicity, and adapting to varying traffic rates. Accordingly, a novel approach is introduced in sensing, whereby a single node is elected to report a sensed event, in a decentralized manner, thereby avoiding redundant reports by other nodes which exhaust network resources. Election is based on the node with the highest likelihood of successfully reporting the event. This protocol is coupled with a localized multi-hop routing protocol, to route that report back to the sink, by electing the most reliable next-hop neighbor to relay the report. Simulation results demonstrate the increase in network lifetime, detection/reporting efficiency, and resilience to varying node density.

Enhancing emergency response systems through leveraging crowdsensing and heterogeneous data

Robust and prompt emergency response is a crucial service that smart cities should provide to citizens, communities, and corporations. Emergency management strategies that are currently supported by cities yield pre-determined protocols that can only handle well-understood incidents. However, there are incidents whose nature, shape, scale, and timing are not as predictable. The lack of adequate data management platforms to harvest emergency-related data from the proliferation of data sources scattered around a city is a major shortfall in current emergency response and risk assessment processes. We propose an improved information infrastructure to assist emergency personnel in responding effectively and proportionally to large-scale, distributed, unstructured natural and man-made hazards such as multi-vehicle accidents, outbreaks of human or animal diseases, major weather events, large fires, and terrorist attacks. The proposed infrastructure will crowdsource the multitude of human and physical sensing resources that can generate data about incidents (e.g. smartphones, sensors, vehicles, etc.) in order to build a comprehensive understanding of emergency situations and provide situational awareness and recommendations to emergency teams on the scene. Our infrastructure consists of three components: (1) large-scale crowdsensing and data quality valuation, (2) heterogeneous data integration and analytics, and (3) decision making, alternative generation and recommendations. Leveraging crowdsensing and heterogeneous data analytics will improve the response coordination to critical incidents and real-time incident management, which will contribute to saving lives and reducing injuries, improving the quality of life, and saving resources by deploying them more effectively.